Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Design and development of self-cleaning PV sliding system

Design and development of self-cleaning PV sliding system Abstract This study describes the designing steps of the proposed self-cleaning system for the photovoltaic (PV) system and experimentally investigates the effectiveness of the proposed self-cleaning PV sliding system for solar power plants under all Indian climate conditions. To investigate the performance of the proposed self-cleaning PV sliding system, we used three PV panels of 20 W with a sliding structure and another set of three PV panels of 20 W with a traditional (fixed) technique, and recorded the output power for the period of December 2019 to April 2021. The experimental results show that the proposed self-cleaning PV sliding system improved efficiency by 18.3%, 13.3% and 6.4%, respectively, in the summer, winter and post-monsoon seasons when compared to the fixed PV system. The results also revealed that energy consumption was very low when compared to the amount of energy gained. The proposed system not only cleans the PV system, but also protects it from hailstorms. The results of this study show that there is a significant improvement in PV efficiency and thus an increase in electricity production under all climate conditions. Open in new tabDownload slide solar energy, photovoltaics, cleaning, soiling, hailstorm protection, sliding PV system Introduction Clean-energy power generation is a vital strategy for mitigation to overcome the challenge of global warming. Sun intensity is higher in the sunbelt region than in other parts of the world, but PV systems in the region can experience soiling that necessitates frequent and costly solar-panel cleaning. The fundamental issue of installing solar PV energy systems in dry and hot areas is dirt deposition on panel surfaces, which, in addition to high temperatures, is the main challenge [1]. Both soiling and high temperature significantly diminish the efficiency of solar panels and increase electricity costs, resulting in a lack of competitiveness for solar energy systems with fossil fuels such as natural gas [2]. The principal source of dust in desert regions is soil, whereas soiling can take place in many other places, as snow and other particles are included. However, there are numerous local sources of deposited dust, such as discharges from coal-based plants, vehicle movements, vehicle emissions and human activities in large cities [3]. The suspension of dust and its deposits in the desert and semi-arid regions is a natural phenomenon and the amount of suspended dust is very high. Previous research has shown that there is a relationship between seasons and the type of deposited impurity. Apart from dust deposition, different types of impurities such as pollen, plants, bird droppings, soot and other types of organic materials have been observed [4]. With wind speeds increasing and the sun warming Earth’s surface, the density of the dust rises. Dust depends on the type of land through which it passes or rises [5]. When the sun comes up in the morning, it delivers its warm rays to Earth’s surface, drying the ground and producing dust, which is then suspended in the air. As the sun sets, dust is deposited on the surface of the PV panels as the wind speed declines [6]. The dry atmosphere, unlike damp weather, helps to cause dust [7]. The deposition rate of dirt on the PV surface depends on numerous elements, including the density of dust in the air, the inclination of the cell, the size of the dust particles, the humidity, wind speed and so on [8–10]. The fact that the sea and sea spray are a source of dust can be quite surprising because they produce ~200 million tonnes of salty dust each year [11, 12]. The chemicals and salts are trapped in the air after the water evaporates [13]. Dust storms carry tonnes of dust in the air and can travel ≤300 kilometres [11, 14]. The air always contains a stunning amount of suspended dust [15]. The presence of dust in the atmosphere, as well as its volatilization, results in a shadow that blocks solar radiation from reaching the PV cells [16]. The deposition of dust also enhances the shadow effect and restricts solar radiation from reaching the panel surface, causing PV cells to heat, which diminishes the PV cell’s productivity and lowers the output power of the PV cell [17]. Because the properties and dust composition vary by geography, the PV panel’s efficiency is affected not only by the amount of dust accumulated, but also by another important factor: dust characteristics. Not only are electrical parameters affected by dust accumulation, but also thermal and optical parameters [18]. Carbon particles have the greatest impact on the reduction of PV performance [19]. Natural dust deposition reduces PV module energy generation significantly [20]. Another study found that dust accumulation resulted in yield losses of 78.3% and efficiency declines of 78% for amorphous Si, 77% and 77% for cadmium telluride, and 70% and 71% for polycrystalline Si modules [21]. In India, the monthly reduction in PV panel efficiency is >70% [22]. This will have an impact on the price of energy, the payback period and other factors. It is therefore necessary to clean the PV system on a regular basis at a low cost and with limited resources. Apart from dust accumulation, hailstorms also affect the performance of solar PV panels [23]. The number of hailstorms has increased in the last few years, especially in Rajasthan, which is dangerous for the solar PV system. A heavy hailstorm can affect the surface of the front glass and break the solar cell. When cracks occur on the front surface of glass, they reduce the solar radiation that enters the solar cell. If cracks occur in a solar cell, current is reduced and the cell is totally isolated. Hailstorms reduce not only total electricity generation, but also PV module life. Thus, hailstorm protection for solar PV panels is critical [24]. This paper describes the performance analysis and design of a self-cleaning solar PV sliding system that not only protects the solar panels from dirt deposition, but also protects them from hailstorms. The principal reasons for this system’s development are to achieve the maximum power of the PV system and ensure that PV modules are protected against dirt deposition and hailstorms. 1 Literature review The literature mentions a variety of cleaning options, such as manual cleaning, mechanical cleaning, autonomous cleaning and self-cleaning. Manual cleaning is one of the simplest ways to clean PV panels, depending on the amount of water needed to clean the PV panel’s surface. This traditional method, in addition to being expensive, necessitates the use of labour. Because water is a rare resource in hot desert conditions, and desalination of water produces fresh water, which necessitates a large amount of thermal or electrical energy, this is not a good idea. Mechanical cleaning comprises various cleaning robots, while manual cleaning is the fundamental form of solar-panel cleaning. A customized robotic system is generally proposed for large solar plants. Such systems are designed to clean the most areas in the shortest amount of time with only a few operators required [25]. The brush with the smaller robots consumes less water. Though these robots provide self-cleaning and are typically highly durable, they necessitate (i) modification; (ii) significant cost-effectiveness, such as specific rails; and (iii) high temperatures and complicated maintenance. There is another type of robot that uses water to clean the panels on the basis of a sprinkler. Although they are efficient in dry, sandy environments, their main drawback is that they demand a lot of water [26, 27]. Mechanical automatic cleaning systems, which are effective but have high capital and operating costs, require their own infrastructure, electricity, maintenance and manpower [27]. The electric-curtain method is also used to clean PV panels, in which a travelling wave is created by an electrical wave that prevents particles from moving on the surface of the photovoltaic system. On rainy days, this system requires additional safety for the electrodes. Another way of protecting dust is to cover the PV surfaces with anti-dust coatings. Two kinds of self-cleaning coating are available: superhydrophilic and superhydrophobic films. TiO2 is the best-known type of coating [28]. The solution needs water, which is not suitable for desert environments [29]. Arabatzis et al. [29] used self-cleaning, clean, anti-reflective glass coatings to test the external behaviour of coated and uncoated PV panels and arrays in various weather situations over several months. During its study, its superhydrophilicity showed that the coated surface was better able to remove dust, which allowed one-stage application to achieve an average power gain of 5–6% for long-term operation. According to Piliougine et al. [30], uncoated modules have a daily loss of soiling of ~3.3%, while coated modules have a daily loss of soiling of 2.5%. According to Luque et al. [31], the soiling rate in the mono-facial module was detected to be ~0.301% per day, whereas that of the bifacial module was determined to be at 0.236% per day. In rainy areas, 5 mm of precipitation may be considered sufficient to clean the solar panels and restore their output capability [32]. Moharram et al. investigated the use of a non-pressurized water system to remove dust particles from PV panels. They observed that after 45 days of cleaning with non-pressurized water, the PV panels’ efficiency decreased considerably. However, when anionic and cationic surfactants were employed, the efficiency remained steady. They proposed reducing the volume of water needed to clean and the energy needed to spray the water with a combination of surfactants [33]. Urrejola et al. found that monthly cleaning and unpredictable rain were the most effective solutions [34]. Table 1 shows the comparison of existing cleaning techniques. Table 1: Comparison of various cleaning techniques Cleaning technique . Merits . Demerits . Functions . Natural cleaning Costless Depends on weather Cleaning and cooling Manual cleaning No need for electricity Labour, water is required, it is costly and time-consuming Cleaning and cooling Mechanical cleaning More effective Sometimes scratching happens Cleaning Electrical screens Effective, no required moving parts Need high voltage and costly Cleaning Superhydrophobic and superhydrophilic coating No need for water and labour Reduces the screen efficiency due to coating Cleaning Vibrating cleaning system Useful for dry weather Required motor and power supply Cleaning Forced-air cleaning system Effective in dry weather Requires power supply and high-pressure air pump Cleaning and cooling Cleaning technique . Merits . Demerits . Functions . Natural cleaning Costless Depends on weather Cleaning and cooling Manual cleaning No need for electricity Labour, water is required, it is costly and time-consuming Cleaning and cooling Mechanical cleaning More effective Sometimes scratching happens Cleaning Electrical screens Effective, no required moving parts Need high voltage and costly Cleaning Superhydrophobic and superhydrophilic coating No need for water and labour Reduces the screen efficiency due to coating Cleaning Vibrating cleaning system Useful for dry weather Required motor and power supply Cleaning Forced-air cleaning system Effective in dry weather Requires power supply and high-pressure air pump Cleaning and cooling Open in new tab Table 1: Comparison of various cleaning techniques Cleaning technique . Merits . Demerits . Functions . Natural cleaning Costless Depends on weather Cleaning and cooling Manual cleaning No need for electricity Labour, water is required, it is costly and time-consuming Cleaning and cooling Mechanical cleaning More effective Sometimes scratching happens Cleaning Electrical screens Effective, no required moving parts Need high voltage and costly Cleaning Superhydrophobic and superhydrophilic coating No need for water and labour Reduces the screen efficiency due to coating Cleaning Vibrating cleaning system Useful for dry weather Required motor and power supply Cleaning Forced-air cleaning system Effective in dry weather Requires power supply and high-pressure air pump Cleaning and cooling Cleaning technique . Merits . Demerits . Functions . Natural cleaning Costless Depends on weather Cleaning and cooling Manual cleaning No need for electricity Labour, water is required, it is costly and time-consuming Cleaning and cooling Mechanical cleaning More effective Sometimes scratching happens Cleaning Electrical screens Effective, no required moving parts Need high voltage and costly Cleaning Superhydrophobic and superhydrophilic coating No need for water and labour Reduces the screen efficiency due to coating Cleaning Vibrating cleaning system Useful for dry weather Required motor and power supply Cleaning Forced-air cleaning system Effective in dry weather Requires power supply and high-pressure air pump Cleaning and cooling Open in new tab In summary, decreasing PV panel energy-generation efficiencies due to dirt deposition has prompted several researchers to develop practical and economical PV cleaning solutions. Because most existing solar cleaning technologies involve water and separate cleaning systems, they can be very expensive and ineffective. The separate cleaning system raises capital costs while using fossil fuel to operate. This is a conflicting issue where a clean source of energy is used while simultaneously consuming fossil fuels. Studies show that a heavy hailstorm can affect the surface of the front glass and break the solar cell. When cracks occur on the front surface of glass, they reduce the solar radiation entering the solar cell specifically. If cracks occur in a solar cell, current is reduced and the cell is totally isolated. A hailstorm reduces not just the total electricity generation, but also the PV module life [24]. In the last few years, it has been seen that the frequency and size of hailstorms have increased, which is not suitable for PV panels. In hot and dry climates like Rajasthan, water is scarce. The use of precious water resources for the purposes of PV cleaning is therefore contrary to the ultimate aims of economic and environmental sustainability. This study therefore attempts to address these issues, based on the true necessity of an economical, efficient and automated technology for cleaning from dirt deposition and protecting from hailstorms. With the motivation of the above literature review, the authors have tried to develop self-cleaning and hail-protection mechanisms for the PV system. In this paper, a method for self-cleaning PV modules has been developed and tested in Indian climate conditions. In this proposed technique, a self-cleaning PV sliding system covers the PV panels during the night and performs the cleaning procedure twice daily. The proposed self-cleaning PV sliding system also provides protection from hailstorms. The proposed self-cleaning system is more effective in summer and winter with less power consumption. 