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On-line designed hybrid controller with adaptive observer for variable-speed wind generation system

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  • Lin, Whei-Min
  • Hong, Chih-Ming
  • Cheng, Fu-Sheng

Abstract

This paper presents a wind-driven induction generator system with a hybrid controller, which combines the advantages of the integral–proportional and the sliding mode controllers. The proposed controller is designed to adjust the turbine speed to extract maximum power from the wind. The integral–proportional speed controller can be designed on-line according to the estimated rotor parameters, and the observed disturbance torque is feed-forward to increase the robustness of the system. The designed integral switching surface with integral–proportional due to on-line tuning produced a new sliding surface to implement the control, and can ensure the robustness under noisy environment. The rotor inertia constant, friction constant, and the disturbed mechanical torque of the induction generator are estimated by a proposed adaptive observer, which is composed of the recursive least square algorithm and a torque observer.

Suggested Citation

  • Lin, Whei-Min & Hong, Chih-Ming & Cheng, Fu-Sheng, 2010. "On-line designed hybrid controller with adaptive observer for variable-speed wind generation system," Energy, Elsevier, vol. 35(7), pages 3022-3030.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:7:p:3022-3030
    DOI: 10.1016/j.energy.2010.03.040
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    References listed on IDEAS

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    1. Yurdusev, M.A. & Ata, R. & Çetin, N.S., 2006. "Assessment of optimum tip speed ratio in wind turbines using artificial neural networks," Energy, Elsevier, vol. 31(12), pages 2153-2161.
    2. Fernandez, L.M. & Garcia, C.A. & Jurado, F., 2008. "Comparative study on the performance of control systems for doubly fed induction generator (DFIG) wind turbines operating with power regulation," Energy, Elsevier, vol. 33(9), pages 1438-1452.
    3. Lin, Whei-Min & Hong, Chih-Ming & Cheng, Fu-Sheng, 2010. "Fuzzy neural network output maximization control for sensorless wind energy conversion system," Energy, Elsevier, vol. 35(2), pages 592-601.
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    Cited by:

    1. Derafshian, Mehdi & Amjady, Nima, 2015. "Optimal design of power system stabilizer for power systems including doubly fed induction generator wind turbines," Energy, Elsevier, vol. 84(C), pages 1-14.
    2. Melício, R. & Mendes, V.M.F. & Catalão, J.P.S., 2011. "Comparative study of power converter topologies and control strategies for the harmonic performance of variable-speed wind turbine generator systems," Energy, Elsevier, vol. 36(1), pages 520-529.
    3. Song, Zhanfeng & Shi, Tingna & Xia, Changliang & Chen, Wei, 2012. "A novel adaptive control scheme for dynamic performance improvement of DFIG-Based wind turbines," Energy, Elsevier, vol. 38(1), pages 104-117.
    4. Saheb-Koussa, Djohra & Haddadi, Mourad & Belhamel, Maiouf & Hadji, Seddik & Nouredine, Said, 2010. "Modeling and simulation of the fixed-speed WECS (wind energy conversion system): Application to the Algerian Sahara area," Energy, Elsevier, vol. 35(10), pages 4116-4125.
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    6. Song, Ziyou & Hou, Jun & Hofmann, Heath & Li, Jianqiu & Ouyang, Minggao, 2017. "Sliding-mode and Lyapunov function-based control for battery/supercapacitor hybrid energy storage system used in electric vehicles," Energy, Elsevier, vol. 122(C), pages 601-612.
    7. Phan, Dinh-Chung & Yamamoto, Shigeru, 2016. "Rotor speed control of doubly fed induction generator wind turbines using adaptive maximum power point tracking," Energy, Elsevier, vol. 111(C), pages 377-388.
    8. Shrabani Sahu & Sasmita Behera, 2022. "A review on modern control applications in wind energy conversion system," Energy & Environment, , vol. 33(2), pages 223-262, March.
    9. Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.

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