Model predictive control of bidirectional AC-DC converter for energy storage system

Parvez Akter, Saad Mekhilef, Nadia Mei Lin Tan, Hirofumi Akagi

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Energy storage system has been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper presents a model predictive algorithm to control a bidirectional AC-DC converter, which is used in an energy storage system for power transferring between the three-phase AC voltage supply and energy storage devices. This model predictive control (MPC) algorithm utilizes the discrete behavior of the converter and predicts the future variables of the system by defining cost functions for all possible switching states. Subsequently, the switching state that corresponds to the minimum cost function is selected for the next sampling period for firing the switches of the AC-DC converter. The proposed model predictive control scheme of the AC-DC converter allows bidirectional power flow with instantaneous mode change capability and fast dynamic response. The performance of the MPC controlled bidirectional AC-DC converter is simulated with MATLAB/Simulink® and further verified with 3.0kW experimental prototypes. Both the simulation and experimental results show that, the AC-DC converter is operated with unity power factor, acceptable THD (3.3% during rectifier mode and 3.5% during inverter mode) level of AC current and very low DC voltage ripple. Moreover, an efficiency comparison is performed between the proposed MPC and conventional VOC-based PWM controller of the bidirectional AC-DC converter which ensures the effectiveness of MPC controller.

Original languageEnglish
Pages (from-to)165-175
Number of pages11
JournalJournal of Electrical Engineering and Technology
Volume10
Issue number1
DOIs
Publication statusPublished - 01 Jan 2015

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Model predictive control
Energy storage
Cost functions
Uninterruptible power systems
Controllers
Electric potential
Volatile organic compounds
Pulse width modulation
MATLAB
Dynamic response
Switches
Sampling

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

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abstract = "Energy storage system has been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper presents a model predictive algorithm to control a bidirectional AC-DC converter, which is used in an energy storage system for power transferring between the three-phase AC voltage supply and energy storage devices. This model predictive control (MPC) algorithm utilizes the discrete behavior of the converter and predicts the future variables of the system by defining cost functions for all possible switching states. Subsequently, the switching state that corresponds to the minimum cost function is selected for the next sampling period for firing the switches of the AC-DC converter. The proposed model predictive control scheme of the AC-DC converter allows bidirectional power flow with instantaneous mode change capability and fast dynamic response. The performance of the MPC controlled bidirectional AC-DC converter is simulated with MATLAB/Simulink{\circledR} and further verified with 3.0kW experimental prototypes. Both the simulation and experimental results show that, the AC-DC converter is operated with unity power factor, acceptable THD (3.3{\%} during rectifier mode and 3.5{\%} during inverter mode) level of AC current and very low DC voltage ripple. Moreover, an efficiency comparison is performed between the proposed MPC and conventional VOC-based PWM controller of the bidirectional AC-DC converter which ensures the effectiveness of MPC controller.",
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Model predictive control of bidirectional AC-DC converter for energy storage system. / Akter, Parvez; Mekhilef, Saad; Tan, Nadia Mei Lin; Akagi, Hirofumi.

In: Journal of Electrical Engineering and Technology, Vol. 10, No. 1, 01.01.2015, p. 165-175.

Research output: Contribution to journalArticle

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