Hybrid adaptive control for variable-speed variable-pitch wind
energy systems using general regression neural network
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This preprint has been retracted.
How to cite. Yin, X.: Hybrid adaptive control for variable-speed variable-pitch wind
energy systems using general regression neural network, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2018-19, 2018.
Received: 19 Feb 2018 – Discussion started: 24 May 2018