Journal cover Journal topic
Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Wind Energy Science (WES).
Hybrid adaptive control for variable-speed variable-pitch wind energy systems using general regression neural network
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.
Citation: Yin, X.: Hybrid adaptive control for variable-speed variable-pitch wind energy systems using general regression neural network, Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2018-19, in review, 2018.
Xiuxing Yin
Xiuxing Yin
Xiuxing Yin

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