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Interleaved high-gain boost converter powered by solar energy using hybrid-based MPP tracking technique

Interleaved high-gain boost converter powered by solar energy using hybrid-based MPP tracking... Abstract This paper presents a solar-powered interleaved high-gain boost converter (IHGBC) that increases voltage gain with fewer ripples in the output voltage in comparison to existing DC–DC converters. The goal of this research is to develop a hybrid-based maximum power point tracking (MPPT) approach with the combination of a flower pollination (FP) algorithm assisted with a perturb & observe (P&O) MPPT approach for solar photovoltaic (SPV) systems integrated with IHGBC. To ensure effective usage of both FP and P&O algorithms, this study incorporates and validates the hybrid-based MPPT approach. The proposed solar-powered IHGBC with a hybrid-based MPPT algorithm has been computationally modelled and simulated using MATLAB® and Simulink® for both uniform and non-uniform irradiation and analysed for voltage gain, ripples in the output waveforms and convergence time. The proposed hybrid-based MPPT is based on a number of flowers that forecast the initial global peak, assisted by P&O in the last stage for faster convergence to attain the maximum power point (MPP). As a result, the hybrid-based MPPT approach alleviates the computational issues encountered in P&O and FP-based MPP approaches. The proposed hybrid MPPT is compared with conventional MPPT for SPV and the results show that the solar-powered IHGBC using a hybrid-based MPPT technique has negligible oscillations of 0.14% with a high-voltage gain of 7.992 and a fast convergence rate of 0.05 seconds compared to individual P&O-based MPPT and FP-based MPPT techniques. The simulation results of the proposed MPPT with IHGBC outperform the conventional MPPT with high-gain converters. Open in new tabDownload slide flower pollination, interleaved high-gain boost converter, maximum power point tracking technique, perturb & observe, solar photovoltaic Introduction The use of renewable energy has exploded due to the scarcity of conventional resources, the high cost of fossil fuels and environmental concerns. Solar photovoltaic (SPV) systems are among the most important renewable resources. However, because of their higher cost and lower efficiency when compared to conventional resources, they face numerous challenges. The fact that SPV strings are non-linear is crucial [1]. Hence, SPV systems must operate at their maximum power point (MPP) to offset this major disadvantage and have substantial control over the efficiency of SPV systems. Therefore, the MPPT approach is used to split the maximum SPV array power from the continuous power, which is crucial in SPV systems. As a result, numerous MPPT approaches for uniform and non-uniform irradiance have been developed [2, 3]. The MPPT approaches are categorized into conventional approaches [4] and soft computing-based approaches [5–8]. Furthermore, the conventional MPPT techniques are classified as incremental conductance [9], hill-climbing [10] and perturb & observe (P&O) [11]. Incremental conductance MPPT is a conventional approach for obtaining the global MPP that comprises a simple linear equation [9]. However, this MPPT requires an additional component to measure the converter output. The hill-climbing MPPT is used to get the MPP of the SPV system under shading conditions [10]. The P&O MPPT is also used to get the MPP from the SPV system, which causes more oscillations [11]. The P&O algorithm focuses on perturbing the SPV system voltage to optimize the DC–DC converter duty cycle and operate under MPP. In shading conditions and non-uniform irradiance, conventional MPPT approaches are unable to achieve the global MPP, which reduces the SPV system’s efficiency. As a result, before employing the P&O approach, it is necessary to concentrate on improving the performance of the conventional MPPT technique. So, soft computing-based MPPT techniques are used to keep track of the global MPP more quickly [5]. The soft computing-based MPPT algorithms include the fuzzy-logic controller (FLC) [12–15], firefly algorithm (FA) [16, 17], bat algorithm (BA) [18], particle swarm optimization (PSO) [19, 20] and flower pollination (FP) [21–24]. These approaches are some examples of soft computing-based approaches to track MPP. In FLC, the formulations of the fuzzification and defuzzification processes in the fuzzy-logic approach are difficult to execute [12–15]. The FA has some drawbacks, such as becoming trapped in multiple local optima and the parameters of the FA are fixed and do not change with time [16, 17]. Furthermore, FA does not memorize or remember any previous better situations for each firefly. In BA, the random initial values of bats can lead to premature convergence [18]. In PSO, a velocity formula is employed in the process of converging the particles to find the best solution [19, 20]. The FP algorithm has the features of fast convergence and robustness but involves a few more oscillations [21–24]. This paper explicates the hybrid-based MPPT technique to track the maximum power from the SPV system in both uniform and non-uniform irradiances [25–27]. The hybrid-based MPPT method is a combination of the FP algorithm with help from the P&O algorithm. It has been recommended for the solar-powered IHGBC to get a high voltage gain with fewer oscillations by using this method. An SPV system’s voltage is relatively low and it is influenced by environmental factors such as irregular or non-uniform availability of solar irradiance, which impacts SPV systems. As a result, high-voltage gain converters are used for SPV systems to increase the gain at the output of the system [28–31]. In the literature, numerous boost converters have been demonstrated to increase the voltage [32–35]. This paper presents an assessment of the solar-powered IHGBC to obtain high voltage gain and compares it with various non-isolated boost converters like conventional boost [36], double-boost [37], modified single-ended primary inductance converter (SEPIC) [38], hybrid-boost [39], quadratic-boost [40] and three-level-boost [41] converters. However, these converters are unable to produce a sufficient voltage gain for SPV systems compared to IHGBCs [42]. Fig. 1 represents the block diagram of the proposed solar-powered IHGBC using a hybrid-based MPPT technique. Fig. 1: Open in new tabDownload slide Proposed solar-powered IHGBC using the hybrid-based MPPT technique. This paper presents an IHGBC that helps to boost the output voltage of SPV. A hybrid MPP tracking approach with the combination of an FP algorithm and P&O algorithm is used to extract the maximum possible power from SPV throughout the day. The proposed MPPT method is also simulated in MATLAB® and compared to standard MPPTs in terms of output gain, convergence and ripples. 1 Solar-powered IHGBC In this paper, a solar-powered IHGBC with hybrid-based FP assisted with a P&O MPPT technique is proposed to attain high voltage gain with negligible oscillations. This section describes the mathematical model of the SPV module; IHGBC modes of operation and its boost ability analysis; a comparison of the IHGBC and the existing boost converters in terms of components and voltage gain for a 0.5 duty cycle. The configuration of the solar-powered IHGBC circuit is shown in Fig. 2. Fig. 2: Open in new tabDownload slide Solar-powered IHGBC circuit configuration. 1.1 Mathematical equivalent circuit of SPV module Fig. 3 depicts the SPV cell equivalent circuit represented by the SPV cell photocurrent (Iph). Rs and Rsh are the essential series and shunt resistances of the SPV cell, respectively. Normally, Rsh has a very high value and Rs is very small, so they may be neglected to make the analysis easier to understand [39]. Practically, several SPV cells are grouped to form an SPV module. SPV modules are used in larger units and these modules are linked together to form an SPV array by connecting SPV modules in series or parallel. They are used in PV generating systems to generate electricity. Fig. 4 depicts the equivalent circuit of the SPV array [40]. Fig. 3: Open in new tabDownload slide SPV cell equivalent circuit. Fig. 4: Open in new tabDownload slide SPV array equivalent circuit. The following parameters are determined using the SPV equivalent circuit depicted in Fig. 3 [4, 43]. Equation (1) describes the output current of the SPV equivalent circuit: I=Isc(expq(V+RsI)KnT−1)−((V+RsI)Rsh)(1) where K is Boltzmann's constant, n is ideal factor of diode and T is cell temperature. Isc, Iph and V represent the short-circuit current, photocurrent and voltage of the SPV cell, respectively. In the photocurrent equation, Iph is described by Equation (2): Iph=[Isc+Ki(T−298)]Ir1000(2) where Ir, T and Ki represent the solar insolation, cell temperature and temperature coefficient of the SPV cell, respectively. The diode saturation current with respect to temperature Io is described in Equation (3): Io=Irs[TTr]3exp[EgonVt(TTr−1)](3) where Tr, Irs, Ego and Vt represent the nominal temperature, reverse saturation current, bandgap energy and diode thermal voltage of the SPV cell, respectively. To generate a SPV module, SPV cells are arranged either in parallel, in series or in series-parallel where Np and Ns are the numbers of cells arranged in parallel and series, respectively. The number of SPV cells linked in series and parallel on the solar KC200GT panel is 54 and 1, respectively. The SPV