A Reinforcement Learning Approach for Frequency Control of Inverted-Based Microgrids⁎. Issue 4 (2019)

• Record Type: Journal Article
• Authors: Adibi, Mahya; Woude, Jacob van der
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 4(2019)
• Page Start: 111
• Page End: 116
• Publication Date: 2019
• Subjects: reinforcement learning -- microgrids -- stability -- frequency synchronization; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.08.164
• Languages: English
• ISSNs: 2405-8963

Abstract

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.