Abstract
In this paper, a new approach is presented for the detection and classification of PQ disturbance in power system by Chirplet transforms(CT), which is the generalized forms of Fourier transform(FT), short-time Fourier transform(STFT) and wavelet transform(WT). WT and wavelet ridge are very useful tools to analyze PQ disturbance signals, but invalid for nonlinear time-varying harmonic signals. CT can detect and identify voltage quality and frequency quality visually, i.e., according to the contour of CT matrix of PQ harmonic signals, the harmonics can be detect and identify to fixed, linear time-varying and nonlinear time-varying visually. It is helpful to choose appropriate WT to analyze harmonics. Simulations show the contours of CT can effectively detect harmonic disturbance occurrence time and duration. Finally, it is validated that the harmonics of the stator current fault signal of the bar-broken electric machine is nonlinear time-varying, and tend to stable status in a short time.
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References
Gaouda, A.M., Kanoun, S.H., Alama, M.A., et al.: Pattern recognition applications for power system disturbance[J]. IEEE Trans. on Power Delivery 17(3), 677–683 (2002)
Gaouda, A.M., Salama, M.M., Sultan, M.R., et al.: Application of multiresolution signal decomposition for monitoring short-duration variations in distribution systems[J]. IEEE Trans. on Power Delivery 15(2), 478–485 (2000)
Santoso, S., Powers, E.J., Grady, W.M., Hoffman, P.: Power quality assessment via wavelet transform analysis[J]. IEEE Trans. Power Delivery 11, 920–930 (1996)
Pillay, P., Bhattacharijee, A.: Application of wavelets to model short term power system disturbances[J]. IEEE Trans. Power Syst. 11, 2031–2037 (1996)
Gouda, M., Salama, M.A., Sultan, M.R., Chikhani, A.Y.: Power quality detection and classification using wavelet multiresolution signal decomposition[J]. IEEE Trans. Power Delivery 14, 1469–1476 (1999)
Borras, D.: Wavelet and neural structure: A new tool for diagnostic of power system disturbances[J]. IEEE Trans. Ind. Applicat. 14, 1469–1476 (2001)
Mann, S., Haykin, S.: Chirplets and wavelets: Novel time-frequency methods. Electronic Letters[J] 28, 114–116 (1992)
Mann, S., Haykin, S.: The chirplet transform: Physical considerations[J]. IEEE Trans. Signal Processing 43(11), 2745–2761 (1995)
Hu, G.S.: Motor Fault Signals Denosing Based Chirplet Transform[J]. Trans. of china electrotechnical society 17(3), 59–62 (2002)
Hu, G.S.: Location of slight fault in electric machine using trigonometric spline chirplet transforms. Power System Technology 27(3), 28–31 (2003)
IEEE Std 1159-1995, IEEE recommended practice for monitoring electric power quality. IEEE, New York (1995)
Xue, H., Yang, R.G.: Power quality disturbance detection method using wavelet packet transform based denosing scheme. Proceedings of the CSEE 24(3), 85–90 (2004)
Liu, A.D., Xiao, X.Y., Deng, W.J.: Detection and analysis of power quality disturbance signal based on discrete cosine transform and wavelet transform. Power System Technology 29(10), 70–74 (2005)
Zhu, F.F., Ren, Z., Huang, W.Y.: A new method to quantitatively detect power quality disturbances based on two real wavelet transforms. Power System Technology 27(11), 52–54 (2003)
Wang, C.S., Wang, J.D.: Classification method of poer quality disturbance based on wavelet packet decomposition. Power System Technology 28(15), 78–82 (2004)
Zhang, Z.P.: A novel method of motor faulted rotor detection based on wavelet ridge. Proceedings of the CSEE 23(1), 81–97 (2003)
Mallat, S.: A wavelet tour of signal processing, 2nd edn. Academic Press, USA (2001)
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Hu, GS., Zhu, FF., Tu, YJ. (2006). Power Quality Disturbance Detection and Classification Using Chirplet Transforms. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_5
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DOI: https://doi.org/10.1007/11903697_5
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