Journal of Electrical Engineering and Technology

  • 제3권4호
  • /
  • Pages.468-475
  • /
  • 2008
  • /
  • 1975-0102(pISSN)
  • /
  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지
DOI QR코드

DOI QR Code

Radial Basis Function Neural Network for Power System Transient Energy Margin Estimation

  • Karami, Ali (Faculty of Engineering, University of Guilan)
  • 발행 : 2008.12.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper presents a method for estimating the transient stability status of the power system using radial basis function(RBF) neural network with a fast hybrid training approach. A normalized transient energy margin(${\Delta}V_n$) has been obtained by the potential energy boundary surface(PEBS) method along with a time-domain simulation technique, and is used as an output of the RBF neural network. The RBF neural network is then trained to map the operating conditions of the power system to the ${\Delta}V_n$, which provides a measure of the transient stability of the power system. The proposed approach has been successfully applied to the 10-machine 39-bus New England test system, and the results are given.

키워드

참고문헌

  1. L.L. Grigsby, Power System Stability and Control: CRC Press, 2007
  2. IEEE/CIGRE Joint Task Force, 'Definition and Classification of Power System Stability', IEEE Trans. Power Systems, Vol. 1, No. 2, 2004
  3. A.L. Bettiol, A. Souza, J.L. Todesco, and J.R Tesch, 'Estimation of Critical Clearing Times Using Neural Network', in Proceedings of IEEE Bologna PowerTech Conference, Bologna, Italy, 2003
  4. P. Kundur, Power System Stability and Control: McGraw-Hill, 1994
  5. M. Pavella and P.G. Murthy, Transient Stability of Power Systems: Theory and Practice: John Wiley, 1994
  6. M.A. Pai, Energy Function Analysis for Power System Stability: Kluwer Academic, 1989
  7. A.A. Fouad and V. Vittal, Power System Transient Stability Analysis Using the Transient Energy Function Method: Prentice-Hall, 1992
  8. Y. Xue, T.V. Cutsem, and M. Pavella, 'Extended Equal Area Criterion Justifications, Generalizations, Applications', IEEE Trans.Power Systems, Vol. 4, pp. 44-52, 1989 https://doi.org/10.1109/59.32456
  9. Y. Xue, T.V. Cutsem, and M. Pavella, 'A Simple Direct Method for Fast Transient Stability Assessment of Large Power Systems', IEEE Trans.Power Systems, Vol. 3, pp. 400-412, 1998 https://doi.org/10.1109/59.192890
  10. D.J. Sobajic and Y.H. Pao, 'Artificial Neural-Net Based Dynamic Security Assessment for Electric Power Systems', IEEE Trans. Power Systems, Vol. 4, No. 1, pp. 220-226, 1989 https://doi.org/10.1109/59.32481
  11. M. Djukanovic, D.J. Sobajic, and Y.H. Pao, 'Neural-Net Based Unstable Machine Identification Using Individual Energy Functions', Int. Journal of Electrical Power and Energy Systems, Vol. 13, No. 5, pp. 255-262, 1991 https://doi.org/10.1016/0142-0615(91)90048-Z
  12. Y.H. Pao and D.J. Sobajic, 'Combined Use of Unsupervised and Supervised Learning for Dynamic Security Assessment', IEEE Trans. Power Systems, Vol. 7, No. 2, pp. 878-884, 1992 https://doi.org/10.1109/59.141799
  13. Q. Zhou, J. Davidson, and A.A. Foaud, 'Application of Artificial Neural Networks in Power System Security and Vulnerability Assessment', IEEE Trans. Power Systems, Vol. 9, No. 1, pp. 525-532, 1994 https://doi.org/10.1109/59.317570
  14. E. Hobson and G.N Allen, 'Effectiveness of Artificial Neural Networks for First Swing Stability Determination of Practical Systems', IEEE Trans. Power Systems, Vol. 9, No. 2, pp. 1062-1068, 1994 https://doi.org/10.1109/59.317625
  15. F. Aboytes and R. Ramirez, 'Transient Stability Assessment in Longitudinal Power Systems Using Artificial Neural Networks', IEEE Trans. Power Systems, Vol. 11, No. 4, pp. 2003-2010, 1996 https://doi.org/10.1109/59.544677
  16. A.G. Bahbah and A.A. Girgis, 'New Method for Generators' Angles and Angular Velocities Prediction for Transient Stability Assessment of Multimachine Power Systems Using Recurrent Artificial Neural Network', IEEE Trans. Power Systems, Vol. 19, No. 2, pp. 1015-1022, 2004 https://doi.org/10.1109/TPWRS.2004.826765
  17. T. Chen, and H. Chen, 'Approximation Capability to Functions of Several Variables Nonlinear Functional, and Operators by Radial Basis Function Neural Network', IEEE Trans. Neural Networks, Vol. 6, pp. 904-910, 1995 https://doi.org/10.1109/72.392252
  18. S. Haykin, Neural Networks: A Comprehensive Foundation, Second Edition: Prentice-Hall, 1999
  19. T. Jain, L. Srivastava, and S.N. Singh, 'Fast Voltage Contingency Screening Using Radial Basis Function Neural Network', IEEE Trans. Power Systems, Vol. 18, No. 4, pp. 359-1366, 2003 https://doi.org/10.1109/TPWRS.2002.807079
  20. N.B. Karayiannis and G.W. Mi, 'Growing Radial Basis Neural Networks: Merging Supervised and Unsupervised Learning with Network Growth techniques', IEEE Trans. Neural Networks, Vol. 8 No. 6, pp. 1492-1506, 1997 https://doi.org/10.1109/72.641471
  21. M.A. Pai, M. Laufenberg, and P.W. Sauer, 'Some Clarifications in the Transient Energy Function Method', International Journal of Electrical Power & Energy Systems, Vol. 18, No. 1, pp. 65-72, 1996 https://doi.org/10.1016/0142-0615(95)00062-3
  22. T. Athay, R. Podmore, and S. Virmani, 'A Practical Method for Direct Analysis of Transient Stability' IEEE Trans. Power Systems, Vol. 98, No. 2, pp. 573-584, 1979 https://doi.org/10.1109/TPAS.1979.319407
  23. H.D. Chiang, F.F. Wu, and P. Varaiya,, 'Foundation of the Potential Energy Boundary Surface Method for Power System Transient Stability Analysis', IEEE Trans. Circuits and Systems, Vol. 35, No. 6, pp. 712-728, 1988 https://doi.org/10.1109/31.1808

피인용 문헌

  1. Transient Stability Analysis of a Two-Machine Power System under Different Fault Clearing Times vol.3, pp.1, 2015, https://doi.org/10.12720/ijeee.3.1.28-33
  2. Partial Discharge Pattern Recognition of Cast Resin Current Transformers Using Radial Basis Function Neural Network vol.9, pp.1, 2014, https://doi.org/10.5370/JEET.2014.9.1.293