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Classification of Power Quality Disturbances at Power System Frequency and Out Of Power System Frequency Using Suport Vector Machines

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Klasyfikacja zakłóceń jakości energii w systemie elektroenergetycznym w częstotliwości sieciowej i poza nią – metoda wektorów nośnych
Języki publikacji
EN
Abstrakty
EN
In this paper, firstly it is tried to classify pure sine and power quality disturbances (PQD) such as voltage sag, voltage swell, voltage with harmonics, transients and flicker at power system frequency (50 Hz). Wavelet transform (WT) is used to extract distinctive features. Wavelet energy criterion is applied to wavelet detail coefficients. It is seen that classification performance of support vector machine (SVM) used as classifier is well. Then pure sine and PQD, that are out of power system frequency, are tried to classify. Curve fitting approach is used for estimating frequency. It is observed that SVM classifies PQD signals well when frequency of pure sine is updated with the frequency of PQD even if they deviate from 50 Hz.
PL
W artykule przedstawiono sposób wykorzystania transformaty falkowej do wykrycia i analizy podstawowych zaburzeń napięcia jakości energii w sieci elektroenergetycznej (50Hz). W celu estymacji częstotliwości zastosowano metodę dopasowania krzywej. Stwierdzono, że metoda wektorów nośnych (ang. Support Vector Machine) poprawnie klasyfikuje zakłócenia mocy, nawet dla częstotliwości odmiennych niż 50Hz.
Rocznik
Strony
284--291
Opis fizyczny
Bibliogr. 56 poz., rys., schem., tab., wykr.
Twórcy
autor
  • Yıldız Teknik Üniversitesi
autor
  • Ondokuz Mayıs Üniversitesi
Bibliografia
  • 1] Sankaran C., Power Quality, CRC Press LLC, 2002
  • [2] Kocatepe C., Umurkan N., Atar F., Yumurtacı R., Karakaş A., Arıkan O., Baysal M., Elektrik Enerjisi Ve harmonikler Kurs Notları, MİSEM, 2005
  • [3] IEEE Std. 1159-1995 IEEE Recommended Practice for Monitoring Electric Power Quality, IEEE Standards Coordinating Committee 22 on Power Quality, USA
  • [4] IEC 61000-1-1 Electromagnetic Compatibility (EMC) Part 1A
  • [5] Gao P., Wu W., Power quality disturbances classification using wavelet and support vector machines, Sixth International Conference on Intelligent Systems Design and Applications, (2006), 201-206
  • [6] Moravej Z., Abdoos A. A., Pazoki M., Detection and Classification of Power Quality Disturbances Using Wavelet Transform and Support Vector Machines, Electric Power Components and Systems, 38 (2009), No. 2, 182-196
  • [7] Lin W. M., Wu C. H., Lin C. H., Cheng F. S., Detection and Classification of Multiple Power Quality Disturbances with Wavelet Multiclass SVM, IEEE Transactions On Power Delivery, 23 (2008), No. 4, 2575-2582
  • [8] Lazzaretti A. E., Ferreira V. H., . Neto H. V, Riella R. J., Omori J., Classification of events in distribution networks using autonomous neural models, 15th International Conference on Intelligent System Applications to Power Systems, (2009), 1-6
  • [9] Huang S. J., Hsieh C. T., Feasibility of Fractal-Based Methods for Visualization of Power System Disturbances, International Journal of Electrical Power & Energy Systems, 23 (2001), No. 1, 31-36
  • [10] Nguyen T., Liao Y., Power Quality Disturbance Classification Utilizing S Transformed Binary Feature Matrix Method, Elect. Power Syst.Res., 79 (2009), No. 4, 569-575
  • [11] Wang M., Ochenkowski P., Mamishev A., A classification of power quality disturbances using time-frequency ambiguity plane and neural networks, IEEE Power Eng. Soc. Summer Mtg., (2001), 1246-1251
  • [12] Wen J., Liu P., A Method for Detection and Classification of Power Quality Disturbances, Automat. Elect. Power Syst., 26 (2002), No. 1, 42-44
  • [13] Santoso S., Powers E.J., Grady W. M., Hofmann P., Power Quality Assessment via Wavelet Transform Analysis, IEEE Trans. Power Delivery, 11 (1996), No. 2, 924-930
