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Details of Grant 

EPSRC Reference: EP/P009743/1
Title: HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms
Principal Investigator: Barnes, Professor M
Other Investigators:
Collu, Dr M Durovic, Dr S Lane, Professor D
Flynn, Dr D Crabtree, Dr CJ Keane, Professor J
Mawby, Professor P Brown, Dr K Crowther, Dr W
Ran, Professor L Watson, Dr SA Kazemtabrizi, Dr B
Nenadic, Professor G
Researcher Co-Investigators:
Project Partners:
BASEC (British Approvals Serv for Cables BPP-TECH CENSIS
DNV GL (UK) European Marine Energy Centre Fugro (UK)
Hi Speed Sustainable Manufacturing Inst Hydrason Solutions Ltd Nova Innovation Ltd
Offshore Renewable Energy Catapult Scottish Power Siemens
University of Edinburgh
Department: Electrical and Electronic Engineering
Organisation: University of Manchester, The
Scheme: Standard Research
Starts: 11 April 2017 Ends: 10 April 2020 Value (£): 3,048,221
EPSRC Research Topic Classifications:
Robotics & Autonomy Wind Power
EPSRC Industrial Sector Classifications:
Energy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Nov 2016 Programme Grant Interviews - 9 November 2016 (Engineering) Announced
Summary on Grant Application Form
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 according to the Crown Estate 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. As the Crown Estate has identified: "There is an increased focus on design for reliability and maintenance in the industry in general, but the reality is that there is a still a long way to go. Wind turbine, foundation and electrical elements of the project infrastructure would all benefit from innovative solutions which can demonstrably reduce O&M spending and downtime". Recent, more detailed, academic studies support this position.

The wind farm is however an extremely complicated system-of-systems consisting of the wind turbines, the collection array and the connection to shore. This consists of electrical, mechanical, thermal and materials engineering systems and their complex interactions. Data needs to be extracted from each of these, assessed as to its significance and combined in models that give meaningful diagnostic and prognstic information. This needs to be achieved without overwhelming the user. Unfortunately, appropriate multi-physics sensing schemes and reliability models are a complex and developing field, and the required knowledge base is presently scattered across a variety of different UK universities and subject specialisms.

This project will bring together and consolidate theoretical underpinning research from a variety of disparate prior research work, in different subject areas and at different universities. Advanced robotic monitoring and advanced sensing techniques will be integrated into diagnostic and prognostic schemes which will allow improved information to be streamed into multi-physics operational models for offshore windfarms. Life-time, reliability and physics of failure models will be adapted to provide a holistic view of wind-farms system health and include these new automated information flows. While aspects of the techniques required in this offshore application have been previously used in other fields, they are innovative for the complex problems and harsh environment in this offshore system-of-systems. 'Marinising' these methods is a substantial challenge in itself. The investigation of an integrated monitoring platform and the reformulation of models and techniques to allow synergistic use of data flow in an effective and efficient diagnostic and prognostic model is ambitious and would allow a major step change over present practice.

Key Findings
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Organisation Website: http://www.man.ac.uk