CPS: Medium: Data-driven Causality Mapping, System Identification and Dynamics Characterization for Future Power Grid

所提出的研究的总体目标是利用有限的观测量来获得大规模通用非线性动力系统的关键信息和表征。在目前的数据驱动动力系统分析中，需要所有的底层状态测量和这些状态的时间演化。在现实世界中访问所有的动态状态测量是不可能的或昂贵的。该提案的目标是开发数据驱动工具，用于动态系统识别、动态事件分类和根本原因分析以及系统演变预测。该研究小组将专门进行研究，利用现有的测量来执行电力系统中发生的各种动态事件的近实时应用。所提出的数据分析方法适用于一般的非线性动态系统，也可以很容易地应用于其他的信息物理系统（CPS）。更广泛地说，在CPS和控制社区中有大量的努力来建模我们每天都与之交互的真实的世界系统（例如交通系统，通信网络，万维网等）。作为动态系统，因此，通过该项目开发的理论和技术将使这些关键系统的在线监测成为可能，使运营商能够从最小的数据流中快速分析这些系统的任何不稳定/异常行为。该项目将促进各种教育和外联活动，包括为妇女和代表性不足的少数民族学生开设新课程、短期课程、学校活动和奖学金。 概述：该提案的目标是为具有有限测量的动态系统开发算子理论数据分析技术，以识别潜在的非线性动态系统并表征其行为，例如组成组件之间的因果相互作用，稳定性监测，识别控制目标。拟议的研究属于“网络物理系统技术”领域。所提出的方法的新奇在于，它们不需要动态状态，但可以利用系统输出，使其适用于现实世界的动态系统。电力系统正在快速发展，传感器的部署越来越多，如具有高精度和高采样频率（高达120 Hz）的相量测量单元（PMU）。 这些测量将被用来开发一个等效的线性表示在一个更高的维度的功能空间，可用于在线识别和表征的非线性动态的电网。此外，机器学习技术将制定学习有效的字典功能的可扩展部署的方法。使用所提出的系统识别方法，该项目将开发基于数据驱动的信息传输的因果关系映射的理论和方法，用于检测和定位系统应力和系统组件之间的动态耦合。电网的具体应用将包括稳定性监测、轨迹预测和控制不利动态行为的目标识别。这些方法由集成的电力网络协同仿真器（IPCC）进行评估，该仿真器集成了电力传输，配电和通信系统，以在各种动态场景下为大型系统生成合成传感器数据。IPCC将能够对导致测量不一致的中间通信网络进行建模，如延迟，丢包，爱荷华州州立大学的硬件在环网络-物理试验台将用于验证和评估一些在线应用，如大型电网拓扑结构的稳定性监测和轨迹预测。该奖项反映了NSF的法定使命，并被认为值得通过评估提供支持使用基金会的知识价值和更广泛的影响审查标准。

The overarching goal of the proposed research is to derive critical information and characterization of large scale generic nonlinear dynamical systems using limited observables. In the present state-of-the-art in data-driven dynamical system analysis, all the underlying state measurements and the time evolution of these states are required. Access to all of the dynamical states measurements in real-world is impossible or expensive. The objective of the proposal is to develop data-driven tools for dynamic system identification, classification and root-cause analysis of dynamic events, and prediction of system evolution. The research team will specifically conduct research on using available measurements to perform near real-time applications for various dynamic events that occur in electric power systems. The data analytics proposed are applicable to general non-linear dynamic systems and can be easily applied to other cyberphysical systems (CPS). More broadly, there is a large effort in the CPS and control community to model real world systems that we all interact with on a daily basis (such as transportation systems, communication networks, world wide web, etc.) as dynamical systems and thus, the theory and techniques developed through this project will enable online monitoring of these critical systems, allowing operators to quickly analyze these systems for any unstable/anomalous behavior from minimal data streams. The project will promote various educational and outreach activities including developing new courses, short courses, activities in schools, and scholarships for women and underrepresented minority students. Overview: The goal of this proposal is to develop operator theoretic data analytics techniques for dynamic systems with limited measurements to identify the underlying non-linear dynamical system and characterize their behavior such as causal interactions between constituent components, stability monitoring, identifying targets for control. The proposed research is in the domain of "Technology for cyber-physical systems". The novelty of the proposed methods is that they do not require the dynamic states but can utilize system outputs, making it applicable to real-world dynamical systems. Power systems are rapidly evolving with increased deployment of sensors like the phasor measurement units (PMUs) that have high accuracy and high sampling frequencies (up to 120 Hz). These measurements will be used to develop an equivalent linear representation in a higher dimensional function space that can be used for online identification and characterization of nonlinear dynamics of the power grid. Further, machine learning techniques will be formulated to learn effective dictionary functions for the scalable deployment of proposed method. Using the proposed system identification method, the project will develop the theory and methodology for data-driven Information Transfer based causality mapping for detection and localization of system stress and dynamic coupling between the systems components. Specific applications for power grids will include stability monitoring, trajectory prediction and identification of targets for controlling adverse dynamic behavior. The methods are evaluated by an integrated power-cyber co-simulator (IPCC) that integrates power transmission, distribution and communication systems to generate synthetic sensor data for large systems under various dynamic scenarios. The IPCC will be able to model intermediate communication networks that cause measurement inconsistencies like delays, packet drop, etc. The Iowa State University's hardware in the loop cyber-physical testbed will be used to validate and evaluate some of the online applications like stability monitoring and trajectory prediction for large power grid topologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.