Inflow Inversion and Error Correction Method for Nacelle-based LiDAR Under Wake Influence

To overcome the measurement priciple defects of nacelle-based LiDAR under wake influence, an inflow wind field inversion method integrating analytical modeling and data-driven calibration was proposed. This method established an analytical inflow wind field model that accounted for both wake effect and rotor induction effect. It incorporated uncertainty modeling of inflow conditions and calibrated key model parameters using field measurement data. Based on the calibrated model, an online correction was conducted to dynamically compensate for LiDAR measurement values, which was validated using field measurement data from a 2 MW wind turbine. Results show that the proposed method can effectively suppress systematic measurement deviation in wake regions without relying on line-of-sight measurement data, while maintaining computational efficiency suitable for real-time correction, and it has good potential for engineering application. This study provides technical support for the application of nacelle-based LiDAR in online perception and operation control of wind turbine units.