Predicting power outages caused by extratropical storms

Abstract

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.

Characteristics of extratropical cyclones and precursors to windstorms in Northern Europe

“…They are associated with precipitation and strong winds which can lead to high impacts in society (e.g. Suursaar et al, 2006;Kufeoglu and Lehtonen, 2014;Holley, 2021;Tervo et al, 2021;Rantanen et al, 2021), for example floods, power outages, and forest and property damage. Since extratropical cyclones play a crucial role in the atmospheric circulation, weather variability and as a cause for societal and economical impacts, the knowledge and understanding of extratropical cyclone climate and dynamics are essential.…”