module output current is described in Equation (4): I=NpIph−NpIo[exp(q(VNs+InRsNp)KnT)−1]−(NpVNs+IRsRsh)(4) The equations described from Equations (1)–(4) are modelled in MATLAB® and Simulink®, and the respective P–V and I–V curves are attained under various values of irradiance (i.e. 1000, 750 and 500 W/m2) and temperature conditions (i.e. 25oC, 40oC and 50oC), which are shown in Fig. 5a and b, respectively. The KC200GT 200-watt panel’s constraints are given in Table 2. Table 1: Component requirements and voltage-gain comparison of various boost converters Converters . Components . . . . Voltage gain (Vout/Vin) . . . Switches . Inductors . Capacitors . Diodes . Expression . Value for D = 0.5 . Conventional boost [36] 1 1 1 1 11−D 2 Double-boost [37] 1 2 2 3 21−D 4 Modified SEPIC [38] 1 3 3 3 D(1−D)2 2 Hybrid-boost [39] 1 1 4 4 3−D1−D 5 Quadratic-boost [40] 1 2 2 3 1(1−D)2 4 Three-level-boost [41] 1 1 5 5 31−D 6 Proposed IHGBC [42] 2 2 4 4 41−D 8 Converters . Components . . . . Voltage gain (Vout/Vin) . . . Switches . Inductors . Capacitors . Diodes . Expression . Value for D = 0.5 . Conventional boost [36] 1 1 1 1 11−D 2 Double-boost [37] 1 2 2 3 21−D 4 Modified SEPIC [38] 1 3 3 3 D(1−D)2 2 Hybrid-boost [39] 1 1 4 4 3−D1−D 5 Quadratic-boost [40] 1 2 2 3 1(1−D)2 4 Three-level-boost [41] 1 1 5 5 31−D 6 Proposed IHGBC [42] 2 2 4 4 41−D 8 Open in new tab Table 1: Component requirements and voltage-gain comparison of various boost converters Converters . Components . . . . Voltage gain (Vout/Vin) . . . Switches . Inductors . Capacitors . Diodes . Expression . Value for D = 0.5 . Conventional boost [36] 1 1 1 1 11−D 2 Double-boost [37] 1 2 2 3 21−D 4 Modified SEPIC [38] 1 3 3 3 D(1−D)2 2 Hybrid-boost [39] 1 1 4 4 3−D1−D 5 Quadratic-boost [40] 1 2 2 3 1(1−D)2 4 Three-level-boost [41] 1 1 5 5 31−D 6 Proposed IHGBC [42] 2 2 4 4 41−D 8 Converters . Components . . . . Voltage gain (Vout/Vin) . . . Switches . Inductors . Capacitors . Diodes . Expression . Value for D = 0.5 . Conventional boost [36] 1 1 1 1 11−D 2 Double-boost [37] 1 2 2 3 21−D 4 Modified SEPIC [38] 1 3 3 3 D(1−D)2 2 Hybrid-boost [39] 1 1 4 4 3−D1−D 5 Quadratic-boost [40] 1 2 2 3 1(1−D)2 4 Three-level-boost [41] 1 1 5 5 31−D 6 Proposed IHGBC [42] 2 2 4 4 41−D 8 Open in new tab Table 2: Specifications of solar-powered IHGBCs Solar-powered IHGBC parameters . Value . FP optimization parameters P = 0.8, γ = 1.5, D = 0.5, iterations = 100 and random duty cycle = 25–75% Voc 32.9 V Isc 8.21 A Maximum power 200 W Voltage at MPP 26.2 V Input voltage (VPV) to IHGBC 26.2 V Duty cycle (D) 0.5 Switching frequency 50 kHz Inductor L1 = L2 = 100 µH Capacitors C1 = C2 = 100 µH and C3 = C4 = 100 µH Solar-powered IHGBC parameters . Value . FP optimization parameters P = 0.8, γ = 1.5, D = 0.5, iterations = 100 and random duty cycle = 25–75% Voc 32.9 V Isc 8.21 A Maximum power 200 W Voltage at MPP 26.2 V Input voltage (VPV) to IHGBC 26.2 V Duty cycle (D) 0.5 Switching frequency 50 kHz Inductor L1 = L2 = 100 µH Capacitors C1 = C2 = 100 µH and C3 = C4 = 100 µH Open in new tab Table 2: Specifications of solar-powered IHGBCs Solar-powered IHGBC parameters . Value . FP optimization parameters P = 0.8, γ = 1.5, D = 0.5, iterations = 100 and random duty cycle = 25–75% Voc 32.9 V Isc 8.21 A Maximum power 200 W Voltage at MPP 26.2 V Input voltage (VPV) to IHGBC 26.2 V Duty cycle (D) 0.5 Switching frequency 50 kHz Inductor L1 = L2 = 100 µH Capacitors C1 = C2 = 100 µH and C3 = C4 = 100 µH Solar-powered IHGBC parameters . Value . FP optimization parameters P = 0.8, γ = 1.5, D = 0.5, iterations = 100 and random duty cycle = 25–75% Voc 32.9 V Isc 8.21 A Maximum power 200 W Voltage at MPP 26.2 V Input voltage (VPV) to IHGBC 26.2 V Duty cycle (D) 0.5 Switching frequency 50 kHz Inductor L1 = L2 = 100 µH Capacitors C1 = C2 = 100 µH and C3 = C4 = 100 µH Open in new tab Fig. 5: Open in new tabDownload slide Characteristics of SPV module with non-uniform irradiances (a) P–V and (b) I–V. 1.2 Operation of solar-powered IHGBC The IHGBC operation is described by the four modes with respect to the ON/OFF positions of the metal-oxide-semiconductor field-effect transistor switches used in the IHGBC, i.e. M1 and M2. The operating modes of IHGBC are shown in Figs 6–8. Fig. 6: Open in new tabDownload slide Mode I and Mode III equivalent circuit of SPV-based IHGBC. Fig. 7: Open in new tabDownload slide Mode II equivalent circuit of SPV-based IHGBC. Fig. 8: Open in new tabDownload slide Mode IV equivalent circuit of SPV-based IHGBC. 1.2.1 Mode I and Mode III operation of IHGBC The switches M1 and M2 of the IHGBC are in conduction and all the diodes D1 to D4 are reverse-biased in this mode. Fig. 6 shows the respective path for the current flow. At this instant, the inductances L1 and L2 of IHGBC are charged by the SPV system, and the L1 and L2 inductance currents are gradually raised. The output capacitors C3 and C4 are associated in series to supply the load. 1.2.2 Mode II operation of IHGBC The switch M2 is turned on, M1 is turned off and only D1 and D3 are in conduction. Fig. 7 shows the respective path for the current flow. At this instant, the inductance of L1 is still being charged, increasing the inductor current of L1. The SPV system charges the capacitor C1. In this mode, SPV, L2 and C2 are associated in series and the energy is delivered to the capacitor C4 and the load. The capacitor C3 discharges energy to the load at the same instant and consequently reduces the inductor current on L2. When the switch M2 is turned on, the IHGBC operates in Mode III. 1.2.3 Mode IV operation of IHGBC The switch, M1 is turned on, and D1 and D3 are in conduction. Fig. 8 shows the respective path for the current flow. At this instant, the inductance of L1 is still being charged, increasing the inductor current of L1. The SPV system charges the capacitor C2. In this mode, SPV, L1 and C1 are associated in series and the energy is delivered to the capacitor C4 and the load. The capacitor C4 discharges energy to the load at the same instant and consequently reduces the inductor current on L1. When the switch M1 is turned off, the IHGBC operates in Mode I again. 1.3 Boost ability analysis of SPV-based IHGBC For the sake of simplicity, all inductors and capacitors of the SPV-based IHGBC are assumed to have the same values. With reference to SPV-based IHGBC modes of operation as illustrated in Fig. 7, the boost factor or voltage gain has been analysed using the volt–second balance principle [42]. When the switch M1 is turned off, for the inductance L1: VL1I.T1on=VL1II.T1off(5) where T1on and T1off represent the turn-on time and turn-off time of switch M1, respectively. From the Mode I, II and III equivalent circuits of IHGBC, VL1 can be written as: VL1I=VPV(6) Using Equations (5) and (6): VL1II=T1onT1off.VL1I=D11−D1.VL1I=D11−D1.VPV(7) where D1=T1onT1on + T1off=duty cycle of M1. When the switch M2 is turned off, for the inductance L2: VL2I.T2on=VL2II.T2off(8) where T2on and T2off represent the turn-on and turn-off times of switch M2, respectively. From the Mode I, II and III equivalent circuits of IHGBC, VL2 can be written as: VL2I=VPV(9) Using Equations (8) and (9): VL2II=T2onT2off.VL2I=D21−D2.VL2I=D21−D2.VPV(10) where D2=T2onT2on + T2off=duty cycle of M2. The voltage on C1 and C2 is obtained from Mode II and Mode IV, and is expressed in Equations (11) and (12), respectively: VC1=VPV+VL2II=VPV+D21−D2.VPV=11−D2.VPV(11) VC2=VPV+VL1II=VPV+D11−D1.VPV=11−D1.VPV(12) Similarly, the voltage on C3 and C4 is obtained from Mode IV and Mode II, and is expressed in Equations (13) and (14), respectively: VC3=VPV+VL1II+VC1=VPV+D11−D1.VPV+11−D2.VPV(13) VC4=VPV+VL2II+VC2=VPV+D21−D2.VPV+11−D1.VPV(14) Assuming equal duty ratios (i.e. D1 = D2 = D), the voltage in the C3 and C4 equations is obtained as represented in Equation (15): VC3=VC4=21−D.VPV(15) From Modes I and II, the proposed IHGBC output can be expressed as: Vo=VC3+VC4(16) Substituting Equation (15) into Equation (16), the IHGBC output is expressed as: Vo=41−D.VPV(17) Therefore, using Equation (13), the boost factor (B) or voltage gain (Vout/Vin) of the IHGBC is given as follows: Boost factor or voltage gain of IHGBC, B=VoVPV=41−D(18) From Equation (18), the voltage gain of the solar-powered IHGBC is increased by eight times for the 0.5 duty cycle [42]. 1.4 Comparison of IHGBCs with various boost converters A comparison is made between the IHGBC and other boost converters, namely conventional, double-boost, modified SEPIC, hybrid-boost, quadratic-boost and three-level-boost converters, in order to distinguish the proposed IHGBC. Table 1 represents the required components, voltage-gain expressions and their values for a 0.5 duty cycle. Fig. 9 depicts the absolute voltage-gain variation with respect to duty-cycle variation for all the presented converters shown in Table 1. In comparison to other boost converters, it demonstrates that the IHGBC has significant voltage gain-boosting capabilities. Fig. 9: Open in new tabDownload slide Voltage gains of various boost converters. 2 MPPT techniques for SPV-based IHGBCs Fig. 10 shows the schematic diagram of the solar-powered IHGBC with a hybrid-based MPPT assisted with the combination of a FP approach assisted with P&O. Furthermore, the effectiveness of the suggested solar-powered IHGBC performance has been evaluated and compared with existing P&O and FP algorithms to verify the MPPT techniques of a SPV system. The proposed IHGBC is simulated using MATLAB® and Simulink® to investigate its enactment in terms of boost factor or voltage gain, convergence time and ripples (%) in the IHGBC output. Furthermore, the paper compares P&O, FP and hybrid-based MPPT algorithms in terms of various performance parameters. Fig. 10: Open in new tabDownload slide Solar-powered IHGBC circuit using hybrid-based MPPT technique. 