Performance comparison of analytical wake models calibrated on a large offshore wind cluster

Abstract

This study benchmarks the performance of multiple analytical wake models using a multi-level hyperparameter optimization framework for calibrating models with SCADA data in the Belgian-Dutch offshore zone. The calibration targets wind coming the northwest (300 to 330 degrees) with wind speeds ranging from 7 to 9 m/s, a wind direction where wakes are clustered across Belgian wind farms. Six wake models are evaluated, namely those described by Jensen (1983), Bastankhah (2014), Niayifar (2016), Zong (2020), Nygaard (2020), and Pedersen (2022). Relative and accumulated relative error between the calibrated wake models and SCADA data are analyzed both on turbine, farm, and cluster level. Statistical moments of the residual error between each model and SCADA data for individual turbines are presented in boxplots for each wind farm and are analyzed using kernel density estimates. Additionally, the calibrated tuning parameters are used to calculate wake losses, demonstrating a strong overlap across models. The analysis reveals that the top-hat TurbOpark model shows the best performance, followed by its Gaussian variant. The Gaussian models by Niayifar (2016) and Zong (2020), as well as the top-hat model by Jensen (1983) all show relatively good performance, while the Gaussian model by Bastankhah (2014) has the worst performance after calibration. While some models perform better than other models, all models show similar trends post-calibration, indicating that with proper calibration, any model can be viable, given its inherent limitations are recognized and managed.