At a glance
- DatesApril 2017 – April 2020
- SponsorEngineering and Physical Sciences Research Council (EPSRC)
- Funded£3.8m (£350k to Cranfield)
- PartnersUniversity of Manchester, Durham University, Heriot-Watt University, University of Warwick
This project will undertake the research necessary for the remote inspection and asset management of offshore wind farms and their connection to shore. This industry has the potential to be worth £2billion annually by 2025 in the UK alone, according to studies for the Crown Estate. At present most Operation and Maintenance (O&M) is still undertaken manually onsite. Remote monitoring through advanced sensing, robotics, data-mining and physics-of-failure models therefore has significant potential to improve safety and reduce costs.
Typically 80-90% of the cost of offshore O&M is a function of accessibility during inspection - the need to get engineers and technicians to remote sites to evaluate a problem and decide what remedial action to undertake. Minimising the need for human intervention offshore is a key route to maximising the potential, and minimising the cost, for offshore low-carbon generation. This will also ensure potential problems are picked up early, when the intervention required is minimal, before major damage has occurred and when maintenance can be scheduled during a good weather window.
This project aims to lower costs and risks of maintenance by reducing human intervention and by using state-of-the art technology. The three key elements are: robotic inspection and advance sensing, artificial intelligence in the form of machine learning, and advanced physics modelling tools.
The advanced sensors will continuously monitor the offshore wind farm assets, and send their data to a central artificial intelligence unit. This unit, using state-of-the-art machine learning algorithms, will be able to recognise the early signs of a potential issue, based on previous observations and outcomes.
At this point, the advanced physics modelling tool, which will be developed, among others, by Cranfield University, will be able to understand why those sensors signals are linked to that previse problem, by virtually reproducing the chain of events in between.
This processed, workable information can then be passed to wind farm operators who can dispatch rapid and precise UAVs to inspect the dry part of the wind turbine, UUV (unmanned underwater vehicles) to inspect the submerged part of the wind turbine, and if necessary also robots to inspect the equipment within the offshore electrical substation. If necessary, the drones dispatch can also be automated.
The project will end with a demonstration of the approach developed. This interactive approach, with drones, learning computers and intelligent modelling all working in synchronisation, will mean that offshore wind farms of the future will be smarter, safer, easier to maintain, and more profitable, ensuring their continued use in the transition away from non-renewable power sources.