AI-enhanced load frequency control in multi-area power systems via a self-tuning PIDF with ANN-based NMPC and hybrid cat-pikas optimization

Ensuring frequency stability in multi-area power systems under diverse disturbances remains a major challenge. This paper proposes an AI-enhanced self-tuning nonlinear-proportional-integrator-derivative denoising filter (NL-PIDF) controller designed within an artificial neural network (ANN)-based nonlinear model predictive control (NMPC) framework and optimized using a novel Hybrid Cat-Pikas Optimization (HCPO) algorithm. The ANN predictor identifies the nonlinear system dynamics, while an error compensator mitigates steady-state offsets caused by prediction errors. To further enhance dynamic stability, a superconducting magnetic energy storage (SMES) unit is integrated in Area 1, and high-voltage direct current (HVDC) tie-lines are employed between selected areas. The approach is evaluated on a nonlinear three-area power system including steam, gas, and combined-cycle turbines, considering key nonlinearities such as the reheater, generation rate constraint (GRC), governor deadband (GDB), and boiler dynamics (BD). Simulation results, supported by time-domain and eigenvalue analyses, demonstrate significant improvements in damping frequency oscillations and inter-area power exchanges compared with conventional controllers. The proposed strategy achieves faster settling, reduced overshoot/undershoot, and enhanced robustness under random step, sinusoidal load disturbances, and wide parameter variations. In such a way that the proposed strategy reduces frequency overshoot by ≈45%, improves settling time by ≈38%, and lowers ITSE by ≈52% compared with conventional tuned PID and recent metaheuristic-based controllers, confirming its robustness against load disturbances and system nonlinearities.