This paper introduces the concept of Deep Reinforcement Learning based architecture for protective relay design in power distribution systems with many distributed energy resources (DERs). The performance of widely-used overcurrent protection scheme is hindered by the presence of distributed generation, power electronic interfaced devices and fault impedance. In this paper, a reinforcement learning-based approach is proposed to design and implement protective relays in the distribution grid. The particular algorithm used is an Long Short-Term Memory (LSTM) enhanced deep neural network that is highly accurate, communication-free and easy to implement. The proposed relay design is tested in OpenDSS simulation on the IEEE 34-node test feeder and demonstrated much more superior performance over traditional overcurrent protection from the aspect of failure rate, robustness and response speed.
@article{arxiv.2003.02422,
title = {Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids},
author = {Dongqi Wu and Dileep Kalathil and Miroslav Begovic and Le Xie},
journal= {arXiv preprint arXiv:2003.02422},
year = {2021}
}
Comments
Submitted to IEEE Transactions of Smart Grid, under review