Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy

Abstract

With the increase in the share of solar energy in the sustainable development, accurate parameter identification plays a significant role in designing optimal solar photovoltaic systems. For this purpose, this paper extensively implements and evaluates the grey wolf optimizer with a dimension learning-based hunting search strategy, an improved version of GWO named I-GWO, in the parameter extraction of photovoltaic cells and modules. According to the experimental results, the double-diode model leads to better fitness than the other diode models in representing the physical behaviors of both photovoltaic cells and photovoltaic modules. For further performance validation, firstly, the internal parameters extracted by the I-GWO algorithm and the corresponding output current data are compared with a number of widely-used parameter extraction methods in the literature. Then, the best goodness-of-fit results achieved by the I-GWO algorithm are evaluated considering many state-of-the-art metaheuristic algorithms in the literature. The accuracy measures including root mean squared error and sum of individual absolute errors show that I-GWO is fairly promising to be the efficient and valuable parameter extraction method for both photovoltaic cells and photovoltaic modules.