2 Description of proposed self-cleaning PV sliding system The proposed self-cleaning PV sliding system comprises rails with three trackers, three 20-W solar PV panels, rollers, an aluminium protective plate, stud, a 12-V DC motor, bearings, cleaning brushes, mechanical supporter, light-activated light-dependent resistor (LDR), dark-activated LDR sensor, rain sensor, 5-V magnetic reed-switch sensor and Arduino UNO. The rollers are attached to both sides of the solar panel to allow the solar PV panels to move easily on the tracker. To prevent rust, we have used plastic rollers here so that they can be moved freely for a long time. A 12-volt DC motor is connected to one end of the stud via a mechanical coupler and the other end of the stud rests on a ball-bearing. The third solar panel is connected to the stud so that it can move linearly on the track with the motor. Strong threads are used to attach the PV solar panels together so that they can move forward throughout the uncovering process. The second solar panel is linked to the third panel by a strong thread and the first panel is linked to the second panel by a strong thread, so that when the motor rotates, the third panel, along with the first and second panels, is covered and uncovered according to command signals that are sent by LDRs and rain sensors. On the protecting plate, we fixed a brush in such a way that it cleans the first panel during covering and uncovering. We mounted the other brushes to clean the downward panels on the edges of the first and second panels. The front and rear views of the proposed self-cleaning PV sliding system are shown in Figs 1 and 2. A block diagram of the motor controller of the proposed self-cleaning system is shown in Fig. 3. Fig. 1: Open in new tabDownload slide Front view of self-cleaning PV sliding system. Fig. 2: Open in new tabDownload slide Rear view of self-cleaning PV sliding system. Fig. 3: Open in new tabDownload slide Block diagram of the motor controller of the self-cleaning PV sliding system. Three sets of polycrystalline PV modules with the proposed self-cleaning system remain exposed in the natural environment. The rain sensor and LDRs are used for the detection of rain and sunlight. When there is sunshine in the morning, the light-activated LDR activates the relay module set, which rotates the DC motor to uncover and bring the solar panels into the sun’s light. The brush on the top cover plate cleans the first panel, the brush on the first panel cleans the next panel, and the brush on the second panel cleans the third panel. All PV panels are cleaned in this manner during the uncovering process. When the sun sets in the evening, the dark-activated LDR triggers the second relay set, which rotates the DC motor in the opposite direction, allowing the panels to be placed beneath the protective plate. During this activity, the cleaning procedure takes place again. As a result, the suggested self-cleaning PV sliding system performs the cleaning procedure twice daily and prevents dust particles from adhering to the PV panels. During hailstorms, raindrops on the rain sensor raise the rain sensor’s output, which activates the relay, which starts the motor in such a way that all three panels come beneath the protective plate, shielding the solar panels from the hail. The reed relay switches, which are located on both ends of the track-rail system, disconnect the relay modules, stopping the motor as soon as all the panels are completely uncovered and covered. During covering, L-section blocks are used to lock the third panel to the second and first panels. Tables 2 and 3 show the mechanical and electronic components of the proposed self-cleaning PV sliding system and Table 4 shows the specifications of the DC motor used in the experiment. Table 2: Mechanical components for proposed self-cleaning PV sliding system Name of major components . Material . Function . Track-rail system Aluminium To provide the sliding track for PV panels Roller Plastic To provide smoothness during the sliding of PV panels Bearing Iron To provide frictionless support for the stud Stud Iron To move the PV panel Nut Iron To connect the PV panel to the stud Mechanical coupler Iron To connect the motor shaft to the stud Cleaning brush Nylon To clean the PV panels Mechanical supports Iron For bearing L-section Aluminium To provide the contact for another panel during covering Thread Nylon To connect the PV panels Protective sheet Aluminium coated fibre To cover the PV panels Name of major components . Material . Function . Track-rail system Aluminium To provide the sliding track for PV panels Roller Plastic To provide smoothness during the sliding of PV panels Bearing Iron To provide frictionless support for the stud Stud Iron To move the PV panel Nut Iron To connect the PV panel to the stud Mechanical coupler Iron To connect the motor shaft to the stud Cleaning brush Nylon To clean the PV panels Mechanical supports Iron For bearing L-section Aluminium To provide the contact for another panel during covering Thread Nylon To connect the PV panels Protective sheet Aluminium coated fibre To cover the PV panels Open in new tab Table 2: Mechanical components for proposed self-cleaning PV sliding system Name of major components . Material . Function . Track-rail system Aluminium To provide the sliding track for PV panels Roller Plastic To provide smoothness during the sliding of PV panels Bearing Iron To provide frictionless support for the stud Stud Iron To move the PV panel Nut Iron To connect the PV panel to the stud Mechanical coupler Iron To connect the motor shaft to the stud Cleaning brush Nylon To clean the PV panels Mechanical supports Iron For bearing L-section Aluminium To provide the contact for another panel during covering Thread Nylon To connect the PV panels Protective sheet Aluminium coated fibre To cover the PV panels Name of major components . Material . Function . Track-rail system Aluminium To provide the sliding track for PV panels Roller Plastic To provide smoothness during the sliding of PV panels Bearing Iron To provide frictionless support for the stud Stud Iron To move the PV panel Nut Iron To connect the PV panel to the stud Mechanical coupler Iron To connect the motor shaft to the stud Cleaning brush Nylon To clean the PV panels Mechanical supports Iron For bearing L-section Aluminium To provide the contact for another panel during covering Thread Nylon To connect the PV panels Protective sheet Aluminium coated fibre To cover the PV panels Open in new tab Table 3: Electrical and electronics components for proposed self-cleaning PV sliding system Name of major components . Quantity . Function . DC motor 1 To move the PV panels forward and backward 2-Relay module 2 To control the direction of the motor Light-activated LDR 1 To send a signal to Arduino UNO for moving forward (to uncover) the PV panel Dark-activated LDR 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Rain sensor 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Arduino UNO 1 Control the whole system Magnetic reed-switch sensor 2 To stop the motor when the PV panel is fully covered or uncovered 12-volt supply 1 Input supply for motor Magnet 2 For reed-switch sensor PV panels 3 To generate electrical energy Name of major components . Quantity . Function . DC motor 1 To move the PV panels forward and backward 2-Relay module 2 To control the direction of the motor Light-activated LDR 1 To send a signal to Arduino UNO for moving forward (to uncover) the PV panel Dark-activated LDR 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Rain sensor 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Arduino UNO 1 Control the whole system Magnetic reed-switch sensor 2 To stop the motor when the PV panel is fully covered or uncovered 12-volt supply 1 Input supply for motor Magnet 2 For reed-switch sensor PV panels 3 To generate electrical energy Open in new tab Table 3: Electrical and electronics components for proposed self-cleaning PV sliding system Name of major components . Quantity . Function . DC motor 1 To move the PV panels forward and backward 2-Relay module 2 To control the direction of the motor Light-activated LDR 1 To send a signal to Arduino UNO for moving forward (to uncover) the PV panel Dark-activated LDR 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Rain sensor 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Arduino UNO 1 Control the whole system Magnetic reed-switch sensor 2 To stop the motor when the PV panel is fully covered or uncovered 12-volt supply 1 Input supply for motor Magnet 2 For reed-switch sensor PV panels 3 To generate electrical energy Name of major components . Quantity . Function . DC motor 1 To move the PV panels forward and backward 2-Relay module 2 To control the direction of the motor Light-activated LDR 1 To send a signal to Arduino UNO for moving forward (to uncover) the PV panel Dark-activated LDR 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Rain sensor 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Arduino UNO 1 Control the whole system Magnetic reed-switch sensor 2 To stop the motor when the PV panel is fully covered or uncovered 12-volt supply 1 Input supply for motor Magnet 2 For reed-switch sensor PV panels 3 To generate electrical energy Open in new tab Table 4 Specifications of DC motor used in the experiment Specification . Value . Units . Type Gear – Input voltage 12 V Maximum load current 3 A Rated speed 300 rpm Stall torque 8 kg-cm Specification . Value . Units . Type Gear – Input voltage 12 V Maximum load current 3 A Rated speed 300 rpm Stall torque 8 kg-cm Open in new tab Table 4 Specifications of DC motor used in the experiment Specification . Value . Units . Type Gear – Input voltage 12 V Maximum load current 3 A Rated speed 300 rpm Stall torque 8 kg-cm Specification . Value . Units . Type Gear – Input voltage 12 V Maximum load current 3 A Rated speed 300 rpm Stall torque 8 kg-cm Open in new tab Experimental Setup and Methodology To investigate the performance of the proposed self-cleaning PV sliding system, we incorporated three PV panels of 20 W with a sliding structure and installed them on the rooftop of Manipal University Jaipur. The solar panels were connected in parallel and the output terminals of the PV system were connected to the resistive load. Another set of three PV panels of 20 W was installed with a conventional method, i.e. fixed. The effectiveness of the proposed cleaning system was tested on photovoltaic panels in Jaipur, India for all seasons. Both sets of three polycrystalline solar panels are installed on a platform that is tilted 27° to the south and the specifications of the PV panels are listed in Table 5. Fig. 4 shows the prototype of the self-cleaning PV system and the experimental set-up. Table 5: Specifications of solar panels used in the experiment under standard testing conditions Specification . Value . Units . Model ELDORA 20P – Type Polycrystalline – Maximum power 20 W Open-circuit voltage 21.44 V Short-circuit current 1.27 A Maximum voltage 17.15 V Maximum current 1.18 A Specification . Value . Units . Model ELDORA 20P – Type Polycrystalline – Maximum power 20 W Open-circuit voltage 21.44 V Short-circuit current 1.27 A Maximum voltage 17.15 V Maximum current 1.18 A Open in new tab Table 5: Specifications of solar panels used in the experiment under standard testing conditions Specification . Value . Units . Model ELDORA 20P – Type Polycrystalline – Maximum power 20 W Open-circuit voltage 21.44 V Short-circuit current 1.27 A Maximum voltage 17.15 V Maximum current 1.18 A Specification . Value . Units . Model ELDORA 20P – Type Polycrystalline – Maximum power 20 W Open-circuit voltage 21.44 V Short-circuit current 1.27 A Maximum voltage 17.15 V Maximum current 1.18 A Open in new tab Fig. 4: Open in new tabDownload slide Experimental set-up. We installed one set of three PV panels without any cleaning method and on the second set of three PV panels, we implemented the proposed cleaning technique and measured the output power of both sets for 1 year. The proposed cleaning method uses no water. The performance of the proposed self-cleaning PV sliding system is determined based on cleaning efficiency and energy usage. A data logger is used to record the performance parameters. Table 6 shows the specifications of the test location. Table 6: Specifics about the test location Description . Value . Place Academic Block 1, Manipal University Jaipur Latitude 26.84°N Longitude 75.56°E Tilt angle 27° Facing South Description . Value . Place Academic Block 1, Manipal University Jaipur Latitude 26.84°N Longitude 75.56°E Tilt angle 27° Facing South Open in new tab Table 6: Specifics about the test location Description . Value . Place Academic Block 1, Manipal University Jaipur Latitude 26.84°N Longitude 75.56°E Tilt angle 27° Facing South Description . Value . Place Academic Block 1, Manipal University Jaipur Latitude 26.84°N Longitude 75.56°E Tilt angle 27° Facing South Open in new tab An IOT-based monitoring system has been used, which collects real-time information and sends it to the ThinkSpeak cloud server [35]. Table 7 shows the technical characteristics of components used in the data acquisition system. The system consists of an ESP8266 Wi-Fi module, INA 260 sensors and the cloud platform ThingSpeak. ThingSpeak displays PV system variables such as generated voltage, current and power. The sensors were verified and calibrated precisely. The readings of current and voltage measured by the INA260 and INA219 sensors were compared with the readings of a standard multimeter (Aplab VC97) at the same time. The entire calibration results were repeatable. For solar radiation, an EKO MS40M pyranometer was used. The Arduino-based data logger recorded the power consumed by the DC motor and stored the information on a microSD card. For this purpose, the INA219 sensor was used. Before each season, the calibration process was repeated. Measurements of the PV panel’s variables were recorded at an interval of 5 minutes from 7.00 am to 6.00 pm for the period of December 2019 to April 2021. The study period was split into four seasons: post-monsoon (September to mid-December), winter (mid-December to February), summer (March to June) and monsoon (July to August). Table 7: Technical characteristics of components used in data acquisition system Name of component . Parameters . Specification . Function . Number . ESP8266 Wi-Fi Module (Lolin NodeMCU) Supply voltage 3.3–5 V For Wi-Fi module 2 Available GPIO pins 10 RAM 36 kb Clock speed 80/160 MHz MCU 32 bits TenSillica L 106 INA 260 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power of PV panels 2 Measure voltage 0–36 V DC Measure current range 0–15 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% INA 219 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power for DC motor 1 Measure voltage 0–26 V DC Measure current range 0–3.2 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% Pyranometer (EKO MS40M) Operating voltage 12–24 V DC To sense solar irradiance 1 Irradiance range 0–2000 W/m2 Wavelength range 285–3000 nm Operating Temperature –40ºC to 80ºC Signal processing error ±1.5 W/m2 Multimeter (Aplab VC97) DC voltage range 400 mV to 1000 V ± 0.5% To calibrate the sensors 1 AC voltage range 400 mV to 750 V ± 0.5% DC current range 400 µA to 10 A ± 1.0% AC current range 400 µA to 10 A ± 1.0% Sampling rate 3 times/second Arduino UNO Microcontroller ATmega328P To feed sensors data 1 Operating voltage 5 V Analogue input pin 6 Digital I/o pin 14 Clock speed 16 MHz MicroSD card with shield Operating voltage 5/3.3 V To store the measured data 1 Interface SPI Compatible MicroSD card Name of component . Parameters . Specification . Function . Number . ESP8266 Wi-Fi Module (Lolin NodeMCU) Supply voltage 3.3–5 V For Wi-Fi module 2 Available GPIO pins 10 RAM 36 kb Clock speed 80/160 MHz MCU 32 bits TenSillica L 106 INA 260 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power of PV panels 2 Measure voltage 0–36 V DC Measure current range 0–15 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% INA 219 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power for DC motor 1 Measure voltage 0–26 V DC Measure current range 0–3.2 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% Pyranometer (EKO MS40M) Operating voltage 12–24 V DC To sense solar irradiance 1 Irradiance range 0–2000 W/m2 Wavelength range 285–3000 nm Operating Temperature –40ºC to 80ºC Signal processing error ±1.5 W/m2 Multimeter (Aplab VC97) DC voltage range 400 mV to 1000 V ± 0.5% To calibrate the sensors 1 AC voltage range 400 mV to 750 V ± 0.5% DC current range 400 µA to 10 A ± 1.0% AC current range 400 µA to 10 A ± 1.0% Sampling rate 3 times/second Arduino UNO Microcontroller ATmega328P To feed sensors data 1 Operating voltage 5 V Analogue input pin 6 Digital I/o pin 14 Clock speed 16 MHz MicroSD card with shield Operating voltage 5/3.3 V To store the measured data 1 Interface SPI Compatible MicroSD card Open in new tab Table 7: Technical characteristics of components used in data acquisition system Name of component . Parameters . Specification . Function . Number . ESP8266 Wi-Fi Module (Lolin NodeMCU) Supply voltage 3.3–5 V For Wi-Fi module 2 Available GPIO pins 10 RAM 36 kb Clock speed 80/160 MHz MCU 32 bits TenSillica L 106 INA 260 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power of PV panels 2 Measure voltage 0–36 V DC Measure current range 0–15 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% INA 219 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power for DC motor 1 Measure voltage 0–26 V DC Measure current range 0–3.2 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% Pyranometer (EKO MS40M) Operating voltage 12–24 V DC To sense solar irradiance 1 Irradiance range 0–2000 W/m2 Wavelength range 285–3000 nm Operating Temperature –40ºC to 80ºC Signal processing error ±1.5 W/m2 Multimeter (Aplab VC97) DC voltage range 400 mV to 1000 V ± 0.5% To calibrate the sensors 1 AC voltage range 400 mV to 750 V ± 0.5% DC current range 400 µA to 10 A ± 1.0% AC current range 400 µA to 10 A ± 1.0% Sampling rate 3 times/second Arduino UNO Microcontroller ATmega328P To feed sensors data 1 Operating voltage 5 V Analogue input pin 6 Digital I/o pin 14 Clock speed 16 MHz MicroSD card with shield Operating voltage 5/3.3 V To store the measured data 1 Interface SPI Compatible MicroSD card Name of component . Parameters . Specification . Function . Number . ESP8266 Wi-Fi Module (Lolin NodeMCU) Supply voltage 3.3–5 V For Wi-Fi module 2 Available GPIO pins 10 RAM 36 kb Clock speed 80/160 MHz MCU 32 bits TenSillica L 106 INA 260 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power of PV panels 2 Measure voltage 0–36 V DC Measure current range 0–15 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% INA 219 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power for DC motor 1 Measure voltage 0–26 V DC Measure current range 0–3.2 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% Pyranometer (EKO MS40M) Operating voltage 12–24 V DC To sense solar irradiance 1 Irradiance range 0–2000 W/m2 Wavelength range 285–3000 nm Operating Temperature –40ºC to 80ºC Signal processing error ±1.5 W/m2 Multimeter (Aplab VC97) DC voltage range 400 mV to 1000 V ± 0.5% To calibrate the sensors 1 AC voltage range 400 mV to 750 V ± 0.5% DC current range 400 µA to 10 A ± 1.0% AC current range 400 µA to 10 A ± 1.0% Sampling rate 3 times/second Arduino UNO Microcontroller ATmega328P To feed sensors data 1 Operating voltage 5 V Analogue input pin 6 Digital I/o pin 14 Clock speed 16 MHz MicroSD card with shield Operating voltage 5/3.3 V To store the measured data 1 Interface SPI Compatible MicroSD card Open in new tab The uncertainties of the measuring sensors INA260 (U1), INA219 (U2), pyranometer (U3) and multimeter (U4) used in this experiment are shown in Table 8. The verification reports for the sensors and measuring devices were used to determine the standard uncertainties for the components. Table 8: Standard uncertainties for the components U1 (%) . U2 (%) . U3 (W/m2) . U4 (%) . 