2.1 P&O-based MPPT This is one of the simplistic techniques used to track the MPP for SPV-based IHGBC. A little disturbance is introduced in this approach to induce the power fluctuation of the SPV system. The SPV output power is sensed on a regular basis and compared to the prior power. This method applies a perturbation to the SPV system or the SPV array voltage. The flowchart of the P&O-based MPPT technique is represented in Fig. 11 for SPV-based IHGBCs. Fig. 11: Open in new tabDownload slide Flowchart of P&O-based MPPT technique for solar-powered IHGBCs. When the algorithm reaches a steady state, it oscillates around the peak point. The amount of the perturbation is usually kept to a minimum to keep the power variation as small as feasible. The algorithm is intended to establish a module reference voltage that corresponds to the peak voltage of the SPV system. The P&O-based MPPT technique is ineffective for tracking peak power under rapidly changing atmospheric conditions. 2.2 FP-based MPP tracking technique The FP-based MPPT algorithm approach is one of the metaheuristic algorithms used to track the MPP for SPV-based IHGBCs. The natural pollination process in flowers serves as inspiration for the FP algorithm. In the FP approach, pollination is very essential. Pollination is the exchange of pollen that occurs as a result of two different kinds of processes: biotic and abiotic. In the biotic procedure, if two flowers that seem alike bloom at the same time, the pollination is referred to as cross-pollination. It is considered pollination when two distinct flowers pollinate each other. In an abiotic operation, this is referred to as self-pollination. In the FP, cross-pollination accounts for 90% of crossings, while self-pollination accounts for the remaining 10%. A probability switch P∈[0, 1] limits the control between cross-pollinations and self-pollinations during the procedure. The number of flowers participating in pollination determines the reproductive flower constancy. The generalized FP-based MPPT technique for SPV-based IHGBCs is represented by the flowchart in Fig. 12. Fig. 12: Open in new tabDownload slide Flowchart of FP-based MPP tracking technique for solar-powered IHGBCs. 2.3 Proposed hybrid-based FP assisted with P&O MPPT technique The evolution of bio-inspired MPPT approaches has been abundant in recent years to achieve proven global MPPT [25–27]. However, the use of the P&O-based MPPT technique is highly encouraged due to its smoother operation and reduced switching stress. In this paper, the benefits of conventional MPPT and bio-inspired MPPT techniques are merged to develop hybrid methods to attain optimal MPPT efficiently and accurately with negligible oscillations. The FP algorithm has been combined with the P&O algorithm to develop a hybrid-based MPPT technique. The MPPT technique using the combination of FP and P&O algorithms is compared against individual algorithms, i.e. FP alone and P&O alone. The circuit configuration of IHGBCs using a hybrid-based MPPT technique is shown in Fig. 10. By employing the hybrid-based MPPT technique on the IHGBC, the converter stress can be substantially removed, ensuring smooth operation with negligible voltage fluctuations. The step-by-step procedure of the hybrid-based MPPT technique of combined FP and P&O is represented by a flowchart in Fig. 13. The following is a detailed description of the steps involved in developing the hybrid-based MPPT technique: Fig. 13: Open in new tabDownload slide Flowchart of hybrid-based MPPT technique for solar-powered IHGBCs. Step 1: Initialize variables such as D, ΔD, P and γ, where D represents the duty cycle, ΔD represents the change in duty, P represents the switch probability and γ represents the  scaling factor Step 2: Initialize the swarms y1(t), y2(t), y3(t), y4(t), y5(t) and y6(t) using Equation (19): Yi=Yi,min+rand (0, 1)(Yi,max−Yi,min), i=1, 2, ….6(19) where i represents the swarm or particle number, and Yi,min and Yi,max represent the duty-cycle limits. This method of random initialization assures that all of the swarms or particles in the population will find themselves in different parts of the search space. Step 3: Power values from P–V characteristics are determined for the initialized duty cycle and the best duty-cycle ‘Gbest’ is equivalent to the ideal global MPP from the initial population. Step 4: Update the initial duty cycle with respect to ‘Gbest’ attained in the initial iteration by global and local pollination to develop different duty values as y1(t + 1), y2(t + 1), y3(t + 1), y4(t + 1), y5(t + 1) and y6(t + 1). Step 5: Determine the ‘Gbest’ value for the different duty cycles that corresponds to the global MPP and verify the present global power with the ‘Gbest’ value from the previous iteration; if the requirement Gbest (t + 1) ~ ±0.05Gbest (t) is met, call in the P&O algorithm; otherwise, continue with the FP algorithm. Moreover, after transferring to P&O, the suggested hybrid-based MPPT technique begins to exploit the global MPP with minimum deviations due to changes in the duty cycle. Overall, the suggested convention improves efficiency and power exploitation abilities significantly. Step 6: The operational point of the SPV in the P–V curve changes with respect to the change in irradiation of a SPV array. As a result, the voltage and current equations given in Equations (20) and (21) of the SPV, which are described in [44], are followed to switch the SPV under abrupt changes in load: VPV(k)−VPV(k−1)VPV(k)≥0.2(20) IPV(k)−IPV(k−1)IPV(k)≥0.1(21) where k represents the iteration number, VPV represents the SPV voltage and IPV represents the SPV current. 3 Simulation results The simulation study of the IHGBC powered by SPV using a hybrid-based MPPT technique is carried out using MATLAB® and Simulink® to ensure that the proposed MPPT technique is valid. Table 2 [23, 38] illustrates the specifications and parameters of the proposed SPV system and IHGBC employed in this investigation. To verify the proposed hybrid-based MPPT technique, two cases are examined by considering uniform and non-uniform irradiance. Furthermore, the results are compared with individual P&O-based and FP-based MPPT techniques. Fig. 14 represents the output of the SPV system and it gives an output of 26.2 V. Fig. 14: Open in new tabDownload slide Output of SPV system. Fig. 15a–c shows the IHGBC output voltage, current and power curves, respectively, using the P&O-based MPPT technique with constant irradiance of 1000 W/m2. Fig. 15: Open in new tabDownload slide Simulation results of IHGBC using P&O-based MPPT technique under constant irradiance for (a) voltage, (b) current and (c) power. From Fig. 15a, it can be seen that the IHGBC output is increased to 207.4 V with a boost factor of 7.916 for a 26.2-V input. Furthermore, Fig. 15b and c shows the output IHGBC current of 0.816 A and power of 169.2 W. Finally, the waveforms of IHGBC using the P&O MPPT technique shown in Fig. 15 exhibit 3.34% oscillations, as shown in the zoomed view, and stabilized at 1.05 seconds. Similarly, Fig. 16a–c depicts the IHGBC waveforms at the output of the P&O-based MPPT technique with non-uniform irradiances of 1000, 750 and 500 W/m2. The uniform irradiance of 1000 W/m2 is considered from 1.2 to 1.6 seconds, 750 W/m2 from 1.6 to 2 seconds, and 500 W/m2 from 2 to 3 seconds. It can be observed that the IHGBC output waveforms are stabilized at 1.05 seconds, similarly to the constant irradiance. Fig. 16: Open in new tabDownload slide Simulation results of IHGBC using P&O-based MPPT technique under non-uniform irradiance for (a) voltage, (b) current and (c) power. According to Fig. 16a–c, the values of MPP for non-uniform irradiance are 169.2 W at 207.4 V and 0.82 A for 1000 W/m2, 108 W at 165 V and 0.65 A for 750 W/m2, and 50 W at 110 V and 0.43 A for 500 W/m2. Fig. 17a–c shows the IHGBC waveforms at the output using an FP-based MPPT technique with constant irradiance of 1000 W/m2. From Fig. 17a, it can be seen that the IHGBC output is increased to 208.5 V with a boost factor of 7.958 for a 26.2-V input. Fig. 17b and c shows the respective IHGBC current of 0.815 A and power of 170 W at the output. The waveforms of IHGBC using the FP-based MPPT technique shown in Fig. 17 exhibit 1.43% oscillations, as shown in the zoomed view, and stabilized at 0.2 seconds. Fig. 17: Open in new tabDownload slide Simulation results of IHGBC using FP-based MPPT technique under constant irradiance for (a) voltage, (b) current and (c) power. Similarly, Fig. 18a–c depicts the IHGBC waveforms at the output of the FP-based MPPT technique with non-uniform irradiances of 1000, 750 and 500 W/m2. The uniform irradiance of 1000 W/m2 is considered from 0 to 0.4 seconds, 750 W/m2 from 0.4 to 0.8 seconds, and 500 W/m2 from 0.8 to 1.2 seconds. It can be observed that the IHGBC output waveforms are stabilized at 0.2 seconds, similarly to constant irradiances. According to Fig. 18a–c, the values of MPP for non-uniform irradiances are 170.13 W at 208.5 V and 0.815 A for 1000 W/m2, 108.97 W at 166 V and 0.64 A for 750 W/m2, and 51.285 W at 113 V and 0.445 A for 500 W/m2. According to Figs 15a and 17a, IHGBC using an FP-based MPPT technique has moderate oscillations, more voltage and a faster steady-state response than the P&O-based MPPT