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.

NSF的这个项目旨在将电网的物理建模纳入机器学习算法的理论中，以利于能源输送系统的监测和控制。虽然我们将开发的基本理论广泛适用于输电和配电系统，但我们关注的电网部分是正在经历最大变革的部分，即配电网。系统的这一部分不仅带来了独特的建模挑战，而且由于分布式能源的集成以及对响应需求和存储的控制，它也正在发生重大变化。这些都是可持续能源供应的关键因素，该项目将为数字技术和机器智能带来变革性的变化，从而加速这一转变。 更具体地说，该提案探讨了一种新的数学方法，用于分析电网信号，植根于基本的电力系统图形为基础的方法和出生的解释系统状态的图形信号的实例。我们的目标是使用来自图形信号处理（GSP）和图形傅立叶分析的见解，提取信号特征，以改进数据驱动的推理和决策算法。目前，GSP机器学习工具是为真实的信号设计的，而不是基于物理的。该项目将填补这一空白，通过提供网格图形信号的理论基础。这需要扩展GSP工具以处理复杂信号，将电网系统参数纳入算法并考虑实际的功率测量系统。我们的目标是有一个更好的表示数据的时空特征相比，通用的机器学习算法和先进的理论工具，在GSP中，这不是基于系统的属性被捕获的信号包络和物理网格。开发的工具将开放源码。除了普惠制的进展外，该项目还将通过与纽约市公立学校的联系产生更广泛的影响，并为K-12学生创建一个简短的课程，并辅以一本插图书，解释能源是如何输送的，以及先进社会需要遵循的道路，以实现脱碳经济的目标。该奖项反映了NSF的法定使命，并已通过评估被认为值得支持使用基金会的知识价值和更广泛的影响审查标准。

项目成果

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

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