Data-Driven Event-Triggered Fixed-Time Load Frequency Control for Multi-Area Power Systems with Input Delays

Load frequency control is essential for maintaining power system stability, especially under uncertainties and input delays. This paper proposes a reinforcement learning-based dual-channel dynamic event-triggered fixed-time load frequency control approach for uncertain multi-area power systems with input delays. A non-singular fast terminal sliding mode technique is employed to guarantee that the tracking error converges within a fixed time. To address system uncertainties and input delays, actor neural networks are designed to estimate the modeling uncertainties and provide compensation, and critic neural networks evaluate execution costs. To further enhance efficiency, a dual-channel event-triggered mechanism is designed, reducing communication overhead through independent dynamic event-triggering strategies for control input and output channels. The stability of the proposed method is rigorously analyzed using the Lyapunov method. Simulation results demonstrate faster convergence, reduced communication costs, and improved frequency stability compared to existing methods.