Direction-Dependent Power Curve Modeling for Multiple Interacting Wind Turbines

You, Mingdi; Liu, Bingjie; Byon, Eunshin; Huang, Shuai; Jin, Jesse S.

doi:10.1109/tpwrs.2017.2737529

Cited by 28 publications

(6 citation statements)

References 18 publications

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“…Specifically, the wakes from upwind turbines can greatly affect the power production of downstream turbines, and this effect depends strongly on the wind direction 3 . Generally, downstream turbines produce less power compared to upwind turbines, but changes in wind direction can cause heterogeneity in the power curve of each turbine such that some upstream turbines can become downstream turbines 4 . Porté-Agel et al 5 presented a study about the effects of wind direction on turbine wakes and power losses at a large wind farm.…”

Section: Introductionmentioning

confidence: 99%

Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling

Rad

Bekker

Arashi

2022

Sci Rep

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Wind energy production depends not only on wind speed but also on wind direction. Thus, predicting and estimating the wind direction for sites accurately will enhance measuring the wind energy potential. The uncertain nature of wind direction can be presented through probability distributions and Bayesian analysis can improve the modeling of the wind direction using the contribution of the prior knowledge to update the empirical shreds of evidence. This must align with the nature of the empirical evidence as to whether the data are skew or multimodal or not. So far mixtures of von Mises within the directional statistics domain, are used for modeling wind direction to capture the multimodality nature present in the data. In this paper, due to the skewed and multimodal patterns of wind direction on different sites of the locations understudy, a mixture of multimodal skewed von Mises is proposed for wind direction. Furthermore, a Bayesian analysis is presented to take into account the uncertainty inherent in the proposed wind direction model. A simulation study is conducted to evaluate the performance of the proposed Bayesian model. This proposed model is fitted to datasets of wind direction of Marion island and two wind farms in South Africa and show the superiority of the approach. The posterior predictive distribution is applied to forecast the wind direction on a wind farm. It is concluded that the proposed model offers an accurate prediction by means of credible intervals. The mean wind direction of Marion island in 2017 obtained from 1079 observations was 5.0242 (in radian) while using our proposed method the predicted mean wind direction and its corresponding $$95\%$$ 95 % credible interval based on 100 generated samples from the posterior predictive distribution are obtained 5.0171 and (4.7442, 5.2900). Therefore, our results open a new approach for accurate prediction of wind direction implementing a Bayesian approach via mixture of skew circular distributions.

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Section: Introductionmentioning

confidence: 99%

Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling

Rad

Bekker

Arashi

2022

Sci Rep

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“…A comprehensive review about wind turbine power curve modeling is given in [4]. Several aspects have been addressed as regards the mismatch between nominal and real-world power curves [5]: the effect of the wind direction [6], in relation to the terrain and wind farm layout, of the wind shear and vertical wind profile [2], or of the turbulence intensity [7]. Currently, it is therefore widely accepted that the power curve of a wind turbine is strongly site-dependent [8].…”

Section: Introductionmentioning

confidence: 99%

Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

Astolfi

2021

Machines

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Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated.

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“…Because wind turbines in downwind rows are impacted by the wakes of upstream turbines, performance differences between wind turbines are created. [8] adds wind direction to the power curve model and gets improved model accuracy. These two papers use additional input parameters in their power curve models but do not give an overall analysis of the factors that influence wind turbine power output.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Power Curve Modeling and Monitoring With Gaussian Process and SPRT

Guo

Infield

2020

IEEE Trans. Sustain. Energy

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The wind turbine power curve is an important indicator of the performance of a wind turbine. Modeling and monitoring the power curve can detect wind turbine operating abnormalities and degradation in a timely manner. This paper firstly points out the drawbacks of the standard binned power curve modeling method of IEC-61400-12-1. Multiple factors that influence the wind energy capture and power output of a wind turbine are analyzed in detail and used as the power curve model inputs. A multivariable power curve model is constructed with a modified Cholesky decomposition Gaussian Process (GP) and validated using wind turbine SCADA data. A Sequential Probability Ratio Test (SPRT) with two groups of hypotheses is introduced to analyze and detect abnormal changes in GP power curve prediction residuals and thus detect abnormal operation. In order to locate failed components when an alarm is identified, longitudinal and transverse data comparisons are proposed to check the operation of specific components. The modeling and monitoring methods proposed in this paper successfully identify faults and locate the faulty component for two wind turbines with anemometer failure and pitch system failure respectively.

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Direction-Dependent Power Curve Modeling for Multiple Interacting Wind Turbines

Cited by 28 publications

References 18 publications

Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling

Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling

Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

Wind Turbine Power Curve Modeling and Monitoring With Gaussian Process and SPRT

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