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Implementation of a Fault Diagnosis System Using Neural Networks for Solar Panel

  • Hye-Rin Hwang
  • Berm-Soo Kim
  • Tae-Hyun Cho
  • In-Soo LeeEmail author
Regular Papers Intelligent Control and Applications
  • 27 Downloads

Abstract

In this paper, we propose a fault diagnosis system for the solar panels of solar-powered street lights that uses an adaptive resonance theory 2 neural network (ART2 NN) and a multilayer neural network (MNN). To diagnose a fault in a solar panel, we use the open-circuit voltage with respect to the duty cycle as input for the two neural networks. As a result, we can use them to double check the fault diagnosis for the solar panel. In addition, we present a graphical user interface for the proposed solar panel fault diagnosis system. The fault diagnosis system we propose has the potential for application in similar systems and devices.

Keywords

Adaptive resonance theory 2 neural network fault diagnosis graphical user interface multilayer neural network open-circuit voltage solar panel 

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Copyright information

© ICROS, KIEE and Springer 2019

Authors and Affiliations

  1. 1.School of Electronics EngineeringKyungpook National UniversityDaeguKorea
  2. 2.MIJIENERTECH Co., LtdDaeguKorea

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