Collaborative Research: Risk Informed Decision Making for Maintenance of Deteriorating Distribution Poles Under Extreme Wind Hazards

美国的电力分配依赖于由木杆支撑的架空线路。该配电系统在强风事件中遭受损坏，导致企业和住宅的电力中断。木质电线杆受到老化和风力危害增加的压力的综合影响，导致不可靠和潜在的不安全配电基础设施。超过1亿个配电杆是关键的能源输送基础设施的一部分，在风暴期间受到极大的影响。目前用于预测电力系统组件的一般集合的故障时间分布的方法不以整体观点解决老化和危险的同时影响。关于杆的失效年龄的研究一直是稀疏的、不完整的和不一致的。这将导致在极寿命的预测增加不确定性，从而阻碍明智和准确的决策有效的电极管理strategy.The项目提出了随机有限元模型的发展与时间相关的衰减模型集成的配电杆，以模拟当前和未来状态的响应配电杆各种气候条件。认识上的不确定性将减少，容量和需求建模中任意不确定性的各种来源将被捕获。一种新的战略，利用n维脆弱性表面与区域飓风灾害模型相结合，将合并估计故障概率，开发预测模型的时间分配杆故障，并量化的置信水平的预测。将在配电网络随机优化的基础上制定一个基于优先级的诊断和风险知情维护框架，其中考虑到物理组件对压力源的地理暴露及其对网络可靠性的重要性。这种方法的成功并不能保证避免故障，但指出了网络中使用维护良好的基础设施可能会降低风暴期间故障风险的区域，并为在正常运行情况下选择预防性更换杆提供了指导。

Distribution of electricity in the United States relies on overhead lines supported by wood poles. This distribution system suffers damage in high wind events resulting in interruption in electrical power to businesses and residencies. The wood poles suffer from the combined effect of aging and increasing stress from wind hazards leading to an unreliable and potentially unsafe distribution infrastructure. Over 100 million distribution poles, which are part of the critical energy delivery infrastructure, suffer immensely during wind storms. Current methods for predicting the distribution of times-to-failure of a general set of power system components do not address simultaneous effects of aging and hazards with a holistic view. Research on age-at-failure of poles has been sparse, incomplete, and inconsistent. This results in increased uncertainties in predictions of pole lifetimes, thus obstructing informed and accurate decision-making on effective pole management strategies.The project proposes the development of stochastic Finite Element models for distribution poles integrated with time-dependent decay models to simulate the current and future state of the response of distribution poles to various climate conditions. Epistemic uncertainties will be reduced and various sources of aleatoric uncertainties in capacity and demand modeling will be captured. A novel strategy utilizing n-dimensional fragility surfaces integrated with regional hurricane hazard models will be merged to estimate failure probabilities, develop predictive models for the time-to-failure of distribution poles, and quantify the confidence levels in the predictions. A priority-based diagnostics and risk-informed maintenance framework will be developed on the basis of stochastic optimization for distribution networks that takes into account both the geographic exposure of the physical components to stressors and their importance for the reliability of the networks. The success afforded by this approach does not guarantee failure avoidance, but points to the regions in networks where use of well-maintained infrastructure may lower the risk of failures during storms and also provides guidance in selecting the poles for preventive replacement under normal operating circumstances.