technique. Fig. 18: Open in new tabDownload slide Simulation results of IHGBC using FP-based MPPT technique under non-uniform irradiance for (a) voltage, (b) current and (c) power. The IHGBC waveforms at the output of a hybrid-based MPPT technique with a constant irradiance of 1000 W/m2 are shown in Fig. 19a–c. Fig. 19: Open in new tabDownload slide Simulation results of IHGBC using hybrid-based MPPT technique under constant irradiance for (a) voltage, (b) current and (c) power. The IHGBC output is increased to 209.4 V with a boost factor of 7.922 for a 26.2-V input, as shown in Fig. 19a. Furthermore, Fig. 19b and c shows the output IHGBC current of 0.813 A and power of 170.24 W. From the waveforms of IHGBC, using the hybrid-based MPPT technique shown in Fig. 19 exhibits 0.14% oscillations, as shown in the zoomed view, and stabilized at 0.05 seconds. Similarly, Fig. 20a–c depicts the IHGBC output waveforms obtained using a hybrid-based MPPT technique with non-uniform irradiances of 1000, 750 and 500 W/m2. The uniform irradiance of 1000 W/m2 is considered from 0 to 0.4 seconds, 750 W/m2 from 0.4 to 0.8 seconds, and 500 W/m2 from 0.8 to 1.2 seconds. It can be observed that the IHGBC output waveforms are stabilized at 0.05 seconds, similarly to constant irradiances. Fig. 20: Open in new tabDownload slide Simulation results of IHGBC using hybrid-based MPPT technique under non-uniform irradiance for (a) voltage, (b) current and (c) power. From Fig. 20a–c, under non-uniform irradiances, the values of MPP are 170.24 W at 209.4 V and 0.813 A for 1000 W/m2, and similarly 109.5 W at 167 V and 0.645 A for 750 W/m2, and 51.75 W at 115 V and 0.45 A for 500 W/m2. Furthermore, from Figs 15, 17 and 19, it is observed that IHGBC using a hybrid-based MPPT technique has fewer oscillations with high voltage gain and a fast steady-state response compared to individual P&O-based MPPT and FP-based MPPT techniques [23]. 3.1 Comparative analysis of P&O-based, FP-based and hybrid-based MPPT techniques for IHGBC Table 3 represents the comparative analysis of solar-powered IHGBC using P&O-based, FP-based and hybrid-based MPPT techniques for an SPV voltage of 26.2 V. It can be noticed that P&O-based MPPT techniques have a higher convergence rate and higher oscillations compared to FP-based and hybrid-based MPPT techniques. In the same way, the FP-based MPPT technique exhibits more oscillations with a moderate convergence rate compared to the hybrid-based MPPT technique, and a poorer convergence rate and moderate oscillations when compared to the P&O-based MPPT techniques. The hybrid-based MPPT technique has fewer oscillations and a poorer convergence rate when compared to individual P&O-based and FP-based MPPT techniques alone [23]. Also, the output power and voltage gain are almost the same for all the MPPT techniques. The analysis reveals that the hybrid-based MPPT technique has a little more voltage gain and power due to negligible oscillations with more tracking speed when compared to the P&O and FP techniques. Table 3: Analysis of solar-powered IHGBC using various MPP tracking techniques Parameter . MPPT techniques . . . . P&O . FP . Hybrid . SPV output (VPV in volts) 26.2 26.2 26.2 Output voltage (Vo in volts) 207.4 208.5 209.4 Voltage ripple (%) 3.34 1.43 0.14 Output current (Io in amperes) 0.816 0.816 0.813 Output power (Po in watts) 169.2 170.13 170.24 Convergence time (ts in seconds) 1.05 0.2 0.05 Voltage gain 7.916 7.958 7.992 Oscillations More Moderate Less MPP tracking speed Less Moderate More Parameter . MPPT techniques . . . . P&O . FP . Hybrid . SPV output (VPV in volts) 26.2 26.2 26.2 Output voltage (Vo in volts) 207.4 208.5 209.4 Voltage ripple (%) 3.34 1.43 0.14 Output current (Io in amperes) 0.816 0.816 0.813 Output power (Po in watts) 169.2 170.13 170.24 Convergence time (ts in seconds) 1.05 0.2 0.05 Voltage gain 7.916 7.958 7.992 Oscillations More Moderate Less MPP tracking speed Less Moderate More Open in new tab Table 3: Analysis of solar-powered IHGBC using various MPP tracking techniques Parameter . MPPT techniques . . . . P&O . FP . Hybrid . SPV output (VPV in volts) 26.2 26.2 26.2 Output voltage (Vo in volts) 207.4 208.5 209.4 Voltage ripple (%) 3.34 1.43 0.14 Output current (Io in amperes) 0.816 0.816 0.813 Output power (Po in watts) 169.2 170.13 170.24 Convergence time (ts in seconds) 1.05 0.2 0.05 Voltage gain 7.916 7.958 7.992 Oscillations More Moderate Less MPP tracking speed Less Moderate More Parameter . MPPT techniques . . . . P&O . FP . Hybrid . SPV output (VPV in volts) 26.2 26.2 26.2 Output voltage (Vo in volts) 207.4 208.5 209.4 Voltage ripple (%) 3.34 1.43 0.14 Output current (Io in amperes) 0.816 0.816 0.813 Output power (Po in watts) 169.2 170.13 170.24 Convergence time (ts in seconds) 1.05 0.2 0.05 Voltage gain 7.916 7.958 7.992 Oscillations More Moderate Less MPP tracking speed Less Moderate More Open in new tab In addition, one of the most notable advantages of the proposed hybrid-based MPPT is that it requires fewer initial parameters than GA (genetic algorithm) and PSO. The initial values of the parameters for each algorithm used in the presented research work are listed in Table 4 and the root mean squared error (RMSE) is used to evaluate the performance. To minimize the error e(t), the RMSE is defined as the objective function for the optimization process in accordance with Equation (22): Table 4: Initial parameters of various MPP techniques MPP technique . Initialization process . . . Parameters . Value . Hybrid-based MPP technique Number of flowers 20 Maximum iterations 100 GA Population size 60 Crossover rate 0.9 Maximum iterations 120 PSO Particles 60 Inertia weight 0.4–0.9 Maximum iterations 100 MPP technique . Initialization process . . . Parameters . Value . Hybrid-based MPP technique Number of flowers 20 Maximum iterations 100 GA Population size 60 Crossover rate 0.9 Maximum iterations 120 PSO Particles 60 Inertia weight 0.4–0.9 Maximum iterations 100 Open in new tab Table 4: Initial parameters of various MPP techniques MPP technique . Initialization process . . . Parameters . Value . Hybrid-based MPP technique Number of flowers 20 Maximum iterations 100 GA Population size 60 Crossover rate 0.9 Maximum iterations 120 PSO Particles 60 Inertia weight 0.4–0.9 Maximum iterations 100 MPP technique . Initialization process . . . Parameters . Value . Hybrid-based MPP technique Number of flowers 20 Maximum iterations 100 GA Population size 60 Crossover rate 0.9 Maximum iterations 120 PSO Particles 60 Inertia weight 0.4–0.9 Maximum iterations 100 Open in new tab RMSE=∑t=0Tsim(Vo(t)−VPV(t))2Tsim(22) where Tsim represents the simulation time. The Fig. 21 shows the convergence curves of hybrid-based MPPT, i.e. FP assisted with P&O, GA and PSO algorithms for IHGBC to obtain the optimum duty cycle. As illustrated in Fig. 21, the hybrid-based MPPT technique is faster at approaching the optimal solution than the GA and PSO algorithms with the lower RMSE and also it shows that the results do not change during the iterative process, which shows how stable it is compared to GA and PSO. Fig. 21: Open in new tabDownload slide Convergence curves of FP–P&O, GA and PSO algorithms. 4 Conclusion In this paper, a solar-powered IHGBC using a hybrid-based MPPT technique with a combination of P&O-based and FP-based MPPT techniques is suggested to locate the MPP under constant and variable irradiance. From the results, it is noticed that the hybrid-based MPPT technique exhibits only 0.14% oscillations with a faster convergence rate of 0.05 seconds compared to individual P&O-based MPPT and FP-based MPPT techniques for solar PV, whereas the P&O-based MPPT technique exhibits 1.43% with a convergence rate of 1.05 seconds and the FP-based MPPT technique exhibits 3.34% oscillations with a moderate convergence rate of 0.12. Therefore, the suggested hybrid-based MPPT technique is demonstrated to be superior to the other MPPT techniques in terms of convergence rate, percentage oscillations and MPP tracking speed. The presented MPPT techniques have been also tested under three different values of irradiance to check the performance of the SPV with the suggested MPPT, and it is concluded that the hybrid-based MPPT technique increases its overall computational performance compared to other MPPT techniques. In addition, the proposed solar-powered IHGBC achieves a high voltage gain compared with other boost converters, namely conventional, double-boost, modified SEPIC, hybrid-boost, quadratic-boost and three-level-boost converters. Finally, the output of solar-powered IHGBCs has been increased by eight times for an optimum duty cycle of 0.5 when compared to conventional boost converters. 5 Future scope The suggested solar-powered IHGBC with hybrid MPPT can be connected to a utility grid via a reduced switch multilevel inverter, which can pump generated energy into the grid. Furthermore, diverse renewable energy sources can be incorporated for the suggested converter to improve energy generation in remote areas. Conflict of interest statement None declared. References [1] Nguyen XH , Nguyen MP. Mathematical modeling of photovoltaic cell/module/arrays with tags in MATLAB/Simulink . Environmental Systems Research , 2015 , 4 : 1 – 13 . 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Interleaved high-gain boost converter powered by solar energy using hybrid-based MPP tracking technique

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Oxford University Press
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Copyright © 2022 National Institute of Clean-and-Low-Carbon Energy
