Study on Obtaining Real Power Curve of Wind Turbines by Using SCADA Data

2021 ◽  
Author(s):  
Juchuan Dai ◽  
Huifan Zeng ◽  
Fan Zhang ◽  
Huanguo Chen ◽  
Mimi Li
Keyword(s):  
Wind Turbines ◽  
Power Curve ◽  
Real Power

scholarly journals Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1105 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Standard Deviation ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Curve Analysis ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Features Selection ◽  
Power Curve ◽  
Working Parameters ◽  
Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

scholarly journals An algorithm for practical power curve estimation of wind turbines

2018 ◽  
Vol 4 (1) ◽  
pp. 93-102 ◽  
Author(s):  
Milad Javadi ◽  
◽  
Alexander M. Malyscheff ◽  
Di Wu ◽  
Chongqing Kang ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Power Curve ◽  
Curve Estimation

Experimental power curve of small-size wind turbines in turbulent urban environment

2015 ◽  
Vol 154 ◽  
pp. 112-121 ◽  
Author(s):  
Luisa C. Pagnini ◽  
Massimiliano Burlando ◽  
Maria Pia Repetto
Keyword(s):  
Urban Environment ◽  
Wind Turbines ◽  
Power Curve

scholarly journals A Novel Power Curve Prediction Method for Horizontal-Axis Wind Turbines Using Artificial Neural Networks

2021 ◽  
Vol 118 (3) ◽  
pp. 507-516
Author(s):  
Vin Cent Tai ◽  
Yong Chai Tan ◽  
Nor Faiza Abd Rahman ◽  
Chee Ming Chia ◽  
Mirzhakyp Zhakiya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Turbines ◽  
Prediction Method ◽  
Horizontal Axis ◽  
Power Curve ◽  
Horizontal Axis Wind Turbines ◽  
Artificial Neural

Nacelle Anemometry for the Electric Power Prediction System

2007 ◽  
Author(s):  
J. Kuroda ◽  
M. Iida ◽  
C. Arakawa
Keyword(s):  
Numerical Simulation ◽  
Flow Field ◽  
Electric Power ◽  
Wind Velocity ◽  
Wind Turbines ◽  
Power Output ◽  
Measurement Data ◽  
Power Curve ◽  
Prediction System ◽  
Power Prediction

The purpose of this study is to establish the nacelle anemometry for the wind forecast. This paper describes the problems of the meteorological anemometry and the nacelle anemometry based on measurement data in Japan. In the results, it is shown that wind velocity measured at the mast is less related with power output of wind turbines than measured at the nacelle. However it seems power curve referred to the nacelle anemometer to shift to lower wind velocity. Then the numerical simulation is carried out for the flow field around the nacelle and the blade as the first step.

Influence of the number of blades on the mechanical power curve of wind turbines

2009 ◽  
Vol 1 (07) ◽  
pp. 825-830 ◽  
Author(s):  
M. Predescu ◽  
A. Bejinariu ◽  
O. Mitroi ◽  
A. Nedelcu
Keyword(s):  
Wind Turbines ◽  
Mechanical Power ◽  
Power Curve
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