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.

利用图同构网络（GIN）进行中压网的 1-n 评估，结果显示相较于传统的数学优化方法，GIN 方法具有更快、更可靠的评估速度，减少了大约 1000 倍的预测时间，为解决计算挑战、提高能源网络评估的可靠性和效率提供了一种有前景的方法。