Automatic Diagnosis Technology of Lightning Fault in Transmission Line

DOI

10.2991/ecae-17.2018.4How to use a DOI?

Keywords

lightning locating system; lightning failure; fault location; automatic fault diagnosis

Abstract

Lightning fault is the main fault of transmission line. Accurate and effective diagnosis of lightning fault can effectively improve the reliability level of transmission line. At present, the determination of lightning strike fault and the location of the lightning strike are determined by manually inquiring the lightning locating system after the lightning strike. In order to improve the real - time determination of lightning fault, the accuracy of lightning strike point positioning and to shorten the outage time due to the lightning strike fault, research on the automatic fault diagnosis technology of the lightning failure of transmission line. According to the lightning fault of the transmission line, the information such as the relevant information of the lightning location system, the GIS information, fault recorder data, the scheduling automation data and the multi-terminal fault location information are used to realize the accurate positioning of the tower and the automatic fault diagnosis function of the transmission line fault. The rapid detection and overhaul of lightning failure is of great significance to the safe and stable operation of power grid.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chao Yang
AU  - Xin Su
AU  - Haiyan Yuan
AU  - Yangyang Liu
AU  - Wanjie Zhang
AU  - Xin Wang
PY  - 2017/12
DA  - 2017/12
TI  - Automatic Diagnosis Technology of Lightning Fault in Transmission Line
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)
PB  - Atlantis Press
SP  - 18
EP  - 22
SN  - 2352-5401
UR  - https://doi.org/10.2991/ecae-17.2018.4
DO  - 10.2991/ecae-17.2018.4
ID  - Yang2017/12
ER  -