Application of soft computing techniques for multi source deregulated power system

In this paper, an interconnected power system is proposed for Automatic Generation Control (AGC) in restructured power environment. The customized AGC scheme is projected in deregulated environment for multi-source combination of hydro, reheat thermal and gas generating units in entire area. Proportional integral derivative controller is offered for AGC scheme and the gains are optimised through soft computing techniques such as Hybrid Chaotic Particle Swarm Optimization (HCPSO) algorithm, Real Coded Genetic Algorithm (RCGA) and also with Artificial Neural Network (ANN). The PSO chosen here carves out the AGC problem through the addition of adaptive inertia weight factor and adaptive constriction factors. The intense trend in deregulated system leads to the aggressiveness in frequency and tie line power deviations. It is observed that the chaos mapping of PSO enhance the rate of convergence using logistics map sequence. The proposed algorithms are tested on three area power system for different electricity contracted scenarios under various operating conditions with Generation Rate Constraint (GRC). Analysis reveals that proposed HCPSO improves significantly the dynamical performances of system such as settling time and overshoot. The comparative results show the robust performance of HCPSO against parametric uncertainties for a wide range of load demands and disturbances.