  • [14] Hong Y. Y., Chen Y. Y., Placement of Power Quality Monitors Using Enhanced Genetic Algorithm and Wavelet Transform, Generation, Transmission & Distribution, 5 (2011), No. 4, 461- 466
  • [15] David G. L., Comments On Hilbert Transform Based Signal Analysis BYU (Microwave Remote Sensing (MERS) Laboratory Technical Report, Brigham Young University, Provo, UT, 2004)
  • [16 ]Aiello M., Cataliotti A., Nuccio S., A chirp-Z transform-based synchronizer for power system measurements, IEEE Trans. Instrument. Meas. The 19th IEEE Instrumentation and Measurement Technology Conference, (2005), 1025-1032
  • [17] Xu Y., Xiangning X., Song Y. H., Automatic classification and analysis of the characteristic parameters for power quality disturbances, IEEE Power Engineering Society General Meeting, (2004), 496-503
  • [18] Styvaktakis E., Bollen M. H. J., Gu I. Y. H., Expert System for Classification and Analysis of Power System Events, IEEE Transactions On Power Delivery, 17(2002), No. 2, 423-428
  • [19] Heydt G. T., Fjeld P. S., Liu C. C., Pierce D., Tu I., Hensley I., Applications of The Windowed FFT to Electric Power Quality Assessment, IEEE Transactions on Power Delivery, 14 (1999) No. 4, 1411-1416
  • [20] Hong Y. Y., Chen Y. Y., Placement of Power Quality Monitors Using Enhanced Genetic Algorithm and Wavelet Transform, Generation, Transmission & Distribution, 5 (2011), No. 4 461- 466
  • [21] He H., Starzyk J. A., A Self Organizing Learning Array System for Power Quality Classification Based on Wavelet Transform, IEEE Trans. Power Delivery, 21 (2006), No. 1, 286-295
  • [22] Liao Y., Lee J. B., A Fuzzy-Expert System for Classifying Power Quality Disturbances, Elect. Power Energy Syst., 26 (2004), No. 3, 199-205
  • [23] Andami H., Jalilian A., Voltage notch detection using fuzzy expert system, Canadian Conference on Electrical and Computer Engineering, (2003), 479-482
  • [24] Lieberman D. G., Troncoso R. J. R., Rios R. A. O., Perez A. G., Yepez E. C., Techniques and Methodologies for Power Quality Analysis and Disturbances Classification in Power Systems: A Rewiev, IET Generation, Transmission and Distribution, 5 (2011), No. 4, 519-529
  • [25] Huang C. H., Lin C. H., Kuo C. L., Chaos Synchronization Based Detector for Power Quality Disturbances Classification in a Power System, IEEE Transaction on Power Delivery, 26 (2011), No. 2, 944-953
  • [26] Erişti H., Demir Y., The Feature Selection Based PowerQuality Event Classification using Wavelet Transform and Logistic Model Tree, Przeglad Electrotechniczny (Electrical Review), 7a (2012), R. 88, 43-48
  • [27] Misiti M., Misiti Y., Oppenheim G., Poggi J. M., Wavelet Toolbox for Use with MATLAB (The Math Works, Inc., 2002)
  • [28] Erişti, H., Uçar A., Demir, Y., Wavelet based feature extraction and selection for classification of power system disturbances using support vector machines, Electric Power Systems Research, 80 (2010), No. 7, 743-752
  • [29] Lee C. Y., Shen Y. X., Optimal Feature Selection for Power Quality Disturbances Classification, IEEE Transaction on Power Delivery, 26 (2011), No. 4, 2342-2351
  • [30] Nguyen T., Strang G., Wavelets and Filter Banks, Wellesley- Cambridge Press, Massachusettes, A.B.D., 1996
  • [31] Gaing Z. L., Huang H. S., Wavelet based neural network for power disturbance classification, IEEE Power Engineering Society General Meeting, (2003), 1621-1628
  • [32] Uyar M., Yıldırım S., Gençoğlu T., Güç kalitesi bozulmalarının sınıflandırılmasında dalgacık dönüşümüyle enerji dağılımına dayalı özelliklerin incelenmesi, Elektrik Elektronik Bilgisayar Biyomedikal Mühendisliği 12. Ulusal Kongre Ve Sergisi, (2007), 1-5
  • [33] Yu X., Wang K., Digital system for detection and classification of power quality disturbance, Power and Energy Engineering Conference, (2009), 1-4
  • [34] Hsieh J.G., Lecture Notes on Support Vector Machines. National Sun Yat-Sen University, Taiwan (R.O.C.), 2003
