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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. 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.
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    Cited by:

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    12. Alizadeh, Mojtaba & Kojori, Shokrollah Shokri, 2015. "Augmenting effectiveness of control loops of a PMSG (permanent magnet synchronous generator) based wind energy conversion system by a virtually adaptive PI (proportional integral) controller," Energy, Elsevier, vol. 91(C), pages 610-629.
    13. Mokhtari, Yacine & Rekioua, Djamila, 2018. "High performance of Maximum Power Point Tracking Using Ant Colony algorithm in wind turbine," Renewable Energy, Elsevier, vol. 126(C), pages 1055-1063.
    14. Pichan, Mohammad & Rastegar, Hasan & Monfared, Mohammad, 2013. "Two fuzzy-based direct power control strategies for doubly-fed induction generators in wind energy conversion systems," Energy, Elsevier, vol. 51(C), pages 154-162.
    15. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    16. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.
    17. Medjber, Ahmed & Guessoum, Abderrezak & Belmili, Hocine & Mellit, Adel, 2016. "New neural network and fuzzy logic controllers to monitor maximum power for wind energy conversion system," Energy, Elsevier, vol. 106(C), pages 137-146.
    18. 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.
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