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An Intelligent Lightning Warning System Based on Electromagnetic Field and Neural Network

Author

Listed:
  • Guoming Wang

    (Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University, Busan 49112, Korea)

  • Woo-Hyun Kim

    (Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University, Busan 49112, Korea)

  • Gyung-Suk Kil

    (Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University, Busan 49112, Korea)

  • Dae-Won Park

    (R&D Center, EMI Solutions Co., LTD., Busan 49112, Korea)

  • Sung-Wook Kim

    (Power Asset Management Team, R&D Center, Hyosung Corporation, Changwon 51529, Korea)

Abstract

Prediction of lightning occurrence has significant relevance for reducing potential damage to electric installations, buildings, and humans. However, the existing lightning warning system (LWS) operates using the threshold method and has low prediction accuracy. In this paper, an intelligent LWS based on an electromagnetic field and the artificial neural network was developed for improving lightning prediction accuracy. An electric field mill sensor and a pair of loop antennas were designed to detect the real-time electric field and the magnetic field induced by lightning, respectively. The change rate of electric field, temperature, and humidity acquired 2 min before lightning strikes, were used for developing the neural network using the back propagation algorithm. After observing and predicting lightning strikes over six months, it was verified that the proposed LWS had a prediction accuracy of 93.9%.

Suggested Citation

  • Guoming Wang & Woo-Hyun Kim & Gyung-Suk Kil & Dae-Won Park & Sung-Wook Kim, 2019. "An Intelligent Lightning Warning System Based on Electromagnetic Field and Neural Network," Energies, MDPI, vol. 12(7), pages 1-11, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1275-:d:219469
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    References listed on IDEAS

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    1. Nur Hazirah Zaini & Mohd Zainal Abidin Ab. Kadir & Mohd Amran Mohd Radzi & Mahdi Izadi & Norhafiz Azis & Nor Izzati Ahmad & Mohd Solehin Mohd Nasir, 2017. "Lightning Surge Analysis on a Large Scale Grid-Connected Solar Photovoltaic System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    2. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model," Energies, MDPI, vol. 11(4), pages 1-15, April.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    Cited by:

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    2. Marit Sigfrid Bakka & Erling Kristian Handal & Torgrim Log, 2020. "Analysis of a High-Voltage Room Quasi-Smoke Gas Explosion," Energies, MDPI, vol. 13(3), pages 1-14, January.

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