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Study on lightning risk assessment and early warning for UHV DC transmission channel
- Source :
- High Voltage (2019)
- Publication Year :
- 2019
- Publisher :
- Wiley, 2019.
-
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.
- 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
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Electricity
QC501-721
Subjects
Details
- Language :
- English
- ISSN :
- 23977264
- Database :
- Directory of Open Access Journals
- Journal :
- High Voltage
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.4ba7f554310d4d97a62519b1c04c462e
- Document Type :
- article
- Full Text :
- https://doi.org/10.1049/hve.2018.5081