Predicting Spatiotemporal Impacts of Weather on Power Systems Using Big Data Science
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Due to the increase in extreme weather conditions and aging infrastructureAging infrastructuredeterioration, 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 systemsPower systemis on the rise. The countrys 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 impactsWeather impacton 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 DataBig Datacan 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.
author list (cited authors)
Kezunovic, M., Obradovic, Z., Dokic, T., Zhang, B., Stojanovic, J., Dehghanian, P., & Chen, P.
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