power curve
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Author(s):  
Bin Yang ◽  
An-yuan Deng ◽  
Peng-fei Duan ◽  
Xiao-lei Kang ◽  
En-gang Wang

2022 ◽  
Vol 184 ◽  
pp. 473-486
Author(s):  
Rory Morrison ◽  
Xiaolei Liu ◽  
Zi Lin

2021 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Davide Astolfi ◽  
Ravi Pandit

Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.


Author(s):  
Jiaying Huang ◽  
Wangqiang Niu ◽  
Xiaotong Wang

Background: In wind power generation, the power curve can reflect the overall power generation performance of a wind turbine. How to make the power curve have high precision and be easy to interpret is a hot research topic. Objective: Because the current power curve modeling method is not comprehensive in feature selection, the simplified model and state curve of a wind turbine are introduced to avoid feature selection and make the model interpret easily. Methods: A power modeling method based on different working conditions is proposed. The wind turbine system is simplified into three physical models of blades, mechanical transmission and generator, and the energy transfer is expressed by mathematical expressions. The operation process of the wind turbine is divided into three phases: constant power (CP), constant speed (CS), and maximum power point tracking (MPPT), and the power expression of each phase is given after the analysis of state curves. Results: The effectiveness of the proposed method is verified by the supervisory control and data acquisition (SCADA) data of a 2MW wind turbine. The experimental results show that the mean absolute percentage error (MAPE) index of the proposed power modeling method based on state curve analysis is 11.56%, which indicates that the power prediction result of this method is better than that of the sixth-order polynomial regression method, whose MAPE is 13.88%. Conclusion: The results show that the proposed method is feasible with high transparency and is interpreted easily.


2021 ◽  
Vol 304 ◽  
pp. 117707
Author(s):  
Runmin Zou ◽  
Jiaxin Yang ◽  
Yun Wang ◽  
Fang Liu ◽  
Mohamed Essaaidi ◽  
...  

Author(s):  
Ayman Al‐Quraan ◽  
Hussein Al‐Masri ◽  
Mohammed Al‐Mahmodi ◽  
Ashraf Radaideh

2021 ◽  
Author(s):  
Alessandro Sebastiani ◽  
Alfredo Peña ◽  
Niels Troldborg ◽  
Alexander Meyer Forsting

Abstract. Blockage effects due to the interaction of five wind turbines in a row are investigated through both Reynolds-averaged Navier-Stokes simulations and site measurements. Since power performance tests are often carried out at sites consisting of several turbines in a row, the objective of this study is to evaluate whether the power performance of the five turbines differs from that of an isolated turbine. A number of simulations are performed, in which we vary the turbine inter-spacing (1.8, 2 and 3 rotor diameters) and the inflow angle between the incoming wind and the orthogonal line to the row (from 0° to 45°). Different values of the free-stream velocity are considered to cover a broad wind speed range of the power curve. Numerical results show consistent power deviations for all the five turbines when compared to the isolated case. The amplitude of these deviations depends on the location of the turbine within the row, the inflow angle, the inter-spacing and the power curve region of operation. We show that the power variations do not cancel out when averaging over a large inflow sector (from −45° to +45°) and find an increase in the power output of up to +1 % when compared to the isolated case. We simulate power performance ‘measurements’ with both a virtual mast and nacelle-mounted lidar and find a combination of power output increase and upstream velocity reduction, which causes an increase of +4 % of the power coefficient. We also use measurements from a real site consisting of a row of five wind turbines to validate the numerical results. From the analysis of the measurements, we also show that the power performance is impacted by the neighboring turbines. Compared to when the inflow is perpendicular to the row, the power output varies of +1.8 % and −1.8 % when the turbine is the most downwind and upwind of the line, respectively.


Author(s):  
Angela Amato ◽  
Bamba Heiba ◽  
Filippo Spertino ◽  
Gabriele Malgaroli ◽  
Alessandro Ciocia ◽  
...  

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