Abstract
Fault classification using supervised machine learning Artificial Neural Network (ANN) is proposed to diagnose some defaults in Stand-alone photovoltaic (SAPV) system, where the data learning includes the voltage and current of PV panels, Battery and load are collected for different operation mode of the system (healthy and faulty). The proposed approach is applied to small SAPV system installed at LREA in the University of Médéa, Algeria in which the results of classification show a high accuracy up to 97%. In addition, a Graphical User Interface (GUI) Matlab is created in computer to display the results of classification by the developed ANN.
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Sabri, N., Tlemçani, A., Chouder, A. (2019). Monitoring Tool for Stand-Alone Photovoltaic System Using Artificial Neural Network. In: Hatti, M. (eds) Renewable Energy for Smart and Sustainable Cities. ICAIRES 2018. Lecture Notes in Networks and Systems, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-030-04789-4_12
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DOI: https://doi.org/10.1007/978-3-030-04789-4_12
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