Wavelet Based Energy Extraction Method for Determining Power Quality Disturbances

C. Kocaman, M. Ozdemir

2018/04/20

Abstract

In this paper, energy of wavelet coefficients are used for determining power quality disturbances (PQD) which are important for power systems. These power quality disturbances are voltage with harmonics, transient and flicker. After analyzing energy coeffients based on wavelet transform,
these PQD are compared with healty condition. 50 Hz pure sine is chosen as reference. These signals are generated by using MATLAB. Sampling frequency is 25.6 kHz. Energy distribution
of detail coefficients are obtained by using 12 level Daubechies-4 discrete wavelet filter. Parseval theorem is applied to wavelet coefficients for obtaining energy distribution of detail coefficients of these relevant disturbances in different resolution levels. Satisfactory results are taken visually when examining energy distrubutions of these PQD. Also it is observed that these PQD can be distinguished visually when their amplitudes are increased. Energy coefficients of PQD and amplitude of PQD are in direct propotion.

Published in: Renewable Energy & Power Quality Journal (RE&PQJ, Nº. 16)
Pages: 651-656 Date of Publication: 2018/04/20
ISSN: 2172-038X Date of Current Version:2018/03/23
REF: 420-18 Issue Date: April 2018
DOI:10.24084/repqj16.420 Publisher: EA4EPQ

Authors and affiliations

C. Kocaman1, M. Ozdemir2
1. Faculty of Aeronautics and Astronautics. Ondokuz Mayis University. Campus of Ballýca Samsun (Turkey)
2. Department of Electical and Electronics Engineering. Ondokuz Mayis University. Campus of Kurupelit Samsun (Turkey)

Key words

Power quality disturbances, wavelet trasform, harmonics, transients, flicker.

References

[1] Swain, S. D., Ray, P. K.; Mohanty, K. B., ``Improvement of Power Quality Using a Robust Hybrid Series Active Power
Filter'', IEEE Trans.on Power Elec., 5(32):3490-3498, 2017.
[2] Gaing, Z.L and Huang, H.S., ``Wavelet Based Neutral Network for Power Disturbance Classification'', IEEE
Power Enginerring Soc. Gen. Meet.,Vol. 3: 1621-1628, 2003.
[3] Janik, P., Lobos, T., ``Automated Classification of Power Quality Disturbances Using SVM and RBF Networks'',
IEEE Trans.on Power Elec.,21(3): 1663-1669, 2006.
[4] Hao, L., Feng, Q. W., Qing, S. C., Bin, L. H., ``The Multiple Power Quality Disturbance Classification Based on Power
System Time Domain Analysis'', 2015 Sixth International Conference on Intelligent System Design and Engineering
Applications (ISDEA), Vol:4, 867-870.
[5] Atasal, M., 2000, ``Guc Kalitesi ve Flicker'', Yuksek Lisans Tezi, .stanbul Teknik Universitesi Fen Bilimleri Enstitusu,
.stanbul, 146 s.
[6] Pehlivanturk, E., 2004. Guc Kalitesi ve Guc Kalitesi Kontrolorleri. Yuksek Lisans Tezi, Y.ld.z Teknik Universitesi Fen Bilimleri Enstitusu, .stanbul, 112 s.
[7] IEEEStd 1159-1995. IEEE Recommended Practice for Monitoring Electric Power Quality, IEEE Standards
Coordinating Committee 22 on Power Quality, USA.
[8] Kumar, R., Singh, B., Shahani, D. T., Jain, C., ``Dual-Tree Complex Wavelet Transform-Based Control Algorithm for
Power Quality Improvement in a Distribution System'', IEEE Journals and Magazines, 64(1): 764-772.
[9] Gaing Z.L.,Huang, H.S., `` Wavelet based neural network for power disturbance classificationh in 2003 IEEE Power
Engineering Society General Meeting, pp. 1621-1628.