Nonlinear model predictive control of wind turbines using LIDAR

Schlipf, David; Kühn, Martin

doi:10.1002/we.1533

Cited by 309 publications

(282 citation statements)

References 29 publications

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“…Following the concept of a rotor-effective wind speed as used by Schlipf et al (2013), this approach is more appropriate for describing the wind speed affecting the rotor than a single-point measurement. It should be noted that our results are hardly affected by using averaged measurements as opposed to data of the central hot wire.…”

Section: Wind Tunnel and Active Gridmentioning

confidence: 99%

On the impact of non-Gaussian wind statistics on wind turbines – an experimental approach

et al. 2017

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Abstract. The effect of intermittent and Gaussian inflow conditions on wind energy converters is studied experimentally. Two different flow situations were created in a wind tunnel using an active grid. Both flows exhibit nearly equal mean velocity values and turbulence intensities but strongly differ in their two point statistics, namely their distribution of velocity increments on a variety of timescales, one being Gaussian distributed, and the other one being strongly intermittent. A horizontal axis model wind turbine is exposed to both flows, isolating the effect on the turbine of the differences not captured by mean values and turbulence intensities. Thrust, torque and power data were recorded and analyzed, showing that the model turbine does not smooth out intermittency. Intermittent inflow is converted to similarly intermittent turbine data on all scales considered, reaching down to sub-rotor scales in space. This indicates that it is not correct to assume a smoothing of intermittent wind speed increments below the size of the rotor.

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Section: Wind Tunnel and Active Gridmentioning

confidence: 99%

On the impact of non-Gaussian wind statistics on wind turbines – an experimental approach

et al. 2017

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“…2,3 However, the question of how best to use these measurements to detect an oncoming gust remains open, as does the question of how best to incorporate such information for use within a preview control scheme (e.g. 4,5,6,7 ). The success of such controllers will inevitably be reliant upon the accuracy of the predictions of the incident wind field.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, much of the existing literature on LiDAR-assisted control of wind turbines 7 and LiDAR-based wind field reconstruction, 16 has employed some form of Taylor's frozen turbulence hypothesis. In the present work, we seek to estimate the wind field not just at a point Coloured regions indicate wind magnitude and the prevailing wind direction is from the southwest.…”

Section: Introductionmentioning

confidence: 99%

Real-time wind field reconstruction from LiDAR measurements using a dynamic wind model and state estimation

Towers

Jones

2014

Wind Energ.

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The use of light detection and ranging (LiDAR) instruments offer many potential benefits to the wind energy industry. Although much effort has been invested in developing such instruments, the fact remains that they provide limited spatio-temporal velocity measurements of the wind field. Moreover, LiDAR measurements only provide the radial (line-of-sight) velocity component of the wind, making it difficult to precisely determine wind magnitude and direction, owing to the so-called 'cyclops' dilemma. Motivated by a desire to extract more information from typical LiDAR data, this paper aims to show that it is possible to accurately estimate, in a real-time fashion, the radial and tangential velocity components of the wind field. We show how such reconstructions can be generated through the synthesis of an unscented Kalman filter that employs a low-order dynamic model of the wind to estimate the unmeasured velocities within the wind field, using repeated measurement updates from typical nacelle-mounted LiDAR instruments. This approach is validated upon synthetic data generated from large eddy simulations of the atmospheric boundary layer. The accuracy of the wind field estimates are validated across a variety of beam configurations, look directions, atmospheric stabilities and imperfect measurement conditions. The main outcome of this paper is a technique that offers the potential to accurately reconstruct wind fields from LiDAR data, overcoming the cyclops dilemma in the process. The ultimate aim of this research is to provide reliable gust detection warning systems to offshore construction workers, in addition to accurate wind field estimates for use in preview turbine pitch control systems.

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“…In [13], model predictive control is uses with the information about the future wind and a nonlinear model of the structural damages produced by repetitive loads to reduce the structural load and fatigue. In [14], the authors have used the wind prediction information obtained from a LIDAR system in a nonlinear model predictive controller to reduce fatigue loads on the tower and blades. This paper presents a framework for specifying the desired closed-loop behaviour of the controlled system based on a control strategy including a reference model.…”

Section: Introductionmentioning

confidence: 99%

Wave disturbance reduction of a floating wind turbine using a reference model-based predictive control

Christiansen

Tabatabaeipour

Bak

et al. 2013

2013 American Control Conference

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Abstract-Floating wind turbines are considered as a new and promising solution for reaching higher wind resources beyond the water depth restriction of monopile wind turbines. But on a floating structure, the wave-induced loads significantly increase the oscillations of the structure. Furthermore, using a controller designed for an onshore wind turbine yields instability in the fore-aft rotation. In this paper, we propose a general framework, where a reference model models the desired closed-loop behavior of the system. Model predictive control combined with a state estimator finds the optimal rotor blade pitch such that the state trajectories of the controlled system tracks the reference trajectories. The framework is demonstrated with a reference model of the desired closedloop system undisturbed by the incident waves. This allows the wave-induced motion of the platform to be damped significantly compared to a baseline floating wind turbine controller at the cost of more pitch action.

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Nonlinear model predictive control of wind turbines using LIDAR

Cited by 309 publications

References 29 publications

On the impact of non-Gaussian wind statistics on wind turbines – an experimental approach

On the impact of non-Gaussian wind statistics on wind turbines – an experimental approach

Real-time wind field reconstruction from LiDAR measurements using a dynamic wind model and state estimation

Wave disturbance reduction of a floating wind turbine using a reference model-based predictive control

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