Predicting power outages caused by extratropical storms
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Authors
Tervo, Roope
Láng, Ilona
Jung, Alexander
Mäkelä, Antti
Date
2021-02-11
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Language
en
Pages
21
607-627
607-627
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NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, Volume 21, issue 2
Abstract
Strong winds induced by extratropical storms cause a large number of power outages, especially in highly forested countries such as Finland. Thus, predicting the impact of the storms is one of the key challenges for power grid operators. This article introduces a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with forest inventory. We start by identifying storm objects from wind gust and pressure fields by using contour lines of 15 m s−1 and 1000 hPa, respectively. The storm objects are then tracked and characterized with features derived from surface weather parameters and forest vegetation information. Finally, objects are classified with a supervised machine-learning method based on how much damage to the power grid they are expected to cause. Random forest classifiers, support vector classifiers, naïve Bayes processes, Gaussian processes, and multilayer perceptrons were evaluated for the classification task, with support vector classifiers providing the best results.Description
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Tervo, R, Láng, I, Jung, A & Mäkelä, A 2021, ' Predicting power outages caused by extratropical storms ', NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, vol. 21, no. 2, 6072021, pp. 607-627 . https://doi.org/10.5194/nhess-21-607-2021