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

CPS：中：未来电网的数据驱动因果关系图、系统识别和动态表征

• 批准号: 1932458
• 负责人: Venkataramana Ajjarapu
• 金额: $ 100万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932458&HistoricalAwards=false
• 关键词: CPS; Medium; Data; driven; Causality

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.

拟议研究的总体目标是使用有限的可观测值导出大规模通用非线性动力系统的关键信息和表征。在目前最先进的数据驱动动力系统分析中，需要所有基础状态测量和这些状态的时间演化。获得现实世界中的所有动态测量结果是不可能的或昂贵的。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化的预测。研究团队将专门开展研究，利用现有的测量结果对电力系统中发生的各种动态事件进行近实时的应用。所提出的数据分析适用于一般非线性动态系统，并且可以轻松应用于其他网络物理系统（CPS）。更广泛地说，CPS 和控制界做出了巨大的努力，将我们每天与之交互的现实世界系统（例如交通系统、通信网络、万维网等）建模为动态系统，因此，通过该项目开发的理论和技术将能够在线监控这些关键系统，使操作员能够从最少的数据流中快速分析这些系统是否存在任何不稳定/异常行为。该项目将促进各种教育和外展活动，包括开发新课程、短期课程、学校活动以及为女性和代表性不足的少数族裔学生提供奖学金。 概述：该提案的目标是为测量有限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。所提出方法的新颖之处在于它们不需要动态，但可以利用系统输出，使其适用于现实世界的动态系统。随着具有高精度和高采样频率（高达 120 Hz）的相量测量单元 (PMU) 等传感器的部署增加，电力系统正在迅速发展。 这些测量将用于在高维函数空间中开发等效线性表示，可用于电网非线性动态的在线识别和表征。此外，将制定机器学习技术来学习有效的字典功能，以实现所提出方法的可扩展部署。使用所提出的系统识别方法，该项目将开发基于数据驱动信息传输的因果关系映射的理论和方法，用于系统应力和系统组件之间的动态耦合的检测和定位。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。这些方法通过集成电力网络联合模拟器（IPCC）进行评估，该模拟器集成了输电、配电和通信系统，为各种动态场景下的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（例如延迟、丢包等）的中间通信网络进行建模。爱荷华州立大学的硬件在环网络物理测试平台将用于验证和评估一些在线应用程序，例如稳定性监控和该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。