DocumentCode :
473587
Title :
Application of EMD and SVD in fault identification
Author :
Zhu, Zhihui ; Sun, Yunlian
Author_Institution :
Sch. of Electr. Eng., Wuhan Univ., Wuhan
fYear :
2007
fDate :
3-6 Dec. 2007
Firstpage :
1247
Lastpage :
1250
Abstract :
The method based on empirical mode decomposition (EMD) and singular value decomposition (SVD) for power fault identification is presented in this paper. First, fault signal was adaptively decomposed into a series of smooth intrinsic mode functions (IMFs) with different time scales via EMD; second, the matrix is formed by different level IMFs and SVD method decompose the matrix to obtain singular value as eigenvector; finally, support vector machines (SVMs) is used as classifier to identify the fault type. The simulation results show that EMD and SVD can well extract the fault feature and SVMs network can attain high accuracy of fault identification.
Keywords :
fault location; power system faults; singular value decomposition; support vector machines; EMD; SVD; SVM; empirical mode decomposition; fault feature extraction; matrix decomposition; power fault identification; singular value decomposition; smooth intrinsic mode functions; support vector machines; Fault diagnosis; Power engineering; empirical mode decomposition; fault identification; singular value decomposition; support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Engineering Conference, 2007. IPEC 2007. International
Conference_Location :
Singapore
Print_ISBN :
978-981-05-9423-7
Type :
conf
Filename :
4510216
Link To Document :
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