Improving Load Frequency Control in Power Systems by Optimizing PID Controllers with the Brown Bear Algorithm

The goal of this study is to improve the PID controller parameters for a two-area cogeneration power plant system by using three optimization techniques: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Brown Bear Optimization Algorithm (BOA). The tuning of the PID controller was evaluated using MATLAB R2024B on a system equipped with an AMD Ryzen 7 7735HS processor, an RTX 4050 graphics card and 32 GB of RAM. The results show that the BOA is better than both the GA and the PSO at finding the best PID parameters, especially for the IAE (Integral of Absolute Error) and ITAE (Integral of Time-weighted Absolute Error) objective functions. The BOA demonstrated improved effectiveness in stabilizing frequency deviations while also minimizing both settling time and error within the system. The enhanced performance of the PID controller through BOA leads to greater stability and efficiency in power generation and distribution, thereby fostering a more dependable automatic power generation control system. This investigation emphasizes the promise of the BOA as a valuable instrument for enhancing PID controllers in extensive interconnected power systems.