Lidar-assisted Control Research Articles

Analysis and evaluation of two reference LiDAR-assisted control designs for wind turbines

LiDAR-assisted wind turbine control holds promise in reducing structural loads and enhancing rotor speed regulation. However, a research gap exists in the practicality and limitations of commercially available fixed-beam LiDARs for large turbines and evaluating commonly employed LiDAR-assisted feedforward approaches. This study addresses these gaps by examining the implications of utilizing fixed-beam LiDARs in two wind turbine sizes and two reference LiDAR-assisted control strategies. A comprehensive evaluation considers coherence variations, uncertainties related to inaccurate pitch angle mapping with the upcoming wind speed, and their combined impact on load reduction. Numerical simulations reveal that an excessively low cut-off frequency in the low-pass filter can compromise preview time compensation. This is problematic in larger turbines, where coherence with limited LiDAR beams is inferior compared to smaller wind turbines, which deteriorates the effectiveness of the LiDAR-assisted control. Among the reference LiDAR-assisted control methods, the evaluation indicates the Schlipf approach has greater load reduction independence, while Bossanyi’s approach, which uses measurement of current blade pitch, yields positive results with fine-tuned baseline controllers. However, allowing baseline controller-induced frequencies to propagate into the controller may increase system excitation at certain frequencies due to the use of the actual pitch angle for feedforward pitch rate calculation.

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

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.

Robustness of LiDAR-assisted controller towards measurement uncertainty

Many studies have concluded that LiDAR-assisted control can be used to increase annual energy production (AEP) as well as reduce extreme loads and fatigue damage on blades, tower, and main bearing components. However, most studies assume perfect LiDAR measurements and do not consider large measurement uncertainties on LiDAR measurements. In this study we evaluate the potential benefits in terms of fatigue load reduction from using a simple robust feedforward controller. In addition, we evaluate the impact of measurement uncertainties exemplified by a time shift between the expected time the wind hits the rotor and the actual time it hits the rotor as well as a mean wind speed measurement bias. Results shows that fatigue loads can be reduced up to 7% on the tower sections and up to 4% on other main components. However, these results only applies when the LiDAR is assumed to give perfect information about what is hitting the rotor plane. The load reduction degrades with the magnitude of a time shift of the arriving LiDAR wind measurements. A delay up to 2.5 seconds still gives reasonable load reductions. Furthermore, a positive wind speed bias does not affect the performance of the LiDAR-assisted controller, whereas a negative bias significantly deteriorates performance.

Wind-field characterization using synthetic lidar measurements and proper orthogonal decomposition

This paper presents a simple least-square method combined with Proper Orthogonal Decomposition to reconstruct full-rotor flow, using synthetic measurements from a pulsed lidar mounted on the turbine hub. The proposed lidar effectively overcomes blade blockage effects, enhancing data availability. Conducted at a wind speed of 11.4 m/s with 10% turbulence intensity, the study assesses wind-field reconstruction accuracy with the proposed method by examining the influence of mode count and measurement range selection. Comparisons with a baseline, derived from averaging line-of-sight across the rotor plane, reveal that including more modes generally improves reconstruction performance, achieving up to 57% error reduction in the wind-field reconstruction over the baseline. However, this benefit is constrained by the availability of measurements at each time step; limited data coupled with an increased number of modes can lead to overfitting, escalating errors. The method demonstrated here offers advantages in characterizing turbine responses, particularly in capturing low-frequency content in the wind-flow. Yet, channels like tower base moment necessitate a substantially higher number of modes for accurate characterization. Overall, this approach shows potential for real-time wind-flow estimation in lidar-assisted control applications.

