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Incipient fault identification of distribution networks based on feature matching of power disturbance data

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Abstract

Accurate identification of incipient cable fault is helpful to improve the reliability of power system. This paper proposes an incipient fault identification method for distribution networks cables based on feature matching of power disturbance data. Firstly, based on the power disturbance data provided by the power quality monitor about the incipient faults, a characteristic analysis is performed, and three characteristics F1 to F3 that can distinguish the incipient faults of the cable are extracted. Then, the common abnormal condition of 10 kV system is simulated, and the feature quantity is extracted to establish the feature database. Finally, a topic search algorithm is used to perform correlation matching, so as to quickly and accurately identify incipient cable faults from a variety of abnormal conditions. This method can be used to predict cable faults and evaluate the health status, while providing system operation engineers and dispatchers with advanced situational awareness and the causes of impending failures to reduce accident losses and hazards. Simulation results show that the proposed method has high accuracy.

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All data, models, and code generated or used during the study appear in the submitted article.

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Acknowledgments

The authors acknowledge the financial support from National Natural Science Foundation of China under Grants 61803233, China Postdoctoral Science Foundation (2018M640645), Qingdao Science and Technology Project (19-6-2-65-cg), SDUST Young Teachers Teaching Talent Training Plan (BJRC20180504).

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Authors and Affiliations

  1. Shandong University of Science and Technology, Qingdao, 266590, China

    Chao Zhang & Na Song

  2. State Grid Shandong Electric Power Research Institute, Jinan, 250003, China

    Yudun Li

Authors
  1. Chao Zhang
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  2. Na Song
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  3. Yudun Li
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Correspondence to Chao Zhang.

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Zhang, C., Song, N. & Li, Y. Incipient fault identification of distribution networks based on feature matching of power disturbance data. Electr Eng 103, 2447–2457 (2021). https://doi.org/10.1007/s00202-021-01232-6

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  • DOI: https://doi.org/10.1007/s00202-021-01232-6

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