Predicting power outages caused by extratropical storms

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Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Authors

Tervo, Roope
Láng, Ilona
Jung, Alexander
Mäkelä, Antti

Date

2021-02-11

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en

Pages

21
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.

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Citation

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