ENHANCING SUSTAINABLE POWER SYSTEM STABILITY USING ANN-JAYA-PID CONTROLLERS FOR LOAD FREQUENCY CONTROL

The increasing penetration of renewable energy sources into contemporary power grids is challenging frequency stabilization because these sources are intermittent and nonlinear. Current load frequency control (LFC) strategies are inadequate in addressing these challenges effectively. This paper proposes an advanced LFC strategy that combines an Artificial Neural Network (ANN) with the Jaya Algorithm to optimize PID controller parameters in a two-area power system. The ANN models the system dynamics under fluctuating load conditions, while the Jaya algorithm optimizes the PID parameters to enhance transient response and robustness. Simulation studies conducted in MATLAB Simulink demonstrate that the ANN-Jaya-PID controller achieves superior frequency regulation performance, exhibiting reduced settling time, overshoot, and steady-state error compared to conventional PID, ANN-PID, and Jaya-optimised PID controllers. The comparison shows that the ANN-JAYA-PID controller surpasses the ANN-PID controller under step load perturbations of 0.1 and 0.2. It achieves notable reductions in transient time of up to 18.61 per cent, overshoot of up to 97.03 per cent, undershoot of up to 0.39 per cent, and ITAE of up to 94.97 per cent, thereby improving system stability and performance. Beyond technical improvements, this research frames the control strategy’s contribution within the context of the United Nations Sustainable Development Goals (SDGs), specifically SDG 7 (Affordable and Clean Energy). By enhancing grid stability and enabling greater renewable penetration, the proposed controller supports the transition to a sustainable, resilient, and low-carbon energy future. The findings underscore the critical role of intelligent control systems in aligning power system innovations with global sustainability objectives.