Performance Evaluation of Fuzzy Logic–Enhanced Perturb and Observe MPPT for Photovoltaic Systems

Abstract

Maximum Power Point Tracking (MPPT) is a crucial technique to optimize energy extraction in photovoltaic (PV) systems under varying environmental conditions such as irradiance and temperature. The conventional Perturb and Observe (P&O) algorithm is widely used due to its simplicity; however, it suffers from steady-state oscillations and slow response to environmental changes. This study proposes an improved MPPT method by integrating a fuzzy logic controller (FLC) with the conventional P&O algorithm to generate an adaptive step size for duty cycle adjustment. The system is modeled and simulated using MATLAB/Simulink, including a PV array and a boost converter, under different operating conditions such as constant irradiance, varying irradiance, and temperature variations. The performance of both methods is evaluated based on output power, voltage and current ripple, efficiency, and dynamic response. The simulation results show that the proposed FLC–P&O method reduces voltage ripple from 5.24% to 2.33% and improves settling time from 6.312 ms to 1.872 ms. Therefore, the integration of fuzzy logic with the P&O algorithm provides a simple yet effective solution to improve MPPT performance in photovoltaic systems operating under dynamic environmental conditions.