Reconstructing turbulent wind-fields using inverse-distance-weighting interpolation and measurements from a pulsed mounted-hub lidar

This study evaluates a numerical multi-beam pulsed lidar mounted on the hub of the NREL 5MW reference wind turbine using the HAWC2 v13.1 numerical sensor for synthetic lidar measurement generation. While initially designed for single-beam operations, it facilitates multi-beam configuration simulations. We conducted an analysis of full-rotor longitudinal wind speed reconstruction by combining inverse-distance-weighting with synthetic sensor data from HAWC2. Utilizing a Mann-generated turbulence box for wind input at U = 11.4 m/s, we examined three lidar configurations for efficacy. The hub-mounted lidar proved efficient in capturing the incoming flow towards the turbine, showing a 60% improvement in overall reconstruction accuracy across the plane compared to the baseline, where hublidar measurements are simple average across the rotor plane. The rotor average wind speed showed a 30% enhancement compared to the baseline. Crucially, the lidar configuration, which impacted the spatial distribution across the rotor plane, emerged as a pivotal factor for effective reconstruction. Proper configuration assessment is essential, especially given the implications of rotational sampling and its impact under various wind conditions, for optimal performance. The proposed method, combining inverse-distance weighting with hub-lidar data for high spatial resolution measurements across the rotor plane, shows significant potential for real-time windflow estimation and lidar-assisted control applications.

Feedforward pitch control for a 15 MW wind turbine using a spinner-mounted single-beam lidar

Abstract. Feedforward blade pitch control is one of the most promising lidar-assisted control strategies due to its significant improvement in rotor speed regulation and fatigue load reduction. A high-quality preview of the rotor-effective wind speed is a key element of control benefits. In this work, a single-beam lidar is simulated in the spinner of a bottom-fixed IEA 15 MW wind turbine. Both continuous-wave (CW) and pulsed lidar systems are considered. The single-beam lidar can rotate with the wind turbine rotor and scan the inflow with a circular pattern, which mimics a multiple-beam nacelle lidar at a lower cost. Also, the spinner-based lidar has an unimpeded view of the inflow without intermittent blockage from the rotating blade. The focus distance and the cone angle of the spinner-based single-beam lidar are optimized for the best wind preview quality based on a rotor-effective wind speed coherence model. Then, the control benefits of using the optimized spinner-based lidar are evaluated for an above-rated wind speed in OpenFAST with an embedded lidar simulator and virtual four-dimensional Mann turbulence fields considering the wind evolution. Results are compared against those using a single-beam nacelle-based lidar. We found that the optimum scanning configurations of both CW and pulsed spinner-based single-beam lidars lead to a lidar scan radius of 0.6 of the rotor radius. Also, results show that a single-beam lidar mounted in the spinner provides many more control benefits (i.e. better rotor speed regulations and higher reductions in the damage equivalent loads on the tower base and blade roots) than the one based on the nacelle. The spinner-based single-beam lidar has a similar performance to a four-beam nacelle lidar when used for feedforward control.

The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback

In this paper, we analyze the performances of two control strategies and their combination on a floating turbine through OpenFAST simulations. The floating wind turbine is modeled based on the demonstrative FLOATGEN, which consists of a 2MW wind turbine mounted on the Damping Pool platform designed by BW Ideol. The lidar-assisted control utilizes lidar wind preview to achieve blade pitch feedforward control. The multi-variable feedback additionally uses the platform pitch rate to determine blade pitch. By simulations with the above-rated wind and irregular wave conditions, both control strategies and their combination has the potential to reduce turbine vibrations. Especially, combining lidar-assisted control and multi-variable feedback control brings the most significant reduction in the standard deviations of rotor speed (>36%), low-speed shaft torque (>30%), and blade root moment (>15%).

Assessing lidar-assisted feedforward and multivariable feedback controls for large floating wind turbines

Abstract. We assess the performance of two control strategies on the IEA 15 MW reference floating wind turbine through OpenFAST simulations. The multivariable feedback (MVFB) control tuned by the toolbox of the Reference OpenSource Controller (ROSCO) is considered to be a benchmark for comparison. We then tune the feedback gains for the multivariable control, considering two cases: with and without lidar-assisted feedforward control. The tuning process is performed using OpenFAST simulations, considering realistic offshore turbulence spectral parameters. We reveal that optimally tuned controls are robust to changes in turbulence parameters caused by atmospheric stability variations. The two optimally tuned control strategies are then assessed using the design load case 1.2 specified by the IEC 61400 standard. Compared with the baseline multivariable feedback control, the one with optimal tuning significantly reduced the tower damage equivalent load, leading to a lifetime extension of 19.7 years with the assumption that the lifetime fatigue is only caused by the design load case 1.2. With the assistance of feedforward control realized using a typical four-beam lidar, compared with the optimally tuned MVFB control, the lifetime of the tower can be further extended by 4.6 years.

