Multi Strategy Approaches for Parameter Estimation of One Diode PV Module

The growing use of photovoltaic (PV) modules, which acts as a substitute for fossil fuels based energy sources is the main motivation of this study, which aims to meet the dire need for sustainable energy solutions. PV modules have difficulties sustaining efficiency in a variety of environmental settings, despite their potential. In order to address this, the current study involves a one diode model of PV module and different methods are used to determine the PV module unknown parameters (I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf>, a, I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ph</inf>, R<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</inf> and R<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf>). The unknown parameters are calculated using Dandelion Optimizer (DO), Genetic Algorithm (GA), Genetic Algorithm Neural Network (GANN), and Hybrid methods. Performance index based on Mean Square Error is evaluated for the aforementioned methods and compared with PSO, ABC, HHO methods from the literature. I-V curves are plotted for R.T.C. France test system and performance index is calculated for the same. Among all methods, GANN method is efficient in attaining better RMSE by closely matching estimated currents with the experimental currents of the considered test system.