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 language | English |
---|---|
Pages (from-to) | 111-116 |
Number of pages | 6 |
Journal | IFAC-PapersOnLine |
Volume | 52 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2019 |
Event | IFAC Workshop on Control of Smart Grid and Renewable Energy Systems, CSGRES 2019 - Jeju, Korea, Republic of Duration: 10 Jun 2019 → 12 Jun 2019 |
Keywords
- frequency synchronization
- microgrids
- reinforcement learning
- stability