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2515-4230
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2515-396X
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10.1093/ce/zkac026
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Abstract

Abstract This paper presents a solar-powered interleaved high-gain boost converter (IHGBC) that increases voltage gain with fewer ripples in the output voltage in comparison to existing DC–DC converters. The goal of this research is to develop a hybrid-based maximum power point tracking (MPPT) approach with the combination of a flower pollination (FP) algorithm assisted with a perturb & observe (P&O) MPPT approach for solar photovoltaic (SPV) systems integrated with IHGBC. To ensure effective usage of both FP and P&O algorithms, this study incorporates and validates the hybrid-based MPPT approach. The proposed solar-powered IHGBC with a hybrid-based MPPT algorithm has been computationally modelled and simulated using MATLAB® and Simulink® for both uniform and non-uniform irradiation and analysed for voltage gain, ripples in the output waveforms and convergence time. The proposed hybrid-based MPPT is based on a number of flowers that forecast the initial global peak, assisted by P&O in the last stage for faster convergence to attain the maximum power point (MPP). As a result, the hybrid-based MPPT approach alleviates the computational issues encountered in P&O and FP-based MPP approaches. The proposed hybrid MPPT is compared with conventional MPPT for SPV and the results show that the solar-powered IHGBC using a hybrid-based MPPT technique has negligible oscillations of 0.14% with a high-voltage gain of 7.992 and a fast convergence rate of 0.05 seconds compared to individual P&O-based MPPT and FP-based MPPT techniques. The simulation results of the proposed MPPT with IHGBC outperform the conventional MPPT with high-gain converters. Open in new tabDownload slide flower pollination, interleaved high-gain boost converter, maximum power point tracking technique, perturb & observe, solar photovoltaic Introduction The use of renewable energy has exploded due to the scarcity of conventional resources, the high cost of fossil fuels and environmental concerns. Solar photovoltaic (SPV) systems are among the most important renewable resources. However, because of their higher cost and lower efficiency when compared to conventional resources, they face numerous challenges. The fact that SPV strings are non-linear is crucial [1]. Hence, SPV systems must operate at their maximum power point (MPP) to offset this major disadvantage and have substantial control over the efficiency of SPV systems. Therefore, the MPPT approach is used to split the maximum SPV array power from the continuous power, which is crucial in SPV systems. As a result, numerous MPPT approaches for uniform and non-uniform irradiance have been developed [2, 3]. The MPPT approaches are categorized into conventional approaches [4] and soft computing-based approaches [5–8]. Furthermore, the conventional MPPT techniques are classified as incremental conductance [9], hill-climbing [10] and perturb & observe (P&O) [11]. Incremental conductance MPPT is a conventional approach for obtaining the global MPP that comprises a simple linear equation [9]. However, this MPPT requires an additional component to measure the converter output. The hill-climbing MPPT is used to get the MPP of the SPV system under shading conditions [10]. The P&O MPPT is also used to get the MPP from the SPV system, which causes more oscillations [11]. The P&O algorithm focuses on perturbing the SPV system voltage to optimize the DC–DC converter duty cycle and operate under MPP. In shading conditions and non-uniform irradiance, conventional MPPT approaches are unable to achieve the global MPP, which reduces the SPV system’s efficiency. As a result, before employing the P&O approach, it is necessary to concentrate on improving the performance of the conventional MPPT technique. So, soft computing-based MPPT techniques are used to keep track of the global MPP more quickly [5]. The soft computing-based MPPT algorithms include the fuzzy-logic controller (FLC) [12–15], firefly algorithm (FA) [16, 17], bat algorithm (BA) [18], particle swarm optimization (PSO) [19, 20] and flower pollination (FP) [21–24]. These approaches are some examples of soft computing-based approaches to track MPP. In FLC, the formulations of the fuzzification and defuzzification processes in the fuzzy-logic approach are difficult to execute [12–15]. The FA has some drawbacks, such as becoming trapped in multiple local optima and the parameters of the FA are fixed and do not change with time [16, 17]. Furthermore, FA does not memorize or remember any previous better situations for each firefly. In BA, the random initial values of bats can lead to premature convergence [18]. In PSO, a velocity formula is employed in the process of converging the particles to find the best solution [19, 20]. The FP algorithm has the features of fast convergence and robustness but involves a few more oscillations [21–24]. This paper explicates the hybrid-based MPPT technique to track the maximum power from the SPV system in both uniform and non-uniform irradiances [25–27]. The hybrid-based MPPT method is a combination of the FP algorithm with help from the P&O algorithm. It has been recommended for the solar-powered IHGBC to get a high voltage gain with fewer oscillations by using this method. An SPV system’s voltage is relatively low and it is influenced by environmental factors such as irregular or non-uniform availability of solar irradiance, which impacts SPV systems. As a result, high-voltage gain converters are used for SPV systems to increase the gain at the output of the system [28–31]. In the literature, numerous boost converters have been demonstrated to increase the voltage [32–35]. This paper presents an assessment of the solar-powered IHGBC to obtain high voltage gain and compares it with various non-isolated boost converters like conventional boost [36], double-boost [37], modified single-ended primary inductance converter (SEPIC) [38], hybrid-boost [39], quadratic-boost [40] and three-level-boost [41] converters. However, these converters are unable to produce a sufficient voltage gain for SPV systems compared to IHGBCs [42]. Fig. 1 represents the block diagram of the proposed solar-powered IHGBC using a hybrid-based MPPT technique. Fig. 1: Open in new tabDownload slide Proposed solar-powered IHGBC using the hybrid-based MPPT technique. This paper presents an IHGBC that helps to boost the output voltage of SPV. A hybrid MPP tracking approach with the combination of an FP algorithm and P&O algorithm is used to extract the maximum possible power from SPV throughout the day. The proposed MPPT method is also simulated in MATLAB® and compared to standard MPPTs in terms of output gain, convergence and ripples. 1 Solar-powered IHGBC In this paper, a solar-powered IHGBC with hybrid-based FP assisted with a P&O MPPT technique is proposed to attain high voltage gain with negligible oscillations. This section describes the mathematical model of the SPV module; IHGBC modes of operation and its boost ability analysis; a comparison of the IHGBC and the existing boost converters in terms of components and voltage gain for a 0.5 duty cycle. The configuration of the solar-powered IHGBC circuit is shown in Fig. 2. Fig. 2: Open in new tabDownload slide Solar-powered IHGBC circuit configuration. 1.1 Mathematical equivalent circuit of SPV module Fig. 3 depicts the SPV cell equivalent circuit represented by the SPV cell photocurrent (Iph). Rs and Rsh are the essential series and shunt resistances of the SPV cell, respectively. Normally, Rsh has a very high value and Rs is very small, so they may be neglected to make the analysis easier to understand [39]. Practically, several SPV cells are grouped to form an SPV module. SPV modules are used in larger units and these modules are linked together to form an SPV array by connecting SPV modules in series or parallel. They are used in PV generating systems to generate electricity. Fig. 4 depicts the equivalent circuit of the SPV array [40]. Fig. 3: Open in new tabDownload slide SPV cell equivalent circuit. Fig. 4: Open in new tabDownload slide SPV array equivalent circuit. The following parameters are determined using the SPV equivalent circuit depicted in Fig. 3 [4, 43]. Equation (1) describes the output current of the SPV equivalent circuit: I=Isc(expq(V+RsI)KnT−1)−((V+RsI)Rsh)(1) where K is Boltzmann's constant, n is ideal factor of diode and T is cell temperature. Isc, Iph and V represent the short-circuit current, photocurrent and voltage of the SPV cell, respectively. In the photocurrent equation, Iph is described by Equation (2): Iph=[Isc+Ki(T−298)]Ir1000(2) where Ir, T and Ki represent the solar insolation, cell temperature and temperature coefficient of the SPV cell, respectively. The diode saturation current with respect to temperature Io is described in Equation (3): Io=Irs[TTr]3exp[EgonVt(TTr−1)](3) where Tr, Irs, Ego and Vt represent the nominal temperature, reverse saturation current, bandgap energy and diode thermal voltage of the SPV cell, respectively. To generate a SPV module, SPV cells are arranged either in parallel, in series or in series-parallel where Np and Ns are the numbers of cells arranged in parallel and series, respectively. The number of SPV cells linked in series and parallel on the solar KC200GT