DocumentCode :
128772
Title :
Synchronous generator incipient fault prediction based on SVM
Author :
Huang Cao ; Yuan Haiwen ; Tian Bo ; Wu Qicai ; Yuan Haibing ; Ling Mu
Author_Institution :
Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China
fYear :
2014
fDate :
9-11 June 2014
Firstpage :
2115
Lastpage :
2118
Abstract :
Aiming at the lack of technology for generator incipient condition monitoring and fault prediction, proposed SVM(Support Vector Machine) is introduced to generator incipient fault prediction. This paper takes the parametric faults of synchronous generator as an example, selects the output voltage as a monitoring signal, and combines with SVM regression prediction algorithms to achieve synchronous generators incipient fault prediction.
Keywords :
fault diagnosis; power engineering computing; support vector machines; synchronous generators; SVM regression prediction algorithms; generator incipient condition monitoring; parametric faults; support vector machine; synchronous generator incipient fault prediction; Circuit faults; Inductance; Mathematical model; Support vector machines; Synchronous generators; Threshold voltage; SVM; Synchronous generator; fault prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4316-6
Type :
conf
DOI :
10.1109/ICIEA.2014.6931520
Filename :
6931520
Link To Document :
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