Fault Detection Based on Hierarchical Cluster Analysis in Wide Area Backup Protection System

Yagang ZHANG, Jinfang ZHANG, Jing MA, Zengping WANG

.

DOI: 10.4236/epe.2009.11004                

Abstract

In wide area backup protection of electric power systems, the prerequisite of protection device's accurate, fast and reliable performance is its corresponding fault type and fault location can be discriminated quickly and defined exactly. In our study, global information will be introduced into the backup protection system. By analyzing and computing real-time PMU measurements, basing on cluster analysis theory, we are using mainly hierarchical cluster analysis to search after the statistical laws of electrical quantities' marked changes. Then we carry out fast and exact detection of fault components and fault sections, and finally accomplish fault isolation. The facts show that the fault detection of fault component (fault section) can be performed successfully by hierarchical cluster analysis and calculation. The results of hierarchical cluster analysis are accurate and reliable, and the dendrograms of hierarchical cluster analysis are in intuition.

Keywords

wide area backup protection, phasor measurement unit, PMU, wide area measurement system, WAMS, fault detection, cluster analysis

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. X. Yuan, "Wide area protection and emergency control to prevent large scale blackout," China Electric Power Press, Beijing, 2007.
[2]	L. Ye, "Study on sustainable development strategy of electric power in China in 2020," Electric Power, Vol. 36, No. 10, 1-72003.
[3]	Y. S. Xue, "Interactions between power market stability and power system stability," Automation of Electric Power Systems, Vol. 26, No. 21-22, pp. 1-6, 1-4, 2002.
[4]	Q. X. Yang, "A review of the application of WAMS information in electric power system protective relaying," Modern Electric Power, Vol. 23, No. 3, pp. 1, 2006.
[5]	J. Yi and X. X. Zhou, "A survey on power system wide-area protection and control," Power System Technology, Vol. 30, No. 8, pp. 7-13, 2006.
[6]	A. G. Phadke and J. S. Thorp, "Synchronized phasor measurements and their applications," Springer-Verlag, New York, 2008.
[7]	T. S. Bi, X. H. Qin, and Q. X. Yang, "A novel hybrid state estimator for including synchronized phasor measurements," Electric Power Systems Research, Vol. 78, No. 8, pp. 1343-1352, 2008.
[8]	C. Wang, C. X. Dou, X. B. Li, and Q. Q. Jia, "A WAMS/PMU-based fault location technique," Electric Power Systems Research, Vol. 77, No. 8, pp. 936-945, 2007.
[9]	C. Rakpenthai, S. Premrudeepreechacharn, S. Uatrongjit, and N. R. Watson, "Measurement placement for power system state estimation using decomposition technique," Electric Power Systems Research, Vol. 75, No. 1, pp. 41-49, 2005.
[10]	J. N. Peng, Y. Z. Sun, and H. F. Wang, "Optimal PMU placement for full network observability using Tabu search algorithm," International Journal of Electrical Power & Energy Systems, Vol. 28, No. 4, pp. 223-231, 2006.
[11]	X. Q. He, "Morden statistical analysis methods and applications," China Renmin University Press, Beijing, 2007.
[12]	X. L. Yu and X. S. Ren, "Multivariate statistical analysis," China Statistic Press, Beijing, 1998.
[13]	Y. T. Zhang and K. T. Fang, "Introduction to multivariate statistical analysis," Science Press, Beijing, 1982.
[14]	A. Z. Arifin and A. Asano, "Image segmentation by histogram thresholding using hierarchical cluster analysis," Pattern Recognition Letters, Vol. 27, No. 13, pp. 1515-1521, 2006.
[15]	X. Otazu and O. Pujol, "Wavelet based approach to cluster analysis: Application on low dimensional data sets," Pattern Recognition Letters, Vol. 27, NO. 14, pp.1590-1605, 2006.
[16]	H. S. Park and D. K. Baik, "A study for control of client value using cluster analysis," Journal of Network and Computer Applications, Vol. 29, No. 4, pp. 262-276, 2006.
[17]	V. Tola, F. Lillo, M. Gallegati, and R. N. Mantegna, "Cluster analysis for portfolio optimization," Journal of Economic Dynamics and Control, Vol. 32, No. 1, pp. 235-258, 2008.
[18]	W. X. Zhao, P. K. Hopke, and K. A. Prather, "Comparison of two cluster analysis methods using single particle mass spectra," Atmospheric Environment, Vol. 42, No. 5, pp. 881-892, 2008.
[19]	M. Templ, P. Filzmoser, and C. Reimann, "Cluster analysis applied to regional geochemical data: Problems and possibilities," Applied Geochemistry, Vol. 23, No. 8, pp. 2198-2213, 2008.
[20]	Y. G. Zhang, P. Zhang, and H. F. Shi, "Statistic character in nonlinear systems," Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, Vol. 5, pp. 2598-2602, 2007.
[21]	Y. G. Zhang, C. J. Wang, and Z. Zhou, "Inherent randomicity in 4-symbolic dynamics," Chaos, Solitons and Fractals, Vol. 28, No. 1, pp. 236-243, 2006.
[22]	Y. G. Zhang and C. J. Wang, "Multiformity of inherent randomicity and visitation density in n-symbolic dynamics," Chaos, Solitons and Fractals, Vol. 33, No. 2, pp. 685-694, 2007.