Non dominated Cuckoo search algorithm optimized controllers to improve the frequency regulation characteristics of wind thermal power system

Controllers in some power system problems are required to satisfy different performance objectives, which could be conflicting with one another. Therefore in the process of their gain tuning, when the problem is formulated within an optimization framework, it becomes necessary to achieve multiple objectives with a method multi-objective optimization method. This work presents a new method of multi-objective optimization method to optimize several controller parameters. The problem deals with optimization of controllers of doubly fed induction generator modeled for frequency regulation in an interconnected two-area wind power integrated thermal power system. The gains of integral controller of automatic generation control loop and the proportional and derivative controllers of doubly fed induction generator inertial control loop are optimized in a coordinated manner by employing a multi-objective non-dominated sorting based Cuckoo search algorithm. The algorithm is formed by synthesizing the parallel searching abilities of Cuckoo search algorithm (CSA) with the non dominated sorting methodology adopted in Non dominated sorting genetic algorithm (NSGA-II). Based on the set of selected instances, the algorithm termed as non dominated sorting Cuckoo search (NSCS), exhibits better efficiency of optimization compared to the NSGA-II, CSA, genetic algorithm, and particle swarm optimization. The performance of the designed controller is further compared with the performance obtained with a modified version of NSCS, which includes the method of archiving in it. The designed set of controllers perform robustly even with the variation in disturbances, parameter and operating conditions in the system.