This dataset is published together with the Paper "CARE to Compare: A real-world dataset for anomaly detection in wind turbine data". Which explains the dataset in detail and defines the CARE-score that can be used to evaluated anomaly detection algorithms on this dataset.

The data consists of 95 datasets, containing 89 years of SCADA time series distributed across 36 different wind turbinesfrom the three wind farms A, B and C. The number of features depends on the wind farm; Wind farm A has 86 features, wind farm B has 257 features and wind farm C has 957 features.

The overall dataset is balanced, as 44 out the 95 datasets contain a labeled anomaly event that leads up to a turbine fault and the other 51 datasets represent normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point and further information about some of the given turbine faults are included.

The data for Wind farm A is based on data from the EDP open data platform (https://www.edp.com/en/innovation/open-data/data), and consists of 5 wind turbines of an onshore wind farm in Portugal. It contains SCADA data and information derived by a given fault logbook which defines start timestamps for specified faults. From this data 22 datasets were selected to be included in this data collection. The other two wind farms are offshore wind farms located in Germany. All three datasets were anonymized due to confidentiality reasons for the wind farms B and C.Each dataset is provided in form of a csv-file with columns defining the features and rows representing the data points of the time series. Files

More detailed information can be found in the included README-file.

The Canadian Wind Turbine Database contains the geographic location and key technology details for wind turbines installed in Canada. This dataset was jointly compiled by researchers at CanmetENERGY-Ottawa and by the Centre for Applied Business Research in Energy and the Environment at the University of Alberta, under contract from Natural Resources Canada. Additional contributions were made by the Department of Civil & Mineral Engineering at the University of Toronto. Note that total project capacity was sourced from publicly available information, and may not match the sum of individual turbine rated capacity due to de-rating and other factors. The turbine numbering scheme adopted for this database is not intended to match the developer’s asset numbering. This database will be updated in the future. If you are aware of any errors, and would like to provide additional information, or for general inquiries, please use the contact email address listed on this page.

Simulated capacity factors in Finland for six wind turbine models, Vestas V90-3.0 MW, V90-2.0 MW, V112-3.3 MW, V126-3.3 MW, V117-3.45 MW and V136-3.45 MW at four turbine hub heights 75, 100, 125, 150 m. Wind speed data are from Finnish Wind Atlas [1, 2], from which the Weibull distribution shape and scale parameters (labelled ‘Weibull all data k’ and ‘Weibull all data A’, respectively) and the frequencies of the wind sectors (‘Frequency all data’) were used.

File FWA_coordinates_2500m.csv holds the geographical coordinates (WGS 84) of the Wind Atlas in 2.5×2.5 km2 resolution.

To simulate a wind farm where each turbine experiences a slightly different wind speed, we used a normal distribution with variance (\sigma^2(v) = 0.2v + 0.6\,\mathrm{m/s}), (where v is wind speed) to smooth (convolute) the original power curves [3, 4].

The calculation of capacity factor cf at wind atlas grid point k is described by the formula

(\mathit{CF}_k = \mathop{\mathbb{E}}_{i, s} g(v_i) \approx \sum_{s=1}^{12} f_{k,s} \sum_{i=1}^N p_{k,s}(v_i) g(v_i) \Delta v),

where g(v) is the power curve function for current wind turbine model, vi the mean wind speed of bin i, fk,s the frequency of occurrence of wind direction s at point k, N the number of wind speed bins, pk,s(v) the Weibull probability density function for sector s at point k at the hub height and Δv the width of the wind speed bin.

References

1. Finnish Meteorological Institute, “Finnish Wind Atlas,” 2008. [Online]. Available: http://www.windatlas.fi. [Accessed: 28-Jun-2016]
2. B. Tammelin, T. Vihma, E. Atlaskin, J. Badger, C. Fortelius, H. Gregow, M. Horttanainen, R. Hyvönen, J. Kilpinen, J. Latikka, K. Ljungberg, N. G. Mortensen, S. Niemelä, K. Ruosteenoja, K. Salonen, I. Suomi, and A. Venäläinen, “Production of the Finnish Wind Atlas,” Wind Energy, vol. 16, no. 1, pp. 19–35, Jan. 2013.
3. Staffell, Iain, and Richard Green. 2014. “How Does Wind Farm Performance Decline with Age?” Renewable Energy 66. Elsevier Ltd: 775–86. doi:10.1016/j.renene.2013.10.041.
4. Staffell, Iain, and Stefan Pfenninger. 2016. “Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output.” Energy 114 (November): 1224–39. doi:10.1016/j.energy.2016.08.068.

