Classification of Power Quality Disturbances with Hilbert-Huang Transform, Genetic Algorithm and Artificial Intelligence/Machine Learning Methods

Öz

In this study, Hilbert-Huang Transform method and statistical features are obtained to classify Power Quality (PQ) Disturbances. The appropriate features are selected by the Genetic Algorithm (GA) and k-Nearest Neighbor classification approach. Models based on Artificial Intelligence and Machine Learning methods are formed and test process is performed by using data from experimental setup. It is produced by using mathematical equations together with noisy conditions (40 dB, 30 dB and 20 dB). In addition, Power Quality Disturbances data from the experimental setup is also used in this study. First of all, Empirical Mode Decomposition (EMD) method is applied to the signals. Then, by applying Hilbert transformation (HT), statistical features and necessary features are extracted. The same procedure is repeated for Ensemble Empirical Mode Decomposition (EEMD). GA + KNN wrapper approach is used to select the required ones according to the number of extracted features. Power Quality Disturbances models are created based on Multilayer Perceptron (MLP) and k-Nearest Neighbour classifier (KNN) methods. The performance of EEMD + HT + GA + KNN classification model for 9 single and 9 multiple types of disruption is 99.15% for synthetic data and 99.02% for experimental data.  Compared to the literature, EEMD + HT + GA + KNN method has the ability to distinguish 9 multiple PQ disturbances and the overall performance gives the best performance with a rate of 99.12%.

Anahtar Kelimeler

• Power quality disturbances,Hilbert-Huang transform,emprical mode decompositon mod,ensemble emprical mode decomposition,genetik algorithm

Güç Kalitesi Bozulmalarının Hilbert-Huang Dönüşümü, Genetik Algoritma Ve Yapay Zeka/Makine Öğrenmesi Yöntemleri İle Sınıflandırılması

Öz

Bu çalışmada Güç Kalitesi (GK) Bozulmalarını sınıflandırmak için Hilbert-Huang Dönüşümü yöntemi ve istatistiksel özellikler ile öznitelikler elde edilmektedir. Elde edilen özniteliklerden uygun olanları Genetik Algoritma (GA) k-En Yakın Komşu sınıflandırma yaklaşımı ile seçilmektedir. Yapay Zeka ve Makine Öğrenmesi yöntemlerine dayalı modeller oluşturulmakta ve deneysel düzenekten alınan veriler kullanılarak test işlemi yapılmaktadır. Gürültülü durumlar (40 dB, 30 dB ve 20 dB) ile birlikte matematiksel eşitlikler kullanılarak üretilmektedir. Bunun yanında deneysel düzenekten elde edilen Güç Kalitesi Bozulma verisi de bu çalışmada kullanılmaktadır. Sinyallere öncelikle Ampirik Kip Ayırışımı (EMD) yöntemi uygulanmaktadır. Daha sonra Hilbert dönüşümü (HT) neticesinde istatistiksel özellikler ile gerekli öznitelikler çıkartılmaktadır. Aynı işlem Grupsal Ampirik Kip Ayrışımı (EEMD) yöntemi için tekrarlanmaktadır. Çıkartılan özniteliklerin sayısı itibari ile gerekli olanlarının seçilebilmesi için GA + KNN sarmalama yaklaşımı kullanılmaktadır. Çok katmanlı algılayıcı (MLP) ve KNN yaklaşımları ile Güç Kalitesi Bozulmalarını sınıflandıran modeller oluşturulmaktadır. 9 adet tekli, 9 adet çoklu bozulma türü için oluşturulan EEMD + HT + GA + KNN sınıflandırma modelinin başarımı sentetik veriler için %99.15, deneysel veriler için % 99.02 olarak elde edilmektedir. Literatürdeki çalışmalar ile kıyaslandığında elde edilen EEMD + HT + GA + KNN yönteminin, 9 adet çoklu GK bozulmasını ayırt edebilme özelliğine sahip olduğu ve %99.12 lik genel başarım oranı ile en iyi başarımı veren yöntem olduğu sonuçlarına varılmaktadır.

Anahtar Kelimeler

• Güç kalitesi,Hilbert-Huang Dönüşümü,Ampirik Mod Ayrıştırması (EMD),Grupsal Ampirik Mod Ayrıştırması (EEMD),Genetik Algoritma

Kaynakça

1. 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.
2. Ribeiro, P. F., Duque, C. A., Ribeiro, P. M., & Cerqueira, A. S. (2013). Power systems signal processing for smart grids. John Wiley & Sons.
3. Lee, Ian WC, and Pradipta K. Dash. "S-transform-based intelligent system for classification of power quality disturbance signals." IEEE Transactions on Industrial Electronics 50.4 (2003): 800-805.
4. Sahani, M., & Dash, P. K. (2018). Variational Mode Decomposition and Weighted Online Sequential Extreme Learning Machine for Power Quality Event Patterns Recognition. Neurocomputing.
5. Ribeiro, E. G., Mendes, T. M., Dias, G. L., Faria, E. R., Viana, F. M., Barbosa, B. H., & Ferreira, D. D. (2018). Real-time system for automatic detection and classification of single and multiple power quality disturbances. Measurement, 128, 276-283.
6. Kapoor, R., Gupta, R., Jha, S., & Kumar, R. (2018). Boosting performance of power quality event identification with KL Divergence measure and standard deviation. Measurement, 126, 134-142.
7. Khokhar, S., Zin, A. A. M., Memon, A. P., & Mokhtar, A. S. (2017). A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement, 95, 246-259.
8. Moravej, Z., Banihashemi, S. A., & Velayati, M. H. (2009). Power quality events classification and recognition using a novel support vector algorithm. Energy Conversion and Management, 50(12), 3071-3077.

