Data Mining Based Receiver Operating Characteristic (ROC) for Fault Detection and Diagnosis in Radial Distribution System

Dian Retno Sawitri, Arif Muntasa, Ketut Edi Purnama, Muhammad Ashari, Mauridhi Hery Purnomo

Abstract


This paper discusses data mining applications, especially the use of support vector machines to identify a fault in the 13 nodes radial distribution system of IEEE standard. The identification process was carried out with support vector machine (SVM). Prior to the identification process by SVM, feature extraction was carried out using wavelet transformation for signal decomposition and signal size reduction. To determine the performance of the identification, ROC (Receiver Operating Characteristic) analysis was used. Based on the curve formed by the ROC a fault in the radial distribution can be identified by SVM with “good” performance This is indicated by the value of the best cut-off point is 0.8 and area under the curve is 0.85854.

Keywords


Data mining; ROC; SVM; wavelet transformation; radial distribution systems

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References


H. Saadat, 1999, ―Power system analysis‖, Mc-Graw Hill Int. Singapore.

C. Olaru, P. Geurts, and L. Wehenkel, 1999, Data mining tools and application in power system engineering‖, Proceedings of the 13th Power System Computation Conference, PSCC99, page 324-330 -june 28-July 2nd.

J. Han and M. Kamber, 2006, ―Data mining‖, Concept and Techniques, 2nd Ed., Morgan Kaufman Publisher.

A. Graps, 1995, ―An introduction to wavelet‖, IEEE Computational Science and Engineering, Summer, vol. 2, num. 2.

M. Karimi, H. Mokhtari, and M. R. Iravani, 2000, ―Wavelet based on-line disturbance detection for power quality applications‖, IEEE Transaction on Power Delivery, Vol. 15, No. 4, October.

Haibo, 2006, ―A self organizing learning array system for power quality classification based on wavelet transform‖, IEEE Transaction on Power Delivery, Vol 21, No. 1, January.

S. Ekici, 2009, Classification of power system disturbance using support vector machine, Expert Systems with Application, pp. 36.

A. Borghetti, M. Bosetti, M. Di Silvestro, C.A. Nucci, and M. Paolone, 2007, ‖Continuous-wavelet transform for fault location in distribution power networks: Definition of mother wavelet inferred from fault originated transients‖, The International Conference on Power Systems Transients, Lyon, France, June 4-7.

C. Cortes and V. Vapnik, 1995, ―Support Vector Network‖, Machine Learning, 20, pp. 273-297.

V. N. Vapnik, 1998, Statistical learning theory. New York: Wiley.

N. Cristianini and J. Shawe-Taylor, 2000, An Introduction to Support Vector Machines. Cambridge, MA: Cambridge Univ. Press.

P. G. V. Axelberg, Irene Yu-Hua Gu, and M. H. J. Bollen, 2007, ―Support vector machine for classification of voltage disturbance‖, IEEE Transaction on Power delivery, Vol. 22, No. 3, July.

T. Fawcett, 2006, ―An introduction to ROC analysis‖, Pattern Recognition Letters, 27, pp 861 – 874.

S. Santoso, E. J. powers, W. M. Grady, and A. C. Parsons, 2000, Power quality disturbance waveform recognition using wavelet based neural classifier part I: Theoretical foundation‖, IEEE Trans. Power Del., vol. 15, no. 1, pp. 222–228, Jan.

Mokhtari, H. M. K. Ghartemani, and M. R. Iravani, 2002, Experimental performance evaluation of a wavelet-based online voltage detection method for power quality application”, IEEE transaction on Power Delivery, Vol. 17, No. 1, January.

T. K. Galil, 2004, ―Power quality disturbance classification using the inductive inference approach‖, IEEE Transaction on Power Delivery, Vol. 19, No. 4,October.

L. S. Moulin, A. P. A. da Silva, M. A. El-Sharkawi, and R. J. Marks, II, ―Support vector machines for transient stability analysis of large-scale power systems‖, IEEE Trans. Power Syst., vol. 19, no. 2, pp. 818–825, May.

E. F. Sanchez-Ubeda, J. Peco, P. Raymont, T. Gbrnez, and S. Baiiales, A. L. Hernhdez, 2001, ―Application of data mining techniques to identify structural congestion problems under uncertainty‖, PPTIEEE Porto Power Tech Conference 10th-13th September, Porto, Portugal

IEEE Distribution Planning Working Group Report, 1991, Radial distribution test feeders‖, IEEE Transactioins on Power Systems,, August, Volume 6, Number 3, pp 975-985.

D. R. Sawitri, A. Muntasa, M. Ashari, and M. H. Purnomo, 2009, ―Identifikasi gangguan sistem distribusi radial berbasis wavelet-support vector machine‖, Proceeding of Conference on Information Technology and Electrical Engineering (CITEE), August 4.

J. Milgram, M. Cheriet, and R. Sabourin, ―One against one or one against all‖: Which One is Better for Handwriting Recognition with SVMs?, www.livia.etsmtl.ca/publications/2006/MILGRAM_IWFHR_2006.pdf.

J. Milgram, M. Cheriet, and R. Sabourin, 2007, ―Image classification using SVMs : One–Against-One Vs One-AgainstAll‖, Proccedings of the 28th Asian Conference on Remote Sensing.

X. He, X. Song, and E. C. Frey, 2008, ―Application of threeclass ROC analysis to task-based image quality assessment of simultaneous Dual-Isotope myocardial perfusion SPECT (MPS)‖, IEEE Transaction on Medical Imaging, Vo. 27, No. 11. Nopember.

X. He, Charles E. M., Benjamin M. W. T., Jonathan M. L., and E. C. Frey, 2006, ―Three class ROC analysis – a decision theoretic approach under the ideal observer framework‖, IEEE Transaction on Medical Imaging, Vo. 25, No. 5. May.




DOI: http://dx.doi.org/10.12962/j20882033.v20i4.84

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