The Feature Selection based Power Quality Event Classification using Wavelet Transform and Logistic Model Tree

• Autorzy: Eristi H. , Demir Y.

Warianty tytułu

PL

Selekcja cech bazująca na klasyfikacji jakości energii z wykorzystaniem transformaty falkowej i modelu drzewa

• Języki publikacji: EN

Abstrakty

EN

This paper presents a new power quality event classification technique using wavelet transform and logistic model tree. The proposed method uses the samples of three cycle duration of three line voltage of power quality events. The features of these samples are obtained by using the wavelet transform and a few different feature extraction techniques. The sequential forward selection method based a feature selection process is done to ensure good classification accuracy by selecting 20 better features from all 90 features generated from the wavelet transform coefficients. The obtained features are used to train a single logistic model tree. The feasibility of the proposed algorithm has been tested using real life power quality events. The result indicates that the feature selection based proposed method reliably classifies all types of power quality events with high accuracy.

PL

W artykule zaproponowano nową metodę oceny jakości energii wykorzystującą transformatę falkową i logistyczny model drzewa. W metodzie analizuje się trzy cykle w trzech liniach napięcia. Możliwa jest klasyfikacja 90 zdarzeń i wybranie 20 typowych cech.

Słowa kluczowe

EN

power quality events; wavelet transform; feature selection; logistic model tree

PL

jakość energii; transformata falkowa; selekcja cech

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 7a
• Strony: 43--48
• Opis fizyczny: Bibliogr. 24 poz., schem., tab., wykr.

Twórcy

autor

Eristi H.

autor

Demir Y.

• Tunceli University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Tunceli/Turkey,

Bibliografia

• [1] Liao, Y. and Lee, J. B., A fuzzy-expert system for classifying power quality disturbances, Electrical Power and Energy Systems, (2004), 26(3), 199–205.
• [2] Moravej, Z., Abdoos, A. A. and Pazoki, M., New combined stransform and logistic model tree technique for recognition and classification of power quality disturbances, Electric Power Components and Systems, (2011), 39(1), 80–98.
• [3] Erişti, H., Uçar, A. and Demir, Y., Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines, Electric Power Systems Research, (2010), 80(7), 743–752.
• [4] Gaouda, A. M., Salama, M. M. A., Sultan, M. R. and Chikhani, A. Y., Power quality detection and classification using wavelet multiresolution signal decomposition, IEEE Transactions on Power Delivery, (1999), 14(4), 1469–1476.
• [5] Pires, V. F., Amaral, T. G. and Martins, J. F., Power quality disturbances classification using the 3-D space representation and PCA based neuro-fuzzy approach, Expert Systems with Applications, (2011), 38(9), 11911–11917.
• [6] Gaing, Z. L., Wavelet-based neural network for power disturbance recognition and classification, IEEE Transactions on Power Delivery, (2004), 19(4), 1560–1568.
• [7] Ekici, S., Classification of power system disturbances using support vector machines, Expert Systems with Applications, (2009), 36(6), 9859–9868.
• [8] Hong, Y. Y. and Wang, C. W., Switching detection/classification using discrete wavelet transform and self-organizing mapping network, IEEE Transactions on Power Delivery, (2005), 20(2), 1662–1668.
• [9] Erişti, H. and Demir, Y., A new algorithm for automatic classification of power quality events based on wavelet transform and SVM, Expert Systems with Applications, (2010), 37(6), 4094-4102.
• [10] Axelberg, P. G. V., Irene, Y. H. G. and Bollen, M. H. J. Support vector machine for classification of voltage disturbances, IEEE Transactions on Power Delivery, (2007), 22(3), 1297–1303.
• [11] Styvaktakis, E., Bollen, M. H. J. and Gu, I. Y. H. Expert system for classification and analysis of power system events, IEEE Transactions on Power Delivery, (2002), 17(2), 423–428.
• [12] Bollen, H. J., Gu, I. Y. H., Axelberg, P. G. V. and Styvaktakis, E. Classification of underlying causes of power quality disturbances: deterministic versus statistical methods, EURASIP Journal on Advances in Signal Processing, (2007), article id: 79747, 17 pp.
• [13] Santoso, S., Lamoree, J., Grady, W. M., Powers, E. J. and Bhatt, S. C., A scalable PQ event identification system, IEEE Transactions on Power Delivery, (2000), 15(2), 738–743.
• [14] Demirci, T., Kalaycıoğlu, A., Küçük, D., Salor, Ö., Güder, M., Pakhuylu, S., Atalık, T., İnan, T., Çadırcı, I., Akkaya, Y., Bilgen, S. and Ermiş, M. , Nationwide real-time monitoring system for electrical quantities and power quality of the electricity transmission system, IET Generation, Transmission & Distribution, 2011, 5(5), 540-550.
• [15] Morsi, W. G. and El-Hawary, M. E., Power quality evaluation in smart grids considering modern distortion in electric power systems. Power Systems Research, (2011), 81(5), 1117–1123.
• [16] Wang, M. H. and Tseng, Y. F., A novel analytic method of power quality using extension genetic algorithm and wavelet transform, Expert Systems with Applications, (2011) 38(10), 12491–12496.
• [17] Morsi, W. G. and El-Hawary, M. E., A new reactive, distortion and non-active power measurement method for nonstationary waveforms using wavelet packet transform, Electric Power Systems Research, (2009), 79(10), 1408–1415.
• [18]Mallat, S. G., A theory for multiresolution signal decomposition: The wavelet representation IEEE Transaction on Pattern Analysis and Machine Intelligence, (1989), 11(7), 674–693.
• [19] Daubechies, I. (1992). Ten lectures on wavelets. Philadelphia, USA: CBMSNSF Regional Conference Series, SIAM.
• [20]Chen, S. and Zhu, H. Y., Wavelet transform for processing power quality disturbances, EURASIP Journal on Advances in Signal Processing, (2007), article ID: 47695, 20 pp.
• [21] Marill T. and Green D. M., On the effectiveness of receptors in recognition systems, IEEE Transactions on Information Theory, (1963), 9(1), 11–17.
• [22] Landwehr, N., Hall, M. and Frank, E., Logistic model trees, Machine Learning, (2005), 59(1/2), 161–205.
• [23] Hosmer D. W. and Lemeshow, S., Applied logistic regression, (2000), 2nd Edition, Wiley-Interscience, New York.
• [24] Friedman, J., Hastie, T. and Tibshirani, R., Additive logistic regression: A statistical view of boosting, Annals of Statistics, (2000), 32(2), 337–374.