An autonomous, robotic and AI enabled biofouling monitoring, cleaning and management system for offshore wind turbine monopile foundations
Offshore wind is proving very attractive for operators, especially due to the higher yields and less resistance from onshore homeowners and stakeholders. It is predicted that it could provide all of the UK’s electricity requirement, with minimal emission and visual impacts.
However, there exist a major barrier to further exploitation due to the high levelized cost of electricity (LCOE) from offshore wind (£140/MWhr), which is 2-3 times higher than other key renewable sources: onshore wind, solar and nuclear (a large non-renewable but low emission source).
The high LCOE is caused by the severe environmental conditions, which results in high operational, reliability and maintenance (O&M) costs, with the seabed turbine foundations (largely monopiles) accounting for over 25% of all life cycle O&M costs, mainly caused by marine biofouling.
Current methods of fouling prevention (deploying cleaning tools such as brushes and power jets by divers (which is dangerous) or ROVs with annual costs ~ £30k/MW are proving ver cpstly and ineffective – creating the need for a solution to tackle this problem.
The Project objective:
The project will develop an automated fouling management system for offshore wind turbine monopile foundations consisting of a team of robots comprising a lead survey robot and other cleaning robots so that the need for divers and ROVs can be reduced and even eliminated.
The team of robots are expected to be deployed on the wind turbine monopile structure at sea level and will be able to travel up or down below sea level to the work place. The robot team will have the capability to travel autonomously in a collective manner over the entire monopile surface focussing on imaging the fouling and measuring its thickness in real time at all locations where it occurs.
Simultaneously the lead robot will instruct one or more cleaning robots to fouled locations to remove the fouling through the deployment of an innovative guided power ultrasound technique. On returning to the sea surface the team of robots can be retreived and transported to the next wind turbine monopile scheduled for inspection and cleaning under the planned maintenance cycles.
Expected impact:
This would mean a £7/MW (5%) reduction in LCOE. This is a significant contribution to the overall LCOE reduction required to make offshore wind competitive with other energy sources and thus reap the full environmental advantages of offshore wind.”
Partners of the consortium behind this innovative project:
1. Innovative Technology & Science Ltd. (InnoTecUK), Project Coordinator
2. Brunel University London, (BIC)
