Improved Threshold Function for Power Quality Disturbance Signal Denoising

Xiao Long Fan; Wei Cheng Xie; De Lei Sha; Yu Li

doi:10.4028/www.scientific.net/AMM.687-691.3636

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Improved Threshold Function for Power Quality...

Improved Threshold Function for Power Quality Disturbance Signal Denoising

Abstract:

Mutation point feature of power quality (PQ) disturbance signals are very conducive to the wavelet-based detection and localization of PQ events, but the PQ signals are often disturbed by noise. In order to suppress noise and keep mutation points, an improved threshold function was proposed. According to the fact that the wavelet coefficients of signal and noise distributed on different scale, the threshold σj2lnk amended by to calculate threshold value for each scale adaptively. (k is the number of wavelet coefficients at level j). In simulation, four type of PQ signals and three degrading degrees are testing; meanwhile, four existing algorithm with wavelet shrinkage are performed for comparison. Results reveal that the proposed scheme not only suppresses noise of PQ signal well but also keeps the mutation points nicely.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

3636-3639

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.3636

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Online since:

November 2014

Authors:

Xiao Long Fan*, Wei Cheng Xie, De Lei Sha, Yu Li

Keywords:

De-Noising, Power Quality Disturbances, SWT, Threshold Function

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* - Corresponding Author

References

[1] H. T. Yang and C. C. Liao. A de-noising scheme for enhancing wavelet-based power quality monitoring system [J]. IEEE Trans on Power Delivery, 2003, 16(3): 353-360.

DOI: 10.1109/61.924810

Google Scholar

[2] Tahsina Farah Sanam, Celia Shahnaz. Noisy speech enhancement based on an adaptive threshold and a modified hard thresholding function in wavelet packet domain [J]. Digital Signal Processing, 2013, 23: 941-951.

DOI: 10.1016/j.dsp.2012.12.001

Google Scholar

[3] Dora M. Ballesteros L, Juan M. Moreno A. Wavelet denoising on hardware devices with perfect reconstruction, low latency and adaptive thresholding [J], Computers and Electrical Engineering, 2013, 39: 1300-1311.

DOI: 10.1016/j.compeleceng.2013.03.005

Google Scholar

[4] C. M. Wu, N. N. Xie. Application of improved wavelet threshold in power quality signal denoising [J], Computer Engineering and Applications, 2012, 48(3): 114-116.

Google Scholar

[5] T. Zhang, J. Zhang and Y. Zhang. Ring laser gyro drift signal denoising based on improved wavelet threshold [J], Chinese Journal of Scientific Instrument, 2011, 32(2): 258-264.

Google Scholar

[6] W. D Liu, S. H Liu and X. F. Hu. Analysis of modified methods of wavelet threshold de-noising function[J]. High Voltage Engineering, 2007, 33(10): 59-63.

Google Scholar