GLOBAL MAXIMUM POWER POINT TRACKING EXTRACTION IN PHOTOVOLTAIC SYSTEMS USING PARTICLE SWARM OPTIMIZATION UNDER PARTIAL SHADING CONDITIONS

Abstract

In recent years, photovoltaic (PV) energy systems have been largely expanded, thanks to advances in technology, the gradual decrease whether in installation or maintenance costs. According to the literature, Numerous maximum power point tracking (MPPT) techniques have been proposed to enable PV systems to operate at their MPP. However, under complex environmental conditions such as partial shading, PV characteristics become more complex, exhibiting multiple local maxima instead of a single global maximum. Conventional MPPT methods often fail to identify the global maximum power point (GMPP) in such scenarios. In order to improve MPPT extraction and to increase accuracy of the MPP tracking in PV systems, we propose in the present work two different optimization-based approaches namely, the Perturb and Observe (P&O) and Particle Swarm Optimization (PSO) technique. In this context, by assessing both approaches under various partial shading conditions, we use MATLAB/Simulink to evaluate and compare their effectiveness.