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融入机器学习算法的理论中，以利于对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，即配电电网。该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对响应需求和存储的控制，它也正在经历重大变化。这些都是可持续能源输送的关键要素，该项目将给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，该方案探索了一种用于电网信号分析的新的数学方法，该方法植根于基于图的基本电力系统方法，并源于将系统状态解释为图信号的实例。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，以允许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信号设计的，而不是基于物理的。该项目将填补这一空白，为网格图形信号理论提供基础。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现实的功率测量系统。与一般的机器学习算法相比，目标是更好地表示数据的时空特征，并提出GSP中的理论工具，这些工具不是基于其属性被信号包络和网格物理捕获的系统。开发的工具将以开源方式提供。除了普惠制方面的进展，该项目还将通过拓展到纽约市公立学校产生更广泛的影响，并在一本图文并茂的书的支持下，为K-12学生创建一个简短的项目，解释能源是如何提供的，以及先进社会需要遵循的道路，以实现无碳经济的目标。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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