使用SINDy-SENDAI通过数据驱动发现多尺度电力系统振荡控制方程
Data-Driven Discovery of Multiscale Power System Oscillation Governing Equations Using SINDy-SENDAI
AI总结：
提出基于SINDy-SENDAI算法的分层多尺度框架，分离传感器数据高低频成分，用SINDy模块识别简约控制方程，可进行物理解释、稳定性评估和预测，优于现有方法。
摘要：
监测机电振荡对维持现代电力系统稳定性至关重要，尤其是在基于逆变器的资源（IBR）渗透率增加的情况下，其引入了新的动态行为。在这项工作中，我们提出了一种基于SINDy-SENDAI算法的分层多尺度框架，以表征广域测量所捕获的暂态动力学。所提出的深度学习架构能够稳健地分离嵌入在传感器数据中的低频和高频成分，并在潜在空间中纳入一个非线性动力系统的稀疏识别（SINDy）模块，以识别简约的控制方程。与传统的深度学习方法不同，传统方法通常会产生具有有限可解释性的黑箱模型，所提出的框架学习了一种明确的动力学表示，从而能够对机电振荡进行物理解释、稳定性评估和预测。鉴于现代电力系统的社会重要性，所提出的方法经过专门设计，以满足实际部署的关键要求，即在各种运行条件下的鲁棒性、可解释性和稳定性能。该框架首先在两区域Kundur测试系统上使用传统模态分析作为基准进行验证，随后在两个真实世界数据集上进行演示：2016年伊比利亚振荡事件和2021年意大利南部电网的环境测量数据。结果表明，SINDy-SENDAI始终优于现有技术的Hankel-DMD方法，并且所学习的潜在动力学具有足够的信息，能够准确地重建和预测原始状态空间中整个系统的行为。
英文摘要：
Monitoring electromechanical oscillations is crucial for maintaining the stability of modern power systems, particularly in the presence of increasing penetrations of inverter-based resources (IBRs), which introduce new dynamic behaviors. In this work, we propose a hierarchical multiscale framework based on the SINDy-SENDAI algorithm to characterize the transient dynamics captured by wide-area measurements. The proposed deep learning architecture robustly separates low- and high-frequency components embedded in sensor data and incorporates a Sparse Identification of Nonlinear Dynamical Systems (SINDy) module in the latent space to identify parsimonious governing equations. In contrast to conventional deep learning approaches that often produce black-box models with limited interpretability, the proposed framework learns an explicit dynamical representation, enabling physical interpretation, stability assessment, and forecasting of electromechanical oscillations. Given the societal importance of modern power systems, the proposed approach is specifically designed to satisfy key requirements for practical deployment, namely robustness, interpretability, and stable performance under diverse operating conditions. The framework is first validated on the two-area Kundur test system using conventional modal analysis as ground truth and subsequently demonstrated on two real-world datasets: the 2016 Iberian oscillatory event and the 2021 ambient measurements from the southern Italian power grid. The results show that SINDy-SENDAI consistently outperforms the state-of-the-art Hankel-DMD method and that the learned latent dynamics are sufficiently informative to accurately reconstruct and predict the behavior of the full system in the original state space.
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