Abstract
In this work, an embedded system (ES) for fault detection and diagnosis of photovoltaic (PV) arrays is presented. Two machine learning (ML) classifiers have been developed for PV fault detection and classification based on the I-V curves. The developed classifiers have been then integrated into a Raspberry Pi 4 to detect and classify faults occurred on a PV array. An open source IoT platform (ThingSpeak™) of MathWorks is used to remotely monitor the PV array parameters. Users could be alerted about the state of the PV array by phone message (SMS) using a GSM module and also by email. The whole system was designed and verified experimentally at the Renewable energy laboratory, the University of Jijel (Algeria). Simulation and experimental results demonstrated the feasibility of the developed ES for fault detection and identification of the inspected PV array. Furthermore, a dedicated guide user interface has also been developed.
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Bouzerdoum, M., Mellit, A., Djazari, N., Laissaoui, M. (2023). Embedded Machine Learning for Fault Detection and Diagnosis of Photovoltaic Arrays Using a Low-Cost Device. In: Hatti, M. (eds) Advanced Computational Techniques for Renewable Energy Systems. IC-AIRES 2022. Lecture Notes in Networks and Systems, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-031-21216-1_9
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