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

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

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

Heuristic Coupling Design-Optimization between a Variable Speed Generator and a Wind Rotor

2017 ◽  
Vol 32 ◽  
pp. 133-138
Author(s):  
Cherif Khelifi ◽  
Fateh Ferroudji ◽  
Farouk Meguellati ◽  
Khaled Koussa
Keyword(s):  
Wind Turbine ◽  
Output Power ◽  
Wind Turbines ◽  
Electricity Market ◽  
Maximum Output Power ◽  
Vertical Axis ◽  
Mechanical Power ◽  
Maximum Output ◽  
Variable Speed ◽  
Electric Generator

A high emergence of wind energy into the electricity market needs a parallel efficient advance of wind power forecasting models. Determining optimal specific speed and drive-train ratio is crucial to describe, comprehend and optimize the coupling design between a wind turbine-rotor and an electric generator (EG) to capture maximum output power from the wind. The selection of the specific design speed to drive a generator is limited. It varies from (1-4) for vertical axis wind turbines and (6-8) for horizontal axis wind turbines. Typically, the solution is an iterative procedure, for selecting the adequate multiplier ratio giving the output power curve. The latter must be relatively appreciated to inlet and nominal rated wind speeds. However, instead of this tedious and costly method, in the present paper we are developing a novel heuristic coupling approach, which is economical, easy to describe and applicable for all types of variable speed wind turbines (VSWTs). The principle method is based on the fact that the mechanical power needed of the wind turbine (WT) to drive the EG must be permanently closer to the maximum mechanical power generated by the (WT).

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

scholarly journals ВІЗУАЛЬНА МАТЕМАТИЧНА МОДЕЛЬ ЕЛЕКТРОМЕХАНІЧНОЇ СИСТЕМИ ВІТРОЕНЕРГЕТИЧНОЇ УСТАНОВКИ З АЕРОДИНАМІЧНИМ МУЛЬТИПЛІКУВАННЯМ

2018 ◽  
Vol 124 (4) ◽  
pp. 45-55
Author(s):  
Д. Г. Алексієвський ◽  
К. В. Манаєв ◽  
О. О. Панкова ◽  
А. В. Таранець ◽  
С. Л. Шмалій
Keyword(s):  
Mathematical Model ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Electromechanical System ◽  
Mechanical Power ◽  
Power Losses ◽  
Mean Values ◽  
Local Mean ◽  
Distribution Of Power

Building a visual mathematical model of the electromechanical wind power system with aerodynamic multiplication. In the process of constructing a visual mathematical model of the electromechanical system of wind turbines with aerodynamic multiplication, a mathematical apparatus for describing the system in local mean values of variables was used. Verification of the mathematical model was carried out in the MATLAB Simulink program. A visual mathematical model of the electromechanical system of wind turbines with aerodynamic multiplication is developed, which includes mechanical power losses on the shaft of the primary wind turbine. The visual mathematical model of the electromechanical system of wind power plant with aerodynamic multiplication taking into account the mechanical power losses on the shaft of the primary wind turbine with uneven distribution of power flows between the three secondary aeromechanical subsystems was proposed for the first time.

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.

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