  • [35] Gunn S. R., Support Vector Machines for Classification and Regression, Technical Report, IRIS Research Group,(1998)
  • [36]Burges C. J. C., A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery., 2 (1998), No. 2, 1-47
  • [37] Liu X, Fu H., A Hybrid Algorithm for Text Classification Problem, Przeglad Electrotechniczny (Electrical Review), 1b (2012), R. 88, 8-11
  • [38] Vapnik, V. The support vector method of function estimation. In J. Suykens &J. Vandewalle (Eds.), Nonlinear modeling: Advanced black-box techniques. KluwerAcademic Publishers, (1998)
  • [39] Ekici S., Classification of power system disturbances using support vector machines, Expert Systems with Applications, 36 (2009), No. 6, 9859-9868
  • [40] Patterson J. D. W., Artificial Neural Networks, Theory and Applications, London, Prentice Hall, (1996), 180-213
  • [41] Cichocki A., Unbehauen R.,, Neural Networks for Optimization and Signal Processing, New York, Wiley, (1993), 88-162
  • [42] Chua K. S., Efficient Computations for Large Least Square Support Vector Machine Classifiers”, Pattern Recognition Letters, 24 (2003), 75-80
  • [43] Salat R., Osowski S., Accurate Fault Location in The Power Transmission Line Using Support Vector Machine Approach, IEEE Trans.on Power Systems, 19 (2004), No. 2, 979-986
  • [44] Hsu C. W., Lin C. J., “A Comparison of Methods for Multiclass Support Vector Machines, IEEE Trans. On Neural Networks, 13 (2002), No 2, 415-425
  • [45] Kocaman Ç., Usta H., Özdemir M., Eminoğlu D., Classification of Two Common Power Quality Disturbances Using Wavelet Based SVM, The 15th IEEE Mediterranean Electrotechnical Conference, (2010), 587-591
  • [46]Yıldırım S., Arıza Teşhisinde Destek Vektör Makinelerinin Kullanımı, M.Sc. dissertation, Dept. Elect. Eng., Fırat Univ., Elazığ, (2006)
  • [47] Kocaman Ç., Özgönenel O, Özdemir M., Terzi Ü, Calculation of fundamental power frequency for digital relaying algorithms, The 10th IET International Conference on Developments in Power System Protection, (2010), 1-5
  • [48]Hamid E. Y., Kawasaki Z. I., Wavelet Based Data Compression of Power System Disturbances Using The Minimum Description Length Criterion, IEEE Transactions On Power Delivery, 17 (2002), No. 2, 460-466
  • [49] Gauda A. M., Wavelet Automated Recognition System For Power System For Power Quality Monitoring, Phd Thesis, University of Waterloo, (1999)
  • [50] Uyar M., Güç Kalitesi Bozulma Türlerinin Akıllı Örüntü Tanıma Yaklaşımları İle Belirlenmesi, Phd Thesis, Dept. Elect. Eng., Fırat Univ., Elazığ, (2008)
  • [51] Borras D., Castilla M., Moreno N., Montano J. C., Wavelet and Neural Structure: A New Tool for Diagnostic of Power System Disturbances, IEEE Trans. Industry Appl., 37 (2001), No. 1, 184-190
  • [52] Gaing Z. L., Wavelet-based Neural Network for Power Disturbance Recognition and Classification, IEEE Trans. Power Delivery, 19 (2004), No. 4, 1560-1568
  • [53] Kocaman Ç., Özdemir M., Comparison of statistical methods and wavelet energy coefficients for determining two common PQ disturbances: sag and swel”, International Conference on Electrical and Electronics Engineering, (2009), 80-84
  • [54] Kocaman Ç., Özdemir M., Dirik, H., Dalgacık katsayılarından enerji yöntemiyle özellik çıkarımı yönteminin güç kalitesi bozunumlarının oluşum yerine göre değişimi, 3. Enerji Verimliliği Ve Kalitesi Sempozyumu, (2009), 138-142
  • [55] Kocaman Ç.,Yapay Us Yöntemleri Kullanılarak Enerji Kalitesi Bozucularının Belirlenmesi, Phd Thesis, Dept. Electric & Electronics Eng., Ondokuz Mayıs Univ., Samsun, (2010)
  • [56] Elektrik İletim Sistemi Arz Güvenilirliği ve Kalitesi Yöntemi Acknowledge: Reference [55] is Dr. Çağrı Arıkan’s Phd thesis. This study is a part of this Phd thesis.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-469d598a-83f0-4703-a514-18c3dd870a90
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