Multilevel optimization control for large-scale systems using genetic algorithms

This paper presents a modified approach to design multilevel controllers using structural perturbation technique for large-scale systems based on genetic algorithms. The powerful capabilities of genetic algorithms in locating the optimal solution to a given optimization problem are exploited to determine the parameters of the controller in order to meet specified performance objectives. Them proposed approach is based on obtaining local feedback controller for each subsystem in the first level by ignoring the interaction, and then a global controller at the second level provides corrective signals to neutralize the interaction effects. The proposed control, approach is used successfully to control the behaviour of a two-area power system. Simulation results are presented to illustrate the effectiveness of the proposed control approach compared to traditional method.