UAV‐Assisted Fault Location Coordinated Strategy for Resilient Distribution Systems

ABSTRACT The orderly and rapid restoration of the distribution system (DS) after disasters depends on the accuracy and reliability of fault location. Disasters may also damage the communication infrastructure, resulting in untimely reporting of fault information detected by remote fault indicators (RFIs). In this paper, we propose an unmanned aerial vehicle (UAV) assisted coordination strategy for status collection and fault location in distribution systems. The strategy utilizes the RFIs’ fault status indication information collected by UAVs in the communication fault area for fault location. A co‐optimization model for information acquisition and fault location is established, taking into account the probability of possible fault scenarios, the total amount of status information collected, and the working time of UAVs for multiple objectives. The model is transformed into a mixed‐integer second‐order cone programming (MISOCP) problem form using logarithmic transformations and linear relaxation methods. By iteratively solving and adding cut constraints, the fault section location and UAV dispatching decision can be determined, and RFI abnormalities can be identified. This strategy effectively harnesses the potential of UAVs for fault location in post‐disaster scenarios within DS, offers valuable insights for post‐disaster information collection and fault location, and enhances the situational awareness capabilities of DS following extreme events.

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Publication Details

Journal
IET Generation Transmission & Distribution
Published
2025-01-01
DOI
https://doi.org/10.1049/gtd2.70097
Primary Topic
Power Systems Fault Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

UAV‐Assisted Fault Location Coordinated Strategy for Resilient Distribution Systems

Zhaohong Bie, Haochen Zhang, Chen Chen, Guowei Liu et al.
IET Generation Transmission & Distribution
Power Systems Fault Detection
article

UAV‐Assisted Fault Location Coordinated Strategy for Resilient Distribution Systems

Zhaohong Bie, Haochen Zhang, Chen Chen, Guowei Liu, Jian Zhong
article en

Abstract

ABSTRACT The orderly and rapid restoration of the distribution system (DS) after disasters depends on the accuracy and reliability of fault location. Disasters may also damage the communication infrastructure, resulting in untimely reporting of fault information detected by remote fault indicators (RFIs). In this paper, we propose an unmanned aerial vehicle (UAV) assisted coordination strategy for status collection and fault location in distribution systems. The strategy utilizes the RFIs’ fault status indication information collected by UAVs in the communication fault area for fault location. A co‐optimization model for information acquisition and fault location is established, taking into account the probability of possible fault scenarios, the total amount of status information collected, and the working time of UAVs for multiple objectives. The model is transformed into a mixed‐integer second‐order cone programming (MISOCP) problem form using logarithmic transformations and linear relaxation methods. By iteratively solving and adding cut constraints, the fault section location and UAV dispatching decision can be determined, and RFI abnormalities can be identified. This strategy effectively harnesses the potential of UAVs for fault location in post‐disaster scenarios within DS, offers valuable insights for post‐disaster information collection and fault location, and enhances the situational awareness capabilities of DS following extreme events.

IET Generation Transmission & DistributionVol. 19(1)
Shenzhen Stock Exchange (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China, China Southern Power Grid
Openalex Percentile: Top 88%
Power Systems Fault Detection
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UAV‐Assisted Fault Location Coordinated Strategy for Resilient Distribution Systems — Zhaohong Bie, Haochen Zhang, et al. · IET Generation Transmission & Distribution (2025) | TGRS Research Map | TGRS