Bonobo optimizer: dynamically adaptive heuristic for enhanced MPPT in photovoltaic systems under partial shading – experimental validation with buck converter
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Soufyane Ait El Ouahab
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Firdaous Bakkali
Abstract
The integration of shunt bypass diodes in photovoltaic (P-V) module to mitigate hot spots frequently leads to the emergence of multiple in the PV array characteristics. Researchers consistently strive to develop, integrate, and refine innovative techniques inspired by various natural processes to achieve a global optimum that enhances the overall efficiency of PV systems. However, these techniques face challenges in adapting parameters to strike a delicate balance between exploration and exploitation, which is essential for circumventing local optima, reducing computation times, and refining precision to optimize energy capture. In this context, this paper introduces a groundbreaking new adaptive Maximum Power Point Tracking (MPPT) controller inspired by the social behavior and reproductive tactics observed in bonobos (BO). This innovative approach is underpinned by two key strategies: fission and fusion, with dynamic parameter adjustment in real-time. this enables for efficient exploration and exploitation of the search space, following the positive and negative phases of the BO. This method was compared with three methods: PSO, DE, and ICS, and evaluated through six simulation scenarios, ranging from 1 to 6 peaks, as well as three experimental scenarios: one uniform and the other two involving partial shading, using an Arduino board and a buck converter. According to the comparative analysis, the new BO algorithm outperforms the three other approaches in all performance evaluation parameters. It shows an average improvement in convergence time of more than 39.18 % and an average precision exceeding 99 %, with minimal oscillation in steady-state operation. This translates into an average MPE efficiency of over 96.66 %. Additionally, the experimental results confirm the findings from the simulations.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: QuillBot: Improve language.
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Conflict of interest: All other authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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