Introduction

This dataset is issued from the third and final field campaign of the French national project SMARTEOLE. It consists in data from 7 wind turbines of a single wind farm (Sole du Moulin Vieux, located in France) for which Wind Farm Control field tests were performed to evaluate the performance of a wake steering strategy for improving the power production.

The wind farm consists of 7x Senvion MM82 wind turbines (rotor diameter of 82m, nominal power of 2.05 MW).

Description

The tests were realized between 17 February – 25 May 2020, with wake steering implemented on turbine SMV6. This dataset covers this full period, and it has been pre-processed to facilitate the analysis of the Wind Farm Control experiment. All timesteps when at least one turbine was stopped were removed, and SCADA nacelle position and wind direction signals have been corrected to remove any north alignment issues. Finally, the time resolution has been standardized at 1-min from the raw data recorded at higher frequencies from the different sensors. For more details about the development of the field campaign and the pre-processing steps followed in the data analysis, please consult the related publication : https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html. Some information can also be found in the related IEA task 44 wiki page.

The following files can be found in the dataset :

• SMARTEOLE_WakeSteering_SCADA_1minData.csv : the Supervisory Control and Data Acquisition (SCADA) data from the 7 turbines.
• SMARTEOLE_WakeSteering_ControlLog_1minData.csv : logs from the control system located on turbine SMV6, responsible for the application of the wake steering. The applied yaw offset on the turbine at each timestep can be found here.
• SMARTEOLE_WakeSteering_WindCube_1minData.csv : data from the ground based WindCube profiler lidar, located between SMV2 and SMV3. This can be used to assess the ambient environmental wind conditions at the farm.
• SMARTEOLE_WakeSteering_Coordinates_staticData.csv : file listing the coordinates of the wind turbines in the farm and WindCube location in traditional Latitude / Longitude system (WGS84) and XY metric system (French Lambert 93).
• SMARTEOLE_WakeSteering_Map.pdf : the map of the farm showing the location of wind turbines and WindCube. This is the exact same map as the one seen in the paper indicated above.
• SMARTEOLE_WakeSteering_NTF_SMV6_staticData.csv : the transfer function used in the paper to correct the wind speed measured by SMV6 to better match the freestream wind speed at 150m upstream (i.e. approximately 1.8 diameters), derived using WindCube nacelle lidar installed on top of the turbine.
• SMARTEOLE_WakeSteering_correction_factors_SMV1237_staticData.csv : the transfer function used in the paper to derive and correct the reference power and wind speed signals —defined as the mean values of the power and wind speeds from SMV1, SMV2, SMV3, and SMV7— to remove biases from the values at SMV6 as a function of wind direction and wind speed. These corrected reference signals are used for quantifying the impact of the wake steering.
• SMARTEOLE_WakeSteering_GuaranteedPowerCurve_staticData.csv : the warranted power and thrust curves for the standard mode (Mode 0) of the MM82 wind turbine.
• SMARTEOLE_WakeSteering_ReadMe.xlsx : read me file indicating for each dataset the signification of the different variables.

Unfortunately, the WindCube nacelle lidar data on top of SMV6 could not be shared, instead the transfer functions derived thanks to this sensor can be used to correct the SCADA channels. The Wind Energy Science publication describes how these transfer functions were obtained.

Acknowledgement

The creation of this dataset was realized in the scope of French national project SMARTEOLE, supported by the Agence Nationale de la Recherche (grant no. ANR-14-CE05-0034).

Furthermore, we would like to thank ENGIE Green for allowing us to make this dataset publicly available.

How to cite this dataset

When using this dataset in future research, please add the following sentence in the Ackowledgement section of your publication :

"The dataset used in this research has been obtained by ENGIE Green in the scope of French national project SMARTEOLE (grant no. ANR-14-CE05-0034)".

When citing the dataset in the core text of a paper, the reference to Simley et al. can simply be used.

Related datasets and publications

Several field test campaigns were realized in the scope of SMARTEOLE project. Although these data are not made publicly available by default, they can be shared in a per-project basis and under the protection of a dedicated NDA. Please refer to the following publications listed below to get an idea of the content of the different datasets.