0.5 0.5 1.5 0.5 for voltage 1.0 for current U1 (%) . U2 (%) . U3 (W/m2) . U4 (%) . 0.5 0.5 1.5 0.5 for voltage 1.0 for current Open in new tab Table 8: Standard uncertainties for the components U1 (%) . U2 (%) . U3 (W/m2) . U4 (%) . 0.5 0.5 1.5 0.5 for voltage 1.0 for current U1 (%) . U2 (%) . U3 (W/m2) . U4 (%) . 0.5 0.5 1.5 0.5 for voltage 1.0 for current Open in new tab 3 Results and discussion To investigate the performance of the proposed self-cleaning PV sliding system, we incorporated three PV panels of 20 W with a sliding structure and another set of three PV panels of 20 W was installed using a conventional method, i.e. fixed. Both sets of three polycrystalline solar panels are installed on a platform tilted 27° to the south. We installed one set of three PV panels without any cleaning procedure, then applied the proposed cleaning methodology to the second PV set and measured the output power at an interval of 5 minutes from 7.00 am to 6.00 pm for all seasons. For the summer, monsoon, post-monsoon and winter seasons, data were collected for 58, 54, 68 and 64 days, respectively. The collected data have a P-value of <0.05, indicating that it is statistically significant. The performance of the proposed self-cleaning PV slider system is determined based on the following points: (i) efficient cleaning; (ii) energy consumption; (iii) effectiveness during hailstorms. 3.1 Efficient cleaning Fig. 5a–d shows the variation in normalized efficiency of the PV system with and without the proposed self-cleaning mechanism. The ratio of the measured power to the power rating under standard test conditions (STC) compared to the irradiance divided by the STC irradiance gives the normalized efficiency, ƞN: Fig. 5: Open in new tabDownload slide Variation in the efficiency of cleaned and uncleaned PV systems in (a) summer season; (b) monsoon season; (c) post-monsoon season; and (d) winter season. ηN=P╱PSTCIm╱ISTC(1) where P is the measured power, PSTC is the STC-rated power, Im is the measured irradiance and ISTC is the reference irradiance (1000 W/m2) [36, 37]. Fig. 5a shows the efficiency of the proposed self-cleaning PV sliding system compared to the uncleaned PV system in the summer season. As shown in the figure, the performance of the cleaned panels significantly improves when compared to the uncleaned panels. This is mainly due to dust accumulation on uncleaned panels, whereas dust is removed every day on panels with the proposed self-cleaning system. During the summer season, the ambient temperature is very high and the humidity in the air is low, so the air easily lifts the dust particles that have accumulated on the PV panels. In this season, the reduction in efficiency in PV systems without a self-cleaning mechanism is 18.43%, while the efficiency drop in a PV system with a self-cleaning mechanism is 3.3% in 58 days. With the proposed self-cleaning PV sliding system, the overall gain in efficiency is 18.3%. The result, as shown in Fig. 5a, reveals that this mechanism is more effective for the summer season. Fig. 5b shows the efficiency of the proposed self-cleaning PV sliding system compared to the uncleaned PV system in the monsoon season. As shown in the figure, the performance of the panels with the proposed self-cleaning PV sliding structure does not improve compared to the uncleaned panels. This drop in performance is not due to dust accumulation; it is due to the repetition of the covering and uncovering process of panels when dark clouds appear and disappear. As a result, the PV system’s exposure time was reduced. During this season, PV systems without a self-cleaning mechanism lose 0.33% of their efficiency, while PV systems with a self-cleaning mechanism lose 5.5% of their efficiency in 54 days. In the monsoon season, the proposed self-cleaning sliding system does not improve the efficiency of the PV panels. The overall drop in efficiency is 5.4%. The result, as shown in Fig. 5b, demonstrates that cleaning is not necessary during the monsoon season. But the proposed system provides not only cleaning, but also protection from hailstorms. So, it is very useful in the monsoon season also. Fig. 5c shows the efficiency of the proposed self-cleaning PV sliding system compared to the uncleaned PV system in the post-monsoon season. As shown in the figure, the performance of the panels with the proposed self-cleaning PV sliding structure slightly improves compared to the uncleaned panels. In the post-monsoon season, the hot and humid weather begins to fade and the air quality improves noticeably. Because there are fewer airborne particles, the sky is clean and the quality of solar radiation is improved. Rain reduces the amount of pollutant particles in the air. The efficiency drop in a PV system without a self-cleaning mechanism is 7.1% in 68 days. This drop in efficiency is due to dust accumulation. While the efficiency drop in a self-cleaning PV system is 0.85%, the overall efficiency gain is 6.40% with the proposed self-cleaning PV sliding system. Results as shown in Fig. 5c reveal that this mechanism is effective for the post-monsoon season. Fig. 5d shows the efficiency of the proposed self-cleaning PV sliding system compared to the uncleaned PV system in the winter season. As shown in the figure, the performance of the panels with the proposed self-cleaning PV sliding structure significantly improves compared to the uncleaned panels. This drop in performance is not only due to dust accumulation on uncleaned panels; there are more factors involved, such as bird droppings, insects and leaves of trees because the autumn season starts, whereas cleaning is done every day on panels with the proposed self-cleaning system. In this season, the efficiency drop in PV systems without a self-cleaning mechanism is 14%, while the efficiency drop in a PV system with a self-cleaning mechanism is 2.2%. With the proposed self-cleaning PV sliding system, the overall gain in efficiency is 13.3%. The results depicted in Fig. 5d show that this mechanism is extremely effective during the winter season. Fig. 6 demonstrates that the proposed cleaning system’s percent gain in efficiency is negative during the monsoon season, indicating that the cleaning mechanism of the proposed system is not required during the monsoon season. This decrease in efficiency is not due to dust accumulation; rather, it is due to the repeated covering and uncovering of panels when dark clouds appear and disappear, resulting in shorter exposure times, lower output power and higher energy consumption of the proposed cleaning system. While the sky is cloudless most of the time during other seasons in Jaipur, exposure time is longer and the average energy consumption of the proposed system is lower. Thus, the gain is positive and the proposed system is more effective and adaptable. According to the findings, the average air dust density is higher in the summer and winter than in the post-monsoon and monsoon seasons [38]. The accumulation of dust on PV panels, which is primarily determined by air dust density, contributes significantly to the PV system’s output energy-generation degradation. In the summer, post-monsoon season and winter seasons, PV systems without a self-cleaning mechanism have efficiency reductions of 18.43%, 7.1% and 14%, respectively. In contrast, in the summer, post-monsoon and winter seasons, the efficiencies of a PV system with a self-cleaning mechanism drop by 3.3%, 0.85% and 2.2%, respectively. The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. Results as shown in Fig. 6 reveal that the proposed sliding system is more effective in the summer and winter seasons. Fig. 6: Open in new tabDownload slide Drop and improvement in efficiency of the PV system with and without the proposed cleaning mechanism for all seasons. Most of the dust deposition occurs at night or before the sun rises, as there is less traffic and less wind blowing. These conditions are very suitable for the stagnation of the small and large dust particles suspended in the air. The PV cell cools before sunrise and dew is formed when water in the air condenses, which interacts with the dust particles, increasing the cell surface adhesion forces and making the layers of dust deposition difficult to clean in a fair manner, resulting in significant losses in the generation of output power. In this proposed technique, a self-cleaning PV sliding system covers the PV panel during the night and performs the cleaning procedure twice daily. As a result, the possibility of dust being deposited and dew developing on the PV surface is reduced. As described above, the proposed self-cleaning PV sliding system provides the cleaning process twice a day. It does not allow dust to be deposited on the PV panels and become adhesive dust. The results show that the cleaning system works significantly. It completely cleans large particles of dust and it removes a substantial amount of small dust particles also. It has been observed that the cleaning system is unable to remove the bird droppings completely in one cleaning day. The result shows that the proposed solar cleaning system works well in winter, post-monsoon and summer seasons. Cleaning is not required in the monsoon season because of the rain. It provides natural cleaning. During the monsoon season, the cleaning system performs the covering and uncovering process repeatedly due to the frequently appearing and disappearing dark clouds, resulting in higher energy consumption. But the proposed system provides not only cleaning, but also protection from hailstorms. So it is very useful in the monsoon season also. 3.2 Energy consumption The energy consumption of the motor is primarily determined by the size and weight of the PV system’s characteristics, although they are not the only factors. The efficiency of motors, the battery and a variety of other elements all influence their energy usage. The proposed cleaning technique consumes energy while in use. As a result, the cleaning process’s energy consumption is another important factor to consider when evaluating the cleaning method’s viability for PV applications. This method uses extremely little energy overall when it is compared with power gain. In the summer season, it has been seen that the dust gets in between the track of the solar sliders, due to which the track experiences more friction and the load on the motor also increases, due to which the energy consumption slightly increases. In the monsoon season, due to the frequent arrival and departure of dark clouds, the cleaning system has to be opened and closed frequently, resulting in increased energy consumption. The results show that the cleaning of solar PV panels is not required in the monsoon season because of the rain, which provides natural cleaning and cooling, and during this time, efficiency is not significantly reduced. During the summer season, the suggested solar sliding PV system consumed 29.58 Whr for 58 days, while the energy generation of the proposed system was 1145.6 Whr higher than that of the fixed PV panel. The total amount of energy gained was 1115.72 Whr. However, during the monsoon season, energy usage increases due to the repeated covering and uncovering of panels as dark clouds appear and disappear. As a result, the PV system’s energy consumption increased. A total energy loss of 345.4 Whr was measured for 54 days. During the post-monsoon season, the energy generation of the proposed PV system was 221 Whr more than with a fixed PV system, while energy consumption was 32.64 Whr for 68 days. In the winter, the proposed system generated 396.8 Whr more energy than a fixed PV system and the energy consumption was 31.36 Whr for 64 days. Fig. 7 shows the energy yield of a PV system with and without the proposed cleaning mechanism, energy consumption and gain for all seasons. Table 9 shows the comparison of the proposed self-cleaning PV sliding system with a drone-based PV cleaning system and Table 10 shows the comparison of the proposed self-cleaning PV sliding system with some literature studies for PV cleaning systems. Table 10: Comparison of proposed self-cleaning PV sliding system with some literature studies for PV cleaning Reference . Cleaning technique . Cleaning frequency . Outcomes . Shehri et al. [41] Nylon brush, cloth and silicon rubber foam The maximum power output of solar panels cleaned with silicone rubber brush increased by ~1% on average when compared to the unbrushed initial power output Shehri et al. [42] Brush 1 The right brush must be chosen to achieve the required level of cleaning while avoiding damage to the solar panels’ surface Arabatzis et al. [29] Self-cleaning and anti-reflective glass coating 16 and 86 days The coated PV panels showed an average gain of 5–6% for the tested period of time under real outdoor conditions Urrejola et al. [34] Brushing with water Monthly cleaning (suggested optimum cleaning period: 45 days) Lowest decay values in summer 2015: 0.14%; highest values in autumn 2015 seasonal average: 0.56%/day; the performance ratio has deteriorated by 17.36% monthly Al-Housani et al. [39] Drone incorporated with brush, microfiber cloth wiper, vacuum cleaner 1 day 1 week 1 month In the winter, the weekly power losses for microfiber-based cloth wiper + vacuum cleaner, mechanical brush + vacuum cleaner, microfiber- based cloth wiper, mechanical brush are 3.42%, 2.95%, 3.63% and 2.28%, respectively Proposed cleaning system Sliding structure with brush 2 times per day The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms Reference . Cleaning technique . Cleaning frequency . Outcomes . Shehri et al. [41] Nylon brush, cloth and silicon rubber foam The maximum power output of solar panels cleaned with silicone rubber brush increased by ~1% on average when compared to the unbrushed initial power output Shehri et al. [42] Brush 1 The right brush must be chosen to achieve the required level of cleaning while avoiding damage to the solar panels’ surface Arabatzis et al. [29] Self-cleaning and anti-reflective glass coating 16 and 86 days The coated PV panels showed an average gain of 5–6% for the tested period of time under real outdoor conditions Urrejola et al. [34] Brushing with water Monthly cleaning (suggested optimum cleaning period: 45 days) Lowest decay values in summer 2015: 0.14%; highest values in autumn 2015 seasonal average: 0.56%/day; the performance ratio has deteriorated by 17.36% monthly Al-Housani et al. [39] Drone incorporated with brush, microfiber cloth wiper, vacuum cleaner 1 day 1 week 1 month In the winter, the weekly power losses for microfiber-based cloth wiper + vacuum cleaner, mechanical brush + vacuum cleaner, microfiber- based cloth wiper, mechanical brush are 3.42%, 2.95%, 3.63% and 2.28%, respectively Proposed cleaning system Sliding structure with brush 2 times per day The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms Open in new tab Table 10: Comparison of proposed self-cleaning PV sliding system with some literature studies for PV cleaning Reference . Cleaning technique . Cleaning frequency . Outcomes . Shehri et al. [41] Nylon brush, cloth and silicon rubber foam The maximum power output of solar panels cleaned with silicone rubber brush increased by ~1% on average when compared to the unbrushed initial power output Shehri et al. [42] Brush 1 The right brush must be chosen to achieve the required level of cleaning while avoiding damage to the solar panels’ surface Arabatzis et al. [29] Self-cleaning and anti-reflective glass coating 16 and 86 days The coated PV panels showed an average gain of 5–6% for the tested period of time under real outdoor conditions Urrejola et al. [34] Brushing with water Monthly cleaning (suggested optimum cleaning period: 45 days) Lowest decay values in summer 2015: 0.14%; highest values in autumn 2015 seasonal average: 0.56%/day; the performance ratio has deteriorated by 17.36% monthly Al-Housani et al. [39] Drone incorporated with brush, microfiber cloth wiper, vacuum cleaner 1 day 1 week 1 month In the winter, the weekly power losses for microfiber-based cloth wiper + vacuum cleaner, mechanical brush + vacuum cleaner, microfiber- based cloth wiper, mechanical brush