Fault Diagnosis Based on Graph Theory and Linear Discriminant Principle in Electric Power Network

Yagang ZHANG, Qian MA, Jinfang ZHANG, Jing MA, Zengping WANG

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DOI: 10.4236/wsn.2010.21009                

Abstract

In this paper, we adopt a novel topological approach to fault diagnosis. In our researches, global information will be introduced into electric power network, we are using mainly BFS of graph theory algorithms and linear discriminant principle to resolve fast and exact analysis of faulty components and faulty sections, and finally accomplish fault diagnosis. The results of BFS and linear discriminant are identical. The main technical contributions and innovations in this paper include, introducing global information into electric power network, developing a novel topological analysis to fault diagnosis. Graph theory algorithms can be used to model many different physical and abstract systems such as transportation and communication networks, models for business administration, political science, and psychology and so on. And the linear discriminant is a procedure used to classify an object into one of several a priori groupings dependent on the individual characteristics of the object. In the study of fault diagnosis in electric power network, graph theory algorithms and linear discriminant technology must also have a good prospect of application.

Keywords

Fault Diagnosis, Graph Theory, BFS, Linear Discriminant Principle, Electric Power Network

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. Cao, “Principal component analysis based fault detection and isolation,” Ph.D. Thesis of George Mason University, 2004.
[2]	M. Marseguerra and E. Zio, “Monte Carlo simulation for model-based fault diagnosis in dynamic systems,” Reliability Engineering & System Safety, Vol. 94, No. 2, pp. 180–186, 2009.
[3]	Y. G. Lei, Z. J. He, and Y. Y. Zi, “A new approach to intelligent fault diagnosis of rotating machinery,” Expert Systems with Applications, Vol. 35, No. 4, pp. 1593– 1600, 2008.
[4]	X. Q. Xiang, J. Z. Zhou, X. L. An, B. Peng, and J. J. Yang, “Fault diagnosis based on Walsh transform and support vector machine,” Mechanical Systems and Signal Processing, Vol. 22, No. 7, pp. 1685–1693, 2008.
[5]	A. M. Pertew, H. J. Marquez, and Q. Zhao, “LMI-based sensor fault diagnosis for nonlinear lipschitz systems,” Automatica, Vol. 43, No. 8, pp. 1464–1469, 2007.
[6]	A. G. Rehorn, E. Sejdi?, and J. Jiang, “Fault diagnosis in machine tools using selective regional correlation,” Mechanical Systems and Signal Processing, Vol. 20, No. 5, pp. 1221–1238, 2006.
[7]	J. X. Yuan, “Wide area protection and emergency control to prevent large scale blackout,” China Electric Power Press, Beijing, 2007.
[8]	L. Ye, “Study on sustainable development strategy of electric power in China in 2020,” Electric Power, Vol. 36, No. 10, pp. 1–7, 2003.
[9]	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, pp. 1–4, 2002.
[10]	Q. X. Yang, “A review of the application of WAMS information in electric power system protective relaying,” Modern Electric Power, No. 3, pp. 1, 2006.
[11]	J. Yi and X. X. Zhou, “A survey on power system wide- area protection and control,” Power System Technology, Vol. 30, pp. 7–13, 2006.
[12]	Y. G. Zhang, P. Zhang and H. F. Shi, “Statistic character in nonlinear systems,” Proceedings of the Sixth Inter- national Conference on Machine Learning and Cyber- netics (ICMLC), Hong Kong, Vol. 5, pp. 2598– 2602, August 2007.
[13]	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.
[14]	Y. G. Zhang and C. J. Wang, “Multiformity of inherent randomicity and visitation density in n-symbolic dyna- mics,” Chaos, Solitons and Fractals, Vol. 33, No. 2, pp. 685–694, 2007.
[15]	J. A. Bondy and U. S. R. Murth, “Graph theory with applications,” Elsevier Science Publishing Co.,Inc., New York, 1976.
[16]	D. E. Knuth, “The art Of computer programming,” Third Edition, Addison-Wesley, Boston, 1997.
[17]	Z. Q. Bian and X. G. Zhang, “Pattern recognition,” Se- cond Edition, Tsinghua University Press, Beijing, 2000.