Data-driven forecasting of FOWT dynamics and load time series using lidar inflow measurements

This study focuses on forecasting the fairlead tension and floater dynamics time series of floating offshore wind turbines (FOWTs) using a data-driven approach that incorporates onboard sensor measurements and lidar inflow data. Sensors on FOWTs can provide data on turbine dynamics, such as rotational and translational movements, and load metrics like mooring line loads. However, these sensors are limited to current state measurements and do not provide future signal projections. In this research, we investigate a data-driven forecasting methodology using a simulated dataset. This dataset encompasses FOWT responses to diverse environmental conditions and the associated lidar measurements. Utilizing a Long Short-Term Memory (LSTM) sequence-to-sequence model, this study forecasts the time series of fairlead tension, surge, and pitch for forecasting horizons of 20, 40, and 60 seconds, considering lidar ranges from 100m to 500m. The performance of these forecasting models is benchmarked against a simple persistence model. The results indicate that incorporating lidar inflow measurements significantly improves the forecasts of fairlead tensions and platform motions. The enhancement for pitch motion forecasts is observed across all forecasting horizons. For fairlead tension and surge motion, the enhancement is observed for the longer horizons of 40s and 60s. These findings underscore the value of lidar data in accurate forecasting and emphasize the need to account for the interplay between lidar range, wind speed, and forecasting horizon to achieve optimal forecast accuracy.