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
基本信息
- 批准号:2210012
- 负责人:
- 金额:$ 36万
- 依托单位:
- 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:
- 关键词:
项目摘要
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
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Reinforcement Learning using Physics Inspired Graph Convolutional Neural Networks
- DOI:10.1109/allerton49937.2022.9929321
- 发表时间:2022-09
- 期刊:
- 影响因子:0
- 作者:Tong Wu;A. Scaglione;D. Arnold
- 通讯作者: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:
- 影响因子:9.6
- 作者:Tong Wu;Ignacio Losada Carreño;A. Scaglione;D. Arnold
- 通讯作者:Tong Wu;Ignacio Losada Carreño;A. Scaglione;D. Arnold
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Anna Scaglione其他文献
Stochastic Dynamic Network Utility Maximization with Application to Disaster Response
随机动态网络效用最大化及其在灾难响应中的应用
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Anna Scaglione;Nurullah Karakoç - 通讯作者:
Nurullah Karakoç
2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, TX, USA, March 23-27, 2020
2020 IEEE 国际普适计算和通信研讨会研讨会,PerCom Workshops 2020,美国德克萨斯州奥斯汀,2020 年 3 月 23-27 日
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Yuan Lai;Gonzalo J. Martinez;Stephen M. Mattingly;Shayan Mirjafari;Subigya Nepal;Andrew T Campbell;A. Dey;Aaron D. Striegel;Marco Jansen;Fatjon Seraj;Wei Wang;P. Havinga;Kaijie Zhang;Zhiwen Yu;Dong Zhang;Zhu Wang;Bin Guo;Julian Graf;Katrin Neubauer;Sebastian Fischer;Rudolf Hackenberg;Elliott Wen;Gerald Weber;Javier Rojo;Daniel Flores;J. García;J. M. Murillo;Javier Berrocal;Mingyu Hou;Tianyu Kang;Li Guo;Edison Thomaz;Beichen Yang;Min Sun;Xiaoyan Hong;Xiaoming Guo;P. Barsocchi;A. Crivello;Michele Girolami;Fabio Mavilia;Vivek Chandel;Shivam Singhal;Avik Ghose;Tetsushi Matsuda;Toru Inada;Susumu Ishihara;Luay Alawneh;Belal Mohsen;Mohammad Al;Ahmed S. Shatnawi;Mahmoud Al;N. B. Rabah;Eoin Brophy;W. Muehlhausen;A. Smeaton;Tomás E. Ward;S. Maskey;S. Badsha;Shamik Sengupta;Ibrahim Khalil;Stanisław Saganowski;Anna Dutkowiak;A. Dziadek;Maciej Dziezyc;Joanna Komoszynska;Weronika Michalska;Adam G. Polak;Michal Ujma;Przemysław Kazienko;Nurullah Karakoç;Anna Scaglione;Fatemeh Mirzaei;Jonathan Lam;Roberto Manduchi;R. K. Ramakrishnan;R. Gavas;Lalit Venkata Subramaninan Viraraghavan;Kumar Hissaria;Arpan Pal;P. Balamuralidhar;S. Ditton;Ali Tekeoglu;K. Bekiroglu;Seshadhri Srinivasan;E. Tonkin;Miquel Perello Nieto;Haixia Bi;Antonis Vafeas;Yuri Tani;M. Garcia;A. Konios;M. A. Mustafa;C. Nugent;G. Morrison;Noah Sieck;Cameron Calpin;Mohammad S. Almalag;M. M. Sandhu;Kai Geissdoerfer;Sara Khalifa;Raja Jurdak;Marius Portmann;Brano Kusy;Alwyn Burger;Chao Qian;Gregor Schiele;Domenik Helms;Peter Zdankin;Marian Waltereit;V. Matkovic;Torben Weis;Syafiq Al Atiiq;Christian Gehrmann;Jae Woong Lee;Sumi Helal;Mathias Mormul;Christoph Stach;L. Krupp;G. Bahle;Agnes Gruenerbl;P. Lukowicz;Nicholas Handaja;Brent Lagesse;Clémentine Gritti;Dennis Przytarski;Bernhard Mitschang;Yeongjun Jeon;Kukho Heo;Soon Ju Kang;Sandeep Biplav Srivastava;Singh Sandha;Vaskar Raychoudhury;Sukanya Randhawa;V. Kapoor;Anmol Agrawal;Young D. Kwon;Kirill A. Shatilov;Lik;Serkan Kumyol;Kit;Yui;Pan Hui;Brittany Lewis;Joshua Hebert;Krishna Venkatasubramanian;Matthew Provost;Kelly Charlebois;Kristina Yordanova;Albert Hein;T. Kirste;Lien;Jun;Wei;Casper Van Gheluwe;I. Šemanjski;Suzanne Hendrikse;S. Gautama;Furqan Jameel;Zheng Chang;Riku Jäntti;Sergio Laso;M. Linaje;Ikram Ullah;N. Meratnia;Steven M. Hernandez;Eyuphan Bulut;Amiah Gooding;Matthew Martin;Maxwell Minard;Smruthi Sandhanam;Travis Stanger;Yana Alexandrova;Ashfaq Khokhar;Goce Trajcevski;Utsav Goswami;Kevin Wang;Gabriel