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

利用低维结构进行电力系统监测的高维同步相量测量数据管理

• 批准号: 1508875
• 负责人: Meng Wang
• 金额: $ 40万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1508875&HistoricalAwards=false
• 关键词: Exploiting; Low; dimensional; Structures; Data

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.

北美的相量测量单元（PMU）每天提供电力系统关键运行参数的TB同步相量测量。在当前的电力系统运行实践中，大量的PMU测量被存储用于过去事件分析，并且用于实时决策的现场数据处理超出了电力系统中使用的现有技术的能力。该建议将为电力系统监测中的各种任务开发一个跨多个时刻在多个PMU中集体处理测量的框架。 研究的目标是开发有效的数据管理和信息提取方法，适合于实时处理大量的PMU数据。该项目的成果将对未来电力系统的可靠运行产生积极影响。在这个项目中开发的高维数据分析的通用技术可以在电力系统以外的其他领域找到应用，例如，互联网监测、社会网络分析、图像和视频处理等。该提案还包含了针对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。通过开发新的通用工具，在电力系统监测任务的动机，该建议将有助于发展领域的PMU为基础的电力系统监测和高维数据分析领域。 该提案将通过以下方式解决PMU测量的数据完整性和数据准确性问题：1.开发新的计算效率高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.开发新的方法，通过在多个通道中共同处理PMU测量来检测电力系统中的事件。3.开发新的基于凸优化的方法来检测对PMU测量的网络数据攻击。4.分析网络数据攻击电力系统的可能性和频率。所有开发的方法将在中央纽约电力系统的实际PMU数据上进行数值评估。该建议将建立一个框架，利用低维结构的数据挑战电力系统监测。它将把目前对低秩方法的理解扩展到PMU数据分析领域。 它将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。该项目将通过研究所提出的方法的理论限制，为基于优化的高维数据分析提供新的见解。