Title :
Power signal classification using Adaptive Wavelet Network
Author :
Bebarta, D.K. ; Biswal, B. ; Rout, A.K. ; Biswal, M.
Author_Institution :
Dept. of CSE, GMR Inst. of Technol., Rajam, India
Abstract :
A new approach to classification of non-stationary power signals based on adaptive wavelet has been considered. This paper proposes a model for non-stationary power signal disturbance classification using adaptive wavelet networks (AWN). A AWN is a combination of two sub-networks consisting of a wavelet layer and adaptive probabilistic network. The AWN has the capability of automatic adjustment of learning cycles for different classes of signals, for minimizing error. AWN models are specifically suitable for application in adaptive environments with time varying nonstationary 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 AWN (Adaptive Wavelet Network) has been compared with that of the Probabilistic Neural Network (PNN).
Keywords :
adaptive signal processing; probability; signal classification; time-varying systems; AWN model; adaptive learning mechanism; adaptive probabilistic network; adaptive wavelet network; fast processing time; learning cycles; probabilistic neural network; time varying nonstationary power signal disturbance classification; wavelet layer; Adaptive systems; Artificial neural networks; Kernel; Probabilistic logic; Training; Voltage fluctuations; Wavelet transforms; Adaptive Wavelet Network (AWN); Morlet wavelet; Non-Stationary power signals; Probabilistic Neural Network (PNN); translation and dilation;
Conference_Titel :
Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference on
Conference_Location :
Ramanathapuram
Print_ISBN :
978-1-4244-7769-2
DOI :
10.1109/ICCCCT.2010.5670773
