Wind farm wake modeling using NWTC design codes

2016 ◽  
Vol 41 (1) ◽  
pp. 62-73 ◽  
Author(s):  
Yujia Hao ◽  
Matthew A Lackner
Keyword(s):  
Wind Field ◽  
Wind Farm ◽  
Wind Condition ◽  
Design Codes ◽  
Large Eddy Simulation Model ◽  
Technology Center ◽  
Eddy Simulation ◽  
Farm Model ◽  
Wake Effects ◽  
Wake Model

Wind turbines located in a wind farm are subject to a wind field that is substantially modified when compared to the ambient wind field due to wake effects. It is clear that in addition to the power, a tool that could model the loads of the waked turbine is needed. In this work, a wind farm wake model is created using a single wake model based on the dynamic wake meandering model to systematically model the wake effects and thus the power and loads of an entire wind farm with an arbitrary wind turbine layout and wind condition. This wind farm wake model is incorporated into the National Wind Technology Center design codes. Preliminary results demonstrate that this wind farm tool yields satisfactory results when compared with Simulator for Wind Farm Applications, a high fidelity computational fluid dynamics Large eddy simulation model, and with experimentally-obtained field data. The integration of the wind farm model with the National Wind Technology Center design codes is described in detail and validation results of the tool are provided.

Optimal Placement of Horizontal- and Vertical-Axis Wind Turbines in a Wind Farm for Maximum Power Generation Using a Genetic Algorithm

2011 ◽  
Author(s):  
Xiaomin Chen ◽  
Ramesh Agarwal
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbines ◽  
Wind Farm ◽  
Optimization Problem ◽  
Vertical Axis ◽  
Optimal Placement ◽  
Vertical Axis Wind Turbines ◽  
Wake Effects ◽  
Horizontal Axis Wind Turbines ◽  
Wake Model

In this paper, we consider the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal–Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally place the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed.

scholarly journals Wind Turbine Wake Modeling in Accelerating Wind Field: A Preliminary Study on a Two-Dimensional Hill

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 153 ◽  
Author(s):  
Omar M. A. M. Ibrahim ◽  
Shigeo Yoshida ◽  
Masahiro Hamasaki ◽  
Ao Takada
Keyword(s):  
Wind Speed ◽  
Wind Tunnel ◽  
Wind Turbine ◽  
Wind Field ◽  
Complex Terrain ◽  
Wind Farm ◽  
Wind Tunnel Test ◽  
Two Dimensional ◽  
Preliminary Study ◽  
Wake Model

Complex terrain can influence wind turbine wakes and wind speed profiles in a wind farm. Consequently, predicting the performance of wind turbines and energy production over complex terrain is more difficult than it is over flat terrain. In this preliminary study, an engineering wake model, that considers acceleration on a two-dimensional hill, was developed based on the momentum theory. The model consists of the wake width and wake wind speed. The equation to calculate the rotor thrust, which is calculated by the wake wind speed profiles, was also formulated. Then, a wind-tunnel test was performed in simple flow conditions in order to investigate wake development over a two-dimensional hill. After this the wake model was compared with the wind-tunnel test, and the results obtained by using the new wake model were close to the wind-tunnel test results. Using the new wake model, it was possible to estimate the wake shrinkage in an accelerating two-dimensional wind field.

The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study

2021 ◽  
pp. 1-28
Author(s):  
Puyi Yang ◽  
Hamidreza Najafi
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Layout Optimization ◽  
Navier Stokes ◽  
Zone Model ◽  
Wind Farm Layout ◽  
Yaw Control ◽  
Eddy Simulation ◽  
Wind Farm Layout Optimization ◽  
Wake Model

Abstract The accuracy of analytical wake models applied in wind farm layout optimization (WFLO) problems is of great significance as the high-fidelity methods such as large eddy simulation (LES) and Reynolds-averaged Navier-Stokes (RANS) are still not able to handle an optimization problem for large wind farms. Based on a variety of analytical wake models developed in the past decades, Flow Redirection and Induction in Steady State (FLORIS) have been published as a tool that integrated several widely used wake models and their expansions. This paper compares four wake models selected from FLORIS by applying three classical WFLO scenarios. The results illustrate that the Jensen wake model is the fastest, but the issue of underestimating the velocity deficit is obvious. The Multi-zone model needs additional tuning on the parameters inside the model to fit specific wind turbines. The Gaussian-curl hybrid (GCH) wake model, as an advanced expansion of the Gaussian wake model, does not perform an observable improvement in the current study, where the yaw control is not included. The Gaussian wake model is recommended for the WFLO projects implemented under the FLROIS framework and has similar wind conditions with the present work.

scholarly journals Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4185
Author(s):  
Nicolas Kirchner-Bossi ◽  
Fernando Porté-Agel
Keyword(s):  
Degrees Of Freedom ◽  
Wind Farm ◽  
Efficient Solutions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Cost Of Energy ◽  
Wake Effects ◽  
Wake Model ◽  
Total Freedom ◽  
The Cost

