Analysis and comparison of turbulence models on wind turbine performance using SCADA data and machine learning technique

Figures & data

Figure 1. Fine Tree Model.

Figure 2. Support Vector Machine Classification.

Table 1. Summary of Related Works

Turbulence Model	Brief Methodology	Highlights	Limitations
Kaimal and Mann Models (Dong et al., 2021)	Investigate the adoption of lidar-assisted control in wind turbine design.	Lidar measurement coherence is lower for the Mann turbulence model compared to the Kaimal Model.	The algorithm is not capable of using SCADA data in the generation of the model.
Eddy-viscosity turbulence model (Rezaeiha et al., 2019)	A comparative analysis of commonly used eddy-viscosity turbulence models.	Results showed that there is a high sensitivity of simulation results to the turbulence model.	The algorithm is not capable of prediction-based turbulence modeling.
Spalart-Allmaras and k-w SST model (Alom et al., 2021)	Presents a numerical study that assessed the effect of surface roughness distribution.	Both turbulence models gave comparable results except for the high angles of attack of the drag.	The algorithm is not capable of using SCADA data for model generation.

Figure 3. Research Framework of the Study.

Table 2. General Information of Wind Turbine Farm

Location	Taiwan
Number of Wind Turbine Heads	Thirteen (13)
Nominal Power Capacity of Wind Turbine	2 MW
Transmission Connection	Direct-Drive
Number of Parameters Used	10
Downloaded Data Interval	10 minutes averaged

Table 3. Data Filter Used in the Study

Data Filter	Notation
Missing Data Filter	delete ${\mathit{x}}_{\mathit{t}}$ if $\left\{{\mathit{x}}_{\mathit{t}}|{\mathit{x}}_{\mathit{ti}}=\mathit{not}\mathit{available}\right\}$
General Boundary Filter	delete ${\mathit{x}}_{\mathit{t}}$ if $\{\left({\mathit{x}}_{\mathit{t}}|{\mathit{x}}_{\mathit{ti}}\mathit{\hspace{0.17em}}>\mathit{upper}\mathit{bound}\right)$ or $\left({\mathit{x}}_{\mathit{t}}|{\mathit{x}}_{\mathit{ti}}\mathit{\hspace{0.17em}}<\mathit{lower}\mathit{bound}\right)\}$
Efficiency Filter	delete ${\mathit{x}}_{\mathit{t}}$ if $\{\left({\mathit{x}}_{\mathit{t}}|{\mathit{x}}_{\mathit{ti}}\mathit{\hspace{0.17em}}>\mathit{upper}\mathit{limit}\right)$ or $\left({\mathit{x}}_{\mathit{t}}|{\mathit{x}}_{\mathit{ti}}\mathit{\hspace{0.17em}}<\mathit{lower}\mathit{limit}\right)\}$

Table 4. Selected Parameters

Generator System	Nacelle System	Rotor System
Wind Speed Generator Speed Generator Voltage Generator Current	Wind Speed Resultant Force Wind Direction Atmospheric Pressure	Wind Speed Rotor Speed Rotor Angle Blade Rotor Angle

Figure 4. Pseudo Code of the Algorithm.

Figure 5. MAE of Turbine Head 5 Generator in Different Models.

Figure 6. MSE of Turbine Head 5 Generator in Different Models.

Figure 7. Wind Speed Data in December 2021.

Figure 8. MSE of all Wind Turbine Heads in the Generator.

Figure 9. MSE of all Wind Turbine Heads in the Rotor.

Figure 10. MSE of all Wind Turbine Heads in the Nacelle.

Dong, L., Lio, W. H., & Simley, E. (2021). On turbulence models and lidar measurements for wind turbine control. Wind Energy Science, 6(6), 1491–1500. https://doi.org/10.5194/wes-6-1491-2021

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Rezaeiha, A., Montazeri, H., & Blocken, B. (2019). On the accuracy of turbulence models for CFD simulations of vertical axis wind turbines. Energy, 180, 838–857. https://doi.org/10.1016/j.energy.2019.05.053

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Alom, N., Saha, U. K., & Dewan, A. (2021). In the quest of an appropriate turbulence model for analyzing the aerodynamics of a conventional Savonius (S-type) wind rotor. Journal of Renewable and Sustainable Energy, 13(2), 023313. https://doi.org/10.1063/5.0034362

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