Prediction of the Wind Turbine Performance by Using a Modiﬁed BEM Theory with an Advanced Brake State Model

Arramach, Jallal; Boutammachte, Noureddine; Bouatem, Abdelfattah; Mers, Ahmed Al

doi:10.1016/j.egypro.2017.07.033

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…The BEM approach gives a good approximation of wind turbine behaviour but has its shortcomings in its basic form [59]. Therefore, several analytical models were developed and integrated within a BEM model to account for, e.g., tip losses [60,61], dynamic stall induced by boundary layer separation [62,63], blade flexibility [64,65] and disturbed wind flow caused by the nacelle or tower [66].…”

Section: Pmsgmentioning

confidence: 99%

“…Nevertheless, it allows combining the merits of different modelling techniques eventually leading to a more realistic virtual replica. Computational Fluid Dynamics [69] FEM structural blade model [70][71][72] Large Eddy Simulation (LES) [78][79][80] FEM model of turbine shaft [103] FEM model of the tower and support structure [89,90] Electromagnetic FEM [109][110][111] Dynamic switching models [127][128][129] Conduction and switching loss models [130,131] Transient wide-bandgap component models [132] Full pitch drivetrain models [150][151][152][153] Full yaw drivetrain models [154,155] Blade-Element Momentum [57] Extensions -Tip losses [60,61] -Dynamic stall [62,63] -Blade flexibility [64,65] -Tower and nacelle flow disturbance [66] -Gaussian [82] or Curl [83] wake model Surrogate models [73][74][75] Multi-body drivetrain model [101,102] Multi-body tower and foundation model [84][85][86]<...>…”

Section: Virtual Replicamentioning

confidence: 99%

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Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

et al. 2021

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The Industry 4.0 concept of a Digital Twin will bring many advantages for wind energy conversion systems, e.g., in condition monitoring, predictive maintenance and the optimisation of control or design parameters. A virtual replica is at the heart of a digital twin. To construct a virtual replica, appropriate modelling techniques must be selected for the turbine components. These models must be chosen with the intended use case of the digital twin in mind, finding a proper balance between the model fidelity and computational load. This review article presents an overview of the recent literature on modelling techniques for turbine aerodynamics, structure and drivetrain mechanics, the permanent magnet synchronous generator, the power electronic converter and the pitch and yaw systems. For each component, a balanced overview is given of models with varying model fidelity and computational load, ranging from simplified lumped parameter models to advanced numerical Finite Element Method (FEM)-based models. The results of the literature review are presented graphically to aid the reader in the model selection process. Based on this review, a high-level structure of a digital twin is proposed together with a virtual replica with a minimum computational load. The concept of a multi-level hierarchical virtual replica is presented.

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Section: Pmsgmentioning

confidence: 99%

Section: Virtual Replicamentioning

confidence: 99%

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

et al. 2021

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“…acting on the blade in the streamtube. In the calculation process, Brake State Model (BSM) is often employed to compute the axial and tangential induction factors (a and a , respectively) [22,23,24,25]. Despite its simplicity and computationally cheap compared with high fidelity modelling method such as Computational Fluid Dynamic (CFD) simulation, BEM is able to produce fairly accurate performance prediction within 20% uncertainty [26,27].…”

Section: Introductionmentioning

confidence: 99%

A method for fast and accurate prediction of wind turbine thrust coefficients using classical momentum theory and power curve

Tai,

Tan,

Moey

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The planning and development of windfarms require accurate prediction of the thrust coefficient (cT ) of wind turbines, which significantly affects the downstream wake. Traditional methods, such as blade element momentum theory (BEMT), often necessitate detailed geometric information of wind turbines for cT computation, information that is not frequently available, especially in the early stages of windfarm planning. This paper aims to address this challenge by presenting a novel and efficient approach to predict cT for horizontal-axis wind turbines (HAWTs). The proposed method integrates classical momentum theory with power curve data to estimate the average axial induction factor (a), thereby enabling the calculation of cT without requiring detailed geometric information of HAWTs. The method was validated against thirty-five existing pitch-controlled HAWTs, with R2 values ranging from 0.9604 to 0.9989. This validation confirms the accuracy of the method, making it a viable alternative to traditional techniques that demand comprehensive wind turbine geometric details. The method has demonstrated both rapidity and precision in cT computation for turbine wake analysis, ensuring high levels of prediction accuracy and potentially lowering the barrier to entry for windfarm development. Unlike existing models predominantly focused on wind turbine power curves, cT modelling has largely been overlooked. This study makes a unique contribution to the field by proposing a novel method for cT prediction, thereby filling a critical gap in windfarm planning and development. However, while the study shows promising results, further research is warranted to explore its applicability in diverse windfarm scenarios and turbine configurations.

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“…In order to consider the centrifugal pumping effect caused by the radial flow of the blades (Himmel Kamp effect), Arramach et al established a model by using the aerodynamic coefficients of the pre-stall and post-stall regions. The numerical calculation program was used to predict the WT aerodynamic forces and power (Arramach et al, 2017). Because the minimum WT flow velocities of standard airfoils were about 7 m/s at stall angles of attack, Yavuz et al (2015) chose NACA4412-NACA6411 slat-airfoil arrangement to investigate the potential performance improvements.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of wind turbines based on SCADA data

Wen

Xie

2020

Wind Engineering

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This paper proposed a new hybrid performance evaluation model of wind turbine (WT) based on Morlet wavelet neural network (MWNN), self-organizing map (SOM) neural network and Markov chain. All the data are collected from supervisory control and data acquisition (SCADA) system. Firstly, the WT power prediction model is presented based on Morlet neural network and the number of input variables, learning rates, and hidden layer nodes are discussed to obtain the optimal one. The prediction deviations under the optimal model are calculated accordingly. Then, the wind power deviations are clustered by SOM neural network, and classified by Markov chain. Two wind turbine anomaly indices (AI), which are represented by AI1 and AI2, are proposed to analyze the WTs’ performance. The results show that the proposed indices could accurately evaluate the operation state of the WTs and have important reference value for improving the operation and maintenance efficiency of the wind farm concerned.

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Prediction of the Wind Turbine Performance by Using a Modiﬁed BEM Theory with an Advanced Brake State Model

Cited by 11 publications

References 21 publications

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

A method for fast and accurate prediction of wind turbine thrust coefficients using classical momentum theory and power curve

Performance evaluation of wind turbines based on SCADA data

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