are 3.42%, 2.95%, 3.63% and 2.28%, respectively Proposed cleaning system Sliding structure with brush 2 times per day The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms Reference . Cleaning technique . Cleaning frequency . Outcomes . Shehri et al. [41] Nylon brush, cloth and silicon rubber foam The maximum power output of solar panels cleaned with silicone rubber brush increased by ~1% on average when compared to the unbrushed initial power output Shehri et al. [42] Brush 1 The right brush must be chosen to achieve the required level of cleaning while avoiding damage to the solar panels’ surface Arabatzis et al. [29] Self-cleaning and anti-reflective glass coating 16 and 86 days The coated PV panels showed an average gain of 5–6% for the tested period of time under real outdoor conditions Urrejola et al. [34] Brushing with water Monthly cleaning (suggested optimum cleaning period: 45 days) Lowest decay values in summer 2015: 0.14%; highest values in autumn 2015 seasonal average: 0.56%/day; the performance ratio has deteriorated by 17.36% monthly Al-Housani et al. [39] Drone incorporated with brush, microfiber cloth wiper, vacuum cleaner 1 day 1 week 1 month In the winter, the weekly power losses for microfiber-based cloth wiper + vacuum cleaner, mechanical brush + vacuum cleaner, microfiber- based cloth wiper, mechanical brush are 3.42%, 2.95%, 3.63% and 2.28%, respectively Proposed cleaning system Sliding structure with brush 2 times per day The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms Open in new tab Table 9: Comparison of proposed self-cleaning PV sliding system with drone-based PV cleaning technique [39, 40] Parameters . Drone-based cleaning system . . Developed self-cleaning PV sliding system . . Summer Winter Summer Winter Improvement in average power output (W/day) 0.4 0.5 1.98 0.77 Average improvement (%) 7 4.8 18.33 13.3 Average cleaning cost (USD/m2) 0.0578 0.0578 0.0001 0.000097 Average cleaning time (min/panel) 0.75–2 0.75–2 0.33–0.57 0.33–0.57 Average energy consumption Required Required 0.51 Whr/day 0.49 Whr/day Capital cost High High Relatively low Relatively low Cleaning frequency per day One time One time Two time Two time Manpower Require Required No No Water No No No No Parameters . Drone-based cleaning system . . Developed self-cleaning PV sliding system . . Summer Winter Summer Winter Improvement in average power output (W/day) 0.4 0.5 1.98 0.77 Average improvement (%) 7 4.8 18.33 13.3 Average cleaning cost (USD/m2) 0.0578 0.0578 0.0001 0.000097 Average cleaning time (min/panel) 0.75–2 0.75–2 0.33–0.57 0.33–0.57 Average energy consumption Required Required 0.51 Whr/day 0.49 Whr/day Capital cost High High Relatively low Relatively low Cleaning frequency per day One time One time Two time Two time Manpower Require Required No No Water No No No No Open in new tab Table 9: Comparison of proposed self-cleaning PV sliding system with drone-based PV cleaning technique [39, 40] Parameters . Drone-based cleaning system . . Developed self-cleaning PV sliding system . . Summer Winter Summer Winter Improvement in average power output (W/day) 0.4 0.5 1.98 0.77 Average improvement (%) 7 4.8 18.33 13.3 Average cleaning cost (USD/m2) 0.0578 0.0578 0.0001 0.000097 Average cleaning time (min/panel) 0.75–2 0.75–2 0.33–0.57 0.33–0.57 Average energy consumption Required Required 0.51 Whr/day 0.49 Whr/day Capital cost High High Relatively low Relatively low Cleaning frequency per day One time One time Two time Two time Manpower Require Required No No Water No No No No Parameters . Drone-based cleaning system . . Developed self-cleaning PV sliding system . . Summer Winter Summer Winter Improvement in average power output (W/day) 0.4 0.5 1.98 0.77 Average improvement (%) 7 4.8 18.33 13.3 Average cleaning cost (USD/m2) 0.0578 0.0578 0.0001 0.000097 Average cleaning time (min/panel) 0.75–2 0.75–2 0.33–0.57 0.33–0.57 Average energy consumption Required Required 0.51 Whr/day 0.49 Whr/day Capital cost High High Relatively low Relatively low Cleaning frequency per day One time One time Two time Two time Manpower Require Required No No Water No No No No Open in new tab Fig. 7: Open in new tabDownload slide Energy yield of PV system with and without proposed cleaning mechanism, energy consumption and gain for all seasons. 3.3 Effectiveness during hailstorms Hailstorms are common in India. The frequency and intensity of hailstorms have increased in India in the last few years, which is a threat to PV panel life. There is no method to protect them from heavy hailstorms. The proposed solar sliding system also provides protection from hailstorms, along with the self-cleaning of PV panels. Two hailstorms were observed on 5 March 2020 and 17 November 2020 in Jaipur. The hailstorm that happened on 5 March was very deadly. Fig. 8 shows the intensity and size of deadly hailstorms. There are a lot of PV panels installed on the top of Manipal University Jaipur without any protection. As shown in Fig. 9b, one of the 300-W PV panels was completely damaged during the massive hailstorm and some had cracks in the front glass. The proposed system kept the PV panels safe, but the corner of the protective plate was damaged, as shown in Fig. 9a. Fig. 8: Open in new tabDownload slide Deadly hailstorm on 5 March 2020. Fig. 9: Open in new tabDownload slide Impact of hailstorm. (a) Proposed system after deadly hailstorm; (b) 300-W PV panel damaged during deadly hailstorm on 5 March 2020. 4 Conclusion Most existing solar cleaning technologies rely on water and separate cleaning systems, which can be prohibitively expensive and inefficient. In hot and dry climates, water is scarce. The use of precious water resources for the purposes of PV cleaning is contrary to the ultimate aims of economic and environmental sustainability. Hailstorms reduce not only total electricity generation, but also the life of PV modules. There is no method for protecting the PV system from hailstorms. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms. In this proposed technique, a self-cleaning PV sliding system covers the PV panels during the night and performs the cleaning procedure twice daily. As a result, the volume of dust deposited and dew developed on the PV surface is greatly reduced. This technology is primarily designed to achieve maximum energy in the PV module and ensure protection against pollution and hailstorms for the PV module. Results show that the proposed self-cleaning PV sliding system improves efficiency by 18.3%, 13.3% and 6.4% when compared to fixed systems in the summer, winter and post-monsoon seasons, respectively. The results of this study show that there is a significant improvement in PV efficiency and, hence, an increase in the production of electricity in all climate conditions. This proposed cleaning mechanism provides a more efficient and energy-efficient technique for cleaning and protecting PV systems throughout the year. Conflict of interest statement The authors declare that there is no conflict of interest. References [1] Tanesab J , Parlevliet D, Whale J, et al. T. The effect of dust with different morphologies on the performance degradation of photovoltaic modules . Sustainable Energy Technol Assess , 2019 , 31 : 347 – 354 . Google Scholar Crossref Search ADS WorldCat [2] Salimi H , Lavasani AM, Danesh-Ashtiani HA, et al. Effect of dust concentration, wind speed, and relative humidity on the performance of photovoltaic panels in Tehran . Energy Sources, Part A: Recovery, Utilization, Environmental Effects , 2019 , 1 – 11 . https://doi.org/10.1080/15567036.2019.1677811. Google Scholar OpenURL Placeholder Text WorldCat [3] Oh S . Analytic and Monte-Carlo studies of the effect of dust accumulation on photovoltaics . Sol Energy , 2019 , 188 : 1243 – 1247 . Google Scholar Crossref Search ADS WorldCat [4] Figgis B , Nouviaire A, Wubulikasimu Y, et al. Investigation of factors affecting condensation on soiled PV modules . Sol Energy , 2018 , 159 : 488 – 500 . Google Scholar Crossref Search ADS WorldCat [5] Touati F , Massoud A, Abu Hamad J, et al. Effects of environmental and climatic conditions on PV efficiency in Qatar. In: International Conference on Renewable Energies and Power Quality (ICREPQ’13) , Bilbao, Spain , 20–22 March 2013 . [6] Jones T , Stark DP, Ellis RS. Dust in the wind: composition and kinematics of galaxy outflows at the peak epoch of star formation . The Astrophysical Journal , 2018 , 863 : 191 . Google Scholar Crossref Search ADS WorldCat [7] Badi HA , Boland J, Bruce D, et al. Dust event impact on photovoltaic systems: role of humidity in soiling and self-cleaning, In: IEEE International Conference on Smart Energy Grid Engineering (SEGE) , Oshawa, ON, Canada , 12–15 August 2018 , 342 – 345 . [8] Hosseini SA , Kermani AM, Arabhosseini A. Experimental study of the dew formation effect on the performance of photovoltaic modules . Renew Energy , 2018 , 130 : 352 – 359 . Google Scholar Crossref Search ADS WorldCat [9] Onishchenko O , Fedun V, Horton W, et al. Dust devils: structural features, dynamics, and climate impact . Climate , 2019 , 7 : 121 – 118 . Google Scholar Crossref Search ADS WorldCat [10] Rashki A , Kaskaoutis DG, Sepehr A. Statistical evaluation of the dust events at selected stations in Southwest Asia: from the Caspian Sea to the Arabian Sea . Catena , 2018 , 165 : 590 – 603 . Google Scholar Crossref Search ADS WorldCat [11] Steffan JJ , Brevik EC, Burgess LC, et al. The effect of soil on human health: an overview . Eur J Soil Sci , 2018 , 69 : 159 – 171 . Google Scholar Crossref Search ADS PubMed WorldCat [12] Kinney PL . Interactions of climate change, air pollution, and human health . Current Environmental Health Reports , 2018 , 5 : 179 – 186 . Google Scholar Crossref Search ADS PubMed WorldCat [13] Li N , Weizheng H, Tang J, et al. Pollution characteristics and human health risks of elements in road dust in Changchun, China . Int J Environ Res Public Health , 2018 , 15 : 1843 . Google Scholar Crossref Search ADS WorldCat [14] Jiang Y , Lu L, Ferro AR, et al. Analysing wind cleaning process on the accumulated dust on solar photovoltaic (PV) modules on flat surfaces . Sol Energy , 2018 , 159 : 1031 – 1036 . Google Scholar Crossref Search ADS WorldCat [15] Bolles K , Sweeney M, Forman S. Meteorological catalysts of dust events and particle source dynamics of affected soils during the 1930s Dust Bowl drought, Southern High Plains, USA . Anthropocene. 2019 , 27 : 100216 . Google Scholar Crossref Search ADS WorldCat [16] Kazem HA , Chaichan MT, Alwaeli AH, et al. Effect of shadows on the performance of solar photovoltaic. In: Sayigh A (ed). Mediterranean Green Buildings & Renewable Energy . Cham : Springer , 2017 , 379 – 385 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC [17] Pandian A , Bansal K, Thiruvadigal DJ, et al. Fire hazards and overheating caused by shading faults on photo voltaic solar panel . Fire Technol , 2016 , 52 : 349 – 364 . Google Scholar Crossref Search ADS WorldCat [18] Gupta V , Sharma M, Pachauri RK, et al. Comprehensive review on effect of dust on solar photovoltaic system and mitigation techniques . Sol Energy , 2019 , 191 : 596 – 622 . Google Scholar Crossref Search ADS WorldCat [19] Darwish ZA , Sopian K, Fudholi A. Reduced output of photovoltaic modules due to different types of dust particles . J Clean Prod , 2021 , 280 : 124317 . Google Scholar Crossref Search ADS WorldCat [20] Enaganti PK , Bhattacharjee A, Ghosh A, et al. Experimental investigations for dust build-up on low-iron glass exterior and its effects on the performance of solar PV systems . Energy , 2022 , 239 : 122213 . Google Scholar Crossref Search ADS WorldCat [21] Chanchangi YN , Ghosh A, Baig H, et al. Soiling on PV performance influenced by weather parameters in Northern Nigeria . Renew Energy , 2021 , 180 : 874 – 892 . Google Scholar Crossref Search ADS WorldCat [22] Kazem HA , Chaichan MT, Al-Waeli AH, et al. A review of dust accumulation and cleaning methods for solar photovoltaic systems . J Clean Prod , 2020 , 276 : 123187 . Google Scholar Crossref Search ADS WorldCat [23] Gupta V , Sharma M, Pachauri RK, et al. Impact of hailstorm on the performance of PV module: a review . Energy Sources Part A , 2019 , 1 : 22 . Google Scholar OpenURL Placeholder Text WorldCat [24] Dhimish M , Holmes V, Mehrdadi B, et al. The impact of cracks on photovoltaic power performance . Journal of Science: Advanced Materials and Devices, 2017 , 2 : 199 – 209 . Google Scholar OpenURL Placeholder Text WorldCat [25] Kegeleers M. The development of a cleaning robot for PV panels . Master’s thesis. Leuven: KU, Technology Campus De Nayer, Sint-Katelijne-Waver , 2015 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC [26] Gheitasi A , Almaliky A, Albaqawi N. Development of an automatic cleaning system for photovoltaic plants. In: IEEE PES Asia-Pacific Power Energy Eng. Conf. (APPEEC), 2015 , Brisbane, QLD, Australia , 15–18 November 2015 , 1 – 4 . [27] Isaifan RJ , Samara A, Suwaileh W, et al. Improved self-cleaning properties of an efficient and easy to scale up TiO2 thin films prepared by adsorptive self-assembly . Sci Rep , 2017 , 7 : 1 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat [28] He G , Zhou C, Li Z. Review of self-cleaning method for solar cell array . Procedia Eng , 2011 , 16 : 640 – 645 . Google Scholar Crossref Search ADS WorldCat [29] Arabatzis I , Todorova N, Fasaki I, et al. Photocatalytic, self-cleaning, antireflective coating for photovoltaic panels: characterization and monitoring in real conditions . Sol Energy , 2018 , 159 : 251 – 259 . Google Scholar Crossref Search ADS WorldCat [30] Piliougine M , Cañete C, Moreno R, et al. Comparative analysis of energy produced by photovoltaic modules with anti-soiling coated surface in arid climates . Appl Energy , 2013 , 112 : 626 – 634 . Google Scholar Crossref Search ADS WorldCat [31] Luque EG , Antonanzas-Torres F, Escobar R. Effect of soiling in bifacial PV modules and cleaning schedule optimization . Energy Convers Manage , 2018 , 174 : 615 – 625 . Google Scholar Crossref Search ADS WorldCat [32] Kimber A , Mitchell L, Nogradi S, et al. The effect of soiling on large grid-connected photovoltaic systems in California and the Southwest Region of the United States . In: IEEE 4th World Conf. Photovoltaic. Energy Conf. , Waikoloa, HI, USA , 7–12 May 2006 , 2391 – 2395 . [33] Moharram KA , Abd-Elhady MS, Kandil HA, et al. Influence of cleaning using water and surfactants on the performance of photovoltaic panels . Energy Convers Manage , 2013 , 68 : 266 – 272 . Google Scholar Crossref Search ADS WorldCat [34] Urrejola E , Antonanzas J, Ayala P, et al. Effect of soiling and sunlight exposure on the performance ratio of photovoltaic technologies in Santiago, Chile . Energy Convers Manage , 2016 , 114 : 338 – 347 . Google Scholar Crossref Search ADS WorldCat [35] Gupta V , Sharma M, Pachauri RK, et al. A low-cost real-time IOT enabled data acquisition system for monitoring of PV system. Energy Sources, Part A: Recovery, Utilization Environ Effects . 2021 , 43 : 2529 – 2543 . Google Scholar Crossref Search ADS WorldCat [36] Herteleer B , Huyck B, Catthoor F, et al. Normalised efficiency of photovoltaic systems: going beyond the performance ratio . Sol Energy , 2017 , 157 : 408 – 418.https://www.sciencedirect.com/science/article/abs/pii/S0038092X1730717X?via%3Dihub Google Scholar Crossref Search ADS WorldCat [37] Adouane M , Al-Qattan A, Alabdulrazzaq B, et al. Comparative performance evaluation of different photovoltaic modules technologies under Kuwait harsh climatic conditions . Energy Rep , 2020 , 6 : 2689 – 2696 . Google Scholar Crossref Search ADS WorldCat [38] Dadhich AP , Goyal R, Dadhich PN. Assessment of spatio-temporal variations in air quality of Jaipur city, Rajasthan, India . The Egyptian Journal of Remote Sensing and Space Science . 2018 , 21 : 173 – 181 . https://www.sciencedirect.com/science/article/pii/S1110982317301357 Google Scholar Crossref Search ADS WorldCat [39] Al-Housani M , Bicer Y, Koç M. Experimental investigations on PV cleaning of large-scale solar power plants in desert climates: comparison of cleaning techniques for drone retrofitting . Energy Convers Manage , 2019 , 185 : 800 – 815 . Google Scholar Crossref Search ADS WorldCat [40] Al-Housani M , Bicer Y, Koç M. Assessment of various dry photovoltaic cleaning techniques and frequencies on the power output of CdTe-Type modules in dusty environments . Sustainability , 2019 , 11 : 2850 . Google Scholar Crossref Search ADS WorldCat [41] Shehri AA , Parrott B, Carrasco P, et al. Accelerated testbed for studying the wear, optical and electrical characteristics of dry-cleaned PV solar panels . Sol Energy , 2017 , 146 : 8 – 19 . Google Scholar Crossref Search ADS WorldCat [42] Shehri AA , Parrott B, Carrasco P, et al. Impact of dust deposition and brush-based dry cleaning on glass transmittance for PV modules applications . Sol Energy , 2016 , 135 : 317 – 324 . Google Scholar Crossref Search ADS WorldCat © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clean Energy Oxford University Press

Design and development of self-cleaning PV sliding system

12 pages

Loading next page...
