Method of Wavelet Denoising in Harmonic Detection

Ying Li Cao; Yong Yang; Guang Yang

doi:10.4028/www.scientific.net/AMR.433-440.6103

Paper Titles

Experimental Study on Dynamic Characteristics and Seismic Response Analysis of Yingxian Wooden Pagoda
p.6077

Improved Fault Diagnosis Method Based on Probabilistic Neural Network
p.6084

Study on Application of Improved Predictive Current Method in STATCOM
p.6089

Calculation of Iron Loss in Large Hydro-Generator under Non-Load
p.6096

Method of Wavelet Denoising in Harmonic Detection
p.6103

Warranty Costs Assessment for Automotive Applications
p.6108

Analysis of Different Approaches for Gun Ammunition to Hit Aerial Target
p.6116

Health Monitoring of New and Aging Pipelines- Development and Application of Instrumented Pigs
p.6121

Research on Safety Monitor System of Coal Mine Based on EPA
p.6128

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Method of Wavelet Denoising in Harmonic Detection

Method of Wavelet Denoising in Harmonic Detection

Abstract:

Harmonic detection is an important step in grid harmonic status evolution and suppression measures implementation. While it’s accuracy is interference to the noise in the power grid. The different characteristics of signal and noise in the wavelet transform are proposed firstly, and the method of soft threshold de-noising based on wavelet multiresolution is analyzed in the paper. Using the method, the noisy harmonic signals in the power grid are processed and the de-noisy signals are analyzed by Fourier Transformation (FT) to obtain the contents of each harmonics. The simulation results show that the harmonic containing rate is close to the original signals after de-noising by WT. That is the method reduces the interference of noise and the accuracy of harmonic detection is improved.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 433-440)

Pages:

6103-6107

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.433-440.6103

Citation:

Cite this paper

Online since:

January 2012

Authors:

Ying Li Cao, Yong Yang, Guang Yang

Keywords:

Harmonic Detection, Lipchitz Exponent, Multiresolution Analysis, Wavelet Soft-Threshold De-Noising

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] MALLAT S. A Theory for Multiresolution Signal Decomposition: the Wavelet Representation[J]. IEEE Trans on Patern Analysis and Intelligence, 1989, 11 (7) : 674～{TTP}-162 693.

DOI: 10.1109/34.192463

Google Scholar

[2] ABALLE A, BETHENCOURT M, BOTANA F J, et al. Using wavelets transform in the analysis of electrochemical noise data[J]. The Journal of the International Society of Electrochemistry: Electrochimica Acta, 1999, 44(26): 4805-4815.

DOI: 10.1016/s0013-4686(99)00222-4

Google Scholar

[3] DAVID L, DONOHO, IAIN M. JOHNSTONE. Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(432): 1200-1224, December (1995).

DOI: 10.1080/01621459.1995.10476626

Google Scholar

[4] A NGRISANI L, DAPONTE P, D'APUZZO M, et al. A Measurement Method Based on the Wavelet Transform for Power Quality Analysis. IEEE Trans on Power Delivery, 1998, 13 (4) : 990～{TTP}-162 998.

DOI: 10.1109/61.714415

Google Scholar

[5] MALLAT S. A Wavelet Tour of Signal Processing[M]. China Machine Press, 2002, 128～{TTP}-162 129.

Google Scholar

[6] DONOHO D L. De-noising by Soft-thresholding. IEEE Trans on Information Theory, 1995, 41 (3) : 613～{TTP}-162 627.

DOI: 10.1109/18.382009

Google Scholar

[7] David L. Donoho and Iain M. Johnstone. Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(432): 1200{1224, December 1995. 4, 4, 4.

DOI: 10.1080/01621459.1995.10476626

Google Scholar