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A Method of De-noise and Harmonics Detection in Power System Based on Periodicity Analysis

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Abstract

A novel method of de-noise and harmonics detection is presented. Firstly, based on periodicity analysis and signals which have been detected, one minimum period signal with slight noise could be calculated. This signal is named as Standard unit-Period (SuP). Then a correction model is constructed based on SuP and 3-sigma rule, aiming to revise the rough harmonic signal. From the view of solutions, SNR could be risen about 15 dB in a high-SNR background. Furthermore, SuP could be applied to improve DFT in harmonic detection by adjusting the length of harmonic signal. It not only helps to avoid errors caused by spectral leakage effectively in particular situations, but also makes detection method run faster and more precise. At last, applications of SuP in several complex situations are simulated. It showed that SuP could deal with various coloured noise effectively, and this method could be used when the system fluctuates with changing parameters.

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References

  1. Z.H Li, S.H Yan, W.Z Hu et al, High accuracy on-line calibration system for current transformers based on clamp-shape rogowski coil and improved digital integrator, MAPAN-J. Metrol. Soc India, 31 (2) (2016), 119–127.

    Google Scholar 

  2. V. Salis et al., Stability assessment of power-converter-based AC systems by LTP theory: eigenvalue analysis and harmonic impedance estimation, IEEE Journal of Emerging & Selected Topics in Power Electronics, 5 (4) (2017), 1513–1525.

  3. Z. Li, H. Li, Z. Zhang et al., An online calibration method for electronic voltage transformers based on IEC 61850-9-2, MAPAN-J. Metrol. Soc India, 29 (2) (2014), 97–105.

    MathSciNet  Google Scholar 

  4. B. Zeng, Q. Tang, B. Qing et al., Spectral analysis method based on improved FFT by Nuttall self-convolution window, Transactions of China Electrotechnical Society, 29 (7) (2014), 59–65.

    Google Scholar 

  5. Y.C. Xu, Y.L. Liu, Z.X. Li et al., Harmonic estimation base on center frequency shift algorithm, MAPAN-J. Metrol. Soc India, 32 (1) (2017), 43–50.

    Google Scholar 

  6. H. Wen, C. Li, W. Yao, Power system frequency estimation of sine-wave corrupted with noise by windowed three-point interpolated DFT. IEEE Transactions on Smart Grid, 99 (2017), 1–10.

    Google Scholar 

  7. D. Belega, D. Petri, D. Dallet, Frequency estimation of a sinusoidal signal via a three-point interpolated DFT method with high image component interference rejection capability, Digital Signal Processing, 24 (1) (2014), 162–169.

    Article  MathSciNet  Google Scholar 

  8. H. Wen,C. Li, T. Lu, Novel three-point interpolation DFT method for frequency measurement of sine-wave, IEEE Transactions on Industrial Informatics, 13 (5) (2017), 2333–2338.

    Article  Google Scholar 

  9. S. Niu, Z. Liang, J. Zhang et al., Harmonic detection approach based on 4-term cosine window triple-spectrum-line interpolation FFT, Chinese Journal of Scientific Instrument, 33 (9) (2012), 2002–2008.

    Google Scholar 

  10. T.J. Du, G.J. Chen, Y. Lei, Novel method for power system harmonic detection based on wavelet transform with aliasing compensation. Proceedings of the CSEE, 25 (3) (2005), 54–60.

    Google Scholar 

  11. M. Singh, R.K. Yadav, R. Kumar, Discrete wavelet transform based measurement of inner race defect width in taper roller bearing, MAPAN-J. Metrol. Soc India, 28 (1) (2013), 17–23.

    Google Scholar 

  12. H. Li, Y. Zhou, F. Tian, S. Li, T. Sun, EEMD harmonic detection method based on the new wavelet threshold denoising pre-treatment, Power System Protection and Control, 44 (2) (2016), 42–48.

    Google Scholar 

  13. H. Li, Y. Zhou, T. Feng et al., Wavelet-based vibration signal de-noising algorithm with a new adaptive threshold function. Chinese Journal of Scientific Instrument, 36 (10) (2015), 2200–2206.

    Google Scholar 

  14. B.Y. Qing, Z.S. Teng, Y.P. Gao et al., Approach for electrical harmonic analysis based on Nuttall window double-spectrum-line interpolation FFT. Proceedings of the CSEE, 28 (25) (2008), 153–158.

    Google Scholar 

  15. S. Zhou, Probability and mathematical statistics (Third Edition), Higher Education Press, (2013), 46–50.

  16. Z. Peng, A novel algorithm for harmonic analysis based on discrete wavelet transforms, Transactions of China Electrotechnical Society, 27 (3) (2012), 252–259.

    Google Scholar 

  17. Z. Bo, Z. Teng, A nuttall self-convolution window-based approach to weighted analysis on power system harmonic. Power System Technology, 35 (8) (2011), 134–139.

    Google Scholar 

  18. S. Sun, Y. Pang, EEMD harmonic detection method based on the new wavelet threshold denoising pre-treatment, Power System Protection and Control, 44 (2) (2016), 42–48.

  19. S.G. Sun, Y. Pang, J.Q. Wang et al., Study of improved EEMD denoising method and application in harmonic detection, Advanced Technology of Electrical Engineering & Energy, 35 (4) (2016), 67–73.

    Google Scholar 

  20. Sun Z, He Z, Zang T. A Kind of Hybrid Convolution Window and Its Application in Harmonic Analysis. Transactions of China Electrotechnical Society, 31, 16, (2016), pp 207-214.

    Google Scholar 

  21. F.P. Miller, A.F. Vandome, J. Mcbrewster, Federal Standard 1037c, (2010).

    Google Scholar 

  22. H. Ming, C. Heng, Active power filter technology and its application, Automation of Electric Power Systems, 3 (20) (2000), 66–70.

    Google Scholar 

  23. T. Abdelrehim, M.H.A. Raouf , Detection of ultrasonic signal using polarized homodyne interferometer with avalanche detector and electrical filter. MAPAN-J. Metrol. Soc India, 29 (1) (2014), 1–8.

    Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Number 5160070515), and by The Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (Grant No. KJ2015QT007).

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Authors and Affiliations

  1. College of Electrical Engineering and New Energy, Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control, China Three Gorges University, Yichang, 443002, Hubei Province, China

    Yanchun Xu, Yufei Du & Shan Cheng

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  1. Yanchun Xu
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  2. Yufei Du
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  3. Shan Cheng
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Correspondence to Yufei Du.

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Xu, Y., Du, Y. & Cheng, S. A Method of De-noise and Harmonics Detection in Power System Based on Periodicity Analysis. MAPAN 33, 169–177 (2018). https://doi.org/10.1007/s12647-017-0248-y

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