A Data-Mining Compensation Approach for Yaw Misalignment on Wind Turbine

Abstract

As an important subsystem that controls the nacelle direction facing toward the inflow wind, the yaw control subsystem plays an indispensable role in wind turbine generation systems. The yaw performance of wind turbines will directly determine the maximum capture capacity of wind energy. However, owing to the lack of effective calibration on wind vane, yaw misalignments are usually present in practice, which highly impacts the performance of yaw control and power generation efficiency. In this study, a data-mining approach on wind vane for yaw misalignment compensation is presented without involving any additional hardware investment. Briefly, the operation data of wind turbines are first evenly divided in terms of measured yaw error, and a power curve is identified for each yaw partition. Quantified generation performance metrics of all power curves are calculated afterwards for yaw misalignment determination, then the yaw control strategy can be finally corrected for wind turbine generation performance improvement. The feasibility and effectiveness of the proposed scheme are testified with simulation and measured data of a 1.5-MW wind turbine.