Optimal design of the grey prediction PID controller for power system stabilizers by evolutionary programming

In this paper, we propose an effective method to design the grey prediction PID controller for power system stabilizers (PSSs). The proposed method requires only a few desired state variables, such as torque angles and rotor speeds. Firstly, we use the optimal reducing technique to reduce the order of the power system model into two state variable systems, and then we use the grey prediction PID controller to predict the control signal of each generator. Moreover, we use the fuzzy controller to enhance the performance of the PID. And then we apply the evolutionary programming (EP) technique to search the optimal parameters of the PID controller and optimal forecasting step of the grey prediction. Finally, we illustrate the advantages of the proposed method with the numerical simulation of a two-machines-infinite-bus power system.

[1]  Yao-nan Yu,et al.  Optimal Power System Stabilization Through Excitation and/or Governor Control , 1972 .

[2]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[3]  Ali Feliachi,et al.  Power system stabilizers design using optimal reduced order models. II. Design , 1988 .

[4]  H. H. Happ,et al.  Power System Control and Stability , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[6]  Yi-Sheng Zhou,et al.  Optimal design for fuzzy controllers by genetic algorithms , 2000 .

[7]  Zongyuan Mao,et al.  On designing an optimal fuzzy neural network controller using genetic algorithms , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[8]  CHING-CHANG WONG,et al.  A simulated annealing approach to switching grey prediction fuzzy control system design , 1998, Int. J. Syst. Sci..

[9]  Michio Sugeno,et al.  An introductory survey of fuzzy control , 1985, Inf. Sci..

[10]  H. Happ Power system control and stability , 1979, Proceedings of the IEEE.

[11]  David B. Fogel,et al.  A Comparison of Evolutionary Programming and Genetic Algorithms on Selected Constrained Optimization Problems , 1995, Simul..

[12]  Ali Feliachi,et al.  Power system stabilizers design using optimal reduced order models. I. Model reduction , 1988 .

[13]  David B. Fogel,et al.  System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling , 1991 .

[14]  T. L. Huang,et al.  Two-level optimal output feedback stabilizer design , 1991 .