In this paper, a new discrete-time data-driven distributed learning control strategy for frequency/voltage regulation and active/reactive power sharing of islanded microgrids is proposed. Instead of using the static droop relationship and the conventional primary-secondary hierarchical control structure, a new control framework is adopted and a neural network is used to learn the control law. The neural network is tuned online using the operational system input/output data with no training phase. As a result, the transient performance of microgrids is improved and a remarkable plug-and-play capability is also achieved. Moreover, the stability of the closed-loop system is analyzed through the Lyapunov approach, where the interactions between different distributed energy resources are considered. The effectiveness of the proposed method is demonstrated by real-time hardware-in-the-loop experiment of a typical microgrid.

中文翻译：

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孤岛微电网的数据驱动分布式在线学习控制

在本文中，提出了一种新的离散时间数据驱动的分布式学习控制策略，用于孤岛微电网的频率/电压调节和有功/无功功率共享。不使用静态下垂关系和传统的一二级分层控制结构，而是采用一种新的控制框架，并使用神经网络来学习控制律。使用操作系统输入/输出数据在线调整神经网络，无需训练阶段。因此，微电网的暂态性能得到了改善，并实现了显着的即插即用能力。此外，通过Lyapunov方法分析了闭环系统的稳定性，其中考虑了不同分布式能源之间的相互作用。