DFIG-Based Wind Energy System Robust Optimal Control by Using of Novel LMI-Based Adaptive MPC

References

• X. Hu, P. Ma, B. Gao, and M. Zhang, “An integrated step-up inverter without transformer and leakage current for grid-connected photovoltaic system,” IEEE Trans. Power Electron., Vol. 34, no. 10, pp. 9814–27, Oct. 2019. doi: 10.1109/TPEL.2019.2895324.
   Web of Science ®Google Scholar
• D. Yu, Y. Mao, B. Gu, S. Nojavan, K. Jermsittiparsert, and M. Nasseri, “A new LQG optimal control strategy applied on a hybrid wind turbine/solid oxide fuel cell/ in the presence of the interval uncertainties,” Sustain. Energy Grids Netw., Vol. 21, pp. 100296, 2020.
   Web of Science ®Google Scholar
• R. Zhang, T. Jiang, F. Li, G. Li, H. Chen, and X. Li, “Coordinated bidding strategy of wind farms and power-to-gas facilities using a cooperative game approach,” IEEE Trans. Sustain. Energy, Vol. 11, no. 4, pp. 2545–55, 2020.
   Web of Science ®Google Scholar
• X. Xu, Z. Wei, Q. Ji, C. Wang, and G. Gao, “Global renewable energy development: Influencing factors, trend predictions and countermeasures,” Resour. Pol., Vol. 63, pp. 101470, 2019.
   Web of Science ®Google Scholar
• J. Aleixandre-Tudó, L. Castelló-Cogollos, J. Aleixandre, and R. Aleixandre-Benavent, “Renewable energies: worldwide trends in research, funding and international collaboration,” Renew. Energy, Vol. 139, pp. 268–78, 2019.
   Web of Science ®Google Scholar
• E. Sanchez, and R. Ruiz-Cruz, Doubly Fed Induction Generators: Control for Wind Energy. Boca Raton, FL: CRC Press, 2016.
   Google Scholar
• L. Xiong, H. Zhang, Y. Li, and Z. Liu, “Improved stability and H∞ performance for neutral systems with uncertain Markovian jump,” Nonlinear Anal.: Hybrid Syst., Vol. 19, pp. 13–25, 2016.
   Web of Science ®Google Scholar
• B. Cao, W. Dong, Z. Lv, Y. Gu, S. Singh, and P. Kumar, “Hybrid microgrid many-objective sizing optimization with fuzzy decision,” IEEE Trans. Fuzzy Syst., Vol. 28, no. 11, pp. 2702–10, Nov. 2020. doi: 10.1109/TFUZZ.2020.3026140.
   PubMed Web of Science ®Google Scholar
• G. Zhu, S. Wang, L. Sun, W. Ge, and X. Zhang, “Output feedback adaptive dynamic surface sliding-mode control for quadrotor UAVs with tracking error constraints,” Complexity, Vol. 2020, Article ID 8537198, pp. 23, 2020. doi:10.1155/2020/8537198.
   Web of Science ®Google Scholar
• X. Zhang, Y. Wang, X. Chen, C. Su, Z. Li, C. Wang, and Y. Peng, “Decentralized adaptive neural approximated inverse control for a class of large-scale nonlinear hysteretic systems with time delays,” IEEE Trans. Syst. Man Cybern.: Syst., Vol. 49, no. 12, pp. 2424–37, 2019.
   Web of Science ®Google Scholar
• Z. Chen, J. Wang, K. Ma, X. Huang, and T. Wang, “Fuzzy adaptive two-bits-triggered control for nonlinear uncertain system with input saturation and output constraint,” Int. J. Adapt Control Signal Process., Vol. 34, no. 4, pp. 543–59, 2020.
   Web of Science ®Google Scholar
• J. Wang, P. Zhu, B. He, G. Deng, C. Zhang, and X. Huang, “An adaptive neural sliding mode control with ESO for uncertain nonlinear systems,” Int. J. Control. Autom. Syst., Vol. 19, pp. 687–97, 2021. doi:10.1007/s12555-019-0972-x.
   Google Scholar
• Y. Huang, J. Wang, F. Wang, and B. He, “Event-triggered adaptive finite-time tracking control for full state constraints nonlinear systems with parameter uncertainties and given transient performance,” ISA Trans., Vol. 108, pp. 131–43, 2021.
   Web of Science ®Google Scholar
• K. Shi, Y. Tang, X. Liu, and S. Zhong, “Secondary delay-partition approach on robust performance analysis for uncertain time-varying Lurie nonlinear control system,” Optim. Control Appl. Meth., Vol. 38, no. 6, pp. 1208–26, 2017.
