NDLI: Wavelet-based neural network for power disturbance classification

Content Provider	IEEE Xplore Digital Library
Author	Zwe-Lee Gaing Hou-Sheng Huang
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng., Kao-Yuan Inst. of Technol., Kaohsiung, Taiwan (Zwe-Lee Gaing)
Abstract	In this paper a wavelet-based neural network classifier for recognizing power quality disturbances is implemented and tested under various transient events. The discrete wavelet (DWT) technique is integrated with the probabilistic neural network (PNN) model to construct the classifier. First, the multi-resolution analysis (MRA) technique of DWT and the Parseval's theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Second, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the features of distorted signal without losing its original property less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.
Starting Page	1621
Ending Page	1628
File Size	671139
Page Count	8
File Format	PDF
ISBN	0780379896
DOI	10.1109/PES.2003.1267398
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Multiresolution analysis Discrete wavelet transforms Voltage fluctuations Testing Distortion Power quality Signal analysis Energy resolution Signal resolution
Content Type	Text
Resource Type	Article

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