panel is 54 and 1, respectively. The SPV module output current is described in Equation (4): I=NpIph−NpIo[exp(q(VNs+InRsNp)KnT)−1]−(NpVNs+IRsRsh)(4) The equations described from Equations (1)–(4) are modelled in MATLAB® and Simulink®, and the respective P–V and I–V curves are attained under various values of irradiance (i.e. 1000, 750 and 500 W/m2) and temperature conditions (i.e. 25oC, 40oC and 50oC), which are shown in Fig. 5a and b, respectively. The KC200GT 200-watt panel’s constraints are given in Table 2. Table 1: Component requirements and voltage-gain comparison of various boost converters Converters . Components . . . . Voltage gain (Vout/Vin) . . . Switches . Inductors . Capacitors . Diodes . Expression . Value for D = 0.5 . Conventional boost [36] 1 1 1 1 11−D 2 Double-boost [37] 1 2 2 3 21−D 4 Modified SEPIC [38] 1 3 3 3 D(1−D)2 2 Hybrid-boost [39] 1 1 4 4 3−D1−D 5 Quadratic-boost [40] 1 2 2 3 1(1−D)2 4 Three-level-boost [41] 1 1 5 5 31−D 6 Proposed IHGBC [42] 2 2 4 4 41−D 8 Converters . Components . . . . Voltage gain (Vout/Vin) . . . Switches . Inductors . Capacitors . Diodes . Expression . Value for D = 0.5 . Conventional boost [36] 1 1 1 1 11−D 2 Double-boost [37] 1 2 2 3 21−D 4 Modified SEPIC [38] 1 3 3 3 D(1−D)2 2 Hybrid-boost [39] 1 1 4 4 3−D1−D 5 Quadratic-boost [40] 1 2 2 3 1(1−D)2 4 Three-level-boost [41] 1 1 5 5 31−D 6 Proposed IHGBC [42] 2 2 4 4 41−D 8 Open in new tab Table 1: Component requirements and voltage-gain comparison of various boost converters Converters . Components . . . . Voltage gain (Vout/Vin) . . . Switches . Inductors . Capacitors . Diodes . Expression . Value for D = 0.5 . Conventional boost [36] 1 1 1 1 11−D 2 Double-boost [37] 1 2 2 3 21−D 4 Modified SEPIC [38] 1 3 3 3 D(1−D)2 2 Hybrid-boost [39] 1 1 4 4 3−D1−D 5 Quadratic-boost [40] 1 2 2 3 1(1−D)2 4 Three-level-boost [41] 1 1 5 5 31−D 6 Proposed IHGBC [42] 2 2 4 4 41−D 8 Converters . Components . . . . Voltage gain (Vout/Vin) . . . Switches . Inductors . Capacitors . Diodes . Expression . Value for D = 0.5 . Conventional boost [36] 1 1 1 1 11−D 2 Double-boost [37] 1 2 2 3 21−D 4 Modified SEPIC [38] 1 3 3 3 D(1−D)2 2 Hybrid-boost [39] 1 1 4 4 3−D1−D 5 Quadratic-boost [40] 1 2 2 3 1(1−D)2 4 Three-level-boost [41] 1 1 5 5 31−D 6 Proposed IHGBC [42] 2 2 4 4 41−D 8 Open in new tab Table 2: Specifications of solar-powered IHGBCs Solar-powered IHGBC parameters . Value . FP optimization parameters P = 0.8, γ = 1.5, D = 0.5, iterations = 100 and random duty cycle = 25–75% Voc 32.9 V Isc 8.21 A Maximum power 200 W Voltage at MPP 26.2 V Input voltage (VPV) to IHGBC 26.2 V Duty cycle (D) 0.5 Switching frequency 50 kHz Inductor L1 = L2 = 100 µH Capacitors C1 = C2 = 100 µH and C3 = C4 = 100 µH Solar-powered IHGBC parameters . Value . FP optimization parameters P = 0.8, γ = 1.5, D = 0.5, iterations = 100 and random duty cycle = 25–75% Voc 32.9 V Isc 8.21 A Maximum power 200 W Voltage at MPP 26.2 V Input voltage (VPV) to IHGBC 26.2 V Duty cycle (D) 0.5 Switching frequency 50 kHz Inductor L1 = L2 = 100 µH Capacitors C1 = C2 = 100 µH and C3 = C4 = 100 µH Open in new tab Table 2: Specifications of solar-powered IHGBCs Solar-powered IHGBC parameters . Value . FP optimization parameters P = 0.8, γ = 1.5, D = 0.5, iterations = 100 and random duty cycle = 25–75% Voc 32.9 V Isc 8.21 A Maximum power 200 W Voltage at MPP 26.2 V Input voltage (VPV) to IHGBC 26.2 V Duty cycle (D) 0.5 Switching frequency 50 kHz Inductor L1 = L2 = 100 µH Capacitors C1 = C2 = 100 µH and C3 = C4 = 100 µH Solar-powered IHGBC parameters . Value . FP optimization parameters P = 0.8, γ = 1.5, D = 0.5, iterations = 100 and random duty cycle = 25–75% Voc 32.9 V Isc 8.21 A Maximum power 200 W Voltage at MPP 26.2 V Input voltage (VPV) to IHGBC 26.2 V Duty cycle (D) 0.5 Switching frequency 50 kHz Inductor L1 = L2 = 100 µH Capacitors C1 = C2 = 100 µH and C3 = C4 = 100 µH Open in new tab Fig. 5: Open in new tabDownload slide Characteristics of SPV module with non-uniform irradiances (a) P–V and (b) I–V. 1.2 Operation of solar-powered IHGBC The IHGBC operation is described by the four modes with respect to the ON/OFF positions of the metal-oxide-semiconductor field-effect transistor switches used in the IHGBC, i.e. M1 and M2. The operating modes of IHGBC are shown in Figs 6–8. Fig. 6: Open in new tabDownload slide Mode I and Mode III equivalent circuit of SPV-based IHGBC. Fig. 7: Open in new tabDownload slide Mode II equivalent circuit of SPV-based IHGBC. Fig. 8: Open in new tabDownload slide Mode IV equivalent circuit of SPV-based IHGBC. 1.2.1 Mode I and Mode III operation of IHGBC The switches M1 and M2 of the IHGBC are in conduction and all the diodes D1 to D4 are reverse-biased in this mode. Fig. 6 shows the respective path for the current flow. At this instant, the inductances L1 and L2 of IHGBC are charged by the SPV system, and the L1 and L2 inductance currents are gradually raised. The output capacitors C3 and C4 are associated in series to supply the load. 1.2.2 Mode II operation of IHGBC The switch M2 is turned on, M1 is turned off and only D1 and D3 are in conduction. Fig. 7 shows the respective path for the current flow. At this instant, the inductance of L1 is still being charged, increasing the inductor current of L1. The SPV system charges the capacitor C1. In this mode, SPV, L2 and C2 are associated in series and the energy is delivered to the capacitor C4 and the load. The capacitor C3 discharges energy to the load at the same instant and consequently reduces the inductor current on L2. When the switch M2 is turned on, the IHGBC operates in Mode III. 1.2.3 Mode IV operation of IHGBC The switch, M1 is turned on, and D1 and D3 are in conduction. Fig. 8 shows the respective path for the current flow. At this instant, the inductance of L1 is still being charged, increasing the inductor current of L1. The SPV system charges the capacitor C2. In this mode, SPV, L1 and C1 are associated in series and the energy is delivered to the capacitor C4 and the load. The capacitor C4 discharges energy to the load at the same instant and consequently reduces the inductor current on L1. When the switch M1 is turned off, the IHGBC operates in Mode I again. 1.3 Boost ability analysis of SPV-based IHGBC For the sake of simplicity, all inductors and capacitors of the SPV-based IHGBC are assumed to have the same values. With reference to SPV-based IHGBC modes of operation as illustrated in Fig. 7, the boost factor or voltage gain has been analysed using the volt–second balance principle [42]. When the switch M1 is turned off, for the inductance L1: VL1I.T1on=VL1II.T1off(5) where T1on and T1off represent the turn-on time and turn-off time of switch M1, respectively. From the Mode I, II and III equivalent circuits of IHGBC, VL1 can be written as: VL1I=VPV(6) Using Equations (5) and (6): VL1II=T1onT1off.VL1I=D11−D1.VL1I=D11−D1.VPV(7) where D1=T1onT1on + T1off=duty cycle of M1. When the switch M2 is turned off, for the inductance L2: VL2I.T2on=VL2II.T2off(8) where T2on and T2off represent the turn-on and turn-off times of switch M2, respectively. From the Mode I, II and III equivalent circuits of IHGBC, VL2 can be written as: VL2I=VPV(9) Using Equations (8) and (9): VL2II=T2onT2off.VL2I=D21−D2.VL2I=D21−D2.VPV(10) where D2=T2onT2on + T2off=duty cycle of M2. The voltage on C1 and C2 is obtained from Mode II and Mode IV, and is expressed in Equations (11) and (12), respectively: VC1=VPV+VL2II=VPV+D21−D2.VPV=11−D2.VPV(11) VC2=VPV+VL1II=VPV+D11−D1.VPV=11−D1.VPV(12) Similarly, the voltage on C3 and C4 is obtained from Mode IV and Mode II, and is expressed in Equations (13) and (14), respectively: VC3=VPV+VL1II+VC1=VPV+D11−D1.VPV+11−D2.VPV(13) VC4=VPV+VL2II+VC2=VPV+D21−D2.VPV+11−D1.VPV(14) Assuming equal duty ratios (i.e. D1 = D2 = D), the voltage in the C3 and C4 equations is obtained as represented in Equation (15): VC3=VC4=21−D.VPV(15) From Modes I and II, the proposed IHGBC output can be expressed as: Vo=VC3+VC4(16) Substituting Equation (15) into Equation (16), the IHGBC output is expressed as: Vo=41−D.VPV(17) Therefore, using Equation (13), the boost factor (B) or voltage gain (Vout/Vin) of the IHGBC is given as follows: Boost factor or voltage gain of IHGBC, B=VoVPV=41−D(18) From Equation (18), the voltage gain of the solar-powered IHGBC is increased by eight times for the 0.5 duty cycle [42]. 1.4 Comparison of IHGBCs with various boost converters A comparison is made between the IHGBC and other boost converters, namely conventional, double-boost, modified SEPIC, hybrid-boost, quadratic-boost and three-level-boost converters, in order to distinguish the proposed IHGBC. Table 1 represents the required components, voltage-gain expressions and their values for a 0.5 duty cycle. Fig. 9 depicts the absolute voltage-gain variation with respect to duty-cycle variation for all the presented converters shown in Table 1. In comparison to other boost converters, it demonstrates that the IHGBC has significant voltage gain-boosting capabilities. Fig. 9: Open in new tabDownload slide Voltage gains of various boost converters. 2 MPPT techniques for SPV-based IHGBCs Fig. 10 shows the schematic diagram of the solar-powered IHGBC with a hybrid-based MPPT assisted with the combination of a FP approach assisted with P&O. Furthermore, the effectiveness of the suggested solar-powered IHGBC performance has been evaluated and compared with existing P&O and FP algorithms to verify the MPPT techniques of a SPV system. The proposed IHGBC is simulated using MATLAB® and Simulink® to investigate its enactment in terms of boost factor or voltage gain, convergence time and ripples (%) in the IHGBC output. Furthermore, the paper compares P&O, FP and hybrid-based MPPT algorithms in terms of various performance parameters. Fig. 10: Open in new tabDownload slide Solar-powered IHGBC circuit using hybrid-based MPPT technique. 