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Improving the Transmission of Low Bandwidth Condition Monitoring System (CMS) Data


Offshore wind turbines are continuously collecting large amounts of condition/ performance data throughout their operation. This data is vital for improving our understanding of turbine performance under different conditions and helps to inform predictive maintenance requirements as operators look to make efficiencies by streamlining their maintenance operations. However, because of the highly variable loads and the aggressive operating conditions, the measurement signals reflecting the condition monitoring parameters are very variable and have an extensive range*.  Transmitting this data back to shore presents a significant challenge as there are limitations on how much data can be transported.

*For more information see: “Kuseyri (2015) Condition monitoring of wind turbines: Challenges and opportunities. ISITES2015 3rd International Symposium on Innovative Technologies in Engineering and Science”

Also, once the data is back at the control centre, substantial amounts of time is spent analysing it and detecting premature failure signatures, which utilises significant human resource and adds cost to the management of wind farms. Without autonomous signal processing, the SCADA (Supervisory Control and Data Acquisition) system can become cluttered with data confusing and clogging up the decision-making process.


Develop an AI signal processing within the turbine’s condition monitoring system that extracts only the key information, reducing the amount of data that needs to be transmitted back to the control centre.

The proposed solutions for this challenge must be deployable without requiring changes to existing manufacturing and design of offshore wind turbines.

Functional Requirements

  • Solutions should, where possible, automate the recognition of data signatures that indicate condition/performance issues to reduce manual processing at an individual turbine level before transmission.
  • Solutions should be able to reduce the size of measurement data to transmit from wind turbine to control centre without losing valuable information.
  • Solutions should store original data signals from sensors for certain periods greater than 1 month and transmit these data to the control centre by remote control when it is required (i.e. if there is a fault requiring detailed investigation).
  • Solution can automatically trigger warning or alarm for monitored components after recognition of condition/performance issues
  • Solution can automatically transmit the measurement data to the control centre after data post-processing

Technical Characteristics

  • Solution should not require major modification of turbine structure and components
  • Solution should be able to monitor major components including;
    • Blade and pitch system
    • Main bearing
    • Gearbox (if required)
    • Generator and converter
    • Tower and subsea structure
  • The size of data to transmit should be smaller than 7.2Mbps (if using standard 3G network, if solution can provide its own higher speed transmission method, then the maximum size of data can be increased)
  • Solution should be able to communicate with existing SCADA and control system

Deployment Timescale

  • Validation of solution: 6 months after prototype of solution is manufactured
  • Field trials: 12 – 24 months (to evaluate solution under operating condition of seasonal variation)

Operating Conditions

  • Solution should meet the required protection system according to
    • EN 50308 Wind turbines- Protective Measures: – Requirements for design, operation and maintenance
    • IEC61400
    • (Or equivalent)
  • Operating temperature: -40°C – 60°C

Cost Requirements

  • The CAPEX of solution should not exceed 1 % of CAPEX of turbine
  • The OPEX of solution should be less than OPEX of a conventional monitoring system
  • The solution should result in a net reduction in the LCOE of offshore wind power

This data is vital for improving our understanding of turbine performance under different conditions and helps to inform predictive maintenance requirements as operators look to make efficiencies by streamlining their maintenance operations.


Turbines are increasing in size, therefore blades are getting longer and powertrain systems are increasing in capacity.

From 2010 to 2016, wind turbine power rating has grown by 60%. In 2016, the average capacity of new wind turbines installed was 4.8 MW, a significant increase from 3.0 MW in 2010, reflecting a period of continuous development.  The first 8MW turbines have been in operation since late 2016.

The service and repair market for wind turbines is experiencing rapid growth due to the accumulation effect of continual wind farm development and installation.

Market size

The blades and powertrains market has seen rapid growth following a sustained offshore wind build programme in Europe led by the UK and Germany.  Europe has 3,589 offshore turbines installed and grid-connected as of January 2017 with a total capacity of 12.631GW across 10 European countries.

Market Forecast

The 11 offshore projects under construction in Europe as of June 2017 will increase installed capacity by a further 4.8GW, equating to around 800 new powertrain systems for offshore wind alone.  In 2016 a total of 4,948MW of new capacity reached FID and it is projected that the total European installed capacity will reach 24.6GW by 2020.

Do you have a potential solution?

Improving CMS monitoring will reduce data gathering needs and improve communication.

Apply now

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