Abstract
By increasing the global demand for electricity, there is a heightened need for power generation. The rising costs of natural gas and regulatory pressures to limit greenhouse gas emissions have escalated the expenses associated with electricity production from fossil fuels. Consequently, there is a growing inclination to utilize alternative energy sources for electricity generation, particularly solar power through photovoltaic systems. Evolutionary algorithms are among the most favored methods for identifying and optimally estimating the parameters of photovoltaic systems, and this article examines their functionality. A critical issue in these systems is the appropriate selection of photovoltaic system parameters. This paper presents a new method for optimal parameters identification of the photovoltaic (PV) systems using a newly modified version of a metaheuristic algorithm. The propose algorithm is based on adapting the performance of the Human Evolutionary Optimizer and is called AHEO algorithm. Simulation results indicate that the estimation of the photovoltaic cell's circuit model parameters has been effectively accomplished, with the mean square error (MSE) associated with the AHEO demonstrating a high level of accuracy and robustness in parameters identification of the PV system.
Published Version
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