# Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning

arXiv: https://arxiv.org/abs/2406.08917 · HTML: https://mlsanity.com/paper/2406.08917/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 74 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Topology Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/1710.10370/) (2017)
- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions](https://mlsanity.com/paper/2205.10129/) (2022)
- [Leveraging power grid topology in machine learning assisted optimal power flow](https://mlsanity.com/paper/2110.00306/) (2021)
- [Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids](https://mlsanity.com/paper/2109.03604/) (2021)
- [PowerDynamics.jl -- An experimentally validated open-source package for the dynamical analysis of power grids](https://mlsanity.com/paper/2101.02103/) (2021)
- [Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction](https://mlsanity.com/paper/2202.08065/) (2022)
- [Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting](https://mlsanity.com/paper/2202.06738/) (2022)
- [Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks](https://mlsanity.com/paper/2206.06369/) (2022)
- [Predicting Dynamic Stability from Static Features in Power Grid Models using Machine Learning](https://mlsanity.com/paper/2210.09266/) (2022)
- [Probabilistic Stability Assessment for Active Distribution Grids](https://mlsanity.com/paper/2106.09624/) (2021)
- [Towards dynamic stability analysis of sustainable power grids using graph neural networks](https://mlsanity.com/paper/2212.11130/) (2022)
- [Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid](https://mlsanity.com/paper/2310.01181/) (2023)
- [Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning](https://mlsanity.com/paper/2402.17500/) (2024)
- [Dispatchable Virtual Oscillator Control for Decentralized Inverter-dominated Power Systems: Analysis and Experiments](https://mlsanity.com/paper/1811.08842/) (2018)
- [A normal form for grid forming power grid actors](https://mlsanity.com/paper/2106.00644/) (2021)

## Built on by (most cited first, 2 of 2)

- [Projected Neural Differential Equations for Learning Constrained Dynamics](https://mlsanity.com/paper/2410.23667/) (2024)
- [Learning Dynamic Stability Landscapes in Synchronization Networks](https://mlsanity.com/paper/2605.23708/) (2026)

## Abstract (arXiv)

Due to the increasing share of renewables, the analysis of the dynamical behavior of power grids gains importance. Effective risk assessments necessitate the analysis of large number of fault scenarios. The computational costs inherent in dynamic simulations impose constraints on the number of configurations that can be analyzed. Machine Learning (ML) has proven to efficiently predict complex power grid properties. Hence, we analyze the potential of ML for predicting dynamic stability of future power grids with large shares of inverters. For this purpose, we generate a new dataset consisting of synthetic power grid models and perform dynamical simulations. As targets for the ML training, we calculate the fault-ride-through probability, which we define as the probability of staying within a ride-through curve after a fault at a bus has been cleared. Importantly, we demonstrate that ML models accurately predict the fault-ride-through probability of synthetic power grids. Finally, we also show that the ML models generalize to an IEEE-96 Test System, which emphasizes the potential of deploying ML methods to study probabilistic stability of power grids.