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。这种配电系统在大风事件中受损，导致企业和居民的电力中断。木杆受到老化和风力灾害带来的压力增加的综合影响，导致配电基础设施不可靠和潜在的不安全。作为关键能源输送基础设施的一部分，超过1亿个配电电线杆在风暴期间遭受了巨大的损失。目前用于预测一组电力系统部件的故障时间分布的方法不能以整体的观点解决老化和危险的同时影响。对电线杆失效年龄的研究一直是稀疏、不完整和不一致的。这导致预测极寿命的不确定性增加，从而阻碍了有效极管理策略的明智和准确决策。该项目建议发展随机有限元模型，将分布极点与时间相关的衰减模型结合起来，以模拟分布极点对各种气候条件的当前和未来响应状态。认知的不确定性将会减少，能力和需求建模中的各种任意不确定性将会被捕获。一种利用n维脆弱面与区域飓风灾害模型相结合的新策略将被用于估计故障概率，开发配电杆故障时间的预测模型，并量化预测的置信度。将在配电网络随机优化的基础上开发基于优先级的诊断和风险知情维护框架，该框架考虑到物理组件在压力源中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以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电线杆进行预防性更换提供了指导。

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.