In recent years, wind farm layout optimization (WFLO) has been extendedly developed to address the minimization of turbine wake effects in a wind farm. Considering that increasing the degrees of freedom in the decision space can lead to more efficient solutions in an optimization problem, in this work the WFLO problem that grants total freedom to the wind farm area shape is addressed for the first time. We apply multi-objective optimization with the power output (PO) and the electricity cable length (CL) as objective functions in Horns Rev I (Denmark) via 13 different genetic algorithms: a traditionally used algorithm, a newly developed algorithm, and 11 hybridizations resulted from the two. Turbine wakes and their interactions in the wind farm are computed through the in-house Gaussian wake model. Results show that several of the new algorithms outperform NSGA-II. Length-unconstrained layouts provide up to 5.9% PO improvements against the baseline. When limited to 20 km long, the obtained layouts provide up to 2.4% PO increase and 62% CL decrease. These improvements are respectively 10 and 3 times bigger than previous results obtained with the fixed area. When deriving a localized utility function, the cost of energy is reduced up to 2.7% against the baseline.

scholarly journals Axial induction controller field test at Sedini wind farm

2020 ◽  
Author(s):  
Ervin A. Bossanyi ◽  
Renzo Ruisi
Keyword(s):  
Steady State ◽  
Time Domain ◽  
Control Algorithm ◽  
Wind Farm ◽  
Dynamic Control ◽  
Wind Condition ◽  
Large Matrix ◽  
Wake Model ◽  
Dynamic Time ◽  
Design And Testing

Abstract. This paper describes the design and testing of an axial induction controller implemented on a row of nine turbines on the Sedini Wind Farm in Sardinia, Italy. This work was performed as part of the EU Horizon 20-20 research project CL Windcon. An engineering wake model, selected for its good fit to historical SCADA data from the site, was used in the LongSim code to optimise turbine power reduction setpoints for a large matrix of steady-state wind conditions. The setpoints were incorporated into a dynamic control algorithm capable of running on site using available wind condition estimates from the turbines. The complete algorithm was tested in dynamic time-domain simulations using LongSim, using a time-varying wind field generated from historical met mast data from the site. The control algorithm was implemented on site, with the wind farm controller toggled on and off at regular intervals to allow the performance with and without the controller to be compared in comparable wind conditions. Data was collected from July 2019 until early February 2020. The results have been analysed and show a positive increase in energy production resulting from the induction control, in line with LongSim model predictions. The measurements also provide a convincing validation of the LongSim model, proving its value for both steady state setpoint optimisation and time-domain simulation of wind farm performance.

Large-Eddy Simulation of Offshore Wind Farms for Power Prediction

2018 ◽  
Author(s):  
Micah Sandusky ◽  
Rey DeLeon ◽  
Inanc Senocak
Keyword(s):  
Boundary Layer ◽  
Large Eddy Simulation ◽  
Atmospheric Boundary Layer ◽  
Wind Field ◽  
Wind Farm ◽  
Power Production ◽  
Offshore Wind ◽  
Detailed Knowledge ◽  
Eddy Simulation ◽  
Large Eddy

Offshore wind turbines are mega structures that span a critical section of the lowest part of atmospheric boundary layer while experiencing significant wind shear. A detailed knowledge of the wind field through a wind farm as part of the atmospheric boundary layer is essential to design efficient farm layouts and estimate power production for grid integration. To address these needs, we present a micro-scale wind prediction model based on a large-eddy simulation paradigm. We consider actuator disk models with and without rotation to simulate the influence of turbines on the wind field and apply our computational capability to the well-known Horns Rev offshore wind farm in Denmark to estimate power production. Instead of using manufacturers power curve to estimate power production, we propose an alternative approach based on the control volume analysis of kinetic energy conservation around turbines.

scholarly journals A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3537
Author(s):  
Jian Teng ◽  
Corey D. Markfort
Keyword(s):  
Wind Turbine ◽  
Analytical Approach ◽  
Wind Farm ◽  
Renewable Energy Sources ◽  
Calibration Procedure ◽  
Total Power ◽  
Wake Effects ◽  
Wake Model ◽  
Operational Data ◽  
Wake Modeling

Wind energy is one of the fastest growing renewable energy sources in the U.S. Wind turbine wakes change the flow field within wind farms and reduce power generation. Prior research has used experimental and computational methods to investigate and model wind farm wake effects. However, these methods are costly and time-consuming to use commercially. In contrast, a simple analytical approach can provide reasonably accurate estimates of wake effects on flow and power. To reducing errors in wake modeling, one must calibrate the model based on a specific wind farm setting. The purpose of this research is to develop a calibration procedure for wind farm wake modeling using a simple analytical approach and wind turbine operational data obtained from the Supervisory Control And Data Acquisition (SCADA) system. The proposed procedure uses a Gaussian-based analytical wake model and wake superposition model. The wake growth rate varies across the wind farm based on the local streamwise turbulence intensity. The wake model was calibrated by implementing the proposed procedure with turbine pairs within the wind farm. The performance of the model was validated at an onshore wind farm in Iowa, USA. The results were compared with the industry standard wind farm wake model and shown to result in an approximate 1% improvement in sitewide total power prediction. This new SCADA-based calibration procedure is useful for real-time wind farm operational optimization.

