CPS: Synergy: Achieving High-Resolution Situational Awareness in Ultra-Wide-Area Cyber-Physical Systems

CPS：协同：在超广域网络物理系统中实现高分辨率态势感知

• 批准号: 1239478
• 负责人: Hairong Qi
• 金额: $ 100万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1239478&HistoricalAwards=false
• 关键词: CPS; Synergy; Achieving; Resolution; Situational

Energy infrastructure is a critical underpinning of modern society. To ensure its reliable operation, a nation-wide or continent-wide situational awareness system is essential to provide high-resolution understanding of the system dynamics such that proper actions can be taken in real-time in response to power system disturbances and to avoid cascading blackouts. The power grid represents a typical highly dynamic cyber-physical system (CPS). The ever-increasing complexity and scale in sensing and actuation, compounded by the limited knowledge of the accurate system state have resulted in major system failures, such as the massive power blackout of August 2003 and the most recent Arizona/California blackout of September 2011. Therefore, methods and tools for monitoring and control of these and other such dynamic systems at high resolution are vital to an emergent generation of tightly coupled, physically distributed CPS. This project employs the power grid as a target application and develops a high-resolution, ultra-wide-area situational awareness system that synergistically integrates sensing, processing, and actuation. First, from the sensing perspective, high resolution is reflected in both measurement accuracy and potential for dense spatial coverage. Wide area, precise, synchronized, and affordable sensing in voltage angle and frequency measurements for large-scale observation is sorely needed to observe system disturbances and capture critical changes in the power grid. The crucial innovation of this work is to make accurate frequency measurement from low voltage distribution systems through the wide deployment of Frequency Disturbance Recorders (FDRs). Second, from a data processing perspective, high resolution is reflected in finer-scale data analysis to reveal hidden information. In practical CPS, events seldom occur in an isolated fashion; cascading events are more common and realistic. A new conceptual framework is presented in the study of event analysis, referred to as event unmixing, where real-world events are considered a mixture of more than one constituent root event. This concept is a key enabler for the analysis of events to go beyond what are immediately detectable in the system. The event formation process is interpreted from a linear mixing perspective and innovative sparsity-constrained unmixing algorithms are presented for multiple event separation and spatial-temporal localization. Third, to discover the high-level spatial-temporal correlation among root events in real time, a descriptive language is developed to discover patterns on the spatial and temporal information of root events. This descriptive language allows embedding pattern descriptions on the desirable and undesirable interactions between events in the system, which will then be compiled into distributed runtime constructs to be executed in deployed systems. Fourth, from the actuation perspective, the system pushes the intelligence toward the lower level of the power grid allowing local devices to make decisions and to react quickly to contingencies based on the high-resolution understanding of the system state, enabling a more direct reconfiguration of the physical makeup of the grid. Finally, the methods and tools are implemented and validated on an existing wide-area power grid monitoring system, the North American frequency monitoring network (FNET).Escalating demands for electricity coupled with an outdated power transmission grid pose a serious threat to the US economy. The transformative nature of this research is to turn a large volume of real-time data into actionable information and help prevent potential outages from happening. The power grid is a typical example of dynamic cyber physical system. Providing high-resolution situational awareness for the power grid has a direct and immediate impact on this and other CPS. The research is coupled with a strong educational component including active recruitment of students from underrepresented groups supported by existing programs and broad dissemination of research findings.

能源基础设施是现代社会的重要支柱。为了确保其可靠运行，全国或大陆范围的态势感知系统对于提供对系统动态的高分辨率了解至关重要，以便能够针对电力系统扰动实时采取适当行动，避免连锁停电。电网是一个典型的高度动态的网络物理系统。传感和执行的复杂性和规模不断增加，再加上对准确系统状态的有限了解，已导致重大系统故障，如2003年8月的大规模停电和最近的2011年9月亚利桑那州/加利福尼亚州的停电。因此，用于以高分辨率监测和控制这些和其他这样的动态系统的方法和工具对于新一代紧密耦合、物理分布的CP至关重要。该项目以电网为目标应用，开发了一种高分辨率、超广域态势感知系统，协同集成了感知、处理和驱动。首先，从传感角度来看，高分辨率既体现在测量精度上，也体现在密集空间覆盖的潜力上。为了观察系统扰动和捕捉电网中的关键变化，迫切需要在电压角度和频率测量中进行大范围、精确、同步和负担得起的传感，以便进行大规模观测。这项工作的关键创新之处在于通过广泛应用频率扰动记录器(FDR)从低压配电系统进行准确的频率测量。第二，从数据处理的角度来看，高分辨率体现在更精细的数据分析上，揭示隐藏的信息。在实际的CP中，事件很少以孤立的方式发生；级联事件更常见和现实。在事件分析的研究中提出了一种新的概念框架，称为事件分解，其中现实世界的事件被认为是一个以上组成根事件的混合。这一概念是使事件分析超越系统中可立即检测到的范围的关键因素。从线性混合的角度解释了事件的形成过程，提出了稀疏约束的多事件分离和时空定位算法。第三，为了实时发现根事件之间的高层次时空相关性，开发了一种描述性语言来发现根事件的时空信息模式。这种描述性语言允许嵌入关于系统中事件之间所需和不所需交互的模式描述，然后这些描述将被编译成分布式运行时构造，以便在部署的系统中执行。第四，从驱动的角度来看，系统将智能推向电网的较低级别，允许本地设备基于对系统状态的高分辨率了解做出决策并对意外情况做出快速反应，从而能够更直接地重新配置电网的物理构成。最后，在现有的广域电网监测系统北美频率监测网(FNET)上实施和验证了方法和工具。不断增长的电力需求加上过时的输电网对美国经济构成了严重威胁。这项研究的变革性本质是将大量实时数据转化为可操作的信息，并帮助防止潜在的停机发生。电网是动态网络物理系统的典型代表。为电网提供高分辨率的态势感知对这一点和其他CPS有直接和直接的影响。这项研究与强大的教育组成部分相结合，包括积极从现有方案支持的代表性不足群体中招收学生，以及广泛传播研究结果。