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

这项研究的首要目标是利用有限的观测值来获得大规模一般非线性动力系统的关键信息和特征。在目前最先进的数据驱动动态系统分析中，需要所有的底层状态测量和这些状态的时间演化。访问真实世界中的所有动态测量是不可能的，也是昂贵的。该提案的目标是开发数据驱动的工具，用于动态系统识别、动态事件的分类和根本原因分析以及系统演变预测。研究小组将专门针对电力系统中发生的各种动态事件进行研究，使用可用的测量来执行近实时应用。所提出的数据分析方法适用于一般的非线性动态系统，并可方便地应用于其他控制物理系统(CPS)。更广泛地说，在CPS和控制社区中，有大量的努力来对我们每天都与之交互的真实世界系统(例如交通系统、通信网络、万维网等)进行建模。因此，通过该项目开发的理论和技术将能够对这些关键系统进行在线监控，使操作员能够从最小的数据流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族裔学生开发新课程、短期课程、学校活动和奖学金。概述：这项建议的目标是为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力系统，并表征其行为，如组件之间的因果相互作用，稳定性监测，确定控制目标。这项拟议的研究属于“网络物理系统技术”领域。所提出的方法的新颖之处在于它们不需要动态状态，而是可以利用系统输出，使其适用于真实世界的动态系统。随着相量测量单元(PMU)等传感器部署的增加，电力系统正在迅速发展，这些传感器具有高精度和高采样频率(高达120赫兹)。这些测量结果将被用来在高维函数空间中建立等价的线性表示，该表示可用于在线辨识和表征电网的非线性动态。此外，将制定机器学习技术来学习有效的字典函数，以便可扩展地部署所提出的方法。利用拟议的系统识别方法，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，以检测和定位系统压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、轨迹预测和识别控制不利动态行为的目标。这些方法由一个集成的电力-网络协同仿真器(IPCC)进行评估，该仿真器集成了输电、配电和通信系统，以生成各种动态场景下的大型系统的合成传感器数据。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、丢包等。爱荷华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，如大型电网拓扑的稳定性监测和轨迹预测。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.