A deep asynchronous actor‐critic learning‐based event‐triggered decentralized load frequency control of power systems with communication delays

Abstract This article proposes a novel asynchronous advantage actor‐critic (A3C) learning‐based dynamic event‐triggered mechanism for the decentralized load frequency regulation to alleviate the local‐area communication burden and influence of the load fluctuations. The proposed dynamic event‐triggered mechanism applies the A3C algorithm to optimally adjust the threshold of the event‐triggered function in real time. In the A3C algorithm framework, the long short‐term memory (LSTM) network is used to estimate the policy function and value function. First, for each control area, a novel model of the decentralized load frequency control (LFC) system is established to design the event‐triggered communication mechanism and deal with the communication delay simultaneously. Then, based on the Lyapunov stability theory, the controller gain parameters of the decentralized LFC system and the margins of the even‐triggering thresholds are derived by solving a series of linear matrix inequalities (LMIs). Finally, a three‐area and four‐area power systems are used to evaluate the proposed decentralized LFC method. Simulation results show that the proposed method can greatly reduce the data transmission times and preserve a satisfactory system performance.