An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

Mittelmeier, Niko; Blodau, Tomas; Steinfeld, Gerald; Rott, Andreas; Kühn, Martin

doi:10.1088/1742-6596/753/3/032052

Cited by 4 publications

(9 citation statements)

References 1 publication

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“…Due to the distance between sensor and reference, the result of the verification can be corrupted by the spatial variance of the second-order moment of wind speed. This phenomenon is discussed in the present work on the example of the verification case in Mittelmeier et al (2016). Here turbine-based measurements of turbulence intensity are verified with reference measurements at an adjacent meteorological mast.…”

Section: Introductionmentioning

confidence: 78%

“…In wind farm control, recent flow models Göçmen et al, 2018;Niayifar and Porté-Agel, 2016) increasingly use measurements of turbulence intensity as input, particularly ambient turbulence intensity. An approach to obtain an estimate of ambient turbulence intensity is to average turbulence intensity measured at upstream turbines of the wind farm.…”

Section: Wind Farm Controlmentioning

confidence: 99%

“…Advanced wind farm controllers typically employ models of wind farm operation (Gebraad et al, 2016; to predict the impact of the control on the flow within the wind farm. Increasingly, turbulence intensity is used as input to flow models (Niayifar and Porté-Agel, 2016;Göçmen et al, 2018). The most common turbulence-related input is ambient turbulence intensity, which is typically obtained from measurements at upstream turbines of the wind farm.…”

Section: Introductionmentioning

confidence: 99%

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Mitigating impact of spatial variance of turbulence in wind energy applications

Kazda

Mann

2020

Wind Energ. Sci.

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Abstract. For the first time an analytical solution for the quantification of the spatial variance of the second-order moment of correlated wind speeds was developed in this work. The spatial variance is defined as random differences in the sample variance of wind speed between different points in space. The approach is successfully verified using simulation and field data. The impact of the spatial variance on three selected applications relevant to the wind energy sector is then investigated including mitigation measures. First, the difference of the second-order moment between front-row wind turbines of Lillgrund wind farm is investigated. The variance of the difference ranges between 25 % and 48 % for turbulence intensities ranging from 7 % to 10 % and a sampling period of 10 min. It is thus suggested to use the second-order moment measured at each individual turbine as input to flow models of wind farm controllers in order to mitigate random error. Second, the impact of the spatial variance of the measured second-order moment on the verification of wind turbine performance is investigated. Misalignment between the mean wind direction and the line connecting the meteorological mast and wind turbine is observed to result in an additional random error in the observed second-order moment of wind speed. In the investigated conditions the random error was up to 34 %. Such a random error adds uncertainty to the turbulence intensity-based classification of the fatigue loads and power output of a wind turbine. To mitigate the random error, it is suggested to either filter the measured data for low angles of misalignment or quantify wind turbine performance using the ensemble-averaged measurements of the same wind conditions. Third, the verification of sensors in wind farms was investigated with respect to the impact of distant reference measurements. In the case of a misalignment between the wind direction and the line connecting sensor and reference, an increased random error will hamper the comparison of the measured second-order moments. The suggested mitigation measures are equivalent to those for the verification of turbine performance.

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Section: Introductionmentioning

confidence: 78%

Section: Wind Farm Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mitigating impact of spatial variance of turbulence in wind energy applications

Kazda

Mann

2020

Wind Energ. Sci.

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“…The development of new sensors for application in wind farms can involve comparing these with distant reference measurements. For example, the use of rotor-effective wind speed to quantify turbulence intensity at a wind turbine was compared to measurements at adjacent meteorological masts by Mittelmeier et al (2016). As shown in the previous section in Figure 5, a small misalignment of the wind direction with the line connecting mast and wind turbine can result in a large random error in the measured second-order moment.…”

Section: Sensor Verification In Wind Farmsmentioning

confidence: 99%

“…The third application area investigated in the present work is the verification of sensors in wind farms. For example in Mittelmeier et al (2016), the use of the rotor-effective wind speed is investigated for the measurement of turbulence intensity.…”

mentioning

confidence: 99%

Mitigating Impact of Spatial Variance of Turbulence in Wind Energy Applications

Kazda¹,

Mann²

2019

Preprint

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Abstract. The first analytical solution for the quantification of the spatial variance of the second-order moment of correlated wind speeds was developed in this work. The spatial variance is defined as random differences in the sample variance of wind speed between different points in space. The approach is successfully verified using simulation and field data. The impact of the spatial variance on three selected applications relevant to the wind energy sector is then investigated including mitigation measures. First, the difference of the second-order moment between front-row wind turbines of Lillgrund wind farm is investigated. The variance of the difference ranges between 25 % and 48 % for turbulence intensities ranging from 7 % to 10 % and a sampling period of 10 min. It is thus suggested to use the second-order moment measured at each individual turbine as input to flow models of wind farm controllers in order to mitigate random error. Second, the impact of the spatial variance of the measured second-order moment on the verification of wind turbine performance is investigated. Misalignment between the mean wind direction and the line connecting the meteorological mast and wind turbine is observed to result in an additional random error in the observed second-order moment of wind speed. In the investigated conditions the random error was up to 34 %. Such random error adds uncertainty to the turbulence intensity-based classification of the fatigue loads and power output of a wind turbine. To mitigate the random error it is suggested to either filter the measured data for low angles of misalignment, or to quantify wind turbine performance using the ensemble averaged measurements of the same wind conditions. Third, the verification of sensors in wind farms was investigated with respect to the impact of distant reference measurements. In case of a misalignment between the wind direction and the line connecting sensor and reference, an increased random error will hamper the comparison of the measured second-order moments. The suggested mitigation measures are equivalent to those for the verification of turbine performance.

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Investigation of wind turbine wakes and wake recovery in a tandem configuration using actuator line model with LES

Onel

Tuncer

2021

Computers & Fluids

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An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

Cited by 4 publications

References 1 publication

Mitigating impact of spatial variance of turbulence in wind energy applications

Mitigating impact of spatial variance of turbulence in wind energy applications

Mitigating Impact of Spatial Variance of Turbulence in Wind Energy Applications

Investigation of wind turbine wakes and wake recovery in a tandem configuration using actuator line model with LES

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