Modelling photovoltaic modules with enhanced accuracy using particle swarm clustered optimization

Abstract

Accurately simulating and operating photovoltaic (PV) modules is vital for thoroughly analyzing their performance under different conditions. The main focus of this paper is to address the inherent nonlinearity in solar PV systems. To achieve this, the particle swarm clustered optimization (PSCO) is applied to extract parameters of solar modules, allowing for a more comprehensive understanding of their behavior. PSCO aims to enhance the accuracy and effectiveness of PV module analysis. For that, PSCO utilizes clusters within the particle population, enabling localized communication and information sharing. By doing so, it effectively facilitates efficient exploration and exploitation of diverse regions, fostering a comprehensive understanding of the behavior of PV modules under different conditions. Through this approach, PSCO maximizes the accuracy and effectiveness of parameter extraction, contributing to advancements in PV system analysis and performance evaluation. The effectiveness of PSCO is demonstrated in extracting parameters for the three-diode model (TDM) of the STP6-120/36 and Photowatt-PWP201 PV modules. PSCO surpasses state-of-the-art algorithms with significantly low root mean square error (RMSE) values of 0.0145 A and 0.0019 A, showcasing its superior accuracy. Additionally, PSCO achieves the lowest power errors of 0.16054 W and 0.01484 W for the respective modules, emphasizing its excellent performance.