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《Journal of Guangxi Normal University(Natural Science Edition)》 2018-04
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Anomalous State Detection of Power Transformer Based on Bidirectional KL Distance Clustering Algorithm

LIN Yue;LIU Tingzhang;HUANG Lirong;XI Xiaoye;PAN Jian;College of Marine Communication Engineering,Hainan Tropical Ocean University;College of Mechatronics Engineering and Automation,Shanghai University;Haikou Power Supply Bureau of Hainan Power Grid Limited Liability Company;  
Since the Euclidean distance has the disadvantage of poor distinguishing ability in the similarity measure of some data sets,ageneral 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.
【Fund】: 国家自然科学基金(61273190)
【CateGory Index】: TM40;TP311.13
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