Advanced fault location method for three-terminal distribution lines using frequency characteristics and µPMU data

The urban distribution network contains a significant number of multi- or three-terminal connections. Fault location in such networks is severely constrained by the lack of necessary conditions for deploying measuring instruments at intermediate connection points and the presence of erroneous or missing distribution line characteristics. This study designs a fault determination time index by analyzing the changes in Micro-phasor measurement unit (µPMU) measurement data at end nodes when an unbalanced fault occurs in the distribution line. A fault localization model incorporating frequency parameters is proposed, considering the influence of source load variations and the regular fluctuations of the fundamental frequency, which affect the characteristics of the distribution lines. To enhance the model's accuracy and efficiency, a solution approach combining Simulated Annealing Algorithm and trust region methods is suggested, addressing the impact of the initial values on the fault localization model calculation process. A three-terminal distribution line model is constructed in Matlab, and various failure scenarios are simulated. Using these frequency values significantly improved the accuracy of the fault location model's estimation results, achieving accuracy more than three times higher than models that do not consider frequency. The fault distance estimation error is reduced to less than 50 m. And, the model's applicable fault scenario is increased to 2000Ω. The results demonstrate that the proposed technique significantly improves the calculation efficiency and accuracy of the fault location model, providing a robust solution for fault location and line parameter estimation in distribution networks.

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Publication Details

Journal
Scientific Reports
Published
2025-02-13
DOI
https://doi.org/10.1038/s41598-025-89229-0
Citations
1
Primary Topic
Power Systems Fault Detection
Type
article
Field-Weighted Citation Impact
1.21

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article

Advanced fault location method for three-terminal distribution lines using frequency characteristics and µPMU data

Houhe Chen, Xiaozhe Song, Shuguang Li, Changjiang Wang
1 citations
Scientific Reports
Power Systems Fault Detection
1.21
article

Advanced fault location method for three-terminal distribution lines using frequency characteristics and µPMU data

Houhe Chen, Xiaozhe Song, Shuguang Li, Changjiang Wang
article en
1 citations

Abstract

The urban distribution network contains a significant number of multi- or three-terminal connections. Fault location in such networks is severely constrained by the lack of necessary conditions for deploying measuring instruments at intermediate connection points and the presence of erroneous or missing distribution line characteristics. This study designs a fault determination time index by analyzing the changes in Micro-phasor measurement unit (µPMU) measurement data at end nodes when an unbalanced fault occurs in the distribution line. A fault localization model incorporating frequency parameters is proposed, considering the influence of source load variations and the regular fluctuations of the fundamental frequency, which affect the characteristics of the distribution lines. To enhance the model's accuracy and efficiency, a solution approach combining Simulated Annealing Algorithm and trust region methods is suggested, addressing the impact of the initial values on the fault localization model calculation process. A three-terminal distribution line model is constructed in Matlab, and various failure scenarios are simulated. Using these frequency values significantly improved the accuracy of the fault location model's estimation results, achieving accuracy more than three times higher than models that do not consider frequency. The fault distance estimation error is reduced to less than 50 m. And, the model's applicable fault scenario is increased to 2000Ω. The results demonstrate that the proposed technique significantly improves the calculation efficiency and accuracy of the fault location model, providing a robust solution for fault location and line parameter estimation in distribution networks.

Scientific ReportsVol. 15(1)
Northeast Electric Power University (CN), Jilin Electric Power Research Institute (China) (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 25%
Power Systems Fault Detection
1.21
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