发表机构
Center for Energy Research; Department of Mechanical and Aerospace Engineering at University of California, San Diego（能源研究中心; 加州大学圣地亚哥分校机械与航空航天工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出数据驱动框架，通过离线数据确定纳什均衡并设计静态输出反馈控制器，在高保真电网模型中验证，较传统控制减少78%超调、加快30%稳定时间。
AI 中文摘要
这项工作提出了一种数据驱动框架，用于为线性时不变多智能体系统（MAS）综合输出反馈控制。我们首先开发了两种算法，利用状态和输入轨迹的离线数据样本来确定MAS的纳什均衡（NE）。接下来，我们制定了一个半定规划，在给定NE解的情况下，计算MAS中各智能体的静态输出反馈控制器。与先前工作相比，我们框架的优点包括更弱的可控性假设、无需在线信息以及数据驱动的状态估计。然后，我们使用所提出的框架为具有构网型和构网型逆变器的非线性多节点电网设计电压和频率调节的控制方案。因此，与先前在小规模数值示例中实施其数据驱动框架的工作相比，我们在MAS的高保真非线性模型中验证了我们数据驱动框架的性能。这种在现实MAS中的实施提供了小规模数值示例可能无法捕捉到的见解。结果表明，使用我们提出的框架，电网成功实现了稳定，逆变器提供了调节服务。与传统策略（如比例积分控制）相比，它可以实现高达78%的超调量减少和30%的更快稳定时间。
英文摘要
This work proposes a data-driven framework that synthesizes output-feedback control for linear time-invariant multi-agent systems (MAS). We first develop two algorithms that use offline data samples of state and input trajectories to determine a Nash equilibrium (NE) for MAS. Next, we formulate a semidefinite program that, given the NE solution, computes the static output-feedback controllers for the agents in MAS. Compared to prior work, virtues of our framework include weaker controllability assumptions, the absence of online information and data-driven estimation of states. Next, we use the proposed framework to design control schemes for voltage and frequency regulation for nonlinear multi-nodal electrical networks with grid-forming and grid-following inverters. Thus, compared to prior work that implements their data-driven frameworks in small-scale numerical examples, we validate the performance of our data-driven framework in high-fidelity nonlinear models of MAS. This implementation in realistic MAS provides insights that small-scale numerical examples may fail to capture. Results show successful grid stabilization and provision of regulation services from the inverters in the grid using our proposed framework. It can attain up to 78% less overshoot and 30% faster settling time than traditional strategies such as proportional-integral control.
