New Hybrid Method Based Differential Evolution for Identifying a Single Diode Photovoltaic Cell Parameters with Temperature Variation

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Abstract At the time when renewable energy is making the headlines, photovoltaic technology has shown significant potential as one of the best energy sources. It is therefore necessary to predict the performance of a photovoltaic system by modeling it accurately and optimally. In this work, we propose a new hybrid algorithm for extracting parameters to improve the performance and the efficiency of a PV cell when subjected to temperature variations. The metaheuristic approach combined with an analytical approach to improve the accuracy and robustness that we call the Improved Differential Evolution (IDE). The performances of the proposed method are evaluated by clicking on cell data. For validation purposes, several analyses and comparisons are made with other methods, and the results illustrate the accuracy and precision of the IDE. The proposed technique is able to estimate the parameters in an optimal way whatever the temperature together with a high convergence speed and a short simulation time.

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