Hybrid Chaotic Particle Swarm Optimization Based Gains For Deregulated Automatic Generation Control

Generation control is an important objective of power system operation. In modern power system, the traditional automatic generation control (AGC) is modified by incorporating the effect of bilateral contracts. This paper investigates application of chaotic particle swarm optimization (CPSO) for optimized operation of restructured AGC system. To obtain optimum gains of controllers, application of adaptive inertia weight factor and constriction factors is proposed to improve performance of particle swarm optimization (PSO) algorithm. It is also observed that chaos mapping using logistic map sequence increases convergence rate of traditional PSO algorithm. The hybrid method presented in this paper gives global optimum gains of controller with significant improvement in convergence rate over basic PSO algorithm. The effectiveness and efficiency of the proposed algorithm have been tested on two area restructure system.