9. Ahila, R., Sadasivam, V., & Manimala, K. (2015). An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances. Applied Soft Computing, 32, 23-37.
10. Babu, N. R., & Mohan, B. J. (2017). Fault classification in power systems using EMD and SVM. Ain Shams Engineering Journal, 8(2), 103-111.
11. Drummond, C. F., & Sutanto, D. (2010). Classification of power quality disturbances using the iterative Hilbert Huang transform.
12. Yang, L., Yu, J., & Lai, Y. (2010, March). Disturbance source identification of voltage sags based on Hilbert-Huang transform. In Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific (pp. 1-4). IEEE.
13. Hafiz, F., Chowdhury, A. H., & Shahnaz, C. (2012, December). An approach for classification of power quality disturbances based on Hilbert Huang transform and Relevance vector machine. In Electrical & Computer Engineering (ICECE), 2012 7th International Conference on (pp. 201-204). IEEE.
14. Önal, Y., & Turhal, Ü. Ç. (2013, May). The orthogonal Hilbert-Huang transform application in voltage flicker analysis. In Power Engineering, Energy and Electrical Drives (POWERENG), 2013 Fourth International Conference on (pp. 700-704). IEEE.
15. Senroy, N., Suryanarayanan, S., & Ribeiro, P. F. (2007). An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality. IEEE Transactions on Power Systems, 22(4), 1843-1850.
16. Manjula, M., Mishra, S., & Sarma, A. V. R. S. (2013). Empirical mode decomposition with Hilbert transform for classification of voltage sag causes using probabilistic neural network. International Journal of Electrical Power & Energy Systems, 44(1), 597-603.
17. Ktonas, P. Y., & Papp, N. (1980). Instantaneous envelope and phase extraction from real signals: theory, implementation, and an application to EEG analysis. Signal Processing, 2(4), 373-385.
18. Picinbono, B. (1997). On instantaneous amplitude and phase of signals. IEEE Transactions on signal processing, 45(3), 552-560.
19. Huang, et al. "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis." Proc. R. Soc. Lond. A (1998) 454, 903–995
20. Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 1(01), 1-41.
21. Holland, J. H., “Genetic algorithms”, Scientific American, 267(1): 66-73, 1992.
22. P. N. Tan, V. Kumar, and M. Steinbach, “Introduction to Data Mining”, Pearson, 2005.
23. Fix E, Hodges JL, Jr (1951) Discriminatory analysis, nonparametric discrimination. USAF School of Aviation Medicine, Randolph Field, Tex., Project 21-49-004, Rept. 4, Contract AF41(128)-31, February 1951.
24. Karasu, S., & Saraç, Z. (2018). Investigation of power quality disturbances by using 2D discrete orthonormal S-transform, machine learning and multi-objective evolutionary algorithms. Swarm and Evolutionary Computation.
25. Karasu, S., & Saraç, Z. (2018). Classification of Power Quality Disturbances with 2D Discrete Wavelet Transform and Bagged Decision Trees Method. JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 21(4), 849-855.
26. Karasu, S., & Saraç, Z. (2018, May). Classification of power quality events signals with pattern recognition methods by using Hilbert transform and genetic algorithms. In 2018 26th Signal Processing and Communications Applications Conference (SIU)(pp. 1-4). IEEE.
27. Karasu, S., & Saraç, Z. (2017, May). Classification of power quality disturbances with S-transform and artificial neural networks method. In Signal Processing and Communications Applications Conference (SIU), 2017 25th (pp. 1-4). IEEE.
28. Mahela, Om Prakash, Abdul Gafoor Shaik, and Neeraj Gupta. "A critical review of detection and classification of power quality events." Renewable and Sustainable Energy Reviews 41 (2015): 495-505.
29. Saini, M. K., & Kapoor, R. (2012). Classification of power quality events–a review. International Journal of Electrical Power & Energy Systems, 43(1), 11-19.
30. Kumar, R., Singh, B., Shahani, D. T., Chandra, A., & Al-Haddad, K. (2015). Recognition of power-quality disturbances using S-transform-based ANN classifier and rule-based decision tree. IEEE Transactions on Industry Applications, 51(2), 1249-1258.
31. Wang, M., Zhou, H., Yang, S., Jin, L., & Jiao, L. (2016). Robust compressive features based power quality events classification with Analog–Digital Mixing Network (ADMN). Neurocomputing, 171, 685-692.
32. Sebastian, P., & DŚa, P. A. (2015, August). Implementation of a Power Quality signal classification system using wavelet based energy distribution and neural network. In Power and Advanced Control Engineering (ICPACE), 2015 International Conference on (pp. 157-161). IEEE.
33. Ray, P. K., Mohanty, S. R., & Kishor, N. (2013). Classification of power quality disturbances due to environmental characteristics in distributed generation system. IEEE Transactions on sustainable energy, 4(2), 302-313.
34. He, S., Li, K., & Zhang, M. (2013). A real-time power quality disturbances classification using hybrid method based on S-transform and dynamics. IEEE transactions on instrumentation and measurement, 62(9), 2465-2475.
35. Khadse, C. B., Chaudhari, M. A., & Borghate, V. B. (2016). Conjugate gradient back-propagation based artificial neural network for real time power quality assessment. International Journal of Electrical Power & Energy Systems, 82, 197-206.
36. Camarena-Martinez, D., Valtierra-Rodriguez, M., Perez-Ramirez, C. A., Amezquita-Sanchez, J. P., de Jesus Romero-Troncoso, R., & Garcia-Perez, A. (2016). Novel downsampling empirical mode decomposition approach for power quality analysis. IEEE Transactions on Industrial Electronics, 63(4), 2369-2378.
37. Biswal, M., & Dash, P. K. (2013). Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier. Digital Signal Processing, 23(4), 1071-1083.