Impacts of Wind Assimilation on Error Correction of Forecasted Dynamic Loads from Wind, Wave, and Current for Offshore Wind Turbines

Abstract

In this study, a dynamic load forecasting model was developed for offshore wind turbines, based on the COAWST (Coupled Ocean-Atmosphere-Wave-Sediment Transport) model, the GRU (Gated Recurrent Unit) algorithm, and a data assimilation module. The model was able to forecast aerodynamic, wave, and current loads acting on the turbines. Four groups of forecasting tests were conducted to evaluate the model’s performance under different strategies and to assess the impact of atmospheric assimilation on improving dynamic load forecasts. The wind turbines in Cangnan Offshore Wind Farm, located in the west of the East China Sea, were chosen as the study object. The results indicated that the model achieved high forecasting accuracy, with the RMSEs (root mean square errors) of 275.59 kN, 335.85 kN, and 313.51 N, for the aerodynamic, wave, and current loads. The errors were reduced by about 13%, 10.09%, and 6.7% when compared with the original COAWST model, and were also lower than the atmospheric and oceanic reanalysis data. Atmospheric data assimilation was demonstrated to reduce the forecasting RMSE of aerodynamic load by about 12%, and its error improvement was able to be combined with GRU-based error correction. Additionally, atmospheric assimilation mitigated the reduction in temporal variability caused by forecasting error correction, preventing a decrease in the standard deviation of aerodynamic load forecasts. However, atmospheric assimilation had minimal impacts on wave and current load forecasts, with the RMSEs increased by about 2.5% and 0.1%, and had almost the same performance in correlation coefficients and standard deviations.