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author:

Liu, Weicheng (Liu, Weicheng.) [1] | Jin, Tao (Jin, Tao.) [2] (Scholars:金涛) | Mon-Nzongo, Daniel Legrand (Mon-Nzongo, Daniel Legrand.) [3] | Tang, Jinquan (Tang, Jinquan.) [4]

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EI

Abstract:

The wide distribution of renewable DC microgrids can easily cause large fluctuations when energy is connected to the grid. In order to further solve the problem of terminal voltage fluctuation in energy transmission, the dual active bridge(DAB) structure is widely used. Discrete Model Predictive Control (DMPC) based on bidirectional dual-phase-shift(BDPS) control is proposed to further improve the stability and the dynamic performance of fast tracking of the output voltage of the DC converter system. By integrating a simplified model of the dual active bridge circuit and the power transmission characteristics of the converter, a predictive model under the given mode is established. Reasonable design of the cost function of the prediction model and feedforward compensation of the prediction value further improve the accuracy of the prediction model. The good dynamic performance of the control method is verified by simulation, and a comparative analysis is also carried out in combination with different control methods. © 2021 IEEE.

Keyword:

Cost functions Forecasting Model predictive control Power converters

Community:

  • [ 1 ] [Liu, Weicheng]Fuzhou University Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou, China
  • [ 2 ] [Jin, Tao]Fuzhou University Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou, China
  • [ 3 ] [Mon-Nzongo, Daniel Legrand]Fuzhou University Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou, China
  • [ 4 ] [Tang, Jinquan]Pearl Electric Corporation, Research and Development Department, Guangzhou, China

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Year: 2021

Page: 161-166

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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