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

Guoming Wang, Woo-Hyun Kim, Gyung-Suk Kil, Dae-Won Park and Sung-Wook Kim
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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

Energies, 2019, vol. 12, issue 7, 1-11

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%.

Keywords: lightning warning system; electric field mill; loop antenna; artificial neural network; prediction accuracy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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