2.1 P&O-based MPPT This is one of the simplistic techniques used to track the MPP for SPV-based IHGBC. A little disturbance is introduced in this approach to induce the power fluctuation of the SPV system. The SPV output power is sensed on a regular basis and compared to the prior power. This method applies a perturbation to the SPV system or the SPV array voltage. The flowchart of the P&O-based MPPT technique is represented in Fig. 11 for SPV-based IHGBCs. Fig. 11: Open in new tabDownload slide Flowchart of P&O-based MPPT technique for solar-powered IHGBCs. When the algorithm reaches a steady state, it oscillates around the peak point. The amount of the perturbation is usually kept to a minimum to keep the power variation as small as feasible. The algorithm is intended to establish a module reference voltage that corresponds to the peak voltage of the SPV system. The P&O-based MPPT technique is ineffective for tracking peak power under rapidly changing atmospheric conditions. 2.2 FP-based MPP tracking technique The FP-based MPPT algorithm approach is one of the metaheuristic algorithms used to track the MPP for SPV-based IHGBCs. The natural pollination process in flowers serves as inspiration for the FP algorithm. In the FP approach, pollination is very essential. Pollination is the exchange of pollen that occurs as a result of two different kinds of processes: biotic and abiotic. In the biotic procedure, if two flowers that seem alike bloom at the same time, the pollination is referred to as cross-pollination. It is considered pollination when two distinct flowers pollinate each other. In an abiotic operation, this is referred to as self-pollination. In the FP, cross-pollination accounts for 90% of crossings, while self-pollination accounts for the remaining 10%. A probability switch P∈[0, 1] limits the control between cross-pollinations and self-pollinations during the procedure. The number of flowers participating in pollination determines the reproductive flower constancy. The generalized FP-based MPPT technique for SPV-based IHGBCs is represented by the flowchart in Fig. 12. Fig. 12: Open in new tabDownload slide Flowchart of FP-based MPP tracking technique for solar-powered IHGBCs. 2.3 Proposed hybrid-based FP assisted with P&O MPPT technique The evolution of bio-inspired MPPT approaches has been abundant in recent years to achieve proven global MPPT [25–27]. However, the use of the P&O-based MPPT technique is highly encouraged due to its smoother operation and reduced switching stress. In this paper, the benefits of conventional MPPT and bio-inspired MPPT techniques are merged to develop hybrid methods to attain optimal MPPT efficiently and accurately with negligible oscillations. The FP algorithm has been combined with the P&O algorithm to develop a hybrid-based MPPT technique. The MPPT technique using the combination of FP and P&O algorithms is compared against individual algorithms, i.e. FP alone and P&O alone. The circuit configuration of IHGBCs using a hybrid-based MPPT technique is shown in Fig. 10. By employing the hybrid-based MPPT technique on the IHGBC, the converter stress can be substantially removed, ensuring smooth operation with negligible voltage fluctuations. The step-by-step procedure of the hybrid-based MPPT technique of combined FP and P&O is represented by a flowchart in Fig. 13. The following is a detailed description of the steps involved in developing the hybrid-based MPPT technique: Fig. 13: Open in new tabDownload slide Flowchart of hybrid-based MPPT technique for solar-powered IHGBCs. Step 1: Initialize variables such as D, ΔD, P and γ, where D represents the duty cycle, ΔD represents the change in duty, P represents the switch probability and γ represents the  scaling factor Step 2: Initialize the swarms y1(t), y2(t), y3(t), y4(t), y5(t) and y6(t) using Equation (19): Yi=Yi,min+rand (0, 1)(Yi,max−Yi,min), i=1, 2, ….6(19) where i represents the swarm or particle number, and Yi,min and Yi,max represent the duty-cycle limits. This method of random initialization assures that all of the swarms or particles in the population will find themselves in different parts of the search space. Step 3: Power values from P–V characteristics are determined for the initialized duty cycle and the best duty-cycle ‘Gbest’ is equivalent to the ideal global MPP from the initial population. Step 4: Update the initial duty cycle with respect to ‘Gbest’ attained in the initial iteration by global and local pollination to develop different duty values as y1(t + 1), y2(t + 1), y3(t + 1), y4(t + 1), y5(t + 1) and y6(t + 1). Step 5: Determine the ‘Gbest’ value for the different duty cycles that corresponds to the global MPP and verify the present global power with the ‘Gbest’ value from the previous iteration; if the requirement Gbest (t + 1) ~ ±0.05Gbest (t) is met, call in the P&O algorithm; otherwise, continue with the FP algorithm. Moreover, after transferring to P&O, the suggested hybrid-based MPPT technique begins to exploit the global MPP with minimum deviations due to changes in the duty cycle. Overall, the suggested convention improves efficiency and power exploitation abilities significantly. Step 6: The operational point of the SPV in the P–V curve changes with respect to the change in irradiation of a SPV array. As a result, the voltage and current equations given in Equations (20) and (21) of the SPV, which are described in [44], are followed to switch the SPV under abrupt changes in load: VPV(k)−VPV(k−1)VPV(k)≥0.2(20) IPV(k)−IPV(k−1)IPV(k)≥0.1(21) where k represents the iteration number, VPV represents the SPV voltage and IPV represents the SPV current. 3 Simulation results The simulation study of the IHGBC powered by SPV using a hybrid-based MPPT technique is carried out using MATLAB® and Simulink® to ensure that the proposed MPPT technique is valid. Table 2 [23, 38] illustrates the specifications and parameters of the proposed SPV system and IHGBC employed in this investigation. To verify the proposed hybrid-based MPPT technique, two cases are examined by considering uniform and non-uniform irradiance. Furthermore, the results are compared with individual P&O-based and FP-based MPPT techniques. Fig. 14 represents the output of the SPV system and it gives an output of 26.2 V. Fig. 14: Open in new tabDownload slide Output of SPV system. Fig. 15a–c shows the IHGBC output voltage, current and power curves, respectively, using the P&O-based MPPT technique with constant irradiance of 1000 W/m2. Fig. 15: Open in new tabDownload slide Simulation results of IHGBC using P&O-based MPPT technique under constant irradiance for (a) voltage, (b) current and (c) power. From Fig. 15a, it can be seen that the IHGBC output is increased to 207.4 V with a boost factor of 7.916 for a 26.2-V input. Furthermore, Fig. 15b and c shows the output IHGBC current of 0.816 A and power of 169.2 W. Finally, the waveforms of IHGBC using the P&O MPPT technique shown in Fig. 15 exhibit 3.34% oscillations, as shown in the zoomed view, and stabilized at 1.05 seconds. Similarly, Fig. 16a–c depicts the IHGBC waveforms at the output of the P&O-based MPPT technique with non-uniform irradiances of 1000, 750 and 500 W/m2. The uniform irradiance of 1000 W/m2 is considered from 1.2 to 1.6 seconds, 750 W/m2 from 1.6 to 2 seconds, and 500 W/m2 from 2 to 3 seconds. It can be observed that the IHGBC output waveforms are stabilized at 1.05 seconds, similarly to the constant irradiance. Fig. 16: Open in new tabDownload slide Simulation results of IHGBC using P&O-based MPPT technique under non-uniform irradiance for (a) voltage, (b) current and (c) power. According to Fig. 16a–c, the values of MPP for non-uniform irradiance are 169.2 W at 207.4 V and 0.82 A for 1000 W/m2, 108 W at 165 V and 0.65 A for 750 W/m2, and 50 W at 110 V and 0.43 A for 500 W/m2. Fig. 17a–c shows the IHGBC waveforms at the output using an FP-based MPPT technique with constant irradiance of 1000 W/m2. From Fig. 17a, it can be seen that the IHGBC output is increased to 208.5 V with a boost factor of 7.958 for a 26.2-V input. Fig. 17b and c shows the respective IHGBC current of 0.815 A and power of 170 W at the output. The waveforms of IHGBC using the FP-based MPPT technique shown in Fig. 17 exhibit 1.43% oscillations, as shown in the zoomed view, and stabilized at 0.2 seconds. Fig. 17: Open in new tabDownload slide Simulation results of IHGBC using FP-based MPPT technique under constant irradiance for (a) voltage, (b) current and (c) power. Similarly, Fig. 18a–c depicts the IHGBC waveforms at the output of the FP-based MPPT technique with non-uniform irradiances of 1000, 750 and 500 W/m2. The uniform irradiance of 1000 W/m2 is considered from 0 to 0.4 seconds, 750 W/m2 from 0.4 to 0.8 seconds, and 500 W/m2 from 0.8 to 1.2 seconds. It can be observed that the IHGBC output waveforms are stabilized at 0.2 seconds, similarly to constant irradiances. According to Fig. 18a–c, the values of MPP for non-uniform irradiances are 170.13 W at 208.5 V and 0.815 A for 1000 W/m2, 108.97 W at 166 V and 0.64 A for 750 W/m2, and 51.285 W at 113 V and 0.445 A for 500 W/m2. According to Figs 15a and 17a, IHGBC using an FP-based MPPT technique has moderate oscillations, more voltage and a faster steady-state