Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids

2020/03/05 by Dongqi Wu, Dileep Kalathil, Wu, Dongqi +6
Computer Science · Engineering · #FOS: Electrical engineering #Full-Duplex Wireless Communications #Islanding Detection in Power Systems #Power Systems Fault Detection #Systems and Control (eess.SY) #cs.SY #eess.SY #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2003.02422

Submitted to IEEE Transactions of Smart Grid, under review

openalex publication_date 2020/03/05 · arxiv created 2021/06/01 · arxiv updated 2021/06/03 · openalex created_date 2022/08/27 · openalex updated_date 2026/07/28

Abstract

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.

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