Journal of Electrical Engineering and Technology

  • 제2권2호
  • /
  • Pages.178-187
  • /
  • 2007
  • /
  • 1975-0102(pISSN)
  • /
  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지
DOI QR코드

DOI QR Code

Real-Coded Genetic Algorithm Based Design and Analysis of an Auto-Tuning Fuzzy Logic PSS

  • 발행 : 2007.06.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

One important issue in power systems is dynamic instability due to loosing balance relation between electrical generation and a varying load demand that justifies the necessity of stabilization. Moreover, Power System Stabilizer (PSS) must have capability of producing appropriate stabilizing signals over a wide range of operating conditions and disturbances. To overcome these drawbacks, this paper proposes a new method for robust design of PSS by using an auto-tuning fuzzy control in combination with Real-Coded Genetic Algorithm (RCGA). This method includes two fuzzy controllers; internal fuzzy controller and supervisor fuzzy controller. The supervisor controller tunes the internal one by on-line applying of nonlinear scaling factors to inputs and outputs. The RCGA-based method is used for off-line training of this supervisor controller. The proposed PSS is tested in three operational conditions; nominal load, heavy load, and in the case of fault occurrence in transmission line. The simulation results are provided to compare the proposed PSS with conventional fuzzy PSS and conventional PSS. By evaluating the simulation results, it is shown that the performance and robustness of proposed PSS in different operating conditions is more acceptable

키워드

참고문헌

  1. Anderson, P.M., Fouad, A.A., Power System Control and Stability, Iowa State Univ. Press, Ames, Iowa, USA, 1977
  2. Yu, Y.N., Electric Power System Dynamics, Academic Press, 1983
  3. Kundur, P., Power System Stability and Control, McGraw-Hill Inc., USA, 1994
  4. Dobrescu, M., Kamwa, I., 'A New Fuzzy Logic Power System Stabilizer Performances', IEEE PES, Power Systems Conference and Exposition, vol. 2, pp. 1056-1061, 10-13 Oct. 2004
  5. Fraile-Ardanuy, J., Zufiria, P.J., 'Adaptive Power System Stabilizer Using ANFIS and Genetic Algorithms', 44th IEEE Conference on Decision and Control, European Control Conference, CDC-ECC '05, pp. 8028-8033, 12-15 Dec. 2005
  6. Andreoiu, A., Bhattacharya, K., 'Robust Tuning of Power System Stabilisers Using a Lyapunov Method Based Genetic Algorithm', IEE Proceedings- Generation, Transmission and Distribution, vol. 149, no. 5, pp. 585-592, Sept. 2002 https://doi.org/10.1049/ip-gtd:20020600
  7. Folly, K.A., 'Multimachine Power System Stabilizer Design Based on a Simplified Version of Genetic Algorithms Combined With Learning', Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems 2005, pp. 240- 246, 6-10 Nov. 2005
  8. Nallathambi, N., Neelakantan, P.N., 'Fuzzy Logic Based Power System Stabilizer', E-Tech, pp. 68-73, 31 July 2004
  9. Akhrif, O., Okou, F.A., Dessaint, L.A., Champagne, R., 'Application of a Multivariable Feedback Linearization Scheme for Rotor Angle Stability and Voltage Regulation of Power Systems', IEEE Trans. on Power Systems, vol. 14, no. 2, pp. 620-628, May 1999 https://doi.org/10.1109/59.761889
  10. Chaturvedi, D.K., Malik, O.P., Kalra, P.K., 'Generalised Neuron-Based Adaptive Power System Stabiliser', IEE Proceedings- Generation, Transmission and Distribution, vol. 151, no. 2, pp. 213-218, 2 March 2004
  11. Hosseinzadeh, N., Kalam, A., 'A Direct Adaptive Fuzzy Power System Stabilizer', IEEE Trans. on Energy Conversion, vol. 14, no. 4, pp. 1564-1571, Dec. 1999 https://doi.org/10.1109/60.815106
  12. Soos, A., Malik, O.P., 'An $H_2$ Optimal Adaptive Power System Stabilizer', IEEE Tran. on Energy Conversion, vol. 17, no. 1, pp. 143-149, March 2002 https://doi.org/10.1109/60.986451
  13. Ruhua You, Eghbali, H.J., Nehrir, M.H., 'An Online Adaptive Neuro-Fuzzy Power System Stabilizer for Multi machine Systems', IEEE Trans. on Power Systems, vol. 18, no. 1, pp. 128-135, Feb. 2003 https://doi.org/10.1109/TPWRS.2002.804961
  14. Hiyama, T., Miyazaki, K., Satoh, H., 'A Fuzzy Logic Excitation System for Stability Enhancement of Power Systems With Multi-mode Oscillations', IEEE Trans. on Energy Conversion, vol. 11, no. 2, pp. 449- 454, June 1996 https://doi.org/10.1109/60.507659
  15. Shijie Cheng, Rujing Zhou, Lin Guan, 'An On-line Self-learning Power System Stabilizer Using a Neural Network Method', IEEE Trans. on Power Systems, vol. 12, no. 2, pp. 926-931, May 1997 https://doi.org/10.1109/59.589773
  16. Hui Ni; Heydt, G.T., Mili, L., 'Power System Stability Agents Using Robust Wide Area Control' IEEE Trans. on Power Systems, vol. 17, no. 4, pp. 1123- 1131, Nov. 2002 https://doi.org/10.1109/TPWRS.2002.805016
  17. Yadaiah, N., Babu, C.V.S.R.K., Bhattacharya, J.L., 'Fuzzy Logic Controllers - An Application to Power Systems', IEEE International Workshop on Soft Computing in Industrial Applications, SMCia/03, pp.1-6, 23-25 June 2003
  18. Abido, A.A., 'Particle Swarm Optimization for Multimachine Power System Stabilizer Design', IEEE Power Engineering Society Summer Meeting 2001, vol. 3, pp. 1346-1351, 15-19 July 2001
  19. Abido, M.A., Abdel-Magid, Y.L., 'Robust Design of Electrical Power-Based Stabilizers Using Tabu Search', IEEE Power Engineering Society Summer Meeting 2001, vol. 3, pp. 1573-1578, 15-19 July 2001
  20. Andreoiu, A., Bhattacharya, K., 'Lyapunov's method based genetic algorithm for multi-machine PSS tuning', IEEE Power Engineering Society Winter Meeting 2002, vol. 2, pp. 1495-1500, 27-31 Jan. 2002
  21. Eshelman L., Schaffer J. Real-coded Genetic Algorithms and Interval-schemata, in L. Whitely, editor, Foundations of Genetic Algorithms, vol. 2, pp. 187- 202, Morgan Kaufmann Publishers, San Francisco, 1993
  22. Young-Moon Park; Un-Chul Moon; Lee, K.Y.; A Self-Organizing Power System Stabilizer using Fuzzy Auto-Regressive Moving Average (FARMA) Model, IEEE Trans. on Energy Conversion, vol. 11, no. 2, pp.442-448, June 1996 https://doi.org/10.1109/60.507658

피인용 문헌

  1. Identification of fuzzy models using a successive tuning method with a variant identification ratio vol.159, pp.21, 2008, https://doi.org/10.1016/j.fss.2007.12.031
  2. The design of polynomial function-based neural network predictors for detection of software defects vol.229, 2013, https://doi.org/10.1016/j.ins.2011.01.026