Comparisons of wake models profiles with LiDAR data measured downstream from an operating onshore wind turbine

2021 ◽  
pp. 0309524X2110445
Author(s):  
Leandro José Lemes Stival ◽  
Fernando Oliveira de Andrade
Keyword(s):  
Wind Turbine ◽  
Wind Velocity ◽  
Cross Sections ◽  
Wind Farm ◽  
Velocity Profiles ◽  
Measured Data ◽  
Lidar Data ◽  
Freestream Velocity ◽  
Wind Velocities ◽  
Longitudinal Profiles

This study analyzes the performance of Park, Frandsen, and Larsen models to simulate wake development downstream from a wind turbine for freestream wind velocities ranging from 5 to 10 m/s. Analyses are performed in terms of normalized freestream velocity recovery for a longitudinal centerline downstream from the turbine and normalized wind velocity profiles for cross-sections located 500 and 700 m downstream from the wind turbine. Simulated results are compared with high resolution LiDAR data measured during operation of a North American wind farm. Comparisons of longitudinal profiles demonstrate that Larsen and Frandsen models provide the best agreement with measured data for the case of 5 m/s freestream wind velocity, whereas Park model performs best for the 6–9 m/s freestream wind velocity bins. Post-processing of measured data indicates asymmetry of wake profiles at the selected cross-sections. At these locations, Larsen model accurately predicts the west side of normalized velocity profiles, whereas Park and Frandsen models only predict the velocity recovery at the wake centerline.

scholarly journals Parametric design analysis of elliptical shroud profile

AIMS Energy ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1147-1169
Author(s):  
Salih Nawaf Akour ◽  
◽  
Mahmoud Azmi Abo Mhaisen
Keyword(s):  
Wind Turbine ◽  
Wind Velocity ◽  
Parametric Design ◽  
Large Family ◽  
Design Analysis ◽  
Average Wind ◽  
Wind Velocities ◽  
Inlet Length ◽  
Commercial Software Package ◽  
Exact Shape

<abstract> <p>Parametric design analysis for Eccentric Rotated Ellipsoid (ERE) shroud profile is conducted whereas the design model is validated experimentally. A relation between shroud inlet, length and exit diameter is established, different ratios related to the wind turbine diameter are introduced, and solution for different ERE family curves that passes on the inlet, throat, and exit points is studied. The performance of the ERE shroud is studied under different wind velocities ranging from 5–10 m/s.</p> <p>The method used in creating the shroud profile is by solving the ERE curve equations to generate large family of solutions. The system is modeled as axisymmetric system utilizing commercial software package. The effect of the parameters; shroud length, exit diameter, inlet diameter, turbine position with respect to the shroud throat, and wind velocity are studied. An optimum case for each shroud length, exit diameter and location of the shroud with respect to the wind turbine throat axis are achieved.</p> <p>The simulation results show an increase in the average wind velocity by 1.63 times of the inlet velocity. This leads to a great improvement in the wind turbine output power by 4.3 times of bare turbine. One of the achieved optimum solutions for the shroud curves has been prototyped for experimental validation. The prototype has been manufactured using 3D printing technology which provides high accuracy in building the exact shape of shroud design curve. The results show very good agreement with the experimental results.</p></abstract>

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 Lidar-based wake tracking for closed-loop wind farm control

2017 ◽  
Author(s):  
Steffen Raach ◽  
David Schlipf ◽  
Po Wen Cheng
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Closed Loop ◽  
Measurement Data ◽  
Lidar Data ◽  
Lidar Measurement ◽  
Lidar System ◽  
Reduced Order ◽  
Model Based ◽  
Wake Model

Abstract. This work presents two advancements towards closed-loop wake redirecting of a wind turbine. First, a model-based wake tracking approach is presented which uses a nacelle-based lidar system facing downwind to obtain information about the wake. The method uses a reduced order wake model to track the wake. The tracking is demonstrated with lidar measurement data from an offshore campaign and with simulated lidar data from a SOWFA simulation. Second, a controller for closed-loop wake steering is presented. It uses the wake tracking information to set the yaw actuator of the wind turbine to redirect the wake to a desired position. Altogether, the two approaches enable a closed-loop wake redirection.

scholarly journals Pengaruh Desain Interior Diffuser Terhadap Peningkatan Performa Diffuser Augmented Wind Turbine (DAWT)

2018 ◽  
Vol 5 (2) ◽  
pp. 70
Author(s):  
Yiyin Klistafani ◽  
Muh Iqbal Mukhsen ◽  
Muh Iqbal Mukhsen
Keyword(s):  
Wind Turbine ◽  
Wind Velocity ◽  
Interior Design ◽  
Freestream Velocity ◽  
Performance Improvements ◽  
Average Wind ◽  
Computational Fluid Dynamics Cfd ◽  
Cfd Method ◽  
Suction Flow ◽  
Average Wind Velocity

