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A New Method for Power Quality Events Detection and Classification using Discrete Wavelet Transform and Correlation Coefficients | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقاله 5، دوره 4، شماره 1، فروردین 2021، صفحه 47-57 اصل مقاله (905.3 K) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2020.33660.1257 | ||
| نویسنده | ||
Navid Ghaffarzadeh  
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| Imam Khomeini International University | ||
| چکیده | ||
| In this paper a novel and simple approach for detection and classification of wide variety range of power quality(PQ) events based on discrete wavelet transform (DWT) and correlation coefficient is presented. For this purpose, two new indices is proposed and by comparing the values of the correlation coefficient between the value of these indices for pre-stored PQ events and for a recorded indistinct signal, type of PQ events will be detected. This algorithm has advantages of DWT and correlation coefficient which, it does not have disadvantage of neural network or neural network-fuzzy based algorithms such as; training, and high dimension input matrices nor it does not have disadvantage of Fourier transform based approach such as unsuitability for non-stationary signal as it does not track signal dynamics properly due to limitation of fixed window width. The effectiveness of this method has been tested using numerous PQ disturbance and simulation results confirm the competency and the ability of the proposed method in PQ disturbances detection and automatic diagnosis. Compared with the other methods, the simulation under different noises conditions, verify the effectiveness of noise immunity, and relatively better accuracy of the proposed method. | ||
| کلیدواژه‌ها | ||
| Classification؛ Correlation Coefficient؛ Discrete Wavelet Transform؛ Power Quality | ||
| مراجع | ||
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[1] J. Arrillaga, M. H. J. Bollen, and N. R.Watson, “Power quality following deregulation,” Proc. IEEE, vol. 88, no. 2, pp. 246–261, Feb. 2000. [2] M. Karimi, H. Mokhtari, and M. R. Iravani, “Wavelet based on-line disturbance detection for power quality applications,” IEEE Trans. PowerDelivery, vol. 15, pp. 1212–1220, Oct. 2000. [3] S. Santoso, W. M. Grady, E. J. Powers, J. Lamoree, and S. C. Bhatt, “Characterization of distribution power quality events with Fourier and wavelet transforms,” IEEE Trans. Power Delivery, vol. 15, pp. 247– 254,Jan. 2000. [4] W. Tong, X. Song, J. Lin, Z. Zhao “Detection and Classification of Power Quality Disturbances Based on Wavelet Packet Decomposition and Support Vector Machines,” ISCP 2006 proceedings, 2006. [5] P. K. Ra, G. Karmakar, F. Y. S. Eddy, A. Krishnan, H. B. Gooi, “Dual Tree Complex Wavelet Transform based Detection of Power Quality Disturbances,” 2018 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), Singapore, pp. 1177-1182, 2018. [6] Mohammad Barghi Latran, Ahmet Teke, “A novel wavelet transform based voltage sag/swell detection algorithm,” International Journal of Electrical Power & Energy Systems, Vol. 71, pp. 131-139, October 2015. [7] Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances, Applied Soft Computing, Vol. 38, pp. 967-977, January 2016. [8] F. R. Zaro and M. A. Abido, "Real-Time Detection and Classification for Voltage Events Based on Wavelet Transform," 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Jordan, pp. 872-877, 2019. [9] P. K. Dash, S. Mishra, M. M. A. Salama, and A. C. Liew, “Classification of power system disturbance using a fuzzy expert system and a Fourier