Assessing lidar-assisted feedforward and multivariable feedback controls for large 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.

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

Gräfe,

Pettas,

Cheng

2024

Performance similarities between standard and retrofit LiDAR-assisted control for wind turbines

Lio,

Meng,

Larsen

2024

LiDAR-assisted control has proven to be a highly effective method for mitigating rotor speed deviations and reducing loads in wind turbines. This effectiveness stems from the ability of feedforward controllers to utilize incoming wind speed information obtained from LiDARs, enabling advanced blade pitch actions before the wind disturbs the turbine. However, the standard implementation of LiDAR-assisted control often necessitates modifications to the existing feedback controller, where the feedforward pitch rate is typically integrated into the feedback controller. This process can be challenging in terms of accessibility and may be limited to specific stakeholders, such as turbine manufacturers. A retrofit design provides an ideal solution, where the retrofit LiDAR-assisted controller modifies the rotor speed measurement to induce pitch actions without requiring alterations to the existing feedback controller. This paper aims to demonstrate the performance similarities between the standard LiDAR-assisted controller and its retrofit counterpart. Specifically, we establish that the retrofit LiDAR-assisted controller, with appropriate tuning, is equivalent to its standard counterpart. This equivalence implies that architecturally dissimilar controllers can yield the same performance in terms of rotor speed deviations and tower load reductions. The presented findings are supported by results obtained from high-fidelity closed-loop turbine simulations.

Detecting abnormal small-scale events by nacelle lidar at the AD8-180 prototype turbine

Meyer,

Adorno,

Wegner

et al. 2024

Identifying anomalous wind events that lead to high aerodynamic loads is essential for mitigating extreme loading on wind turbines. Building upon a recently published method, we herein introduce an extended methodology for early small-scale abnormal wind event detection employing a pulsed nacelle-lidar, potentially triggering preemptive control actions within the wind turbine system. The extended method integrates the consideration of multiple measurement ranges within the induction zone and the evaluation of a horizontally asymmetric wind detection parametrized through a difference in lidar line-of-sight (LoS) measurements. A comparative assessment of various detection algorithms is conducted, leading to the identification of optimal parameter configurations and an evaluation of their reliability. With the proposed methodology, the false positive rate can be reduced with respect to the original methodology, although not all extreme loading events are detected.