A vortex sheet based analytical model of the curled wake behind yawed wind turbines

Motivated by the need for compact descriptions of the evolution of non-classical wakes behind yawed wind turbines, we develop an analytical model to predict the shape of curled wakes. Interest in such modelling arises due to the potential of wake steering as a strategy for mitigating power reduction and unsteady loading of downstream turbines in wind farms. We first estimate the distribution of the shed vorticity at the wake edge due to both yaw offset and rotating blades. By considering the wake edge as an ideally thin vortex sheet, we describe its evolution in time moving with the flow. Vortex sheet equations are solved using a power series expansion method, and an approximate solution for the wake shape is obtained. The vortex sheet time evolution is then mapped into a spatial evolution by using a convection velocity. Apart from the wake shape, the lateral deflection of the wake including ground effects is modelled. Our results show that there exists a universal solution for the shape of curled wakes if suitable dimensionless variables are employed. For the case of turbulent boundary layer inflow, the decay of vortex sheet circulation due to turbulent diffusion is included. Finally, we modify the Gaussian wake model by incorporating the predicted shape and deflection of the curled wake, so that we can calculate the wake profiles behind yawed turbines. Model predictions are validated against large-eddy simulations and laboratory experiments for turbines with various operating conditions.

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Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽

10.3390/en14144291 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4291

Author(s):

Paxis Marques João Roque ◽

Shyama Pada Chowdhury ◽

Zhongjie Huan

Keyword(s):

Wind Speed ◽

Wind Power ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Wind Farms ◽

Operating Conditions ◽

Wake Effect ◽

Wind Generators ◽

Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

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Condition Monitoring of Wind Turbines Based on the Scattering Transform of Vibration Data

10.1115/gt2021-60280 ◽

2021 ◽

Author(s):

Junyu Qi ◽

Alexandre Mauricio ◽

Konstantinos Gryllias

Keyword(s):

Condition Monitoring ◽

Wind Turbines ◽

Wind Farms ◽

Operating Conditions ◽

Vibration Data ◽

Bearing Diagnostics ◽

Scattering Transform ◽

The One ◽

Envelope Spectrum ◽

High Effort

Abstract As a renewable, unlimited and free resource, wind energy has been intensively deployed in the past to generate electricity. However, the maintenance of Wind Turbines (WTs) can be challengeable. On the one hand, most wind farms operate in remote areas and on the other hand, the dimension of WTs’ tip/hub/rotor are usually enormous. In order to prevent abrupt breakdowns of WTs, a number of Condition Monitoring (CM) methods have been proposed. Focusing on bearing diagnostics, Squared Envelope Spectrum is one of the most common techniques. Moreover in order to identify the optimum demodulation frequency band, fast Kurtogram, Infogram and Sparsogram are nowadays popular tools evaluating respectively the Kurtosis, the Negentropy and the Sparsity. The analysis of WTs usually requires high effort due to the complexity of the drivetrain and the varying operating conditions and therefore there is still need for research on effective and reliable CM techniques for WT monitoring. Thus the purpose of this paper is to investigate a blind and effective CM approach based on the Scattering Transform. Through the comparison with state of the art techniques, the proposed methodology is found more powerful to detect a fault on six validated WT datasets.

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Modeling and simulation of forces applied to the horizontal axis wind turbine rotors by the vortex method coupled with the method of the blade element

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v12.i1.pp413-420 ◽

2021 ◽

Vol 12 (1) ◽

pp. 413

Author(s):

Ibtissem Barkat ◽

Abdelouahab Benretem ◽

Fawaz Massouh ◽

Issam Meghlaoui ◽

Ahlem Chebel

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Wind Farms ◽

Vortex Method ◽

Operating Conditions ◽

Horizontal Axis ◽

Rotor Blades ◽

Good Representation ◽

Horizontal Axis Wind Turbine ◽

Blade Element

This article aims to study the forces applied to the rotors of horizontal axis wind turbines. The aerodynamics of a turbine are controlled by the flow around the rotor, or estimate of air charges on the rotor blades under various operating conditions and their relation to the structural dynamics of the rotor are critical for design. One of the major challenges in wind turbine aerodynamics is to predict the forces on the blade as various methods, including blade element moment theory (BEM), the approach that is naturally adapted to the simulation of the aerodynamics of wind turbines and the dynamic and models (CFD) that describes with fidelity the flow around the rotor. In our article we proposed a modeling method and a simulation of the forces applied to the horizontal axis wind rotors turbines using the application of the blade elements method to model the rotor and the vortex method of free wake modeling in order to develop a rotor model, which can be used to study wind farms. This model is intended to speed up the calculation, guaranteeing a good representation of the aerodynamic loads exerted by the wind.

