arXiv:2601.21035v1 Announce Type: new
Abstract: Validating engineering wake models under real-world operational conditions is essential for improving wind farm performance predictions. This study uses a unique dataset from the Lillgrund offshore wind farm collected during the Horizon 2020 TotalControl project, combining synchronous SCADA and LiDAR measurements under baseline operation (no intentional yaw offset) and active wake steering. Four analytical wake model combinations are assessed, employing different formulations for velocity deficit, added turbulence, wake superposition, and deflection, implemented in the LongSim software developed by DNV. The analysis focuses on time-averaged wake velocity deficits and turbine- and farm-level power output, with model accuracy quantified using mean absolute error metrics. The models reproduce general wake deficit trends and wake deflection across a range of atmospheric conditions, with normalised velocity deficit errors between 7% and 15%. Power prediction errors increase with farm depth, with turbine-level errors between 3% and 23% and farm-level errors between -13% and +30%. Some analytical models achieve accuracy comparable to reported LES results while requiring substantially lower computational cost. The results highlight the value of field campaigns for benchmarking engineering wake models and inform trade-offs between model fidelity and operational practicality for wake steering applications.