# Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid

arXiv: https://arxiv.org/abs/2310.01181 · HTML: https://mlsanity.com/paper/2310.01181/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis](https://mlsanity.com/paper/2001.03690/) (2020)
- [On Calibration of Graph Neural Networks for Node Classification](https://mlsanity.com/paper/2206.01570/) (2022)
- [Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks](https://mlsanity.com/paper/2103.07248/) (2021)
- [Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning](https://mlsanity.com/paper/2211.05855/) (2022)

## Built on by (most cited first, 3 of 3)

- [Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning](https://mlsanity.com/paper/2406.08917/) (2024)
- [Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations](https://mlsanity.com/paper/2501.07186/) (2025)
- [Exploring the impact of adaptive rewiring in Graph Neural Networks](https://mlsanity.com/paper/2602.10754/) (2026)

## Abstract (arXiv)

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.