Hybrid constrained genetic algorithm/particle swarm optimisation load flow algorithm

A hybrid constrained genetic algorithm and particle swarm optimisation (PSO) method for the evaluation of the load flow in heavy-loaded power systems is developed. The new algorithm is applied to find the maximum loading points of three IEEE test systems. The experimental determination of the best values of the parameters for use in the PSO part of the hybrid algorithm is also reported.

[1]  Zhao Hui,et al.  Optimal Design of Power System Stabilizer Using Particle Swarm Optimization , 2006 .

[2]  I. Dobson.  Observations on the geometry of saddle node bifurcation and voltage collapse in electrical power systems , 1992 .

[3]  L. Ni,et al.  Parallel processing for the load flow of power systems: the approach and applications , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[4]  A. Li,et al.  Development of constrained-genetic-algorithm load-flow method , 1997 .

[5]  M. A. Abido.  Optimal des'ign of Power System Stabilizers Using Particle Swarm Opt'imization , 2002, IEEE Power Engineering Review.

[6]  Chuangxin Guo,et al.  A multiagent-based particle swarm optimization approach for optimal reactive power dispatch , 2005 .

[7]  Joong-Rin Shin,et al.  A particle swarm optimization for economic dispatch with nonsmooth cost functions , 2005, IEEE Transactions on Power Systems.

[8]  Colin D. Simpson,et al.  Industrial Electronics , 1936, Nature.

[9]  K.P. Wong,et al.  Two-phase Particle Swarm Optimization for Load Flow Analysis , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[10]  A.C.Z. de Souza,et al.  Comparison of performance indices for detection of proximity to voltage collapse , 1996 .

[11]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  K.P. Wong,et al.  Locating Type-1 Load Flow Solutions using Hybrid Evolutionary Algorithm , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[13]  Naoto Yorino,et al.  A simplified approach to estimate maximum loading conditions in the load flow problem , 1993 .

[14]  D. Hill,et al.  Voltage stability indices for stressed power systems , 1993 .

[15]  C. Castro,et al.  A critical evaluation of step size optimization based load flow methods , 2000 .

[16]  William F. Tinney,et al.  Power Flow Solution by Newton's Method , 1967 .

[17]  Kitpo Wong,et al.  Advanced constrained genetic algorithm load flow method , 1999 .

[18]  James S. Thorp,et al.  An efficient algorithm to locate all the load flow solutions , 1993 .

[19]  Chao-Ming Huang,et al.  A particle swarm optimization to identifying the ARMAX model for short-term load forecasting , 2005 .

[20]  Carlos A. Castro,et al.  Fast computation of security margins to voltage collapse based on sensitivity analysis , 2006 .

[21]  C.A. Castro,et al.  Fast computation of voltage stability security margins using nonlinear programming techniques , 2006, IEEE Transactions on Power Systems.

[22]  Thomas J. Overbye,et al.  Improved techniques for power system voltage stability assessment using energy methods , 1991 .

[23]  F. Alvarado,et al.  Computation of closest bifurcations in power systems , 1994 .

[24]  Venkataramana Ajjarapu,et al.  The continuation power flow: a tool for steady state voltage stability analysis , 1991 .

[25]  Kiyotaka Izumi,et al.  A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems , 2005, IEEE Transactions on Industrial Electronics.

[26]  O. Alsac,et al.  Fast Decoupled Load Flow , 1974 .

[27]  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).

[28]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..