Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2013, Article ID 263905, 7 pages
http://dx.doi.org/10.1155/2013/263905
Research Article

Operational Risk Assessment of Distribution Network Equipment Based on Rough Set and D-S Evidence Theory

1School of Economics and Management, North China Electric Power University, Beijing 102206, China
2State Grid East Inner Mongolia Electric Power Co., Ltd., Hohhot Power Supply Bureau, Hohhot 010050, China

Received 29 August 2013; Revised 22 November 2013; Accepted 22 November 2013

Academic Editor: Antonio J. M. Ferreira

Copyright © 2013 Cunbin Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. A. M. Javadian and M. R. Haghifam, Eds., “Analysis of protection system's risk in distribution networks with DG,” International Journal of Electrical Power & Energy Systems, vol. 44, no. 1, pp. 688–695, 2013. View at Google Scholar
  2. J. H. Yang, Z. H. Yang, and L. Jun, Eds., “Risk assessment research on power transformers based on risk probability for power transformer,” Power System and Clean Energy, vol. 28, no. 3, pp. 044–049, 2012 (Chinese). View at Google Scholar
  3. Y. L. Dong, Y. J. Gu, and K. Yang, Eds., “Criticality analysis on equipment in power plant based on Monte Carlo simulation,” Proceedings of the Chinese Society of Electrical Engineering, vol. 23, no. 8, pp. 201–205, 2003. View at Google Scholar · View at Scopus
  4. K. Bai and R. H. Luo, “Study on risk assessment of transmission and distribution equipments,” Electric Power, vol. 42, no. 10, pp. 048–051, 2009. View at Google Scholar
  5. Z. H. Dai, Z. P. Wang, and Y. Jiao, Eds., “Dynamic reliability assessment of protection system based on dynamic fault tree and Monte Carlo simulation,” Proceedings of the Chinese Society of Electrical Engineering, vol. 31, no. 19, pp. 105–113, 2011. View at Google Scholar · View at Scopus
  6. Z. L. Yang, Research on special equipment risk management [Ph.D. thesis], Tianjin University, Tianjin, China, 2008.
  7. X. P. Yang, H. P. Nan, and J. Zhang, Eds., “Application of information fusion technology on fault diagnosis of hydropower generating unit,” Journal of Hydroelectric Engineering, vol. 23, no. 6, pp. 111–115, 2004. View at Google Scholar · View at Scopus
  8. X. L. Wang and H. Y. Dong, Eds., “State evaluation of secondary device in 750 kV power grid based on information fusion,” Proceedings of the Chinese Society of Universities, vol. 25, no. 1, pp. 040–046, 2013. View at Google Scholar
  9. D. Xiao and P. Sun, Eds., “Transformer risk assessment based on the neural network and genetic algorithm,” Yunnan Electric Power, vol. 40, no. 5, pp. 020–024, 2012 (Chinese). View at Google Scholar
  10. Y. H. Feng and X. J. Liu, Eds., “Power transformer risk assessment and repair decision based on cloud model,” Rural Electrification, vol. 12, pp. 041–042, 2012 (Chinese). View at Google Scholar
  11. Q. Yu and W. G. Li, Eds., “A risk assessment method of power transformer based on fuzzy analytic hierarchy process and neural network,” Journal of Hunan University(Natural Sciences), vol. 39, no. 5, pp. 059–064, 2012. View at Google Scholar
  12. Y. Y. Wang, Y. Yuan, J. Li, L. Du, and J. Zhou, Eds., “Fuzzy risk assessment model of power transformer based on Borda number theory,” High Voltage Engineering, vol. 34, no. 12, pp. 2668–2673, 2008. View at Google Scholar · View at Scopus
  13. J. N. Mordeson, “Rough set theory applied to (fuzzy) ideal theory,” Fuzzy Sets and Systems, vol. 121, no. 2, pp. 315–324, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  14. L. Y. Zhai, L. P. Khoo, and S. Fok, Eds., “Feature extraction using rough set theory and genetic algorithms-an application for the simplification of product quality evaluation,” Computers and Industrial Engineering, vol. 43, no. 4, pp. 661–676, 2002. View at Publisher · View at Google Scholar · View at Scopus
  15. E. H. T. Francis and L. X. Shen, “Economic and financial prediction using rough sets model,” European Journal of Operational Research, vol. 141, no. 3, pp. 641–659, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  16. E. H. T. Francis and L. X. Shen, “Fault diagnosis based on Rough Set Theory,” Engineering Applications of Artificial Intelligence, vol. 16, no. 1, pp. 39–43, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. F. Witlox and H. Tindemans, Eds., “The application of rough sets analysis in activity-based modelling. Opportunities and constraints,” Expert Systems with Applications, vol. 27, no. 4, pp. 585–592, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. J. B. Geng, W. Qiu, X. Kong, and H. Liu, Eds., “Technical condition evaluation for devices based on rough set theory and D-S evidence theory,” Systems Engineering and Electronics, vol. 30, no. 1, pp. 112–115, 2008. View at Google Scholar · View at Scopus
  19. T. Li and Q. Feng, Eds., “Threat assessment based on entropy weight grey incidence and D-S theory of evidence,” Application Research of Computers, vol. 30, no. 2, pp. 380–382, 2013. View at Google Scholar
  20. X. F. Fan and M. J. Zuo, “Fault diagnosis of machines based on D-S evidence theory. Part 2: application of the improved D-S evidence theory in gearbox fault diagnosis,” Pattern Recognition Letters, vol. 27, no. 5, pp. 377–385, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Guo and W. Li, “Combination rule of D-S evidence theory based on the strategy of cross merging between evidences,” Expert Systems with Applications, vol. 38, no. 10, pp. 13360–13366, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. X. F. Qian, Application of rough set theory in the transformer fault diagnosis [M.S. thesis], Nanjing University of Science & Technology, Jiangsu, China, 2005.