Advancing Graph Signal Processing Techniques for Monitoring and Control of Electric Distribution Power Systems

先进的图形信号处理技术用于配电电力系统的监测和控制

• 批准号: 2210012
• 负责人: Anna Scaglione
• 金额: $ 36万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210012&HistoricalAwards=false
• 关键词: Advancing; Graph; Signal; Processing; Techniques

This NSF project aims to incorporate the physical modeling of the electric grid in the theory of machine learning algorithms, to benefit the monitoring and control of energy delivery systems. While the basic theory we will develop applies broadly to transmission and distribution systems, the section of the grid we are focusing on is the one that is undergoing the greatest transformation, which is the distribution grid. This section of the system not only poses unique modeling challenges, it is also undergoing significant changes because of the integration of distributed energy resources and the control of responsive demand and storage. These are the key ingredients to sustainable energy delivery, and the project will bring transformative changes to the digital technology and machine intelligence that can accelerate this transition. More specifically, the proposal explores a novel mathematical approach for the analysis of grid signals, rooted in fundamental power systems graph-based methods and born out of interpreting the system state as an instance of graph signals. The goal is to use the insights that come from Graph Signal Processing (GSP) and from graph Fourier analysis, to extract signals features that allow to improve data driven inference and decision algorithms. At this time, GSP machine learning tools are designed for real signals and are not physics based. The project will fill this gap, by providing the underpinning for a theory of grid graph signals. This entails extending the GSP tools to tackle complex signals, incorporating the grid system parameters in the algorithm and considering realistic power measurements systems. The goal is to have a better representation of the spatial-temporal characteristics of the data as compared to generic machine learning algorithms and advance the theoretical tools in GSP which are not based on systems whose properties are captures by the signals envelopes and on the physics of the grid. The tools developed will be made available open source. In addition to the advances in GSP, the project will have broader impact through its outreach to New York City public schools and create a short program for K-12 students, supported by an illustrated book, explaining how energy is delivered and the path that advanced societies need to follow to achieve the goal of a decarbonized economy.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.

该NSF项目旨在将电网的物理建模纳入机器学习算法理论中，以使能源输送系统的监视和控制受益。尽管我们将开发的基本理论广泛地适用于传输和分布系统，但我们关注的网格部分是经历最大转换的网格，即分布网格。系统的这一部分不仅提出了独特的建模挑战，而且由于分布式能源的整合以及响应速度需求和存储的控制，它还正在经历重大变化。这些是可持续能源传递的关键要素，该项目将为数字技术和机器智能带来变革性的变化，以加速这种过渡。 更具体地说，该提案探讨了一种新型的数学方法，用于分析网格信号，该方法植根于基本功率系统基于图形的方法，并以将系统状态解释为图形信号的实例。目的是使用来自图形信号处理（GSP）和图形傅立叶分析的见解，以提取允许改善数据驱动推理和决策算法的信号功能。目前，GSP机器学习工具是为真实信号而设计的，不是基于物理的。该项目将通过为网格图信号理论提供基础来填补这一空白。这需要扩展GSP工具以应对复杂信号，并将网格系统参数包含在算法中，并考虑现实的功率测量系统。与通用机器学习算法相比，目标是更好地表示数据的空间特征，并推进GSP中的理论工具，而GSP中的理论工具不是基于其属性是由信号信封捕获和网格物理的系统。开发的工具将被提供开源。除了GSP的进步外，该项目还将通过向纽约市公立学校的宣传来产生更大的影响，并为K-12学生创建一个简短的计划，并得到了一本插图书的支持，解释了如何提供能量，而高级社会需要遵循的途径，以实现脱氧经济的目标。这些奖项通过评估了NSF的范围，这反映了NSF的范围，其范围的范围是众多的支持者，其范围是众多的支持。 标准。

项目成果

Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms

• DOI: 10.1109/tsg.2023.3239740
• 发表时间: 2022-03
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: Tong Wu;Ignacio Losada Carreño;A. Scaglione;D. Arnold

Reinforcement Learning using Physics Inspired Graph Convolutional Neural Networks

• DOI: 10.1109/allerton49937.2022.9929321
• 发表时间: 2022-09
• 期刊: 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
• 作者: Tong Wu;A. Scaglione;D. Arnold