scholarly journals Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression

2021 ◽  
Vol 11 (7) ◽  
pp. 3048
Author(s):  
Bo Jing ◽  
Zheng Qian ◽  
Hamidreza Zareipour ◽  
Yan Pei ◽  
Anqi Wang
Keyword(s):  
Quantile Regression ◽  
Wind Turbine ◽  
Wind Power ◽  
Power Distribution ◽  
Wind Farms ◽  
Power Curve ◽  
Energy Assessment ◽  
Curve Modeling ◽  
Relationship Of ◽  
Logistic Functions

The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Firstly, we combine the asymmetric absolute value function from the quantile regression (QR) cost function with logistic functions (LF), so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles without considering the prior distribution of wind power. Among them, three optimization algorithms are selected to make comparative studies. Secondly, an adaptive outlier filtering method is developed based on QRLF, which can eliminate the outliers by the symmetrical relationship of power distribution. Lastly, supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling.

scholarly journals Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

2019 ◽  
Vol 9 (22) ◽  
pp. 4930 ◽  
Author(s):  
Shenglei Pei ◽  
Yifen Li
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Power Output ◽  
Parametric Models ◽  
Wind Data ◽  
Support Vector ◽  
Power Curve ◽  
Hybrid Strategy ◽  
Curve Modeling ◽  
Power Curves

A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

Output wind power curve optimization based on design wind speed concept

2020 ◽  
pp. 0309524X1990100
Author(s):  
Cherif Khelifi ◽  
Fateh Ferroudji
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Wind Farms ◽  
Operating Conditions ◽  
Speed Ratio ◽  
Power Curve ◽  
Design Wind Speed

The output wind power curve versus wind speed is the most important characterization parameter of wind turbines. It allows quantifying and analyzing the design performances of wind turbines, monitoring its database, and controlling the operation modes and manufacturing products. Wind power curve can be used to select the proper rotor size to estimate the potential of wind energy at candidate wind sites and to assess the control device of the operating conditions. Developing model strategies for wind farms has the basic objectives such as the optimization of wind power produced and the minimization of dynamic loads to provide the best quality of output wind power at reasonable cost. Optimal design of wind turbines requires maximum-closing to the cubical output wind power curve despite technical and economic considerations. This study aims to determine the design wind speed of a wind turbine based on modeling-optimization of the output wind power curve under certain working conditions. The procedure is applied to a unit wind turbine in Gamesa wind farm (G52/850, 10.2 MW, http://www.thewindpower.net ) connected to an electrical grid located in south-west Algeria and extrapolated for other windy sites in Algeria. From simulation results, the design wind speed to inlet wind speed ratio [Formula: see text] increased from 0.35 to 7.68 once [Formula: see text] increased from 0.001 to 2.9999. Consequently, the output wind power predicted an increase of about 17.7% and an annual specific wind energy factor of about 2.55%–4% than nominal value given by the manufacturer, reducing the unit average cost of the electricity, generated by wind farms, by about 18.75%.

Wind turbine power curve modeling using radial basis function neural networks and tabu search

2021 ◽  
Vol 163 ◽  
pp. 2137-2152
Author(s):  
Despina Karamichailidou ◽  
Vasiliki Kaloutsa ◽  
Alex Alexandridis
Keyword(s):  
Neural Networks ◽  
Tabu Search ◽  
Radial Basis Function ◽  
Wind Turbine ◽  
Basis Function ◽  
Power Curve ◽  
Radial Basis ◽  
Curve Modeling

Statistical power curve modeling to estimate wind turbine power output

2019 ◽  
pp. 0309524X1989167
Author(s):  
Bharti Dongre ◽  
Rajesh Kumar Pateriya
Keyword(s):  
Wind Turbine ◽  
Statistical Power ◽  
Polynomial Regression ◽  
Smoothing Splines ◽  
Optimal Number ◽  
Specific Power ◽  
Power Curve ◽  
Curve Modeling ◽  
A Site ◽  
Power Curves

In the wind industry, the power curve serves as a performance index of the wind turbine. The machine-specific power curves are not sufficient to measure the performance of wind turbines in different environmental and geographical conditions. The aim is to develop a site-specific power curve of the wind turbine to estimate its output power. In this article, statistical methods based on empirical power curves are implemented using various techniques such as polynomial regression, splines regression, and smoothing splines regression. In the case of splines regression, instead of randomly selecting knots, the optimal number of knots and their positions are identified using three approaches: particle swarm optimization, half-split, and clustering. The National Renewable Energy Laboratory datasets have been used to develop the models. Imperial investigations show that knot-selection strategies improve the performance of splines regression. However, the smoothing splines-based power curve model estimates more accurately compared with all others.

Wind Power Curve Modeling With Asymmetric Error Distribution

2020 ◽  
Vol 11 (3) ◽  
pp. 1199-1209 ◽  
Author(s):  
Yun Wang ◽  
Qinghua Hu ◽  
Shenglei Pei
Keyword(s):  
Wind Power ◽  
Error Distribution ◽  
Power Curve ◽  
Curve Modeling

Effect of Air Density on Output Power of Wind Turbine

2013 ◽  
Vol 336-338 ◽  
pp. 1114-1117 ◽  
Author(s):  
Ying Zhi Liu ◽  
Wen Xia Liu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Output Power ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Air Density ◽  
Wind Farm Design ◽  
Farm Design

This paper elaborates the effect of wind speed on the output power of the wind farms at different locations. It also describes the correction of the power curve and shows the comparison chart of the standard power curve and the power curve after correction. In China's inland areas, wind farms altitude are generally higher, the air density is much different from the standard air density. The effect of air density on wind power output must be considered during the wind farm design.

Wind turbine power curve modeling based on hybrid fuzzy clustering algorithms

2018 ◽  
Author(s):  
Lucas L. Carneiro ◽  
Thiago P. Das Chagas ◽  
Thiago C. Vieira ◽  
Erik G. P. da Silva ◽  
Pedro Henrique S. Coutinho
Keyword(s):  
Wind Turbine ◽  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Power Curve ◽  
Curve Modeling

scholarly journals Wind Turbine Power Curve Design for Optimal Power Generation in Wind Farms Considering Wake Effect

Energies ◽  
2017 ◽  
Vol 10 (3) ◽  
pp. 395 ◽  
Author(s):  
Jie Tian ◽  
Dao Zhou ◽  
Chi Su ◽  
Mohsen Soltani ◽  
Zhe Chen ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Wind Farms ◽  
Power Curve ◽  
Wake Effect ◽  
Curve Design ◽  
Optimal Power
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