Optimal drive train management of wind turbine using LiDAR-assisted predictive control strategy

Optimal drive train management of wind turbine using LiDAR-assisted predictive control strategy

Evaluation of lidar-assisted wind turbine control under various turbulence characteristics

Abstract. Lidar systems installed on the nacelle of wind turbines can provide a preview of incoming turbulent wind. Lidar-assisted control (LAC) allows the turbine controller to react to changes in the wind before they affect the wind turbine. Currently, the most proven LAC technique is the collective pitch feedforward control, which has been found to be beneficial for load reduction. In literature, the benefits were mainly investigated using standard turbulence parameters suggested by the IEC 61400-1 standard and assuming Taylor's frozen hypothesis (the turbulence measured by the lidar propagates unchanged to the rotor). In reality, the turbulence spectrum and the spatial coherence change by the atmospheric stability conditions. Also, Taylor's frozen hypothesis does not take into account the coherence decay of turbulence in the longitudinal direction. In this work, we consider three atmospheric stability classes, unstable, neutral, and stable, and generate four-dimensional stochastic turbulence fields based on two models: the Mann model and the Kaimal model. The generated four-dimensional stochastic turbulence fields include realistic longitudinal coherence, thus avoiding assuming Taylor's frozen hypothesis. The Reference Open-Source Controller (ROSCO) by NREL is used as the baseline feedback-only controller. A reference lidar-assisted controller is developed and used to evaluate the benefit of LAC. Considering the NREL 5.0 MW reference wind turbine and a typical four-beam pulsed lidar system, it is found that the filter design of the LAC is not sensitive to the turbulence characteristics representative of the investigated atmospheric stability classes. The benefits of LAC are analyzed using the aeroelastic tool OpenFAST. According to the simulations, LAC's benefits are mainly the reductions in rotor speed variation (up to 40 %), tower fore–aft bending moment (up to 16.7 %), and power variation (up to 20 %). This work reveals that the benefits of LAC can depend on the turbulence models, the turbulence parameters, and the mean wind speed.

Review of LIDAR-assisted Control for Offshore Wind Turbine Applications

Nacelle-mounted, forward-facing Light Detection and Ranging (LIDAR) technology is able to provide knowledge of the incoming wind so that wind turbines can prepare in advance, through feedforward control. LIDAR can aid in improving wind turbine performance across the full operating range, assisting with torque control in below rated wind speeds, pitch control in above rated wind speeds and yaw control for correctly aligning the turbine rotor with the incoming wind direction. The motivations are for decreasing structural loads, resulting in reduced maintenance and extended lifetimes of turbines and their components, and increasing power capture, both of which can lead to reductions in the levelised cost of energy. This paper provides a review of control strategies that have been employed for LIDAR-assisted turbine control. This paper reviews the computational and practical studies that have been performed for both bottom-fixed and floating turbines and the journey that the field has undertaken since its conceptualisation. Detail is provided of the key differences between fixed and floating offshore turbine dynamics. The paper concludes with guidance for future work within the field, with a focus on floating turbines, as the extent of the literature is scarce when compared to bottom-fixed. Suggestions are offered for how the future studies can better account for the current and future industry landscape. Opportunities for testing of LIDAR-assisted floating turbine control in the field, its benefits for floating substructure design, and the steps needed to be taken to ensure its increased utilisation on industrial projects are also discussed.

