Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis

Qais H. Alsafasfeh, Ikhlas Abdel-Qader, Ahmad M. Harb

Department of Electrical and Computer Engineering, Western Michigan University, Kalamazoo, USA.
Electrical Engineering Department, Tafila Technical University, Tafila, Jordan.
Energy Engineering Department, German Jordanian University, Amman, Jordan.

DOI: 10.4236/epe.2012.46064                

Abstract

A vital attribute of electrical power network is the continuity of service with a high level of reliability. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing on fault detection, classification and localization. In this paper, a new protective relaying framework to detect, classify and localize faults in an electrical power transmission system is presented. This work will extract phase current values during ( )th of a cycle to generate unique signatures. By utilizing principal component analysis (PCA) methods, this system will identify and classify any fault instantaneously. Also, by using the curve fitting polynomial technique with our index pattern obtained from the unique fault signature, the location of the fault can be determined with a significant accuracy.

Keywords

Fault Detection and Classification; Protective Relaying; PCA; PSCAD

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	M. Kezunovic, C. C. Liu, J. McDonald and L. E. Smith, “Automated Fault Analysis,” IEEE Tutorial, IEEE Power Engineering Society, 2000.
[2]	M. Kezunovic and I. Rikalo, “Detect and Classify Faults Using Neural Nets,” IEEE Computer Applications in Power, Vol. 9, No. 4, 1996, pp. 42-47. doi:10.1109/67.539846
[3]	K. Ferreira, “Fault Location for Power Transmission Systems Using Magnetic Field Sensing Coils,” Master Thesis, Worcester Polytechnic Institute, April 2007.
[4]	A. Jain, A. Thoke and R. Patel, “Fault Classification of Double Circuit Transmission Line Using Artificial Neural Network,” International Journal of Electrical and Electronics Engineering, Vol. 1, No. 4, 2008, pp. 230-235.
[5]	A. Jain and A. Thoke, “Classification of Single Line to Ground Faults on Double Circuit Transmission Line Using ANN,” International Journal of Computer and Electrical Engineering, Vol. 1, No. 2, 2009, pp. 196-203.
[6]	M. Sanaye-Pasand and H. Khorashadi-Zadeh, “Transmission Line Fault Detection & Phase Selection Using ANN,” International Conference on Power Systems Transients— IPST, New Orleans, 2003, pp.
[7]	H. Zheng-You, C. Xiao-Qing and F. Ling, “Wavelet Entropy Measure Definition and Its Application for Transmission Line Fault Detection and Identification (Part II: Fault Detection in Transmission Line),” International Conference on Power System Technology, Chongqing, 22-26 October 2006, pp. 1-5.
[8]	K. S. Swarup, N. Kamaraj and R. Rajeswari, “Fault Diagnosis of Parallel Transmission Lines Using Wavelet Based ANFIS,” International Journal of Electrical and Power Engineering, Vol. 1, No. 4, 2007, pp. 410-415.
[9]	V. Malathi and N. Marimuthu, “Multi-Class Support Vector Machine Approach for Fault Classification in Power Transmission Line,” IEEE International Conference on Sustainable Energy Technologies ICSET 2008, Singapore, 24-27 November 2008, pp. 67-71.
[10]	S. El Safty and A. El-Zonkoly, “Applying Wavelet Entropy Principle in Fault Classification, International,” Journal of Electrical Power & Energy Systems, Vol. 31, No. 10, 2009, pp. 604-607. doi:10.1016/j.ijepes.2009.06.003
[11]	J. Upendar, C. P. Gupta and G. K. Singh, “Discrete Wavelet Transform and Genetic Algorithm Based Fault Classification of Transmission Systems, National Power Systems Conference (NPSC-2008),” Indian Institute of Technology, Bombay, December 2008, pp. 223-228.
[12]	D. Biswarup and V. Reddy, “Fuzzy-Logic-Based Fault Classification Scheme for Digital Distance Protection,” IEEE Transactions on Power Delivery, Vol. 20, No. 1, 2005, pp. 609-616.
[13]	K. Razi, M Hagh and G Ahrabian, “High Accurate Fault Classification of Power Transmission Lines Using Fuzzy Logic,” International Power Engineering Conference IPEC, Singapore, 3-6 December 2007, pp. 42-46.
[14]	S. Vaslilc and M. kezunovic, “Fuzzy ART Neural Network Algorithm for Classifying the Power System Faults,” IEEE Transactions on Power Delivery, Vol. 20, No. 2, 2005, pp. 1306-1314. doi:10.1109/TPWRD.2004.834676
[15]	S. Samantaray, P. Dash and G. Panda, “Transmission Line Fault Detection Using Time-Frequency Analysis,” IEEE Indicon 2005 Conference, Chennai, 2005, pp. 162-166.
[16]	E. Styvaktakis, M. Bollen and I. Gu, “Automatic Classification of Power System Events Using rms Voltage Measurements,” IEEE Power Engineering Society Summer Meeting, Chicago, 25 July 2002, pp. 824-829.
[17]	H. Saadat, “Power System Analysis,” McGraw Hill, New Yoek, 2002.
[18]	I. Jollife, “Principal Component Analysis,” Verlag, Springer, 1986.
[19]	J. Karhunen and J. Joutsensalo, “Generalizations of Principal Component Analysis, Optimization Problems, and Neural Networks,” Neural Networks, Vol. 8, No. 4, 1995, pp. 549-562. doi:10.1016/0893-6080(94)00098-7
[20]	S. Nayar and T. Poggio, “Early Visual Learning,” Oxford University Press, New York, 1996.
[21]	I. Abdel-Qader, S. Pashaie-Rad, O. Abudayyeh and S. Yehia, “PCA-Based Algorithm for Unsupervised Bridge Crack Detection,” Advances in Engineering Software, Vol. 37, No. 12, 2006, pp. 771-778. doi:10.1016/j.advengsoft.2006.06.002
[22]	D. Widdows, “Geometry and Meaning,” University of Chicago Press, Chicago, 2003.