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EAGER: Real-Time: Decision and Control of Complex Engineered Systems Enabled by Machine Learning and High-performance Computing

EAGER：实时：机器学习和高性能计算支持的复杂工程系统的决策和控制

基本信息

批准号：
1839733
负责人：
Mario Rotea
金额：
$ 29.99万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839733&HistoricalAwards=false
关键词：
EAGER Real Time Decision Control

项目摘要

In recent years, there have been significant advances in machine learning - statistical techniques that enable computers to "learn" using available data. Machine learning methods have demonstrated great success in image recognition, language translation, speech processing, and other consumer applications. This has led to great interest globally in academia, industry, and government. The drawback in purely machine learning methods is that it does not use the knowledge of physical properties of specific system which could significantly improve the performance of these methods. This EArly-concept Grant for Exploratory Research (EAGER) project will lead to fundamental results and methods that combine the advantages of machine learning techniques and knowledge of physical attributes of the system to enable decision making and control of complex engineered systems. The research will be conducted in the context of control of large wind energy plants. Maximizing power production despite variable and uncertain operating conditions in large wind plants is an unsolved problem that is ripe for transformative approaches and innovation. The research from this project is likely to transition to industry by leveraging connections with the NSF I-UCRC for Wind Energy Science, Research and Technology (WindSTAR) as wind plant owners and operators constantly seek new ways to improve annual energy production and reduce the cost of electricity from wind.The main idea of this EAGER project is to leverage advances in deep learning and high performance computing simulations for the control of complex engineered systems. Our hypothesis is that techniques from (semi-supervised) machine learning can be tailored to extract information from high performance simulation data to deal with the joint problem of identifying control system architectures and control algorithms for real-time decision making in complex engineered systems. The research goals of this project have great potential to contribute to the convergence of high performance computing simulations and data, machine learning, and controls to advance the state-of-art tools for controlling complex engineered systems. The testbed for the project is a wind plant. As turbines become larger, and are placed closer to one another, the aerodynamic coupling amongst turbines will increase resulting in a truly large-scale complex engineered system that must perform despite environmental uncertainty and variability of turbine components. Specific goals of this project include: Advanced learning algorithms for extracting control system architecture and training algorithms from large eddy simulation data of the wind farms; Real-time decision algorithms to select architecture and algorithms from site-specific libraries discovered in the first goal; and Real-time algorithms for tuning key parameters of the control solutions for additional improvements in the overall energy production.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.

近年来，机器学习取得了重大进展-统计技术使计算机能够使用可用数据进行“学习”。机器学习方法在图像识别、语言翻译、语音处理和其他消费者应用中取得了巨大成功。这引起了全球学术界、工业界和政府的极大兴趣。纯机器学习方法的缺点是它没有使用特定系统的物理特性的知识，这可以显着提高这些方法的性能。EARLY概念探索性研究资助（EAGER）项目将产生基本的结果和方法，这些结果和方法将机器学习技术的优势与系统物理属性的知识联合收割机相结合，以实现复杂工程系统的决策和控制。该研究将在大型风能发电厂控制的背景下进行。尽管大型风力发电厂的运行条件多变且不确定，但最大限度地提高发电量是一个尚未解决的问题，对于变革性方法和创新来说已经成熟。该项目的研究可能会通过利用与NSF I-UCRC风能科学的联系过渡到工业，研究和技术（WindSTAR）随着风力发电厂业主和运营商不断寻求新的方法来提高年能源产量并降低风电成本，EAGER项目的主要思想是利用深度学习和高性能计算模拟的进步来控制风力发电。复杂的工程系统我们的假设是，（半监督）机器学习的技术可以定制为从高性能仿真数据中提取信息，以处理识别控制系统架构和控制算法的联合问题，以便在复杂的工程系统中进行实时决策。该项目的研究目标具有很大的潜力，有助于高性能计算模拟和数据，机器学习和控制的融合，以推进控制复杂工程系统的最先进工具。该项目的试验台是一个风力发电厂。随着涡轮机变得更大，并且彼此更靠近地放置，涡轮机之间的空气动力学耦合将增加，从而导致尽管环境不确定性和涡轮机部件的可变性，但必须执行的真正大规模的复杂工程系统。该项目的具体目标包括：先进的学习算法，用于从风电场的大涡模拟数据中提取控制系统架构和训练算法;实时决策算法，用于从第一个目标中发现的特定于站点的库中选择架构和算法;和真实的-时间算法，用于调整控制解决方案的关键参数，以进一步改善整体能源生产。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Real-time identification of clusters of turbines

实时识别涡轮机集群

DOI：
10.1088/1742-6596/1618/2/022032
发表时间：
2020
期刊：
Journal of Physics: Conference Series
影响因子：
0
作者：
Bernardoni, Federico;Ciri, Umberto;Rotea, Mario;Leonardi, Stefano
通讯作者：
Leonardi, Stefano

Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation

DOI：
10.1109/dcas53974.2022.9845651
发表时间：
2021-06
期刊：
2022 IEEE 15th Dallas Circuit And System Conference (DCAS)
影响因子：
0
作者：
Farzad Karami;N. Kehtarnavaz;M. Rotea
通讯作者：
Farzad Karami;N. Kehtarnavaz;M. Rotea

Identification of wind turbine clusters for effective real time yaw control optimization

DOI：
10.1063/5.0036640
发表时间：
2021-07
期刊：
Journal of Renewable and Sustainable Energy
影响因子：
2.5
作者：
F. Bernardoni;U. Ciri;M. Rotea;S. Leonardi
通讯作者：
F. Bernardoni;U. Ciri;M. Rotea;S. Leonardi

Identification of turbine clusters during time varying wind direction

风向随时间变化时涡轮机集群的识别

DOI：
10.23919/acc53348.2022.9867223
发表时间：
2022
期刊：
2022 American Control Conference (ACC
影响因子：
0
作者：
Bernardoni, Federico;Ciri, Umberto;Rotea, Mario A.;Leonardi, Stefano
通讯作者：
Leonardi, Stefano

Real-time Wind Direction Estimation using Machine Learning on Operational Wind Farm Data

使用机器学习对运行风电场数据进行实时风向估计

DOI：
10.1109/cdc45484.2021.9683613
发表时间：
2021
期刊：
2021 60th IEEE Conference on Decision and Control (CDC
影响因子：
0
作者：
Karami, Farzad;Zhang, Yujie;Rotea, Mario A.;Bernardoni, Federico;Leonardi, Stefano
通讯作者：
Leonardi, Stefano