An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances

Mehmet İsmail Gursoy; Seydi Vakkas Ustun; Ahmet Serdar Yilmaz

doi:10.29137/umagd.350231

EN

An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances

Abstract

Determination and investigation of incidents affecting Power Quality (PQ) is very important for consumers. In this study, estimation of PQ events is obtained to determine the disturbances of PQ by using Empirical Wavelet Transform (EWT) and Discrete Wavelet Transform (DWT) methods and with this estimated parameters. PQ disturbances were examined with Support Vector Machine (SVM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) classification methods. Voltage signals (sag, swell, interruption, transient and normal) used in the classification of PQ disturbances were recorded from grid with the aid of a microcontroller based on device designed with a sampling frequency of 6.4 kHz. Classification consequences using Machine Learning Methods show that DWT outperforms over EWT for feature extraction processing and the classification accuracy is tabled. Classification by ANN and ANFIS through the use of conjecture parameters in PQ disturbances based on DWT Method has been recommended.

Keywords

Power Quality,Discrete Wavelet Transform,Empirical Wavelet Transform,Support Vector Machine,Artificial Neural Networks,Adaptive Neuro-Fuzzy Inference System

References

Abdoos, A. A., Mianaei, P. K. & Ghadikolaei M. R. (2016). Combined VMD-SVM based Feature Selection Method for Classification of Power Quality Events. Applied Soft Computing. 38. 637-646. doi: 10.1016/j.asoc.2015.10.038
Bayhan,S. & Yılmaz, D. (2009). Güç Sistemlerinde meydana gelen dalga şekli bozukluklarının dalgacık dönüşümü yardımıyla tespiti. Technological Applied Sciences. 4(2). 151-162
Cho, S.H., Jang, G. & Kwon, S.H. (2010). Time Frequancy Analysis of Power Quality Disturbances via the Gabor – Wigner Transform. IEEE Transactions on Power Delivery. 25(1). doi: 10.1109/TPWRD.2009.2034832
Daubechies, I. (1990). The wavelet transform time frequency localization and signal analysis. IEEE Trans. On information Theory. 36(5). 961-1005. doi: 10.1109/18.57199
Dugan, R.C., McGranaghan, M.F., Santoso, S. & Beaty, H.W.(2004). Electrical Power Systems Quality (2th ed.). New York
Ekici, S. (2009). Classification of power system disturbances using support vector mashines. Expert Systems with Applications. 36(6). 9859-9868. doi: 10.1016/j.eswa.2009.02.002
Eristi, H., Ucar, A. & Demir, Y. (2010). Wavelet-based feature extraction and selection for classification of power system disturbances using support vector mashines. Electric Power Systems Research. 80(7). 743-752. doi: 10.1016/j.epsr.2009.09.021
Gaing, Z.L. & Huang, H.S. (2003). Wavelet-based Neural Network for Power Disturbance classification. Power Engineering Society General Meeting. 1621-1628. doi: 10.1109/PES.2003.1267398
Gaing, Z.L. (2004). Wavelet – Based Neural Network for Power Disturbance Recognition and Classification. IEEE Transactions on Power Delivery 19(4). 1560-1568. doi: 10.1109/TPWRD.2004.835281
Gaouda, A.M., Salama, M.M.A., Sultan, M.R. & Chikhani, A.Y. (1999). Power Quality detection and classification using wavelet-multiresulation signal decomposition. IEEE Transactions on Power Delivery. 14(4). 1469-1476. doi: 10.1109/61.796242

