MESAFE KORUMA İÇİN BİR ÖRÜNTÜ TANIMA UYGULAMASI

Öz

Bu makalede, mesafe koruma işlevi için örüntü sınıflandırıcısı olarak Destek Vektör Makineler (DVM) yöntemini kullanan bir yöntem sunulmuştur. Önerilen yöntem enerji iletim hattının farklı noktalarında meydana gelen arızaları algılayarak röle işlevi için bir çıkış üretilmektedir. Yöntem, üç faz akım ve gerilim bilgilerinden faydalanmaktadır. DVM’nin eğitiminde ve test edilmesinde kullanılan akım ve gerilim bilgileri Alternative Transient Program (ATP) ile gerçekleştirilen bir iletim hattı benzetiminden elde edilmiştir. Gerçekleştirilen dijital röle uygulamasında, DVM’nin oldukça yüksek bir başarı oranı ile iletim hattında meydana gelen arızaları sınıflandırdığı görülmüştür.

Anahtar Kelimeler

• Örüntü tanıma, iletim hattı arızaları, destek vektör makineleri.

Kaynakça

1. Hanninen, S., Single Phase Earth Faults In High Impedance Grounded Networks, Characteristics, Indication and Location, Espoo 2001, Technical Research Centre of Finland, VTT Publications 453, 78s, 2001.
2. Kezunovic, M., Digital protective relaying algorithms and systems-an overview, Electric Power Systems Research, 4, 3, 167-80, 1981.
3. Davall, P.W., Yeung, G.A., A software design for a computer based impedance relay for transmission line protection, IEEE Trans. on Power App. and Systems, PAS-99, 1, 235-45, 1980.
4. Youssef, O.A.S., Fundamental approach to impedance relaying, IEEE Trans. on Power Delivery, 7, 4, 1861-70, 1992.
5. Sachdev, M.S., Sidhu, T.S., A technique for estimating the location of a transmission line shunt fault, Electrical and Computer Engineering, Canadian Conference on, 1, 562-565, 1993.
6. Crossley, P.A., McLaren, P.G., Distance protection based on travelling waves, IEEE Trans. on Power Apparatus and Systems, PAS-102, 9, 2971-2983, 1983.
7. Bollen, M.H.J., Travelling-wave based protection of double-circuit lines, IEE Proceedings-C, 140, 1, 37-47, 1993.
8. Morlet, J., Wave propagation and sampling theory and complex waves, Technical Report, Geophysics, 47, 2, 222-236, 1982.

9. Cichocki, A., Lobos, T., Artificial neural networks for real-time estimation of basic waveforms of voltages and currents, IEEE Trans. on Power Systems, 9, 2, 612-8, 1994.
10. Perez, L.G., Flechsiz, A.J., Meador, J.L., Obradovic, A., Training an artificial neural network to discriminate between magnetizing inrush and internal faults, IEEE Trans. on Power
11. Delivery, 9, 1, 434-41, 1994.
12. Igel, M., Koglin, H.J., Schegner, P., New algorithms for earth fault distance protection in insulated and compensated networks, European Transaction in Electrical Power, 1, 5, 253-259, 1991.
13. Kim, C.H., Kim, H., Ko, Y., Byun, S.H., Aggarwal, R.K., Johns, A.T., A novel faultdetection technique of high-impedance arcing faults in transmission lines using the wavelet
14. transform, IEEE Trans. on Power Delivery, 17, 4, 921-929, 2002.
15. Prikler, L., Hoidalen, H.K., ATPDraw Version 3.5 User’s Manual, Preliminary Release 1.0., 198-200, 2002.
16. CanAm EMTP User Group, Alternative Transients Program (ATP) - Rule Book, 1992.
17. Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J.M., Wavelet Toolbox, For Use with MATLAB, The MathWorks, User’s Guide, Ver:3, 2005.
18. Özgönenel, O., Önbilgin, G., Kocaman, Ç., Wavelets and its applications of power system protection, Gazi University Journal of Science 17, 2, 77-90, 2004.
19. Daubechies, I., Ten Lectures On Wavelets, 2nd ed.. Philadelphia: SIAM, CBMS-NSF regional conference series in applied mathematics, 61p, 1992.
20. Kaiser, G., A Friendly Guide To Wavelets, Birkhauser Boston, 324p, 1994.
21. Ekici, S., Yildirm, S., Fault Location Estimation on Transmission Lines Using Wavelet Transform and Artificial Neural Network. Proceedings of the 2006 International Conference on Artificial Intelligence, ICAI 2006, Las Vegas, USA, 1: 181-184, 2006.
22. Ekici, S., Yildirim, S., Poyraz, M., Energy and Entropy-Based Feature Extraction for Locating Fault on Transmission Lines by Using Neural Network and Wavelet Packet Decomposition, Expert Systems with Applications, doi:10.1016/j.eswa.2007.05.011, 2007.
23. Mo, F., Kinsner, W., Probabilistic neural networks for power line fault classification, IEEE Canadian Conference on Electrical and Computer Eng., 585-588, 1998.
24. Jiang, Z.G., Fu, H.G., Li, L.J., Support vector machine for mechanical faults classification, Journal of Zhejiang University Science, 6A, 5, 433-439, 2005.
25. Smola, A.J. and Schölkopf, B., A tutorial on support vector regression, Technical Report NC2-TR-1998-030, 200-222, 1998.
26. Burges, C.J.C., A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2, 121-167, 1998.
27. Dash, P.K., Samantary, S.R., Ganapati, P., Fault classification and section identification of an advanced series-compensated transmission line using support vector machine, Power Delivery, IEEE Transactions on, 22, 67-73, 2007.
28. Marwala, T., Mahola, U., Chakraverty, S., Fault classification in cylinders using multi-layer perceptrons, support vector machines and guassian mixture models, The Intl. Symposium on Neural Networks and Soft Computing, NNSC-2005, 14, 2, 307-316, 2007.
29. Pöyhönen, S., Support Vector Machine Based Classification In Condition Monitoring of Induction Motors, Doktora Tezi, Helsinki University of Technology, 63p, 2004.
30. Vapnik, V., The Nature of Statistical Learning Theory, New York: Springer Verlag, New York, 187p, 1995.
31. Vojtech, F., Hlavac, V., Statistical Pattern Recognition Toolbox for MATLAB (SPRTOOL), User’s Guide, 2007.
32. Kohavi, R., A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 2, 12, 1137-1143, 1995.