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Real-Coded Genetic Algorithm Based Design and Analysis of an Auto-Tuning Fuzzy Logic PSS
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 Title & Authors
Real-Coded Genetic Algorithm Based Design and Analysis of an Auto-Tuning Fuzzy Logic PSS
Hooshmand, Rahmat-Allah; Ataei, Mohammad;
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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
Auto-Tuning Fuzzy Controller;Dynamic Stability;Power System Stabilizer;Real Coded Genetic Algorithm;
 Cited by
A Simultaneous Perturbation Stochastic Approximation (SPSA)-Based Model Approximation and its Application for Power System Stabilizers,Ko, Hee-Sang;Lee, Kwang-Y.;Kim, Ho-Chan;

International Journal of Control, Automation, and Systems, 2008. vol.6. 4, pp.506-514
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Anderson, P.M., Fouad, A.A., Power System Control and Stability, Iowa State Univ. Press, Ames, Iowa, USA, 1977

Yu, Y.N., Electric Power System Dynamics, Academic Press, 1983

Kundur, P., Power System Stability and Control, McGraw-Hill Inc., USA, 1994

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

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

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 crossref(new window)

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

Nallathambi, N., Neelakantan, P.N., 'Fuzzy Logic Based Power System Stabilizer', E-Tech, pp. 68-73, 31 July 2004

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 crossref(new window)

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

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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

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

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

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

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

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 crossref(new window)