Dynamic wake tracking using a cost-effective LiDAR and Kalman filtering: Design, simulation and full-scale validation

Lio, Wai Hou; Larsen, Gitte; Thorsen, Gunhild Rolighed

doi:10.1016/j.renene.2021.03.081

Cited by 14 publications

(12 citation statements)

References 29 publications

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“…The performance is influenced by several factors, most notably the turbulence intensity. The same is seen in various literature sources [2,6,7]. The TI affects the meandering motion, ambient disturbances and the wake deficit shape.…”

Section: Ekf Parametrizationsupporting

confidence: 72%

“…Smaller cut-off frequencies ω c for the dynamic model could generally be considered. This would match with the method in Lio et al [6], who propose an estimation of ω c based on the auto-correlation of v c . In comparison to ω c suggested by the DWM model, the authors see a value ω c smaller by a factor of two for application in HAWC2 simulations and a factor of ten for field experiments with a Vestas V52 turbine at the Risø site.…”

Section: Ekf Parametrizationsupporting

confidence: 68%

“…ambient wind speed or deficit shape) and increasing the precision via advancements in the measurement model and in the noise tuning. Recent publications [6,7] give inspiration here and show that an online estimation of the wake deficit shape is indeed feasible, possibly as a trade-off with the position accuracy. Furthermore, the method should be tested with field measurements.…”

Section: Discussionmentioning

confidence: 94%

“…The latter splits up into suitable pre-processing of the measurements, the Extended Kalman Filter (EKF) and eventual adaptive noise tuning. An EKF is a Kalman Filter which can incor-Figure 1: Overview sketch of the dynamic wake tracking algorithm, showing the subcomponents and their interaction (inspired by [6]) ). The dynamic model describes the expected change of the system state x k within one time step k, so in this case it yields the expected new position of the wake based on its current lateral and vertical position y w , z w and their associated characteristic transversal velocities v c , w c .…”

Section: Methodsmentioning

confidence: 99%

“…Such methods either aim at qualitative impingement detection [2,3] or time-averaged location tracking [4,5], where typically the dynamics caused by the meandering behaviour of the wake are not taken into account. The lidar based approaches include the wake deficit shape to the estimation [1,6] and also account for the dynamic meandering [6]. A combination of both aspects is proposed in [7], using blade loads but also taking the meandering dynamics into account.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering

Onnen

Larsen

Lio

et al. 2022

J. Phys.: Conf. Ser.

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In a wind farm setting, the location of the wake to which a downwind turbine is exposed is of high interest. It can be used for closed-loop active wake control, ultimately leading to fatigue load reduction and higher power generation. This work proposes a method for dynamic tracking of the meandering wake centre. The rotor takes the role of a sensor, with its blades sampling through the incoming wind field. The measurement of the flapwise blade root bending moment and its decomposition into the non-rotating yaw and tilt moments is used. The latter are linked to the lateral and vertical wake location via a parametric model, tuned with training data from aeroelastic simulations. The implementation of an Extended Kalman Filter (EKF) adds robustness to the tracking and allows to include physical knowledge of the wake meandering to the estimation. For this, the governing equations of the dynamic wake meandering model (DWM) are used to describe the meandering motion as a random walk process. The performance, possibilities and limitations of the tracking method in various inflow conditions are shown and discussed. Generally the wake tracking works satisfying, with estimation errors below 10% of the rotor diameter under moderate turbulence intensity. The Extended Kalman Filter formulation provides the confidence in the tracked wake position. The work shows how this can be effectively used for wake impingement detection.

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Section: Ekf Parametrizationsupporting

confidence: 72%

Section: Ekf Parametrizationsupporting

confidence: 68%

Section: Discussionmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%