# Fault Detection for agents on power grid topology optimization: A Comprehensive analysis

arXiv: https://arxiv.org/abs/2406.16426 · HTML: https://mlsanity.com/paper/2406.16426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 16 of 16)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance](https://mlsanity.com/paper/2304.11127/) (2023)
- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Learning to run a power network challenge for training topology controllers](https://mlsanity.com/paper/1912.04211/) (2019)
- [PyTorch Tabular: A Framework for Deep Learning with Tabular Data](https://mlsanity.com/paper/2104.13638/) (2021)
- [Action Set Based Policy Optimization for Safe Power Grid Management](https://mlsanity.com/paper/2106.15200/) (2021)
- [Reinforcement Learning for Electricity Network Operation](https://mlsanity.com/paper/2003.07339/) (2020)
- [Power Grid Congestion Management via Topology Optimization with AlphaZero](https://mlsanity.com/paper/2211.05612/) (2022)
- [PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks](https://mlsanity.com/paper/2212.02397/) (2022)
- [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021)
- [Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents](https://mlsanity.com/paper/2304.00765/) (2023)
- [Exploring grid topology reconfiguration using a simple deep reinforcement learning approach](https://mlsanity.com/paper/2011.13465/) (2020)
- [HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach](https://mlsanity.com/paper/2405.00629/) (2024)
- [GANDALF: Gated Adaptive Network for Deep Automated Learning of Features](https://mlsanity.com/paper/2207.08548/) (2022)
- [Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design](https://mlsanity.com/paper/2207.10330/) (2022)

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

- [Graph Reinforcement Learning for Power Grids: A Comprehensive Survey](https://mlsanity.com/paper/2407.04522/) (2024)
- [Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach](https://mlsanity.com/paper/2503.15190/) (2025)

## Abstract (arXiv)

Optimizing the topology of transmission networks using Deep Reinforcement Learning (DRL) has increasingly come into focus. Various DRL agents have been proposed, which are mostly benchmarked on the Grid2Op environment from the Learning to Run a Power Network (L2RPN) challenges. The environments have many advantages with their realistic grid scenarios and underlying power flow backends. However, the interpretation of agent survival or failure is not always clear, as there are a variety of potential causes. In this work, we focus on the failures of the power grid simulation to identify patterns and detect them in advance. We collect the failed scenarios of three different agents on the WCCI 2022 L2RPN environment, totaling about 40k data points. By clustering, we are able to detect five distinct clusters, identifying common failure types. Further, we propose a multi-class prediction approach to detect failures beforehand and evaluate five different prediction models. Here, the Light Gradient-Boosting Machine (LightGBM) shows the best failure prediction performance, with an accuracy of 82%. It also accurately classifies whether a the grid survives or fails in 87% of cases. Finally, we provide a detailed feature importance analysis that identifies critical features and regions in the grid.