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

  • Nassim SabriEmail author
  • Abdelhalim Tlemçani
  • Aissa Chouder
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 62)

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.

Keywords

SAPV system ANN Accuracy GUI matlab 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nassim Sabri
    • 1
    Email author
  • Abdelhalim Tlemçani
    • 1
  • Aissa Chouder
    • 2
  1. 1.Laboratory of Electrical Engineering and Automatics LREAUniversity of MédéaMédéaAlgeria
  2. 2.Laboratoire de Géni Electrique (LGE)University of Mohamed Boudiaf de M’silaM’silaAlgeria

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