A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation

The reactive power optimization is an effective method to improve voltage level, decrease network losses and maintain the power system running under normal conditions. This paper provides a method combining particle swarm optimization (PSO) with linear interior point to handle the problems remaining in the traditional arithmetic of time-consuming convergence and demanding initial values. Furthermore, since chaotic mapping enjoys certainty, ergodicity and stochastic property, the paper introduces chaos mapping into the particle swarm optimization, the paper presents a new arithmetic based on a hybrid method of chaotic particle swarm optimization and linear interior point. Thanks to the superior overall exploration ability of particle swarm optimization and the local exploration ability of linear interior point within the neighborhood of the optimal point, the new method can improve the performance of both convergence and results' precision. Tested by IEEE-30, the new method provided in this paper is proved effective and practical in the optimization of shunt capacitors and tap position of load-ratio voltage transformer.

[1]  M. A. Abido,et al.  Optimal power flow using particle swarm optimization , 2002 .

[2]  S. M. Shahidehpour,et al.  Linear reactive power optimization in a large power network using the decomposition approach , 1990 .

[3]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[4]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  K. C. Mamandur,et al.  Optimal Control of Reactive Power flow for Improvements in Voltage Profiles and for Real Power Loss Minimization , 1981, IEEE Transactions on Power Apparatus and Systems.

[6]  James Kennedy,et al.  Proceedings of the 1998 IEEE International Conference on Evolutionary Computation [Book Review] , 1999, IEEE Transactions on Evolutionary Computation.

[7]  Yoshikazu Fukuyama,et al.  A hybrid particle swarm optimization for distribution state estimation , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[8]  L. L. Grigsby,et al.  Voltage Optimization Using Combined Linear Programming and Gradient Techniques , 1984, IEEE Power Engineering Review.

[9]  J. Teng,et al.  A Novel ACS-Based Optimum Switch Relocation Method , 2002, IEEE Power Engineering Review.

[10]  Osvaldo R. Saavedra,et al.  A Cauchy-based evolution strategy for solving the reactive power dispatch problem , 2002 .

[11]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[12]  D. Bhagwan Das,et al.  Reactive power dispatch with a hybrid stochastic search technique , 2002 .

[13]  C.A. Roa-Sepulveda,et al.  A solution to the optimal power flow using simulated annealing , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[14]  Sanjay Mehrotra,et al.  On the Implementation of a Primal-Dual Interior Point Method , 1992, SIAM J. Optim..

[15]  Osvaldo R. Saavedra,et al.  Optimal reactive power dispatch using evolutionary computation: extended algorithms , 1999 .