Design and Assessment of a LIDAR-Based Model Predictive Wind Turbine Control

The development of the Light Detection and Ranging (LIDAR) technology has enabled wider options for wind turbine control, in particular regarding disturbance rejection. The LIDAR measurements provide a spatial, preview wind information, based on which the controller has a better chance to cope with the wind disturbance before it affects the turbine operation. In this paper, a model predictive controller for above-rated wind turbine control was developed with the use of pseudo-LIDAR wind measurements data. A predictive control algorithm was developed based on a linearised wind turbine model, in which the disturbance from the incoming wind was computed by the LIDAR simulator. The optimal control action was applied to the nonlinear turbine model. The developed controller was compared with the baseline control and a previously developed LIDAR-assisted control combining a feedback-and-feedforward design. Our simulation studies on a 5 MW nonlinear wind turbine model, under different wind conditions, demonstrated that the developed LIDAR-based predictive control achieved improved performance in the presence of small variations in the out-of-plane rotor torque and fore-aft tower acceleration, as well as a smoother generator speed regulation and satisfied pitch activity control constraints.

Wind turbine LIDAR-assisted control: Power improvement, wind coherence and loads reduction

This paper aims to firstly review various LIDAR-assisted wind turbine control methods proposed in the past ten years for improving power production in the below rated wind speed region in order to clarify their performance and quantify the potential benefits and drawbacks of LIDAR-assisted control. Secondly, a new LIDAR-assisted control algorithm based on Extremum Seeking Control (ESC) using LIDAR preview information for tracking the optimal power coefficient value is presented and compared to the traditional control strategy. The results show that the newly proposed LIDAR-assisted control strategy is beneficial in the below rated wind speed region when the aerodynamic properties of blades are changed largely compared to the original design. Lastly, the effects of the wind field coherence between the LIDAR measured points and the rotor plane on the LIDAR-assisted control for loads reduction in above rated region is discussed. An important conclusion is that the major benefit of LIDAR-assisted wind turbine control is to improve collective pitch control for reducing fatigue loads in above rated region, when the coherence bandwidth between the LIDAR measured wind speed and the rotor effective wind speed is high.

On LiDAR-assisted wind turbine retrofit control and fatigue load reductions

The use of upstream wind speed measurement has motivated the development of LiDAR-assisted control for enhancing rotor speed tracking and fatigue structural load reductions. However, conventional LiDAR-assisted control designs often require altering the existing controller architecture, for example, by sending an additional feed-forward pitch angle signal to the pitch actuator or by incorporating the feed-forward pitch rate into the feedback controller. This work proposes LiDAR-assisted retrofit control solutions that can be implemented without any prior knowledge or modifications of the existing feedback controller. Specifically, the feed-forward pitch action is provoked by modifying the rotor speed measurement. Three retrofit methods are proposed according to the level of retrofit requirement and complexity. Numerical simulation results showed that all three LiDAR-assisted retrofit solutions could achieve good thrust-related load reductions. Thus, the proposed LiDAR-assisted retrofit solutions present a simple control upgrade to extend the lifetime of existing turbines.

Updates on the OpenFAST Lidar Simulator

Lidar systems are able to measure the wind speed remotely by detecting the aerosol movement caused by wind. A nacelle-based lidar system scanning the wind in front of a wind turbine can provide a preview of the incoming wind before the wind interacts with the turbine. Implementing a realistic lidar simulator into the wind turbine aero-elastic simulation tool can be beneficial for various wind energy related fields, such as lidar-assisted control, load validation, and load monitoring. In previous work, a lidar simulation module has been integrated into the open-source aero-elastic simulation tool OpenFAST, covering already lidar characteristics like different scan patterns, the volume averaging along the beam, and the coupling with the nacelle motion due to turbine tower dynamics. This paper focuses on adding further features to the lidar simulation module of OpenFAST to make the lidar simulation more realistic: the evolving turbulence, the blade blockage effect, and the adjustable data availability. Further, the wind preview quality with the new features is assessed.

LIDAR based multivariable H∞ feedforward control for load reduction in wind turbines

The performance of LIDAR assisted control is highly affected by the quality of the wind measurement. Consequently, it seems convenient to include some kind of uncertainty model during the design of the LIDAR-based feedforward controllers. Robust control methodologies provide a great tool to deal with uncertain systems. In this paper, three H∞ feedforward controllers have been designed based on linear frequency-domain models of the system. Because the control problem is multivariable and multiobjective, the controllers show different combinations of control actions -pitch and generator torque- and control objectives -generator speed regulation and tower base load reduction-. The controllers have been tested in a nonlinear simulation using the aerolastic code OpenFAST, a turbulent wind field and the onshore NREL 5MW wind turbine model.

