scholarly journals Wake Management in Wind Farms: An Adaptive Control Approach

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1247 ◽  
Author(s):  
Harsh Dhiman ◽  
Dipankar Deb ◽  
Vlad Muresan ◽  
Valentina Balas
Keyword(s):  
Adaptive Control ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Transfer Functions ◽  
Wind Farms ◽  
Control Approach ◽  
Wind Turbine System ◽  
Effective Assessment ◽  
Yaw Angle

Advanced wind measuring systems like Light Detection and Ranging (LiDAR) is useful for wake management in wind farms. However, due to uncertainty in estimating the parameters involved, adaptive control of wake center is needed for a wind farm layout. LiDAR is used to track the wake center trajectory so as to perform wake control simulations, and the estimated effective wind speed is used to model wind farms in the form of transfer functions. A wake management strategy is proposed for multi-wind turbine system where the effect of upstream turbines is modeled in form of effective wind speed deficit on a downstream wind turbine. The uncertainties in the wake center model are handled by an adaptive PI controller which steers wake center to desired value. Yaw angle of upstream wind turbines is varied in order to redirect the wake and several performance parameters such as effective wind speed, velocity deficit and effective turbulence are evaluated for an effective assessment of the approach. The major contributions of this manuscript include transfer function based methodology where the wake center is estimated and controlled using LiDAR simulations at the downwind turbine and are validated for a 2-turbine and 5-turbine wind farm layouts.

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.

Assessment of the noise produced by wind farms with the acoustically analogous techniques without stopping the noise source

2018 ◽  
Vol 43 (2) ◽  
pp. 201-209
Author(s):  
Gino Iannace ◽  
Amelia Trematerra ◽  
Umberto Berardi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Noise Source ◽  
Sound Field ◽  
Wind Farms ◽  
Sound Level ◽  
Acoustic Measurements ◽  
Relative Contribution

In Italy, wind turbines with a nominal power below 1 MW can be installed following simplified authorization procedures and are therefore becoming the preferred choice for promoters. The assessment of the noise of wind farms is not easy, due to economic reasons, with it being difficult to stop and assess the relative contribution of each wind turbine. Several acoustic measurements were taken inside homes located near a wind farm consisting of three wind turbines, each with a nominal power of 1 MW. The acoustic measurements were taken by placing sound level meters inside the houses at different wind speed values and wind directions. The acoustic measurements were taken using the acoustically analogous place technique. For economic reasons, the plant cannot be switched off. In this case, the sound field generated by the operation of the wind turbines was measured by placing two sound level meters in a house.

The Equivalent Method of Wind Farms Considering Wake Effect

2013 ◽  
Vol 860-863 ◽  
pp. 237-241
Author(s):  
Jing Ru Yan ◽  
Jin Yao Zhu ◽  
Xue Bing Zheng ◽  
Ran Li
Keyword(s):  
Wind Speed ◽  
Energy Loss ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Output Characteristic ◽  
Wake Effect ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Equivalent Method

It analyses the model of wake effect of wind farm in detail. Considering the energy loss caused by wake effect on the wind speed of wind turbine in different locations, the output of whole wind farm can be evaluated via the model, including the wind speed distribution. Then, it determines a kind of equivalent method of wind farm based on the output characteristic of the port of wind farm.

scholarly journals A Comprehensive Survey of Accurate and Efficient Aggregation Modeling for High Penetration of Large-Scale Wind Farms in Smart Grid

2019 ◽  
Vol 9 (4) ◽  
pp. 769 ◽  
Author(s):  
Fang Liu ◽  
Junjie Ma ◽  
Wendan Zhang ◽  
Min Wu
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Smart Grids ◽  
Wind Farm ◽  
Wind Farms ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
High Penetration ◽  
Comprehensive Survey

As one of the important renewable energies, wind power has been exploited worldwide. Modeling plays an important role in the high penetration of wind farms in smart grids. Aggregation modeling, whose benefits include low computational complexity and high computing speed, is widely used in wind farm modeling and simulation. To contribute to the development of wind power generation, a comprehensive survey of the aggregation modeling of wind farms is given in this article. A wind farm aggregation model consists of three parts, respectively, the wind speed model, the wind turbine generator (WTG) model, and the WTG transmission system model. Different modeling and aggregation methods, principles, and formulas for the above three parts are introduced. First, the features and emphasis of different wind speed models are discussed. Then, the aggregated wind turbine generator (WTG) models are divided into single WTG and multi-WTG aggregation models, considering the aggregation of wind turbines and generators, respectively. The calculation methods for the wind conditions and parameters of different aggregation models are discussed. Finally, the WTG transmission model of the wind farm from the aggregation bus is introduced. Some research directions are highlighted in the end according to the issues related to the aggregation modeling of wind farms in smart grids.

