Distribution Cable Fault Location Method Based on ICEEDMAN-NTEO

With the acceleration of urbanization, fault location in power distribution cables has become increasingly challenging due to their concealed nature and complex operating environments. Traditional dual-terminal traveling wave ranging methods suffer from inaccuracies in wave velocity calibration and fault occurrence time estimation. To address these limitations, this paper proposes a fault location method for distribution cables based on an improved ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) combined with a Novel High-order Time-frequency Energy Operator (NHTEO). First, the dual-terminal traveling wave ranging method is enhanced by analyzing the reflection and refraction characteristics of traveling waves during propagation. This modification eliminates the dependency on wave velocity and fault timing, enabling high-precision localization using only four key parameters. Second, to mitigate noise interference and modal aliasing in fault signals, an improved ICEEMDAN algorithm integrating Kernel Principal Component Analysis (KPCA) and fractal dimension fusion is proposed. Coupled with the NHTEO, this approach effectively suppresses high-frequency noise and improves the detection accuracy of wavefront arrival times. Validation through PSCAD simulations under diverse fault conditions-including varying fault locations, transition resistances, and fault types-demonstrates that the proposed method achieves a maximum ranging error of 0.165 km. Compared to conventional approaches, the method exhibits significantly enhanced precision and robust performance. This technique provides an effective solution for rapid fault localization in distribution networks.

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Publication Details

Published
2025-03-21
DOI
https://doi.org/10.1109/sgai64825.2025.11009543
Citations
1
Primary Topic
Power Systems Fault Detection
Type
article
Field-Weighted Citation Impact
1.21
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Distribution Cable Fault Location Method Based on ICEEDMAN-NTEO

Dengfeng Fu, Yu Zheng, Keyu Yue, Minzhen Wang et al.
1 citations
Power Systems Fault Detection
1.21
article

Distribution Cable Fault Location Method Based on ICEEDMAN-NTEO

Dengfeng Fu, Yu Zheng, Keyu Yue, Minzhen Wang, Guangxin Zhang, Zhigang Wang, Wenxin Wu
article en
1 citations

Abstract

With the acceleration of urbanization, fault location in power distribution cables has become increasingly challenging due to their concealed nature and complex operating environments. Traditional dual-terminal traveling wave ranging methods suffer from inaccuracies in wave velocity calibration and fault occurrence time estimation. To address these limitations, this paper proposes a fault location method for distribution cables based on an improved ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) combined with a Novel High-order Time-frequency Energy Operator (NHTEO). First, the dual-terminal traveling wave ranging method is enhanced by analyzing the reflection and refraction characteristics of traveling waves during propagation. This modification eliminates the dependency on wave velocity and fault timing, enabling high-precision localization using only four key parameters. Second, to mitigate noise interference and modal aliasing in fault signals, an improved ICEEMDAN algorithm integrating Kernel Principal Component Analysis (KPCA) and fractal dimension fusion is proposed. Coupled with the NHTEO, this approach effectively suppresses high-frequency noise and improves the detection accuracy of wavefront arrival times. Validation through PSCAD simulations under diverse fault conditions-including varying fault locations, transition resistances, and fault types-demonstrates that the proposed method achieves a maximum ranging error of 0.165 km. Compared to conventional approaches, the method exhibits significantly enhanced precision and robust performance. This technique provides an effective solution for rapid fault localization in distribution networks.

Ningbo University (CN), Jilin Electric Power Research Institute (China) (CN)
Openalex Percentile: Top 21%
Power Systems Fault Detection
1.21
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