Comparison Between GA and PSO Optimized PID and PSS for SMIB System

We discuss in this paper the use of genetic algorithms (GA) and particle swarm optimization (PSO) techniques to optimize the parameters of the power system stabilizer (PSS) and proportional-integral-derivative (PID) controllers for a single-machine infinite-bus bar (SMIB) system. These optimization methods are of interest due to their ability to solve complex engineering problems and improve the stability and efficiency of electrical systems. The aim of this study is to compare the performance of GA and PSO in optimizing the parameters of the PSS and PID controllers for the SMIB system. By evaluating the performance metrics such as load angle and rotor speed variations, we seek to determine the effectiveness of each optimization technique in achieving the desired stability and efficiency. The simulation results indicate the importance of carefully selecting the optimization method when optimizing the parameters of the PSS and PID controllers. We found that what may be suitable for the PSS controller in terms of optimization method may not necessarily be suitable for the PID controller. Hence, a judicious choice of the optimization method is crucial to achieve optimal performance.