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.

中文翻译：

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一种基于深度异步actor-critic学习的事件触发分布式负载频率控制电力系统的通信延迟

本文提出了一种新颖的基于异步优势actor-critic（A3C）学习的动态事件触发机制，用于分散负载频率调节，以减轻局域通信负担和负载波动的影响。所提出的动态事件触发机制应用A3C算法实时优化调整事件触发函数的阈值。在A3C算法框架中，使用长短期记忆（LSTM）网络来估计策略函数和价值函数。首先，针对每个控制区域，建立了分散负载频率控制（LFC）系统的新模型，以设计事件触发的通信机制并同时处理通信延迟。然后，基于李雅普诺夫稳定性理论，通过求解一系列线性矩阵不等式（LMI），得到分散式LFC系统的控制器增益参数和偶触发阈值的裕度。最后，使用三区域和四区域电力系统来评估所提出的分散式 LFC 方法。仿真结果表明，所提出的方法可以大大减少数据传输次数，保持令人满意的系统性能。