Fractional order PID controller tuned-artificial Protozoa optimiser for LFR in multi-area interconnected power systems

Maintaining a stable frequency in multi-area power systems is critical for reliable power delivery, particularly under varying load conditions. However, conventional Load Frequency Regulation (LFR) systems often lack the responsiveness and adaptability needed to address generation-load imbalances efficiently. This study addresses the problem by proposing a robust LFR strategy using a Fractional-Order Proportional-Integral-Derivative (FOPID) controller, whose parameters are optimally tuned through the Artificial Protozoa Optimization (APO) algorithm—a novel population-based metaheuristic inspired by the behavioral patterns of protozoa. The significance of this work lies in its application to a two-area non-reheat thermal power system, where dynamic performance and system stability are vital. The APO algorithm was implemented to adjust the five parameters of the FOPID controller, and the system’s response was evaluated under step load disturbances in both areas. The results show that the APO-tuned FOPID controller achieves superior frequency regulation compared to conventional PID controllers and outperforms recent strategies such as the Neural Network Algorithm (NNA) and the Artificial Rabbits Algorithm (ARA). Key performance metrics, including settling time, overshoot, and undershoot, were significantly improved, while the Integral of Time-weighted Absolute Error (ITAE) was minimized. These findings demonstrate the APO-FOPID controller's ability to maintain transient stability and robustness against load variations, which is essential for secure and reliable grid operation. Overall, the proposed method offers a valuable solution for modern power systems requiring enhanced adaptability and control precision in load frequency regulation.