Abstract
This study proposes a novel approach to identify and classify faults in photovoltaic systems. Specifically, a hybrid model is developed by integrating an artificial neural network with a differential evolution algorithm. The differential evolution algorithm is utilized to optimize the neural network's topology and enhance the accuracy of the fault detection and categorization system. The experimental results demonstrate the effectiveness of the proposed method in improving both prediction accuracy and training accuracy. Thus, this study contributes to the development of advanced techniques for monitoring and maintaining the reliability of photovoltaic systems.
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Acknowledgements
We acknowledge the financial support for this research from the “Centre National pour la Recherche Scientifique et Technique”, CNRST, Morocco.
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Saliha, S., Nabil, E.A., Mohamed, F. (2024). Intelligent PV Fault Detection and Categorization Based on Metaheuristic Algorithm and Feedforward Neural Network. In: Bendaoud, M., El Fathi, A., Bakhsh, F.I., Pierluigi, S. (eds) Advances in Electrical Systems and Innovative Renewable Energy Techniques. ICESA 2023. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-49772-8_11
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