SMARTEOLE Field Test 1

• Ahmad T. et al., Field Implementation and Trial of Coordinated Control of WIND Farms, IEEE Transactions on Sustainable Energy, 9(3), 2018, 10.1109/TSTE.2017.2774508.
• Duc T., Optimization of wind farm power production using innovative control strategies, Master’s thesis, DTU Wind Energy-M-0161, 2017.
• Duc T. et al., Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm, Wind Energy Science, 4(2), 2019, 10.5194/wes-4-287-2019.
• Torres Garcia E. et al., Statistical characteristics of interacting wind turbine wakes from a 7-month LiDAR measurement campaign, Renewable Energy, 130, 2019, 10.1016/j.renene.2018.06.030.
• Hegazy A. et al., LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models, Renewable Energy, 181, 2022, 10.1016/j.renene.2021.09.019.

SMARTEOLE Field Test 2

• Tagliatti F., Investigation of Wind Turbine Fatigue Loads under Wind Farm Control: Analysis of Field Measurements, Master’s thesis, DTU Wind Energy-M-0302, 2019.
• Göçmen T. et al., FarmConners wind farm flow control benchmark – Part 1: Blind test results, Wind Energy Science, 7(5), 2022, 10.5194/wes-7-1791-2022.

SMARTEOLE Field Test 3

• Simley E. et al., Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, Wind Energy Science, 6(6) 2021, 10.5194/wes-6-1427-2021.

Release Notes

• v1.0 (2022-11-24) : first version of the dataset.

The dataset file contains data regarding the electrical and mechanical operational actual quantities and parameters obtained and recorded by the internal inverter controller of a Skystream 3.7 small wind turbine (SWT) installed on the roof of the High Voltage Laboratory at the Institute of Energy and Environment (IEE) of the University of Sao Paulo (USP), Brazil, recorded from 2017 to 2022.

The main electrical parameters are the energy, voltages, and currents in the connection grid point and power frequency. Mechanical information can be retrieved, such as the rotation and the wind speed. The temperature, measured in some location points to the nacelle and inverter, is also recorded. Several other parameters concerning the SWT inverter operation, such as the dc voltages on its internal bus, alarms, and flags, are also presented.

The files in the dataset are named as "data_swt_iee_usp_YYYY.csv" where YYYY is the referring year. In the files, the semicolon symbol (;) is used as a column separator, while the dot symbol (.) represents the decimal separator. The first row of the CSV file corresponds to the header row to help identify data as described in the file "data_description.txt". Sampling rate one record per minute.

The first line on each year-based file represents the header table description of the data columns.

The complete and detailed information about the installation, localization,and analysis is in the article published at: https://doi.org/10.3390/wind2040037

General description of wind turbine: The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system.

Location of site: The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E.

Control and measurement systems and signals: The turbine is regulated via a variable-speed and collective variable pitch control system.

SHM Motivation: Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes.

Data Description: The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout.

The measurements/instrumentation setup, type and layout is provided in the pdf files.

The data: the data is provided in zip files corresponding to four use-cases as follows:

• Normal operation data for system identification
• Aerodynamic imbalance on one blade
• Rotor icing event
• Failure of the flexible coupling of the linear drive of the collective pitch system

The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows:

• Time-series data in HDF5 format
• Metadata:
  • Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)
  • Sensor specification (Aventa_sensors.json )
  • Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)
• Semantic artifacts:
  • WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)
  • Sensor specification JSON schema (sensors_schema.json)
• Media: Pictures of leading edge roughness and a clip of wind turbine operation
• Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)

Additional data is available upon request, please contact:

• Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)
• Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)

For further details or questions, please contact:

Prof. Dr. Eleni Chatzi
Chair of Structural Mechanics & Monitoring

ETH Zürich
http://www.chatzi.ibk.ethz.ch/

SCADA Data from Aventa AV-7 (6kW) Research Wind Turbine, located in Taggenberg (ZH), Lat:47.52 Long:8.68236. The turbine is owned by IET-OST. The data covers time period from 2022-01-01 to 2023-07-20, sampled at 1Hz. The dataset is intended for environmental and operational analysis.
15 SCADA Channels contain timeseries observations (at 1Hz) of: Rotor speed in RPM, Generator speed in RPM, Stator temperature in C, Windspeed in m/s, Converter active power in kW, Wind direction w.r.t. nacelle measured in degrees, System supply voltage 24V , Pitch angle in degrees, Turbine status.
Structured metadata about wind turbine characteristics and SCADA channels is included as JSON files and CSV. Additional information is available upon request.