The main objective of numerical simulation in thisstudies is to determine the effect of diffuser’s interior design onincreasing the diffuser augmented wind turbine (DAWT)performance by observing wind velocity increment. Numericalstudies were carried out using the computational Fluid Dynamics(CFD) method through a two-dimensional steady approach withAnsys Fluent 18.2 and Ansys Workbench 18.2 software. Thepresent studies spesifically investigate the shapes of diffuser,namely flat diffuser and curved diffuser. The studies demonstratethat the curved diffuser generates stronger increment of the windvelocity than flat diffuser (at centreline), which 1.842 times thefreestream velocity, while the flat diffuser is only able to increseup to 1.742 times the freestream velocity. The curved diffusershows the highest increment of the average wind velocity alongdiffuser with the greatest increment of 78.66 % and the flatdiffuser is only able to provide average wind velocity incrementup to 44.81%. The curved interor of diffuser is able to enlarge thewake area, so the effect of the suction flow entering the diffuserbecomes stronger. Therefore, curved diffuser is better to provideDAWT performance improvements.

scholarly journals Lidar-based wake tracking for closed-loop wind farm control

2017 ◽  
Vol 2 (1) ◽  
pp. 257-267 ◽  
Author(s):  
Steffen Raach ◽  
David Schlipf ◽  
Po Wen Cheng
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Closed Loop ◽  
Measurement Data ◽  
Lidar Data ◽  
Lidar Measurement ◽  
Lidar System ◽  
Reduced Order ◽  
Model Based ◽  
Wake Model

Abstract. This work presents two advancements towards closed-loop wake redirection of a wind turbine. First, a model-based wake-tracking approach is presented, which uses a nacelle-based lidar system facing downwind to obtain information about the wake. The method uses a reduced-order wake model to track the wake. The wake tracking is demonstrated with lidar measurement data from an offshore campaign and with simulated lidar data from a simulation with the Simulator fOr Wind Farm Applications (SOWFA). Second, a controller for closed-loop wake steering is presented. It uses the wake-tracking information to set the yaw actuator of the wind turbine to redirect the wake to a desired position. Altogether, the two approaches enable a closed-loop wake redirection.

An Investigation of the Low Performance of the First Wind Farm in Thailand: A Case of Poor Wind Turbine-Site Matching

2013 ◽  
Vol 724-725 ◽  
pp. 469-475
Author(s):  
Akraphon Janon ◽  
Tanakorn Wongwuttanasatian ◽  
Gumphol Faikaow ◽  
Panumas Srinor
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Performance Indicator ◽  
Wind Farms ◽  
Measured Data ◽  
Capacity Factor ◽  
Contributing Factors ◽  
Short Period ◽  
Low Performance ◽  
Turbine Capacity

This research investigates causes of the low performance of the first commercial wind farm in Thailand. The measured data suggests that this wind farm is uncompetitive. We found that this is due to poor turbine-site matching. In contrary to a traditionally held belief, the hub-height and turbine capacity are not the contributing factors. Key performance indicators are obtained for use as benchmarks in future wind farm appraisal. Then a turbine selection method is proposed to increase the capacity factor (CF) of the wind farm. CF is used as the main performance indicator, which can be compared to other wind farms. The real capacity factor (CFR) determined using measured data is 14.90%. This CFR is considerably lower than the estimated capacity factor (CFE) of 21.53%. The low CFR is due to grid instability. In addition, the CFR is lower than the CFE by a factor of 0.69. This information is valuable to investors and wind farm developers in a wind farm feasibility study. A graphical wind turbine-site matching is proposed. Wind turbine-site matching is achieved by using normalised power output plots and power density plots on a probability density graph of the wind site. This process consumes a short period of time. An improved turbine-site matching is achieved.

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.

Experimental Study for SPAR Type Floating Offshore Wind Turbine With Blade-Pitch Control

2013 ◽  
Author(s):  
Toshiki Chujo ◽  
Yoshimasa Minami ◽  
Tadashi Nimura ◽  
Shigesuke Ishida
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Offshore Wind ◽  
Scale Model ◽  
Offshore Wind Turbine ◽  
Experimental Proof ◽  
Pitch Control ◽  
Model Experiments ◽  
North Eastern ◽  
North Eastern Part

The experimental proof of the floating wind turbine has been started off Goto Islands in Japan. Furthermore, the project of floating wind farm is afoot off Fukushima Prof. in north eastern part of Japan. It is essential for realization of the floating wind farm to comprehend its safety, electric generating property and motion in waves and wind. The scale model experiments are effective to catch the characteristic of floating wind turbines. Authors have mainly carried out scale model experiments with wind turbine models on SPAR buoy type floaters. The wind turbine models have blade-pitch control mechanism and authors focused attention on the effect of blade-pitch control on both the motion of floater and fluctuation of rotor speed. In this paper, the results of scale model experiments are discussed from the aspect of motion of floater and the effect of blade-pitch control.

scholarly journals Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2319
Author(s):  
Hyun-Goo Kim ◽  
Jin-Young Kim
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Free Stream ◽  
Turbine Blades ◽  
Wind Farms ◽  
Lidar Measurement ◽  
Wind Turbine Blades ◽  
Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

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