Model Predictive Control of a Voltage-Source Inverter With Seamless Transition Between Islanded and Grid-Connected Operations Academic Article uri icon

abstract

  • © 1982-2012 IEEE. Inverter-based distributed generation (DG) system is becoming an attractive solution for high penetration of renewable energy sources to the main grid. DG system should be able to supply power to the local loads whenever necessary even in case of utility power outage. Thus, the inverters in DG systems are expected to operate in both grid-connected and islanded mode, where they are acting as a current source for the ac grid and a voltage-source for the load, respectively. Transition between modes of operation is nontrivial and can cause deviations in voltage and current, because of mismatch in frequency, phase, and amplitude between the inverter output voltage and the grid voltage. Thus, it is necessary to have seamless transition between grid-connected and islanded mode. This paper presents a new control strategy with seamless transfer characteristics for a grid-connected voltage-source inverter using model predictive control (MPC) framework. The main objectives of the proposed predictive controller are: 1) decoupled power control in grid-connected mode, which enables the proposed power electronics interface to provide ancillary services such as reactive power compensation; 2) load voltage control in islanded mode; and 3) seamless transition between modes of operation through proposed synchronization and phase adjustment algorithm. The proposed controller features simplicity to implement since only one cost function should be minimized for all modes of operation, and hence no ambiguity in the control algorithm that could cause mode transition problems. An autotuning strategy for weight factors in MPC cost function is proposed to simplify the weight factor tuning strategy. The stability analysis of the proposed controller is provided. Simulation and experimental results validate the expected performance and effectiveness of the proposed control strategy.

author list (cited authors)

  • Li, X., Zhang, H., Shadmand, M. B., & Balog, R. S.

citation count

  • 80

publication date

  • October 2017