Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading

Abstract

This study conducts a comparative analysis of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for Flexible Power Point Tracking (FPPT) in photovoltaic (PV) systems. The GA-based FPPT algorithm exhibits superior performance in power output, tracking accuracy, and convergence speed compared to conventional methods. In contrast, the PSO-based FPPT algorithm is designed to mitigate oscillations around steady-state operating points under partial shading conditions (PSC) by incorporating power limitation control. This allows the FPPT-PSO algorithm to effectively track the global maximum power point (GMPP) without fluctuating around steady-state points. The findings of this comparative analysis highlight the significance of adaptive FPPT algorithms in enhancing system reliability and maximizing power extraction under dynamic environmental conditions. The GA-based approach excels in optimizing power generation metrics, while the PSO-based approach specializes in maintaining stability and precision under challenging operational scenarios such as partial shading. By exploring the strengths and limitations of each algorithm, this study provides valuable in-sights into the selection and implementation of FPPT strategies in PV systems.