 
/lp/oxford-university-press/design-and-development-of-self-cleaning-pv-sliding-system-8FMpHGjN07
Publisher
Oxford University Press
Copyright
Copyright © 2022 National Institute of Clean-and-Low-Carbon Energy
ISSN
2515-4230
eISSN
2515-396X
DOI
10.1093/ce/zkac015
Publisher site
See Article on Publisher Site

Abstract

Abstract This study describes the designing steps of the proposed self-cleaning system for the photovoltaic (PV) system and experimentally investigates the effectiveness of the proposed self-cleaning PV sliding system for solar power plants under all Indian climate conditions. To investigate the performance of the proposed self-cleaning PV sliding system, we used three PV panels of 20 W with a sliding structure and another set of three PV panels of 20 W with a traditional (fixed) technique, and recorded the output power for the period of December 2019 to April 2021. The experimental results show that the proposed self-cleaning PV sliding system improved efficiency by 18.3%, 13.3% and 6.4%, respectively, in the summer, winter and post-monsoon seasons when compared to the fixed PV system. The results also revealed that energy consumption was very low when compared to the amount of energy gained. The proposed system not only cleans the PV system, but also protects it from hailstorms. The results of this study show that there is a significant improvement in PV efficiency and thus an increase in electricity production under all climate conditions. Open in new tabDownload slide solar energy, photovoltaics, cleaning, soiling, hailstorm protection, sliding PV system Introduction Clean-energy power generation is a vital strategy for mitigation to overcome the challenge of global warming. Sun intensity is higher in the sunbelt region than in other parts of the world, but PV systems in the region can experience soiling that necessitates frequent and costly solar-panel cleaning. The fundamental issue of installing solar PV energy systems in dry and hot areas is dirt deposition on panel surfaces, which, in addition to high temperatures, is the main challenge [1]. Both soiling and high temperature significantly diminish the efficiency of solar panels and increase electricity costs, resulting in a lack of competitiveness for solar energy systems with fossil fuels such as natural gas [2]. The principal source of dust in desert regions is soil, whereas soiling can take place in many other places, as snow and other particles are included. However, there are numerous local sources of deposited dust, such as discharges from coal-based plants, vehicle movements, vehicle emissions and human activities in large cities [3]. The suspension of dust and its deposits in the desert and semi-arid regions is a natural phenomenon and the amount of suspended dust is very high. Previous research has shown that there is a relationship between seasons and the type of deposited impurity. Apart from dust deposition, different types of impurities such as pollen, plants, bird droppings, soot and other types of organic materials have been observed [4]. With wind speeds increasing and the sun warming Earth’s surface, the density of the dust rises. Dust depends on the type of land through which it passes or rises [5]. When the sun comes up in the morning, it delivers its warm rays to Earth’s surface, drying the ground and producing dust, which is then suspended in the air. As the sun sets, dust is deposited on the surface of the PV panels as the wind speed declines [6]. The dry atmosphere, unlike damp weather, helps to cause dust [7]. The deposition rate of dirt on the PV surface depends on numerous elements, including the density of dust in the air, the inclination of the cell, the size of the dust particles, the humidity, wind speed and so on [8–10]. The fact that the sea and sea spray are a source of dust can be quite surprising because they produce ~200 million tonnes of salty dust each year [11, 12]. The chemicals and salts are trapped in the air after the water evaporates [13]. Dust storms carry tonnes of dust in the air and can travel ≤300 kilometres [11, 14]. The air always contains a stunning amount of suspended dust [15]. The presence of dust in the atmosphere, as well as its volatilization, results in a shadow that blocks solar radiation from reaching the PV cells [16]. The deposition of dust also enhances the shadow effect and restricts solar radiation from reaching the panel surface, causing PV cells to heat, which diminishes the PV cell’s productivity and lowers the output power of the PV cell [17]. Because the properties and dust composition vary by geography, the PV panel’s efficiency is affected not only by the amount of dust accumulated, but also by another important factor: dust characteristics. Not only are electrical parameters affected by dust accumulation, but also thermal and optical parameters [18]. Carbon particles have the greatest impact on the reduction of PV performance [19]. Natural dust deposition reduces PV module energy generation significantly [20]. Another study found that dust accumulation resulted in yield losses of 78.3% and efficiency declines of 78% for amorphous Si, 77% and 77% for cadmium telluride, and 70% and 71% for polycrystalline Si modules [21]. In India, the monthly reduction in PV panel efficiency is >70% [22]. This will have an impact on the price of energy, the payback period and other factors. It is therefore necessary to clean the PV system on a regular basis at a low cost and with limited resources. Apart from dust accumulation, hailstorms also affect the performance of solar PV panels [23]. The number of hailstorms has increased in the last few years, especially in Rajasthan, which is dangerous for the solar PV system. A heavy hailstorm can affect the surface of the front glass and break the solar cell. When cracks occur on the front surface of glass, they reduce the solar radiation that enters the solar cell. If cracks occur in a solar cell, current is reduced and the cell is totally isolated. Hailstorms reduce not only total electricity generation, but also PV module life. Thus, hailstorm protection for solar PV panels is critical [24]. This paper describes the performance analysis and design of a self-cleaning solar PV sliding system that not only protects the solar panels from dirt deposition, but also protects them from hailstorms. The principal reasons for this system’s development are to achieve the maximum power of the PV system and ensure that PV modules are protected against dirt deposition and hailstorms. 1 Literature review The literature mentions a variety of cleaning options, such as manual cleaning, mechanical cleaning, autonomous cleaning and self-cleaning. Manual cleaning is one of the simplest ways to clean PV panels, depending on the amount of water needed to clean the PV panel’s surface. This traditional method, in addition to being expensive, necessitates the use of labour. Because water is a rare resource in hot desert conditions, and desalination of water produces fresh water, which necessitates a large amount of thermal or electrical energy, this is not a good idea. Mechanical cleaning comprises various cleaning robots, while manual cleaning is the fundamental form of solar-panel cleaning. A customized robotic system is generally proposed for large solar plants. Such systems are designed to clean the most areas in the shortest amount of time with only a few operators required [25]. The brush with the smaller robots consumes less water. Though these robots provide self-cleaning and are typically highly durable, they necessitate (i) modification; (ii) significant cost-effectiveness, such as specific rails; and (iii) high temperatures and complicated maintenance. There is another type of robot that uses water to clean the panels on the basis of a sprinkler. Although they are efficient in dry, sandy environments, their main drawback is that they demand a lot of water [26, 27]. Mechanical automatic cleaning systems, which are effective but have high capital and operating costs, require their own infrastructure, electricity, maintenance and manpower [27]. The electric-curtain method is also used to clean PV panels, in which a travelling wave is created by an electrical wave that prevents particles from moving on the surface of the photovoltaic system. On rainy days, this system requires additional safety for the electrodes. Another way of protecting dust is to cover the PV surfaces with anti-dust coatings. Two kinds of self-cleaning coating are available: superhydrophilic and superhydrophobic films. TiO2 is the best-known type of coating [28]. The solution needs water, which is not suitable for desert environments [29]. Arabatzis et al. [29] used self-cleaning, clean, anti-reflective glass coatings to test the external behaviour of coated and uncoated PV panels and arrays in various weather situations over several months. During its study, its superhydrophilicity showed that the coated surface was better able to remove dust, which allowed one-stage application to achieve an average power gain of 5–6% for long-term operation. According to Piliougine et al. [30], uncoated modules have a daily loss of soiling of ~3.3%, while coated modules have a daily loss of soiling of 2.5%. According to Luque et al. [31], the soiling rate in the mono-facial module was detected to be ~0.301% per day, whereas that of the bifacial module was determined to be at 0.236% per day. In rainy areas, 5 mm of precipitation may be considered sufficient to clean the solar panels and restore their output capability [32]. Moharram et al. investigated the use of a non-pressurized water system to remove dust particles from PV panels. They observed that after 45 days of cleaning with non-pressurized water, the PV panels’ efficiency decreased considerably. However, when anionic and cationic surfactants were employed, the efficiency remained steady. They proposed reducing the volume of water needed to clean and the energy needed to spray the water with a combination of surfactants [33]. Urrejola et al. found that monthly cleaning and unpredictable rain were the most effective solutions [34]. Table 1 shows the comparison of existing cleaning techniques. Table 1: Comparison of various cleaning techniques Cleaning technique . Merits . Demerits . Functions . Natural cleaning Costless Depends on weather Cleaning and cooling Manual cleaning No need for electricity Labour, water is required, it is costly and time-consuming Cleaning and cooling Mechanical cleaning More effective Sometimes scratching happens Cleaning Electrical screens Effective, no required moving parts Need high voltage and costly Cleaning Superhydrophobic and superhydrophilic coating No need for water and labour Reduces the screen efficiency due to coating Cleaning Vibrating cleaning system Useful for dry weather Required motor and power supply Cleaning Forced-air cleaning system Effective in dry weather Requires power supply and high-pressure air pump Cleaning and cooling Cleaning technique . Merits . Demerits . Functions . Natural cleaning Costless Depends on weather Cleaning and cooling Manual cleaning No need for electricity Labour, water is required, it is costly and time-consuming Cleaning and cooling Mechanical cleaning More effective Sometimes scratching happens Cleaning Electrical screens Effective, no required moving parts Need high voltage and costly Cleaning Superhydrophobic and superhydrophilic coating No need for water and labour Reduces the screen efficiency due to coating Cleaning Vibrating cleaning system Useful for dry weather Required motor and power supply Cleaning Forced-air cleaning system Effective in dry weather Requires power supply and high-pressure air pump Cleaning and cooling Open in new tab Table 1: Comparison of various cleaning techniques Cleaning technique . Merits . Demerits . Functions . Natural cleaning Costless Depends on weather Cleaning and cooling Manual cleaning No need for electricity Labour, water is required, it is costly and time-consuming Cleaning and cooling Mechanical cleaning More effective Sometimes scratching happens Cleaning Electrical screens Effective, no required moving parts Need high voltage and costly Cleaning Superhydrophobic and superhydrophilic coating No need for water and labour Reduces the screen efficiency due to coating Cleaning Vibrating cleaning system Useful for dry weather Required motor and power supply Cleaning Forced-air cleaning system Effective in dry weather Requires power supply and high-pressure air pump Cleaning and cooling Cleaning technique . Merits . Demerits . Functions . Natural cleaning Costless Depends on weather Cleaning and cooling Manual cleaning No need for electricity Labour, water is required, it is costly and time-consuming Cleaning and cooling Mechanical cleaning More effective Sometimes scratching happens Cleaning Electrical screens Effective, no required moving parts Need high voltage and costly Cleaning Superhydrophobic and superhydrophilic coating No need for water and labour Reduces the screen efficiency due to coating Cleaning Vibrating cleaning system Useful for dry weather Required motor and power supply Cleaning Forced-air cleaning system Effective in dry weather Requires power supply and high-pressure air pump Cleaning and cooling Open in new tab In summary, decreasing PV panel energy-generation efficiencies due to dirt deposition has prompted several researchers to develop practical and economical PV cleaning solutions. Because most existing solar cleaning technologies involve water and separate cleaning systems, they can be very expensive and ineffective. The separate cleaning system raises capital costs while using fossil fuel to operate. This is a conflicting issue where a clean source of energy is used while simultaneously consuming fossil fuels. Studies show that a heavy hailstorm can affect the surface of the front glass and break the solar cell. When cracks occur on the front surface of glass, they reduce the solar radiation entering the solar cell specifically. If cracks occur in a solar cell, current is reduced and the cell is totally isolated. A hailstorm reduces not just the total electricity generation, but also the PV module life [24]. In the last few years, it has been seen that the frequency and size of hailstorms have increased, which is not suitable for PV panels. In hot and dry climates like Rajasthan, water is scarce. The use of precious water resources for the purposes of PV cleaning is therefore contrary to the ultimate aims of economic and environmental sustainability. This study therefore attempts to address these issues, based on the true necessity of an economical, efficient and automated technology for cleaning from dirt deposition and protecting from hailstorms. With the motivation of the above literature review, the authors have tried to develop self-cleaning and hail-protection mechanisms for the PV system. In this paper, a method for self-cleaning PV modules has been developed and tested in Indian climate conditions. In this proposed technique, a self-cleaning PV sliding system covers the PV panels during the night and performs the cleaning procedure twice daily. The proposed self-cleaning PV sliding system also provides protection from hailstorms. The proposed self-cleaning system is more effective in summer and winter with less power consumption. 