   Web of Science ®Google Scholar
• D. Zhi, and L. Xu, “Direct power control of DFIG with constant switching frequency and improved transient performance,” IEEE Trans. Energy Convers., Vol. 22, pp. 110–8, 2007.
   Web of Science ®Google Scholar
• I. Takahashi, and T. Noguchi, “A new quick-response and high-efficiency control strategy of an induction motor,” IEEE Trans. Ind. Appl., Vol. IA-22, no. 5, pp. 820–27, Sept. 1986. doi: 10.1109/TIA.1986.4504799.
   Web of Science ®Google Scholar
• M. Depenbrock, “Direct self-control (DSC) of inverter-fed induction machine,” IEEE Trans. Power Electron., Vol. 3, pp. 420–9, 1988.
   Google Scholar
• E. Aydin, A. Polat, and L. T. Ergene, “Vector control of DFIG in wind power applications,” in 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, 2016, pp. 478–83. doi: 10.1109/ICRERA.2016.7884383.
   Google Scholar
• D. Zhi, L. Xu, and B. Williams, “Model-based predictive direct power control of doubly fed induction generators,” IEEE Trans. Power Electron, Vol. 25, pp. 341–51, 2010.
   Web of Science ®Google Scholar
• Y. Zhang, J. Jiao, D. Xu, D. Jiang, Z. Wang, and C. Tong, “Model predictive direct power control of doubly fed induction generators under balanced and unbalanced network conditions,” Vol. 56, no. 1, pp. 771–86, Jan.–Feb. 2020. doi: 10.1109/TIA.2019.2947396.
   Google Scholar
• J. Dong, S. Li, S. Wu, T. He, B. Yang, H. Shu, and J. Yu, “Nonlinear observer-based robust passive control of doubly-fed induction generators for power system stability enhancement via energy reshaping,” Energies, Vol. 10, no. 8, pp. 1082, 2017. doi:10.3390/en10081082.
   Web of Science ®Google Scholar
• B. Yang, L. Jiang, L. Wang, W. Yao, and Q. Wu, “Nonlinear maximum power point tracking control and modal analysis of DFIG based wind turbine,” Int. J. Electr. Power Energy Syst., Vol. 74, pp. 429–36, 2016.
   Web of Science ®Google Scholar
• M. Fei, and B. Pal, “Modal analysis of grid-connected doubly fed induction generators,” IEEE Trans. Energy Convers., Vol. 22, pp. 728–36, 2007.
   Web of Science ®Google Scholar
• W. Qiao, “Dynamic modeling and control of doubly fed induction generators driven by wind turbines,” in Proceedings of the IEEE/PES Power Systems Conference and Exposition, Seattle, WA, USA, 2009.
   Google Scholar
• H. Sheng, W. Huang, T. Zhang, and X. Huang, “Robust adaptive fuzzy control of compressor surge using backstepping,” Arab. J. Sci. Eng., Vol. 39, no. 12, pp. 9301–8, 2014.
   Web of Science ®Google Scholar
• F. Tahir, and I. M. Jaimoukha, “Robust model predictive control through dynamic state-feedback: An LMI approach,” in Preprints of the 18th IFAC World Congress, Milano, Italy, 2011.
   Google Scholar
• H. Zhang, J. Hao, C. Wu, Y. Li, and A. Sahab, “A novel LMI-based robust adaptive model predictive control for DFIG-based wind energy conversion system,” Syst. Sci. Control Eng., Vol. 7, no. 1, pp. 311–20, 2019.
   Web of Science ®Google Scholar
• A. Gholami, A. Sahab, A. Tavakoli, and B. Alizadeh, “A novel LMI-based robust model predictive control for DFIG-based wind energy conversion systems,” Kybernetika, Vol. 55, no. 6, pp. 1034–49, 2019.
   Web of Science ®Google Scholar
• M. Duong, F. Grimaccia, S. Leva, M. Mussetta, and E. Ogliari, “Pitch angle control using hybrid controller for all operating regions of SCIG wind turbine system,” Renew. Energy, Vol. 70, pp. 197–203, 2014.
   Web of Science ®Google Scholar
• W. Yao, L. Jiang, J. Wen, Q. Wu, and S. Cheng, “Wide-area damping controller for power system interarea oscillations: A networked predictive control approach,” IEEE Trans. Control Syst. Technol., Vol. 23, no. 1, pp. 27–36, 2014
   Web of Science ®Google Scholar