Importance of lidar measurement timing accuracy for wind turbine control

Dunne, Fiona; Pao, Lucy Y.; Schlipf, David; Scholbrock, Andrew

doi:10.1109/acc.2014.6859337

Cited by 26 publications

(19 citation statements)

References 10 publications

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“…Values of coherence range from 0 to 1, where a value of 0 indicates that the two signals are completely uncorrelated while a value of 1 indicates perfect correlation. A reasonable value of coherence for representing the boundary between frequency components that are uncorrelated and correlated is 0.5 [51]. Therefore, a useful metric for indicating measurement quality is the frequency at which the measurement coherence crosses below 0.5:…”

Section: Metrics For Assessing Measurement Qualitymentioning

confidence: 99%

Optimizing Lidars for Wind Turbine Control Applications—Results from the IEA Wind Task 32 Workshop

Simley¹,

Fürst

Haizmann

et al. 2018

Remote Sensing

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IEA Wind Task 32 serves as an international platform for the research community and industry to identify and mitigate barriers to the use of lidars in wind energy applications. The workshop "Optimizing Lidar Design for Wind Energy Applications" was held in July 2016 to identify lidar system properties that are desirable for wind turbine control applications and help foster the widespread application of lidar-assisted control (LAC). One of the main barriers this workshop aimed to address is the multidisciplinary nature of LAC. Since lidar suppliers, wind turbine manufacturers, and researchers typically focus on their own areas of expertise, it is possible that current lidar systems are not optimal for control purposes. This paper summarizes the results of the workshop, addressing both practical and theoretical aspects, beginning with a review of the literature on lidar optimization for control applications. Next, barriers to the use of lidar for wind turbine control are identified, such as availability and reliability concerns, followed by practical suggestions for mitigating those barriers. From a theoretical perspective, the optimization of lidar scan patterns by minimizing the error between the measurements and the rotor effective wind speed of interest is discussed. Frequency domain methods for directly calculating measurement error using a stochastic wind field model are reviewed and applied to the optimization of several continuous wave and pulsed Doppler lidar scan patterns based on commercially-available systems. An overview of the design process for a lidar-assisted pitch controller for rotor speed regulation highlights design choices that can impact the usefulness of lidar measurements beyond scan pattern optimization. Finally, using measurements from an optimized scan pattern, it is shown that the rotor speed regulation achieved after optimizing the lidar-assisted control scenario via time domain simulations matches the performance predicted by the theoretical frequency domain model.

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Section: Metrics For Assessing Measurement Qualitymentioning

confidence: 99%

Optimizing Lidars for Wind Turbine Control Applications—Results from the IEA Wind Task 32 Workshop

Simley¹,

Fürst

Haizmann

et al. 2018

Remote Sensing

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“…Thus, this study gives confidence that the proposed model can accurately represent the important lidar properties and it can be used to optimize the lidar focus point positions to maximize the coherence between lidar and turbine. A common parameter used in the lidar optimization is the wavenumber where the coherence drops to a value of 0.5 (Dunne et al, 2014), which can be calculated precisely by the model.…”

Section: Resultsmentioning

confidence: 99%

Lidar Estimation of Rotor-Effective Wind Speed – An Experimental Comparison

Held¹,

Mann²

2019

Preprint

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Abstract. Lidar systems have the potential of alleviating structural loads on wind turbines by providing a preview of the incoming wind field to the control system. For a collective pitch controller the important quantity of interest is the rotor-effective wind speed (REWS). In this study, we present a model of the coherence between the REWS and its estimate from continuous-wave nacelle-mounted lidar systems. The model uses the spectral tensor definition of the Mann model. Model results were compared to field data gathered from a 2- and 4-beam nacelle lidar mounted on a wind turbine. The comparison shows close agreement for the coherence and the data fits better to the proposed model than to a model based on the Kaimal turbulence model, which underestimates the coherence. Inflow conditions with larger length scales led to a higher coherence between REWS and lidar estimates than inflow turbulence of smaller length scale. When comparing the two lidar systems, it was shown that the 4-beam lidar is able to resolve small turbulent structures with a higher degree of coherence. Further, the advection speed by which the turbulent structures are transported from measurement to rotor plane can be estimated by 10 minute averages of the lidar estimation of REWS. The presented model can be used as a computationally efficient tool to optimize the position of the lidar focus points in order to maximize the coherence.

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“…around 1 s: Feed forward control is used to compensate wind changes to reduce structural loads. For example, the blade pitch, the rotor-effective wind speed is needed only a short time before the wind reaches the rotor to overcome the pitch actuator dynamics [13,14].…”

Section: Wind Turbine and Wind Farm Controlmentioning

confidence: 99%

Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36

et al. 2019

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The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.

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Importance of lidar measurement timing accuracy for wind turbine control

Cited by 26 publications

References 10 publications

Optimizing Lidars for Wind Turbine Control Applications—Results from the IEA Wind Task 32 Workshop

Optimizing Lidars for Wind Turbine Control Applications—Results from the IEA Wind Task 32 Workshop

Lidar Estimation of Rotor-Effective Wind Speed – An Experimental Comparison

Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36

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