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\begin{document}

\title{Evaluating Machine Learning Models for the Fast Identification of
Contingency Cases}



\author[1]{Florian Schaefer}%
\author[1]{Jan-Hendrik Menke}%
\author[1]{Martin Braun}%
\affil[1]{University of Kassel}%


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  \date{June 15, 2024}


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\begin{abstract}
Fast approximations of power flow results are beneficial in power system
planning and live operation. In planning, millions of power flow
calculations are necessary if multiple years, different control
strategies, or contingency policies are to be considered. In live
operation, grid operators must assess if grid states comply with
contingency requirements in a short time. In this paper, we compare
regression and classification methods to either predict multi-variable
results, e.g., bus voltage magnitudes and line loadings, or binary
classifications of time steps to identify critical loading situations.
We test the methods on three realistic power systems based on time
series in 15min and 5min resolution of one year. We compare different
machine learning models, such as multilayer perceptrons (MLPs), decision
trees, k-nearest neighbors, gradient boosting, and evaluate the required
training time and prediction times as well as the prediction errors. We
additionally determine the amount of training data needed for each
method and show results, including the approximation of untrained
curtailment of generation. Regarding the compared methods, we identified
the MLPs as most suitable for the task. The MLP-based models can predict
critical situations with an accuracy of 97-98\% and a very low number of
false negative predictions of 0.0-0.64\%.%
\end{abstract}%



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