Integrating Fuzzy Logic with AI for Real-Time Efficiency Optimization in Solar Cells: A Simulation-Based Analysis

Abstract

This research explores the integration of fuzzy logic with artificial intelligence (AI) for real-time efficiency optimization of solar cells. The study addresses the limitations of traditional Maximum Power Point Tracking (MPPT) methods, which often fail to adapt effectively to rapidly changing environmental conditions. A simulation-based approach using MATLAB/Simulink was employed to model photovoltaic (PV) cells and implement fuzzy logic controllers (FLCs) optimized with Genetic Algorithms (GAs). The key objective was to develop a system capable of dynamically adjusting solar cell operations in response to varying irradiance, temperature, and shading conditions, thereby maximizing energy output. The results demonstrate that the fuzzy logic-based MPPT system, enhanced with GAs, significantly improved efficiency, maintaining MPPT efficiency levels mostly above 85% across various scenarios. This adaptability underscores the system’s potential for real-world applications, especially in regions with highly variable weather patterns. The integration of AI techniques, such as GAs, provided continuous optimization of the FLCs, ensuring robust performance and resilience against environmental fluctuations. The study concludes that the integration of fuzzy logic with AI offers a transformative approach to solar energy optimization, enhancing the reliability and efficiency of renewable energy systems and providing a foundation for future advancements in solar technology.