Model predictive voltage control of three-phase four-switch converter in island mode considering capacitor voltage balancing control
DOI:10.7667/PSPC20191204
Key Words:three-phase four-switch converter  capacitor voltage balancing  voltage predictive control  cost function  island mode
Author NameAffiliation
WANG Yonghui XJ Electric Co., Xuchang 461000, China 
HE Shuaibiao School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China 
FENG Jintao Xuchang KETOP Testing Research Institute Co., Ltd., Xuchang 461000, China 
MA Xiaoqiang State Grid Sanmenxia Power Supply Company, Sanmenxia 472000, China 
QIAO Zhonghua State Grid Sanmenxia Power Supply Company, Sanmenxia 472000, China 
JIN Nan School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China 
WANG Yiwang Suzhou Vocational University, Suzhou 215104, China 
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Abstract:The three-phase four-switch converter is a fault-tolerant structure of a three-phase six-switch converter. In the island mode, the DC-link current flows into the fault phase through the capacitor, resulting in the separation capacitor voltage unbalance, which affects the power quality of the load side and reduces the service life of the capacitor. To solve this problem, a model predictive voltage control strategy considering neutral point voltage balance is proposed. First, the voltage vector is analyzed in the island mode, and voltage prediction model is established based on αβ two-phase stationary coordinate. On this basis, the neutral point voltage balance control is designed through extracting the DC component in the midpoint voltage deviation of the DC side capacitor. The current compensation value is calculated by the prediction model and then model predictive voltage control in the inland mode is realized. The proposed control strategy does not require phase-locked loop or PWM modulation. Simulation and experiment results verify the effectiveness of the proposed control strategy. This work is supported by National Natural Science Foundation of China (No. 51607159).
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