Exploiting Low-dimensional Structures in Data Management of High-dimensional Synchrophasor Measurements for Power System Monitoring

北美的相量测量单元(PMU)每天为电力系统的关键运行参数提供兆兆字节的同步相量测量。在目前的电力系统运行实践中，大量的PMU测量值被存储用于过去的事件分析，而用于实时决策的现场数据处理超出了现有电力系统技术的能力。这一建议将为电力系统监测中的各种任务开发一个跨多个时间点的多个PMU测量的集体处理框架。研究目标是开发适合于实时处理大量PMU数据的高效数据管理和信息提取方法。该项目的实施结果将对未来电力系统的可靠运行产生积极影响。该项目开发的高维数据分析通用技术可能会在电力系统以外的其他领域找到应用，如互联网监控、社交网络分析、图像和视频处理等。该提案还包含了面向K-12学生、本科生和研究生的综合教育议程。该提案首次将电力系统监测和基于低维模型的高维分析领域联系起来。通过开发针对电力系统监测任务的新的通用工具，该建议将有助于基于PMU的电力系统监测领域和高维数据分析领域的发展。该提案将围绕提高PMU测量的数据完整性和数据准确性，通过以下方式解决以下挑战和有待解决的问题：1.开发新的计算高效的丢失数据恢复方法，以填补在通信过程中丢失的测量数据。2.通过集中处理多个通道中的PMU测量，开发新的电力系统事件检测方法。3.提出了一种新的基于凸优化的PMU网络数据攻击检测方法。4.对电力系统遭受网络数据攻击的可能性和频率进行了分析。利用纽约中央电力系统PMU的实际数据，对所提出的方法进行了数值评估。该建议将利用低维结构建立一个数据挑战电力系统监测框架。它将把目前对低秩法的理解扩展到PMU数据分析领域。通过对电力系统网络数据攻击检测的研究，将高维数据分析与图论相结合。该项目将通过调查提出的方法的理论局限性，为基于优化的高维数据分析提供新的见解。

Phasor measurements units (PMUs) in North America provide terabytes of synchronized phasor measurements of key operational parameters of power systems on a daily basis. In current practice of power system operation, the large amounts of PMU measurements are stored for past event analysis, and the in-situ data processing for real-time decision is beyond the capability of current technologies used in power systems. This proposal will develop a framework of collective processing of measurements in multiple PMUs across multiple time instants for various tasks in power system monitoring. The research goal is to develop efficient data management and information extraction methods that are suitable for real-time processing of large volumes of PMU data. The outcome of this project will positively impact the reliable operation of future power systems. The generic techniques for high-dimensional data analysis developed in this project can potentially find applications in other areas beyond power systems, e.g., Internet monitoring, social network analysis, image and video processing, etc. This proposal also contains an integrated educational agenda for K-12 students, undergraduates and graduate students.This proposal for the first time bridges the areas of power system monitoring and high-dimensional analysis based on low-dimensional models. By developing new generic tools that are motivated by tasks in power system monitoring, this proposal will contribute to the development of the field of PMU-based power system monitoring and the field of high-dimensional data analysis. Focusing on improving data integrity and data accuracy of PMU measurements, this proposal will address the following challenges and open questions by:1. Developing new computationally efficient missing data recovery methods to fill in the measurements that are lost during communication. 2. Developing new methods to detect events in power systems by collectively processing PMU measurements in multiple channels. 3. Developing new convex-optimization-based methods to detect cyber data attacks to PMU measurements. 4. Analyzing the likelihood and frequency of cyber data attacks to power systems.All the developed methods will be numerically evaluated on actual PMU data in Central New York Power System.This proposal will establish a framework of data-challenged power system monitoring by exploiting low-dimensional structures. It will extend the current understanding of low rank methods to the field of PMU data analysis. It will connect high-dimensional data analysis with graph theory through the research on the detection of cyber data attacks to power systems. This project will provide new insights to optimization-based high-dimensional data analysis by investigating the theoretical limits of proposed methods.