AI 中文总结
该研究提出PC-SINDYc框架，结合物理引导库构造等技术辨识微电网频率动态，集成MPC实现实时频率控制，仿真显示其控制性能优于PI控制器等方法。
AI 中文摘要
本文提出了一种名为PC-SINDYc的新型框架，用于含分布式能源资源的微电网（MGs）的辨识与频率控制。该算法通过利用物理引导的库构造、总体最小二乘回归和随机样本一致性，能从相量测量单元（PMU）数据中鲁棒地辨识出微电网的真实频率动态，同时考虑噪声、延迟和约束激活情况。基于辨识得到的模型，PC-SINDYc框架进一步集成了模型预测控制器（MPC）以实现实时频率控制。我们证明，在温和条件下，PC-SINDYc可确保微电网的渐近稳定性。对4母线和13母线微电网的仿真结果表明，PC-SINDYc能在离线辨识阶段未出现的各类扰动下有效控制微电网频率，其性能优于比例积分（PI）控制器、传统SINDYc以及最先进的强化学习方法。
英文摘要
This paper proposes PC-SINDYc, a novel framework for the identification and frequency control of microgrids (MGs) with distributed energy resources. By leveraging physics-guided library construction, total least squares regression, and random sample consensus, the regression algorithm of PC-SINDYc robustly identifies the true frequency dynamics of MGs from phasor measurement unit (PMU) data, considering noise, delays, and constraint activations. Based on the identified model, the PC-SINDYc framework further incorporates a model predictive controller (MPC) for real-time frequency control. We prove that, under mild conditions, PC-SINDYc ensures asymptotic stability of the MG. Simulations on 4-bus and 13-bus MGs demonstrate that PC-SINDYc effectively controls MG's frequency across various disturbances unseen during the offline identification, outperforming PI controllers, conventional SINDYc, and state-of-the-art reinforcement learning methods.
