Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform

Chen Xiaojing; Li Kaicheng; Meng Qingxu; Cai Delong; Luo Yi

doi:doi:10.11648/j.jeee.20170505.16

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Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform

Chen Xiaojing, Li Kaicheng, Meng Qingxu, Cai Delong, Luo Yi

Published in Journal of Electrical and Electronic Engineering (Volume 5, Issue 5)

Received: 5 December 2017 Accepted: Published: 6 December 2017

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Abstract

With the increasingly penetration of nonlinear loads in the power system, power quality (PQ) has become a significant issue for the power utilities and end users. In order to improve the PQ, the PQ detection is essential. In this paper, a new method for detecting the PQ disturbances via empirical wavelet transform (EWT) and Hilbert (HT) is proposed. Firstly, EWT is applied to the signal for obtaining different modes. Then the instantaneous amplitude and frequency of each mode are calculated by using the HT. By applying it to two stationary signals and two non-stationary signals, the efficiency of the proposed method is evaluated. With no frequency aliasing like the S transform (ST), the proposed method presents more accurate results than the ST.

Published in	Journal of Electrical and Electronic Engineering (Volume 5, Issue 5)
DOI	10.11648/j.jeee.20170505.16
Page(s)	192-197
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Power Quality Disturbances, Detection, Empirical Wavelet Transform, Hilbert

References

[1]	P. R. Babu, P. K. Dash, and S. K. Swain, S. Sivanagaraju, “A new fast discrete S-transform and decision tree for the classification and monitoring of power quality disturbance waveforms”. International Transactions on Electrical Energy Systems.2013; 24(9):1279-1300.
[2]	S. Santoso, W. M. Grady, E. J. Powers, J. Lamoree, S. C. Bhatt, “Characterization of distribution power quality events with fourier and wavelet transforms”. IEEE TRANSACTIONS ON POWER DELIVERY. 2000; 15(1). 247-254.
[3]	N. C. F. Tse, J. Y. C. Chan, W. H. Lau, L. L. Lai, “Hybrid wavelet and Hilbert transform with frequency-shifting decomposition for power quality analysis”. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. 2012; 61(12): 3225-3233.
[4]	D. K. Alves, F. B. Costa, R. L. A. Ribeiro, “Real-time power measurement using the maximal overlap discrete wavelet-packet transform”. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS.2017; 64(4).3177-3187.
[5]	P. K. Dash, B. K. Panigrahi, G. Panda, “Power quality analysis using S-transform”. IEEE TRANSACTIONS ON POWER DELIVERY. 2003; 18(2). 406-411.
[6]	M. Biswal, P. K. Dash, “Estimation of time-varying power quality indices with an adaptive window-based fast generalized S-transform”. IET Science, Measurement and Technology. 2012; 6(4): 189-197.
[7]	D. Camarena-Martinez, M. Valtierra-Rodriguez, C. A. Perez-Ramirez, et al., “Novel down sampling empirical mode decomposition approach for power quality analysis”. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. 2016; 63(4).2369-2378.
[8]	M. Jasa Afroni, D. Sutanto, D. Stirling, “Analysis of nonstationary power-quality waveforms using iterative Hilbert Huang transform and SAX algorithm”. IEEE TRANSACTIONS ON POWER DELIVERY. 2013; 28(4). 2134-2144.
[9]	J. Li, Z. Teng, Qiu. Tang, J. Song, “Detection and classification of power quality disturbances using double resolution S-transform and DAG-SVMs”. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT.2016; 65(10).2302-2312.
[10]	R. Kumar, B. Singh, D. T. Shahani, A. Chandra, K. Al-Haddad, “Recognition of power-quality disturbances using S-transform-based ANN classifier and rule-based decision tree”. IEEE Transactions on industry applications.2015; 51(2). 1249-1258.
[11]	J. Gilles, “Empirical wavelet transform”. IEEE TRANSACTIONS ON SIGNAL PROCESSING. 2013; 61(16):3999-4010.
[12]	K. Thirumala, A. C. Umarikar, T. ain; “Estimation of single-phase and three-phase power-quality indices using empirical wavelet transform”. IEEE TRANSACTIONS ON POWER DELIVERY.2015;30(1):2015. 445-454.
[13]	K. Thirumala, Shantanu, T. Jain, A. C. Umarikar, “Visualizing time-varying power quality indices using generalized empirical wavelet transform”. Electric power systems research. 2017,143:99-109.