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A review of recent evolutionary computation-based techniques in wind turbines layout optimization problems

Open Computer Science ◽

10.2478/s13537-011-0004-2 ◽

2011 ◽

Vol 1 (1) ◽

Cited By ~ 6

Author(s):

S. Salcedo-Sanz ◽

B. Saavedra-Moreno ◽

A. Paniagua-Tineo ◽

L. Prieto ◽

A. Portilla-Figueras

Keyword(s):

Evolutionary Computation ◽

Wind Turbines ◽

Optimization Problems ◽

Wind Farms ◽

Research Area ◽

Optimization Models ◽

Cost Models ◽

Objective Functions ◽

Computational Framework ◽

Wake Model

AbstractThis paper presents a mini-review of the main works recently published about optimal wind turbines layout in wind farms. Specifically, we focus on discussing articles where evolutionary computation techniques have been applied, since this computational framework has obtained very good results in different formulations of the problem. A summary of the main concepts needed to face the problem are also included in the article, such as a basic wake model and several cost models and objective functions previously used in the literature. This review includes works published in the most significant journals and international conferences, and it gives a brief remark of the optimization models proposed and the implemented algorithms, so it can be useful for readers who want to be quickly introduced in this research area.

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Optimization of Wind Turbine Placement in Offshore Wind Farms

Volume 2: 31st Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2011-47935 ◽

2011 ◽

Author(s):

Simeng Li ◽

J. Iwan D. Alexander

Keyword(s):

Wind Turbines ◽

Spatial Configuration ◽

Unit Cost ◽

Wind Farms ◽

Offshore Wind ◽

Offshore Wind Farms ◽

Wind Speeds ◽

Wake Interactions ◽

Wake Model ◽

Wind Distribution

In this paper, a Genetic Algorithm is used to find optimized spatial configurations of wind turbines in offshore or flat terrain wind farms. The optimization is made by obtaining maximizing power output per unit cost. A wake model which permits the calculation of single wakes, multiple wakes and wake interactions is employed to estimate wind speeds at each turbine for a given external wind distribution function and a given spatial configuration. The optimization is applied to cases of unidirectional wind, variable direction winds and variable wind speed. The placement of a turbine can be set at any location following the approach of Mittal et al. Results are obtained for different spacing limits between turbines and wind farms of different sizes. The results for some patterns of optimized placements of wind turbines are discussed in the context of the wind distributions and the wake model employed.

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An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions

Energies ◽

10.3390/en13040891 ◽

2020 ◽

Vol 13 (4) ◽

pp. 891 ◽

Cited By ~ 3

Author(s):

Mohsen Vahidzadeh ◽

Corey D. Markfort

Keyword(s):

Wind Turbines ◽

Sonic Anemometer ◽

Wind Farms ◽

Operating Conditions ◽

Power Curve ◽

Power Prediction ◽

Horizontal Axis Wind Turbine ◽

Scada System ◽

Proposed Model ◽

Power Curves

Power generation from wind farms is traditionally modeled using power curves. These models are used for assessment of wind resources or for forecasting energy production from existing wind farms. However, prediction of power using power curves is not accurate since power curves are based on ideal uniform inflow wind, which do not apply to wind turbines installed in complex and heterogeneous terrains and in wind farms. Therefore, there is a need for new models that account for the effect of non-ideal operating conditions. In this work, we propose a model for effective axial induction factor of wind turbines that can be used for power prediction. The proposed model is tested and compared to traditional power curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and turbulence measurements from a nearby meteorological tower are used in the models. The results for a period of four months showed an improvement of 51% in power prediction accuracy, compared to the standard power curve.

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Optimizing the layout of wind turbines by upgrading a wind farm

E3S Web of Conferences ◽

10.1051/e3sconf/202129701038 ◽

2021 ◽

Vol 297 ◽

pp. 01038

Author(s):

Abdelouahad Bellat ◽

Khalifa Mansouri ◽

Abdelhadi Raihani

Keyword(s):

Genetic Algorithm ◽

Objective Function ◽

Wind Turbines ◽

Wind Farm ◽

Wind Farms ◽

Power Calculation ◽

Cost Of Energy ◽

Reasonable Cost ◽

Wake Model ◽

Different Characteristics

The optimization of the size of wind farms is little studied in the literature. The objective of this study is to renew the existing wind farms by inserting new wind turbines with different characteristics. To evaluate our approach, a genetic algorithm was chosen to optimize our objective function, which aims to maximize the power of the wind farm studied at a reasonable cost, the Jensen wake model was chosen for the power calculation of the park. The results obtained from the simulation on the Horns-rev wind farm showed a significant increase in energy and a relatively reasonable cost of energy.

