Distribution Network Fault Diagnosis Based on Hybrid Model and Data-Driven Approach with Grey Wolf Hunting and Spatial Contraction Strategy

Miaomiao Zhou; Mengshi Li; Haiting Shan; Q. H. Wu

doi:10.17775/CSEEJPES.2024.03930

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Distribution Network Fault Diagnosis Based on Hybrid Model and Data-Driven Approach with Grey Wolf Hunting and Spatial Contraction Strategy

Regular Papers | 更新时间：2026-06-12

- Distribution Network Fault Diagnosis Based on Hybrid Model and Data-Driven Approach with Grey Wolf Hunting and Spatial Contraction Strategy
- Distribution Network Fault Diagnosis Based on Hybrid Model and Data-Driven Approach with Grey Wolf Hunting and Spatial Contraction Strategy
- 中国电机工程学会电力与能源系统学报(英文) 2026年12卷第3期页码：1448-1457
- 作者机构：
  
  School of Electric Power Engineering, South China University of Technology,Guangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2024.03930
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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Miaomiao Zhou, Mengshi Li, Haiting Shan, 等. Distribution Network Fault Diagnosis Based on Hybrid Model and Data-Driven Approach with Grey Wolf Hunting and Spatial Contraction Strategy[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(3):1448-1457.

Miaomiao Zhou, Mengshi Li, Haiting Shan, et al. Distribution Network Fault Diagnosis Based on Hybrid Model and Data-Driven Approach with Grey Wolf Hunting and Spatial Contraction Strategy[J]. CSEE Journal of Power and Energy Systems, 2026, 12(3): 1448-1457.
Miaomiao Zhou, Mengshi Li, Haiting Shan, 等. Distribution Network Fault Diagnosis Based on Hybrid Model and Data-Driven Approach with Grey Wolf Hunting and Spatial Contraction Strategy[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(3):1448-1457. DOI： 10.17775/CSEEJPES.2024.03930.

Miaomiao Zhou, Mengshi Li, Haiting Shan, et al. Distribution Network Fault Diagnosis Based on Hybrid Model and Data-Driven Approach with Grey Wolf Hunting and Spatial Contraction Strategy[J]. CSEE Journal of Power and Energy Systems, 2026, 12(3): 1448-1457. DOI： 10.17775/CSEEJPES.2024.03930.

摘要

Abstract

Swift and precise fault location in distribution networks is paramount for minimising outage losses and expediting power restoration. This paper proposes a hybrid fault location technique based on data and model fusion

which formulates the fault localization task as an optimization problem. The technique tackles three key problems: fault classification

line identification and precise location in distribution networks. The technique hinges on the Grey Wolf Hunting (GWH) algorithm

which has significant advantages in exploring and exploiting capabilities

and the spatial contraction strategy (SCS)

which significantly curtails computational expense. Simulation results demonstrate the proposed technique's commendable performance in recognition accuracy and location precision

alongside robust noise immunity. Meanwhile

this hybird data and model fusion driven technique can be flexibly applied to different distribution networks.

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