Reducing LIDAR wind speed measurement error with optimal filtering

Simley, Eric; Pao, Lucy Y.

doi:10.1109/acc.2013.6579906

Cited by 29 publications

(36 citation statements)

References 11 publications

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“…where SRL is the cross-power spectral density between the rotoreffective wind speed v0 and the lidar measurement v0L, and SLL is the PSD of the lidar measurement (Simley and Pao, 2013). The transfer function in Equation (10) is comprised of two stages.…”

Section: Evaluation Of the Resultsmentioning

confidence: 99%

“…Here, Taylor's Hypothesis is used for the wind evolution (EVO) and therefore measurements of the several distances can be combined by shifting them in time . The transfer function between v0L and v0 is known to be the optimal prefilter (PF) for the lidar estimate to remove all uncorrelated frequencies (Simley and Pao, 2013;Schlipf et al, 2013a). Here, a second-order low pass filter (Butterworth) with a cut-off frequency of fcutoff = 0.201 Hz is fitted to the transfer function, see Figure 3.…”

Section: Collective Pitch Feedforward Controller Design For Realisticmentioning

confidence: 99%

“…However, since the wind speed signals v0 and v0L are modeled as jointly Gaussian random processes, the filter SRL/SLL is not only the MMSE linear estimator of v0, but the optimal MMSE estimator of v0 among all possible estimators (Kailath et al, 2000). By using a MMSE filter to estimate v0 from v0L prior to the ideal feedforward controller stage, the controller (10) also minimizes the PSD of Ω (Simley and Pao, 2013).…”

Section: −1mentioning

confidence: 99%

“…The obtained spectrum of the rotor speed is compared to the theoretical optimal spectrum that can be achieved with the used lidar setup. The optimal spectrum is calculated using a linearized turbine model and an analytic model of the correlation between the lidar measurements and the reaction of the wind turbine (Simley and Pao, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Collective Pitch Feedforward Control of Floating Wind Turbines Using Lidar

Schlipf¹,

Simley²,

Lemmer³

et al. 2015

JOWE

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In this work a collective pitch feedforward controller for floating wind turbines is presented. The feedforward controller provides a pitch rate update to a conventional feedback controller based on a wind speed preview. The controller is designed similar to the one for onshore turbines, which has proven its capability to improve wind turbine control performance in field tests. In a first design step, perfect wind preview and a calm sea is assumed. Under these assumptions the feedforward controller is able to compensate almost perfectly the effect of changing wind speed to the rotor speed of a full nonlinear model over the entire full load region. In a second step, a nacelle-based lidar is simulated scanning the same wind field which is used also for the aero-hydro-servo-elastic simulation. With model-based wind field reconstruction methods, the rotor effective wind speed is estimated from the raw lidar data and is used in the feedforward controller after filtering out the uncorrelated frequencies. Simulation results show that even with a more realistic wind preview, the feedforward controller is able to significantly reduce rotor speed and power variations. Furthermore, structural loads on the tower, rotor shaft, and blades are decreased. A comparison to a theoretical investigation shows that the reduction in rotor speed regulation is close to the optimum.

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Section: Evaluation Of the Resultsmentioning

confidence: 99%

Section: Collective Pitch Feedforward Controller Design For Realisticmentioning

confidence: 99%

Section: −1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Collective Pitch Feedforward Control of Floating Wind Turbines Using Lidar

Schlipf¹,

Simley²,

Lemmer³

et al. 2015

JOWE

Self Cite

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“…Second, the signal has to be transferred in a timely fashion to the control system. To avoid unnecessary and harmful control action and obtain optimal performance, the wind speed from the lidar has to be filtered by an adaptive low-pass filter based on the transfer function G RL between the rotor-effective wind speed and its lidar estimate [13], [14]. Thus, a minimum distance can be calculated to have a timely control action.…”

Section: B Lidar System Measurement Configurationmentioning

confidence: 99%

Optimization of a feed-forward controller using a CW-lidar system on the CART3

Haizmann

Schlipf

Raach

et al. 2015

2015 American Control Conference (ACC)

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Abstract-This work presents results from a new field-testing campaign conducted on the three-bladed Controls Advanced Research Turbine (CART3) at the National Renewable Energy Laboratory in 2014. Tests were conducted using a commercially available, nacelle-mounted continuous-wave lidar system from ZephIR Lidar for the implementation of a lidar-based collective pitch feed-forward controller. During the campaign, the data processing of the lidar system was optimized for higher availability. Furthermore, the optimal scan distance was investigated for the CART3 by means of a spectra-based analytical model and found to match the lidar's capabilities well. Throughout the campaign the predicted correlation between the lidar measurements and the turbine's reaction was confirmed from the measured data. Additionally, the baseline feedback controller's gains were tuned based on a simulation study that included the lidar system to achieve further load reductions. This led to some promising first results, which are presented at the end of this paper.

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Evaluation of a wind speed estimator for effective hub-height and shear components

Simley

Pao

2014

Wind Energ.

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Estimates of the effective wind speed disturbances acting on a wind turbine are useful in a variety of control applications. With some simplifications, it is shown that for zero yaw error, any wind field interacting with a turbine can be equivalently described using a hub-height (uniform) component as well as linear horizontal and vertical shear components. A Kalman filter-based wind speed estimator is presented for estimation of these effective hub-height and shear components. The wind speed estimator is evaluated in the frequency domain using the FAST aeroelastic simulator with the National Renewable Energy Laboratory's 5 MW reference wind turbine model and realistic hub-height and shear disturbances. In addition, the impact of the inflow model, used to simulate the rotor aerodynamics, on the Kalman filter performance is investigated. It is found that the estimator accuracy strongly depends on the inflow model used. In general, the estimator performs well up to a bandwidth of 1 Hz when the inflow model used for simulation matches the model used to create the linear Kalman filter model and blade pitch angle remains close to the linearization operating point. However, inaccuracies in the linear model of the turbine when dynamic inflow is used for simulation as well as nonlinearities in the turbine dynamics due to blade pitch actuation cause performance to degrade. Finally, the improvement gained by employing a non-causal wind speed estimator is assessed, showing a minor increase in performance.

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Reducing LIDAR wind speed measurement error with optimal filtering

Cited by 29 publications

References 11 publications

Collective Pitch Feedforward Control of Floating Wind Turbines Using Lidar

Collective Pitch Feedforward Control of Floating Wind Turbines Using Lidar

Optimization of a feed-forward controller using a CW-lidar system on the CART3

Evaluation of a wind speed estimator for effective hub-height and shear components

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