Collaborative Research: AMPS: Rare Events in Power Systems: Novel Mathematics, Statistics and Algorithms.

该项目的目标是建立一个全面的AI/ML理论和算法框架，用于检测，跟踪，预测和缓解电力系统中极端和罕见但后果严重的事件。AI/ML的绝大多数传统应用都涉及学习分布的“中间”。由于深度学习（DL）的突破，应用程序已经成为工业界和学术界“插值”的常规练习。基于大量的“典型”信息，一个通用的DL任务的重点是设计算法，提取和构建的功能，代表了最常见的特点，大量的科学数据。这种传统的DL情况和极端事件的DL之间的区别在于，在后一种情况下，任务是外推。此外，在正常情况下有益的大量科学数据成为外推法的祸根，这种外推法侧重于提取罕见但重要的事件-黑天鹅-就像大海捞针一样，众所周知，难以发现和跟踪，然后随着事件的发展用来作出可靠的预测和可能的缓解措施。换句话说，基于非常有限的信息，研究目标是提取规律性模式，这些模式可以在长时间和长时间尺度上持续存在，然后导致潜在的罕见极端。PI将研究与2020年夏季的极端高温或2021年春季德克萨斯州的极端寒冷有关的模型;电力系统停电，如2004年东海岸停电。这些方法将具有更广泛的适用性，例如，如果预测和检测其他物理和网络中的故障。PI将研究三个领域的具体目标：（A）电力系统的物理信息统计建模，（B）电力系统极值的计算推理方法，以及（C）模型中错误的学习和量化。他们将应用在新框架内开发的方法，包括电力系统中罕见但破坏性的级联故障的早期检测。该方法的数学/理论核心是将电力系统特定的约束集成到一般的极值理论（EVT）中。这种整合将通过EVT与物理信息机器学习，概率图模型和最优运输理论的互补方法的合成来实现。在计算方面，PI将利用EVT为大规模随机系统开发有效的模型校准，推理和学习算法，通过适当参数化的非线性真实的或复值代数和微分方程与随机随机输入来描述。此外，他们的理论和计算工作将有助于在更广泛的范围内将罕见事件控制和预防方法扩展到其他系统和国家重要性的应用。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智力价值和更广泛的影响审查标准进行评估的支持。

The project goal is to build a comprehensive theoretical and algorithmic framework of AI/ML for detection, tracking, forecasting and mitigation of extreme and rare but consequential events in power systems. The overwhelming majority of conventional applications of AI/ML involve learning the `middle' of the distribution. Applications have become mostly routine exercises in `interpolation' in both industry and academia, thanks to the Deep Learning (DL) breakthrough. Based on copious amounts of `typical' information, a generic DL task focuses on designing algorithms which extract and build features which represent the most common characteristics of the massive scientific data. The difference between this conventional DL situation and DL for extreme events is that, in the latter setting, the task is one of extrapolation. Moreover, massive scientific data, beneficial in normal regimes, becomes a curse for extrapolation which focuses on extracting rare but significant events -- the black swans -- which are, like a needle in a haystack, notoriously difficult to detect and track, and then use to make reliable forecasts and possible mitigations as events develop. In other words, based on very limited information, the research objective is to extract regularity patterns, which can persist over long spatial and temporal scales, that then lead to potential rare extremes. The PI will study models that relate to such as the extreme heat of the summer of 2020 or the extreme cold in Texas in the spring of 2021; power system blackouts, like the 2004 East Coast blackout. The methods will have even broader applicability, for example, if prediction and detection of failures in other physical and cyber networks. PI will investigate specific objectives in three areas: (A) Physics-Informed Statistical Modeling for power systems, (B) Computational Methods of Inference for Extremes in power systems, and (C) Learning and Quantification of Errors in the Models. They will apply the methodology developed within the novel framework to including early detection of rare but devastating cascading failures in power systems. The mathematical/theoretical core of the methodology will consist in integration of power-system-specific constraints into the general Extreme Value Theory (EVT). This integration will be achieved via synthesis of EVT with the complementary approaches from the Physics Informed Machine Learning, Probabilistic Graphical Models and Optimal Transport theory. On the computational side, PI will utilize EVT to develop efficient model calibration, inference and learning algorithms for large-scale stochastic systems, described via properly parameterized non-linear real or complex-valued algebraic and differential equations with random stochastic input. Moreover, their coupled theoretical and computational efforts will be useful in a broader context for extending the rare event control and prevention methodology to other systems and applications of national importance.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.