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Projects: Projects for Investigator
Reference Number EP/P009743/1
Title HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms
Status Completed
Energy Categories Renewable Energy Sources(Wind Energy) 100%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 50%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Dr M Barnes
No email address given
Electrical & Electronic Engineering
University of Manchester
Award Type Standard
Funding Source EPSRC
Start Date 11 April 2017
End Date 10 November 2020
Duration 43 months
Total Grant Value £3,048,221
Industrial Sectors Energy
Region North West
Programme Energy : Energy
 
Investigators Principal Investigator Dr M Barnes , Electrical & Electronic Engineering, University of Manchester (99.985%)
  Other Investigator Professor P Mawby , School of Engineering, University of Warwick (0.001%)
Professor L Ran , School of Engineering, University of Warwick (0.001%)
Dr K Brown , School of Engineering and Physical Sciences, Heriot-Watt University (0.001%)
Professor D Lane , School of Engineering and Physical Sciences, Heriot-Watt University (0.001%)
Dr D Flynn , School of Engineering and Physical Sciences, Heriot-Watt University (0.001%)
Dr CJ Crabtree , Engineering, Durham University (0.001%)
Dr B Kazemtabrizi , Engineering, Durham University (0.001%)
Mr PR Green , Electrical & Electronic Engineering, University of Manchester (0.001%)
Dr O Marjanovic , Electrical & Electronic Engineering, University of Manchester (0.001%)
Dr S Djurovic , Electrical & Electronic Engineering, University of Manchester (0.001%)
Dr SA Watson , Electrical & Electronic Engineering, University of Manchester (0.001%)
Dr W Crowther , Mechanical, Aerospace and Civil Engineering, University of Manchester (0.001%)
Dr M Collu , Naval Architecture & Marine Engineering, University of Strathclyde (0.001%)
Professor G Nenadic , Computer Science, University of Manchester (0.001%)
Professor J Keane , Computer Science, University of Manchester (0.001%)
  Industrial Collaborator Project Contact , University of Edinburgh (0.000%)
Project Contact , Fugro GeoServices Ltd (0.000%)
Project Contact , European Marine Energy Centre (EMEC) (0.000%)
Project Contact , SP Energy Networks (0.000%)
Project Contact , Siemens plc (0.000%)
Project Contact , High Speed Sustainable Manufacturing Institute Ltd (HSSMI Ltd) (0.000%)
Project Contact , Offshore Renewable Energy Catapult (0.000%)
Project Contact , DNV GL (UK) (0.000%)
Project Contact , Nova Innovation Ltd (0.000%)
Project Contact , BPP-TECH (0.000%)
Project Contact , Hydrason Solutions Ltd (0.000%)
Project Contact , British Approvals Service for Cables (BASEC) (0.000%)
Project Contact , CENSIS (0.000%)
Web Site
Objectives
Abstract 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.
Publications (none)
Final Report (none)
Added to Database 13/11/18