linear combiner,” IEEE Transaction on Power Delivery, vol. 15, pp. 472–477, Apr 2000. [10] T. X. Zhu, S. K. Tso, K. L. Lo “Wavelet-Based Fuzzy Reasoning Approach to Power-Quality Disturbance Recognition,” IEEE Transactions on Power Delivery, vol. 19, no. 4, October 2004. [11] G. S. Hu, J. Xie, F. F. Zhu “Classification Of Power Quality Disturbances Using Wavelet And Fuzzy Support Vector Machines,” IEEE Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, PP. 3981-3984, 18-21 August 2005. [12] J. Chung, E. J. Powers, W. M. Grady, and S. C. Bhatt, “Power disturbance classifier using a rule-based method and wavelet packet-based hidden Markov model,” IEEE Transactions on Power Delivery, vol. 17, pp.233–241, Jan. 2002. [13] M. Kezunovic and I. Rikalo, “Detect and classify faults using neural nets,” IEEE Comput. Appl. Power, vol. 9, no. 4, pp. 42–47, Oct. 1996. [14] C. B. Khadse, M. A. Chaudhari, V. B. Borghate, “Conjugate gradient back-propagation based artificial neural network for real time power quality assessment,” International Journal of Electrical Power & Energy Systems, Vol. 82, pp. 197-206, November 2016. [15] M. Yilmaz, E. Kayabasi, M. Akbaba, “Determination of the effects of operating conditions on the output power of the inverter and the power quality using an artificial neural network,” Engineering Science and Technology, an International Journal, Vol. 22, Issue 4, pp. 1068-1076, August 2019. [16] L. Hua, W. Yuguo, Z. Wei “Power Quality Disturbances Detection and Classification Using Complex Wavelet Transformation and Artificial Neural Network,” IEEE Proceedings of the 26th Chinese Control Conference, Zhangjiajie, Hunan, China, pp. 208-212, July 2007. [17] R. W. A. Ibrahim, M. Morcos, “Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey,” IEEE Trans. Power Delivery, vol. 17, pp. 738–743, Apr. 2002. [18] S. Santoso, E. J. Powers and W. M. Grady, “Power quality disturbance identification using wavelet transforms and artificial neural networks,” in Proc. IEEE Int. Conf. Harmonics and Quality of Power, Las Vegas, NV, pp. 615–618, Oct. 16–18, 1996. [19] S. Santoso, E. J. Powers, W. M. Grady, A. C. Parsons, “Power Quality Disturbance Waveform Recognition Using Wavelet-Based Neural Classifier- Part1: Theoretical Foundation,” IEEE Transaction on Power Delivery, vol. 15, no. 1, pp. 222-228, 2000. [20] S. Santoso, E. J. Powers, W. M. Grady, A. C. Parsons, “Power Quality Disturbance Waveform Recognition Using Wavelet-Based Neural Classifier- Part2: Application,” IEEE Transaction on Power Delivery, vol. 15, no. 1, pp. 229-235, 2000. [21] P. Kanirajan, V.Suresh Kumar, “Power quality disturbance detection and classification using wavelet and RBFNN,” Applied Soft Computing, Vol. 35, pp. 470-481, October 2015. [22] S. Sridhar, K. U. Rao and S. Jade, "Detection and classification of PQ disturbances in the supply to induction motor using wavelet transform and feedforward neural network," 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, pp. 1-5, 2015. [23] S. Khokhar,A.A.M. Zin, A. P. Memon, A. S. Mokhtar, “A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network,” Measurement, Vol. 95, pp. 246-259, January 2017. [24] J. Huang, M. Negnevitsky, and D. T. Nguyen, “A neural-fuzzy classifier for recognition of power quality disturbances,” IEEE Transactions on Power Delivery, vol. 17, pp. 609–616, Apr. 2002. [25] A. Elmitwally, S. Farghal, M. Kandil, S. Abdelkar, M. Elketab, “Proposed Wavelet Neurofuzzy Combined System for Power Quality Violations Detection and Diagnosis,” IEE proc. Transm. Distrib., vol. 148, no. 1, pp. 15-20, 2001. [26] B. Kiruthiga, R. Narmatha Banu, D. Devaraj, Detection