Evaluation of the “fan scan” based on three combined nacelle lidars for advanced wind field characterisation

Nacelle-mounted lidars are finding more and more applications in the wind industry. Their use cases range from power performance testing to lidar-assisted control. Depending on the application, different wind field parameters are reconstructed from the lidar’s line-of-sight wind measurements. Hereby, the respective scan geometry of the lidar determines the capabilities and limitations for the reconstruction of wind field parameters. In this study, we introduce the fan scan, a scanning pattern made of multiple nacelle-mounted lidars with the aim of an advanced wind field assessment to reconstruct more than three wind field parameters, which has been implemented at the Testfeld BHV site in Bremerhaven, Germany. Analytically derived uncertainties as well as calibration results for both, line-of-sight speeds and reconstructed wind field parameters, are presented indicating a good agreement with reference measurements. Additionally, a method for the reconstruction of the horizontal wind shear is introduced and applied to the fan scan data, highlighting the extended utilization possibilities of nacelle lidars.

Four-dimensional wind field generation for the aeroelastic simulation of wind turbines with lidars

Abstract. Lidar-assisted control (LAC) of wind turbines is a control concept that takes advantage of a nacelle-mounted lidar (a remote sensing device) to measure upstream wind speeds of a turbine to allow a preview of the incoming turbulence. Because the turbine will not be exposed to the identical turbulence as that measured by the lidar in advance, the simulation of a LAC system will be more realistic if wind evolution can be modeled in the wind field generation. Since the commonly used 3D stochastic wind field generation method does not include wind evolution, the main goal of this research is to extend the 3D method to 4D to enable the modeling of wind evolution along the wind direction. The most novel part of this research is that we propose a two-step Cholesky decomposition approach for the factorization of the coherence matrices in the wind field generation. With this approach, 4D wind fields can be generated by combining multiple statistically independent 3D wind fields. To enable better integration of the 4D method into the common workflow of wind turbine simulations, we implement the 4D method as the open-access tool evoTurb in combination with TurbSim and Mann turbulence generator. Moreover, since 4D wind field generation is supposed to be coupled with lidar simulations, and considering the range weighting effect of lidars and eventually multiple range gates, a 4D wind field will contain many more simulation points than a 3D one. To avoid excessive computational effort, we further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide some insights to optimize the application of 4D wind field generation.

On turbulence models and lidar measurements for wind turbine control

Abstract. To provide comprehensive information that will assist in making decisions regarding the adoption of lidar-assisted control (LAC) in wind turbine design, this paper investigates the impact of different turbulence models on the coherence between the rotor-effective wind speed and lidar measurement. First, the differences between the Kaimal and Mann models are discussed, including the power spectrum and spatial coherence. Next, two types of lidar systems are examined to analyze the lidar measurement coherence based on commercially available lidar scan patterns. Finally, numerical simulations have been performed to compare the lidar measurement coherence for different rotor sizes. This work confirms the association between the measurement coherence and the turbulence model. The results indicate that the lidar measurement coherence with the Mann turbulence model is lower than that with the Kaimal turbulence model. In other words, the potential value creation of LAC based on simulations during the wind turbine design phase, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors. As a result, this paper suggests that the impacts of different turbulence models should be considered uncertainties while evaluating the benefits of LAC.

What are the benefits of lidar-assisted control in the design of a wind turbine?

Abstract. This paper explores the potential benefits brought by the integration of lidar-assisted control (LAC) in the design of a wind turbine. The study identifies which design drivers can be relaxed by LAC, as well as by how much these drivers could be reduced before other conditions become the drivers. A generic LAC load-reduction model is defined and used to redesign the rotor and tower of three representative turbines, differing in terms of wind class, size, and power rating. The load reductions enabled by LAC are used to save mass, increase hub height, or extend lifetime. For the first two strategies, results suggest only modest reductions in the levelized cost of energy, with potential benefits essentially limited to the tower of a large offshore machine. On the other hand, lifetime extension appears to be the most effective way of exploiting the effects of LAC.