A study on SVM with feature selection for fault diagnosis of power systems

Author

Wang, Yufei ; Wu, Chunguo ; Wan, Liming ; Liang, Yanchun

Author_Institution

Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China

Volume

2

fYear

2010

fDate

26-28 Feb. 2010

Firstpage

173

Lastpage

176

Abstract

When faults occur in power systems, it is hard to manually deal with the fault data reported by the system of supervisory control and data acquisition (SCADA) because of the huge amount of alarm information. In this paper, we study the problem of power system fault diagnosis by using support vector machine (SVM), and enhance the ability of fault diagnosis through optimizing support vectors. The results of simulation tests demonstrate the effectiveness of the proposed automatic fault diagnosis method.

Keywords

SCADA systems; fault diagnosis; optimisation; power engineering computing; power system faults; support vector machines; SCADA; SVM; alarm information; feature selection; power system fault diagnosis; simulation tests; supervisory control and data acquisition; support vector machine; support vectors optimization; Fault diagnosis; Machine learning; Pattern recognition; Power system control; Power system faults; Power system simulation; Power systems; SCADA systems; Support vector machine classification; Support vector machines; fault diagnosis; optimize; power system; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-5585-0

Electronic_ISBN

978-1-4244-5586-7

Type

conf

DOI

10.1109/ICCAE.2010.5451443

Filename

5451443