文章基本信息

标题：A Reinforcement Learning Approach for Frequency Control of Inverted-Based Microgrids
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作者：Mahya Adibi ; Jacob van der Woude
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：4
页码：111-116
DOI：10.1016/j.ifacol.2019.08.164
语种：English
出版社：Elsevier
摘要：AbstractIn 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.
关键词：Keywordsreinforcement learningmicrogridsstabilityfrequency synchronization