2 Description of proposed self-cleaning PV sliding system The proposed self-cleaning PV sliding system comprises rails with three trackers, three 20-W solar PV panels, rollers, an aluminium protective plate, stud, a 12-V DC motor, bearings, cleaning brushes, mechanical supporter, light-activated light-dependent resistor (LDR), dark-activated LDR sensor, rain sensor, 5-V magnetic reed-switch sensor and Arduino UNO. The rollers are attached to both sides of the solar panel to allow the solar PV panels to move easily on the tracker. To prevent rust, we have used plastic rollers here so that they can be moved freely for a long time. A 12-volt DC motor is connected to one end of the stud via a mechanical coupler and the other end of the stud rests on a ball-bearing. The third solar panel is connected to the stud so that it can move linearly on the track with the motor. Strong threads are used to attach the PV solar panels together so that they can move forward throughout the uncovering process. The second solar panel is linked to the third panel by a strong thread and the first panel is linked to the second panel by a strong thread, so that when the motor rotates, the third panel, along with the first and second panels, is covered and uncovered according to command signals that are sent by LDRs and rain sensors. On the protecting plate, we fixed a brush in such a way that it cleans the first panel during covering and uncovering. We mounted the other brushes to clean the downward panels on the edges of the first and second panels. The front and rear views of the proposed self-cleaning PV sliding system are shown in Figs 1 and 2. A block diagram of the motor controller of the proposed self-cleaning system is shown in Fig. 3. Fig. 1: Open in new tabDownload slide Front view of self-cleaning PV sliding system. Fig. 2: Open in new tabDownload slide Rear view of self-cleaning PV sliding system. Fig. 3: Open in new tabDownload slide Block diagram of the motor controller of the self-cleaning PV sliding system. Three sets of polycrystalline PV modules with the proposed self-cleaning system remain exposed in the natural environment. The rain sensor and LDRs are used for the detection of rain and sunlight. When there is sunshine in the morning, the light-activated LDR activates the relay module set, which rotates the DC motor to uncover and bring the solar panels into the sun’s light. The brush on the top cover plate cleans the first panel, the brush on the first panel cleans the next panel, and the brush on the second panel cleans the third panel. All PV panels are cleaned in this manner during the uncovering process. When the sun sets in the evening, the dark-activated LDR triggers the second relay set, which rotates the DC motor in the opposite direction, allowing the panels to be placed beneath the protective plate. During this activity, the cleaning procedure takes place again. As a result, the suggested self-cleaning PV sliding system performs the cleaning procedure twice daily and prevents dust particles from adhering to the PV panels. During hailstorms, raindrops on the rain sensor raise the rain sensor’s output, which activates the relay, which starts the motor in such a way that all three panels come beneath the protective plate, shielding the solar panels from the hail. The reed relay switches, which are located on both ends of the track-rail system, disconnect the relay modules, stopping the motor as soon as all the panels are completely uncovered and covered. During covering, L-section blocks are used to lock the third panel to the second and first panels. Tables 2 and 3 show the mechanical and electronic components of the proposed self-cleaning PV sliding system and Table 4 shows the specifications of the DC motor used in the experiment. Table 2: Mechanical components for proposed self-cleaning PV sliding system Name of major components . Material . Function . Track-rail system Aluminium To provide the sliding track for PV panels Roller Plastic To provide smoothness during the sliding of PV panels Bearing Iron To provide frictionless support for the stud Stud Iron To move the PV panel Nut Iron To connect the PV panel to the stud Mechanical coupler Iron To connect the motor shaft to the stud Cleaning brush Nylon To clean the PV panels Mechanical supports Iron For bearing L-section Aluminium To provide the contact for another panel during covering Thread Nylon To connect the PV panels Protective sheet Aluminium coated fibre To cover the PV panels Name of major components . Material . Function . Track-rail system Aluminium To provide the sliding track for PV panels Roller Plastic To provide smoothness during the sliding of PV panels Bearing Iron To provide frictionless support for the stud Stud Iron To move the PV panel Nut Iron To connect the PV panel to the stud Mechanical coupler Iron To connect the motor shaft to the stud Cleaning brush Nylon To clean the PV panels Mechanical supports Iron For bearing L-section Aluminium To provide the contact for another panel during covering Thread Nylon To connect the PV panels Protective sheet Aluminium coated fibre To cover the PV panels Open in new tab Table 2: Mechanical components for proposed self-cleaning PV sliding system Name of major components . Material . Function . Track-rail system Aluminium To provide the sliding track for PV panels Roller Plastic To provide smoothness during the sliding of PV panels Bearing Iron To provide frictionless support for the stud Stud Iron To move the PV panel Nut Iron To connect the PV panel to the stud Mechanical coupler Iron To connect the motor shaft to the stud Cleaning brush Nylon To clean the PV panels Mechanical supports Iron For bearing L-section Aluminium To provide the contact for another panel during covering Thread Nylon To connect the PV panels Protective sheet Aluminium coated fibre To cover the PV panels Name of major components . Material . Function . Track-rail system Aluminium To provide the sliding track for PV panels Roller Plastic To provide smoothness during the sliding of PV panels Bearing Iron To provide frictionless support for the stud Stud Iron To move the PV panel Nut Iron To connect the PV panel to the stud Mechanical coupler Iron To connect the motor shaft to the stud Cleaning brush Nylon To clean the PV panels Mechanical supports Iron For bearing L-section Aluminium To provide the contact for another panel during covering Thread Nylon To connect the PV panels Protective sheet Aluminium coated fibre To cover the PV panels Open in new tab Table 3: Electrical and electronics components for proposed self-cleaning PV sliding system Name of major components . Quantity . Function . DC motor 1 To move the PV panels forward and backward 2-Relay module 2 To control the direction of the motor Light-activated LDR 1 To send a signal to Arduino UNO for moving forward (to uncover) the PV panel Dark-activated LDR 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Rain sensor 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Arduino UNO 1 Control the whole system Magnetic reed-switch sensor 2 To stop the motor when the PV panel is fully covered or uncovered 12-volt supply 1 Input supply for motor Magnet 2 For reed-switch sensor PV panels 3 To generate electrical energy Name of major components . Quantity . Function . DC motor 1 To move the PV panels forward and backward 2-Relay module 2 To control the direction of the motor Light-activated LDR 1 To send a signal to Arduino UNO for moving forward (to uncover) the PV panel Dark-activated LDR 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Rain sensor 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Arduino UNO 1 Control the whole system Magnetic reed-switch sensor 2 To stop the motor when the PV panel is fully covered or uncovered 12-volt supply 1 Input supply for motor Magnet 2 For reed-switch sensor PV panels 3 To generate electrical energy Open in new tab Table 3: Electrical and electronics components for proposed self-cleaning PV sliding system Name of major components . Quantity . Function . DC motor 1 To move the PV panels forward and backward 2-Relay module 2 To control the direction of the motor Light-activated LDR 1 To send a signal to Arduino UNO for moving forward (to uncover) the PV panel Dark-activated LDR 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Rain sensor 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Arduino UNO 1 Control the whole system Magnetic reed-switch sensor 2 To stop the motor when the PV panel is fully covered or uncovered 12-volt supply 1 Input supply for motor Magnet 2 For reed-switch sensor PV panels 3 To generate electrical energy Name of major components . Quantity . Function . DC motor 1 To move the PV panels forward and backward 2-Relay module 2 To control the direction of the motor Light-activated LDR 1 To send a signal to Arduino UNO for moving forward (to uncover) the PV panel Dark-activated LDR 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Rain sensor 1 To send signal to Arduino UNO for moving backward (to cover) the PV panel Arduino UNO 1 Control the whole system Magnetic reed-switch sensor 2 To stop the motor when the PV panel is fully covered or uncovered 12-volt supply 1 Input supply for motor Magnet 2 For reed-switch sensor PV panels 3 To generate electrical energy Open in new tab Table 4 Specifications of DC motor used in the experiment Specification . Value . Units . Type Gear – Input voltage 12 V Maximum load current 3 A Rated speed 300 rpm Stall torque 8 kg-cm Specification . Value . Units . Type Gear – Input voltage 12 V Maximum load current 3 A Rated speed 300 rpm Stall torque 8 kg-cm Open in new tab Table 4 Specifications of DC motor used in the experiment Specification . Value . Units . Type Gear – Input voltage 12 V Maximum load current 3 A Rated speed 300 rpm Stall torque 8 kg-cm Specification . Value . Units . Type Gear – Input voltage 12 V Maximum load current 3 A Rated speed 300 rpm Stall torque 8 kg-cm Open in new tab Experimental Setup and Methodology To investigate the performance of the proposed self-cleaning PV sliding system, we incorporated three PV panels of 20 W with a sliding structure and installed them on the rooftop of Manipal University Jaipur. The solar panels were connected in parallel and the output terminals of the PV system were connected to the resistive load. Another set of three PV panels of 20 W was installed with a conventional method, i.e. fixed. The effectiveness of the proposed cleaning system was tested on photovoltaic panels in Jaipur, India for all seasons. Both sets of three polycrystalline solar panels are installed on a platform that is tilted 27° to the south and the specifications of the PV panels are listed in Table 5. Fig. 4 shows the prototype of the self-cleaning PV system and the experimental set-up. Table 5: Specifications of solar panels used in the experiment under standard testing conditions Specification . Value . Units . Model ELDORA 20P – Type Polycrystalline – Maximum power 20 W Open-circuit voltage 21.44 V Short-circuit current 1.27 A Maximum voltage 17.15 V Maximum current 1.18 A Specification . Value . Units . Model ELDORA 20P – Type Polycrystalline – Maximum power 20 W Open-circuit voltage 21.44 V Short-circuit current 1.27 A Maximum voltage 17.15 V Maximum current 1.18 A Open in new tab Table 5: Specifications of solar panels used in the experiment under standard testing conditions Specification . Value . Units . Model ELDORA 20P – Type Polycrystalline – Maximum power 20 W Open-circuit voltage 21.44 V Short-circuit current 1.27 A Maximum voltage 17.15 V Maximum current 1.18 A Specification . Value . Units . Model ELDORA 20P – Type Polycrystalline – Maximum power 20 W Open-circuit voltage 21.44 V Short-circuit current 1.27 A Maximum voltage 17.15 V Maximum current 1.18 A Open in new tab Fig. 4: Open in new tabDownload slide Experimental set-up. We installed one set of three PV panels without any cleaning method and on the second set of three PV panels, we implemented the proposed cleaning technique and measured the output power of both sets for 1 year. The proposed cleaning method uses no water. The performance of the proposed self-cleaning PV sliding system is determined based on cleaning efficiency and energy usage. A data logger is used to record the performance parameters. Table 6 shows the specifications of the test location. Table 6: Specifics about the test location Description . Value . Place Academic Block 1, Manipal University Jaipur Latitude 26.84°N Longitude 75.56°E Tilt angle 27° Facing South Description . Value . Place Academic Block 1, Manipal University Jaipur Latitude 26.84°N Longitude 75.56°E Tilt angle 27° Facing South Open in new tab Table 6: Specifics about the test location Description . Value . Place Academic Block 1, Manipal University Jaipur Latitude 26.84°N Longitude 75.56°E Tilt angle 27° Facing South Description . Value . Place Academic Block 1, Manipal University Jaipur Latitude 26.84°N Longitude 75.56°E Tilt angle 27° Facing South Open in new tab An IOT-based monitoring system has been used, which collects real-time information and sends it to the ThinkSpeak cloud server [35]. Table 7 shows the technical characteristics of components used in the data acquisition system. The system consists of an ESP8266 Wi-Fi module, INA 260 sensors and the cloud platform ThingSpeak. ThingSpeak displays PV system variables such as generated voltage, current and power. The sensors were verified and calibrated precisely. The readings of current and voltage measured by the INA260 and INA219 sensors were compared with the readings of a standard multimeter (Aplab VC97) at the same time. The entire calibration results were repeatable. For solar radiation, an EKO MS40M pyranometer was used. The Arduino-based data logger recorded the power consumed by the DC motor and stored the information on a microSD card. For this purpose, the INA219 sensor was used. Before each season, the calibration process was repeated. Measurements of the PV panel’s variables were recorded at an interval of 5 minutes from 7.00 am to 6.00 pm for the period of December 2019 to April 2021. The study period was split into four seasons: post-monsoon (September to mid-December), winter (mid-December to February), summer (March to June) and monsoon (July to August). Table 7: Technical characteristics of components used in data acquisition system Name of component . Parameters . Specification . Function . Number . ESP8266 Wi-Fi Module (Lolin NodeMCU) Supply voltage 3.3–5 V For Wi-Fi module 2 Available GPIO pins 10 RAM 36 kb Clock speed 80/160 MHz MCU 32 bits TenSillica L 106 INA 260 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power of PV panels 2 Measure voltage 0–36 V DC Measure current range 0–15 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% INA 219 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power for DC motor 1 Measure voltage 0–26 V DC Measure current range 0–3.2 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% Pyranometer (EKO MS40M) Operating voltage 12–24 V DC To sense solar irradiance 1 Irradiance range 0–2000 W/m2 Wavelength range 285–3000 nm Operating Temperature –40ºC to 80ºC Signal processing error ±1.5 W/m2 Multimeter (Aplab VC97) DC voltage range 400 mV to 1000 V ± 0.5% To calibrate the sensors 1 AC voltage range 400 mV to 750 V ± 0.5% DC current range 400 µA to 10 A ± 1.0% AC current range 400 µA to 10 A ± 1.0% Sampling rate 3 times/second Arduino UNO Microcontroller ATmega328P To feed sensors data 1 Operating voltage 5 V Analogue input pin 6 Digital I/o pin 14 Clock speed 16 MHz MicroSD card with shield Operating voltage 5/3.3 V To store the measured data 1 Interface SPI Compatible MicroSD card Name of component . Parameters . Specification . Function . Number . ESP8266 Wi-Fi Module (Lolin NodeMCU) Supply voltage 3.3–5 V For Wi-Fi module 2 Available GPIO pins 10 RAM 36 kb Clock speed 80/160 MHz MCU 32 bits TenSillica L 106 INA 260 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power of PV panels 2 Measure voltage 0–36 V DC Measure current range 0–15 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% INA 219 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power for DC motor 1 Measure voltage 0–26 V DC Measure current range 0–3.2 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% Pyranometer (EKO MS40M) Operating voltage 12–24 V DC To sense solar irradiance 1 Irradiance range 0–2000 W/m2 Wavelength range 285–3000 nm Operating Temperature –40ºC to 80ºC Signal processing error ±1.5 W/m2 Multimeter (Aplab VC97) DC voltage range 400 mV to 1000 V ± 0.5% To calibrate the sensors 1 AC voltage range 400 mV to 750 V ± 0.5% DC current range 400 µA to 10 A ± 1.0% AC current range 400 µA to 10 A ± 1.0% Sampling rate 3 times/second Arduino UNO Microcontroller ATmega328P To feed sensors data 1 Operating voltage 5 V Analogue input pin 6 Digital I/o pin 14 Clock speed 16 MHz MicroSD card with shield Operating voltage 5/3.3 V To store the measured data 1 Interface SPI Compatible MicroSD card Open in new tab Table 7: Technical characteristics of components used in data acquisition system Name of component . Parameters . Specification . Function . Number . ESP8266 Wi-Fi Module (Lolin NodeMCU) Supply voltage 3.3–5 V For Wi-Fi module 2 Available GPIO pins 10 RAM 36 kb Clock speed 80/160 MHz MCU 32 bits TenSillica L 106 INA 260 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power of PV panels 2 Measure voltage 0–36 V DC Measure current range 0–15 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% INA 219 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power for DC motor 1 Measure voltage 0–26 V DC Measure current range 0–3.2 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% Pyranometer (EKO MS40M) Operating voltage 12–24 V DC To sense solar irradiance 1 Irradiance range 0–2000 W/m2 Wavelength range 285–3000 nm Operating Temperature –40ºC to 80ºC Signal processing error ±1.5 W/m2 Multimeter (Aplab VC97) DC voltage range 400 mV to 1000 V ± 0.5% To calibrate the sensors 1 AC voltage range 400 mV to 750 V ± 0.5% DC current range 400 µA to 10 A ± 1.0% AC current range 400 µA to 10 A ± 1.0% Sampling rate 3 times/second Arduino UNO Microcontroller ATmega328P To feed sensors data 1 Operating voltage 5 V Analogue input pin 6 Digital I/o pin 14 Clock speed 16 MHz MicroSD card with shield Operating voltage 5/3.3 V To store the measured data 1 Interface SPI Compatible MicroSD card Name of component . Parameters . Specification . Function . Number . ESP8266 Wi-Fi Module (Lolin NodeMCU) Supply voltage 3.3–5 V For Wi-Fi module 2 Available GPIO pins 10 RAM 36 kb Clock speed 80/160 MHz MCU 32 bits TenSillica L 106 INA 260 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power of PV panels 2 Measure voltage 0–36 V DC Measure current range 0–15 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% INA 219 Sensor (Adafruit) Supply voltage 3.3–5.5 V DC To sense voltage, current, power for DC motor 1 Measure voltage 0–26 V DC Measure current range 0–3.2 A Operating temperature –40ºC to 125℃ Accuracy ±0.5% Pyranometer (EKO MS40M) Operating voltage 12–24 V DC To sense solar irradiance 1 Irradiance range 0–2000 W/m2 Wavelength range 285–3000 nm Operating Temperature –40ºC to 80ºC Signal processing error ±1.5 W/m2 Multimeter (Aplab VC97) DC voltage range 400 mV to 1000 V ± 0.5% To calibrate the sensors 1 AC voltage range 400 mV to 750 V ± 0.5% DC current range 400 µA to 10 A ± 1.0% AC current range 400 µA to 10 A ± 1.0% Sampling rate 3 times/second Arduino UNO Microcontroller ATmega328P To feed sensors data 1 Operating voltage 5 V Analogue input pin 6 Digital I/o pin 14 Clock speed 16 MHz MicroSD card with shield Operating voltage 5/3.3 V To store the measured data 1 Interface SPI Compatible MicroSD card Open in new tab The uncertainties of the measuring sensors INA260 (U1), INA219 (U2), pyranometer (U3) and multimeter (U4) used in this experiment are shown in Table 8. The verification reports for the sensors and measuring devices were used to determine the standard uncertainties for the components. Table 8: Standard uncertainties for the components U1 (%) . U2 (%) . U3 (W/m2) . U4 (%) . 