Optimized PID Controller for Load Frequency Control in Single-Area Power Systems Utilizing Particle Swarm Optimization (PSO) and Genetic Algorithms(GA)

Maintaining the stability and reliability of present-day power systems is a major task, particularly in light of the rising integration of renewable energy sources and the complexity of interconnected networks. Maintaining the system frequency and balancing the tie-line power across multiarea power grids need load frequency control (LFC). However, traditional proportional-integral-derivative (PID) controllers are frequently employed, they often struggle to adapt to dynamic operating conditions and sudden load changes. To overcome these challenges, this article utilizes metaheuristic optimization methods, particularly Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), to optimize PID controllers for improved LFC performance. A thorough analysis was performed on single-area power systems using MATLAB/Simulink, with the objective function called the Integral Time Absolute Error (ITAE) for evaluating the performance. The simulation results indicate that PSO-tuned PID controllers surpass those optimized by the GA in terms of frequency stability, minimizing overshoot, and reducing settling time. These findings underscore the effectiveness of PSO in quickly reaching optimal solutions, while remaining robust under varying load conditions. This research advances intelligent control strategies for power system stability by setting a stage for future applications in renewable-integrated smart grids.