Benfei Wang

A Digital Method of Power-Sharing and Cross-Regulation Suppression for Single-Inductor Multiple-Input Multiple-Output DC–DC Converter

This paper presents a digital method to regulate the single-inductor multiple-input multiple-output (SI-MIMO) dc–dc converter, which can integrate renewable sources and loads as the hybrid renewable energy system with less components. The digital method is able to achieve power-sharing and cross-regulation suppression, and it is named as PS-CRS. PS-CRS is based on model predictive control, power sharing, and time multiplexing. A power-sharing controller is developed to regulate the power from multiple power sources. A cross-regulation-suppression controller is designed to control the output voltages independently with reduced cross regulation, which is the critical issue of the single-inductor multiple-output dc–dc converter. Experimental prototype of a single-inductor dual-input dual-output buck converter is built for verification. The steady-state operation and dynamic performance cases are studied. The results demonstrate that PS-CRS is able to regulate the SI-MIMO dc–dc converter effectively and robustly.

Low cross regulation SIMO DC/DC converter with model predictive voltage control

This paper presents a model predictive voltage control method for single-inductor multiple-output (SIMO) DC/DC converters operating in the continuous conduction mode (CCM). The state-space model for a SIMO DC/DC converter system is formulated and a Model Predictive Control (MPC) strategy, which has the capability to track the reference voltage automatically and reduce the impact of cross regulation problem, is proposed to regulate the system. Considering that the switches in the converter only have ON/OFF modes, the quadratic programming algorithm is employed to obtain the MPCs solution. Simulation studies are conducted in MATLAB/Simulink to verify the performance of the proposed method.

Dynamic model predictive voltage control for single-input dual-output flyback converter

This paper presents a dynamic model predictive voltage control (DMPVC) method for the single-input dualoutput (SIDO) flyback converter. In this converter, cross regulation is one of the most critical issues because its primary winding is shared by its secondary windings so that the two output voltages are interacted with each other. The proposed method has the ability to reduce cross regulation significantly, i.e., the two output voltages can be regulated individually. The statespace models of the converter for the DMPVC method are formulated, as well as the corresponding cost function which determines the optimal control actions. In order to verify the performance of the DMPVC method, simulation platform is built in the MATLAB/Simulink environment. The results indicate that the DMVPC method is capable of controlling the SIDO flyback converter with expected line and cross regulations along with seamless mode transition among various operation modes, including buck-buck mode, buck-boost mode and boost-boost mode.

Approximate-model-based predictive current control for buck converter in CCM

This paper presents an approximate-model-based predictive current control (AMPCC) method for current regulation of buck converter, when it operates in continuous-conduction mode (CCM). In addition, the inductor current constraints are incorporated to further reduce the output voltage ripple. The output current of the buck converter is directly manipulated by the proposed AMPCC controller without PWM modulator. A hardware prototype is built and the experimental verification is conducted. The steady-state operation, the influence of inductor current constraints and the dynamic performance of AMPCC are presented and studied. Experimental results demonstrate that the proposed AMPCC method is able to regulate the output current of the buck converter in CCM effectively and robustly under different operation scenarios.

Energy Management and Control for Grid Connected Hybrid Energy Storage System under Different Operating Modes

DC-coupled microgrids are simple as they do not require any synchronization when integrating different distributed energy generations. However, the control and energy management strategy between the renewable energy sources and the energy storages under different operating modes is a challenging task. In this paper, a new energy management scheme is proposed for the grid connected hybrid energy storage with the battery and the supercapacitor under different operating modes. The main advantages of the proposed energy management scheme are effective power sharing between the different energy storage systems, faster dc link voltage regulation to generation and load disturbances, dynamic power sharing between the battery and the grid based on the battery state of charge, reduced rate of charge/discharge of battery current during steady state and transient power fluctuations, improved power quality features in ac grid and seamless mode transitions. The effectiveness of the proposed method is validated by both simulation and experimental studies.

Enumerated-MPC-based dynamic voltage restorer using lc filter with damping resistor

This paper presents an enumerated model predictive control (MPC) based method for the dynamic voltage restorer (DVR), which consists of a series-connected transformer and a voltage source inverter. The filter adopted in the DVR is a LC filter with a damping resistor. The design of the discrete model for the voltage source inverter and the corresponding controller are discussed in this paper. The proposed method is capable to regulate the DVR to compensate the sag, swell and harmonic distortions of the grid voltage. Simulation studies are conducted to verify the performance of the proposed enumerated MPC-based method, and the simulation results demonstrate that the proposed method is effective to achieve the protection of the loads from the grid voltage sag, swell and harmonic distortion.

A stand-alone hybrid pv/fuel cell power system using single-inductor dual-input single-output boost converter with model predictive control

This paper presents a stand-alone hybrid PV/fuel cell system using single-inductor dual-input single-output (SI-DISO) boost converter. Compared to conventional hybrid power system, the proposed system significantly reduce the number of components, the system size and the corresponding cost. A model predictive control (MPC) method has been developed to control the SI-DISO boost converter to achieve the input power regulation and output voltage regulation. In order to verify the performance of the proposed hybrid PV/fuel cell power system based on SI-DISO boost converter and the corresponding MPC method, several simulation studies are conducted in Matlab/Simulink. The simulation results demonstrate that the proposed hybrid power system works effectively.

A DC microgrid integrated dynamic voltage restorer with model predictive control

This paper presents a DC microgrid integrated dynamic voltage restorer (DVR) system to mitigate the grid voltage sag and swell. The DC source of the DVR is the DC microgrid, including the PV array and hybrid energy storage system consisting of lithium-ion battery and supercapacitor. Compared to the conventional DVR supplied by pure energy storages, the DC microgrid will prolong the operation the DVR because the renewable energy source can supply the DVR or charge the HESS occasionally. The DVR is regulated by a model predictive control (MPC) method to conduct the compensation of voltage sag and swell. The design of the MPC method for DVR and controller for the system are presented in detail in this paper. In order to validate the performance of the DC microgrid integrated DVR system and the corresponding control method, several simulation studies are conducted including the scenarios of voltage sag and voltage swell. The simulation results indicate that the proposed system works effectively and achieve compensation of the grid voltage sag and swell successfully.