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

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  • Intelligent Control and Applications
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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.

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Authors and Affiliations

  1. School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu, 41566, Korea

    Hye-Rin Hwang, Tae-Hyun Cho & In-Soo Lee

  2. MIJIENERTECH Co., Ltd, Daegu, Korea

    Berm-Soo Kim

Authors
  1. Hye-Rin Hwang
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  2. Berm-Soo Kim
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  3. Tae-Hyun Cho
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  4. In-Soo Lee
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Corresponding author

Correspondence to In-Soo Lee.

Additional information

Recommended by Associate Editor Jiuxiang Dong under the direction of Editor Guang-Hong Yang.

Hye-Rin Hwang received her B.S. degree in Electrical Engineering from Yeungnam University in 2009, an M.S. degree in Computer Engineering from Kyungpook National University in 2013, and a Ph.D. degree in Electronics Engineering in Kyungpook National University in 2018. Her research interests include Photovoltaic systems, fault diagnosis, auto-restoration control for solar street light and solar charge controller.

Berm-Soo Kim received his B.S., and M.S. degrees in Engineering Chemistry from Yeungnam University, in 1990 and 1992 and a Ph.D. degree in Chemical System Engineering in 2001 from the University of Tokyo. Currently, he is CEO of MIJIENERTECH Co., Ltd, Daegu, Korea. His research interests include photovoltaic systems, solar street light, catalyst for VOCs removal.

Tae-Hyun Cho received his B.S. and M.S. degrees in Electronics Engineering from Kyungpook National University, in 2013 and 2019, respectively. His research interests include fault diagnosis, battery SOC, intelligent control using neural networks.

In-Soo Lee received his B.S., M.S. and Ph.D. degrees in Electronics Engineering from Kyungpook National University, in 1986, 1989 and 1997, respectively. From Mar. 1997 to Feb. 2008, he was a professor in the School of Electronic and Electrical Engineering at Sangju National University. Since Mar. 2008, he has been at Kyungpook National University where he is currently a professor in the School of Electronics Engineering. From Aug. 2005 to Jan. 2007, he was a research scholar at San Diego State University. His current research interests include intelligent sensor system, fault diagnosis, fault tolerant control, intelligent control using neural networks, and robotics.

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Hwang, HR., Kim, BS., Cho, TH. et al. Implementation of a Fault Diagnosis System Using Neural Networks for Solar Panel. Int. J. Control Autom. Syst. 17, 1050–1058 (2019). https://doi.org/10.1007/s12555-018-0153-3

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  • DOI: https://doi.org/10.1007/s12555-018-0153-3

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