Application of a Novel Improved Crow Search Algorithm for Optimal Designing of PID Controller in Two Area Thermal Power System

Numerous control areas, or regions, make up the connected power networks. Each control area's responsibility is to provide enough power to meet the load requirements of its customers while also keeping the interchanged power and system frequency at predetermined levels. Thus, to ensure that the quality of the power supplied is maintained at the necessary level, automated generation control, also known as load frequency controller, or LFC for short, is devised and put into operation to automatically balance generated power and load demand in each control region. A novel artificial intelligence search technique is applied in this research to determine the optimal load frequency controller (LFC) parameters for PID controller. A PID controller is anticipated to be included in a two area thermal system. To obtain optimal results, three optimization strategies have been analyzed: Particle Swarm Optimization (PSO), Crow Search Algorithm (CSA), and Improved Crow Search Algorithm (ICSA). The time domain objective function is minimized by using the Improved Crow Search Algorithm (ICSA) to find the optimum controller parameters. To show the better efficiency of the proposed ICSA in tuning PID controller, the performance characteristics of the proposed technique has been contrasted to that of the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO). The proposed ICSA based PID proves to be better than CSA based PID and PSO based PID in terms undershoots and overshoots.