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

Ashwin Venkataramanan; Ali Mehrizi-Sani

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arXiv · 2207.05020

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

Ashwin Venkataramanan·Ali Mehrizi-Sani

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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.

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BibTeX RIS

Ashwin Venkataramanan, Ali Mehrizi-Sani. 2022-07-11. Reinforcement Learning$\unicode{x2013}$Based Transient Response Shaping for Microgrids. https://arxiv.org/abs/2207.05020

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