DocumentCode :
1708253
Title :
Power transformer fault diagnosis based on immune evolutionary clustering algorithm
Author :
Hong-xia, Xie ; Li-ping, Shi
Volume :
2
fYear :
2010
Abstract :
In this paper, Fuzzy C-Means (FCM) algorithm is combined with the immune evolutionary algorithm to propose a new method which can be applied to transformer fault diagnosis. First, the paper makes attributes reduction in decision table with the use of rough set, then the continuous attributes in the decision table use immune evolutionary clustering algorithm for discrete processing, and then builds transformer fault diagnosis system. Experiments show that the transformer fault diagnosis system has the feasibility and high accuracy.
Keywords :
evolutionary computation; fault diagnosis; fuzzy set theory; pattern clustering; power engineering computing; power transformers; rough set theory; decision table; discrete processing; fuzzy C-Means algorithm; immune evolutionary clustering algorithm; power transformer fault diagnosis; rough set theory; Clustering algorithms; Discharges; Fault diagnosis; Immune system; Power transformers; Set theory; Signal processing algorithms; Fault Diagnosis; Fuzzy Cluster; Immune Evolutionary; rough set;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Systems (ICSPS), 2010 2nd International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-6892-8
Electronic_ISBN :
978-1-4244-6893-5
Type :
conf
DOI :
10.1109/ICSPS.2010.5555225
Filename :
5555225
Link To Document :
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