MODEL BASED PREDICTIVE CONTROL STRATEGY FOR GRID-CONNECTED WIND ENERGY SYSTEM
DOI:
https://doi.org/10.29284/ijasis.8.1.2022.1-8Keywords:
Wind energy system, MPC controller, boost converter, wind turbine, pitch angle and grid-connected systemAbstract
In recent years, the move to green energy systems for power generation has greatly increased, and Wind Energy Systems (WES) are among the most efficient natural resources. Various control techniques can be employed to improve the amount of power generation and control the system's inverter. This paper implements a new Model Predictive Controller (MPC) for the Wind Energy Conversion System (WECS), which is connected to the grid. The system has wind energy as its input, and its AC source is converted into DC by a rectifier. A boost converter is injected for a constant and boosted DC output voltage from the rectifier, which is then converted to an AC supply by an inverter, controlled by MPC, and then connected to the grid. The proposed system is designed and simulated using the MATLAB tool. The results are verified, and the control technique is very efficient, enhancing system stability for the power generation in WECS.
References
S. Vazquez, J. I. Leon, L. G. Franquelo, J. Rodriguez, H. A. Young, A. Marquez and P. Zanchetta, “Model predictive control: A review of its applications in power electronics,” IEEE industrial electronics magazine, vol. 8, no. 1, 2014, pp. 16-31.
S. Kouro, P. Cortés, R. Vargas, U. Ammann and J. Rodríguez, “Model predictive control—A simple and powerful method to control power converters,” IEEE Transactions on industrial electronics, vol. 56, no. 6, 2008, pp. 1826-1838.
M. Bayat and H. K. Karegar, “Predictive control of wind energy conversion system,” 1st IEEE International Conference on the Developments in Renewable Energy Technology, 2009, pp. 1-5.
X. Li, H. Zhang, M. B. Shadmand and R. S. Balog, “Model predictive control of a voltage-source inverter with seamless transition between islanded and grid-connected operations,” IEEE Transactions on Industrial Electronics, vol. 64, no. 10, 2017, pp. 7906-7918.
M. Mirzaei, N. K. Poulsen and H. H. Niemann, “Robust model predictive control of a wind turbine,” American Control Conference, 2012, pp. 4393-4398.
A. Gholami, A. Sahab, A. Tavakoli and B. Alizadeh, “DFIG-Based Wind Energy System Robust Optimal Control by Using of Novel LMI-Based Adaptive MPC,” IETE Journal of Research, 2021, pp. 1-10.
A. Jain, G. Schildbach, L. Fagiano and M. Morari, “On the design and tuning of linear model predictive control for wind turbines,” Renewable Energy, vol. 80, 2015, pp. 664-673.
G. Ofualagba and E. U. Ubeku, “Wind energy conversion system-wind turbine modelling,” IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, 2008, pp. 1-8.
Y. Shan, J. Hu, Z. Li and J. M. Guerrero, “A model predictive control for renewable energy-based AC microgrids without any PID regulators,” IEEE Transactions on Power Electronics, vol. 33, no. 11, 2018, pp. 9122-9126.
W. R. Sultana, S. K. Sahoo, S. Sukchai, S. Yamuna and D. Venkatesh, “A review on state of art development of model predictive control for renewable energy applications,” Renewable and sustainable energy reviews, vol. 76, 2017, pp. 391-406.
M. Aguirre, S. Kouro, J. Rodriguez and H. Abu-Rub, “Model predictive control of interleaved boost converters for synchronous generator wind energy conversion systems,” IEEE International Conference on Industrial Technology, 2015, pp. 2295-2301.
A. Parisio, E. Rikos and L. Glielmo, “A model predictive control approach to microgrid operation optimization,” IEEE Transactions on Control Systems Technology, vol. 22, no. 5, 2014, pp. 1813-1827.
M. Khalid and A. V. Savkin, “A model predictive control approach to the problem of wind power smoothing with controlled battery storage,” Renewable Energy, vol. 35, no. 7, 2010, pp. 1520-1526.
C. Wu, S. Gao, Y. Liu, H. Han and S. Jiang, “ Wind Power Smoothing With Energy Storage System: A Stochastic Model Predictive Control Approach,” IEEE Access, vol. 9, 2021, pp. 37534-37541.
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