On Probabilistic Assessment of Exergy Analysis of a Wind Turbine for Optimum Performance

2021 ◽  
Author(s):  
Mubashir Ali Siddiqui ◽  
Muhammad Uzair Yousuf ◽  
Muhammad Kashan Rashid ◽  
Ahsan Ahmed
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Probability Density ◽  
Wind Farm ◽  
Exergy Efficiency ◽  
Second Law ◽  
Optimum Performance ◽  
Wind Speeds ◽  
One Year ◽  
Performance Conditions

Judgment on the performance of a wind turbine depends upon its first law efficiency as well as its second law efficiency. This paper focuses on the second law efficiency, i.e., the exergy efficiency of a wind turbine. The work introduces a novel technique to determine the optimum performance conditions of a wind turbine. Jhimpir city, Pakistan, has been selected as a case study. The wind speed distribution of the selected area is analyzed using different probability density functions. Three-parameter Weibull Distribution turns out to be the best probability density function fitting the wind speed variation. Probability distribution of total wind exergy is performed, and a one-year variation of wind exergy is plotted, showing maximum exergy around the middle of the year. The exergy efficiency of the turbine using a power curve and wind exergy is determined at different wind speeds. Probabilities of various exergy efficiencies are also determined. Results show that higher exergy efficiency has a high probability but so does low exergy efficiency due to seasonal variations. The proposed method can be extended to any wind farm provided the geographical and meteorological parameters of the site.

scholarly journals Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

2021 ◽  
Vol 6 (6) ◽  
pp. 1427-1453
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

scholarly journals Results from a Wake Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake Steering Performance

2021 ◽  
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the three-month experiment period, we estimate that wake steering reduced wake losses by 5.7 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.8 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the predicted achieved yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

scholarly journals Analysis of the Wind Turbine Selection for the Given Wind Conditions

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7740
Author(s):  
Waldemar Kuczyński ◽  
Katarzyna Wolniewicz ◽  
Henryk Charun
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Medium Size ◽  
Wind Speeds ◽  
The North ◽  
Wind Measurements ◽  
Hourly Distribution ◽  
The Given

The aim of the current paper is to present an approach to a wind turbine selection based on an annual wind measurements. The proposed approach led to a choice of an optimal device for the given wind conditions. The research was conducted for two potential wind farm locations, situated on the north of Poland. The wind measurements pointed out a suitability of the considered localizations for a wind farm development. Six types of wind turbines were investigated in each localization. The power of the wind turbines were in the range of 2.0 to 2.5 MW and with a medium size of the rotor being in the range of 82 to 100 m. The purpose of the research was to indicate a wind turbine with the lowest sensitivity to the variation of wind speed and simultaneously being most effective energetically. The Weibull density distribution was used in the analyses for three values of a shape coefficients k. The energy efficiency of the considered turbines were also assessed. In terms of the hourly distribution of the particular wind speeds, the most effective wind turbines were those with a nominal power of 2 MW, whereas the least effective were those with the nominal power of 2.3–2.5 MW. The novelty of the proposed approach is to analyze the productivity for many types of wind turbines in order to select the one which is the most effective energy producer. The analyses conducted in the paper allowed to indicate a wind turbine which generates the highest amount of energy independently on the wind speed variation.

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>

Design Optimization of Small Wind Turbines for Low Wind Regimes

1984 ◽  
Vol 106 (3) ◽  
pp. 347-350 ◽  
Author(s):  
D. E. Cromack ◽  
Debbie Oscar
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Design Optimization ◽  
Wind Turbines ◽  
Wind Farm ◽  
Maximum Energy ◽  
Optimized Design ◽  
Market Place ◽  
Wind Speeds ◽  
Wind Regimes

The optimized design of a wind turbine depends on numerous parameters involving the entire machine (rotor, generator, etc.) and on the intended operating environment as described by the wind characteristics as well as on the load or application. This paper presents the design optimization process, identifies and discusses the influence of the various parameters, and then reviews the procedure by looking at two examples. The results of this process emphasize the relative importance of the design on rated wind speed, rotor rpm, generator size, and rotor blade characteristics. These results in general show that wind turbines have been designed for excessively high-rated wind speeds and generator capacities, except for those machines intended for wind farm applications and sites with particularly high winds. Machines designed for residential use should be sized to closely match the expected load and should be rated at a wind speed close to that value where the maximum energy contribution occurs. This wind speed is much lower than the rated wind speed for most currently available machines particularly for regions of relatively low annual mean wind speeds. Simplicity of design leads to a lower cost system, lower maintenance, and operating costs and greater reliability. It is this simply designed and optimized wind turbine that will have lasting success in the commercial market place.

scholarly journals Prediction and Validation of the Annual Energy Production of a Wind Turbine Using WindSim and a Dynamic Wind Turbine Model

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6604
Author(s):  
Yuan Song ◽  
Insu Paek
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Dynamic Simulation ◽  
Energy Production ◽  
Wind Farm ◽  
Dynamic Performance ◽  
Dynamic Simulations ◽  
Wind Farm Design ◽  
One Year ◽  
Turbine Model

In this study, dynamic simulations of a wind turbine were performed to predict its dynamic performance, and the results were experimentally validated. The dynamic simulation received time-domain wind speed and direction data and predicted the power output by applying control algorithms. The target wind turbine for the simulation was a 2 MW wind turbine installed in an onshore wind farm. The wind speed and direction data for the simulation were obtained from WindSim, which is a commercial computational fluid dynamics (CFD) code for wind farm design, and measured wind speed and direction data with a mast were used for WindSim. For the simulation, the wind turbine controller was tuned to match the power curve of the target wind turbine. The dynamic simulation was performed for a period of one year, and the results were compared with the results from WindSim and the measurement. It was found from the comparison that the annual energy production (AEP) of a wind turbine can be accurately predicted using a dynamic wind turbine model with a controller that takes into account both power regulations and yaw actions with wind speed and direction data obtained from WindSim.

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.

SHAPING OF AN AUTONOMOUS POWER SYSTEM FOR GUARANTEED POWER SUPPLY WITH THE PREDOMINANCE WIND ENERGY

2018 ◽  
pp. 28-50
Author(s):  
S. G. Ignatiev ◽  
S. V. Kiseleva
Keyword(s):  
Energy Storage ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Demand ◽  
Diesel Generator ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.

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.

scholarly journals Inter-annual variability of wind climates and wind turbine annual energy production

2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Standard Deviation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speeds ◽  
Annual Variability ◽  
Eastern Usa

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

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