期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 37, 期 6, 页码 4094-4102出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2009.11.015
关键词
Automatic classification; Power quality events; Feature extraction; Wavelet transform; Support vector machines
类别
资金
- Firat University Scientific Research Unit (FUBAP) [FUBAP-1605]
This paper presents a new approach for automatic classification of power quality events, which is based on the wavelet transform and support vector machines. In the proposed approach, an effective single feature vector representing three phase event signals is extracted after signals are applied normalization and segmentation process. The kernel and penalty parameters of the support vector machine (SVM) are determined by cross-validation. The parameter set that gives the smallest misclassification error is retained. ATP/EMTP model for six types of power system events, namely phase-to-ground fault, phase-to-phase fault, three-phase fault, load switching, capacitor switching and transformer energizing, are constructed. Both the noisy and noiseless event signals are applied to the proposed algorithm. Obtained results indicate that the proposed automatic event classification algorithm is robust and has ability to distinguish different power quality event classes easily. (C) 2009 Elsevier Ltd. All rights reserved.
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