DocumentCode
2720271
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
Volume
1
fYear
2003
fDate
2-6 Nov. 2003
Firstpage
454
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE
Print_ISBN
0-7803-7906-3
Type
conf
DOI
10.1109/IECON.2003.1280023
Filename
1280023
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