Abstract
This paper presents an innovative approach for maximum power point tracking (MPPT) in photovoltaic (PV) systems, employing an optimized self-organizing fuzzy tuning system enhanced by a genetic algorithm (GA). The method encodes essential parameters—such as scaling factors, membership function parameters, and controller policies—into bit-strings, which are processed by the GA to find near-optimal solutions. A specific fitness function is used to ensure superior dynamic performance. Experimental results confirm the effectiveness of this approach, demonstrating a significant reduction in the number of required parameters without sacrificing performance. Comparative analysis shows that this method outperforms other MPPT techniques, including GA and fuzzy logic controllers (FLC), achieving a notable tracking efficiency of 98.14% within 4 s. This efficiency surpasses other methods, which have lower efficiencies and longer convergence times. Across various irradiance levels (0.3 kW/m2 to 0.9 kW/m2), the proposed approach consistently achieves the highest tracking efficiency (98.32% to 98.14%), underscoring its potential as an optimal solution for PV system optimization. This study introduces a novel optimization technique for PV systems and provides empirical evidence of its robust performance under diverse environmental conditions.
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Douiri, M.R., Chermite, C. (2025). Optimal Fuzzy-Genetic Self-tuning for Tracking Photovoltaic Peak Power. In: Martínez-Villaseñor, L., Ochoa-Ruiz, G. (eds) Advances in Soft Computing. MICAI 2024. Lecture Notes in Computer Science(), vol 15247. Springer, Cham. https://doi.org/10.1007/978-3-031-75543-9_7
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