Advertisement

Power Quality Disturbances Detection Based on EMD

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

The power quality (PQ) disturbance signals have the characteristics of short duration and strong randomness, and often form complex disturbances, which make the disturbance signals difficult to detect and identify. In this paper, EMD algorithm is introduced to decompose the PQ disturbance signals and calculate the intrinsic mode function (IMF) of the disturbance signals. Then, Hibert transform are performed for each IMF to obtain the characteristic information of the disturbance signal. EMD transform is used to detect the type, duration, frequency and amplitude of PQ disturbances. To verify the effectiveness of the algorithm, several kinds of PQ disturbance signals are simulated with transient harmonic, voltage interruption, voltage drop and voltage surge and complex disturbances. Experimental results show that the algorithm can accurately detect power quality interferences. This paper provides a new method for the detection of PQ disturbances and a new idea for the power management.

Keywords

Power quality Intrinsic mode function Empirical mode decomposition HHT 

Notes

Acknowledgement

This study is supported by Provincial Natural Science Foundation of Anhui (KJ2018A0618).

References

  1. 1.
    Arrillaga, J.A., Watson, N.R., Chen, S.: Power System Quality Assessment. Wiley, New York (2000)Google Scholar
  2. 2.
    Fuchs, E., Trajanoska, B., Orhouzee, S., Renner, H.: Comparison of wavelet and Fourier analysis in power quality. In: Proceedings of the Electric Power Quality and Supply Reliability Conference, pp. 1–7 (2012)Google Scholar
  3. 3.
    Hao, Q., Zhao, R., Tong, C.: Interharmonics analysis based on interpolating windowed FFT algorithm. IEEE Trans. Power Delivery 22, 1064–1069 (2007)CrossRefGoogle Scholar
  4. 4.
    Robertson, D.C., Camps, O.I., Mayer, J.S., Gish, W.B.: Wavelets and electromagnetic power system transients. IEEE Trans. Power Delivery 11, 1050–1058 (1996)CrossRefGoogle Scholar
  5. 5.
    Poisson, O., Rioual, P., Meunier, M.: Detection and measurement of power quality disturbances using wavelet transform. IEEE Trans. Power Delivery 15(3), 1039–1044 (2000)CrossRefGoogle Scholar
  6. 6.
    Latran, M.B., Teke, A.: A novel wavelet transform based voltage sag/swell detection algorithm. Int. J. Electr. Power Energy Syst. 71, 131–139 (2015)CrossRefGoogle Scholar
  7. 7.
    Huang, N.E., Chern, C.C., Huang, K., Salvino, L.W., Long, S.R., Fan, K.L.: A new spectral representation of earthquake data: Hilbert spectral analysis of station TCU 129. Bull. Seismol. Soc. Am. 91(5), 1310–1338 (2001)CrossRefGoogle Scholar
  8. 8.
    Liu, A.D., Xiao, X.Y., Deng, W.J.: Detection and analysis of power quality disturbance signal based on discrete transform and wavelet transform. Power Syst. Technol. 29(10), 70–74 (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Hohai University Wentian CollegeMa’anshanChina

Personalised recommendations