Fault Section Location Method for Distribution Network Based on Multi-Dimensional Waveform Difference Clustering Analysis

Hanjing LUO , Xiangjun ZENG , Kun YU , Zhi LI , Zhicheng XIE , Jun DENG

›› 2024, Vol. 18 ›› Issue (6) : 58 -68.

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›› 2024, Vol. 18 ›› Issue (6) : 58 -68. DOI: 10.13648/j.cnki.issn1674-0629.2024.06.008
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Fault Section Location Method for Distribution Network Based on Multi-Dimensional Waveform Difference Clustering Analysis

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Abstract

The resonant grounding system has a complex operating environment and variable single-phase grounding fault occurrence status. The distribution network traditional fault section location methods based on a single characteristic quantity have their own location blind zones, and the reliability and applicability of methods are also difficult to be guaranteed. To solve the above problems, a fault section location method for distribution network based on multi-dimensional waveform difference clustering analysis without tuning is proposed. According to the waveform difference characteristics of the zero-sequence current transient and steady-state components at each detection point upstream and downstream of the fault point, the transient and steady-state waveform differences between adjacent detection points are quantified by discrete Fréchet distance algorithm, and fuzzy C-means (FCM) is used to synthesize multi-feature information to divide the normal section and fault section. The location blind zones with single feature are eliminated effectively and the accurate fault section location is realized adaptively. The simulation results of PSCAD/EMTDC and 10kV true experimental test show that the proposed method not only ensures reliability and applicability under different fault conditions, but also has high resistance resistance and noise resistance.

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resonant grounding system / fuzzy C-means clustering / discrete Fréchet distance / zero sequence current / fault location

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Hanjing LUO,Xiangjun ZENG,Kun YU,Zhi LI,Zhicheng XIE,Jun DENG. Fault Section Location Method for Distribution Network Based on Multi-Dimensional Waveform Difference Clustering Analysis. 2024, 18(6): 58-68 DOI:10.13648/j.cnki.issn1674-0629.2024.06.008

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Funding

the National Natural Science Foundation(51737002)

Hunan Provincial Natural Science Foundation of China(2021JJ30729)

Hunan Provincial Education Department Project(22A0231)

Hunan Province Postgraduate Research and Innovation Project(CX20210792)

the Science and Technology Project of State Grid Zhejiang Electric Power Corporation(2021FD05)

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