Power signal classification using dynamic wavelet network |
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| Authors: | B. Biswal P.K. Dash B.K. Panigrahi J.B.V. Reddy |
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| Affiliation: | 1. Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar 751024, India;2. SMIEEE, Centre for Research in Electrical Sciences, Bhubaneswar, India;3. Indian Institute of Technology, New Delhi, India;4. Department of Science & Technology, New Delhi, India;1. Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan;2. Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China |
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| Abstract: | 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). |
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