Microgrids (MGs) are building blocks of smart power systems formed by integrating local power generation resources, energy storage systems, and power-consuming units. While MGs offer many benefits, including increased resilience and flexibility, there remains a need for improved control and protection techniques that can ensure their performance and automatic restoration in response to dynamic operating conditions and failure events. Recently, researchers have explored model-free emotional-learning adaptive strategies based on the emotional response of human brains to control MGs. These model-free control strategies are well-suited for handling the complexity, nonlinearity, and uncertainty present in MGs and offer several advantages over traditional approaches. This paper provides an overview of different emotional learning techniques applied to MG control and protection, their challenges, and future trends. In addition, we draw parallels between the hierarchical control architecture of MGs and the emotional learning process in the human brain, discussing their operational strategies and key areas of research. Finally, the future implementations of brain emotional learning in the control and protection of MGs are discussed, and concluding remarks on the potential of this approach are provided.

中文翻译：

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微电网控制和保护的大脑建模：最新技术、挑战和未来趋势


微电网（MG）是通过整合本地发电资源、储能系统和用电单位而形成的智能电力系统的组成部分。虽然 MG 提供了许多好处，包括增强的弹性和灵活性，但仍然需要改进的控制和保护技术，以确保其性能并自动恢复以响应动态操作条件和故障事件。最近，研究人员探索了基于人脑情绪反应的无模型情绪学习自适应策略来控制 MG。这些无模型控制策略非常适合处理 MG 中存在的复杂性、非线性和不确定性，并且与传统方法相比具有多种优势。本文概述了应用于 MG 控制和保护的不同情绪学习技术、它们的挑战和未来趋势。此外，我们将 MG 的分层控制架构与人脑的情感学习过程进行了类比，讨论了它们的操作策略和关键研究领域。最后，讨论了大脑情感学习在 MG 控制和保护中的未来应用，并对这种方法的潜力进行了总结。