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Determination of maximum power point from photovoltaic system using genetic algorithm

Determination of maximum power point from photovoltaic system using genetic algorithm The purpose of this paper is to present a novel approach to the determination of the maximum power point (MPP) in the photovoltaic system using genetic algorithm (GA). The optimization is realised on two types of photovoltaic (PV) modules: monocrystalline and polycrystalline solar modules, with the same rated peak power (400 Wp) but different electrical output data.Design/methodology/approachThe proposed algorithm is a nature-based algorithm that uses genetic operators such as reproduction, crossover and mutation to realise the search through the investigated area of solutions. To determine the MPP of the PV modules, a two-diode model of a PV cell is used. Based on the input electrical data for the analysed PV module, as well as the mathematical model of the PV, the algorithm can estimate the current and voltage at the MPP for given solar irradiation and cell temperature. The analysis is made for several different irradiations, but in work, the results are presented for irradiations of: 100, 500 and 1,000 W/m2 and cell temperatures of 0, 25 and 40 °C.FindingsFrom the presented results and performed analysis, it can be concluded that GA gives adequate results for both modules and for all working conditions. From the obtained results, it can be concluded that the optimization algorithm performs better when applied to the monocrystalline module works better especially in conditions with larger cell temperature, in comparison with the performance of the optimization algorithm applied to the polycrystalline module. On the other hand, the optimization algorithm applied to the polycrystalline module works better for the other working scenarios with smaller cell temperatures.Practical implicationsFrom the performed analysis, it can be concluded that the use GA as an optimization tool for the determination of the MPP can be successfully implemented. In addition, to improve the overall performance of the PV system, it is also necessary to forecast the weather conditions of the location where the PV system would be installed to forecast the cell temperature and the solar irradiation. This is necessary to choose the right PV module and inverter for the given location.Originality/valueAn optimization technique using GA as an optimization tool has been developed and successfully applied in the determination of the MPP for a PV system. The results are compared with the analytically determined values as well as with the values given by the producer, and they show good agreement. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png COMPEL: Theinternational Journal for Computation and Mathematics in Electrical and Electronic Engineering Emerald Publishing

Determination of maximum power point from photovoltaic system using genetic algorithm

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0332-1649
eISSN
0332-1649
DOI
10.1108/compel-11-2021-0445
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to present a novel approach to the determination of the maximum power point (MPP) in the photovoltaic system using genetic algorithm (GA). The optimization is realised on two types of photovoltaic (PV) modules: monocrystalline and polycrystalline solar modules, with the same rated peak power (400 Wp) but different electrical output data.Design/methodology/approachThe proposed algorithm is a nature-based algorithm that uses genetic operators such as reproduction, crossover and mutation to realise the search through the investigated area of solutions. To determine the MPP of the PV modules, a two-diode model of a PV cell is used. Based on the input electrical data for the analysed PV module, as well as the mathematical model of the PV, the algorithm can estimate the current and voltage at the MPP for given solar irradiation and cell temperature. The analysis is made for several different irradiations, but in work, the results are presented for irradiations of: 100, 500 and 1,000 W/m2 and cell temperatures of 0, 25 and 40 °C.FindingsFrom the presented results and performed analysis, it can be concluded that GA gives adequate results for both modules and for all working conditions. From the obtained results, it can be concluded that the optimization algorithm performs better when applied to the monocrystalline module works better especially in conditions with larger cell temperature, in comparison with the performance of the optimization algorithm applied to the polycrystalline module. On the other hand, the optimization algorithm applied to the polycrystalline module works better for the other working scenarios with smaller cell temperatures.Practical implicationsFrom the performed analysis, it can be concluded that the use GA as an optimization tool for the determination of the MPP can be successfully implemented. In addition, to improve the overall performance of the PV system, it is also necessary to forecast the weather conditions of the location where the PV system would be installed to forecast the cell temperature and the solar irradiation. This is necessary to choose the right PV module and inverter for the given location.Originality/valueAn optimization technique using GA as an optimization tool has been developed and successfully applied in the determination of the MPP for a PV system. The results are compared with the analytically determined values as well as with the values given by the producer, and they show good agreement.

Journal

COMPEL: Theinternational Journal for Computation and Mathematics in Electrical and Electronic EngineeringEmerald Publishing

Published: Aug 5, 2022

Keywords: Photovoltaic systems; Solar energy; Optimization; Genetic algorithm; Maximum power; Determination

References