Atmospheric turbulence affects wind turbine nacelle transfer functions

Martin, Clara M. St.; Lundquist, Julie K.; Clifton, Andrew; Poulos, Gregory S.; Schreck, S.

doi:10.5194/wes-2-295-2017

Cited by 25 publications

(12 citation statements)

References 22 publications

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“…These measurements occur behind the blades of a turbine and the wind speeds are consequently impacted by wake effects. St. Martin et al (2017) showed that there is a substantial difference at wind speeds of greater than 9 m s −1 and accounted for the wake effects of using nacelle wind speeds for power conversion by applying a fifth-order polynomial fit between an upwind met tower and the nacelle wind speed data.…”

Section: Shagaya Wind Farmmentioning

confidence: 99%

The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

McCandless

Haupt

2019

Wind Energ. Sci.

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Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared with a super-turbine power conversion for a hypothetical wind farm of 100 2 MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with a 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with a 2 m s−1 standard deviation of wind speeds as a consequence of Jensen's inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen's inequality. These significant differences can lead to wind power forecasters overestimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicate that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen's inequality.

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Section: Shagaya Wind Farmmentioning

confidence: 99%

The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

McCandless

Haupt

2019

Wind Energ. Sci.

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“…As regards the monitoring of wind turbine performance, the main issue is that a wind turbine has a multivariate dependence on environmental conditions [12,13] and working parameters. SCADA control systems typically record and store (with some minutes of sampling time) the main working parameters and also internal temperatures collected at meaningful wind turbine sub-components.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Operation Curves Modelling Techniques

Astolfi

2021

Electronics

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Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity.

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“…Earlier work shows that the NTF-based FSWS estimation achieves an R 2 (coefficient of determination) of 0.98 for a linear regression 11 and 0.99 for a fifth-order polynomial fitting. 12 The annual energy production (AEP) calculated by the NTF-based FSWS estimation yields an error less than 1% than that based on met-tower measurement. 12 In particular, the use of spinner anemometer installed at the hub cover has shown more accurate and reliable FSWS estimation for upwind turbines.…”

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confidence: 99%

“…12 The annual energy production (AEP) calculated by the NTF-based FSWS estimation yields an error less than 1% than that based on met-tower measurement. 12 In particular, the use of spinner anemometer installed at the hub cover has shown more accurate and reliable FSWS estimation for upwind turbines. 13 In this paper, we propose an improved wind turbine region-2 ESC controller framework with an estimated C P as feedback, where the FSWS estimation is based on the measurement of nacelle-mounted anemometer.…”

mentioning

confidence: 99%

“…This proof-of-concept study is performed with control simulation using FAST, the wind turbine aeroelastic simulation software by NREL. In this study, on the basis of the acceptance of the validity of the NTF method in the existing literatures, [10][11][12][13] we assume that the FSWS can be obtained with a no-bias estimation but contaminated with some white noise whose variance is similar to that of the regression errors in the aforementioned literatures. A comparative study later reveals that the improvement in ESC performance will not be compromised by a moderate level error in the FSWS estimation.…”

mentioning

confidence: 99%

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Nacelle anemometer measurement‐based extremum‐seeking wind turbine region‐2 control for improved convergence in fluctuating wind

Xiao

2020

Wind Energy

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For region‐2 operation of wind turbines in practice, the optimal torque gain can deviate from the nominal value because of the variations in turbine and wind conditions. The extremum‐seeking control (ESC) has shown its potential as a model‐free region‐2 control solution in some recent work; however, the ESC with rotor power feedback suffers from undesirable convergence under fluctuating wind. In this paper, we propose to use an estimated power coefficient as the objective function for the torque‐gain ESC, where the hub‐height free‐stream wind speed (FSWS) is estimated with the nacelle anemometer measurement on the basis of the so‐called nacelle transfer function (NTF) between the nacelle anemometer and met‐tower measurement. A sensitivity analysis is performed to quantify the impact of the wind speed estimation error on the estimation of power coefficient. An ESC integrated interregion switching scheme is proposed to avoid the load increase. Simulation results show that, compared with the power feedback‐based ESC, the proposed method can greatly improve the convergence rate of ESC under fluctuating wind, even under relatively large wind speed estimation error. Evaluation for the fatigue loads of wind turbine shows that the proposed control strategy induces mild increase of the wind turbine load.

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Atmospheric turbulence affects wind turbine nacelle transfer functions

Cited by 25 publications

References 22 publications

The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

Wind Turbine Operation Curves Modelling Techniques

Nacelle anemometer measurement‐based extremum‐seeking wind turbine region‐2 control for improved convergence in fluctuating wind

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