A New Method for Extracting Transient Signal Feature in Transmission System Based on Tsallis Wavelet Entropy

Abstract:

To reduce the computing complexity of Shannon wavelet entropy(WE), Tsallis WE algorithm was proposed and implemented by combining Tsallis entropy with lifting wavelet transform(LWT), which provided a new method to extract features of transient signals in transmission system. By adjusting the nonextension index, the property of Tsallis entropy was analyzed, and the relations between Tsallis entropy and Shannon entropy were discussed. Taking for instance Tsallis wavelet energy entropy(WEE), the computing complexity of Tsallis WE was analyzed and compared with Shannon WE. In order to verify the practicality of the new algorithm, the paper carried out not only the simulation test for transient faults in transmission system model, but also the processing of practical harmonics and lighting signal based on DSP, which showed that in comparison with Shannon WE the new algorithm can ensure the accuracy of feature extraction for transient signals , but its runtime has been partially reduced.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 2417-2422
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.2417
• Online since: January 2012
• Authors: Ji Kai Chen, Hao Yu Li, Shi Yan Yang, Bao Quan Kou
• Keywords: Entropy, Feature Extraction, Lifting Wavelet Transform, Transmission System

References

[1] Diao Yanhua and Li Chunming, Study on transient signal detection in power system based on wavelet transform, 2008 International Symposium on Intelligent Information Technology Application, vol. 3, pp.816-820, (2008).

DOI: 10.1109/iita.2008.473

[2] P. Chiradeja, C. Pothisarn, Identification of the fault location for three-terminal transmission lines using discrete wavelet transforms, Transmission & Distribution Conference & Exposition: Asia and Pacific, pp.1-4, (2009).

DOI: 10.1109/td-asia.2009.5356924

[3] HE Zheng-you, Chen Xiaoqing and Luo Guoming, Wavelet Entropy Measure Definition and Its Application for Transmission Line Fault Detection and Identification (Part I: Definition and Methodology), IEEE 2006 International Conference on Power System Technology, pp.1-6, Oct. (2006).

DOI: 10.1109/icpst.2006.321939

[4] HE Zheng-you, Chen Xiaoqing and Fu Ling, Wavelet Entropy Measure Definition and Its Application for Transmission Line Fault Detection and Identification(Part II: Fault Detection in Transmission line), IEEE 2006 International Conference on Power System Technology, pp.1-5, Oct. (2006).

DOI: 10.1109/icpst.2006.321940

[5] HE Zheng-you, Chen Xiaoqing and Zhang Bin, Wavelet Entropy Measure Definition and Its Application for Transmission Line Fault Detection and Identification (Part III: Transmission line faults transients identification), IEEE 2006 International Conference on Power System Technology, pp.1-5, Oct. (2006).

DOI: 10.1109/icpst.2006.321941

[6] C. Tsallis, R. S. Mendes and A. R. Plastino, The role of constraints within generalized nonextensive statistics, Physica A, vol. 261, no. 3, pp.534-554, 1998.

DOI: 10.1016/s0378-4371(98)00437-3

[7] S. Furuichi, K. Yanagi and K. Kuriyama, Fundamental properties of Tsallis relative entropy, Journal of Mathematical Physics, vol. 45, no. 123, pp.4868-4877, (2004).

DOI: 10.1063/1.1805729

[8] A. Chame and E. V. L. de. Mello, The fluctuation-dissipation theorem in the framework of the Tsallis statistics, Journal of Physics A Mathematical and General, vol. 27, no. 11, pp.3663-3670, 1994.

DOI: 10.1088/0305-4470/27/11/016

[9] W. Sweldens, The Lifting Scheme: A Construction of Second Generation Wavelets, SIAM Journal in Math. Analysis, vol. 29, no. 2, pp.511-546, (1998).

DOI: 10.1137/s0036141095289051

[10] I. Daubechies and W. Sweldens, Factoring wavelet transforms into lifting steps, Journal Fourier and Application, vol. 4, no. 3, pp.247-269, (1998).

DOI: 10.1007/bf02476026