  Stable operation of the electrical power system requires the power grid
frequency to stay within strict operational limits. With millions of consumers
and thousands of generators connected to a power grid, detailed human-build
models can no longer capture the full dynamics of this complex system. Modern
machine learning algorithms provide a powerful alternative for system modelling
and prediction, but the intrinsic black-box character of many models impedes
scientific insights and poses severe security risks. Here, we show how
eXplainable AI (XAI) alleviates these problems by revealing critical
dependencies and influences on the power grid frequency. We accurately predict
frequency stability indicators (such as RoCoF and Nadir) for three major
European synchronous areas and identify key features that determine the power
grid stability. Load ramps, specific generation ramps but also prices and
forecast errors are central to understand and stabilize the power grid.
