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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

基本信息

批准号：
1736364
负责人：
Guang Lin
金额：
$ 12万
依托单位：
Purdue 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=1736364&HistoricalAwards=false
关键词：
Collaborative Research AMPS Multi Fidelity

项目摘要

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，并且需要整合间歇性可再生资源和动态负荷组成，这些模型不适合分析智能电网。模型预测中的巨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，可能危及电网的可靠运行。因此，在智能电网建模中引入机器学习和预测多速率建模等新工具实现的新监测功能至关重要。处理不确定性的经典方法由于速度太慢而无法收敛到一个解，因此无法有效地用于电网的实时控制，导致求解效率低下。这种困难源于需要对非常复杂的电网进行数千次采样才能得到合理准确的解决方案。该项目的目标是在机器学习和电力系统实时预测建模的研究和教育方面取得重大进展，特别是在智能电网方面。近年来，机器学习和实时预测建模受到越来越多的关注。广泛的研究工作已经投入到这些主题，并开发了新的数值方法来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实时做出关键决策。例如，太阳能和风能将取决于天气状况。因此，机器学习和实时预测建模对于电力系统稳定性分析和社会网络预测等许多重要的实际问题至关重要。对于大型电力系统，确定性模拟可能非常耗时，并且进行预测性模拟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从这些分层降阶模型中融合信息。该项目旨在解决这些关键挑战。一套新颖的基于深度学习的多保真度算法（深度高斯过程）将被开发用于电力系统的实时预测。本研究项目中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，以实现有效的电力系统降维。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，它们将显著推进当前深度学习和实时预测建模的技术水平。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者的人数。该项目将向不同群体的本科生和少数族裔学生展示机器学习和预测建模。