Static and Dynamic Yaw Misalignments of Wind Turbines and Machine Learning Based Correction Methods Using LiDAR Data

A yaw misalignment can be static or dynamic depending on its variation over time. For static, a few popular correction methods exist. LiDAR is one of the promising solutions because it can provide accurate wind measurements compared to a vane sensor and because it is cost effective when used for a limited time. We extend the LiDAR method to dynamic yaw misalignment correction by modeling the misalignment error's dependence on wind direction, wind speed, and rotor speed. An analytical framework is developed, and machine learning algorithms are trained to estimate the LiDAR's wind direction using SCADA data only. In this way, dynamic errors within SCADA data can be mitigated even after LiDAR is removed. Three machine learning algorithms, linear regression, random forests, and gradient boosting, are investigated. To evaluate the algorithms, SCADA and LiDAR data were collected at two wind farm sites in South Korea. The analysis shows that machine learning algorithms are capable of mitigating both static and dynamic yaw misalignments. While error reduction of only 22.6% was achieved with a static method, error reduction of 44.4% was achieved with a machine learning method. The validity was double checked by investigating turbine-to-turbine transferability of the dynamic correction model.