Collaborative Research: AMPS: Multi-Fidelity Modeling via Machine Learning for Real-time Prediction of Power System Behavior

合作研究：AMPS：通过机器学习进行多保真度建模，实时预测电力系统行为

• 批准号: 1736088
• 负责人: George Karniadakis
• 金额: $ 12万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1736088&HistoricalAwards=false
• 关键词: Collaborative; Research; AMPS; Multi; Fidelity

The operation of current power systems depend on deterministic and static models, which are not suitable for analyzing smart power grids due to the increasing large-volume of data collected by the grids and sensors and the need to integrate intermittent renewable resources and dynamic load compositions. Large uncertainty in the model prediction is problematic as it does now allow careful planning, and failure to identify large fluctuations and possible instabilities could endanger the reliable operation of the power grid. Hence, it is crucial to incorporate new monitoring capabilities realized by new tools such as machine learning and predictive multi-rate modeling in modeling the smart grid. Classical methods that deal with uncertainty lead to inefficient solutions as they are too slow to converge to a solution and hence they cannot be used effectively for real-time control of power grids. This difficulty stems from the requirement of sampling the very complex power grid thousands of times in order to arrive to a reasonably accurate solution. The goal of this project is to establish significant advances in research and education in the development of machine learning and real-time predictive modeling of power systems, with particular focus on the smart grid.Machine learning and real-time predictive modeling have received increasing attention in recent years. Extensive research effort has been devoted to these topics, and novel numerical methods have been developed to efficiently deal with sensor data and complex engineering systems. Both machine learning and real-time predictive modeling enable us to better extract the useful information from available sensor data and make critical decision in real time with the presence of uncertainties. For example, solar and wind energy will depend on the weather condition. Machine learning and real-time predictive modeling are thus critical to many important practical problems such as power system stability analysis and social cyber-network prediction, etc. For large-scale power systems, deterministic simulations can be very time-consuming, and conducting predictive simulations further increases the simulation cost and can be prohibitively expensive. One of the biggest challenges in machine learning and real-time predictive modeling is how to develop hierarchical reduced-order models and how to fuse information from such hierarchical reduced-order models. This project aims to address these critical challenges. A novel set of deep-learning based multi-fidelity algorithms (deep Gaussian processes) will be developed for real-time prediction of power systems. The approach under development in this research project is based on scalable algorithms for building deep-learning based reduced-order models for efficient power system dimension reduction. The new algorithms will be based on building multi-fidelity models via deep learning for power systems, and they will significantly advance the current state of the art of deep learning and real-time predictive modeling. The project will also integrate educational opportunities and will expand the population of modelers who use machine learning and predictive modeling tools to solve network problems. The project will expose a diverse group of undergraduates and minority students to machine learning and predictive modeling.

当前电力系统的运行依赖于确定性和静态模型，由于电网和传感器收集的数据量越来越大，并且需要集成间歇性可再生资源和动态负荷组成，因此不适合分析智能电网。模型预测中的大不确定性是有问题的，因为它现在允许仔细规划，如果不能识别大的波动和可能的不稳定性，可能会危及电网的可靠运行。因此，将机器学习和预测多速率建模等新工具实现的新监控功能纳入智能电网建模中至关重要。经典的方法，处理不确定性导致效率低下的解决方案，因为他们太慢，收敛到一个解决方案，因此，他们不能有效地用于实时控制的电网。这一困难源于需要对非常复杂的电网进行数千次采样，以便获得合理准确的解决方案。本项目的目标是在电力系统的机器学习和实时预测建模的研究和教育方面取得重大进展，特别是以智能电网为重点。机器学习和实时预测建模近年来越来越受到关注。广泛的研究工作已经致力于这些主题，和新的数值方法已经开发出有效地处理传感器数据和复杂的工程系统。机器学习和实时预测建模使我们能够更好地从可用的传感器数据中提取有用的信息，并在存在不确定性的情况下真实的时间内做出关键决策。例如，太阳能和风能将取决于天气条件。因此，机器学习和实时预测建模对于许多重要的实际问题至关重要，例如电力系统稳定性分析和社会网络预测等。对于大规模电力系统，确定性仿真可能非常耗时，并且进行预测仿真进一步增加了仿真成本，并且可能非常昂贵。机器学习和实时预测建模的最大挑战之一是如何开发分层降阶模型以及如何融合来自这种分层降阶模型的信息。该项目旨在应对这些关键挑战。将开发一套新的基于深度学习的多保真度算法（深度高斯过程），用于电力系统的实时预测。该研究项目正在开发的方法基于可扩展算法，用于构建基于深度学习的降阶模型，以实现高效的电力系统降维。新算法将基于通过深度学习为电力系统构建多保真度模型，它们将显著推进深度学习和实时预测建模的当前技术水平。该项目还将整合教育机会，并将扩大使用机器学习和预测建模工具来解决网络问题的建模人员的数量。该项目将使不同群体的本科生和少数民族学生接触机器学习和预测建模。

项目成果

Neural-net-induced Gaussian process regression for function approximation and PDE solution

用于函数逼近和 PDE 求解的神经网络诱导高斯过程回归

• DOI: 10.1016/j.jcp.2019.01.045
• 发表时间: 2018-06
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Guofei Pang;Liu Yang;George Em Karniadakis
• 通讯作者: George Em Karniadakis

Bi-directional coupling between a PDE-domain and an adjacent Data-domain equipped with multi-fidelity sensors

• DOI: 10.1016/j.jcp.2018.07.039
• 发表时间: 2018-12
• 期刊: J. Comput. Phys.
• 作者: Dongkun Zhang;Liu Yang;G. Karniadakis

Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression

• DOI: 10.4208/cicp.oa-2020-0151
• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Yixiang Deng;Guang Lin;Xiu Yang