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Fuzzy neural network output maximization control for sensorless wind energy conversion system

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

Abstract

This paper presents the design of an online training fuzzy neural network (FNN) controller with a high-performance speed observer for the induction generator (IG). The proposed output maximization control is achieved without mechanical sensors such as the wind speed or position sensor, and the new control system will deliver maximum electric power with light weight, high efficiency, and high reliability. The estimation of the rotor speed is designed on the basis of the sliding mode control theory.

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  • 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.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:2:p:592-601
    DOI: 10.1016/j.energy.2009.10.030
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    References listed on IDEAS

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    1. 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.
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    Cited by:

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    5. 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.
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    12. 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.
    13. 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.
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    16. Masmoudi, Abdelkarim & Abdelkafi, Achraf & Krichen, Lotfi, 2011. "Electric power generation based on variable speed wind turbine under load disturbance," Energy, Elsevier, vol. 36(8), pages 5016-5026.
    17. Ganjefar, Soheil & Ghassemi, Ali Akbar & Ahmadi, Mohamad Mehdi, 2014. "Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network," Energy, Elsevier, vol. 67(C), pages 444-453.
    18. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.
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