Gilles, J. (2013). Emprical Wavelet Transform, IEEE Transactions on Signal Processing. 61(16). 3999-4011. doi: 10.1109/TSP.2013.2265222
Hamdy,A., Abdelaziz, A. Y. & Badı, M. A. (2013). Recognition of Power Quality Events Using Artificial Neural Networks. International Journal on Power Engineering and Energy (IJPEE). 4(1). 348-352. doi: 10.12986/IJPEE.2013.004
Huang, E.N., Shen, Z., Long, S.R, Wu, M.C., Shih,H.H, Zheng, Q., Yen, N.C., Tung, C.C. & Liu, H.H. (1998). The Emprical mode decomposition and the Hilbert Spectrum for nonlinear and non-statinary time series analysis. Proc. R.Soc. Lond. A, 454. 903-995.
IEEE std. 1159-1995: IEEE Recommended Practice for Monitoring Electric Power Quality.
Jang, R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on System, 23(3). 665-685. doi: 10.1109/21.256541
Kanirajan, P. & Suresh Kumar, V. (2015). Power Quality Disturbance Detection and Classification Using Wavelet and RBFNN. Applied Soft Computing. 35. 470-481. doi: 10.1016/j.asoc.2015.05.048
Lin, C.T. & Lee, C.S.G. (1991). Neural-Network-Based Fuzzy Logic Control and Decision System. IEEE Transactions on Computers. 40(12). 1320-1337. doi: 10.1109/12.106218
Naderian, S. & Salemnia, A. (2015). Detection and Classification of Power-Quality Events Using Discrete Gabor Transform and Support Vector Mechine. The 6th International Power Electronics Drive Systems and Technologies Conference (PEDSTC2015). Tehran, Iran. 544-549. doi: 10.1109/PEDSTC.2015.7093333
Ray, P.K. & Kishor, N. (2014). Optimal Feature and Decision Tree-Based Classification of Power Quality Disturbances in Distributed Generation Systems. IEEE Transactions on Sustainable Energy. 5(1). 200-208. doi: 10.1109/TSTE.2013.2278865
Santoso, S., Powers, E.J., Grady,W.M. & Hofman,P. (1996). Power Quality assessment via wavelet transform analiysis. IEEE Transactions on Power Delivery. 11(2). 924-930. doi: 10.1109/61.489353
Sebestian, P. & DSa, P.A. (2015). Implementation of a Quality Signal Classification System using Wavelet based Energy Distribution and Neural Network. International Conferance on Power and Advanced Control Engineering. 157-161. Doi: 10.1109/ICPACE.2015.7274935
Stockwell, R.G., Monsinha, L. & Lowe, R.P. (1996). Localization of the complex spectrom: the S transform. IEEE Transactions on Signal Processing. 44(4). 1056-1063. doi: 10.1109/78.492555
Subasi, A., Yilmaz, A.S. & Tufan, K. (2011). Detection of Generated and Measured Transient Power Quality Events Using Teager Energy Operator. Energy Conversion and Management, 52(4). 1959–1967. doi: 10.1016/j.enconman.2010.11.006
Sumathi, S. & Surekha, P. (2010). Computational Intelligence Paradigms Theory and Applications using Matlab. CRC Press Taylor & Francis Group, NewYork, USA.
Uyar, M., Yıldırım, S. & Gencoglu, M.T. (2008). An Effective wavelet – based feature extraction method for classification of power quality diturbance signals. Electric Power Systems Research, 78(10). 1747-1755. doi: 10.1016/j.epsr.2008.03.002
Uyar, M., Yıldırım, S. & Gençoğlu, M.T. (2011). Güç Kalitesindeki Bozulma Türlerinin Sınıflandırılması için bir örüntü tanıma yaklaşımı. Gazi Üniversitesi Muhendislik Mimimarlık Fakültesi Dergisi. 26(1). 14-56.
Xiong, S., Xia, L. & Bu, L. (2015). An Effective S-transform Feature Extraction Method for Classification of Power Quality Disturbance Signals. Chinese Automation Congress (CAC). 1555-1560. doi: 10.1109/CAC.2015.7382748
Xiong, S., Xia, L. & Bu, L. (2015). Classification of Composite Power Quality Disturbance Using Support Vector Mashines. Chinese Automation Congress (CAC). 1522-1527. doi: 10.1109/CAC.2015.7382742
Yilmaz, A.S., Subasi, A., Bayrak, M., Karsli, V.M. & Ercelebi, E. (2007). Application of lifting based wavelet transforms to characterize power quality events. Energy Conversion and Management. 48(1). 112–123. doi: 10.1016/j.enconman.2006.05.003
Zhan, W., Xiangjun, Z., Xiaoxi, H. & Jingying, H. (2012). The multi-disturbance complex power quality signal HHT detection technique. Innovative Smart Grid Technologies Asia,Tianjin, 2012. doi: 10.1109/ISGT-Asia.2012.6303259

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Mehmet İsmail Gursoy
ADIYAMAN ÜNİVERSİTESİ
0000-0002-2285-5160
Türkiye

Seydi Vakkas Ustun
ADIYAMAN ÜNİVERSİTESİ
Türkiye

Ahmet Serdar Yilmaz
Kahramanmaraş Sütçü İmam Universitesi
Türkiye

Publication Date

January 29, 2017

Submission Date

November 9, 2017

Acceptance Date

December 5, 2017

Published in Issue

Year 2018 Volume: 10 Number: 1

DOI

https://doi.org/10.29137/umagd.350231

IZ

https://izlik.org/JA22NK36BA

APA

Gursoy, M. İ., Ustun, S. V., & Yilmaz, A. S. (2017). An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances. International Journal of Engineering Research and Development, 10(1), 158-171. https://doi.org/10.29137/umagd.350231

AMA

1.Gursoy Mİ, Ustun SV, Yilmaz AS. An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances. IJERAD. 2017;10(1):158-171. doi:10.29137/umagd.350231

Chicago

Gursoy, Mehmet İsmail, Seydi Vakkas Ustun, and Ahmet Serdar Yilmaz. 2017. “An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances”. International Journal of Engineering Research and Development 10 (1): 158-71. https://doi.org/10.29137/umagd.350231.

EndNote

Gursoy Mİ, Ustun SV, Yilmaz AS (January 1, 2017) An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances. International Journal of Engineering Research and Development 10 1 158–171.

IEEE

[1]M. İ. Gursoy, S. V. Ustun, and A. S. Yilmaz, “An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances”, IJERAD, vol. 10, no. 1, pp. 158–171, Jan. 2017, doi: 10.29137/umagd.350231.

ISNAD

Gursoy, Mehmet İsmail - Ustun, Seydi Vakkas - Yilmaz, Ahmet Serdar. “An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances”. International Journal of Engineering Research and Development 10/1 (January 1, 2017): 158-171. https://doi.org/10.29137/umagd.350231.

JAMA

1.Gursoy Mİ, Ustun SV, Yilmaz AS. An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances. IJERAD. 2017;10:158–171.

MLA

Gursoy, Mehmet İsmail, et al. “An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances”. International Journal of Engineering Research and Development, vol. 10, no. 1, Jan. 2017, pp. 158-71, doi:10.29137/umagd.350231.

Vancouver

1.Mehmet İsmail Gursoy, Seydi Vakkas Ustun, Ahmet Serdar Yilmaz. An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances. IJERAD. 2017 Jan. 1;10(1):158-71. doi:10.29137/umagd.350231