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

ENERGIES(2020)

引用 10|浏览4
暂无评分
摘要
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.
更多
查看译文
关键词
wind turbine,lidar,yaw power,LSTM-NN yaw control,wind evolution,FAST/Simulink
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要