Title :
A quantitative comparison of wavelet based feature vectors for classification of power quality disturbances
Author :
Dash, P.K. ; Lee, I.W.C. ; Basu, K.P. ; Morris, Stella ; Sharaf, A.M.
Author_Institution :
Silicon Inst. of Technol., Bhubaneswar, India
Abstract :
This paper presents a comparison between different wavelet feature vectors for power quality disturbance classification problems. Three different wavelet algorithms are simulated and applied on nine classes of power quality disturbances. Neural networks are then used to compute the classification accuracy of the feature vectors. Certain characteristics of the wavelet feature vectors are apparent from the results.
Keywords :
neural nets; pattern classification; power engineering computing; power supply quality; power system faults; wavelet transforms; neural networks; power quality disturbance classification; wavelet algorithms; wavelet based feature vectors; Continuous wavelet transforms; Discrete wavelet transforms; Multiresolution analysis; Neural networks; Power industry; Power quality; Testing; Voltage; Wavelet domain; Wavelet transforms;
Conference_Titel :
Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE
Print_ISBN :
0-7803-7906-3
DOI :
10.1109/IECON.2003.1280023
