Identification of Yaw Error Inherent Misalignment for Wind Turbine Based on SCADA Data: A Data Mining Approach

As one of the main control subsystem implemented for controlling the wind turbine nacelle position in parallel with the inflow wind, the yaw control system directly determines the power generation performance of wind turbines. Thus, accurate measurement of yaw error is important in yaw control strategy. However, the existence of inherent misalignments on yaw error usually severely impact the performance of yaw control strategies. Aiming at the identification of yaw error inherent misalignment, a data-mining based inherent misalignment identification and compensation approach for yaw error is proposed. The raw data set is firstly preprocessed and further segmented into different partitions. A curve fitting technique is finally implemented for yaw error inherent misalignment estimation. The simulation data set from a simulation software GH Bladed is used to testify the effectiveness of this approach, and the result shows high accuracy on yaw error inherent misalignment identification.