Revisiting the modeling of wind turbine power curves using neural networks and fuzzy models: an application-oriented evaluation

2021 ◽  
Author(s):  
Guilherme A. Barreto ◽  
Igor S. Brasil ◽  
Luis Gustavo M. Souza
Keyword(s):  
Neural Networks ◽  
Wind Turbine ◽  
Fuzzy Models ◽  
Power Curves

Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks

2019 ◽  
Vol 140 ◽  
pp. 477-492 ◽  
Author(s):  
G. Ciulla ◽  
A. D’Amico ◽  
V. Di Dio ◽  
V. Lo Brano
Keyword(s):  
Neural Networks ◽  
Wind Turbine ◽  
Real World ◽  
Power Curves

Stochastic Models of Particle Trajectories in Turbulent Atmosphere Flows by Using the LANGEVIN Equations

Proceedings ◽  
2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Youssra El Qasemy ◽  
Abdelfatah Achahbar ◽  
Abdellatif Khamlichi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Classical Method ◽  
Degradation Mechanism ◽  
Transformation Process ◽  
Langevin Equations ◽  
Turbulence Data ◽  
Power Curves ◽  
And Diffusion ◽  
Fluctuating Wind

The stochastic behavior of wind speed is a particular characteristic of wind energy production, which affects the degradation mechanism of the turbine, resulting in stochastic charging on the wind turbine. A model stochastic is used in this study to evaluate the efficiency of wind turbine power of whatever degree given fluctuating wind turbulence data. This model is based on the Langevin equations, which characterize, by two coefficients, drift and diffusion functions. These coefficients describe the behavior of the transformation process from the input wind speed to the output data that need to be determined. For this present work, the computation of drift and diffusion functions has been carried out by using the stochastic model to assess the output variables in terms of the torque and power curves as a function of time, and it is compared by the classical method. The results show that the model stochastic can define the efficiency of wind turbine generation more precisely.

Data Confidence Applied to Wind Turbine Power Curves

2020 ◽  
Author(s):  
Roberto Milton Scheffel ◽  
Jose Luis Conradi Hoffmann ◽  
Leonardo Passig Horstmann ◽  
Gustavo Medeiros de Araujo ◽  
Antonio Augusto Frohlich ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Power Curves

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

Development and Field Testing of an Inclined Flanged Compact Diffuser for a Micro Wind Turbine

2014 ◽  
Author(s):  
Sandip Kale ◽  
S. N. Sapali
Keyword(s):  
Wind Turbine ◽  
Wind Velocity ◽  
Wind Turbines ◽  
Energy Production ◽  
Cost Effective ◽  
Field Testing ◽  
International Electrotechnical Commission ◽  
Average Wind Speed ◽  
Average Wind ◽  
Power Curves

Micro wind turbines installed in various applications, experience average wind speed for most of the time during operations. Power produced by the wind turbine is proportional to the cubic power of the wind velocity and a small increase in wind velocity results increases power output significantly. The approach wind velocity can be increased by covering traditional wind turbine with a diffuser. Researchers are continuously working to develop a compact, lightweight, cost effective and feasible diffuser for wind turbines. The present work carried out to develop a diffuser with these stated objectives. A compact, lightweight inclined flanged diffuser developed for a micro wind turbine. Bare micro wind turbine and wind turbine covered with developed efficient inclined flanged diffuser tested in the field as per International Electrotechnical Commission (IEC) standards and results presented in the form of power curves. The prediction of annual energy production for both wind turbines determined as per IEC standards.

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