期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 35, 期 1-2, 页码 143-149出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2007.06.005
关键词
weighted support vector machines (WSVMs); power quality; disturbances; classification; wavelet packet energy entropy
In this paper, wavelet packet energy entropy and weighted support vector machines are used to automatically detect and classify power quality (PQ) disturbances. Electric power quality is an aspect of power engineering that has been with Lis since the inception of power systems. The types of concerned disturbances include voltage sags, swells, interruptions. Wavelet packet are utilized to denoise the digital signals, to decompose the signals and then to obtain five common features for the sampling PQ disturbance signals. A weighted support vector machine is designed and trained by 5-dimension feature space points for making it decision regarding the type of the disturbance. Simulation cases illustrate the effectiveness. (C) 2007 Elsevier Ltd. All rights reserved.
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