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Nonlinear control of the doubly-fed induction generator in wind power systems

Author

Listed:
  • Soares, Orlando
  • Gonçalves, Henrique
  • Martins, António
  • Carvalho, Adriano

Abstract

This paper describes the models of a wind power system, such as the turbine, generator, power electronics converters and controllers, with the aim to control the generation of wind power in order to maximize the generated power with the lowest possible impact in the grid voltage and frequency during normal operation and under the occurrence of faults. The presented work considers a wind power system equipped with the doubly-fed induction generator and a vector-controlled converter connected between the rotor and the grid. The paper presents comparative results between proportional-integral controllers and neural networks based controllers, showing that better dynamic characteristics can be obtained using neural networks based controllers.

Suggested Citation

  • Soares, Orlando & Gonçalves, Henrique & Martins, António & Carvalho, Adriano, 2010. "Nonlinear control of the doubly-fed induction generator in wind power systems," Renewable Energy, Elsevier, vol. 35(8), pages 1662-1670.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:8:p:1662-1670
    DOI: 10.1016/j.renene.2009.12.008
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    References listed on IDEAS

    as
    1. Tapia, A. & Tapia, G. & Ostolaza, J.X., 2004. "Reactive power control of wind farms for voltage control applications," Renewable Energy, Elsevier, vol. 29(3), pages 377-392.
    2. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
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    Citations

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    Cited by:

    1. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    2. El-Kharashi, Eyhab & Farid, Azmy Wadie, 2015. "Accurate assessment of the output energy from the doubly fed induction generators," Energy, Elsevier, vol. 93(P1), pages 406-415.
    3. Jabbari Asl, Hamed & Yoon, Jungwon, 2016. "Power capture optimization of variable-speed wind turbines using an output feedback controller," Renewable Energy, Elsevier, vol. 86(C), pages 517-525.
    4. Bin Li & Jiahao Zhu & Ranran Zhou & Guoxing Wen, 2022. "Adaptive Neural Network Sliding Mode Control for a Class of SISO Nonlinear Systems," Mathematics, MDPI, vol. 10(7), pages 1-12, April.
    5. Boutoubat, M. & Mokrani, L. & Machmoum, M., 2013. "Control of a wind energy conversion system equipped by a DFIG for active power generation and power quality improvement," Renewable Energy, Elsevier, vol. 50(C), pages 378-386.
    6. M. Abdelbasset Mahboub & Said Drid & M. A. Sid & Ridha Cheikh, 2017. "Sliding mode control of grid connected brushless doubly fed induction generator driven by wind turbine in variable speed," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 788-798, November.
    7. Justo, Jackson John & Mwasilu, Francis & Jung, Jin-Woo, 2015. "Doubly-fed induction generator based wind turbines: A comprehensive review of fault ride-through strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 447-467.

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