Title :
Study on power transformers fault diagnosis based on Fuzzy Kernel C-Means Clustering and Dempster-Shafer theory fusion method
Author :
Liu, Dong-Hui ; Lv, Dan-dan ; Sun, Xiao-Yun ; Bian, Jian-peng
Author_Institution :
Sch. of Electr. Technol. & Inf. Sci., Hebei Univ. of Sci. & Technol., Shijiazhuang, China
Abstract :
Because of the compactness characteristics of oil-immersed transformer fault, a model of oil-immersed transformer fault diagnosis is based on the collaborative method of Fuzzy Kernel C-Means Clustering (FKCM) and multi-source information data fusion. The basic idea is that the trained samples are clustered first by using FKCM, then a Dempster-Shafer(D-S) evidential theory Fusion method is used to train the chose samples, the simulation result shows that the above method can effectively diagnose the transformer fault condition. Two kinds of data fusion methods are given in this paper, which are compared in the transformer fault diagnosis.
Keywords :
case-based reasoning; fault diagnosis; fuzzy set theory; pattern clustering; power engineering computing; power transformer insulation; power transformers; sensor fusion; transformer oil; Dempster-Shafer theory fusion method; FKCM; fault diagnosis; fuzzy kernel c-means clustering; multisource information data fusion; oil-immersed transformer fault; power transformers; transformer fault condition; Fault diagnosis; Kernel; Oil insulation; Power transformers; Sensor fusion; Uncertainty; D-S evidential theory; Fuzzy Kernel C-Means Clustering; Information fusion;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580659