Overview The SUMR-D CART2 turbine data are recorded by the CART2 wind turbine's supervisory control and data acquisition (SCADA) system for the Advanced Research Projects Agency–Energy (ARPA-E) SUMR-D project located at the National Renewable Energy Laboratory (NREL) Flatirons Campus. For the project, the CART2 wind turbine was outfitted with a highly flexible rotor specifically designed and constructed for the project. More details about the project can be found here: https://sumrwind.com/. The data include power, loads, and meteorological information from the turbine during startup, operation, and shutdown, and when it was parked and idle. Data Details Additional files are attached: sumr_d_5-Min_Database.mat - a database file in MATLAB format of this dataset, which can be used to search for desired data files; sumr_d_5-Min_Database.xlsx - a database file in Microsoft Excel format of this dataset, which can be used to search for desired data files; loadcartU.m - this script loads in a CART data file and puts it in your workspace as a Matlab matrix (you can call this script from your own Matlab scripts to do your own analysis); charts.mat - this is a dependency file needed for the other scripts (it allows you to make custom preselections for cartPlotU.m); cartLoadHdrU.m - this script loads in the header file information for the data file (the header is embedded in each data file at the beginning); cartPlotU.m - this is a graphic user interface (GUI) that allows you to interactively look at different channels (to use it, run the script in Matlab, and load in the data file(s) of interest; from there, you can select different channels and plot things against each other; note that this script has issues with later versions of MATLAB; the preferred version to use is R2011b). Data Quality Wind turbine blade loading data were calibrated using blade gravity calibrations prior to data collection and throughout the data collection period. Blade loading was also checked for data quality following data collection as strain gauge measurements drifted throughout the data collection. These drifts in the strain gauge measurements were removed in post processing.

This dataset contains:

A kmz file for Kelmarsh wind farm in the UK (for opening in e.g. Google Earth)

Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)

10-minute SCADA and events data from the 6 Senvion MM92's at Kelmarsh wind farm, grouped by year from 2016 to mid-2021, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period

Data mappings from primary SCADA to csv signal names

Site substation/PMU meter data where available for the same period

Site fiscal/grid meter data where available for the same period

MERRA2 and ERA5 since 2000 up to the same period

The dataset has been released by Cubico Sustainable Investments Ltd under a CC-BY-4.0 open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. I'll try and add or link to additional file formats that might be easier to work with (e.g. for use with specific analysis software), and update this dataset periodically (e.g. twice a year), but please prompt me as required.

Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.

If you would like to get access to other datasets we may hold (e.g. more recent data, data from our other sites, ~30s resolution data, etc.), please let me know, and, if you have any questions or want to discuss open data and this or other initiatives, please contact me and I will endeavour to help.

I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.

For contact my email address is charlie.plumley@cubicoinvest.com.

You can also access data from Penmanshiel wind farm here.

This dataset contains:

• A kmz file for Penmanshiel wind farm in the UK (for opening in e.g. Google Earth)
• Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.)
• 10-minute SCADA and events data from the 14 Senvion MM82's at Penmanshiel wind farm, grouped by year from 2016 to mid-2021, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period, and there is no turbine WT03
• Data mappings from primary SCADA to csv signal names
• Site substation/PMU meter data where available for the same period
• Site fiscal/grid meter data where available for the same period

The dataset has been released by Cubico Sustainable Investments Ltd under a CC-BY-4.0 open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. I'll try and add or link to additional file formats that might be easier to work with (e.g. for use with specific analysis software), and update this dataset periodically (e.g. twice a year), but please prompt me as required.

Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry.

If you would like to get access to other datasets we may hold (e.g. more recent data, data from our other sites, ~30s resolution data, etc.), please let me know, and, if you have any questions or want to discuss open data and this or other initiatives, please contact me and I will endeavour to help.

I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way.

For contact my email address is charlie.plumley@cubicoinvest.com.

