The Transactions of the Korean Institute of Electrical Engineers A (대한전기학회논문지:전력기술부문A)

The Korean Institute of Electrical Engineers (대한전기학회)
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A study on Fault Diagnosis in Power systems Using Probabilistic Neural Network

확률신경회로망을 이용한 전력계통의 고장진단에 관한 연구

  • Published : 2001.02.01
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Abstract

This paper presents the new methods of fault diagnosis through multiple alarm processing of protective relays and circuit breakers in power systems using probabilistic neural networks. In this paper, fault section detection neural network (FSDNN) for fault diagnosis is designed using the alarm information of relays or circuit breakers. In contrast to conventional methods, the proposed FSDNN determines the fault section directly and fast. To show the possibility of the proposed method, it is simulated through simulation panel for Sinyangsan substation system in KEPCO (Korea Electric Power Corporation) and the case studies show the effectiveness of the probabilistic neural network mehtod for the fault diagnosis.

Keywords

References

  1. A. A. Girgis and M. B. Johnsm, 'A Hybrid Expert System for Faulted Section Identification, Fault Type Classification and Selection of Fault Location Algorithms,' IEEE Trans. on Power Delivery, vol. 4, no. 2m April 1989, pp. 978-985 https://doi.org/10.1109/61.25578
  2. H. Yang, W. Chang, and C. Huang, 'On-Line Fault Diagnosis of Power Substation Using Connectionist Expert System', IEEE Trans. on Power Systems, vol. 10, no. 1, Feb. 1995, pp. 323-331 https://doi.org/10.1109/59.373914
  3. P. D. Wasserman, Advanced Methods in Neural Computing, Van Nostrand Reinhold, 1993
  4. H. Yang, W. Chang, and C. Huang, 'A New Neural Networks Approach to On-Line Fault Section Estimation Using Information of Protective Relays and Circuit Breakers', IEEE Trans. on Power Delivery, vol.9, no. 1, Jan. 1994, pp. 220-230 https://doi.org/10.1109/61.277693
  5. A. P. Alves da Silva, A. H. F. Insfran, P. M. da Silveira, and G. Lambert-Torres, 'Fault Localization Estimation via Associative Memories', Intelligent System Application to Power systems, 1994, pp. 477-484
  6. E. Handschin, W. Hoffmann, and D. Kuhlmann, 'Fault Location in Electrical Energy Systems Using Artificial Neural Networks', Intelligent System Application to Power systems, 1994, pp. 557-564
  7. M. Kezunovic, I. Rikalo, and D. J. Sobajic, 'Neural Network Applications to Real-Time and Off-Line Fault Analysis', Intelligent System Application to Power systems, 1994, pp. 29-36
  8. J. Momoh, L. Dias, K. Butler, and D. Laird, 'Selection of Artificial Neural Networks for Distribution System Fault Diagnosis', Intelligent System Application to Power systems, 1994, pp. 37-43
  9. K. G. Narendra, V. K. Sood, K. Khorasani, and R. Patel, Application of a Radial Basis Function (RBF) Neural Network for Fault Diagnosis in a HVDC System, IEEE Trans. on Power Systems, vol. 13, no. 1, Feb. 1998, pp. 177-183 https://doi.org/10.1109/59.651633
  10. D. F. Specht, 'Probabilistic Neural Networks', Neural Network, vol. 3, 1990, pp. 109-118 https://doi.org/10.1016/0893-6080(90)90049-Q