Predicting Spatiotemporal Impacts of Weather on Power Systems Using Big Data Science
Predicting Spatiotemporal Impacts of Weather on Power Systems Using Big Data Science
复制标题
利用大数据科学预测天气对电力系统的时空影响
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Po
中科院分区：
文献类型：
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作者：
M. Kezunovic;Z. Obradovic;T. Dokic;Bei Zhang;Jelena Stojanovic;P. Dehghanian;Po
由于极端天气条件的增加和老化的基础设施恶化，电网中断的数量和频率急剧上升，主要是由于网络组件高度暴露于天气因素。总的来说，75%的停电是由天气造成的故障直接造成的（例如，闪电、风的冲击），或由于磨损与天气暴露（例如长时间过热）导致的设备故障而间接造成的。此外，可再生能源在电力系统中的渗透率正在上升。该国的太阳能发电能力预计到2016年底将翻一番。可再生能源对天气条件的严重依赖导致其高度可变和间歇性。为了开发自动化的方法来评估天气对电力系统的影响，大量的数据需要进行综合分析。本章所解决的问题是如何将这些大数据进行集成，时空关联和实时分析，以提高现代电网应对天气引起的紧急情况的能力。
Due to the increase in extreme weather conditions and aging infrastructure deterioration, the number and frequency of electricity network outages is dramatically escalating, mainly due to the high level of exposure of the network components to weather elements. Combined, 75% of power outages are either directly caused by weather-inflicted faults (e.g., lightning, wind impact), or indirectly by equipment failures due to wear and tear combined with weather exposure (e.g. prolonged overheating). In addition, penetration of renewables in electric power systems is on the rise. The country’s solar capacity is estimated to double by the end of 2016. Renewables significant dependence on the weather conditions has resulted in their highly variable and intermittent nature. In order to develop automated approaches for evaluating weather impacts on electric power system, a comprehensive analysis of large amount of data needs to be performed. The problem addressed in this chapter is how such Big Data can be integrated, spatio-temporally correlated, and analyzed in real-time, in order to improve capabilities of modern electricity network in dealing with weather caused emergencies.