Enhanced Maximum Power Point Tracking for Photovoltaic Systems Using Adaptive Fuzzy Control
DOI:
https://doi.org/10.18196/jrc.v6i3.26451Keywords:
Adaptive Fuzzy Control, Lyapunov Stability, Dynamic MPPT Optimization, P&O Improvement, Photovoltaic SystemsAbstract
The growing need for clean energy has made solar panels an essential solution. However, the nonlinear behavior of photovoltaic (PV) systems under varying weather conditions necessitates advanced control strategies to ensure optimal energy harvesting. This paper presents an enhanced Maximum Power Point Tracking (MPPT) approach that integrates the conventional Perturb and Observe (P&O) method with an Indirect Adaptive Fuzzy Controller (IAFC). While P&O is known for its simplicity, it suffers from steady-state oscillations and slow response during environmental changes. To address these issues, the IAFC adaptively adjusts the perturbation step using a Lyapunov-based rule to improve convergence and minimize power fluctuations. The proposed method achieves Maximum Power Point tracking within less than 0.025 s, compared to 0.05 s for the conventional P&O algorithm. This enhances the credibility of our dynamic performance claim. Specifically, unlike prior fuzzy-P&O hybrids with fixed rule sets, our method leverages Lyapunov-based adaptation to dynamically adjust the control action, improving convergence and robustness under changing conditions. We also included a quantitative metric showing a 75% reduction in power fluctuations compared to conventional P&O. Simulation results under varying sunlight conditions demonstrate fast convergence and improved power stability. The proposed IAFC method clearly outperforms classical P&O in tracking accuracy, responsiveness, and overall energy yield.
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