Reinforcement Learning$\unicode{x2013}$Based Transient Response Shaping for Microgrids

TL;DR

This paper presents a reinforcement learning-based supplementary controller that autonomously improves the transient response of inverter-based resources in microgrids, verified through simulation on a medium voltage test system.

Contribution

It introduces a novel RL-based control method for transient shaping in microgrids, demonstrating autonomous adaptive set point adjustments to mitigate transients.

Findings

Effective transient mitigation demonstrated in simulation

Controller adapts based on current system state

Improves stability and response time of inverter resources

Abstract

This work explores the usage of a supplementary controller for improving the transient performance of inverter$\unicode{x2013}$based resources (IBR) in microgrids. The supplementary controller is trained using a reinforcement learning (RL)$\unicode{x2013}$based algorithm to minimize transients in a power converter connected to a microgrid. The controller works autonomously to issue adaptive, intermediate set points based on the current state and trajectory of the observed or tracked variable. The ability of the designed controller to mitigate transients is verified on a medium voltage test system using PSCAD/EMTDC.