Applications of Data Mining Theory in Electrical Engineering
Yagang ZHANG, Jing MA, Jinfang ZHANG, Zengping WANG
DOI: 10.4236/eng.2009.13025   PDF    HTML     8,275 Downloads   16,793 Views   Citations


In this paper, we adopt a novel applied approach to fault analysis based on data mining theory. In our researches, global information will be introduced into the electric power system, we are using mainly cluster analysis technology of data mining theory to resolve quickly and exactly detection of fault components and fault sections, and finally accomplish fault analysis. The main technical contributions and innovations in this paper include, introducing global information into electrical engineering, developing a new application to fault analysis in electrical engineering. Data mining theory is defined as the process of automatically extracting valid, novel, potentially useful and ultimately comprehensive information from large databases. It has been widely utilized in both academic and applied scientific researches in which the data sets are generated by experiments. Data mining theory will contribute a lot in the study of electrical engineering.

Share and Cite:

Y. ZHANG, J. MA, J. ZHANG and Z. WANG, "Applications of Data Mining Theory in Electrical Engineering," Engineering, Vol. 1 No. 3, 2009, pp. 211-215. doi: 10.4236/eng.2009.13025.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Y. Shi, “Dynamic data mining on multi-dimensional data,” Ph. D. thesis of State University of New York at Buffalo, 2006.
[2] J. W. Han and M. Kamber, “Data mining: Concepts and techniques,” Second Edition, Morgan Kaufmann, Elsevier, San Francisco, 2006.
[3] D. Dursun, F. Christie, M. Charles and R. Deepa, “Analysis of healthcare coverage: A data mining approach,” Expert Systems with Applications, Vol. 36, No. 2, pp. 995–1003, 2009.
[4] Y. J. Kwon, O. A. Omitaomu, and G. N. Wang, “Data mining approaches for modeling complex electronic circuit design activities,” Computers & Industrial Engineering, Vol. 54, No. 2, pp. 229–241, 2008.
[5] K. G. Srinivasa, K. R. Venugopal, and L. M. Patnaik, “A self–adaptive migration model genetic algorithm for data mining applications,” Information Sciences, Vol. 177, No. 20, pp. 4295–4313, 2007.
[6] J. Cao, “Principal component analysis based fault dection and isolation”, Ph. D. thesis of George Mason University of Virginia, 2004.
[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 International Conference on Machine Learning and Cybernetics (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 dynamics,” Chaos, Solitons and Fractals, Vol. 33, No. 2, pp. 685–694, 2007.
[15] R. C. Robinson, “An introduction to dynamical systems: Continuous and discrete,” Pearson Education, New Jersey, 2004.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.