Optimizing Solar Power: Advanced Maximum Power Point Tracking Via Fuzzy Logic for Enhanced Performance and Efficiency

This research presents an advanced Maximum Power Point Tracking (MPPT) strategy that uses a Fuzzy Logic Controller (FLC) to improve the efficiency and performance of solar power systems. Classic MPPT techniques, such as fractional open-circuit voltage (FOCV), incremental conductance (INC), and perturbation and observation (P&O), often encounter complex structures, slow responses to sudden environmental variations, and inaccurate tracking, leading to significant energy losses and decreased system efficiency. The system utilizes the error and the difference in error (E & ΔE) between the predicted and actual inputs as inputs, and generates the duty cycle (D) as the output. By the circumstances of broad range of climatic conditions, the experiments and simulations involving irradiance levels ranging from 750 W/m² to 1000 W/m² and temperatures varying from 20°C to 45°C, prove the efficacy of the proposed FLC algorithm. These tests demonstrate the system's adaptability to environmental changes. Quantitative results demonstrate a substantial efficiency enhancement of 0.83% over conventional perturbation and observation (P&O) methods, which achieve 0.65%. The result demonstrates that not only is the FLC-based MPPT strategy effective and robust, but it is also well-suited in practice, providing a scalable and effective solution for maximizing solar energy exploitation.