Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Finite control set model predictive control integrated with disturbance observer for battery energy storage power conversion system

  • Gao, Ning (Department of Electrical Engineering, Shanghai Maritime University) ;
  • Zhang, Bingtao (Department of Electrical Engineering, Shanghai Maritime University) ;
  • Wu, Weimin (Department of Electrical Engineering, Shanghai Maritime University) ;
  • Blaabjerg, Frede (Department of Energy Technology, Aalborg University)
  • Received : 2020.09.06
  • Accepted : 2020.12.02
  • Published : 2021.02.20
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Abstract

A typical battery energy storage system consists of a combination of battery packs and a grid-tied power conversion system. The control algorithm of the power conversion system plays an important role when interfacing the DC energy stored in battery packs with the conventional AC grid to generate an obedient bidirectional power flow. Finite control set model predictive control is believed to be one of the most effective choices for controlling power conversion systems. However, the performance of such a control strategy heavily depends on the accuracy of the predictive model. Parameter mismatch in the model leads to prediction error, which deteriorates the overall power quality performance of the power conversion system. Therefore, this paper studies a robust finite control set model predictive control method based on a discrete disturbance observer to eliminate the negative effects caused by model inaccuracy and uncertainty. The stability issue of the additional observer is discussed from the perspective of closed-loop poles. Parameter scan is performed to provide assistance in designing the feedback matrix. Finally, simulations and experimental results obtained from a downscaled prototype rated at 4.2 kVA are conducted as a validation of the presented control algorithm.

Keywords

Acknowledgement

The research in this paper is supported by the National Natural Science Foundation of China (No. 51907119) and Shanghai Sailing Program (No. 19YF1418700).

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