AMPS: Stochastic Aging Modeling and Failure Prediction of Power Grid

AMPS：电网随机老化建模和故障预测

• 批准号: 1923201
• 负责人: Mohsen Zayernouri
• 金额: $ 23.13万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923201&HistoricalAwards=false
• 关键词: AMPS; Stochastic; Aging; Modeling; Failure

In the case of foreign attacks, identifying the most sensitive regions in the American power grid while predicting cascade failure is a vital task with significant national interest due to the energy distribution and security concerns. Such large-scale networks are influenced dynamically by a substantial number of stochastic inputs, such as weather, aging, external attacks, composing a high-dimensional uncertainty space that propagates. Our stochastic models and sensitivity analysis we obtain reliable predictions of the network's failure uncertainty evolution and identify sensitive regions, facilitating decision making and preventive actions during catastrophic events. This study will break a new ground on identifying the most vulnerable regions also enhancing the reliability and resilience of power grid as a whole. This is of critical importance, where the environment and public safety are at risk. the novelty of our approach is that we carry out a 'detailed stochastic failure modeling' for a small yet representing component, e.g., electric wire/cable and a mechanical part subject to thermo-electro-mechanical load, and then, to develop a 'physics and math informed lumped element modeling' of failure and aging for the expanded and global system. Therefore, in our approach, the underlying physics and data-infused modeling would minimally be compromised as it is in the common practice. One graduate student will be supported in each year of this 3 year grant.The power grid network in the United States is an engineering wonder of complexity, interconnectivity and robustness. However, the high connectivity can potentially lead to cascading failures with the loss of single components. The majority of disturbances is caused by natural events, such as storms, hurricanes, tornadoes, earthquakes. Still, one third of major blackouts have non-natural causes, including human errors, machine failure and intentional attacks. We formulate a new computational-mathematical phase-field based model for thermo-electro- mechanical failure analysis of generating points (sites) on the power grid and the power-transmission lines. Moreover, we develop a reduced-order model which can be in real-time fashioned examined subject to different working/weather conditions across the US. Subsequently, we will approximate the 'probability failure' of the prominent source/generating points also the aging grid network links. We study the propagation of failure uncertainty front through the power grid and identify regions of 'critically high risk' which would be affected by the malfunction of power grid. The model is a robust tool for faster decision making in case of blackouts, prevention of malicious attacks and in prudent, secure expansion of the already complex power networks. With data obtained through smart grid used in monitoring power grid operations, the probabilistic framework serves as a real-time asset in the prediction of rare catastrophic events. In terms of deliverables, in addition to published papers and organization of mini-symposiums in conferences and workshops, we will also provide properly documented open source codes accompanied with benchmark power-grid simulation examples.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.

在外国攻击的情况下，识别美国电网中最敏感的区域，同时预测级联故障是一项重要的任务，由于能源分配和安全问题，具有重大的国家利益。这样的大规模网络受到大量随机输入的动态影响，如天气，老化，外部攻击，构成一个传播的高维不确定性空间。我们的随机模型和敏感性分析，我们获得可靠的预测网络的故障不确定性的演变，并确定敏感区域，促进决策和预防措施，在灾难性事件。这项研究将在识别最脆弱地区方面开辟新天地，并提高整个电网的可靠性和弹性。在环境和公共安全面临风险的情况下，这一点至关重要。我们方法的新奇在于，我们对一个小而有代表性的部件进行了“详细的随机故障建模”，例如，电线/电缆和受到热-电-机械负载的机械部件，然后，开发用于扩展的和全局系统的故障和老化的“物理和数学通知的集总元件建模”。因此，在我们的方法中，底层物理和数据注入建模将尽可能少地受到损害，因为这是常见的做法。美国的电网网络是一个复杂的工程奇迹，互联性和鲁棒性。然而，高连通性可能会导致级联故障和单个组件的丢失。大多数扰动是由自然事件引起的，如风暴、飓风、龙卷风、地震。尽管如此，三分之一的大停电是由非自然原因造成的，包括人为错误、机器故障和故意攻击。提出了一种新的基于计算数学相场的模型，用于分析电网和输电线路上发电点（点）的热机电故障。此外，我们开发了一个降阶模型，可以在美国各地不同的工作/天气条件下进行实时检查。随后，我们将近似的“概率故障”的突出的源/发电点，以及老化的网格网络链接。我们研究了通过电网故障不确定性的传播，并确定区域的“临界高风险”，这将影响到电网故障。该模型是一个强大的工具，可以在停电的情况下更快地做出决策，防止恶意攻击，并谨慎，安全地扩展已经复杂的电力网络。通过智能电网获得的数据用于监测电网运行，概率框架可作为预测罕见灾难性事件的实时资产。在成果方面，除了发表论文和组织小型研讨会和研讨会外，我们还将提供适当记录的开源代码以及基准电网仿真示例。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

Dynamic nonlocal passive scalar subgrid-scale turbulence modeling

动态非局部被动标量亚网格尺度湍流建模

• DOI: 10.1063/5.0106733
• 发表时间: 2022
• 期刊: Physics of Fluids
• 影响因子: 4.6
• 作者: Seyedi, S. Hadi;Akhavan-Safaei, Ali;Zayernouri, Mohsen
• 通讯作者: Zayernouri, Mohsen

Anomalous features in internal cylinder flow instabilities subject to uncertain rotational effects

• DOI: 10.1063/5.0021815
• 发表时间: 2020-09-01
• 期刊: PHYSICS OF FLUIDS
• 影响因子: 4.6
• 作者: Akhavan-Safaei, Ali;Seyedi, S. Hadi;Zayernouri, Mohsen
• 通讯作者: Zayernouri, Mohsen

A non-local spectral transfer model and new scaling law for scalar turbulence

• DOI: 10.1017/jfm.2022.1066
• 发表时间: 2021-11
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Ali Akhavan-Safaei;Mohsen Zayernouri

Fractional Modeling in Action: a Survey of Nonlocal Models for Subsurface Transport, Turbulent Flows, and Anomalous Materials

• DOI: 10.1007/s42102-022-00085-2
• 发表时间: 2021-09
• 期刊: Journal of Peridynamics and Nonlocal Modeling
• 作者: J. Suzuki;Mamikon A. Gulian;Mohsen Zayernouri;M. D'Elia

Deep Learning of Subgrid-Scale Dynamics in Scalar Turbulence and Generalization to other Transport Regimes

标量湍流中次网格尺度动力学的深度学习及其对其他传输机制的推广

• DOI: 10.1615/jmachlearnmodelcomput.2023048824
• 发表时间: 2023
• 期刊: Journal of Machine Learning for Modeling and Computing
• 作者: Akhavan-Safaei, Ali;Zayernouri, Mohsen
• 通讯作者: Zayernouri, Mohsen