scholarly journals Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2482
Author(s):  
Tao ◽  
Xu ◽  
Feijóo ◽  
Kuenzel ◽  
Bokde
Keyword(s):  
Wind Farm ◽  
Logistic Function ◽  
Computational Method ◽  
Power Curve ◽  
Wake Effect ◽  
Wind Speeds ◽  
The Monte Carlo Method ◽  
Wake Effects ◽  
Wake Model ◽  
Power Curves

This work presents a computational method for the simulation of wind speeds and for the calculation of the statistical distributions of wind farm (WF) power curves, where the wake effects and terrain features are taken into consideration. A three-parameter (3-P) logistic function is used to represent the wind turbine (WT) power curve. Wake effects are simulated by means of the Jensen’s wake model. Wind shear effect is used to simulate the influence of the terrain on the WTs located at different altitudes. An analytical method is employed for deriving the probability density function (PDF) of the WF power output, based on the Weibull distribution for describing the cumulative wind speed behavior. The WF power curves for four types of terrain slopes are analyzed. Finally, simulations applying the Monte Carlo method on different sample sizes are provided to validate the proposed model. The simulation results indicate that this approximated formulation is a possible substitute for WF output power estimation, especially for the scenario where WTs are built on a terrain with gradient.

scholarly journals Analytical model for the power-yaw sensitivity of wind turbines operating in full wake

2019 ◽  
Author(s):  
Jaime Liew ◽  
Albert M. Urbán ◽  
Søren Juhl Andersen
Keyword(s):  
Wind Turbines ◽  
Wind Field ◽  
Wind Farm ◽  
Power Production ◽  
Loss Coefficient ◽  
Wake Flow ◽  
Wind Farm Optimization ◽  
Wake Effects ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations

Abstract. Wind turbines are designed to align themselves with the incoming wind direction. However, turbines often experience unintentional yaw misalignment, which can significantly reduce the power production. The unintentional yaw misalignment increase for turbines operating in wake of upstream turbines. Here, the combined effects of wakes and yaw misalignment are investigated with the resulting reduction in power production. A model is developed, which considers the trajectory of each turbine blade element as it passes through the waked wind field in order to determine a power-yaw loss coefficient. The simple model is verified using the HAWC2 aeroelastic code, where wake flow fields have been generated using both medium and high-fidelity computational fluid dynamics simulations. It is demonstrated that the spatial variation of the incoming wind field, due to the presence of wake(s), plays a significant role in the power loss due to yaw misalignment. Results show that disregarding these effects on the power-yaw loss coefficient can yield a 3.5 % overestimation in the power production of a turbine misaligned by 30°. The presented analysis and model is relevant to low-fidelity wind farm optimization tools, which aim to capture the effects of wake effects and yaw misalignment as well as uncertainty on power output.

scholarly journals A new three-dimensional wake model for the real wind farm layout optimization

2021 ◽  
pp. 014459872110569
Author(s):  
Zhaohui Luo ◽  
Wei Luo ◽  
Junhang Xie ◽  
Jian Xu ◽  
Longyan Wang
Keyword(s):  
Wind Turbine ◽  
Power Output ◽  
Wind Farm ◽  
Three Dimensional ◽  
Wind Condition ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Optimization Study ◽  
Wake Model

The utilization of wind energy has attracted extensive attentions in the last few decades around the world, providing a sustainable and clean source to generate electricity. It is a common phenomenon of wake interference among wind turbines and hence the optimization of wind farm layout is of great importance to improve the wind turbine yields. More specifically, the accuracy of the three-dimensional wake model is critical to the optiamal design of a real wind farm layout considering the combinatorial effect of wind turbine interaction and topography. In this paper, a novel learning-based three-dimensional wake model is proposed and subsequently validated by comparison to the high-fidelity wake simulation results. Moreover, due to the fact that the inevitable deviation of actual wind scenario from the anticipated one can greatly jeopardize the wind farm optimization outcome, the inaccuracy of wind condition prediction using the existing meteorologic data with limited-time measurement is incorporated into the optimization study. Different scenarios including short-, medium-, and long-term wind data are studied specifically with the wind speed/direction prediction errors of [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], [Formula: see text] 0.08 m/s, [Formula: see text] 1.75 [Formula: see text] and [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text], respectively. An advanced objective function which simultaneously maximizes the power output and minimizes the power variance is employed for the optimization study. Through comparison, it is found that the optimized wind farm layout yields over 210 kW more total power output on average than the existed wind farm layout, which verifies the effectiveness of the wind farm layout optimization tool. The results show that as the measurement time for predicting the wind condition gets longer, the total wind farm power output average increases while the error of power output prediction decreases. For the wind farm with 20 wind turbines installed, the individual power output is above 500 kW with an error of 90 kW under the short-term wind [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], while it is above 530 kW with an error of 10 kW under the long-term wind [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text].

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