Nguyen;Federico Montori;L. Bedogni;Gianluca Iselli;L. Bononi;Saptaparni Kumar;Haochen Pan;Roger Wang;Lewis Tseng;K. Hirayama;S. Saiki;Masahide Nakamura;Kiyoshi Yasuda;Samy El;Ismail Arai;Ahmad Salman;B. B. Park;Yuya Sano;Yuito Sugata;Teruhiro Mizumoto;H. Suwa;K. Yasumoto;P. Kouris;Marietta Sionti;Chrysovalantis Korfitis;Stella Markantonatou;Naima Khan;Nirmalya Roy;D. Jaiswal;D. Chatterjee;Ramesh Kumar;Ana Cristina Franco;Da Silva;Pascal Hirmer;Jan Schneider;Seda Ulusal;Matheus Tavares;Tomokazu Matsui;Kosei Onishi;Shinya Misaki;Manato Fujimoto;Hayata Satake;Yuki Kobayashi;Ryotaro Tani;Hiroshi Shigeno;Avijoy Chakma;Abu Zaher;Md Faridee;M Sajjad Hossain;Cleo Forman;Pablo Thiel;Raymond Ptucha;Miguel Dominguez;Cecilia Ovesdotter Alm;S. Mozgai;Arno Hartholt;Albert Rizzo - 通讯作者:
Albert Rizzo
Network-Constrained Reinforcement Learning for Optimal EV Charging Control
用于最佳电动汽车充电控制的网络约束强化学习
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Tong Wu;Anna Scaglione;Adrian;Daniel Arnold;S. Peisert - 通讯作者:
S. Peisert
Routing and data compression in sensor networks: stochastic models for sensor data that guarantee scalability
- DOI:
10.1109/isit.2003.1228188 - 发表时间:
2003-09 - 期刊:
- 影响因子:0
- 作者:
Anna Scaglione - 通讯作者:
Anna Scaglione
Statistical analysis of the capacity of MIMO frequency selective Rayleigh fading channels with arbitrary number of inputs and outputs
- DOI:
10.1109/isit.2002.1023550 - 发表时间:
2002-06 - 期刊:
- 影响因子:0
- 作者:
Anna Scaglione - 通讯作者:
Anna Scaglione
Anna Scaglione的其他文献
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{{ truncateString('Anna Scaglione', 18)}}的其他基金
I-Corps: Geospatial Trend Detection for Hydro-power and Critical Infrastructure Design
I-Corps:水电和关键基础设施设计的地理空间趋势检测
- 批准号:
2344120 - 财政年份:2023
- 资助金额:
$ 36万 - 项目类别:
Standard Grant
Travel Grant: Urban Tech Academy meeting on electrified multimodal transportation
旅行补助金:城市技术学院关于电气化多式联运的会议
- 批准号:
2336001 - 财政年份:2023
- 资助金额:
$ 36万 - 项目类别:
Standard Grant
CCF-BSF: CIF: Small: Identification and Isolation of Malicious Behavior in Multi-Agent Optimization Algorithms
CCF-BSF:CIF:小:多代理优化算法中恶意行为的识别和隔离
- 批准号:
1714672 - 财政年份:2017
- 资助金额:
$ 36万 - 项目类别:
Standard Grant
EAGER: The Identification of Social Systems Trust: Theory and Experimental Validation
EAGER:社会系统信任的识别:理论与实验验证
- 批准号:
1553746 - 财政年份:2015
- 资助金额:
$ 36万 - 项目类别:
Standard Grant
Collaborative Research: EAGER: Renewables: A function space theory for continuous-time flexibility scheduling in electricity markets
合作研究:EAGER:可再生能源:电力市场连续时间灵活性调度的函数空间理论
- 批准号:
1549923 - 财政年份:2015
- 资助金额:
$ 36万 - 项目类别:
Standard Grant
CCF: Small: Online Learning and Exploitation of the Radio Frequency Spectrum with Sub-Nyquist Sampling
CCF:小型:采用亚奈奎斯特采样的射频频谱在线学习和利用
- 批准号:
1534957 - 财政年份:2014
- 资助金额:
$ 36万 - 项目类别:
Standard Grant
CIF: Large: Collaborative Research: Cooperation and Learning Over Cognitive Networks
CIF:大型:协作研究:认知网络上的合作与学习
- 批准号:
1531050 - 财政年份:2014
- 资助金额:
$ 36万 - 项目类别:
Continuing Grant
CCF: Small: Online Learning and Exploitation of the Radio Frequency Spectrum with Sub-Nyquist Sampling
CCF:小型:采用亚奈奎斯特采样的射频频谱在线学习和利用
- 批准号:
1320065 - 财政年份:2013
- 资助金额:
$ 36万 - 项目类别:
Standard Grant
CIF: Large: Collaborative Research: Cooperation and Learning Over Cognitive Networks
CIF:大型:协作研究:认知网络上的合作与学习
- 批准号:
1011811 - 财政年份:2010
- 资助金额:
$ 36万 - 项目类别:
Continuing Grant
NeTS: Medium: Collaborative Research: Unlocking Capacity for Wireless Access Networks through Robust Cooperative Cross-Layer Design
NetS:媒介:协作研究:通过稳健的协作跨层设计释放无线接入网络的容量
- 批准号:
0905267 - 财政年份:2009
- 资助金额:
$ 36万 - 项目类别:
Standard Grant
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