You can also access data from Kelmarsh wind farm here.

The Chalmers wind turbine has variable speed operation with a direct driven generator and a frequency converter, it also has a digital control system developed by Chalmers. The wind turbine has a rated power of 45 kW and rated speed of 75 rpm. The wooden tower is 30 m high, the blades of carbon fibres are 7.5 m long, and the turbine diameter is 15.9 m. The individually blade pitch system is electrical. The turbine is situated on the island Björkö at Skarviksvägen, 20 km west of Göteborg city. The coordinates are: 57.71818820625921, 11.683382148764485.

69 SCADA and structural vibration and loads Channels timeseries (sampled at 20 and 100 Hz) such as nacelle accelerations, tower and blades bending moments are included.

Structured metadata about wind turbine characteristics, SCADA, vibration and loads channels are included as JSON files and CSV.

This particular dataset consisting of high frequency sampled data, is intended for condition and structural health analysis.

The data covers:

• the measurements sampled at 100 Hz correspond to the period from 05 July 2022 to 9 June 2023
• the measurements sampled at 20 Hz correspond to the period from 05 July 2022 to 2 August 2023

This repository includes:

Time-series data in csv format:

• B1_CL4_20.csv (this is the data sampled at 20 Hz)
• B1_CL4_100.csv (this is the data sampled at 100 Hz)

Metadata:

• Bjorko_Sensors_Specs_Metadata.csv (Sensors signals specification in csv format)
• Bjorko_modes_mapping.csv (numerical integer value representing the wind turbine controller system mode in csv format)
• Bjorko_modes_mapping.json (numerical integer value representing the wind turbine controller system mode in csv JSON format)
• Bjorko_digital_io_states_mappings.csv (Description of digital input and output states in the wind turbine controller system in csv format)

Media:

• Chalmers-Wind turbine.pdf (description of the wind turbine including pictures)
• Chalmers wind turbine description 220121-short.pdf (description of the wind turbine including pictures)

Semantic artifacts:

• N/A

Other:

• N/A

Additional information is available upon request.

Snapshot img

What is the Intelligent Remote Terminal Unit Market Size?

The intelligent remote terminal unit market size is forecast to increase by USD 2.70 billion at a CAGR of 8% between 2024 and 2029. The Intelligent Remote Terminal Unit (RTU) market is experiencing significant growth due to the increasing need for remote monitoring in industrial facilities. This trend is driven by the benefits of real-time data collection and analysis, which enable efficient operations and preventive maintenance. Another key trend is the growing adoption of Supervisory Control and Data Acquisition (SCADA) systems in water monitoring applications, as they offer improved automation and data management capabilities. However, the market also faces challenges, including inadequate cybersecurity measures in the SCADA system, which pose a significant risk to data integrity and system reliability. As a result, there is a pressing need for advanced security solutions to protect against cyber threats and ensure the safe and effective operation of Intelligent RTUs.

What will be the size of Market during the forecast period?

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Market Segmentation

The market report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments.

Type

  Wireless intelligent RTU
  Wired intelligent RTU


End-user

  Oil and gas
  Power generation
  Chemical
  Water and wastewater
  Others


Geography

  Europe

    Germany
    UK
    Italy


  North America

    US


  APAC

    China


  Middle East and Africa



  South America

Which is the largest segment driving market growth?

The oil and gas segment is estimated to witness significant growth during the forecast period. Intelligent Remote Terminal Units (IRTUs) serve a pivotal function in various industries, particularly in sectors where remote monitoring, control, and automation are indispensable, such as the oil and gas industry. IRTUs function as control terminal devices at wellheads, facilitating real-time monitoring of essential parameters including pressure, temperature, flow rates, and fluid levels. They enable remote control of valves and chokes, optimizing production rates while ensuring safety and environmental compliance. In tank farms, IRTUs are instrumental in monitoring liquid levels, temperatures, and densities of stored commodities like crude oil, refined products, and chemicals. These devices automate inventory management, optimize storage capacity utilization, and ensure adherence to safety regulations for handling hazardous materials.

