Open AccessPower transformer fault diagnosis using FCM and improved PCA
Author(s) -
Kari Tusongjiang,
Gao Wensheng
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0851
Subject(s) - principal component analysis , cluster analysis , pattern recognition (psychology) , euclidean distance , transformer , fuzzy logic , computer science , dissolved gas analysis , fault (geology) , data mining , artificial intelligence , algorithm , engineering , voltage , transformer oil , seismology , geology , electrical engineering
In order to improve fault diagnosis accuracy of power transformer, a new fault diagnosis model based on fuzzy C‐means (FCM) clustering algorithm and improved principal component analysis (IPCA) is presented. First, dissolve gas analysis samples are clustered with FCM and cluster centre for each fault type is regarded as reference sequence. Then, the IPCA approach is implemented to obtain main principal components containing 95% of original information. Finally, Euclidean distances between principal components of reference sequence and testing sample are calculated to identify final fault type. Cases studies and test results show that the proposed approach achieves recognition of transformer fault effectively and has a higher diagnostic accuracy than the international electrotechnical commission (IEC) ratio method and the improved three ratio method.