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A Predictive Power Control Strategy for DFIGs Based on a Wind Energy Converter System

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  • Xiaoliang Yang

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
    College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Guorong Liu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
    College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411100, China)

  • Anping Li

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Le Van Dai

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Faculty of Electrical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam)

Abstract

A feasible control strategy is proposed to control a doubly fed induction generator based on the wind energy converter system (DFIG-WECS). The main aim is to enhance the steady state and dynamic performance under the condition of the parameter perturbations and external disturbances and to satisfy the stator power response of the system. Within the proposed control method, the control scheme for the rotor side converter (RSC) is developed on the model predictive control. Firstly, the self-adaptive reference trajectory is established from the deduced discrete state-space equation of the generator. Then, the rotor voltage is calculated by minimizing the global performance index under the current prediction steps at the sampling instant. Through the control scheme for the grid side converter (GSC) and wind turbine, we have re-applied the conventional control. The effectiveness of the proposed control strategy is verified via time domain simulation of a 150 kW-575 V DFIG-WECS using Matlab/Simulink. The simulation result shows that the control of the DFIG with the proposed control method can enhance the steady and dynamic response capability better than the conventional ones when the system faces errors due to the parameter perturbations, external disturbances and the rotor speed.

Suggested Citation

  • Xiaoliang Yang & Guorong Liu & Anping Li & Le Van Dai, 2017. "A Predictive Power Control Strategy for DFIGs Based on a Wind Energy Converter System," Energies, MDPI, vol. 10(8), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1098-:d:105981
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    References listed on IDEAS

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    1. Vandai Le & Xinran Li & Yong Li & Tran Le Thang Dong & Caoquyen Le, 2016. "An Innovative Control Strategy to Improve the Fault Ride-Through Capability of DFIGs Based on Wind Energy Conversion Systems," Energies, MDPI, vol. 9(2), pages 1-23, January.
    2. Minh Quan Duong & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Kim Hung Le, 2015. "Improving Transient Stability in a Grid-Connected Squirrel-Cage Induction Generator Wind Turbine System Using a Fuzzy Logic Controller," Energies, MDPI, vol. 8(7), pages 1-22, June.
    3. Mohd Zin, Abdullah Asuhaimi B. & Pesaran H.A., Mahmoud & Khairuddin, Azhar B. & Jahanshaloo, Leila & Shariati, Omid, 2013. "An overview on doubly fed induction generators′ controls and contributions to wind based electricity generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 692-708.
    4. Duong, Minh Quan & Grimaccia, Francesco & Leva, Sonia & Mussetta, Marco & Ogliari, Emanuele, 2014. "Pitch angle control using hybrid controller for all operating regions of SCIG wind turbine system," Renewable Energy, Elsevier, vol. 70(C), pages 197-203.
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    Cited by:

    1. Zhicheng Lin & Song Zheng & Zhicheng Chen & Rong Zheng & Wang Zhang, 2019. "Application Research of the Parallel System Theory and the Data Engine Approach in Wind Energy Conversion System," Energies, MDPI, vol. 12(5), pages 1-20, March.
    2. Jian Zhang & Yong Wan & Quan Ouyang & Meng Dong, 2023. "Nonlinear Stochastic Adaptive Control for DFIG-Based Wind Generation System," Energies, MDPI, vol. 16(15), pages 1-19, July.
    3. Crestian Almazan Agustin & Jen-te Yu & Cheng-Kai Lin & Xiang-Yong Fu, 2019. "A Modulated Model Predictive Current Controller for Interior Permanent-Magnet Synchronous Motors," Energies, MDPI, vol. 12(15), pages 1-20, July.
    4. Cheng-Kai Lin & Jen-te Yu & Hao-Qun Huang & Jyun-Ting Wang & Hsing-Cheng Yu & Yen-Shin Lai, 2018. "A Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drive Systems," Energies, MDPI, vol. 11(7), pages 1-29, July.

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