0.5 0.5 1.5 0.5 for voltage 1.0 for current U1 (%) . U2 (%) . U3 (W/m2) . U4 (%) . 0.5 0.5 1.5 0.5 for voltage 1.0 for current Open in new tab Table 8: Standard uncertainties for the components U1 (%) . U2 (%) . U3 (W/m2) . U4 (%) . 0.5 0.5 1.5 0.5 for voltage 1.0 for current U1 (%) . U2 (%) . U3 (W/m2) . U4 (%) . 0.5 0.5 1.5 0.5 for voltage 1.0 for current Open in new tab 3 Results and discussion To investigate the performance of the proposed self-cleaning PV sliding system, we incorporated three PV panels of 20 W with a sliding structure and another set of three PV panels of 20 W was installed using a conventional method, i.e. fixed. Both sets of three polycrystalline solar panels are installed on a platform tilted 27° to the south. We installed one set of three PV panels without any cleaning procedure, then applied the proposed cleaning methodology to the second PV set and measured the output power at an interval of 5 minutes from 7.00 am to 6.00 pm for all seasons. For the summer, monsoon, post-monsoon and winter seasons, data were collected for 58, 54, 68 and 64 days, respectively. The collected data have a P-value of <0.05, indicating that it is statistically significant. The performance of the proposed self-cleaning PV slider system is determined based on the following points: (i) efficient cleaning; (ii) energy consumption; (iii) effectiveness during hailstorms. 3.1 Efficient cleaning Fig. 5a–d shows the variation in normalized efficiency of the PV system with and without the proposed self-cleaning mechanism. The ratio of the measured power to the power rating under standard test conditions (STC) compared to the irradiance divided by the STC irradiance gives the normalized efficiency, ƞN: Fig. 5: Open in new tabDownload slide Variation in the efficiency of cleaned and uncleaned PV systems in (a) summer season; (b) monsoon season; (c) post-monsoon season; and (d) winter season. ηN=P╱PSTCIm╱ISTC(1) where P is the measured power, PSTC is the STC-rated power, Im is the measured irradiance and ISTC is the reference irradiance (1000 W/m2) [36, 37]. Fig. 5a shows the efficiency of the proposed self-cleaning PV sliding system compared to the uncleaned PV system in the summer season. As shown in the figure, the performance of the cleaned panels significantly improves when compared to the uncleaned panels. This is mainly due to dust accumulation on uncleaned panels, whereas dust is removed every day on panels with the proposed self-cleaning system. During the summer season, the ambient temperature is very high and the humidity in the air is low, so the air easily lifts the dust particles that have accumulated on the PV panels. In this season, the reduction in efficiency in PV systems without a self-cleaning mechanism is 18.43%, while the efficiency drop in a PV system with a self-cleaning mechanism is 3.3% in 58 days. With the proposed self-cleaning PV sliding system, the overall gain in efficiency is 18.3%. The result, as shown in Fig. 5a, reveals that this mechanism is more effective for the summer season. Fig. 5b shows the efficiency of the proposed self-cleaning PV sliding system compared to the uncleaned PV system in the monsoon season. As shown in the figure, the performance of the panels with the proposed self-cleaning PV sliding structure does not improve compared to the uncleaned panels. This drop in performance is not due to dust accumulation; it is due to the repetition of the covering and uncovering process of panels when dark clouds appear and disappear. As a result, the PV system’s exposure time was reduced. During this season, PV systems without a self-cleaning mechanism lose 0.33% of their efficiency, while PV systems with a self-cleaning mechanism lose 5.5% of their efficiency in 54 days. In the monsoon season, the proposed self-cleaning sliding system does not improve the efficiency of the PV panels. The overall drop in efficiency is 5.4%. The result, as shown in Fig. 5b, demonstrates that cleaning is not necessary during the monsoon season. But the proposed system provides not only cleaning, but also protection from hailstorms. So, it is very useful in the monsoon season also. Fig. 5c shows the efficiency of the proposed self-cleaning PV sliding system compared to the uncleaned PV system in the post-monsoon season. As shown in the figure, the performance of the panels with the proposed self-cleaning PV sliding structure slightly improves compared to the uncleaned panels. In the post-monsoon season, the hot and humid weather begins to fade and the air quality improves noticeably. Because there are fewer airborne particles, the sky is clean and the quality of solar radiation is improved. Rain reduces the amount of pollutant particles in the air. The efficiency drop in a PV system without a self-cleaning mechanism is 7.1% in 68 days. This drop in efficiency is due to dust accumulation. While the efficiency drop in a self-cleaning PV system is 0.85%, the overall efficiency gain is 6.40% with the proposed self-cleaning PV sliding system. Results as shown in Fig. 5c reveal that this mechanism is effective for the post-monsoon season. Fig. 5d shows the efficiency of the proposed self-cleaning PV sliding system compared to the uncleaned PV system in the winter season. As shown in the figure, the performance of the panels with the proposed self-cleaning PV sliding structure significantly improves compared to the uncleaned panels. This drop in performance is not only due to dust accumulation on uncleaned panels; there are more factors involved, such as bird droppings, insects and leaves of trees because the autumn season starts, whereas cleaning is done every day on panels with the proposed self-cleaning system. In this season, the efficiency drop in PV systems without a self-cleaning mechanism is 14%, while the efficiency drop in a PV system with a self-cleaning mechanism is 2.2%. With the proposed self-cleaning PV sliding system, the overall gain in efficiency is 13.3%. The results depicted in Fig. 5d show that this mechanism is extremely effective during the winter season. Fig. 6 demonstrates that the proposed cleaning system’s percent gain in efficiency is negative during the monsoon season, indicating that the cleaning mechanism of the proposed system is not required during the monsoon season. This decrease in efficiency is not due to dust accumulation; rather, it is due to the repeated covering and uncovering of panels when dark clouds appear and disappear, resulting in shorter exposure times, lower output power and higher energy consumption of the proposed cleaning system. While the sky is cloudless most of the time during other seasons in Jaipur, exposure time is longer and the average energy consumption of the proposed system is lower. Thus, the gain is positive and the proposed system is more effective and adaptable. According to the findings, the average air dust density is higher in the summer and winter than in the post-monsoon and monsoon seasons [38]. The accumulation of dust on PV panels, which is primarily determined by air dust density, contributes significantly to the PV system’s output energy-generation degradation. In the summer, post-monsoon season and winter seasons, PV systems without a self-cleaning mechanism have efficiency reductions of 18.43%, 7.1% and 14%, respectively. In contrast, in the summer, post-monsoon and winter seasons, the efficiencies of a PV system with a self-cleaning mechanism drop by 3.3%, 0.85% and 2.2%, respectively. The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. Results as shown in Fig. 6 reveal that the proposed sliding system is more effective in the summer and winter seasons. Fig. 6: Open in new tabDownload slide Drop and improvement in efficiency of the PV system with and without the proposed cleaning mechanism for all seasons. Most of the dust deposition occurs at night or before the sun rises, as there is less traffic and less wind blowing. These conditions are very suitable for the stagnation of the small and large dust particles suspended in the air. The PV cell cools before sunrise and dew is formed when water in the air condenses, which interacts with the dust particles, increasing the cell surface adhesion forces and making the layers of dust deposition difficult to clean in a fair manner, resulting in significant losses in the generation of output power. In this proposed technique, a self-cleaning PV sliding system covers the PV panel during the night and performs the cleaning procedure twice daily. As a result, the possibility of dust being deposited and dew developing on the PV surface is reduced. As described above, the proposed self-cleaning PV sliding system provides the cleaning process twice a day. It does not allow dust to be deposited on the PV panels and become adhesive dust. The results show that the cleaning system works significantly. It completely cleans large particles of dust and it removes a substantial amount of small dust particles also. It has been observed that the cleaning system is unable to remove the bird droppings completely in one cleaning day. The result shows that the proposed solar cleaning system works well in winter, post-monsoon and summer seasons. Cleaning is not required in the monsoon season because of the rain. It provides natural cleaning. During the monsoon season, the cleaning system performs the covering and uncovering process repeatedly due to the frequently appearing and disappearing dark clouds, resulting in higher energy consumption. But the proposed system provides not only cleaning, but also protection from hailstorms. So it is very useful in the monsoon season also. 3.2 Energy consumption The energy consumption of the motor is primarily determined by the size and weight of the PV system’s characteristics, although they are not the only factors. The efficiency of motors, the battery and a variety of other elements all influence their energy usage. The proposed cleaning technique consumes energy while in use. As a result, the cleaning process’s energy consumption is another important factor to consider when evaluating the cleaning method’s viability for PV applications. This method uses extremely little energy overall when it is compared with power gain. In the summer season, it has been seen that the dust gets in between the track of the solar sliders, due to which the track experiences more friction and the load on the motor also increases, due to which the energy consumption slightly increases. In the monsoon season, due to the frequent arrival and departure of dark clouds, the cleaning system has to be opened and closed frequently, resulting in increased energy consumption. The results show that the cleaning of solar PV panels is not required in the monsoon season because of the rain, which provides natural cleaning and cooling, and during this time, efficiency is not significantly reduced. During the summer season, the suggested solar sliding PV system consumed 29.58 Whr for 58 days, while the energy generation of the proposed system was 1145.6 Whr higher than that of the fixed PV panel. The total amount of energy gained was 1115.72 Whr. However, during the monsoon season, energy usage increases due to the repeated covering and uncovering of panels as dark clouds appear and disappear. As a result, the PV system’s energy consumption increased. A total energy loss of 345.4 Whr was measured for 54 days. During the post-monsoon season, the energy generation of the proposed PV system was 221 Whr more than with a fixed PV system, while energy consumption was 32.64 Whr for 68 days. In the winter, the proposed system generated 396.8 Whr more energy than a fixed PV system and the energy consumption was 31.36 Whr for 64 days. Fig. 7 shows the energy yield of a PV system with and without the proposed cleaning mechanism, energy consumption and gain for all seasons. Table 9 shows the comparison of the proposed self-cleaning PV sliding system with a drone-based PV cleaning system and Table 10 shows the comparison of the proposed self-cleaning PV sliding system with some literature studies for PV cleaning systems. Table 10: Comparison of proposed self-cleaning PV sliding system with some literature studies for PV cleaning Reference . Cleaning technique . Cleaning frequency . Outcomes . Shehri et al. [41] Nylon brush, cloth and silicon rubber foam The maximum power output of solar panels cleaned with silicone rubber brush increased by ~1% on average when compared to the unbrushed initial power output Shehri et al. [42] Brush 1 The right brush must be chosen to achieve the required level of cleaning while avoiding damage to the solar panels’ surface Arabatzis et al. [29] Self-cleaning and anti-reflective glass coating 16 and 86 days The coated PV panels showed an average gain of 5–6% for the tested period of time under real outdoor conditions Urrejola et al. [34] Brushing with water Monthly cleaning (suggested optimum cleaning period: 45 days) Lowest decay values in summer 2015: 0.14%; highest values in autumn 2015 seasonal average: 0.56%/day; the performance ratio has deteriorated by 17.36% monthly Al-Housani et al. [39] Drone incorporated with brush, microfiber cloth wiper, vacuum cleaner 1 day 1 week 1 month In the winter, the weekly power losses for microfiber-based cloth wiper + vacuum cleaner, mechanical brush + vacuum cleaner, microfiber- based cloth wiper, mechanical brush are 3.42%, 2.95%, 3.63% and 2.28%, respectively Proposed cleaning system Sliding structure with brush 2 times per day The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms Reference . Cleaning technique . Cleaning frequency . Outcomes . Shehri et al. [41] Nylon brush, cloth and silicon rubber foam The maximum power output of solar panels cleaned with silicone rubber brush increased by ~1% on average when compared to the unbrushed initial power output Shehri et al. [42] Brush 1 The right brush must be chosen to achieve the required level of cleaning while avoiding damage to the solar panels’ surface Arabatzis et al. [29] Self-cleaning and anti-reflective glass coating 16 and 86 days The coated PV panels showed an average gain of 5–6% for the tested period of time under real outdoor conditions Urrejola et al. [34] Brushing with water Monthly cleaning (suggested optimum cleaning period: 45 days) Lowest decay values in summer 2015: 0.14%; highest values in autumn 2015 seasonal average: 0.56%/day; the performance ratio has deteriorated by 17.36% monthly Al-Housani et al. [39] Drone incorporated with brush, microfiber cloth wiper, vacuum cleaner 1 day 1 week 1 month In the winter, the weekly power losses for microfiber-based cloth wiper + vacuum cleaner, mechanical brush + vacuum cleaner, microfiber- based cloth wiper, mechanical brush are 3.42%, 2.95%, 3.63% and 2.28%, respectively Proposed cleaning system Sliding structure with brush 2 times per day The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms Open in new tab Table 10: Comparison of proposed self-cleaning PV sliding system with some literature studies for PV cleaning Reference . Cleaning technique . Cleaning frequency . Outcomes . Shehri et al. [41] Nylon brush, cloth and silicon rubber foam The maximum power output of solar panels cleaned with silicone rubber brush increased by ~1% on average when compared to the unbrushed initial power output Shehri et al. [42] Brush 1 The right brush must be chosen to achieve the required level of cleaning while avoiding damage to the solar panels’ surface Arabatzis et al. [29] Self-cleaning and anti-reflective glass coating 16 and 86 days The coated PV panels showed an average gain of 5–6% for the tested period of time under real outdoor conditions Urrejola et al. [34] Brushing with water Monthly cleaning (suggested optimum cleaning period: 45 days) Lowest decay values in summer 2015: 0.14%; highest values in autumn 2015 seasonal average: 0.56%/day; the performance ratio has deteriorated by 17.36% monthly Al-Housani et al. [39] Drone incorporated with brush, microfiber cloth wiper, vacuum cleaner 1 day 1 week 1 month In the winter, the weekly power losses for microfiber-based cloth wiper + vacuum cleaner, mechanical brush + vacuum cleaner, microfiber- based cloth wiper, mechanical brush are 3.42%, 2.95%, 3.63% and 2.28%, respectively Proposed cleaning system Sliding structure with brush 2 times per day The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms Reference . Cleaning technique . Cleaning frequency . Outcomes . Shehri et al. [41] Nylon brush, cloth and silicon rubber foam The maximum power output of solar panels cleaned with silicone rubber brush increased by ~1% on average when compared to the unbrushed initial power output Shehri et al. [42] Brush 1 The right brush must be chosen to achieve the required level of cleaning while avoiding damage to the solar panels’ surface Arabatzis et al. [29] Self-cleaning and anti-reflective glass coating 16 and 86 days The coated PV panels showed an average gain of 5–6% for the tested period of time under real outdoor conditions Urrejola et al. [34] Brushing with water Monthly cleaning (suggested optimum cleaning period: 45 days) Lowest decay values in summer 2015: 0.14%; highest values in autumn 2015 seasonal average: 0.56%/day; the performance ratio has deteriorated by 17.36% monthly Al-Housani et al. [39] Drone incorporated with brush, microfiber cloth wiper, vacuum cleaner 1 day 1 week 1 month In the winter, the weekly power losses for microfiber-based cloth wiper + vacuum cleaner, mechanical brush + vacuum cleaner, microfiber- based cloth wiper, mechanical brush are 3.42%, 2.95%, 3.63% and 2.28%, respectively Proposed cleaning system Sliding structure with brush 2 times per day The efficiency gains are 18.3%, 13.3% and 6.4% in the summer, winter and post-monsoon seasons, respectively. