The use of preview wind measurements for blade pitch control

Laks, Jason; Pao, Lucy Y.; Wright, Alan; Kelley, Neil; Jonkman, Bonnie

doi:10.1016/j.mechatronics.2011.02.003

Cited by 77 publications

(56 citation statements)

References 6 publications

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“…The preview time associated with d = 170 m for U = 11.4 m/s is d/U = 14.9 seconds. From a control systems perspective, where perfect wind speed measurements were assumed, Laks et al 7 and Ozdemir et al 22 find that a preview time of approximately 0.45 seconds is required for a 600 kW wind turbine operating at U = 18 m/s. Dunne et al 5 find that a preview time of roughly 3.5 seconds is required for the 5-MW wind turbine model used in this study operating near U = 11.4 m/s.…”

Section: Discussionmentioning

confidence: 99%

“…6,7 Multiple lidar measurements are used to form a best-fit estimate of the collective and shear components to mitigate cyclic loads. This category of individual pitch control works well in steady winds.…”

Section: Introductionmentioning

confidence: 99%

“…The second category of individual pitch controllers uses a separate preview measurement for each blade. 7,8 The lidar measurements are not fit to a simplified model, but are used to control each blade independently. This paper analyzes lidar measurements for the latter type of individual pitch control where the correlation between a rotating lidar measurement and the rotating wind speed experienced by a blade, called the blade effective wind speed, is of interest.…”

Section: Introductionmentioning

confidence: 99%

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Correlation between Rotating LIDAR Measurements and Blade Effective Wind Speed

Simley

Pao

2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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Preview wind speed measurements from a forward looking Light Detection and Ranging (LIDAR) system located in the hub of a wind turbine can be used by a feedforward blade pitch control system to mitigate structural loads. For individual blade pitch control, a separate preview estimate of the effective wind speed encountered by each blade must be available. One way of providing an estimate of the blade effective wind speed is to implement a hub mounted spinning lidar that scans the wind field at the rotational rate of the rotor such that the measured wind will reach the blade after some delay. In this way, both the lidar measurement and the wind turbine blade rotationally sample the wind field. The benefit gained by using preview wind speed measurements strongly depends on the correlation between the measured wind and the wind that interacts with the blades. In this research, the coherence between rotating lidar measurements and blade effective wind speed is calculated and analyzed using a spectral model of the wind field. The simulated wind field uses an isotropic von Karman spectrum and contains a model of wind evolution described by a longitudinal spatial coherence function. The coherence between stationary measurements and stationary blade effective wind speeds decreases to zero near the 1P rotational frequency of the turbine. However, measurement coherence between rotating lidar measurements and blade effective wind speed remains much higher and does not decay until higher frequencies. Nomenclature CPSD

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correlation between Rotating LIDAR Measurements and Blade Effective Wind Speed

Simley

Pao

2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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“…Recently, much research has been done on wind turbine feedforward control systems using preview LIDAR measurements [1]- [6]. Most of the effort has been focused on either reducing generator speed error and tower loads using collective blade pitch control [6] or tower and blade loads using individual pitch control [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the effort has been focused on either reducing generator speed error and tower loads using collective blade pitch control [6] or tower and blade loads using individual pitch control [1]- [5]. Collective pitch feedforward control requires a preview estimate of the rotor effective wind speed, or the wind speed that is "felt" by the wind turbine rotor.…”

Section: Introductionmentioning

confidence: 99%

Reducing LIDAR wind speed measurement error with optimal filtering

Simley

Pao

2013

2013 American Control Conference

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Abstract-Recent research has shown the potential for reduction in wind turbine generator speed error and structural loads with the introduction of feedforward control using preview LIDAR measurements. Several sources of error exist in the estimation of the wind speeds that will interact with the turbine rotor, including LIDAR distortion and coherence loss due to wind evolution. If a feedforward controller is designed assuming perfect wind speed measurements, however, the error in the disturbance estimate may cause feedforward control to increase output errors. Here we derive the minimum mean square error feedforward controller for imperfect measurements using statistical descriptions of the wind. We show that the resulting controller is the ideal feedforward controller, assuming perfect measurements, in series with a Wiener prefilter to reduce the mean square error of the disturbance estimate. We derive the optimal filter in the frequency domain assuming infinite preview as well as the optimal filter in the time domain with preview time constraints. Examples illustrating the error reduction with optimal prefiltering are provided for simulated control and measurement scenarios.

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Blade root moment sensor failure detection based on multibeam LIDAR for fault‐tolerant individual pitch control of wind turbines

Stotsky

2014

Energy Science & Engineering

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Detection of blade root moment sensor failures is an important problem for fault-tolerant individual pitch control, which plays a key role in reduction of uneven blade loads of large wind turbines. A new method for detection of blade root moment sensor failures which is based on variations induced by a vertical wind shear is described in this paper. The detection is associated with monitoring of statistical properties of the difference between amplitudes of the first harmonic of the blade load, which is calculated in two different ways. The first method is based on processing of the load sensor signal, which contains a number of harmonics. The first harmonic is recovered via least squares estimation of the blade load signal with harmonic regressor and strictly diagonally dominant (SDD) information matrix. The second method is a model-based method of estimation of the first harmonic, which relies on the blade load model and upwind speed measurements provided by multibeam Light Detection and Ranging (LIDAR). This is a new application for future LIDAR-enabled wind turbine technologies. Moreover, adaptation of the load model in a uniform wind field is proposed. This adaptation improves accuracy of the load estimation and hence the performance of the blade load sensor failure detection method.

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The use of preview wind measurements for blade pitch control

Cited by 77 publications

References 6 publications

Correlation between Rotating LIDAR Measurements and Blade Effective Wind Speed

Correlation between Rotating LIDAR Measurements and Blade Effective Wind Speed

Reducing LIDAR wind speed measurement error with optimal filtering

Blade root moment sensor failure detection based on multibeam LIDAR for fault‐tolerant individual pitch control of wind turbines

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