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Fault Location Method Based on SVM and Similarity Model Matching

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  • Chenyu Zhang
  • Xiaodong Yuan
  • Mingming Shi
  • Jinggang Yang
  • Huiyu Miao

Abstract

To locate the fault location accurately and solve the problem quickly is the key to improve the power supply capacity of power grid. This paper presents a fault location method based on SVM fault branch selection algorithm and similarity matching. Firstly, an SVM-based fault branch filter classifier was constructed based on the positive sequence component feature matrix data of each monitoring point, which can accurately select the branch where the current fault is located. Then, based on the positive sequence voltage distribution characteristics, the Euclidean distance and Pearson correlation coefficient (PCC) are used to establish the similarity objective function of fault location. And then, the fault is accurately located by the objective function. Finally, the proposed method is validated by using an IEEE-14 node network. The results show that the proposed method is effective and accurate.

Suggested Citation

  • Chenyu Zhang & Xiaodong Yuan & Mingming Shi & Jinggang Yang & Huiyu Miao, 2020. "Fault Location Method Based on SVM and Similarity Model Matching," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, September.
    • Handle: RePEc:hin:jnlmpe:2898479
    DOI: 10.1155/2020/2898479
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    Cited by:

    1. Hamid Mirshekali & Rahman Dashti & Karsten Handrup & Hamid Reza Shaker, 2021. "Real Fault Location in a Distribution Network Using Smart Feeder Meter Data," Energies, MDPI, vol. 14(11), pages 1-16, June.

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