Statistical calculation of thrust curve of a wind turbine based on available power curve and general specifications data

2021 ◽  
Author(s):  
Evgeny Atlaskin ◽  
Irene Suomi ◽  
Anders Lindfors
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Wake Effect ◽  
Thrust Coefficient ◽  
Simple Method ◽  
Wind Speeds ◽  
Power Curves

<p>Power curves for a substantial number of wind turbine generators (WTG) became available in a number of public sources during the recent years. They can be used to estimate the power production of a wind farm fleet with uncertainty determined by the accuracy and consistency of the power curve data. However, in order to estimate power losses inside a wind farm due to wind speed reduction caused by the wake effect, information on the thrust force, or widely used thrust coefficient (Ct), is required. Unlike power curves, Ct curves for the whole range of operating wind speeds of a WTG are still scarcely available in open sources. Typically, power and Ct curves are requested from a WTG manufacturer or wind farm owner under a non-disclosure agreement. However, in a research study or in calculations over a multitude of wind farms with a variety of wind turbine models, collecting this information from owners may be hardly possible. This study represents a simple method to define Ct curve statistically using power curve and general specifications of WTGs available in open sources. Preliminary results demonstrate reasonable correspondence between simulated and given data. The estimations are done in the context of aggregated wind power calculations based on reanalysis or forecast data, so that the uncertainty of wake wind speed caused by the uncertainty of predicted Ct is comparable, or do not exceed, the uncertainty of given wind speed. Although the method may not provide accurate fits at low wind speeds, it represents an essential alternative to using physical Computational Fluid Dynamics (CFD) models that are both more demanding to computer resources and require detailed information on the geometry of the rotor blades and physical properties of the rotor, which are even more unavailable in open sources than power curves.</p>

Analysis of Senegal Type Vertical Axis Wind Turbines Arrangement in Wind Farm

2019 ◽  
Vol 13 ◽  
Author(s):  
Li Zheng ◽  
Zhang Wenda ◽  
Han Ruihua ◽  
Qi Weiqiang
Keyword(s):  
Wind Speed ◽  
Flow Field ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Vertical Axis ◽  
Energy Utilization ◽  
Unit Distance ◽  
Vertical Axis Wind Turbines

Background: In a wind farm, the wind speed of the downstream wind turbine will be lower than the wind speed of the upstream wind turbine due to the influence of the wake. Therefore, the wake of wind turbines is one of the uncertain factors predicting the annual power generation of wind farms. The study of the wake can effectively improve the efficiency of power generation. The arrangement of vertical axis wind turbines in wind farms is rarely studied. Therefore, it is important to study the vertical layout of wind turbines under the influence of wakes to obtain the best layout and unit spacing. Objective: To obtain the optimal layout and unit distance of wind turbines in Senegal wind turbines by studying the arrangement of Senegal vertical axis wind turbines in wind farms. Method: Based on the ANSYS CFX flow field calculation module, the fluid dynamics model of the Senegal fan was established and the flow field simulation analysis was carried out. Based on the Jensen wake model and its improved model, three layout methods for wind farm wind turbines are proposed: two units are arranged in series, two units are arranged in parallel, and three units are staggered. Through the simulation model, the wind energy utilization coefficient and wind speed of the wind turbine in the wind farm are obtained. Results: The optimal separation distance between the units was analyzed from four different angles: wind energy utilization coefficient, torque analysis, downstream tail flow and wind speed cloud contour. Finally, based on the optimal arrangement and unit distance, a triangular staggered wind farm composed of 10 units is established, and the integrated flow field characteristics of the whole wind farm are simulated and analyzed. The integrated flow field wake characteristics of the wind farm are obtained. Conclusion: In all three arrangements, the optimum distance between the units should be three times the diameter of the wind turbine. This arrangement ensures that most of the units are unaffected by the wake, the area affected by the low velocity wake of the wind farm is small, and the area affected by the high speed wake is large.

scholarly journals A Study on the Effect of Closed-Loop Wind Farm Control on Power and Tower Load in Derating the TSO Command Condition

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 2004 ◽  
Author(s):  
Hyungyu Kim ◽  
Kwansu Kim ◽  
Insu Paek
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Power Output ◽  
Wind Farm ◽  
Closed Loop ◽  
Environmental Changes ◽  
Wind Farms ◽  
Feedback Gain ◽  
Controller Gain ◽  
The Impact

