Optimization Design of Proportional-integral Controllers in High-voltage DC System Based on an Improved Particle Swarm Optimization Algorithm

Abstract The control system plays an important role in the high-voltage direct-current transmission system. An improved particle swarm optimization algorithm is proposed and employed to design the optimal proportional-integral controllers in high-voltage direct-current system in this article. Simulation examples were implemented on the CIGRE HVDC Benchmark Model [Szechtman, M., Wess, T., and Thio, C. V., “First benchmark model for HVDC control studies,” CIGRE WG 14.02 Electra, No. 135, pp. 54–73, 1991], and the results of the standard particle swarm optimization algorithm and stable boundary law method were also given as contrasts. Simulation results showed that the proportional-integral controller designed by the proposed method can satisfy the requirements of stability and dynamic response performance indexes of high-voltage direct-current transmission system.

[1]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[2]  Benjamin E. DeRoy.  Automatic control theory , 1966 .

[3]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[4]  Yoshikazu Fukuyama,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 2000 .

[5]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[6]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[7]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[8]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[9]  A. J. Urdaneta,et al.  A Hybrid Particle Swarm Optimization for Distribution State Estimation , 2002, IEEE Power Engineering Review.

[10]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[11]  M. Szechtman,et al.  First benchmark model for HVDC control studies , 1991 .

[12]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .