scholarly journals Evaluation of AEP Predictions for Commercial Wind Farms in Sweden

2020 ◽  
Vol 10 (22) ◽  
pp. 7995
Author(s):  
Erik Möllerström ◽  
Daniel Lindholm
Keyword(s):  
South Africa ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Wind Farms ◽  
Project Planning ◽  
Data Availability ◽  
Energy Calculation ◽  
The Mean ◽  
Over Time

Based on data from 1162 wind turbines, with a rated power of at least 1.8 MW, installed in Sweden after 2005, the accuracy of the annual energy production (AEP) predictions from the project planning phases has been compared to the wind-index-corrected production. Both the production and the predicted AEP data come from the database Vindstat, which collects information directly from wind turbine owners. The mean error was 7.1%, which means that, overall, the predicted AEP has been overestimated. The overestimation was higher for wind turbines situated in open terrain than in forest areas and was higher overall than that previously established for the British Isles and South Africa. Dividing the result over the installation year, the improvement which had been expected due to the continuous refinement of the methods and better data availability, was not observed over time. The major uncertainty comes from the predicted AEP as reported by wind turbine owners to the Vindstat database, which, for some cases, might not come from the wind energy calculation from the planning phase (i.e., the P50-value).

scholarly journals Improvement of AEP Predictions with Time for Swedish Wind Farms

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3475
Author(s):  
Erik Möllerström ◽  
Sean Gregory ◽  
Aromal Sugathan
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Project Planning ◽  
Data Availability ◽  
Electricity Production ◽  
Farm Size ◽  
Expected Performance ◽  
Performance Decline ◽  
The Mean

Based on data from 2083 wind turbines installed in Sweden from 1988 onwards, the accuracy of the predictions of the annual energy production (AEP) from the project planning phases has been compared to the actual wind-index-corrected production. Both the electricity production and the predicted AEP come from Vindstat, a database that collects information directly from wind turbine owners. The mean error for all analyzed wind turbines was 13.0%, which means that, overall, the predicted AEP has been overestimated. There has been an improvement of accuracy with time with an overestimation of 8.2% for wind turbines installed in the 2010s, however, the continuous improvement seems to have stagnated around 2005 despite better data availability and continuous refinement of methods. Dividing the results by terrain, the error is larger for wind turbines in open and flat terrain than in forest areas, indicating that the reason behind the error is not the higher complexity of the forest terrain. Also, there is no apparent increase of error with wind farm size which could have been expected if wind farm blockage effect was a main reason for the overestimations. Besides inaccurate AEP predictions, a higher-than-expected performance decline due to inadequate maintenance of the wind turbines may be a reason behind the AEP overestimations. The main sources of error are insecurity regarding the source of AEP predictions and the omission of mid-life alterations of rated power.

scholarly journals Effect of Wind Turbine Classes on the Electricity Production of Wind Farms in Cyprus Island

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yiannis A. Katsigiannis ◽  
George S. Stavrakakis ◽  
Christodoulos Pharconides
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Wind Farms ◽  
Electricity Production ◽  
Wind Speeds ◽  
Wind Potential

This paper examines the effect of different wind turbine classes on the electricity production of wind farms in two areas of Cyprus Island, which present low and medium wind potentials: Xylofagou and Limassol. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from five different manufacturers have been used. For each manufacturer, two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC II and IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (IEC III class) in both locations, in terms of energy production. This improvement is higher for the location with the lower wind potential and starts from 7%, while it can reach more than 50%.

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

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 739 ◽  
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.

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

2021 ◽  
pp. 1748006X2098426
Author(s):  
Sergio Martin-del-Campo ◽  
Fredrik Sandin ◽  
Stephan Schnabel
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Dictionary Learning ◽  
Population Distribution ◽  
Wind Farms ◽  
Data Availability ◽  
Rolling Element Bearing ◽  
Data Sampling ◽  
Selection Of

We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

scholarly journals Resource and waste quantification scenarios for wind turbine decommissioning in the United Kingdom

2020 ◽  
Author(s):  
Kiran Tota-Maharaj ◽  
Alexander McMahon
Keyword(s):  
United Kingdom ◽  
Waste Management ◽  
Wind Turbine ◽  
End Of Life ◽  
Wind Turbines ◽  
Rapid Rise ◽  
The United Kingdom ◽  
Management Procedures ◽  
The Uk ◽  
Over Time

AbstractWind power produces more electricity than any other form of renewable energy in the United Kingdom (UK) and plays a key role in decarbonisation of the grid. Although wind energy is seen as a sustainable alternative to fossil fuels, there are still several environmental impacts associated with all stages of the lifecycle of a wind farm. This study determined the material composition for wind turbines for various sizes and designs and the prevalence of such turbines over time, to accurately quantify waste generation following wind turbine decommissioning in the UK. The end of life stage is becoming increasingly important as a rapid rise in installation rates suggests an equally rapid rise in decommissioning rates can be expected as wind turbines reach the end of their 20–25-year operational lifetime. Waste data analytics were applied in this study for the UK in 5-year intervals, stemming from 2000 to 2039. Current practices for end of life waste management procedures have been analysed to create baseline scenarios. These scenarios have been used to explore potential waste management mitigation options for various materials and components such as reuse, remanufacture, recycling, and heat recovery from incineration. Six scenarios were then developed based on these waste management options, which have demonstrated the significant environmental benefits of such practices through quantification of waste reduction and greenhouse gas (GHG) emissions savings. For the 2015–2019 time period, over 35 kilotonnes of waste are expected to be generated annually. Overall waste is expected to increase over time to more than 1200 kilotonnes annually by 2039. Concrete is expected to account for the majority of waste associated with wind turbine decommissioning initially due to foundations for onshore turbines accounting for approximately 80% of their total weight. By 2035–2039, steel waste is expected to account for almost 50% of overall waste due to the emergence of offshore turbines, the foundations of which are predominantly made of steel.

Development and Field Testing of an Inclined Flanged Compact Diffuser for a Micro Wind Turbine

2014 ◽  
Author(s):  
Sandip Kale ◽  
S. N. Sapali
Keyword(s):  
Wind Turbine ◽  
Wind Velocity ◽  
Wind Turbines ◽  
Energy Production ◽  
Cost Effective ◽  
Field Testing ◽  
International Electrotechnical Commission ◽  
Average Wind Speed ◽  
Average Wind ◽  
Power Curves

Micro wind turbines installed in various applications, experience average wind speed for most of the time during operations. Power produced by the wind turbine is proportional to the cubic power of the wind velocity and a small increase in wind velocity results increases power output significantly. The approach wind velocity can be increased by covering traditional wind turbine with a diffuser. Researchers are continuously working to develop a compact, lightweight, cost effective and feasible diffuser for wind turbines. The present work carried out to develop a diffuser with these stated objectives. A compact, lightweight inclined flanged diffuser developed for a micro wind turbine. Bare micro wind turbine and wind turbine covered with developed efficient inclined flanged diffuser tested in the field as per International Electrotechnical Commission (IEC) standards and results presented in the form of power curves. The prediction of annual energy production for both wind turbines determined as per IEC standards.

Machine Learning Aided Prediction of Rain Erosion Damage on Wind Turbine Blade Sections

2021 ◽  
Author(s):  
Alessio Castorrini ◽  
Paolo Venturini ◽  
Fabrizio Gerboni ◽  
Alessandro Corsini ◽  
Franco Rispoli
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Energy Production ◽  
Turbine Blades ◽  
Wind Farms ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Coating Materials ◽  
Erosion Modelling ◽  
Rain Erosion

Abstract Rain erosion of wind turbine blades represents an interesting topic of study due to its non-negligible impact on annual energy production of the wind farms installed in rainy sites. A considerable amount of recent research works has been oriented to this subject, proposing rain erosion modelling, performance losses prediction, structural issues studies, etc. This work aims to present a new method to predict the damage on a wind turbine blade. The method is applied here to study the effect of different rain conditions and blade coating materials, on the damage produced by the rain over a representative section of a reference 5MW turbine blade operating in normal turbulence wind conditions.

Simulation of Flow Field on a Large Scale Wind Turbine

2008 ◽  
Author(s):  
I. Janajreh ◽  
C. Ghenai
Keyword(s):  
Flow Field ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Cross Sections ◽  
Large Scale ◽  
Wind Farms ◽  
Operating Characteristics ◽  
Cross Sectional ◽  
Capital Costs

Large scale wind turbines and wind farms continue to evolve mounting 94.1GW of the electrical grid capacity in 2007 and expected to reach 160.0GW in 2010 according to World Wind Energy Association. They commence to play a vital role in the quest for renewable and sustainable energy. They are impressive structures of human responsiveness to, and awareness of, the depleting fossil fuel resources. Early generation wind turbines (windmills) were used as kinetic energy transformers and today generate 1/5 of the Denmark’s electricity and planned to double the current German grid capacity by reaching 12.5% by year 2010. Wind energy is plentiful (72 TW is estimated to be commercially viable) and clean while their intensive capital costs and maintenance fees still bar their widespread deployment in the developing world. Additionally, there are technological challenges in the rotor operating characteristics, fatigue load, and noise in meeting reliability and safety standards. Newer inventions, e.g., downstream wind turbines and flapping rotor blades, are sought to absorb a larger portion of the cost attributable to unrestrained lower cost yaw mechanisms, reduction in the moving parts, and noise reduction thereby reducing maintenance. In this work, numerical analysis of the downstream wind turbine blade is conducted. In particular, the interaction between the tower and the rotor passage is investigated. Circular cross sectional tower and aerofoil shapes are considered in a staggered configuration and under cross-stream motion. The resulting blade static pressure and aerodynamic forces are investigated at different incident wind angles and wind speeds. Comparison of the flow field results against the conventional upstream wind turbine is also conducted. The wind flow is considered to be transient, incompressible, viscous Navier-Stokes and turbulent. The k-ε model is utilized as the turbulence closure. The passage of the rotor blade is governed by ALE and is represented numerically as a sliding mesh against the upstream fixed tower domain. Both the blade and tower cross sections are padded with a boundary layer mesh to accurately capture the viscous forces while several levels of refinement were implemented throughout the domain to assess and avoid the mesh dependence.

scholarly journals Tourist Viewshed Externalities and Wind Energy Production

2017 ◽  
Vol 46 (2) ◽  
pp. 224-241 ◽  
Author(s):  
Jacob R. Fooks ◽  
Kent D. Messer ◽  
Joshua M. Duke ◽  
Janet B. Johnson ◽  
Tongzhe Li ◽  
...  
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Hotel Room ◽  
Negative Effect ◽  
The Impact ◽  
Large Wind

This study uses an experiment where ferry passengers are sold hotel room “views” to evaluate the impact of wind turbines views on tourists’ vacation experience. Participants purchase a chance for a weekend hotel stay. Information about the hotel rooms was limited to the quality of the hotel and its distance from a large wind turbine, as well as whether or not a particular room would have a view of the turbine. While there was generally a negative effect of turbine views, this did not hold across all participants, and did not seem to be effected by distance or hotel quality.

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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