A Comprehensive Comparative Analysis of Fuzzy Logic and Neural Network Control Architectures for Optimizing Photovoltaic System Efficiency

Abstract

Getting the most power out of a solar photovoltaic (PV) system is tough when weather conditions refuse to stay still. Maximum Power Point Tracking (MPPT) is the standard engineering fix used to squeeze every bit of efficiency out of these panels as sunlight and temperature constantly swing up and down. For decades, the industry has relied on basic, rule-of-thumb tracking loops like Perturb and Observe (P&O). While P&O is simple, it is also notoriously flawed: it never actually stops moving, creating constant "hunting" power ripples when the weather is clear, and lagging terribly when quick clouds pass over. To address this, our research activity is focused on performing a computational comparison of the performance of a heuristic Mamdani-type Fuzzy Logic Controller with a backpropagation Artificial Neural Network. The entire comparative framework was constructed within the open-source Python environment, with the mathematical core implemented in NumPy, Pandas, scikit-fuzzy, and TensorFlow. A standard silicon based PV module's characteristic performance under varying solar irradiance was simulated using a programed single-diode model circuit. The simulation responses shows, while the FLC takes the advantage of needing a significantly decreased training data for deployment, the improved performance is contributed by the optimized ANN that can trace the new MPP within 0.02 s of the irradiance change, eliminate steady-state power ripples, and reach the terminal efficiency of more than 98.7% under dynamic conditions. The study demonstrates the feasibility of substituting commercial industrial engineering software with an open-source computational framework in Python for optimization of photovoltaic infrastructure.