The research of grid-connected inverter control based on decaying amplification aggregation strategy in predictive control
- 21 Downloads
The Grid-connected inverter control strategies are becoming a more attractive subject as the growing requirements for renewable power. The model predictive control (MPC) strategy is thought as a prospective method for the control of inverters due to its capability of handling constraints and nonlinearity properly. However, the complicated computations at each sampling instant makes MPC hardly applied. For the fast implementing of MPC on grid connected inverter control, in this paper, a decaying amplification aggregation strategy based model predictive control is presented on three-phase LCL-filter Grid Connected Inverter control. Additionally, a cumulative incremental state space model is used to depress the possible error. The proposed algorithms can significantly reduce the online computational complexity of MPC and have an infinite prediction horizon. The stability is also discussed and simulation results of a 20 KW grid-connected inverter demonstrate the effectiveness and fast calculation ability of this strategy.
KeywordsGrid-connected inverter Model predictive control (MPC) Decaying amplification aggregation MPC (DAA-MPC)
This work is supported by the National Science Foundation of China (Grant Nos. 61463037, 61521063, 61590924) and National Program on Key Basic Research Project of China (973 Program, Grant No. 2014CB249200).
- 2.Rodriguez, J., Cortes, P.: Predictive control of power converters and electrical drives. (2012)Google Scholar
- 6.Schroder, D.: Predictive control strategies for converter and inverter. In: IEEE International Conference on Industrial Technology, pp. 1–8 (2009)Google Scholar
- 8.Kennel, R., Linder, A.: Predictive control of inverter supplied electrical drives.In: PESC Record - IEEE Annual Power Electronics, vol. 2, pp. 761–766 (2000)Google Scholar
- 9.Linder, A., Kennel, R.: Model predictive control for electrical drives. In: IEEE Power Electronics Specialists Conference, pp. 1793–1799 (2005)Google Scholar
- 12.Zhang, Y., Lin, H.: Simplified model predictive current control method of voltage-source inverter. In: IEEE International Conference on Power Electronics and Ecce Asia, pp. 1726–1733 (2011)Google Scholar
- 13.Geldenhuys, J.M.C., Mouton, H.D.T., Rix, A., Geyer, T.: Model predictive current control of a grid connected converter with LCL-filter. In: Control and Modeling for Power Electronics, pp. 1–6 (2016)Google Scholar
- 17.Rodriguez, J., Cortes, P., Kennel, R., Kazmierkowski, M.P.: Model predictive control-a simple and powerful method to control power converters. In: Power Electronics and Motion Control Conference. Ipemc ‘09. IEEE International 2009, pp. 1826–1838 (2009)Google Scholar
- 18.Li, D., Xi, Y., Lu, J., Gao, F.: Synthesis of real-time-feedback-based 2d iterative learning control–model predictive control for constrained batch processes with unknown input nonlinearity. Ind. Eng. Chem. Res. 55(51) (2016)Google Scholar
- 21.Chen, W., Li, D., Xi, Y.: The application research of forward DC-DC converter control based on decaying amplification aggregation strategy in predictive control. In: Control and Decision Conference, pp. 314–319 (2015)Google Scholar
- 22.Du X. N.: The study and analysis of new optimization strategies in model predictive control [Ph.D. dissertation], Shanghai Jiao Tong University (2001)Google Scholar