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Power signal classification using dynamic wavelet network
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DOI：10.1016/j.asoc.2008.03.005.png)
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摘要
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A new approach to classification of non-stationary power signals based on dynamic wavelet has been considered. This paper proposes a model for non-stationary power signal disturbance classification using dynamic wavelet networks (DWN). A DWN is a combination of two sub-networks consisting of a wavelet layer and adaptive probabilistic network. The DWN has the capability of automatic adjustment of learning cycles for different classes of signals, for minimizing error. DWN models are specifically suitable for application in dynamic environments with time varying non-stationary power signals. The test results showed accurate classification, fast and adaptive learning mechanism, fast processing time and overall model effectiveness in classifying various non-stationary power signals. The classification result of the DWN has been compared with that of the probabilistic neural network (PNN). (C) 2008 Elsevier B.V. All rights reserved.
Keyword:
Non-stationary power signals
Dynamic wavelet network (DWN)
Morlet wavelet
Translation and dilation
Probabilistic neural network (PNN)
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期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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Power quality detection and classification using wavelet-multiresolution signal decomposition基于小波多分辨率信号分解的电能质量检测与分类
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