response than the P&O-based MPPT technique. Fig. 18: Open in new tabDownload slide Simulation results of IHGBC using FP-based MPPT technique under non-uniform irradiance for (a) voltage, (b) current and (c) power. The IHGBC waveforms at the output of a hybrid-based MPPT technique with a constant irradiance of 1000 W/m2 are shown in Fig. 19a–c. Fig. 19: Open in new tabDownload slide Simulation results of IHGBC using hybrid-based MPPT technique under constant irradiance for (a) voltage, (b) current and (c) power. The IHGBC output is increased to 209.4 V with a boost factor of 7.922 for a 26.2-V input, as shown in Fig. 19a. Furthermore, Fig. 19b and c shows the output IHGBC current of 0.813 A and power of 170.24 W. From the waveforms of IHGBC, using the hybrid-based MPPT technique shown in Fig. 19 exhibits 0.14% oscillations, as shown in the zoomed view, and stabilized at 0.05 seconds. Similarly, Fig. 20a–c depicts the IHGBC output waveforms obtained using a hybrid-based MPPT technique with non-uniform irradiances of 1000, 750 and 500 W/m2. The uniform irradiance of 1000 W/m2 is considered from 0 to 0.4 seconds, 750 W/m2 from 0.4 to 0.8 seconds, and 500 W/m2 from 0.8 to 1.2 seconds. It can be observed that the IHGBC output waveforms are stabilized at 0.05 seconds, similarly to constant irradiances. Fig. 20: Open in new tabDownload slide Simulation results of IHGBC using hybrid-based MPPT technique under non-uniform irradiance for (a) voltage, (b) current and (c) power. From Fig. 20a–c, under non-uniform irradiances, the values of MPP are 170.24 W at 209.4 V and 0.813 A for 1000 W/m2, and similarly 109.5 W at 167 V and 0.645 A for 750 W/m2, and 51.75 W at 115 V and 0.45 A for 500 W/m2. Furthermore, from Figs 15, 17 and 19, it is observed that IHGBC using a hybrid-based MPPT technique has fewer oscillations with high voltage gain and a fast steady-state response compared to individual P&O-based MPPT and FP-based MPPT techniques [23]. 3.1 Comparative analysis of P&O-based, FP-based and hybrid-based MPPT techniques for IHGBC Table 3 represents the comparative analysis of solar-powered IHGBC using P&O-based, FP-based and hybrid-based MPPT techniques for an SPV voltage of 26.2 V. It can be noticed that P&O-based MPPT techniques have a higher convergence rate and higher oscillations compared to FP-based and hybrid-based MPPT techniques. In the same way, the FP-based MPPT technique exhibits more oscillations with a moderate convergence rate compared to the hybrid-based MPPT technique, and a poorer convergence rate and moderate oscillations when compared to the P&O-based MPPT techniques. The hybrid-based MPPT technique has fewer oscillations and a poorer convergence rate when compared to individual P&O-based and FP-based MPPT techniques alone [23]. Also, the output power and voltage gain are almost the same for all the MPPT techniques. The analysis reveals that the hybrid-based MPPT technique has a little more voltage gain and power due to negligible oscillations with more tracking speed when compared to the P&O and FP techniques. Table 3: Analysis of solar-powered IHGBC using various MPP tracking techniques Parameter . MPPT techniques . . . . P&O . FP . Hybrid . SPV output (VPV in volts) 26.2 26.2 26.2 Output voltage (Vo in volts) 207.4 208.5 209.4 Voltage ripple (%) 3.34 1.43 0.14 Output current (Io in amperes) 0.816 0.816 0.813 Output power (Po in watts) 169.2 170.13 170.24 Convergence time (ts in seconds) 1.05 0.2 0.05 Voltage gain 7.916 7.958 7.992 Oscillations More Moderate Less MPP tracking speed Less Moderate More Parameter . MPPT techniques . . . . P&O . FP . Hybrid . SPV output (VPV in volts) 26.2 26.2 26.2 Output voltage (Vo in volts) 207.4 208.5 209.4 Voltage ripple (%) 3.34 1.43 0.14 Output current (Io in amperes) 0.816 0.816 0.813 Output power (Po in watts) 169.2 170.13 170.24 Convergence time (ts in seconds) 1.05 0.2 0.05 Voltage gain 7.916 7.958 7.992 Oscillations More Moderate Less MPP tracking speed Less Moderate More Open in new tab Table 3: Analysis of solar-powered IHGBC using various MPP tracking techniques Parameter . MPPT techniques . . . . P&O . FP . Hybrid . SPV output (VPV in volts) 26.2 26.2 26.2 Output voltage (Vo in volts) 207.4 208.5 209.4 Voltage ripple (%) 3.34 1.43 0.14 Output current (Io in amperes) 0.816 0.816 0.813 Output power (Po in watts) 169.2 170.13 170.24 Convergence time (ts in seconds) 1.05 0.2 0.05 Voltage gain 7.916 7.958 7.992 Oscillations More Moderate Less MPP tracking speed Less Moderate More Parameter . MPPT techniques . . . . P&O . FP . Hybrid . SPV output (VPV in volts) 26.2 26.2 26.2 Output voltage (Vo in volts) 207.4 208.5 209.4 Voltage ripple (%) 3.34 1.43 0.14 Output current (Io in amperes) 0.816 0.816 0.813 Output power (Po in watts) 169.2 170.13 170.24 Convergence time (ts in seconds) 1.05 0.2 0.05 Voltage gain 7.916 7.958 7.992 Oscillations More Moderate Less MPP tracking speed Less Moderate More Open in new tab In addition, one of the most notable advantages of the proposed hybrid-based MPPT is that it requires fewer initial parameters than GA (genetic algorithm) and PSO. The initial values of the parameters for each algorithm used in the presented research work are listed in Table 4 and the root mean squared error (RMSE) is used to evaluate the performance. To minimize the error e(t), the RMSE is defined as the objective function for the optimization process in accordance with Equation (22): Table 4: Initial parameters of various MPP techniques MPP technique . Initialization process . . . Parameters . Value . Hybrid-based MPP technique Number of flowers 20 Maximum iterations 100 GA Population size 60 Crossover rate 0.9 Maximum iterations 120 PSO Particles 60 Inertia weight 0.4–0.9 Maximum iterations 100 MPP technique . Initialization process . . . Parameters . Value . Hybrid-based MPP technique Number of flowers 20 Maximum iterations 100 GA Population size 60 Crossover rate 0.9 Maximum iterations 120 PSO Particles 60 Inertia weight 0.4–0.9 Maximum iterations 100 Open in new tab Table 4: Initial parameters of various MPP techniques MPP technique . Initialization process . . . Parameters . Value . Hybrid-based MPP technique Number of flowers 20 Maximum iterations 100 GA Population size 60 Crossover rate 0.9 Maximum iterations 120 PSO Particles 60 Inertia weight 0.4–0.9 Maximum iterations 100 MPP technique . Initialization process . . . Parameters . Value . Hybrid-based MPP technique Number of flowers 20 Maximum iterations 100 GA Population size 60 Crossover rate 0.9 Maximum iterations 120 PSO Particles 60 Inertia weight 0.4–0.9 Maximum iterations 100 Open in new tab RMSE=∑t=0Tsim(Vo(t)−VPV(t))2Tsim(22) where Tsim represents the simulation time. The Fig. 21 shows the convergence curves of hybrid-based MPPT, i.e. FP assisted with P&O, GA and PSO algorithms for IHGBC to obtain the optimum duty cycle. As illustrated in Fig. 21, the hybrid-based MPPT technique is faster at approaching the optimal solution than the GA and PSO algorithms with the lower RMSE and also it shows that the results do not change during the iterative process, which shows how stable it is compared to GA and PSO. Fig. 21: Open in new tabDownload slide Convergence curves of FP–P&O, GA and PSO algorithms. 4 Conclusion In this paper, a solar-powered IHGBC using a hybrid-based MPPT technique with a combination of P&O-based and FP-based MPPT techniques is suggested to locate the MPP under constant and variable irradiance. From the results, it is noticed that the hybrid-based MPPT technique exhibits only 0.14% oscillations with a faster convergence rate of 0.05 seconds compared to individual P&O-based MPPT and FP-based MPPT techniques for solar PV, whereas the P&O-based MPPT technique exhibits 1.43% with a convergence rate of 1.05 seconds and the FP-based MPPT technique exhibits 3.34% oscillations with a moderate convergence rate of 0.12. Therefore, the suggested hybrid-based MPPT technique is demonstrated to be superior to the other MPPT techniques in terms of convergence rate, percentage oscillations and MPP tracking speed. The presented MPPT techniques have been also tested under three different values of irradiance to check the performance of the SPV with the suggested MPPT, and it is concluded that the hybrid-based MPPT technique increases its overall computational performance compared to other MPPT techniques. In addition, the proposed solar-powered IHGBC achieves a high voltage gain compared with other boost converters, namely conventional, double-boost, modified SEPIC, hybrid-boost, quadratic-boost and three-level-boost converters. Finally, the output of solar-powered IHGBCs has been increased by eight times for an optimum duty cycle of 0.5 when compared to conventional boost converters. 5 Future scope The suggested solar-powered IHGBC with hybrid MPPT can be connected to a utility grid via a reduced switch multilevel inverter, which can pump generated energy into the grid. Furthermore, diverse renewable energy sources can be incorporated for the suggested converter to improve energy generation in remote areas. Conflict of interest statement None declared. References [1] Nguyen XH , Nguyen MP. Mathematical modeling of photovoltaic cell/module/arrays with tags in MATLAB/Simulink . Environmental Systems Research , 2015 , 4 : 1 – 13 . 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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

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Clean EnergyOxford University Press

Published: Jun 1, 2022

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