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


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.


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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