Title :
The study of transformer fault diagnosis based on means kernel clustering and SVM multi-class object simplified structure
Author :
Sun, Xiaoyun ; Bian, Jianpeng ; Liu, Donghui ; Li, Zhenquan
Author_Institution :
Sch. of Electr. Technol. & Inf. Sci., Hebei Univ. of Sci. & Technol., Shijiazhuang
Abstract :
A model based on means kernel clustering and support vector machine (SVM) multi-class object simplified structure is proposed for transformer fault diagnosis. The basic idea is, firstly, the training samples are clustered by means kernel clustering algorithm, then the right ones clustered by means kernel clustering are put into the classifier of SVM multi-class object simplified structure and trained by this structure, finally, the fault of the transformer can be detected. The result shows that the precision is better than the traditional one, and the reliability and effectiveness using above method is satisfied in fault diagnosis.
Keywords :
fault diagnosis; pattern clustering; power engineering computing; power transformers; support vector machines; transformers; SVM multiclass object simplified structure; means kernel clustering; support vector machine; transformer fault diagnosis; Artificial neural networks; Dissolved gas analysis; Fault diagnosis; Gases; Kernel; Oil insulation; Power transformer insulation; Power transformers; Support vector machine classification; Support vector machines; Fault diagnosis; Means kernel clustering; Multi-class object simplified structure; SVM;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4593769