Determination of maximum power point from photovoltaic system using genetic algorithm

• Published in: Compel Vol. 41; no. 4; pp. 1107 - 1119
• Main Authors: Najdoska, Angela, Cvetkovski, Goga Vladimir
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 05.08.2022; Emerald Group Publishing Limited
• Subjects: Diodes; Emissions; Energy; Genetic algorithms; Greenhouse gases; Irradiation; Mathematical models; Maximum power; Mutation; Operators (mathematics); Optimization; Photovoltaic cells; Polycrystals; Population; Simulation; Solar radiation; Variables; Weather forecasting; Photovoltaic systems; Determination; Maximum power; Genetic algorithm; Optimization; Solar energy
• ISSN: 0332-1649; 2054-5606; 0332-1649
• DOI: 10.1108/COMPEL-11-2021-0445

Purpose 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/approach The 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. Findings From 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 implications From 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/value An 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.