and classification of power quality disturbances or events by adaptive NFS classifier, Soft Computing, volume 24, pp.10351–10362, 2020. [27] T. X. Zhu,S. K. Tso and K. L. Lo “wavelet-based fuzzy reasoning approach to power-quality disturbance recognition,” IEEE Transactions on Power Delivery, vol. 19, no. 4, pp. 1928-1935, October 2004. [28] M. B. I. Reaz, F. Choong, M. S. Sulaiman, F. M.Yasin and M. Kamada, “Expert System for Power Quality Disturbance Classifier,” IEEE Transactions on power delivery, vol. 22, no. 3, pp. 1979-1988, July 2007. [29] O. Sen, S. Zhengxiang, W. Jianhua, C. Degui, “Application of LVQ neural networks combined with genetic algorithm in power quality signals classification,” Power System Technology, vol. 1, pp. 491-495, 13-17 Oct. 2002. [30] M.A.S. Masoum, S. Jamali, N. Ghaffarzadeh, “Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks,” Vol. 4, Issue 4, pp. 193 – 205, July 2010. [31] R. Kapoor, R. Gupta, L.H. Son, S. Jha, R. Kumar, Detection of power quality event using histogram of oriented gradients and support vector machine, Measurement, Vol. 120, pp. 52-75, May 2018. [32] K. Thirumala, M. S. Prasad, T. Jain and A. C. Umarikar, "Tunable-Q Wavelet Transform and Dual Multiclass SVM for Online Automatic Detection of Power Quality Disturbances," IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 3018-3028, July 2018. [33] Ucar F, Alcin OF, Dandil B, Ata F. “Power quality event detection using a fast extreme learning machine,” Energies, vol. 11, no. 1 pp.145, 2018. [34] Hooshmand R, Enshaee A. “Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm”. Electric Power System Research, vol. 80, no. 12, pp. 1552‐1561, 2010. [35] Muthusamy TA, Ramanathan N. “An expert system based on least mean square and neural network for classification of power system disturbances,” International Journal on Future Revolution in Computer Science & Communication Engineering, vol.1, pp.308‐313, 2018. [36] Biswal B, Biswal M, Mishra S, Jalaja R. “Automatic classification of power quality events using balanced neural tree,” IEEE Transactions on Industrial Electronics, vol. 61, no. 1, pp.521‐530, 2014. [37] Biswal B, Dash PK, Panigrahi BK. “Non-stationary power signal processing for pattern recognition using HS-transform,” Applied Soft Computing, vol. 9, no. 1, pp.107‐117, 2009. [38] Ray PK, Mohanty SR, Kishor N, Catalão JP. “Optimal feature and decision tree‐based classification of power quality disturbances in distributed generation systems,” IEEE Transactions on Sustainable Energy, vol. 5, no. 1, pp.200‐208, 2014. [39] Meher SK, Pradhan AK. “Fuzzy classifiers for power quality events analysis,” Electric Power Systems Research, vol. 80, no.1, pp.71‐76, 2010. [40] Babu PR, Dash PK, Swain SK, Sivanagaraju S. “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, vol. 24, no. 9, pp.1279‐1300, 2014. [41] Sheibani M, Ketabi A, Nosratabadi M. “Optimal power quality meters placement with consideration of single line and meter loss contingencies,” International Journal of Industrial Electronics, Control and Optimization, vol. 1, no. 2, pp. 81-89, September 2018. [42] A. J. Hayter, Probability and Statistics, 2nd edition, CA, Wadsworth Group, 2002, p. 137. [43] D.L. Donoho, “De-noising by soft-thresholding,” IEEE, Trans. on Inf. Theory, vol. 41, no. 3, pp. 613-627, 1995. [44] M. Misiti, Y. Misiti, G. Oppenheim, J. M. Poggi, “Wavelet Toolbox 4 for User's Guide,” Copyright 1997–2007 By The Mathworks, Inc. [45] C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transform: A Primer. Englewood Cliffs, NJ: Prentice-Hall, 1998. | ||
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