期刊
IEEE TRANSACTIONS ON ENERGY CONVERSION
卷 36, 期 3, 页码 1919-1930出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEC.2020.3043709
关键词
Microgrids; Optimal control; Topology; Resistance; Nonlinear systems; Heuristic algorithms; Performance evaluation; DC microgrid; nonlinear optimal control; power buffer; reinforcement learning; sparsity promoting
资金
- National Science Foundation [ECCS-1804, TEC-00509-2020]
This article provides a data-driven optimal solution to reduce interactions between different control loops of power buffers while minimizing a closed-loop performance function. Reinforcement learning methods are used to deal with optimal control of nonlinear systems, and a Tabu Search method is employed to address the resulting combinatorial problem. The proposed solutions are validated in a controller/hardware-in-the-loop environment for a DC microgrid.
A power buffer is a power electronics converter with a large capacitor that shields a weak DC grid from abrupt load changes. Distributed control solutions have been shown to be superior to the decentralized ones; however, the effects of the communication network topology on the control performance of these buffers have not yet been studied. This article offers a data-driven optimal solution to reduce the interactions between different control loops of power buffers while minimizing a closed-loop performance function. Reinforcement learning methods deal with the optimal control of nonlinear systems, and a Tabu Search method addresses the resulting combinatorial problem. The proposed solutions are validated for a DC microgrid in a controller/hardware-in-the-loop environment.
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