Advancements in MPPT for Solar Energy: A Review of Modified PSO Techniques in Photovoltaic Systems

Abstract

This research paper presents the design and development of a modified Particle Swarm Optimization (PSO) controller for enhancing the efficiency of Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The proposed controller aims to optimize the power output of solar panels under varying environmental conditions, ultimately maximizing the energy harvesting efficiency. The research focuses on the modification of traditional PSO algorithms to address specific challenges associated with MPPT in PV systems, such as partial shading, temperature variations, and non-linear characteristics of solar panels. Solar energy has become a prominent source of renewable power, and maximizing the efficiency of Photovoltaic (PV) systems is crucial for harnessing its full potential. This paper presents a comprehensive review of advancements in Maximum Power Point Tracking (MPPT) techniques with a focus on Modified Particle Swarm Optimization (PSO) algorithms applied to PV systems. MPPT is vital for extracting maximum power from solar panels, particularly under varying environmental conditions. The study begins by providing an overview of traditional MPPT methods and their limitations. It then delves into the application of PSO algorithms, exploring how modifications to the traditional PSO approach have been employed to enhance MPPT accuracy and convergence speed. Various modified PSO techniques are discussed, including adaptive inertia weight, dynamic acceleration coefficients, and hybrid approaches that combine PSO with other optimization methods.