  Ensuring electricity grid reliability becomes increasingly challenging with
the shift towards renewable energy and declining conventional capacities.
Distribution System Operators (DSOs) aim to achieve grid reliability by
verifying the n-1 principle, ensuring continuous operation in case of component
failure. Electricity networks' complex graph-based data holds crucial
information for n-1 assessment: graph structure and data about stations/cables.
Unlike traditional machine learning methods, Graph Neural Networks (GNNs)
directly handle graph-structured data. This paper proposes using Graph
Isomorphic Networks (GINs) for n-1 assessments in medium voltage grids. The GIN
framework is designed to generalise to unseen grids and utilise graph structure
and data about stations/cables. The proposed GIN approach demonstrates faster
and more reliable grid assessments than a traditional mathematical optimisation
approach, reducing prediction times by approximately a factor of 1000. The
findings offer a promising approach to address computational challenges and
enhance the reliability and efficiency of energy grid assessments.
