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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2018-19
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2018-19
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research articles 24 May 2018

Research articles | 24 May 2018

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This preprint has been retracted.

Hybrid adaptive control for variable-speed variable-pitch wind energy systems using general regression neural network

Xiuxing Yin Xiuxing Yin
  • State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China

Abstract. This paper presents a novel hybrid adaptive control approach for the VS-VP half-direct driven WECS by combining pitch control with variable generator torque regulation in different operating regions. A general regression neural network (GRNN) is employed to derive the reference commands of generator torque and pitch angle from the real-time signals of generator power and speed. Furthermore, a fast and effective nonlinear PID pitch controller is presented to track the reference command of pitch angle in the full load region. The proposed GRNN based hybrid adaptive control strategies have been developed and validated using simulations. This study shows that the proposed method is much faster, more accurate and effective than conventional linear control approach.

This preprint has been retracted.
Xiuxing Yin
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Xiuxing Yin
Xiuxing Yin
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