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The oil and gas segment was valued at USD 2.62 billion in 2019. and showed a gradual increase during the forecast period. Furthermore, deployed at the sensor network layer, IRTUs interact with the transmission network layer through network interfaces. They collect sensor data and convert it into IP data or serial port data for transmission via communication networks. IRTUs employ various communication protocols, including GPRS/CDMA, for remote communication. IRTUs are equipped with microprocessors, power supplies, and I/O devices for executing control functions, such as analog input/output, remote control, remote signaling, remote adjustment, and central monitoring. They support event-triggered cycles and are integral to automation devices, smart grid systems, and telemetry systems, as well as smart city systems.

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North America is estimated to contribute 31% 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 market in Europe is experiencing significant growth, driven by investments in various end-user industries, including power generation, agriculture, telecommunications, water and wastewater treatment, and oil and gas. Among these, the power generation sector is a major contributor to the market's growth, with Europe's increasing focus on renewable energy sources, such as wind and solar, driving demand for IRTUs. Developed economies in Europe, such as Germany and the UK, are leading the way in renewable energy investments, with offshore wind installations accounting for approximately one-fifth of Europe's gross annual wind installations in 2021. Additionally, the agriculture sector is also adopting IRTUs to optimize irrigation and monitor livestock, while the telecommunications industry is utilizing IRTUs to enhance network connectivity and efficiency.

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Operational Technology Security Market Size 2024-2028

The operational technology (OT) security market size is forecast to increase by USD 28.50 billion at a CAGR of 20.37% between 2023 and 2028. The market is experiencing significant growth due to several key drivers. One major factor is the increasing need to enhance business efficiency by integrating advanced technologies like 5G and the Industrial Internet of Things (IIoT) into OT systems. The growing dependence on the Internet for industrial operations also necessitates strong OT security measures. Moreover, the high cost of ownership for OT infrastructure necessitates investments in reliable security solutions. Component Insights reveal that OT endpoint security and OT network segmentation are critical areas of focus for market participants. Anomaly detection is another essential technology for identifying and mitigating potential security threats in real-time.

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Operational Technology (OT) security refers to the protection of industrial control systems, SCADA networks, and other critical infrastructure components from cyber threats. With the increasing digitization of industrial processes and the integration of the Internet of Things (IoT), 5G technology, cloud technology, and edge computing into operational technology, the security landscape is becoming more complex. OT security is essential for the protection of industrial equipment and critical infrastructure against cyberattacks. These systems are often interconnected and require high levels of interoperability, making them vulnerable to threats.

Furthermore, IT teams must ensure that OT security is integrated into their overall security strategy to mitigate risks. Cybersecurity challenges in OT environments include the lack of standardized security protocols, limited resources for security updates and patches, and the need for real-time response to threats. OT security solutions must be able to provide network security, asset management, application security, and threat detection capabilities. Network security is a critical component of OT security, with microsegmentation and zero trust being effective strategies for securing OT networks. By implementing a zero trust approach, organizations can ensure that only authorized devices and users have access to the network.

Market Segmentation

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

Application

  Power generation and electrical
  Manufacturing
  Transportation and logistics
  Mining
  Others


End-user

  SMEs
  Large enterprises


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Application Insights

The power generation and electrical segment is estimated to witness significant growth during the forecast period. Operational Technology (OT) security solutions have gained significant importance in the power generation and electrical industries due to the increasing use of smart infrastructure and the associated security challenges. These systems, including wind turbines, solar photovoltaic (PV) arrays, building control systems, and Supervisory Control and Data Acquisition (SCADA) networks, incorporate IT infrastructure and are susceptible to cyberattacks. The vulnerabilities in these OT systems can lead to serious consequences, particularly in critical infrastructure sectors such as power generation and electrical systems. The increasing number of cybersecurity breaches in gas pipelines has further highlighted the need for strong security measures to protect against vulnerabilities in these systems.

Furthermore, companies are investing in these solutions to ensure the protection of their critical infrastructure and industrial control systems from potential cyber threats. Interoperability between different OT systems and IT networks is also a major concern, necessitating the need for comprehensive security solutions.

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The power generation and electrical segment was valued at USD 2.31 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

APAC is estimated to contribute 34% 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.

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In North America, the market holds significant importance on a global scale due to the region's early adoption of advanced technologies. The industrial sector in North America is well-established a