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

TL;DR

This paper presents a deep reinforcement learning approach using LSTM neural networks for designing robust, communication-free protective relays in distribution grids with high DER penetration, outperforming traditional methods.

Contribution

Introduces a novel LSTM-based deep reinforcement learning method for protective relay design in DER-rich distribution systems, enhancing robustness and response speed.

Findings

Significantly reduces failure rate compared to traditional overcurrent protection.

Demonstrates improved robustness in fault detection.

Achieves faster response times in simulation tests.

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.