Wind field reconstruction using nacelle based lidar measurements for floating wind turbines

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.

Section: Methodsmentioning

confidence: 99%

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

Gräfe,

Pettas,

Cheng

2024

“…A numerical approach is employed for the provision of model training data, as well as the demonstration of the prediction performance. We use the aero-elastic wind turbine simulation tool openFAST [10] in combination with the numerical lidar simulation framework ViConDAR [3,11] to create the input and output variables for the neural network.…”

Section: Methodsmentioning

confidence: 99%

“…Lidar measurements from nacelle based lidar systems on FOWTs are affected by floater motions. As investigated in [3], different effects of motion influence can be distinguished. Rotational motions of the floater-tower assembly cause tilted beam geometries compared to a fixed system, resulting in changing LOS velocity measurements.…”

Section: Nacelle Based Lidar Measurements On Fowtmentioning

confidence: 99%

“…In this study, we investigate an approach using wind speed measurements from a forwardlooking nacelle based lidar as input to a neural network to estimate fairlead tensions. As shown [3], nacelle based lidar measurements on FOWTs are influenced by platform motions, in particular the fore-aft motion of the nacelle. Therefore, the lidar wind speed measurement contains information about the inflow as well as the dynamic behavior of the floater.…”

Section: Introductionmentioning

confidence: 99%

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Lidar-based virtual load sensors for mooring lines using artificial neural networks

Gräfe,

Özinan,

Cheng

2023

Floating offshore wind turbines are equipped with a variety of sensors, which are measuring data, valuable for the control and monitoring of the turbine. However, reliable measurements are difficult or costly for some physical quantities. This includes load estimates for mooring lines and fairleads. In this study, we investigate an approach using wind speed measurements from a forward-looking nacelle-based lidar as inputs to long short-term memory networks to estimate fairlead tensions. Nacelle-based lidar wind speed measurements on floating offshore wind turbines are influenced by platform motions, in particular by the rotational pitch displacement and the surge displacement of the floater. Therefore, the lidar wind speed measurement contains information about the dynamic behavior of the floater. In turn, the floater’s dynamics determine the fair lead loads. Thus in this study, we directly use the lidar-measured line of sight (LOS) wind speeds to estimate mooring line tensions. The model training data is obtained using the aero-elastic wind turbine simulation tool openFAST in combination with the numerical lidar simulation framework ViConDAR. Results show, that lidar-based virtual load sensors can reproduce mean fairlead tension as well as low-frequency fluctuations, with varying accuracy dependant on the combination of input features. For the model which is only using LOS wind speed measurements as input a normalized root mean squared error of 0.55 was obtained.

“…In this work, ViConDAR [14,15], for Virtual Constrained turbulence and liDAR measurements, has been used to scan wind fields and provide simulated lidar measurements.…”

Section: Introductionmentioning

confidence: 99%

Impact of probe volume and peak detection methods on lidar rotor effective wind speed and turbulence intensity estimations

Costa,

Peña,

Pettas

et al. 2023

Lidar simulation techniques are a suitable and increasingly reliable alternative for testing lidar measuring strategies and illustrating their response when combined with modelled wind fields. In this work, two simulation tools are combined to assess the uncertainty in the derivation of the rotor effective wind speed and the wind speed variance from a forward-looking nacelle-mounted continuous wave lidar wind speed estimations. These uncertainties are analysed for a variety of atmospheric turbulence levels and lidar measuring strategies. A synthetic turbulence generator is used to create the reference wind fields. Subsequently, a lidar simulator operated in a continuous-wave mode is used to scan the synthetic wind fields and perform a sensitivity analysis by comparing first- and second-order statistics against reference values. The lidar simulator is enhanced with three Doppler peak detection methods, namely the maximum, the median and the centroid, to extract radial wind speeds from the velocities found within the probe volume. The results show that probe volume and peak detection methods influence the uncertainty of the wind speed variance. The uncertainty in time-averaged and instantaneous rotor effective wind speed estimations is not sensitive to the lidar spatial averaging or peak detection methods investigated. Finally, we saw that the turbulence intensity influences the derived lidar quantities and is the main driver of the variations in rotor effective wind speed uncertainty estimations.