Fault diagnosis for power system transmission line based on PCA and SVMs

被引：0
作者：
Guo, Yuanjun [1 ]
Li, Kang [1 ]
Liu, Xueqin [1 ]
机构：
[1] School of Electronics, Electrical Engineering and Computer Science, Queen's University of Belfast, Belfast
来源：
Communications in Computer and Information Science | 2013年 / 355卷
关键词：
Fault Diagnosis; Principal-Component Analysis (PCA); Support vector machine (SVM); Transmission Line Faults;
D O I：
10.1007/978-3-642-37105-9_58
中图分类号：
学科分类号：
摘要：
This paper presents the application of a fault detection method based on the principal component analysis (PCA) and support vector machine (SVM) for the detection and classification of faults in power system transmission lines. Consider that the data may be huge with a number of strongly correlated variables, method which incorporates both the principal component analysis (PCA) and support vector machine (SVM) is proposed. This algorithm has two stages. The first stage involves the use of the PCA to reduce the dimensionality as well as to find violating point of the signals according to the confidential limit. The features of each fault extracted from the data are used in the second stage to construct SVM networks. The second stage is to use pattern recognition method to distinguish the phase of the faulty situation. The proposed scheme is able to solve the problems encountered in traditional magnitude and frequency based methods. The benefits of this improvement are demonstrated. © Springer-Verlag Berlin Heidelberg 2013.
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页码：524 / 532
页数：8
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