标题:Application of affinity propagation algorithm based on manifold distance for transformer PD pattern recognition
作者:Wei, B. G.; Huo, K. X.; Yao, Z. F.; Lou, J.; Li, X. Y.
通讯作者:Wei, BG
作者机构:[Wei, B. G.; Yao, Z. F.] Shanghai Elect Power Co, Elect Power Res Inst, 171 Handan Rd, Shanghai, Peoples R China.; [Huo, K. X.; Lou, J.; Li, X. Y.] 更多
会议名称:2nd International Conference on Mechatronics and Electrical Systems (ICMES)
会议日期:DEC 15-25, 2017
来源:2017 2ND INTERNATIONAL CONFERENCE ON MECHATRONICS AND ELECTRICAL SYSTEMS (ICMES 2017)
出版年:2018
卷:339
期:1
DOI:10.1088/1757-899X/339/1/012014
摘要:It is one of the difficult problems encountered in the research of condition maintenance technology of transformers to recognize partial discharge (PD) pattern. According to the main physical characteristics of PD, three models of oil-paper insulation defects were set up in laboratory to study the PD of transformers, and phase resolved partial discharge (PRPD) was constructed. By using least square method, the grey-scale images of PRPD were constructed and features of each grey-scale image were 28 box dimensions and 28 information dimensions. Affinity propagation algorithm based on manifold distance (AP-MD) for transformers PD pattern recognition was established, and the data of box dimension and information dimension were clustered based on AP-MD. Study shows that clustering result of AP-MD is better than the results of affinity propagation (AP), k-means and fuzzy c-means algorithm (FCM). By choosing different k values of k-nearest neighbor, we find clustering accuracy of AP-MD falls when k value is larger or smaller, and the optimal k value depends on sample size.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046286657&doi=10.1088%2f1757-899X%2f339%2f1%2f012014&partnerID=40&md5=6c72cbace44b9e603308c58bc90de24d
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