AMPS: Stochastic Aging Modeling and Failure Prediction of Power Grid

在外国攻击的情况下，由于能源分布和安全问题，确定美国电网中最敏感的区域并预测级联故障是一项具有重大国家利益的重要任务。这种大规模网络受到大量随机输入（如天气、老化、外部攻击）的动态影响，构成了一个传播的高维不确定性空间。通过随机模型和敏感性分析，我们获得了网络故障不确定性演变的可靠预测，并确定了敏感区域，促进了灾难性事件期间的决策和预防措施。这项研究将在识别最脆弱地区方面开辟新的领域，并提高整个电网的可靠性和弹性。在环境和公共安全受到威胁的情况下，这一点至关重要。我们方法的新颖之处在于，我们对一个小而有代表性的组件进行了“详细的随机故障建模”，例如，电线/电缆和受热-电-机械负载影响的机械部件，然后，为扩展和全局系统开发了一个“物理和数学信息的集总元素建模”，用于故障和老化。因此，在我们的方法中，底层物理和数据注入的建模将最小化，因为它是在常见的实践中。在这项为期三年的资助中，每年将有一名研究生获得资助。美国的电网网络是一个复杂、互联和健壮的工程奇迹。然而，高连接性可能会导致单个组件丢失的级联故障。大多数干扰是由自然事件引起的，如风暴、飓风、龙卷风、地震。然而，三分之一的大停电是由非自然原因造成的，包括人为失误、机器故障和故意攻击。本文建立了一种新的基于相场的计算数学模型，用于分析电网和输电线路上的发电点（点）的热电机械故障。此外，我们开发了一个减少订单的模型，可以在美国不同的工作/天气条件下进行实时检查。随后，我们将近似于突出的电源/发电点以及老化的电网网络链路的“概率失效”。研究了故障不确定性前沿在电网中的传播，确定了电网故障可能影响的“临界高风险”区域。该模型是一个强大的工具，可以在停电情况下更快地做出决策，防止恶意攻击，以及谨慎、安全地扩展已经复杂的电力网络。通过智能电网获取的数据用于监测电网运行，概率框架在预测罕见灾难性事件中具有实时资产的作用。在可交付成果方面，除了发表论文和在会议和研讨会上组织小型研讨会外，我们还将提供适当文档化的开源代码，并附带基准电网仿真示例。该奖项反映了美国国家科学基金会的法定使命，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。

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.