Project Details
Description
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.
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.
| Acronym | HOME Offshore |
|---|---|
| Status | Finished |
| Effective start/end date | 2/04/17 → 31/03/20 |
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
-
A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines
Lin, Z., Cevasco, D. & Collu, M., 2 Dec 2019, (E-pub ahead of print) In: Applied Energy. 13 p., 114228.Research output: Contribution to journal › Article › peer-review
Open AccessFile39 Link opens in a new tab Citations (Scopus)69 Downloads (Pure) -
Progress on the development of a holistic coupled model of dynamics for offshore wind farms: phase II - study on a data-driven based reduced-order model for a single wind turbine
Lin, Z., Stetco, A., Carmona-Sanchez, J., Cevasco, D., Collu, M., Nenadic, G., Marjanovic, O. & Barnes, M., 14 Jun 2019. 9 p.Research output: Contribution to conference › Paper › peer-review
Open AccessFile1 Link opens in a new tab Citation (Scopus)59 Downloads (Pure) -
An analysis of the impact of an advanced aero-hydro-servo-elastic model of dynamics on the generator-converter dynamics, for an offshore fixed 5MW PMSG wind turbine
Carmona-Sanchez, J., Lin, Z., Collu, M., Barnes, M., Marjanovic, O. & Cevasco, D., 5 Feb 2019, 15th IET International Conference on AC and DC Power Transmission (ACDC 2019) - Proceedings. Piscataway, NJ: IEEE, 6 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
Open AccessFile5 Link opens in a new tab Citations (Scopus)71 Downloads (Pure)
Datasets
-
Dataset for European Installed Offshore Wind Turbines (until year end 2017)
Cevasco, D. (Creator) & Collu, M. (Creator), Cranfield University, 26 Oct 2018
DOI: 10.17862/cranfield.rd.6133673, https://cranfield.figshare.com/
Dataset