Traveling wave network location method based on adaptive waveform similarity

High-voltage transmission technology can effectively address the issue of uneven spatial and temporal energy distribution, leading to its rapid development in recent years. To address the challenge of accurately identifying the source of the second traveling wave head in complex transmission network scenarios with existing single-ended fault location methods, a traveling wave network fault location method based on adaptive waveform similarity is proposed. The paper analyzes the propagation process of traveling waves in transmission lines and quantitatively derives the time-domain expression of the traveling wave waveform. The BFS algorithm is enhanced by incorporating the propagation characteristics of traveling waves, allowing for the determination of all paths from any location in the topological network to the measurement points. Based on the path information and the derived expression, the traveling wave waveform at the measurement points for the fault location is calculated. An optimization algorithm is used to iteratively solve for unknown parameters such as fault location, traveling wave speed, and fault point information, with the objective of maximizing the similarity between the adaptive waveform and the real waveform by adaptively adjusting the waveform shape. When the similarity between the adaptive waveform and the real waveform is maximized, the adaptive fault location is identified as the actual fault location. Verified through the PSCAD simulation platform, this method can achieve accurate location under different fault conditions.

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Publication Details

Journal
Heliyon
Published
2024-09-01
DOI
https://doi.org/10.1016/j.heliyon.2024.e37576
Citations
3
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.80
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article

Traveling wave network location method based on adaptive waveform similarity

Yu Liu, Wanglong Wan, Zheng Qin, Minggao Deng
3 citations
Heliyon
Traffic Prediction and Management Techniques
0.80
article

Traveling wave network location method based on adaptive waveform similarity

Yu Liu, Wanglong Wan, Zheng Qin, Minggao Deng
article en
3 citations

Abstract

High-voltage transmission technology can effectively address the issue of uneven spatial and temporal energy distribution, leading to its rapid development in recent years. To address the challenge of accurately identifying the source of the second traveling wave head in complex transmission network scenarios with existing single-ended fault location methods, a traveling wave network fault location method based on adaptive waveform similarity is proposed. The paper analyzes the propagation process of traveling waves in transmission lines and quantitatively derives the time-domain expression of the traveling wave waveform. The BFS algorithm is enhanced by incorporating the propagation characteristics of traveling waves, allowing for the determination of all paths from any location in the topological network to the measurement points. Based on the path information and the derived expression, the traveling wave waveform at the measurement points for the fault location is calculated. An optimization algorithm is used to iteratively solve for unknown parameters such as fault location, traveling wave speed, and fault point information, with the objective of maximizing the similarity between the adaptive waveform and the real waveform by adaptively adjusting the waveform shape. When the similarity between the adaptive waveform and the real waveform is maximized, the adaptive fault location is identified as the actual fault location. Verified through the PSCAD simulation platform, this method can achieve accurate location under different fault conditions.

HeliyonVol. 10(18)
Hunan University (CN), Hunan Xiangdian Test Research Institute (China) (CN)
Openalex Percentile: Top 29%
Traffic Prediction and Management Techniques
0.80
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Traveling wave network location method based on adaptive waveform similarity — Yu Liu, Wanglong Wan, et al. · Heliyon (2024) | TGRS Research Map | TGRS