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

合作研究：AMPS：电力系统中的罕见事件：新颖的数学、统计和算法。

• 批准号: 2229012
• 负责人: Michael Chertkov
• 金额: $ 15万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229012&HistoricalAwards=false
• 关键词: Collaborative; Research; AMPS; Rare; Events

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.

该项目的目标是建立一个全面的人工智能/机器学习理论和算法框架，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。人工智能/机器学习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，应用程序已成为工业界和学术界“插值”的常规练习。基于大量“典型”信息，通用深度学习任务侧重于设计算法，提取和构建代表海量科学数据最常见特征的特征。这种传统的深度学习情况与极端事件的深度学习之间的区别在于，在后一种情况下，任务是外推的。此外，大量的科学数据在正常情况下是有益的，但却成为外推法的祸根，外推法的重点是提取罕见但重要的事件——黑天鹅——这些事件就像大海捞针一样，众所周知难以检测和追踪，然后用于随着事件的发展做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，研究目标是提取可以在较长的空间和时间尺度上持续存在的规律性模式，从而导致潜在的罕见极端情况。 PI 将研究与 2020 年夏季极热或 2021 年春季德克萨斯州极冷等相关的模型；电力系统停电，例如 2004 年东海岸停电。这些方法将具有更广泛的适用性，例如，预测和检测其他物理和网络网络中的故障。 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) 电力系统极值推理的计算方法，以及 (C) 模型中误差的学习和量化。他们将应用在新框架内开发的方法来早期检测电力系统中罕见但具有破坏性的级联故障。该方法的数学/理论核心在于将特定于电力系统的约束集成到通用极值理论（EVT）中。这种集成将通过 EVT 与物理信息机器学习、概率图形模型和最优传输理论的补充方法的综合来实现。在计算方面，PI 将利用 EVT 为大规模随机系统开发高效的模型校准、推理和学习算法，通过具有随机随机输入的适当参数化的非线性实数或复值代数和微分方程进行描述。此外，他们耦合的理论和计算工作将在更广泛的背景下发挥作用，将罕见事件控制和预防方法扩展到具有国家重要性的其他系统和应用。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。