Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Abstract

Recently, solar energy has become an attractive topic for researchers as it has been preferred among renewable energy sources due to its advantages such as unlimited energy supply and low maintenance expenses. The precise modeling of the solar cells and the model’s parameter estimate are two of the most important and difficult topics in photovoltaic systems. A solar cell’s behavior can be predicted based on its current-voltage characteristics and unknown model parameters. Therefore, many meta-heuristic search algorithms have been proposed in the literature to solve the PV parameter estimation problem. In this study, the enhanced crayfish optimization algorithm (ECOA) with opposition-based learning (OBL) strategies was proposed to estimate the parameters of the three different PV modules. A thorough simulation study was conducted to demonstrate the performance of the ECOA algorithm in tackling benchmark challenges and PV parameter estimate problems. In the first simulation study, using the three OBL strategies, six variations of the COA were created. The performances of these variations and the classic COA have been tested on CEC2020 benchmark problems. To determine the best COA variation, the results were analyzed using Friedman and Wilcoxon tests. In the second simulation study, the best variation, called ECOA, and the base COA were applied to estimate the parameters of three PV modules. According to the simulation results, the ECOA algorithm achieved 1.0880%, 37.8378%, and 0.8106% lower error values against the base COA for the parameter estimation of the STP6-120/36, Photowatt-PWP201, and STM6-40/36 PV modules. Moreover, the sensitivity analysis was performed in order to determine the parameters influencing the PV module’s performance. Accordingly, the change in the photo-generated current and diode ideality factor in the single-diode model affects the performance of PV modules the most. The comprehensive analysis and results showed the ECOA’s superior performance in parameter estimation of three PV modules compared to other algorithms found in the literature.