The analysis of turbulence intensity based on wind speed data in onshore wind farms

Ren, Guorui; Liu, Jinfu; Wan, Jie; Li, Fēi; Guo, Yuhan; Yu, Daren

doi:10.1016/j.renene.2018.02.080

Cited by 55 publications

(38 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar met-mast measurements have been reported for the Horns Rev 1 and Lillgrund offshore wind farms in the study of [13]. In this study, the highest value is set to 27%, which can be found in complex onshore terrain [14]. The turbulence is stochastic in nature; therefore, more than one realisation per case should be simulated for robust statistics and consistent load prediction.…”

Section: Input Spacementioning

confidence: 62%

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

et al. 2020

View full text Add to dashboard Cite

Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg–Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model.

show abstract

Section: Input Spacementioning

confidence: 62%

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

et al. 2020

View full text Add to dashboard Cite

show abstract

“…We used the multi-resolution wavelet Mallat algorithm to analyze the measured wind power data in the time domain. We could obtain the dependents between the uncertainty part and average power in the wind power signal, which can be used to describe the quantitative characterization model [14,15]:…”

Section: Time and Frequency Separation Characteristics Of Wind Powermentioning

confidence: 99%

“…Moreover, as the average power of single (full-field) wind turbine increases, the wind power fluctuation intensity tends to substantially be unchanged. And The detailed data processing and calculation fitting process can refer to [14,15].…”

Section: Time and Frequency Separation Characteristics Of Wind Powermentioning

confidence: 99%

“…Because wind turbines channel wind to generate winder power, studying the characteristics of wind speed is fundamental. In [14], a large number of actual wind speed data was used for the range of wind speed fluctuations in wind farms to correct the quantitative characterization model to develop and improve it as per the IEC standard. On this basis, literature [15] further evaluated the frequency modulation capability and explored the application of the quantitative characterization model for a range of wind speed fluctuations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Separation characteristics between time domain and frequency domain of wireless power communication signal in wind farm

Wan

Yao

Peng

et al. 2020

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

Understanding the intrinsic characteristics of wind power is important for the safe and efficient parallel function of wind turbines in large-scale wind farms. Current research on the spectrum characteristics of wind power focuses on estimation of power spectral density, particularly the structural characteristics of Kolmogorov's scaling law. In this study, the wavelet Mallat algorithm, which is different from the conventional Fourier transform, with compactly supported characteristics is used to extract the envelope of the signal and analyze the instantaneous spectral characteristics of wind power signals. Then, the theory for the change in the center frequency of the wind power is obtained. The results showed that within a certain range, the center frequency decreases as the wind power increases by using enough wind farm data. In addition, the center frequency remains unchanged when the wind power is sufficiently large. Together with the time domain characteristics of wind power fluctuation, we put forward the time-frequency separation characteristics of wind power and the corresponding physical parameter expressions, which corresponds to wind speed's amplitude and frequency modulation characteristics. Lastly, the physical connotation of the time-frequency separation characteristics of wind power from the perspective of atmospheric turbulent energy transport mechanism and wind turbine energy transfer mechanism is established.

show abstract

“…Wind turbulence intensity is an important parameter for the induced loads evaluation by the wind on the wind turbines components as well as for their design (Ren et al 2018;Dimitrov et al 2015). In the atmospheric boundary layer where wind turbines are generally installed, this phenomenon of turbulence is high (Finnigan 1994).…”

Section: Introductionmentioning

confidence: 99%

Assessment of IEC Normal Turbulence Model and Modelling of the Wind Turbulence Intensity for Small Wind Turbine Design in Tropical Area: Case of the Coastal Region of Benin

Donnou

Akpo

Hounguè

et al. 2020

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

The wind turbulence intensity observed on a site have an influence the wind turbine energy production and the lifetime of the blades. It is therefore primordial to master this parameter for the optimization of the production. So therefore, this study is interested on the modelling of the wind turbulence intensity at 10 m above the ground on the coast of Benin. Four years of wind data measured on the site of Cotonou Port Authority (PAC) from 2011 to 2014 and recorded with a temporal resolution of 10 min were used. From the transport equation of turbulent kinetic energy followed by a numerical simulation based on the Nelder-Mead algorithm developed under the Matlab software, we proposed five new models for estimating the wind turbulence intensity. The results of the different simulations reveal that four of proposed models and based on the roughness, the speed of friction and the length of Obukhov better fit the data, during the periods of January, April, June, July, August, September and December. The estimators of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) vary from (0.02; 0.01) in December to (0.09; 0.07) in August. As for the model which is a function of roughness and the wind shear coefficient (expressed only according to the wind speed), it gives better performance whatever the time of the year and the atmosphere stability conditions. The estimations errors are included between (0.02; 0.01) obtained in December and (0.08; 0.06) observed in March. A comparative study between the existing models in the literature and the best model proposed in this study showed that only this model gives the best adjustment with the data. It can therefore be used on the sites where turbulence is influenced by the roughness and the atmosphere stability. Finally, from this model a new wind turbine design class has been proposed for the site of Cotonou. It takes into account the actual levels of turbulence observed and thus allow to optimize the energy production. ©2020. CBIORE-IJRED. All rights reserved

show abstract

The analysis of turbulence intensity based on wind speed data in onshore wind farms

Cited by 55 publications

References 16 publications

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

Separation characteristics between time domain and frequency domain of wireless power communication signal in wind farm

Assessment of IEC Normal Turbulence Model and Modelling of the Wind Turbulence Intensity for Small Wind Turbine Design in Tropical Area: Case of the Coastal Region of Benin

Contact Info

Product

Resources

About