DocumentCode :
2610082
Title :
Fault Diagnosis of Power Transformer Using Dynamic Clustering Algorithm
Author :
Hao, Xiong ; Jia, Lv ; Chonghan, Liu ; Li, Zhou ; Caixin, Sun
Author_Institution :
Extra-High Voltage Bur., Chongqing Electr. Power Corp., Chongqing
fYear :
2008
fDate :
9-12 Nov. 2008
Firstpage :
710
Lastpage :
714
Abstract :
A novel dynamic clustering algorithm based on artificial immune network is proposed in this paper. Firstly artificial immune network can get memory polls, which effectively represent the characteristics of fault samples, using the ability of immune memory and learning. Then genetic algorithm is used to dynamically optimize and select the best memory cells as initial clustering centers of kernel-based possibility clustering algorithm. A lot of fault samples are analyzed by this algorithm, and the results are compared with those obtained by BPNN. The results indicate that the samples can effectively be classified through the algorithm and precision of fault diagnosis can be improved.
Keywords :
artificial immune systems; fault diagnosis; genetic algorithms; learning (artificial intelligence); pattern clustering; power engineering computing; power transformers; artificial immune network; dynamic clustering algorithm; fault diagnosis; genetic algorithm; kernel-based possibility clustering algorithm; power transformer; Algorithm design and analysis; Clustering algorithms; Fault diagnosis; Genetic algorithms; Heuristic algorithms; Oil insulation; Power engineering and energy; Power system dynamics; Power system reliability; Power transformers; Artificial immune network; Dynamic clustering; Fault diagnosis; Genetic algorithm; Kernel-based possibility clustering; Power transformer;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
High Voltage Engineering and Application, 2008. ICHVE 2008. International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-3823-5
Electronic_ISBN :
978-1-4244-2810-6
Type :
conf
DOI :
10.1109/ICHVE.2008.4774033
Filename :
4774033
Link To Document :
بازگشت