Year 2018, Volume 10 , Issue 1, Pages 158 - 171 2017-01-29

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

Mehmet İsmail GURSOY [1] , Seydi Vakkas USTUN [2] , Ahmet Serdar YİLMAZ [3]


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.

Power Quality, Discrete Wavelet Transform, Empirical Wavelet Transform, Support Vector Machine, Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System
  • 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
Primary Language en
Subjects Engineering, Multidisciplinary
Journal Section Articles
Authors

Orcid: 0000-0002-2285-5160
Author: Mehmet İsmail GURSOY
Institution: ADIYAMAN ÜNİVERSİTESİ
Country: Turkey


Author: Seydi Vakkas USTUN
Institution: ADIYAMAN ÜNİVERSİTESİ
Country: Turkey


Author: Ahmet Serdar YİLMAZ
Institution: Kahramanmaraş Sütçü İmam Universitesi
Country: Turkey


Dates

Publication Date : January 29, 2017

APA GURSOY, M , USTUN, S , YİLMAZ, A . (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 . DOI: 10.29137/umagd.350231