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Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means.

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Title: Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means.
Authors: Li, Bo1 (AUTHOR), Qian, Guochao1,2 (AUTHOR), Tang, Lijun1 (AUTHOR), Sun, Peng2 (AUTHOR) 23126349@bjtu.edu.cn, Wu, Zhensheng2 (AUTHOR)
Source: Energies (19961073). Jul2025, Vol. 18 Issue 13, p3271. 25p.
Subjects: Fault location (Engineering), Distributed sensors, Economic efficiency, Clustering algorithms, Fuzzy clustering technique, Electric power distribution grids, Particle swarm optimization
Abstract: To solve the problem of high cost and low efficiency of measuring equipment in traditional distribution network fault location, a fault section location and line selection strategy combining dynamic binary particle swarm optimization (DBPSO) configuration and fuzzy C-means (FCM) clustering is proposed in this paper. Firstly, the DBPSO algorithm is used to optimize the configuration scheme of the distributed voltage and current sensing device, which reduces the number of measuring devices and system cost on the premise of ensuring the global observability of the distribution network. When a fault occurs in the distribution network, the sensor device based on optimal configuration collects fault feature data, combines it with the FCM clustering algorithm to classify nodes according to fault feature similarity, and divides the most significant fault-affected section as the core fault area. Further, by calculating the Euclidean distance between each node in the fault section and the cluster center, the fault line is accurately identified. Finally, a fault simulation model based on an IEEE 11-node system is constructed to verify the effectiveness of the proposed method. The results show that, compared with the traditional fault section location and route selection strategy, this method can reduce the number of measurement devices optimally configured by 19–36% and significantly reduce the number of algorithm iterations. In addition, it can realize rapid fault location and precise line screening at a low equipment cost under multiple fault types and different fault locations, which significantly improves fault location accuracy while reducing economic investment. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Bo%22">Li, Bo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qian%2C+Guochao%22">Qian, Guochao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Lijun%22">Tang, Lijun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Peng%22">Sun, Peng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 23126349@bjtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Zhensheng%22">Wu, Zhensheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jul2025, Vol. 18 Issue 13, p3271. 25p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Fault+location+%28Engineering%29%22">Fault location (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+sensors%22">Distributed sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+efficiency%22">Economic efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+clustering+technique%22">Fuzzy clustering technique</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+distribution+grids%22">Electric power distribution grids</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To solve the problem of high cost and low efficiency of measuring equipment in traditional distribution network fault location, a fault section location and line selection strategy combining dynamic binary particle swarm optimization (DBPSO) configuration and fuzzy C-means (FCM) clustering is proposed in this paper. Firstly, the DBPSO algorithm is used to optimize the configuration scheme of the distributed voltage and current sensing device, which reduces the number of measuring devices and system cost on the premise of ensuring the global observability of the distribution network. When a fault occurs in the distribution network, the sensor device based on optimal configuration collects fault feature data, combines it with the FCM clustering algorithm to classify nodes according to fault feature similarity, and divides the most significant fault-affected section as the core fault area. Further, by calculating the Euclidean distance between each node in the fault section and the cluster center, the fault line is accurately identified. Finally, a fault simulation model based on an IEEE 11-node system is constructed to verify the effectiveness of the proposed method. The results show that, compared with the traditional fault section location and route selection strategy, this method can reduce the number of measurement devices optimally configured by 19–36% and significantly reduce the number of algorithm iterations. In addition, it can realize rapid fault location and precise line screening at a low equipment cost under multiple fault types and different fault locations, which significantly improves fault location accuracy while reducing economic investment. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/en18133271
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      – Code: eng
        Text: English
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        PageCount: 25
        StartPage: 3271
    Subjects:
      – SubjectFull: Fault location (Engineering)
        Type: general
      – SubjectFull: Distributed sensors
        Type: general
      – SubjectFull: Economic efficiency
        Type: general
      – SubjectFull: Clustering algorithms
        Type: general
      – SubjectFull: Fuzzy clustering technique
        Type: general
      – SubjectFull: Electric power distribution grids
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
    Titles:
      – TitleFull: Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means.
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            NameFull: Li, Bo
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            NameFull: Qian, Guochao
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            NameFull: Tang, Lijun
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            NameFull: Sun, Peng
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            NameFull: Wu, Zhensheng
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            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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              Value: 18
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