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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/nhess-2020-205
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-205
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Aug 2020

03 Aug 2020

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A revised version of this preprint is currently under review for the journal NHESS.

Predicting power outages caused by extratropical storms

Roope Tervo1,, Ilona Láng1,, Alexander Jung2, and Antti Mäkelä1 Roope Tervo et al.
  • 1Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
  • 2Aalto University, Dept of Computer Science, B.O. 11000, 00076 Aalto, Finland
  • These authors contributed equally to this work.

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 Classifier, Support Vector Classifier, Naive Bayes, Gaussian Processes, and Multilayer Perceptron were evaluated for the classification task, Support Vector Classifier providing the best results.

Roope Tervo et al.

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Roope Tervo et al.

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Latest update: 27 Nov 2020
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Short summary
Predicting the number of power outages caused by extratropical storms is the key challenge for power grid operators. We introduce a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with forest inventory. The storms are first identified from the data and then classified using several machine learning methods. While there is plenty of room to improve, the results are already usable, Support Vector Classifier providing the best performance.
Predicting the number of power outages caused by extratropical storms is the key challenge for...
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