Optimised yaw model for adaptive wind direction characteristic based on a data-driven approach

Abstract

The wind direction characteristics of different wind turbines (WTs) in different wind farms are diverse and some may have obvious differences. The WTs are prone to ‘over-yaw’ and ‘under-yaw’ conditions adopting constant yaw threshold and the same control strategy, which reduces the utilisation efficiency of wind energy. This study makes statistics on the variation characteristics of the WT historical wind direction record data, and obtains the maximum probability angle of wind direction change that is used as the control error angle of yaw start (CEAYS). Then, the wind direction value is monitored in real-time, and the average wind direction change angle in the sliding window is calculated. When the deviation angle is larger than CEAYS, the characteristics of current wind direction are classified based on the clustering of historical wind direction, and a back-propagation neural network is used to predict the deviation angle of future short-term wind direction. The adaptive yaw strategy is constructed by CEAYS and deviation angle. The yaw calculation model is analysed and verified with the increase of power extraction and power consumption as the optimisation indexes. The calculation results show that each WT achieved an average 1% increase in power extraction.