NDLI: Support vector and multilayer perceptron neural networks applied to power systems transient stability analysis with input dimensionality reduction

Content Provider	IEEE Xplore Digital Library
Author	Moulin, L.S. da Silva, A.P.A. El-Sharkawi, M.A. Marks, R.J.
Copyright Year	2002
Description	Author affiliation: Inst. of Electr. Eng., Fed. Eng. Sch., Brazil (Moulin, L.S.; da Silva, A.P.A.)
Abstract	The neural network (NN) approach to power system transient stability analysis (TSA) has been presented as a potential tool for online applications, but the high dimensionality of the power systems turns it necessary to implement feature extraction techniques to make the application feasible in practice. At the same time, feature extraction can offer sensitivity information to help the identification of input features best suited for control action. This paper presents a new learning-based nonlinear classifier, the support vector machines (SVMs) NNs, showing its suitability for power system TSA. It can be seen as a different approach to cope with the problem of high dimensionality due to its fast training capability, which can be combined with existing feature extraction techniques. SVMs' theoretical motivation is conceptually explained and they are applied to the IEEE 50 generator system TSA problem. Aspects of model adequacy, training time and classification accuracy are discussed and compared to stability classifications obtained by multi-layer perceptrons (MLPs). Both models are trained with complete and reduced Input features sets.
Starting Page	1308
Ending Page	1313
File Size	1021780
Page Count	6
File Format	PDF
ISBN	0780375181
DOI	10.1109/PESS.2002.1043580
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-07-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multilayer perceptrons Neural networks Multi-layer neural network Power system transients Stability analysis Feature extraction Power system analysis computing Power system stability Support vector machines Support vector machine classification
Content Type	Text
Resource Type	Article

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