EVALUATION OF MAXIMUM POWER POINT TRACKING ALGORITHMS FOR SOLAR PV APPLICATIONS

Abstract

Solar photovoltaic (PV) systems are widely adopted as clean and sustainable energy sources, but their efficiency is highly dependent on environmental conditions such as irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques are essential to ensure optimal power extraction from PV systems under varying conditions. This paper presents a comparative study of commonly used MPPT techniques for solar photovoltaic applications. Conventional algorithms such as Perturb and Observe and Incremental Conductance are compared with intelligent techniques including Fuzzy Logic and Artificial Neural Network–based MPPT. Performance evaluation is carried out in terms of tracking efficiency, convergence speed, power output, and steady-state oscillations. Simulation results demonstrate that intelligent MPPT techniques provide superior performance under dynamic conditions. The study highlights the advantages and limitations of each MPPT technique for practical solar PV applications