MPPT Control and Optimization of a PV Generator using an Improved Particle Swarm Algorithm

Abstract

Extracting the maximum power from photovoltaic (PV) energy conversion systems under non-uniform conditions is an increasing challenge. This work describes an Improved Particle Swarm Optimizer (IPSO) addressed to increase the Maximum Power Point Tracking (MPPT) capabilities for PV solar energy generators operating on three-phase AC loads. Since traditional MPPT methods are ineffective and slow to reach Global Maximum Power Point (GMPPs) under irregular environmental conditions, the proposed IPSO-based MPPT algorithm is enhanced by an effective dispersion mechanism. A re-initialization process is performed for the search for GMPPs. Demonstrative findings along with IPSO performance comparisons with the most commonly used MPPT approaches, namely Perturb-Observe (P&O) and Incremental Conductance (INC), have been conducted. Simulation results and discussion highlight the superiority of IPSO-MPPT trackers mainly in terms of improving maximum power generation, with energy efficiencies of 99.07% for IPSO, 98.44% for P&O and 93.34% for INC, damping of steady-state oscillations, fastness and accuracy for capturing GMPPs.