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms Open in new tab Table 9: Comparison of proposed self-cleaning PV sliding system with drone-based PV cleaning technique [39, 40] Parameters . Drone-based cleaning system . . Developed self-cleaning PV sliding system . . Summer Winter Summer Winter Improvement in average power output (W/day) 0.4 0.5 1.98 0.77 Average improvement (%) 7 4.8 18.33 13.3 Average cleaning cost (USD/m2) 0.0578 0.0578 0.0001 0.000097 Average cleaning time (min/panel) 0.75–2 0.75–2 0.33–0.57 0.33–0.57 Average energy consumption Required Required 0.51 Whr/day 0.49 Whr/day Capital cost High High Relatively low Relatively low Cleaning frequency per day One time One time Two time Two time Manpower Require Required No No Water No No No No Parameters . Drone-based cleaning system . . Developed self-cleaning PV sliding system . . Summer Winter Summer Winter Improvement in average power output (W/day) 0.4 0.5 1.98 0.77 Average improvement (%) 7 4.8 18.33 13.3 Average cleaning cost (USD/m2) 0.0578 0.0578 0.0001 0.000097 Average cleaning time (min/panel) 0.75–2 0.75–2 0.33–0.57 0.33–0.57 Average energy consumption Required Required 0.51 Whr/day 0.49 Whr/day Capital cost High High Relatively low Relatively low Cleaning frequency per day One time One time Two time Two time Manpower Require Required No No Water No No No No Open in new tab Table 9: Comparison of proposed self-cleaning PV sliding system with drone-based PV cleaning technique [39, 40] Parameters . Drone-based cleaning system . . Developed self-cleaning PV sliding system . . Summer Winter Summer Winter Improvement in average power output (W/day) 0.4 0.5 1.98 0.77 Average improvement (%) 7 4.8 18.33 13.3 Average cleaning cost (USD/m2) 0.0578 0.0578 0.0001 0.000097 Average cleaning time (min/panel) 0.75–2 0.75–2 0.33–0.57 0.33–0.57 Average energy consumption Required Required 0.51 Whr/day 0.49 Whr/day Capital cost High High Relatively low Relatively low Cleaning frequency per day One time One time Two time Two time Manpower Require Required No No Water No No No No Parameters . Drone-based cleaning system . . Developed self-cleaning PV sliding system . . Summer Winter Summer Winter Improvement in average power output (W/day) 0.4 0.5 1.98 0.77 Average improvement (%) 7 4.8 18.33 13.3 Average cleaning cost (USD/m2) 0.0578 0.0578 0.0001 0.000097 Average cleaning time (min/panel) 0.75–2 0.75–2 0.33–0.57 0.33–0.57 Average energy consumption Required Required 0.51 Whr/day 0.49 Whr/day Capital cost High High Relatively low Relatively low Cleaning frequency per day One time One time Two time Two time Manpower Require Required No No Water No No No No Open in new tab Fig. 7: Open in new tabDownload slide Energy yield of PV system with and without proposed cleaning mechanism, energy consumption and gain for all seasons. 3.3 Effectiveness during hailstorms Hailstorms are common in India. The frequency and intensity of hailstorms have increased in India in the last few years, which is a threat to PV panel life. There is no method to protect them from heavy hailstorms. The proposed solar sliding system also provides protection from hailstorms, along with the self-cleaning of PV panels. Two hailstorms were observed on 5 March 2020 and 17 November 2020 in Jaipur. The hailstorm that happened on 5 March was very deadly. Fig. 8 shows the intensity and size of deadly hailstorms. There are a lot of PV panels installed on the top of Manipal University Jaipur without any protection. As shown in Fig. 9b, one of the 300-W PV panels was completely damaged during the massive hailstorm and some had cracks in the front glass. The proposed system kept the PV panels safe, but the corner of the protective plate was damaged, as shown in Fig. 9a. Fig. 8: Open in new tabDownload slide Deadly hailstorm on 5 March 2020. Fig. 9: Open in new tabDownload slide Impact of hailstorm. (a) Proposed system after deadly hailstorm; (b) 300-W PV panel damaged during deadly hailstorm on 5 March 2020. 4 Conclusion Most existing solar cleaning technologies rely on water and separate cleaning systems, which can be prohibitively expensive and inefficient. In hot and dry climates, water is scarce. The use of precious water resources for the purposes of PV cleaning is contrary to the ultimate aims of economic and environmental sustainability. Hailstorms reduce not only total electricity generation, but also the life of PV modules. There is no method for protecting the PV system from hailstorms. The proposed self-cleaning system protects the PV system from dust deposition and hailstorms. In this proposed technique, a self-cleaning PV sliding system covers the PV panels during the night and performs the cleaning procedure twice daily. As a result, the volume of dust deposited and dew developed on the PV surface is greatly reduced. This technology is primarily designed to achieve maximum energy in the PV module and ensure protection against pollution and hailstorms for the PV module. Results show that the proposed self-cleaning PV sliding system improves efficiency by 18.3%, 13.3% and 6.4% when compared to fixed systems in the summer, winter and post-monsoon seasons, respectively. The results of this study show that there is a significant improvement in PV efficiency and, hence, an increase in the production of electricity in all climate conditions. This proposed cleaning mechanism provides a more efficient and energy-efficient technique for cleaning and protecting PV systems throughout the year. Conflict of interest statement The authors declare that there is no conflict of interest. References [1] Tanesab J , Parlevliet D, Whale J, et al. T. The effect of dust with different morphologies on the performance degradation of photovoltaic modules . Sustainable Energy Technol Assess , 2019 , 31 : 347 – 354 . Google Scholar Crossref Search ADS WorldCat [2] Salimi H , Lavasani AM, Danesh-Ashtiani HA, et al. Effect of dust concentration, wind speed, and relative humidity on the performance of photovoltaic panels in Tehran . Energy Sources, Part A: Recovery, Utilization, Environmental Effects , 2019 , 1 – 11 . https://doi.org/10.1080/15567036.2019.1677811. Google Scholar OpenURL Placeholder Text WorldCat [3] Oh S . Analytic and Monte-Carlo studies of the effect of dust accumulation on photovoltaics . Sol Energy , 2019 , 188 : 1243 – 1247 . Google Scholar Crossref Search ADS WorldCat [4] Figgis B , Nouviaire A, Wubulikasimu Y, et al. Investigation of factors affecting condensation on soiled PV modules . Sol Energy , 2018 , 159 : 488 – 500 . Google Scholar Crossref Search ADS WorldCat [5] Touati F , Massoud A, Abu Hamad J, et al. Effects of environmental and climatic conditions on PV efficiency in Qatar. In: International Conference on Renewable Energies and Power Quality (ICREPQ’13) , Bilbao, Spain , 20–22 March 2013 . [6] Jones T , Stark DP, Ellis RS. Dust in the wind: composition and kinematics of galaxy outflows at the peak epoch of star formation . The Astrophysical Journal , 2018 , 863 : 191 . Google Scholar Crossref Search ADS WorldCat [7] Badi HA , Boland J, Bruce D, et al. Dust event impact on photovoltaic systems: role of humidity in soiling and self-cleaning, In: IEEE International Conference on Smart Energy Grid Engineering (SEGE) , Oshawa, ON, Canada , 12–15 August 2018 , 342 – 345 . [8] Hosseini SA , Kermani AM, Arabhosseini A. Experimental study of the dew formation effect on the performance of photovoltaic modules . Renew Energy , 2018 , 130 : 352 – 359 . Google Scholar Crossref Search ADS WorldCat [9] Onishchenko O , Fedun V, Horton W, et al. Dust devils: structural features, dynamics, and climate impact . Climate , 2019 , 7 : 121 – 118 . Google Scholar Crossref Search ADS WorldCat [10] Rashki A , Kaskaoutis DG, Sepehr A. Statistical evaluation of the dust events at selected stations in Southwest Asia: from the Caspian Sea to the Arabian Sea . Catena , 2018 , 165 : 590 – 603 . Google Scholar Crossref Search ADS WorldCat [11] Steffan JJ , Brevik EC, Burgess LC, et al. The effect of soil on human health: an overview . Eur J Soil Sci , 2018 , 69 : 159 – 171 . Google Scholar Crossref Search ADS PubMed WorldCat [12] Kinney PL . Interactions of climate change, air pollution, and human health . Current Environmental Health Reports , 2018 , 5 : 179 – 186 . Google Scholar Crossref Search ADS PubMed WorldCat [13] Li N , Weizheng H, Tang J, et al. Pollution characteristics and human health risks of elements in road dust in Changchun, China . Int J Environ Res Public Health , 2018 , 15 : 1843 . Google Scholar Crossref Search ADS WorldCat [14] Jiang Y , Lu L, Ferro AR, et al. Analysing wind cleaning process on the accumulated dust on solar photovoltaic (PV) modules on flat surfaces . Sol Energy , 2018 , 159 : 1031 – 1036 . Google Scholar Crossref Search ADS WorldCat [15] Bolles K , Sweeney M, Forman S. Meteorological catalysts of dust events and particle source dynamics of affected soils during the 1930s Dust Bowl drought, Southern High Plains, USA . Anthropocene. 2019 , 27 : 100216 . Google Scholar Crossref Search ADS WorldCat [16] Kazem HA , Chaichan MT, Alwaeli AH, et al. Effect of shadows on the performance of solar photovoltaic. In: Sayigh A (ed). Mediterranean Green Buildings & Renewable Energy . Cham : Springer , 2017 , 379 – 385 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC [17] Pandian A , Bansal K, Thiruvadigal DJ, et al. Fire hazards and overheating caused by shading faults on photo voltaic solar panel . Fire Technol , 2016 , 52 : 349 – 364 . Google Scholar Crossref Search ADS WorldCat [18] Gupta V , Sharma M, Pachauri RK, et al. Comprehensive review on effect of dust on solar photovoltaic system and mitigation techniques . Sol Energy , 2019 , 191 : 596 – 622 . Google Scholar Crossref Search ADS WorldCat [19] Darwish ZA , Sopian K, Fudholi A. Reduced output of photovoltaic modules due to different types of dust particles . J Clean Prod , 2021 , 280 : 124317 . Google Scholar Crossref Search ADS WorldCat [20] Enaganti PK , Bhattacharjee A, Ghosh A, et al. Experimental investigations for dust build-up on low-iron glass exterior and its effects on the performance of solar PV systems . Energy , 2022 , 239 : 122213 . Google Scholar Crossref Search ADS WorldCat [21] Chanchangi YN , Ghosh A, Baig H, et al. Soiling on PV performance influenced by weather parameters in Northern Nigeria . Renew Energy , 2021 , 180 : 874 – 892 . Google Scholar Crossref Search ADS WorldCat [22] Kazem HA , Chaichan MT, Al-Waeli AH, et al. A review of dust accumulation and cleaning methods for solar photovoltaic systems . J Clean Prod , 2020 , 276 : 123187 . Google Scholar Crossref Search ADS WorldCat [23] Gupta V , Sharma M, Pachauri RK, et al. Impact of hailstorm on the performance of PV module: a review . Energy Sources Part A , 2019 , 1 : 22 . Google Scholar OpenURL Placeholder Text WorldCat [24] Dhimish M , Holmes V, Mehrdadi B, et al. The impact of cracks on photovoltaic power performance . Journal of Science: Advanced Materials and Devices, 2017 , 2 : 199 – 209 . Google Scholar OpenURL Placeholder Text WorldCat [25] Kegeleers M. The development of a cleaning robot for PV panels . Master’s thesis. Leuven: KU, Technology Campus De Nayer, Sint-Katelijne-Waver , 2015 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC [26] Gheitasi A , Almaliky A, Albaqawi N. Development of an automatic cleaning system for photovoltaic plants. In: IEEE PES Asia-Pacific Power Energy Eng. Conf. (APPEEC), 2015 , Brisbane, QLD, Australia , 15–18 November 2015 , 1 – 4 . [27] Isaifan RJ , Samara A, Suwaileh W, et al. Improved self-cleaning properties of an efficient and easy to scale up TiO2 thin films prepared by adsorptive self-assembly . Sci Rep , 2017 , 7 : 1 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat [28] He G , Zhou C, Li Z. Review of self-cleaning method for solar cell array . Procedia Eng , 2011 , 16 : 640 – 645 . Google Scholar Crossref Search ADS WorldCat [29] Arabatzis I , Todorova N, Fasaki I, et al. Photocatalytic, self-cleaning, antireflective coating for photovoltaic panels: characterization and monitoring in real conditions . Sol Energy , 2018 , 159 : 251 – 259 . Google Scholar Crossref Search ADS WorldCat [30] Piliougine M , Cañete C, Moreno R, et al. Comparative analysis of energy produced by photovoltaic modules with anti-soiling coated surface in arid climates . Appl Energy , 2013 , 112 : 626 – 634 . Google Scholar Crossref Search ADS WorldCat [31] Luque EG , Antonanzas-Torres F, Escobar R. Effect of soiling in bifacial PV modules and cleaning schedule optimization . Energy Convers Manage , 2018 , 174 : 615 – 625 . Google Scholar Crossref Search ADS WorldCat [32] Kimber A , Mitchell L, Nogradi S, et al. The effect of soiling on large grid-connected photovoltaic systems in California and the Southwest Region of the United States . In: IEEE 4th World Conf. Photovoltaic. Energy Conf. , Waikoloa, HI, USA , 7–12 May 2006 , 2391 – 2395 . [33] Moharram KA , Abd-Elhady MS, Kandil HA, et al. Influence of cleaning using water and surfactants on the performance of photovoltaic panels . Energy Convers Manage , 2013 , 68 : 266 – 272 . Google Scholar Crossref Search ADS WorldCat [34] Urrejola E , Antonanzas J, Ayala P, et al. Effect of soiling and sunlight exposure on the performance ratio of photovoltaic technologies in Santiago, Chile . Energy Convers Manage , 2016 , 114 : 338 – 347 . Google Scholar Crossref Search ADS WorldCat [35] Gupta V , Sharma M, Pachauri RK, et al. A low-cost real-time IOT enabled data acquisition system for monitoring of PV system. Energy Sources, Part A: Recovery, Utilization Environ Effects . 2021 , 43 : 2529 – 2543 . Google Scholar Crossref Search ADS WorldCat [36] Herteleer B , Huyck B, Catthoor F, et al. Normalised efficiency of photovoltaic systems: going beyond the performance ratio . Sol Energy , 2017 , 157 : 408 – 418.https://www.sciencedirect.com/science/article/abs/pii/S0038092X1730717X?via%3Dihub Google Scholar Crossref Search ADS WorldCat [37] Adouane M , Al-Qattan A, Alabdulrazzaq B, et al. Comparative performance evaluation of different photovoltaic modules technologies under Kuwait harsh climatic conditions . Energy Rep , 2020 , 6 : 2689 – 2696 . Google Scholar Crossref Search ADS WorldCat [38] Dadhich AP , Goyal R, Dadhich PN. Assessment of spatio-temporal variations in air quality of Jaipur city, Rajasthan, India . The Egyptian Journal of Remote Sensing and Space Science . 2018 , 21 : 173 – 181 . https://www.sciencedirect.com/science/article/pii/S1110982317301357 Google Scholar Crossref Search ADS WorldCat [39] Al-Housani M , Bicer Y, Koç M. Experimental investigations on PV cleaning of large-scale solar power plants in desert climates: comparison of cleaning techniques for drone retrofitting . Energy Convers Manage , 2019 , 185 : 800 – 815 . Google Scholar Crossref Search ADS WorldCat [40] Al-Housani M , Bicer Y, Koç M. Assessment of various dry photovoltaic cleaning techniques and frequencies on the power output of CdTe-Type modules in dusty environments . Sustainability , 2019 , 11 : 2850 . Google Scholar Crossref Search ADS WorldCat [41] Shehri AA , Parrott B, Carrasco P, et al. Accelerated testbed for studying the wear, optical and electrical characteristics of dry-cleaned PV solar panels . Sol Energy , 2017 , 146 : 8 – 19 . Google Scholar Crossref Search ADS WorldCat [42] Shehri AA , Parrott B, Carrasco P, et al. Impact of dust deposition and brush-based dry cleaning on glass transmittance for PV modules applications . Sol Energy , 2016 , 135 : 317 – 324 . Google Scholar Crossref Search ADS WorldCat © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy

Journal

Clean EnergyOxford University Press

Published: Jun 1, 2022

References