A Hybrid Fuzzy-Gradient MPPT Method for Enhancing Photovoltaic Efficiency

Abstract

This paper addresses the limitations of traditional Maximum Power Point Tracking (MPPT) algorithms in photovoltaic (PV) systems under fluctuating irradiance. Fixed step-size methods such as Perturb and Observe (P&O) often suffer from tracking drift and instability due to their inability to distinguish between actual power changes and environmental disturbances. To overcome these challenges, a Hybrid Fuzzy-Gradient Perturb and Observe (HFG-P&O) algorithm is proposed. The method integrates Fuzzy Logic to provide rapid response to large power deviations and Adaptive Gradient Descent (AGD) to fine-tune the step size during steady-state conditions. A dual-mode control mechanism dynamically selects between the two modes based on the magnitude of power variation. The proposed HFG-P&O was validated through MATLAB/Simulink simulations using real PV panel data under dynamic irradiance scenarios. Results show 98.5% tracking efficiency, a 63% reduction in power oscillations, and 47% faster convergence compared to conventional and enhanced P&O methods. These improvements are achieved without sacrificing system stability. The algorithm offers intelligent step-size adaptation, enhanced robustness to changing environmental conditions, and effective drift elimination, making it a promising solution for real-world PV applications.