Wind farm analysis using SailoR diagram-based diagnostics to quantify yaw misalignment correction

Abstract

This study introduces a novel application of indices derived from the SailoR diagram for detecting and correcting yaw misalignment in wind turbines, which involves an original visualization of principal component analysis. The angular disparity between principal components, visualized as ellipses on the SailoR diagram, serves as a metric for comparing deviations between anemometers within a designated group (External Deviation index) and between the turbine’s anemometer and its yaw angle system (Internal Deviation index). A normalized criterion, incorporating anomalies in power production (Power Bias index), yaw angle, and absolute angle deviation, is established to compare the error of each turbine relative to its respective group. By integrating these indices with the general deviation of each turbine, the analysis effectively discriminates between external and internal error sources and quantifies the magnitude of each deviation. A case study involving a real wind farm with three distinct turbine groups demonstrates the efficacy of this integrated methodology as a valuable tool for initial diagnosis, enabling the identification, discrimination (internal/external), and quantification of yaw misalignment.