HOME Offshore is a research project funded by the UK Engineering and Physical Sciences Research Council (EPSRC) which partners 5 leading UK universities. The project will investigate the use of advanced sensing, robotics, virtual reality models and artificial intelligence to reduce maintenance cost and effort for offshore windfarms. Predictive and diagnostic techniques will allow problems to be picked up early, when easy and inexpensive maintenance will allow problems to be readily fixed. Robots and advanced sensors will be used to minimise the need for human intervention in the hazardous offshore environment.
The remote inspection and asset management of offshore wind farms and their connection to shore, is an industry which will be worth up to £2 billion annually by 2025 in the UK alone. 80% to 90% of the cost of offshore Operation and Maintenance according to the Crown Estate is generated by access requirements: such as the need to get engineers and technicians to remote sites to evaluate a problem and decide what action to undertake. Such inspection takes place in a remote and hazardous environment and requires highly trained personnel, of which there is likely to be a shortage in coming years. Additionally, much condition monitoring data which is presently generated is not useful or not used effectively. The project therefore aims to make generate more ‘actionable data’ – useful information that can reduce operation and maintenance costs and improve safety.
Investigating the flashover effects of a shielded drone in the UoM High Voltage facility
Research has been conducted into the feasibility of inspecting HVDC substations whilst they are operational. This requires the robotic inspection platform to function in the presence of high external electromagnetic fields. Investigations have been undertaken to understand the effects of such fields on the operation of a UAV and possible mitigation strategies, such as shielding and robust control systems. Work has also been undertaken into the sensor fusion of LiDAR scans and thermal images to be able to geo-locate thermal hotspots caused by faults.
Geo-locating thermal hotspots by fusing LiDAR scans and thermal images
A simulation showing the electric field strength on vulnerable areas of the UAV in the presence of a high external electrostatic field
An overview of the robotics and VR work
Department of Electrical and Electronic Engineering,
The University of Manchester
Sackville Street Building