Wind turbine manufacturing has been experiencing significant growth in the years leading to 2024, despite general macroeconomic uncertainty experienced during the period. Most states today are required to generate a portion of their electricity from renewable sources, including wind via turbines. The federal renewable electricity production tax credit (PTC) subsidizes energy producers based on the amount of renewable energy they generate, driving growing wind power capacity. Turbine purchases are often timed to benefit from federal tax incentives, which has resulted in considerable volatility during the current period. The Trump Administration's lack of emphasis on renewable energy eroded industry investment, but industry revenue has still been growing at a CAGR of 8.4% over the past five years, reaching an estimated $12.1 billion in 2024. This notably includes a spike of 75.1% in 2020 alone, as the anticipated phaseout of the PTC at the end of 2020 sparked investments. PTCs were eventually extended. Still, the industry is expected to see a slight decline of 0.6% in 2024, as elevated interest rates stifle investment. Meanwhile, profit has seen an overall decline in recent years amid rising costs. The fickle nature of the industry has led to consolidation as larger and more diversified companies are better able to withstand the boom-and-bust years caused by uncertainty in federal policy. The rapid evolution of wind turbines and the potential rise of offshore wind over the coming years has also led to mergers as the industry's major players seek to increase research and development efforts. General Electric Company and Siemens Gamesa Renewable Energy, two of the largest industry operators, have both made large acquisitions and gains over the past five years. These companies, along with Vestas Wind Systems A/S and Nordex Acciona, largely control the US wind market. Moving forward, heightened focus on energy policies prioritizing green and renewable energy, from both the private and public sectors, will buoy revenue. Policies included in the Inflation Reduction Act will drive investment. Overall, industry revenue is expected to increase at a CAGR of 1.3% to $12.9 billion in 2029. Motivated by declining wind turbine prices and renewed investment, the industry will retain significant growth potential, with the wind sector expected to emerge as an increasingly viable alternative energy source.

This data provides locations and technical specifications of legacy versions (ver. 1.0 - ver. X.X) of the United States Wind Turbines database. Each release, typically done quarterly, updates the database with newly installed wind turbines, removes wind turbines that have been identified as dismantled, and applies other verifications based on updated imagery and ongoing quality-control. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), the American Wind Energy Association (AWEA), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single data set. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. A locational error of plus or minus 10 meters for turbine locations was tolerated. Technical specifications for turbines were assigned based on the wind turbine make and models as provided by manufacturers and project developers directly, and via FAA datasets, information on the wind project developer or turbine manufacturer websites, or other online sources. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. Similarly, some turbines were not yet built, not built at all, or for other reasons cannot be verified visually. Location and turbine specifications data quality are rated and a confidence is recorded for both. None of the data are field verified. The current version is available for download at https://doi.org/10.5066/F7TX3DN0. The USWTDB Viewer, created by the USGS Energy Resources Program, lets you visualize, inspect, interact, and download the most current USWTDB version only, through a dynamic web application. https://eerscmap.usgs.gov/uswtdb/viewer/

DeepOWT (deep learning derived global offshore wind turbines) is an independent and openly accessible data set of offshore wind energy infrastructure locations and their temporal deployment dynamics on a global scale. It is derived by applying deep learning based object detection on ESA's spaceborne Sentinel-1 synthetic aperture radar (SAR) archive. DeepOWT provides OWT locations along with their quarterly deployment stages from 2016 until 2021. It differentiates between platforms under construction, OWTs which are readily deployed and offshore wind farm substations, such as transformer stations. Related publication

File metadata


    File
    Time
    Periods
    Geometry
    Entries


    DeepOWT.geojson (Dataset)
    2016Q3-2021Q2
    20
    points
    9941


    gt_2021Q2_nsb.geojson (Ground Truth)
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    4354


    gt_2021Q2_ecs.geojson (Ground Truth)
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    2844


    gt_2019Q4_nsb.geojson (Ground Truth)
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    gt_2019Q4_ecs.geojson (Ground Truth)
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    gt_2016Q3-2021Q1_nsb.geojson (GT)
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    gt_2016Q3-2021Q1_ecs.geojson (GT)
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    gt_nsb_gridded.geojson (GT North Sea Basin)


    polygon
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    gt_ecs_gridded.geojson (GT East China Sea)


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Mapping of integer values used in the dataset to semantic classes


    Integer
    Semantic label
    Abbreviation




    0
    open sea
    sea


    1
    under construction
    const


    2
    offshore wind turbine
    owt


    3
    offshore wind farm substation
    sub

In 2023, around 425.2 terawatt hours of wind electricity were generated in the United States. Wind has advanced to become the main source of renewable power generation in the U.S., ahead of conventional hydropower.

Clean energy on the rise

Recent years have seen significant increases in U.S. clean energy investments, specially the years between 2020 and 2022. In 2022, renewable investments rose to 141 billion U.S. dollars, an increase of almost 25 percent compared to the previous year. Larger investments in clean energy in the past decade have brought higher generation of wind and solar power.

The globalized U.S. wind market

Based in Copenhagen, the Danish company Vestas holds a large portion of the global wind manufacturer market share. In 2923, Vestas electricity deliveries were the highest to the U.S. Though the U.S. has generated increasing amounts of wind power, it continues to source much of its wind power turbines and equipment from international companies such as Vestas.

The American company, GE Wind, was the largest wind turbine manufacturer in the United States in terms of installation capacity at around 3,752 megawatts in 2023. With a total capacity of approximately 1,918 megawatts, the Danish manufacturer, Vestas, ranked second in that year. General Electric invests in renewables GE Wind is a branch from the General Electric division, GE Renewable Energy. Created in 2015, GE Renewable combined the wind power assets of two GE purchases, and the headquarters was moved from upstate New York to Paris, France. Though based abroad, GE Wind remains the only American company among the leading wind turbine manufacturers in the United States. The globalized wind turbine market With its extensive overseas operations, Copenhagen-based Vestas dominates the international wind turbine manufacturer market with over 15 billion euros in revenue in 2022. In 2023, Vestas delivered more wind turbines to the United States than to any other country, followed by deliveries to Brazil. The United States has the second highest cumulative installed capacity of wind power worldwide after China, but still relies heavily on international manufacturers such as Vestas or Nordex.

This dataset provides locations and technical specifications of wind turbines in the United States, almost all of which are utility-scale. Utility-scale turbines are ones that generate power and feed it into the grid, supplying a utility with energy. They are usually much larger than turbines that would feed a house or business. The regularly updated database contains wind turbine records that have been collected, digitized, and locationally verified. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), American Clean Power (ACP) Association (formerly American Wind Energy Association (AWEA)), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single dataset. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. A locational error of plus or minus 10 meters for turbine locations was tolerated. Technical specifications for turbines were assigned based on the wind turbine make and models as provided by manufacturers and project developers directly, and via FAA datasets, information on the wind project developer or turbine manufacturer websites, or other online sources. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. Similarly, some turbines were not yet built, not built at all, or for other reasons cannot be verified visually. Location and turbine specifications data quality are rated, and confidence is recorded for both. None of the data are field verified.

Connected wind farm locations in Ireland (ex Northern Ireland) extracted from SEAI's Wind Atlas. Connected wind farms as per Eirgrid and ESB Networks reports which correspond to the wind farm layer on the SEAI Wind Atlas. (See references at description end.) In interpreting the information, PLEASE NOTE the following cautions: 1) Grid coordinates of substations have been used where the wind farm grid coordinates were not available in the planning application information. However, as a substation may or may not be located within the site of the wind farm it serves, it should be noted that the precise accuracy of this coordinate information cannot be assured. 2) Possible stand-alone projects not intended for connection to the Irish electricity grid are not included in this map. 3) The map entries are not necessarily all discrete wind farms. Some could be extensions to existing wind farms, and some could be formal registered capacity additions involving no physical addition of new wind turbines. Zipped collections of shapefiles are available in two spatial reference or coordinate systems: 1) Irish Transverse Mercator (ITM, EPSG:2157) 2) WGS 84 Web Mercator (EPSG:3857) References ESB Generator Statistics EirGrid Connected and Contracted Generators -- Ireland

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Wind Turbine Market Size and Trends

The wind turbine market size is forecast to increase by USD 47.9 million at a CAGR of 9.3% between 2023 and 2028. The market is experiencing significant growth due to the increasing emphasis on clean energy and reducing carbon footprint in response to climate change. Wind turbines have emerged as an efficient and economical renewable energy resource, providing electricity generation that reduces reliance on fossil fuels and associated carbon dioxide emissions. Government initiatives to promote the use of renewable energy and improve air quality are driving market growth. Additionally, wind energy production offers agricultural income through land lease agreements and the potential for co-location with farming operations. This trend is expected to continue as the world transitions to a more sustainable energy future. Keywords: wind turbines, climate change, electricity generation, fossil fuels, carbon dioxide emissions, clean energy, air quality, public health, agricultural income.

Market Overview

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Wind turbines have emerged as a crucial component in the global shift towards clean energy and reducing carbon footprint. With the increasing awareness of the negative impacts of fossil fuels on climate change, air quality, and public health, the demand for renewable energy sources, including wind power, has been on the rise. Wind turbines play a significant role in electricity generation, contributing to the reduction of carbon dioxide emissions. The use of wind turbines not only benefits the environment but also offers economic advantages. For instance, landowners can earn income by leasing their land for wind farm installations. Moreover, wind energy is an excellent option for off-grid power, providing electricity to remote areas where traditional power sources are not readily available. The wind power market in the US is expected to grow significantly in the coming years.

Furthermore, the transition to wind power and other renewable energy sources is essential for reducing our reliance on fossil fuels and mitigating the negative impacts on the environment and public health. Wind turbines offer a viable solution for electricity generation, with the added benefits of energy efficiency, economic opportunities, and a reduced carbon footprint. In conclusion, wind turbines are a crucial component in the transition to a cleaner and more sustainable energy future. With the growing demand for renewable energy sources and the increasing awareness of the negative impacts of fossil fuels, the wind turbine market is poised for significant growth in the US and beyond.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Type

  Onshore
  Offshore


Geography

  APAC

    China
    India


  Europe

    Germany
    Spain


  North America

    US


  Middle East and Africa



  South America

By Type Insights

The onshore segment is estimated to witness significant growth during the forecast period. The wind turbine market is experiencing significant growth due to the global push towards clean energy and reducing carbon footprint in electricity generation. Onshore wind turbines accounted for the largest share of the global market in 2023, with steady growth anticipated compared to offshore wind farms.

Get a glance at the market share of various segments Download the PDF Sample

The metallurgical segment was the largest and valued at USD 44.40 million in 2018. Real-time wind turbine monitoring systems, which optimize performance and efficiency, are gaining popularity in onshore applications due to their ease of implementation. APAC is expected to dominate the onshore wind power generation sector due to favorable regulations. By adopting wind turbines, countries can reduce their reliance on fossil fuels, decrease carbon dioxide emissions, improve air quality, and enhance public health. Investing in wind turbines is an excellent agricultural income source, making it an attractive option for farmers and rural communities.

Regional Analysis

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Europe is estimated to contribute 37% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. The Asia Pacific (APAC) region is experiencing a surge in energy demand due to population growth and improving living standards. In response, there is a heightened focus on renewable energy sources, particularly wind energy, for power generation. China and India are anticipated to dominate the installation of wind turbines in th

In the manuscript, we investigate condition monitoring methods based on unsupervised dictionary learning.

The dataset includes the raw time-domain vibration signals from six turbines within the same wind farm (near geographical location). All the wind turbines are of the same type and possess a three-stage gearbox. All measurement data corresponds to the axial direction of an accelerometer mounted on the housing of the output shaft bearing of each turbine. The sampling rate is 12.8 kilosamples/second and each signal segment is 1.28 seconds long (16384 samples).

There are six files, which contains the vibration data from each of the six wind turbines. Within each file, each row corresponds to a different measurement. Furthermore, the first column represents the time expressed in years since the vibration data started to be recorded. The second column is the speed expressed in cycles per minute. The remaining columns are the vibration signal time series expressed in Gs.

The dataset was originally published in DiVA and moved to SND in 2024.

Wind turbines are an important source of renewable energy. There is a rapid growth in the number of wind turbine installations across the globe. These installations are visible in high resolution aerial imagery. However, it can be tedious to analyze imagery and mark these installations manually. This deep learning model can automate the detection of wind turbines by interpreting high resolution imagery.Using the modelFollow the guide to use the model. This model requires deep learning libraries to be installed, install them using Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (60 cm) imagery.OutputFeature class containing detected wind turbines.Applicable geographiesThe model is expected to work well across USA and Netherlands.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.96.Training dataThis model has been trained on an Esri proprietary wind turbines dataset.Sample resultsHere are a few results from the model.

The data in this repository consists of 4 files. This includes a readme file [readme.txt], a file summarizing the wind speed [All_Windspeed_Data.csv], a file for the resulting power outputs [All_Power_Data.csv],and a zip-file including detailed data for each wind farm [Data_Per_Wind_Farm.zip]. Each file can be downloaded seperatly or colectivly by clicking the "Download all"-Button.The structure of this repository is as follows:├── readme.txt (this file)├── All_Power_Data.csv (Power time series of wind farms)├── All_Windspeed_Data.csv (Windspeed time series of wind farms)├── Data_Per_Wind_Farm (folder including csv-files for each wind farm) ├── Baie_de_Saint_Brieuc ├── Baltic_Eagle ├── Beatrice ├── Borkum_Riffgrund ├── Borssele_(Phase_1,2) ├── Borssele_(Phase_3,4) ├── Dieppe_et_Le_Treport ├── Dogger_Bank_(Phase_A,B) ├── East_Anglia_One ├── Gemini ├── Gode_Wind ├── Greater_Gabbard ├── Gwynt_y_Mor ├── Hautes_Falaises ├── Hohe_See ├── Hollandse_Kust_Noord ├── Hollandse_Kust_Zuid ├── Horns_Rev ├── Hornsea_(Project_1) ├── Hornsea_(Project_2) ├── Iles_dYeu_et_de_Noirmoutir ├── Kriegers_Flak ├── London_Array ├── Moray_Firth ├── Race_Bank ├── Seagreen ├── Seamade ├── Triton_Knoll ├── WalneyIn the 29 files included in the zip-file [Data_Per_Wind_Farm.zip], we report detailed data for each wind farm. Therein, each column includs one variable while each row represents one point in time. Namely, the columns contain:- time- u-component of wind 100m above ground- v-component of wind 100m above ground- forecasted surface roughness (fsr)- scaled windspeed at hub heigts (heigt given in parentheses - multiple time series possible)- Wind direction in degrees- Power of wind turbines (type given in parentheses - multiple time series possible)- Turn_off (0: turbine turned off because of strong winds, 1: turbines active)- Power (resulting power output of wind farm over all turbine types).Starting from January 1, 1980, 00:00 am UTC in the first row, the data set ranges up to December 31, 2019, 11:00 pm in the last of 350640 rows.Similar to the detailed files per wind farm, each row in the two csv files [All_Power_Data.csv , All_Windspeed_Data.csv] reporting wind speed at hub height and total power represent one point in time for the same period.In the [All_Power_Data.csv] each row gives the sythetic resulting power outout in MW of one wind farm. I.e., the dataset includes 29 columns one for each wind farm. In the [All_Windspeed_Data.csv] each row gives the calculated windspeed im 100m above ground in m/s at the position of each wind farm. I.e., the dataset includes 29 columns one for each wind farm. Data generated using Copernicus Climate Change Service information [1980-2019] and containing modified Copernicus Climate Change Service information [1980-2019].

Wind turbine data for completed Alaska wind energy projects. Data includes the wind farm operator, the number of turbines and model, rated power and output, commission date, project cost, and power output type. Source: Alaska Energy Authority, Alaska Industrial Development and Export Authority

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Energy Authority Wind Program Overview

The Global Wind Power Tracker (GWPT) is a worldwide dataset of utility-scale wind facilities. It includes wind farm phases with capacities of 10 megawatts (MW) or more. A wind project phase is generally defined as a group of one or more wind turbines that are installed under one permit, one power purchase agreement, and typically come online at the same time. The GWPT catalogs every wind farm phase at this capacity threshold of any status, including operating, announced, under development, under construction, shelved, cancelled, mothballed, or retired. Each wind farm included in the tracker is linked to a wiki page on the GEM wiki.

Architecture

Global Energy Monitor’s Global Wind Power Tracker uses a two-level system for organizing information, consisting of both a database and wiki pages with further information. The database tracks individual wind farm phases and includes information such as project owner, status, installation type, and location. A wiki page for each wind farm is created within the Global Energy Monitor wiki. The database and wiki pages are updated annually.