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Determining an Appropriate Parameter of Analytical Wake Models for Energy Capture and Layout Optimization on Wind Farms

Energies ◽

10.3390/en13030739 ◽

2020 ◽

Vol 13 (3) ◽

pp. 739 ◽

Cited By ~ 2

Author(s):

Kyoungboo Yang

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Turbulence Intensity ◽

Energy Production ◽

Wind Farm ◽

Wind Farms ◽

Gaussian Models ◽

Advantages And Disadvantages ◽

Power Deficit ◽

Wake Model

The wake of a wind turbine is a crucial factor that decreases the output of downstream wind turbines and causes unsteady loading. Various wake models have been developed to understand it, ranging from simple ones to elaborate models that require long calculation times. However, selecting an appropriate wake model is difficult because each model has its advantages and disadvantages as well as distinct characteristics. Furthermore, determining the parameters of a given wake model is crucial because this affects the calculation results. In this study, a method was introduced of using the turbulence intensity, which can be measured onsite, to objectively define parameters that were previously set according to the subjective judgement of a wind farm designer or general recommended values. To reflect the environmental effects around a site, the turbulence intensity in each direction of the wind farm was considered for four types of analytical wake models: the Jensen, Frandsen, Larsen, and Jensen–Gaussian models. The prediction performances of the wake models for the power deficit and energy production of the wind turbines were compared to data collected from a wind farm. The results showed that the Jensen and Jensen–Gaussian models agreed more with the power deficit distribution of the downstream wind turbines than when the same general recommended parameters were applied in all directions. When applied to energy production, the maximum difference among the wake models was approximately 3%. Every wake model clearly showed the relative wake loss tendency of each wind turbine.

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An equivalent modeling method for Offshore Wind Farms based on fault characteristics analysis

E3S Web of Conferences ◽

10.1051/e3sconf/201910701004 ◽

2019 ◽

Vol 107 ◽

pp. 01004

Author(s):

Haiyan Tang ◽

Guanglei Li ◽

Linan Qu ◽

Yan Li

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Wind Farms ◽

Modeling Method ◽

Operating Conditions ◽

Offshore Wind ◽

Equivalent Model ◽

Offshore Wind Farm ◽

Offshore Wind Farms ◽

Characteristics Analysis

A large offshore wind farm usually consists of dozens or even hundreds of wind turbines. Due to the limitation of the simulation scale, it is necessary to develop an equivalent model of offshore wind farms for power system studies. At present, the aggregation method is widely adopted for wind farm equivalent modeling. In this paper, the topology, electrical parameters, operating conditions and individual turbine characteristics of the offshore wind farms are taken into consideration. Firstly, the output power distribution of offshore wind farm, the voltage distribution of the collector system and the fault ride-through characteristics of wind turbines are analyzed. Then, a dynamic equivalent modeling method for offshore wind farms is developed based on the fault characteristics analysis. Finally, the proposed method is validated through time-domain simulation.

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Modified Power Curves for Prediction of Power Output of Wind Farms

Energies ◽

10.3390/en12091805 ◽

2019 ◽

Vol 12 (9) ◽

pp. 1805 ◽

Cited By ~ 9

Author(s):

Mohsen Vahidzadeh ◽

Corey D. Markfort

Keyword(s):

High Resolution ◽

Wind Speed ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Sonic Anemometer ◽

Wind Farms ◽

Operating Conditions ◽

Horizontal Axis Wind Turbine ◽

Power Curves

Power curves are used to model power generation of wind turbines, which in turn is used for wind energy assessment and forecasting total wind farm power output of operating wind farms. Power curves are based on ideal uniform inflow conditions, however, as wind turbines are installed in regions of heterogeneous and complex terrain, the effect of non-ideal operating conditions resulting in variability of the inflow must be considered. We propose an approach to include turbulence, yaw error, air density, wind veer and shear in the prediction of turbine power by using high resolution wind measurements. In this study, two modified power curves using standard ten-minute wind speed and high resolution one-second data along with a derived power surface were tested and compared to the standard operating curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and wind speed measurements from a nearby meteorological tower are used in the models. The results show that all of the proposed models perform better than the standard power curve while the power surface results in the most accurate power prediction.

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