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Deadbeat Predictive Current Control for Modular Multilevel Converters With Enhanced Steady-State Performance and Stability
Model predictive control (MPC) methods are popularly employed in modular multilevel converters (MMCs) due to their fast dynamic response and multiobjective control capability. However, they present some inherent problems, such as computational complexity, variable switching frequency, poor steady-st...
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Published in: | IEEE transactions on power electronics 2020-07, Vol.35 (7), p.6878-6894 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Model predictive control (MPC) methods are popularly employed in modular multilevel converters (MMCs) due to their fast dynamic response and multiobjective control capability. However, they present some inherent problems, such as computational complexity, variable switching frequency, poor steady-state performance, and tedious weighting factor selection. This article develops a deadbeat predictive current control method for MMCs. This method can realize the reference tracking of ac current and circulating current in one sampling period without error, and thus provide a fast dynamic response as conventional MPC methods. Besides, switching state or voltage level evaluation, cost function calculation as well as weighting factor selection are not required. Therefore, it has a very low calculation burden, which is independent of the number of submodules (SMs). Since a modulation stage is utilized, a fixed switching frequency and consequently a satisfactory steady-state performance are obtained. The effects of time delay, parameter mismatch, and SM capacitor voltage ripple on the control algorithm are discussed. Also, the corresponding improvement measures are provided to further enhance the steady-state performance and system stability of MMCs. The effectiveness and performance of the developed control algorithm are verified by experimental results. |
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ISSN: | 0885-8993 1941-0107 |
DOI: | 10.1109/TPEL.2019.2955485 |