Status Categories

• Announced: Proposed projects that have been described in corporate or government plans but have not yet taken concrete steps such as applying for permits.
• Development: Projects that are actively moving forward in seeking governmental approvals, land rights, or financing.
• Construction: Site preparation and equipment installation are underway.
• Operating: The project has been formally commissioned; commercial operation has begun.
• Shelved: Suspension of operation has been announced, or no progress has been observed for at least two years.
• Cancelled: A cancellation announcement has been made, or no progress has been observed for at least four years.
• Retired: The project has been decommissioned.
• Mothballed: The project is disused, but not dismantled.

Research Process

The Global Wind Power Tracker data set draws on various public data sources, including:

• Government data on individual power wind farms (such as India Central Electricity Authority’s “Plant Wise Details of All India Renewable Energy Projects” and the U.S. EIA 860 Electric Generator Inventory), country energy and resource plans, and government websites tracking wind farm permits and applications;
• Reports by power companies (both state-owned and private);
• News and media reports;
• Local non-governmental organizations tracking wind farms or permits.

Global Energy Monitor researchers perform data validation by comparing our dataset against proprietary and public data such as Platts World Energy Power Plant database and the World Resource Institute’s Global Power Plant Database, as well as various company and government sources.

Wiki Pages

For each wind farm, a wiki page is created on Global Energy Monitor’s wiki. Under standard wiki convention, all information is linked to a publicly-accessible published reference, such as a news article, company or government report, or a regulatory permit. In order to ensure data integrity in the open-access wiki environment, Global Energy Monitor researchers review all edits of project wiki pages.

Mapping

To allow easy public access to the results, Global Energy Monitor worked with GreenInfo Network to develop a map-based and table-based interface using the Leaflet Open-Source JavaScript library. In the case of exact coordinates, locations have been visually determined using Google Maps, Google Earth, Wikimapia, or OpenStreetMap. For proposed projects, exact locations, if available, are from permit applications, or company or government documentation. If the location of a wind farm or proposal is not known, Global Energy Monitor identifies the most accurate location possible based on available information.

As of June 2022, the MySE 16.0-242 from MingYang Smart Energy was the largest wind turbine with a 242 rotor diameter and a nameplate capacity of 16 megawatts. As of that time it was still under construction and was expected to be online by 2026.

Wind turbines

The main function of wind turbines is to convert the wind’s kinetic energy into electrical power. Energy generated from wind turbines is undoubtedly one of the cleanest forms of producing electrical power from a renewable source. Wind turbines can be used to generate large amounts of electricity in wind farms. Wind power is considered one of the fastest growing sources of electricity in the world. Newly installed wind power capacity worldwide reached approximately 93.6 gigawatts in 2021.

Vestas: a leader in wind turbine manufacturing

Vestas is one of the largest wind turbine manufacturers worldwide. The Danish-based company’s focus revolves primarily around the production of wind turbines. In 2020, the company had the highest revenue among wind turbine manufacturers, surpassing 18 billion U.S. dollars. Vestas has turbines installed in over 80 countries and had the largest wind commissioned capacity in 2021. Apart from turbine manufacturing, Vestas is also involved in selling, installing, and maintaining operational power plants.

The number of active wind power turbines in Denmark increased every year from 2011 until the peak in 2023, when it reached 6,974. In 2020, the number had decreased slightly, down to 6,924 active wind turbines. Most of the active wind turbines in Denmark were onshore, around 6,326 compared to 648 offshore.

Wind power coverage   

In 2022, over 53 percent of the total electricity consumption in Denmark was covered by wind power. This was an increase from the previous year, when the coverage had reached 43.7 percent. Except for a drop in 2016, 2018, and 2021, the share of wind power coverage increased markedly since 2009, when only around 19 percent of Denmark’s total electricity consumption was covered by wind power.

Wind power production   

How much wind power was produced to cover the large share of the electricity consumption? In 2022, 19 terawatt hours of wind power were produced in Denmark, which was an increase compared to the previous year. That year, the highest production was in December when it reached around 1.8 terawatt hours, and the lowest in June, when 836 gigawatt hours were produced.

The Wind Turbine Market Research Report is Segmented by Location of Deployment (Onshore and Offshore), Capacity (Small, Medium, and Large), and Geography (North America, Europe, Asia-Pacific, South America, and Middle East and Africa). The Report PDF Offers the Installed Capacity, Wind Turbine Market Share, Forecasts, and Price Trend in GW for all the Above-Mentioned Segments.