Study on lightning risk assessment and early warning for UHV DC transmission channel. Issue 2 (30th April 2019)

• Record Type: Journal Article
• Authors: Gu, Shanqiang; Wang, Jian; Wu, Min; Guo, Juntian; Zhao, Chun; Li, Jian
• Is Part Of: High voltage. Volume 4:Issue 2(2019)
• Page Start: 144
• Page End: 150
• Publication Date: 2019-04-30
• Subjects: atmospheric electricity -- lightning protection -- backpropagation -- risk management -- neural nets -- poles and towers -- HVDC power transmission -- alarm systems -- power transmission faults -- power transmission reliability -- power engineering computing; lightning risk assessment -- UHV DC transmission channel -- operation data -- lightning faults -- lightning characteristics -- lightning protection performance -- strength distributions -- lightning risk source -- risk assessment process -- DC line -- failure risk -- lightning detection data -- early warning model -- lightning damage risk -- effective warning ratio -- failure‐to‐warn ratio -- electrogeometric model -- main ultra‐high voltage DC transmission channels -- density analysis -- polarity effect -- lightning shielding failure risk -- single tower -- backpropagation neural network algorithm -- detection data -- atmospheric electric fields -- radar echoes; High voltages -- Periodicals; 621.3191
• DOI: 10.1049/hve.2018.5081
• Languages: English
• ISSNs: 2397-7264

Abstract

Abstract : Operation data show lightning faults account for >70% for the main ultra‐high voltage (UHV) DC transmission channels, very different from the design view. In order to accurately master the lightning characteristics and the lightning protection performance of the line so as to propose solutions to weak points, this study firstly obtains and analyses the density and strength distributions of lightning risk source. Then the study proposes a set of risk assessment process where the key model electrogeometric model is improved according to the polarity effect of DC line. Then the work realises the calculation of the lightning shielding failure risk of single tower and whole line. The example shows the assessment result is consistent to the line's actual operation. Next, to further evaluate and predict the lightning risk in real time, the study adopts the backpropagation neural network algorithm to integrate the lightning detection data, atmospheric electric fields, and radar echoes to develop the early warning model of lightning risk source, and proposed a method to realise the early warning of lightning damage risk for UHV DC channels. The results show that the effective warning ratio is 73% and the failure‐to‐warn ratio is 27% which indicates very good application effects.