Journal of Guangxi Normal University(Natural Science Edition) ›› 2018, Vol. 36 ›› Issue (4): 20-26.doi: 10.16088/j.issn.1001-6600.2018.04.003

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Anomalous State Detection of Power Transformer Basedon Bidirectional KL Distance Clustering Algorithm

LIN Yue1,2, LIU Tingzhang2*, HUANG Lirong3, XI Xiaoye2, PAN Jian2   

  1. 1.College of Marine Communication Engineering, Hainan Tropical Ocean University, Sanya Hainan 572022, China;
    2.College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;
    3.Haikou Power Supply Bureau of Hainan Power Grid Limited Liability Company, Haikou Hainan 570203, China
  • Received:2017-11-09 Published:2018-10-20

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Abstract: Since the Euclidean distance has the disadvantage of poor distinguishing ability in the similarity measure of some data sets,a general model and analysis method of power transformer state anomaly detection based on bidirectional KL (Kullback-Leibler) distance clustering algorithm is proposed in this paper. The model is analyzed by the historical monitoring data of a substation in Huzhou. The results show that the method is effective and the accuracy is improved compared with the traditional method.

Key words: Euclidean distance, KL distance, clustering, power transformer, anomaly detection

CLC Number: 

  • TP274
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