Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform
Journal of Electrical and Electronic Engineering
Volume 5, Issue 5, October 2017, Pages: 192-197
Received: Dec. 5, 2017; Published: Dec. 6, 2017
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Authors
Chen Xiaojing, State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China; College of Electronic and Information, Yangtze University, Jingzhou, 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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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.
Keywords
Power Quality Disturbances, Detection, Empirical Wavelet Transform, Hilbert
To cite this article
Chen Xiaojing, Li Kaicheng, Meng Qingxu, Cai Delong, Luo Yi, Detection of Power Quality Disturbances Using Empirical Wavelet Transform and Hilbert Transform, Journal of Electrical and Electronic Engineering. Vol. 5, No. 5, 2017, pp. 192-197. doi: 10.11648/j.jeee.20170505.16
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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