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APA Style

Chen Xiaojing, Li Kaicheng, Meng Qingxu, Cai Delong, Luo Yi. (2017). Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform. Journal of Electrical and Electronic Engineering, 5(5), 192-197. https://doi.org/10.11648/j.jeee.20170505.16

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ACS Style

Chen Xiaojing; Li Kaicheng; Meng Qingxu; Cai Delong; Luo Yi. Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform. J. Electr. Electron. Eng. 2017, 5(5), 192-197. doi: 10.11648/j.jeee.20170505.16

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AMA Style

Chen Xiaojing, Li Kaicheng, Meng Qingxu, Cai Delong, Luo Yi. Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform. J Electr Electron Eng. 2017;5(5):192-197. doi: 10.11648/j.jeee.20170505.16

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@article{10.11648/j.jeee.20170505.16,
  author = {Chen Xiaojing and Li Kaicheng and Meng Qingxu and Cai Delong and Luo Yi},
  title = {Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {5},
  number = {5},
  pages = {192-197},
  doi = {10.11648/j.jeee.20170505.16},
  url = {https://doi.org/10.11648/j.jeee.20170505.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20170505.16},
  abstract = {With the increasingly penetration of nonlinear loads in the power system, power quality (PQ) has become a significant issue for the power utilities and end users. In order to improve the PQ, the PQ detection is essential. In this paper, a new method for detecting the PQ disturbances via empirical wavelet transform (EWT) and Hilbert (HT) is proposed. Firstly, EWT is applied to the signal for obtaining different modes. Then the instantaneous amplitude and frequency of each mode are calculated by using the HT. By applying it to two stationary signals and two non-stationary signals, the efficiency of the proposed method is evaluated. With no frequency aliasing like the S transform (ST), the proposed method presents more accurate results than the ST.},
 year = {2017}
}

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TY  - JOUR
T1  - Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform
AU  - Chen Xiaojing
AU  - Li Kaicheng
AU  - Meng Qingxu
AU  - Cai Delong
AU  - Luo Yi
Y1  - 2017/12/06
PY  - 2017
N1  - https://doi.org/10.11648/j.jeee.20170505.16
DO  - 10.11648/j.jeee.20170505.16
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 192
EP  - 197
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20170505.16
AB  - With the increasingly penetration of nonlinear loads in the power system, power quality (PQ) has become a significant issue for the power utilities and end users. In order to improve the PQ, the PQ detection is essential. In this paper, a new method for detecting the PQ disturbances via empirical wavelet transform (EWT) and Hilbert (HT) is proposed. Firstly, EWT is applied to the signal for obtaining different modes. Then the instantaneous amplitude and frequency of each mode are calculated by using the HT. By applying it to two stationary signals and two non-stationary signals, the efficiency of the proposed method is evaluated. With no frequency aliasing like the S transform (ST), the proposed method presents more accurate results than the ST.
VL  - 5
IS  - 5
ER  -

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Author Information

Chen Xiaojing

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China
Li Kaicheng

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China
Meng Qingxu

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China
Cai Delong

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China
Luo Yi

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China

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APA Style

Chen Xiaojing, Li Kaicheng, Meng Qingxu, Cai Delong, Luo Yi. (2017). Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform. Journal of Electrical and Electronic Engineering, 5(5), 192-197. https://doi.org/10.11648/j.jeee.20170505.16

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ACS Style

Chen Xiaojing; Li Kaicheng; Meng Qingxu; Cai Delong; Luo Yi. Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform. J. Electr. Electron. Eng. 2017, 5(5), 192-197. doi: 10.11648/j.jeee.20170505.16

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AMA Style

Chen Xiaojing, Li Kaicheng, Meng Qingxu, Cai Delong, Luo Yi. Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform. J Electr Electron Eng. 2017;5(5):192-197. doi: 10.11648/j.jeee.20170505.16

Copy | Download

@article{10.11648/j.jeee.20170505.16,
  author = {Chen Xiaojing and Li Kaicheng and Meng Qingxu and Cai Delong and Luo Yi},
  title = {Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {5},
  number = {5},
  pages = {192-197},
  doi = {10.11648/j.jeee.20170505.16},
  url = {https://doi.org/10.11648/j.jeee.20170505.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20170505.16},
  abstract = {With the increasingly penetration of nonlinear loads in the power system, power quality (PQ) has become a significant issue for the power utilities and end users. In order to improve the PQ, the PQ detection is essential. In this paper, a new method for detecting the PQ disturbances via empirical wavelet transform (EWT) and Hilbert (HT) is proposed. Firstly, EWT is applied to the signal for obtaining different modes. Then the instantaneous amplitude and frequency of each mode are calculated by using the HT. By applying it to two stationary signals and two non-stationary signals, the efficiency of the proposed method is evaluated. With no frequency aliasing like the S transform (ST), the proposed method presents more accurate results than the ST.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform
AU  - Chen Xiaojing
AU  - Li Kaicheng
AU  - Meng Qingxu
AU  - Cai Delong
AU  - Luo Yi
Y1  - 2017/12/06
PY  - 2017
N1  - https://doi.org/10.11648/j.jeee.20170505.16
DO  - 10.11648/j.jeee.20170505.16
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 192
EP  - 197
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20170505.16
AB  - With the increasingly penetration of nonlinear loads in the power system, power quality (PQ) has become a significant issue for the power utilities and end users. In order to improve the PQ, the PQ detection is essential. In this paper, a new method for detecting the PQ disturbances via empirical wavelet transform (EWT) and Hilbert (HT) is proposed. Firstly, EWT is applied to the signal for obtaining different modes. Then the instantaneous amplitude and frequency of each mode are calculated by using the HT. By applying it to two stationary signals and two non-stationary signals, the efficiency of the proposed method is evaluated. With no frequency aliasing like the S transform (ST), the proposed method presents more accurate results than the ST.
VL  - 5
IS  - 5
ER  -

Copy | Download