The feature selection based power quality event classification using wavelet transform and logistic model tree;Selekcja cech bazująca na klasyfikacji jakości energii z wykorzystaniem transformaty falkowej i modelu drzewa

Przeglad Elektrotechniczny

Volumn 88, Issue 7 A, 2012, Pages 43-48

  Eristi, Huseyin ^a   Demir, Yakup ^b  

^a TUNCELI UNIVERSITY   (Turkey)

^b FIRAT UNIVERSITY   (Turkey)

Author keywords

Feature selection; Logistic model tree; Power quality events; Wavelet transform

Indexed keywords

EID: 84863609589     PISSN: 00332097     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

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., Kalaycioǧlu, A., Küçük, D., Salor, Ö., Güder, M., Pakhuylu, S., Atalik, T., Inan, T., Çadirci, 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.