CS  Vol.2 No.3 , July 2011
An Improved Chirplet Transform and Its Application for Harmonics Detection
Abstract: The chirplet transform is the generalization form of fast Fourier transform , short-time Fourier transform, and wavelet transform. It has the most flexible time frequency window and successfully used in practices. However, the chirplet transform has not inherent inverse transform, and can not overcome the signal reconstructing problem. In this paper, we proposed the improved chirplet transform (ICT) and constructed the inverse ICT. Finally, by simulating the harmonic voltages, The power of the improved chirplet transform are illustrated for harmonic detection. The contours clearly showed the harmonic occurrence time and harmonic duration.
Cite this paper: nullG. Hu and F. Zhu, "An Improved Chirplet Transform and Its Application for Harmonics Detection," Circuits and Systems, Vol. 2 No. 3, 2011, pp. 107-111. doi: 10.4236/cs.2011.23016.

[1]   R. G. Stockwell, L. Mansinha and R. P. Lowe, “Location of the Complex Spectrum: The S-Transform,” IEEE Transactions on Signal Processing, Vol. 44, No. 4, 1996, pp. 998-1001. doi:10.1109/78.492555

[2]   D. Gabor, “Theory of Communication,” Journal of Institution of Electrical Engineers, Vol. 93, No. 3, 1946, pp. 429-457.

[3]   R. N. Bracewell, “The Fourier Transform and Its Applications,” McGraw-Hill, New York, 1978.

[4]   S. Mallat, “A Wavelet Tour of Signal Processing,” 2nd Edition, Academic Press, Waltham, 2001.

[5]   L. Cohen, “Time-Frequency Distributions—A Review,” Proceedings of the IEEE, Vol. 77, No. 7, 1989, pp. 941-981. doi:10.1109/5.30749

[6]   F. Hlawatsch and G. F. Boudreuax-Bartels, “Linear and Quadratic Time-Frequency Signal Representations,” Proceedings of Signal Processing Magazine, Vol. 9, No. 2, 1992, pp. 21-67.

[7]   M. V. Chilukur and P. K. Dash, “Multiresolution S-Transform-Based Fuzzy Recognition System for Power Quality Events,” IEEE Transactions on Power Delivery, Vol. 19, No. 1, 2004, pp. 323-330. doi:10.1109/TPWRD.2003.820180

[8]   S. Mann and S. Haykin, “The Chirplet Transform: Physical Considerations,” IEEE Transactions on Signal Processing, 1995, Vol. 43, No. 11, pp. 2745-2761. doi:10.1109/78.482123

[9]   G.-S. Hu, F.-F. Zhu and Y.-J. Tu, “Power Quality Disturbance Detection and Classification Using Chirplet Transform,” Lecture Notes in Computer Science, Vol. 4247, 2006, pp. 34-41. doi:10.1007/11903697_5

[10]   Z. Ren, G. S. Hu, W. Y. Huang and F. F. Zhu, “Motor Fault Signals Denosing Based on Chirplet Transform,” Transactions of China Electrotechnical Society, Vol. 17, No. 3, 2002, pp. 59-62.

[11]   G. S. Hu and F. F. Zhu, “Location of slight Fault in Electric Machine Using Trigonometric Spline Chirplet Transforms,” Power System Technology, Vol. 27, No. 2, 2003, pp. 28-31.