  The topology optimization of transmission networks using Deep Reinforcement
Learning (DRL) has increasingly come into focus. Various researchers have
proposed different DRL agents, which are often benchmarked on the Grid2Op
environment from the Learning to Run a Power Network (L2RPN) challenges. The
environments have many advantages with their realistic chronics and underlying
power flow backends. However, the interpretation of agent survival or failure
is not always clear, as there are a variety of potential causes. In this work,
we focus on the failures of the power grid to identify patterns and detect them
a priori. We collect the failed chronics of three different agents on the WCCI
2022 L2RPN environment, totaling about 40k data points. By clustering, we are
able to detect five distinct clusters, identifying different failure types.
Further, we propose a multi-class prediction approach to detect failures
beforehand and evaluate five different models. Here, the Light
Gradient-Boosting Machine (LightGBM) shows the best performance, with an
accuracy of 86%. It also correctly identifies in 91% of the time failure and
survival observations. Finally, we provide a detailed feature importance
analysis that identifies critical features and regions in the grid.
