A Reinforcement Learning Approach for Frequency Control of Inverted-Based Microgrids

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Abstract

In this paper, we present a reinforcement learning control scheme for optimal frequency synchronization in a lossy inverter-based microgrid. Compared to the existing methods in the literature, we relax the restrictions on the system, i.e. being a lossless microgrid, and the transmission lines and loads to have constant impedances. The proposed control scheme does not require a priori information about system parameters and can achieve frequency synchronization in the presence of dominantly resistive and/or inductive line and load impedances, model parameter uncertainties, time varying loads and disturbances. First, using Lyapunov theory a feedback control is formulated based on the unknown dynamics of the microgrid. Next, a performance function is defined based on cumulative rewards towards achieving convergence to the nominal frequency. The performance function is approximated by a critic neural network in real-time. An actor network is then simultaneously learning a parameterized approximation of the nonlinear dynamics and optimizing the approximated performance function obtained from the critic network. The performance of our control scheme is validated via simulation on a lossy microgrid case study in the presence of disturbances.

Original languageEnglish
Pages (from-to)111-116
Number of pages6
JournalIFAC-PapersOnLine
Volume52
Issue number4
DOIs
Publication statusPublished - 2019
EventIFAC Workshop on Control of Smart Grid and Renewable Energy Systems, CSGRES 2019 - Jeju, Korea, Republic of
Duration: 10 Jun 201912 Jun 2019

Keywords

  • frequency synchronization
  • microgrids
  • reinforcement learning
  • stability

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