Enhancing Photovoltaic-Powered DC Shunt Motor Performance for Water Pumping through Fuzzy Logic Optimization

Abstract

This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module’s operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios.