Effect of wind turbine nacelle on turbine wake dynamics in large wind farms

2019 ◽  
Vol 869 ◽  
pp. 1-26 ◽  
Author(s):  
Daniel Foti ◽  
Xiaolei Yang ◽  
Lian Shen ◽  
Fotis Sotiropoulos
Keyword(s):  
Wind Turbine ◽  
Coherent Structures ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Practical Significance ◽  
Mean Velocity ◽  
Velocity Deficit ◽  
Farm Scale ◽  
Turbine Wake

Wake meandering, a phenomenon of large-scale lateral oscillation of the wake, has significant effects on the velocity deficit and turbulence intensities in wind turbine wakes. Previous studies of a single turbine (Kang et al., J. Fluid. Mech., vol. 774, 2014, pp. 374–403; Foti et al., Phys. Rev. Fluids, vol. 1 (4), 2016, 044407) have shown that the turbine nacelle induces large-scale coherent structures in the near field that can have a significant effect on wake meandering. However, whether nacelle-induced coherent structures at the turbine scale impact the emergent turbine wake dynamics at the wind farm scale is still an open question of both fundamental and practical significance. We take on this question by carrying out large-eddy simulation of atmospheric turbulent flow over the Horns Rev wind farm using actuator surface parameterisations of the turbines without and with the turbine nacelle taken into account. While the computed mean turbine power output and the mean velocity field away from the nacelle wake are similar for both cases, considerable differences are found in the turbine power fluctuations and turbulence intensities. Furthermore, wake meandering amplitude and area defined by wake meanders, which indicates the turbine wake unsteadiness, are larger for the simulations with the turbine nacelle. The wake influenced area computed from the velocity deficit profiles, which describes the spanwise extent of the turbine wakes, and the spanwise growth rate, on the other hand, are smaller for some rows in the simulation with the nacelle model. Our work shows that incorporating the nacelle model in wind farm scale simulations is critical for accurate predictions of quantities that affect the wind farm levelised cost of energy, such as the dynamics of wake meandering and the dynamic loads on downwind turbines.

Characterizing the Transitional Behavior of Wind Turbine Wake From Near to Far Wake Regimes

2021 ◽  
Author(s):  
Ravi Kumar ◽  
Ojing Siram ◽  
Niranjan Sahoo ◽  
Ujjwal K. Saha
Keyword(s):  
Wind Turbine ◽  
Energy Demand ◽  
Wind Farm ◽  
Flow Characteristics ◽  
Wind Farms ◽  
Horizontal Axis Wind Turbine ◽  
Velocity Deficit ◽  
Turbine Wake ◽  
Far Wake ◽  
Wake Characteristics

Abstract Knowledge of wind energy harvesting is an ever-growing process, and to meet the enormous energy demand, wind farms shall have a significant role. An efficient wind farm is required to have an in-depth knowledge of turbine wake characteristics. This article presents an experimental investigation of the wake expansion process defined by the transition of wake from near to far wake regimes. The study has been performed on models horizontal axis wind turbine (HAWT) composed of NACA 0012 profile, keeping the ratio of root chord to tip chord length is 5:2. A constant temperature hot-wire anemometer (HWA) has been used to examine the rotor’s fluctuating flow field. The subsequent time-averaged normalizes velocity deficit, and vortex shedding frequency are used for the flow characteristics. Time-averaged velocity deficit measurement suggests a drop in upstream velocity by 20–30% within the vicinity of rotor tip downstream of the rotor plane. The study shows that flow recovery is initiating from the near wake regime around 1.08R. Further, the spectral findings indicates the low frequency dominance within 4R (R being the rotor radius), and the Strouhal number falls close to 0.23. The present wind tunnel study on wake characteristics throws significant insight into further enhancing the WT wake modeling.

scholarly journals Distinct Turbulent Regions in the Wake of a Wind Turbine and Their Inflow-Dependent Locations: The Creation of a Wake Map

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5392
Author(s):  
Ingrid Neunaber ◽  
Michael Hölling ◽  
Richard J. A. M. Stevens ◽  
Gerard Schepers ◽  
Joachim Peinke
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Isotropic Turbulence ◽  
Turbulence Modeling ◽  
Wind Farms ◽  
Reference Case ◽  
Mean Velocity ◽  
Turbulence Analysis ◽  
Turbine Wake ◽  
Wind Turbine Wake

Wind turbines are usually clustered in wind farms which causes the downstream turbines to operate in the turbulent wakes of upstream turbines. As turbulence is directly related to increased fatigue loads, knowledge of the turbulence in the wake and its evolution are important. Therefore, the main objective of this study is a comprehensive exploration of the turbulence evolution in the wind turbine’s wake to identify characteristic turbulence regions. For this, we present an experimental study of three model wind turbine wake scenarios that were scanned with hot-wire anemometry with a very high downstream resolution. The model wind turbine was exposed to three inflows: laminar inflow as a reference case, a central wind turbine wake, and half of the wake of an upstream turbine. A detailed turbulence analysis reveals four downstream turbulence regions by means of the mean velocity, variance, turbulence intensity, energy spectra, integral and Taylor length scales, and the Castaing parameter that indicates the intermittency, or gustiness, of turbulence. In addition, a wake core with features of homogeneous isotropic turbulence and a ring of high intermittency surrounding the wake can be identified. The results are important for turbulence modeling in wakes and optimization of wind farm wake control.

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

scholarly journals Utility-Scale Wind Turbine Wake Characterization Using Nacelle-Based Long-Range Scanning Lidar

2014 ◽  
Vol 31 (7) ◽  
pp. 1529-1539 ◽  
Author(s):  
Matthew L. Aitken ◽  
Julie K. Lundquist
Keyword(s):  
Wind Turbine ◽  
Long Range ◽  
Wind Farm ◽  
Thrust Coefficient ◽  
Velocity Deficit ◽  
Scanning Lidar ◽  
Range Scanning ◽  
Utility Scale ◽  
Turbine Wake ◽  
Wind Turbine Wake

Abstract To facilitate the optimization of turbine spacing at modern wind farms, computational simulations of wake effects must be validated through comparison with full-scale field measurements of wakes from utility-scale turbines operating in the real atmosphere. Scanning remote sensors are particularly well suited for this objective, as they can sample wind fields over large areas at high temporal and spatial resolutions. Although ground-based systems are useful, the vantage point from the nacelle is favorable in that scans can more consistently transect the central part of the wake. To the best of the authors’ knowledge, the work described here represents the first analysis in the published literature of a utility-scale wind turbine wake using nacelle-based long-range scanning lidar. The results presented are of a field experiment conducted in the fall of 2011 at a wind farm in the western United States, quantifying wake attributes such as the velocity deficit, centerline location, and wake width. Notable findings include a high average velocity deficit, decreasing from 60% at a downwind distance x of 1.8 rotor diameters (D) to 40% at x = 6D, resulting from a low average wind speed and therefore a high average turbine thrust coefficient. Moreover, the wake width was measured to expand from 1.5D at x = 1.8D to 2.5D at x = 6D. Both the wake growth rate and the amplitude of wake meandering were observed to be greater for high ambient turbulence intensity and daytime conditions as compared to low turbulence and nocturnal conditions.

Numerical Analysis of a Large-Scale Hydraulic Wind Farm With Integrated Hydraulic Energy Storage

2016 ◽  
Author(s):  
Daniel Buhagiar ◽  
Tonio Sant
Keyword(s):  
Wind Turbine ◽  
Output Power ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Strong Relationship ◽  
Pressure Control ◽  
Open Loop ◽  
Offshore Wind ◽  
Pipeline Network

Offshore wind farms are presently facing numerous technical challenges that are affecting their viability. High failure rates of expensive nacelle-based electronics and gearboxes are particularly problematic. On-going research is investigating the possibility of shifting to a seawater-based hydraulic power transmission, whereby wind turbines pressurise seawater that is transmitted across a high-pressure pipeline network. A 9-turbine hydraulic wind farm with three different configurations is simulated in the present work and a previously developed method for open-loop pressure control of a single turbine has been adapted for this multiple-turbine scenario. A conceptual quasi-constant-pressure accumulator is also included in the model. This system is directly integrated within each hydraulic wind turbine and it allows the output power from the wind farm to be scheduled on an hourly basis. The shift in control methodology when integrating storage is illustrated in the present work. Simulation results indicate a strong relationship between hydraulic performance attributes and the specific wind turbine array layout. The beneficial effects of storage can also be observed, particularly in smoothing the output power and rendering it more useable. Finally, the energy yields from 24-hour simulations of the 9-turbine wind farms are calculated. Integrated storage leads to a slight increase in yield since it eliminates bursts of high flow, which induce higher frictional losses in the pipeline network.

Probabilistic Models for Wind Turbine and Wind Farm Performance

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Sanjay R. Arwade ◽  
Matthew A. Lackner ◽  
Mircea D. Grigoriu
Keyword(s):  
Markov Model ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Probabilistic Models ◽  
Performance Metrics ◽  
Wind Farms ◽  
Component Reliability ◽  
Mc Simulation ◽  
Farm Performance

A Markov model for the performance of wind turbines is developed that accounts for component reliability and the effect of wind speed and turbine capacity on component reliability. The model is calibrated to the observed performance of offshore turbines in the north of Europe, and uses wind records obtained from the coast of the state of Maine in the northeast United States in simulation. Simulation results indicate availability of 0.91, with mean residence time in the operating state that is nearly exponential and has a mean of 42 days. Using a power curve typical for a 2.5 MW turbine, the capacity factor is found to be beta distributed and highly non-Gaussian. Noticeable seasonal variation in turbine and farm performance metrics are observed and result from seasonal fluctuations in the characteristics of the wind record. The input parameters to the Markov model, as defined in this paper, are limited to those for which field data are available for calibration. Nevertheless, the framework of the model is readily adaptable to include, for example: site specific conditions; turbine details; wake induced loading effects; component redundancies; and dependencies. An on-off model is introduced as an approximation to the stochastic process describing the operating state of a wind turbine, and from this on-off process an Ornstein–Uhlenbeck (O–U) process is developed as a model for the availability of a wind farm. The O–U model agrees well with Monte Carlo (MC) simulation of the Markov model and is accepted as a valid approximation. Using the O–U model in design and management of large wind farms will be advantageous because it can provide statistics of wind farm performance without resort to intensive large scale MC simulation.

scholarly journals FLOWSTAR-Energy: a high resolution wind farm wake model

2016 ◽  
Author(s):  
Amy Stidworthy ◽  
David Carruthers
Keyword(s):  
Boundary Layer ◽  
Wind Turbine ◽  
Complex Terrain ◽  
Wind Farm ◽  
Wind Farms ◽  
Measured Data ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wake Model ◽  
Turbine Wake

Abstract. A new model, FLOWSTAR-Energy, has been developed for the practical calculation of wind farm energy production. It includes a semi-analytic model for airflow over complex surfaces (FLOWSTAR) and a wind turbine wake model that simulates wake-wake interaction by exploiting some similarities between the decay of a wind turbine wake and the dispersion of plume of passive gas emitted from an elevated source. Additional turbulence due to the wind shear at the wake edge is included and the assumption is made that wind turbines are only affected by wakes from upstream wind turbines. The model takes account of the structure of the atmospheric boundary layer, which means that the effect of atmospheric stability is included. A marine boundary layer scheme is also included to enable offshore as well as onshore sites to be modelled. FLOWSTAR-Energy has been used to model three different wind farms and the predicted energy output compared with measured data. Maps of wind speed and turbulence have also been calculated for two of the wind farms. The Tjaæreborg wind farm is an onshore site consisting of a single 2 MW wind turbine, the NoordZee offshore wind farm consists of 36 V90 VESTAS 3 MW turbines and the Nysted offshore wind farm consists of 72 Bonus 2.3 MW turbines. The NoordZee and Nysted measurement datasets include stability distribution data, which was included in the modelling. Of the two offshore wind farm datasets, the Noordzee dataset focuses on a single 5-degree wind direction sector and therefore only represents a limited number of measurements (1,284); whereas the Nysted dataset captures data for seven 5-degree wind direction sectors and represents a larger number of measurements (84,363). The best agreement between modelled and measured data was obtained with the Nysted dataset, with high correlation (0.98 or above) and low normalised mean square error (0.007 or below) for all three flow cases. The results from Tjæreborg show that the model replicates the Gaussian shape of the wake deficit two turbine diameters downstream of the turbine, but the lack of stability information in this dataset makes it difficult to draw conclusions about model performance. One of the key strengths of FLOWSTAR-Energy is its ability to model the effects of complex terrain on the airflow. However, although the airflow model has been previously compared extensively with flow data, it has so far not been used in detail to predict energy yields from wind farms in complex terrain. This will be the subject of a further validation study for FLOWSTAR-Energy.

scholarly journals Investigation of the dissipation in the wake of a wind turbine array

2021 ◽  
Author(s):  
Ingrid Neunaber ◽  
Joachim Peinke ◽  
Martin Obligado
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Bluff Body ◽  
Wind Farms ◽  
Energy Transition ◽  
Mean Velocity ◽  
Maintenance Time ◽  
Bluff Body Wake ◽  
Turbine Wake ◽  
Wind Turbine Wake

Abstract. Within the energy transition, more and more wind turbines are clustered in big wind farms, often offshore. Therefore, an optimal positioning of the wind turbines is crucial to optimize both the annual power production and the maintenance time. Good knowledge of the wind turbine wake and the turbulence within is thus important. However, although wind turbine wakes have been subject to various studies, they are still not fully understood. One possibility to improve the comprehension is to look into the modeling of bluff body wakes. These wakes have been the subject of intensive study for decades, and by means of the scaling behavior of the centerline mean velocity deficit, the nature of the turbulence inside a wake can be classified. In this paper, we introduce the models for equilibrium and non-equilibrium turbulence from classical wake theory as introduced by A. Townsend and W. George, and we test whether the requirements are fulfilled in the wake of a wind turbine. Finally, we apply the theory to characterize the wind turbine wake, and we compare the results to the Jensen and the Bastankhah-Porté-Agel model. We find that the insight into the classical bluff body wake can be used to further improve the wind turbine wake models. Particularly, the classical bluff body wake models perform better than the wind turbine wake models due to the presence of a virtual origin in the scalings, and we demonstrate the possibility of improving the wind turbine wake models by implementing this parameter. We also see how the dissipation changes across the wake which is important to model wakes within wind farms correctly.

scholarly journals ESCOAMENTO NA ESTEIRA DE TURBINAS EÓLICAS: ANÁLISE ESPECTRAL DA TURBULÊNCIA MEDIANTE TESTES EM TÚNEL DE VENTO

2016 ◽  
Vol 38 ◽  
pp. 46
Author(s):  
Adrián Roberto Wittwer ◽  
Rodrigo Dorado ◽  
Gervásio Annes Degrazia ◽  
Acir Mércio Loredo-Souza ◽  
Bardo Ernst Josef Bodmann
Keyword(s):  
Boundary Layer ◽  
Wind Tunnel ◽  
Wind Farm ◽  
Wake Flow ◽  
Mean Velocity ◽  
Velocity Fluctuations ◽  
Velocity Deficit ◽  
Scale Models ◽  
Spectral Evaluation ◽  
Turbine Wake

The interaction between the incident wind and wind turbines in a wind farm causes mean velocity deficit and increased levels of turbulence in the wake. The turbulent flow is characterized by the superposition of the wind turbine wakes. In this work, the technique of turbulence spectral evaluation to reduce scale models in a boundary layer wind tunnel is presented, and different measurements of velocity fluctuations are analyzed. The results allow evaluating the spectrum configuration at different frequency ranges and the differences of the spectral behavior between the incident wind and the turbine wake flow.  

Analysis of Grid Code Technical Requirements for Wind Farms Connected to the Grid in Penghu Island

2015 ◽  
Vol 737 ◽  
pp. 199-203
Author(s):  
Shao Hong Tsai ◽  
Yuan Kang Wu ◽  
Ching Yin Lee ◽  
Wen Ta Tsai
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Turbine ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Turbine Control ◽  
System A ◽  
Technical Requirements

Modern wind turbine technology has been a great improvement over the past couple decades, leading to large scale wind power penetration. The increasing penetration of wind power resulted in emphasizing the importance of reliable and secure operation of power systems, especially in a weak power system. In this paper, the main wind turbine control schemes, the wind penetration levels and wind farm dynamic behavior for grid code compliance were investigated in the Penghu wind power system, a weak isolated power system.

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