Summary of Fault Detection For Agents on Power Grid Topology Optimization: a Comprehensive Analysis, by Malte Lehna and Mohamed Hassouna and Dmitry Degtyar and Sven Tomforde and Christoph Scholz
Fault Detection for agents on power grid topology optimization: A Comprehensive analysis
by Malte Lehna, Mohamed Hassouna, Dmitry Degtyar, Sven Tomforde, Christoph Scholz
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes using Deep Reinforcement Learning (DRL) to optimize transmission network topology, leveraging environments like Grid2Op from the L2RPN challenges. DRL agents have been benchmarked on these environments, but agent survival or failure interpretation can be unclear due to various causes. This work focuses on identifying patterns and detecting failures in advance by analyzing failed scenarios from three agents on the WCCI 2022 L2RPN environment, totaling 40k data points. Clustering reveals five distinct clusters, indicating common failure types. A multi-class prediction approach is proposed to detect failures beforehand, with Light Gradient-Boosting Machine (LightGBM) showing the best performance, achieving 82% accuracy in failure prediction and 87% accuracy in classifying grid survival or failure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses Deep Reinforcement Learning (DRL) to improve power grid simulations. It looks at why some agents fail and tries to predict when this will happen. They collect data from three agents that failed on a special environment, called L2RPN, which has real-life scenarios and simulates how power flows through the grid. By grouping similar failures together, they find five common types. Then, they develop a way to predict these failures beforehand. The best model they tested is called LightGBM, which can correctly predict if the grid will fail or not 87% of the time. |
Keywords
» Artificial intelligence » Boosting » Clustering » Reinforcement learning
