PERFORMANCE ENHANCEMENT OF SOLAR PV SYSTEMS USING PSO-BASED MAXIMUM POWER POINT TRACKING

Abstract

The increasing demand for renewable energy has accelerated the deployment of photovoltaic (PV) systems in residential, commercial, and industrial applications. However, the nonlinear characteristics of PV modules and continuously changing environmental conditions significantly affect the power extraction capability of solar energy systems. Maximum Power Point Tracking (MPPT) techniques are therefore essential to ensure optimal utilization of available solar energy. Conventional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance (INC) exhibit limitations including oscillations around the maximum power point and reduced tracking performance under rapidly changing irradiance conditions. This paper proposes a Particle Swarm Optimization (PSO)-based MPPT technique to enhance the performance of solar PV systems. The PSO algorithm efficiently determines the optimal operating point by utilizing cooperative particle search mechanisms to maximize PV output power. The proposed method is integrated with a DC–DC boost converter to regulate the PV operating voltage. MATLAB/Simulink simulations are conducted under varying irradiance and temperature conditions to evaluate system performance. The obtained results demonstrate faster convergence, improved tracking accuracy, reduced steady-state oscillations, and higher energy extraction efficiency compared with conventional MPPT techniques. The proposed PSOMPPT approach offers a reliable and intelligent solution for improving the overall performance of photovoltaic energy conversion systems.