LSTM-NN Yaw Control of Wind Turbines Based on Upstream Wind Information

Wenting Chen, Hang Liu, Yonggang Lin, Wei Li, Yong Sun and Di Zhang
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Wenting Chen: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Hang Liu: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Yonggang Lin: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Wei Li: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Yong Sun: Zhejiang Windey Co, Ltd, Hangzhou 310012, China
Di Zhang: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China

Energies, 2020, vol. 13, issue 6, 1-23

Abstract: Based on wind lidar, a novel yaw control scheme was designed that utilizes forecast wind information. The new scheme can reduce the power loss caused by the lag of accurate measurement data in the traditional yaw control strategy. A theoretical analysis of the power loss caused by the traditional wind measurement inherent error and the wind direction based traditional yaw control strategy was conducted. The yaw angle error and yaw stop/start frequency in an actual wind field were statistically analyzed, and a novel Long Short Term-Neural Network (LSTM-NN) yaw control strategy based on wind lidar information was proposed. An accurate forecast of the wind direction could reduce the power loss caused by the inherent yaw misalignment, while an accurate forecast of wind speed could increase the stop/start frequency in the medium speed section within the partial load range and reduce the frequency in the low speed section within the partial load range. Thus, the power captured could be increased by 3.5% under certain wind conditions without increasing the yaw duty. Based on a simple wind evolution model and a novel yaw control strategy, the validity of the yaw control strategy was verified in a FAST/Simulink simulation model.

Keywords: wind turbine; lidar; yaw power; LSTM-NN yaw control; wind evolution; FAST/Simulink (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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