Application of Hybrid Geo-Spatially Granular Fragility Curves to Improve Power Outage Predictions

M. Allen, S. Fernandez, O. Omitaomu, K. A. Walker
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引用次数: 14

Abstract

Fragility curves depict the relationship between a weather variable (wind speed, gust speed, ice accumulation, precipitation rate) and the observed outages for a targeted infrastructure network. This paper describes an empirical study of the county by county distribution of power outages and one minute weather variables during Hurricane Irene with the objective of comparing 1) ‘as built’ fragility curves (statistical approach) to engineering ‘as designed’ (bottom up) fragility curves for skill in forecasting outages during future hurricanes; 2) county specific fragility curves to find examples of significant deviation from average behavior; and 3) the engineering practices of outlier counties to suggest future engineering studies of robustness. Outages in more than 90% of the impacted counties could be anticipated through an average or ‘generic’ fragility curve. The remaining counties could be identified and handled as exceptions through geographic data sets. The counties with increased or decreased robustness were characterized by terrain more or less susceptible to persistent flooding in areas where above ground poles located their foundations. Land use characteristics of the area served by the power distribution system can suggest trends in the “as built” power grid vulnerabilities to extreme weather events that would be subjects for site specific studies.
混合地理-空间颗粒脆性曲线在改进停电预测中的应用
脆弱性曲线描述了天气变量(风速、阵风速度、积冰量、降水率)与观测到的目标基础设施网络中断之间的关系。本文描述了在飓风艾琳期间逐个县的停电分布和一分钟天气变量的实证研究,目的是比较1)“建成”的脆弱性曲线(统计方法)与工程“设计”的脆弱性曲线(自下而上)在预测未来飓风期间停电的技能;2)县特有的脆弱性曲线,寻找明显偏离平均行为的例子;(3)分析了离群县的工程实践,为今后的鲁棒性工程研究提供了建议。通过平均或“一般”脆弱性曲线,可以预测90%以上受影响县的停电情况。其余的县可以通过地理数据集作为例外来识别和处理。稳健性增加或减少的县的特点是地形或多或少容易受到地面上杆子所在地区持续洪水的影响。配电系统所服务地区的土地利用特征可以表明“建成”电网对极端天气事件的脆弱性趋势,这将是具体地点研究的主题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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