Monitoring Tool for Stand-Alone Photovoltaic System Using Artificial Neural Network

Nassim Sabri,Aissa Chouder,Abdelhalim Tlemçani

doi:10.1007/978-3-030-04789-4_12

Monitoring Tool for Stand-Alone Photovoltaic System Using Artificial Neural Network

Nassim Sabri, Aissa Chouder + Show 1 more

https://doi.org/10.1007/978-3-030-04789-4_12

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Publication Date: Nov 24, 2018

Citations: 2

Affiliation: University Yahia Fares of Medea, Université de M'Sila

#Machine Learning Artificial Neural Network #Stand-alone Photovoltaic + Show 8 more

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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 Medea, 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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