This study was conducted to analyze the impact of surrounding environmental changes on the feedback gain and performance of a closed-loop wind farm controller that reduces the error between total power output of wind farm and power command of transmission system operator. To analyze the impact of environment changes on wind farm controller feedback gain, the feedback gain was manually changed from 0 to 0.9 with a 0.1 interval. In this study, wind speed and wind direction changes were considered as environment changes; it was found by simulation code that the wind farm controller gain is in inverse proportion to wake recovery rate. In other words, the feedback gain should be higher if the distance between upstream and downstream wind turbine is not sufficient to wake recovery. Furthermore, the feedback gain should be lower when the upstream wind turbine generates a relatively weak wake by operating above the rated wind speed. The wind farm simulation was performed using reference 5 MW wind turbines from the National Renewable Energy Laboratory (NREL), which are numerically modeled for each element so that wind farm power output and tower load can be calculated according to the variation of the power command by using a modified wake model with improved accuracy. All the simulations performed in this study were carried out to review the power output accuracy of wind farms, but only if the transmission system operator’s power command was lower than the available power of wind farm. In this study, the gain of the wind farm controller was applied differently depending on the wind speed and direction to consider benefits in terms of power and tower load, especially if the wake effect of the upstream wind turbine was rapidly transferred to the downstream wind turbine. Ultimately, a simple, but more effective, power distribution method was proposed for distributing power commands to wind turbines that constitute wind farms and the study indicated the need for controller gain adjustment based on surrounding environmental changes.

About the Extension of Wind Turbine Power Curve in the High Wind Region

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Test Case ◽  
Power Curve ◽  
High Wind ◽  
High Wind Speed ◽  
Wind Speeds

The financial sustainability and the profitability of wind farms strongly depend on the efficiency of the conversion of wind kinetic energy. This motivates further research about the improvement of wind turbine power curve. If the site is characterized by a considerable occurrence of very high wind speeds, it can become particularly profitable to update the power curve management. This is commonly done by raising the cut-out velocity and the high wind speed cut-in regulating the hysteresis logic. Doing this, on one side, the wind turbine possibly undergoes strong vibration and loads. On the other side, the energy improvement is almost certain and the point is quantifying precisely its magnitude. In this work, the test case of an onshore wind farm in Italy is studied, featuring 17 2.3 MW wind turbines. Through the analysis of supervisory control and data acquisition (SCADA) data, the energy improvement from the extension of the power curve in the high wind speed region is simulated and measured. This could be useful for wind farm owners evaluating the realistic profitability of the installation of the power curve upgrade on their wind turbines. Furthermore, the present work is useful for the analysis of wind turbine behavior under extremely stressing load conditions.

scholarly journals Recovery Processes in a Large Offshore Wind Farm

2021 ◽  
Author(s):  
Tanvi Gupta ◽  
Somnath Baidya Roy
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Mesoscale Model ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Recovery Processes ◽  
Mesoscale Processes

Abstract. Wind turbines in a wind farm extract energy from the atmospheric flow and convert it into electricity, resulting in a localized momentum deficit in the wake that reduces energy availability for downwind turbines. Atmospheric momentum convergence from above, below and sides into the wakes replenish the lost momentum, at least partially, so that turbines deep inside a wind farm can continue to function. In this study, we explore recovery processes in a hypothetical offshore wind farm with particular emphasis on comparing the spatial patterns and magnitudes of horizontal and vertical recovery processes and understanding the role of mesoscale processes in momentum recovery in wind farms. For this purpose, we use the Weather Research and Forecasting (WRF) model, a state-of-the-art mesoscale model equipped with a wind turbine parameterization, to simulate a hypothetical large offshore wind farm with different wind turbine spacings under realistic initial and boundary conditions. Results show that vertical turbulent transport of momentum from aloft is the main contributor to recovery in wind farms except in cases with strong background winds and high inter-turbine spacing where horizontal advective momentum transport can also contribute equally. Vertical recovery shows a systematic dependence on wind speed and wind farm density that can be quantified using low-order empirical equations. Wind farms significantly alter the mesoscale flow patterns, especially for densely packed wind farms under high wind speed conditions. In these cases, the mesoscale circulations created by the wind farms can transport high momentum air from aloft into the atmospheric boundary layer (ABL) and thus aid in recovery in wind farms. This is a novel study that is one of the first to look at wind farm replenishment processes under realistic meteorological conditions including the role of mesoscale processes. Overall, this study significantly advances our understanding of recovery processes in wind farms and wind farm-ABL interactions.

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