Improved PSO based automatic generation control of multi-source nonlinear power systems interconnected by AC/DC links

Abstract

This paper presents the automatic generation control of two unequal areas with diverse power generation sources like thermal, hydro, wind and diesel power plants. Three evolutionary optimization techniques named Bacteria Foraging algorithm, Particle swarm optimization (PSO) and Improved PSO (IPSO) have been applied to tune the PID controller for the power system under study. In this paper an improved PSO technique with a constraint treatment mechanism called dynamic search space squeezing strategy is devised to accelerate the optimization process in the PSO algorithm. The dynamic performance of two unequal areas with diverse sources is investigated by the proposed IPSO optimized PID controller and with the cost function integral of time multiplied absolute error (ITAE) considering 1% step load perturbation in either one of the control areas and all of the control areas. It is found that significant improvement in the system dynamic performance is achieved by considering parallel AC/DC lines in comparison to only AC tie lines between control areas. The parameters obtained with proposed approach at nominal condition need not be required to reset while performing sensitivity analysis. Also, satisfactory system performance is obtained when subjected to random load perturbation. Furthermore, the wind and diesel sources are major contributor of power generations in load disturbances and considered as ultimate participating sources to meet the peak load for improvement of dynamics of power system.

Keywords

• automatic generation control
• dynamic performance
• multi source power system
• hvdc link
• renewable energy sources
• dynamic search space squeezing strategy