F4 - Increased use of automation to reduce risk in installation and operation (O&M)

F - Operations, Management, Maintenance and Safety

Status - published
Last updated on: 21/06/2022


Human activity is a governing hazard in the offshore environment, need for reduced human risk exposure in offshore operations.


Increased use of automation to reduce human risk exposure in ORE installation and in operation and maintenance (O&M).

Context And Need

The increased number and maintenance needs for offshore assets, makes human interventions one of the main hazards in the ORE sector and should be avoided where possible/feasible.

Offshore assets are facing a difficult reliability vs. lifecycle cost challenge. Increasing system reliabilities by increasing component redundancies will increase cost, but may overall reduce the lifecycle cost of the asset, by overcompensating the initially higher CAPEX through reduced production losses and lower O&M cost.

The targeted use of automation has the potential to reduce human risk exposure. This will require dedicated testing and implementation efforts of automation solutions.


In order to reduce the risk to human life in servicing O&M requirements of ORE structures, redundant systems to reduce time off for maintenance and human intervention may be considered. This may be achieved through increasing system reliabilities by increasing component redundancies, however this is a design trade-off with cost. Evaluating and specifying the ideal trade-off point between system reliability and lifecycle cost is needed, as well as better understanding of O&M uncertainties and the adoption of risk-based approaches to minimise risk in ORE O&M.

Impact Potential

  • The main impact is to reduce human hazard exposure in the offshore environment, this will also lead to reduction in CAPEX and OPEX in ORE.
  • A balanced and measured increase of reliability has the potential to reduce OPEX cost through decreased production losses and reduced human interventions.
  • If O&M uncertainties can be reduced the overall OPEX cost and production losses can be reduced.

Research Status

Automation of routine operations is implemented, e.g. Supervisory Control and Data Acquisition (SCADA) data; one off (installation) operations require significant human intervention. Some research to automate processes in offshore wind installations/sub-systems ongoing.

Some evidence of implementation (transformers for offshore wind), applicable to other components

Several O&M models in use, many are deterministic/empirical and would benefit from probabilistic methods.

Active research projects:


Supergen Flexible Funding Research

  • Demonstrating a machine learning system to integrate metocean data, sensor networks, and model output for improved coverage and accuracy: Lead Institution: University of Exeter
    Machine Learning for Low-Cost Offshore Modelling (MaLCOM) will develop a modelling methodology to provide rapid, accurate nowcasts and forecasts of the wave conditions at a regional scale using limited input data and requiring drastically reduced computational power. MaLCOM will use the historical outputs of a physics-based model and in-situ measurements to build a statistical representation, termed a surrogate model, between measurements and modelled conditions throughout a region using a machine learning approach. The developed surrogate model will provide two key benefits: (1) immediate spatial assessment of conditions with very little computational power required, such that it could be deployed on a mobile device or autonomous vessel and (2) improved accuracy of metocean forecasts through integrating in-situ measurements. This project will allow refinement and demonstration of this resource modelling and forecasting concept for marine energy sites.
  • A hybrid and scalable digital twin for intelligent direct drive powertrain condition monitoring
    Lead Institution: University of Strathclyde

    As larger wind turbines with newer powertrain technologies are introduced in the offshore wind sector, state-of-the-art machine learning techniques that use past field data are no longer directly applicable. Operational alarms based on physical models of older turbines are often no longer valid with new powertrain technology. This represents a key vulnerability in the offshore wind sector. This project will develop a hybrid digital twin combining transfer learning and physical modelling approaches that will be able to model normal and abnormal behaviour for new turbines before operational data is available. As turbines move further offshore, operators are motivated to reduce the number of turbine visits for cost and safety reasons. The hybrid models proposed in this application could be used to reduce the number of powertrain inspection and service visits. The requirement for visits will be reduced through the digital twin providing additional health indicators and recommendations to the operators, and by adding confidence to the use of existing health indicators provided by SCADA and monitoring systems


We would also like to invite UK researchers and industry stakeholders within ORE to submit links to research projects, both past and present, for inclusion within the landscape.

Therefore, if you have a UK-based research project within an area of ORE that you feel is relevant to a specific research theme or challenge within the Research Landscape, click HERE to submit your research project to the research landscape.


PhD projects in Offshore Renewable Energy

In order to better understand the breadth of ORE research currently being conducted in the UK, the Supergen ORE Hub has collated from its academic network, UK Centres for Doctoral Training and Industrial partners, a list of PhDs currently being undertaken in ORE.

Access a PDF of the list and find out more about including your PhD.

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