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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en18133271 |
