Screening methodology of correlated wind turbines for wind direction prediction based on yawing manoeuvre data

Abstract

Accurate prediction of wind direction can improve wind energy utilisation and extend the life of the wind turbine yaw system effectively. The ‘measurement–sharing–correlation–prediction–verification’ (MSCPV) wind parameter sensing technology based on the turbine network provides a new perspective for wind direction prediction, in which the screening of correlated wind turbines is a key step. In this study, a calculation method of wind direction spatial correlation is proposed, and correlated turbines are selected according to the correlation strength. The study first analyses the limitations of traditional wind direction correlation based on the principle of MSCPV and proposes the concept of correlated turbine screening based on yawing correlation. Then, the modelling and calculation of yawing correlation are analysed in detail. The prediction based on correlated wind turbines selected by different methods is evaluated. Case study results show that compared to wind direction correlation and wind turbines' distance, the yawing correlation has a better effect on the screening of the spatially correlated turbines. The prediction accuracy based on different correlated turbines is positively correlated with the degree of yawing correlation, indicating the proposed screening method is effective.