Open Access

Wavelet-Based Algorithm for Signal Analysis

EURASIP Journal on Advances in Signal Processing20072007:038916

DOI: 10.1155/2007/38916

Received: 6 August 2006

Accepted: 24 November 2006

Published: 10 January 2007

Abstract

This paper presents a computational algorithm for identifying power frequency variations and integer harmonics by using wavelet-based transform. The continuous wavelet transform (CWT) using the complex Morlet wavelet (CMW) is adopted to detect the harmonics presented in a power signal. A frequency detection algorithm is developed from the wavelet scalogram and ridges. A necessary condition is established to discriminate adjacent frequencies. The instantaneous frequency identification approach is applied to determine the frequencies components. An algorithm based on the discrete stationary wavelet transform (DSWT) is adopted to denoise the wavelet ridges. Experimental work has been used to demonstrate the superiority of this approach as compared to the more conventional one such as the fast Fourier transform.

[1234567891011121314]

Authors’ Affiliations

(1)
Division of Building Science and Technology, City University of Hong Kong
(2)
School of Engineering and Mathematical Sciences, City University

References

  1. IEEE recommended practice for monitoring electric power quality IEEE Standards Board, June 1995
  2. Lai LL, Chan WL, Tse CT, So ATP: Real-time frequency and harmonic evaluation using artificial neural networks. IEEE Transactions on Power Delivery 1999,14(1):52-59. 10.1109/61.736681View ArticleGoogle Scholar
  3. Chan WL, So ATP, Lai LL: Harmonics load signature recognition by wavelets transforms. Proceedings of International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT '00), April 2000, London, UK 666-671. IEEE Catalog no. 00EX382View ArticleGoogle Scholar
  4. Tse NCF: Practical application of wavelet to power quality analysis. Proceedings of IEEE Power Engineering Society General Meeting, June 2006, Montreal, Quebec, Canada 5. IEEE Catalogue no. 06CH37818C, CD ROMGoogle Scholar
  5. Addison PS: The Illustrated Wavelet Transform Handbook. Institute of Physics, Bristol, UK; 2002.View ArticleMATHGoogle Scholar
  6. Pham VL, Wong KP: Wavelet-transform-based algorithm for harmonic analysis of power system waveforms. IEE Proceedings: Generation, Transmission and Distribution 1999,146(3):249-254. 10.1049/ip-gtd:19990316Google Scholar
  7. Strang G, Nguyen T: Wavelets and Filter Banks. Wellesley-Cambridge, Wellesley, Mass, USA; 1996.MATHGoogle Scholar
  8. Huang S-J, Hsieh C-T, Huang C-L: Application of Morlet wavelets to supervise power system disturbances. IEEE Transactions on Power Delivery 1999,14(1):235-241. 10.1109/61.736728View ArticleGoogle Scholar
  9. Teolis A: Computational Signal Processing with Wavelets. Birkhäuser, Boston, Mass, USA; 1998.MATHGoogle Scholar
  10. Misiti M, Misiti Y, Oppenheim G: Wavelet Toolbox for Use with Matlab. The Mathworks, Natick, Mass, USA; 1996.MATHGoogle Scholar
  11. Mallet S: A Wavelet Tour of Signal Processing. Academic Press, San Diego, Calif, USA; 1998.Google Scholar
  12. Carmona RA, Hwang WL, Torrésani B: Multiridge detection and time-frequency reconstruction. IEEE Transactions on Signal Processing 1999,47(2):480-492. 10.1109/78.740131View ArticleGoogle Scholar
  13. Carmona RA, Hwang WL, Torresani B: Characterization of signals by the ridges of their wavelet transforms. IEEE Transactions on Signal Processing 1997,45(10):2586-2590. 10.1109/78.640725View ArticleGoogle Scholar
  14. Antoniadis A, Oppenheim G (Eds): Wavelets and Statistics, Lecture Notes in Statistics. Springer, New York, NY, USA; 1995.Google Scholar

Copyright

© N. C. F. Tse and L. L. Lai. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement