# Nested Reinforcement Learning Based Control for Protective Relays in Power Distribution Systems

arXiv: https://arxiv.org/abs/1906.10815 · HTML: https://mlsanity.com/paper/1906.10815/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids](https://mlsanity.com/paper/2110.06324/) (2021)
- [Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality](https://mlsanity.com/paper/2211.04584/) (2022)
- [Offline Reinforcement Learning for Distribution-Grid Protection](https://mlsanity.com/paper/2609.24703/) (2026)

## Abstract (arXiv)

This paper envisions a new control architecture for the protective relay setting in future power distribution systems. With deepening penetration of distributed energy resources at the end users level, it has been recognized as a key engineering challenge to redesign the protective relays in the future distribution system. Conceptually, these protective relays are the discrete ON/OFF control devices at the end of each branch and node in a power network. The key technical difficulty lies in how to set up the relay control logic so that the protection could successfully differentiate heavy load and faulty operating conditions. This paper proposes a new nested reinforcement learning approach to take advantage of the structural properties of distribution networks and develop a new set of training methods for tuning the protective relays.