Projects
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| Reference Number | EP/Z001501/1 | |
| Title | Development of a real-time opportunistic maintenance strategy for floating offshore wind turbines | |
| Status | Funded | |
| Energy Categories | Renewable Energy Sources (Wind Energy) 100%; | |
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 10%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 10%; ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 80%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Professor J Wang School of Engineering Liverpool John Moores University |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 July 2024 | |
| End Date | 30 June 2026 | |
| Duration | 24 months | |
| Total Grant Value | £206,086 | |
| Industrial Sectors | ||
| Region | North West | |
| Programme | UKRI MSCA | |
| Investigators | Principal Investigator | Professor J Wang , School of Engineering, Liverpool John Moores University (100.000%) |
| Web Site | ||
| Objectives | ||
| Abstract | This project aims to develop a real-time opportunistic maintenance strategy for Floating Offshore Wind Turbines (FOWTs) to support reductions in operation and maintenance costs and unplanned downtime, as well as maximization of energy production efficiency. Specifically, a failure information transformation framework will be developed to transfer failure information from other types of wind turbines (onshore and fixed bottom) and used for FOWTs. This is due to the abundance of failure datasets for other wind turbine types compared to that of FOWTs. The framework will analyze the differences in failure characteristics of multiple types wind turbines and provide new cumulative failure datasets of offshore wind turbines, enhancing the data foundation of the wind energy sector. Subsequently, a novel maintenance process simulation tool coupled with scientific algorithms will be created to simulate the maintenance processes of FOWTs accurately and reliably, including non-standard lifecycle datasets. The model will address non-standard failure and repair operations for FOWTs, supporting the generalization of maintenance process simulation. Additionally, an opportunistic maintenance process determination framework will be constructed to provide an offline maintenance strategy for FOWTs. The model works in real-time and is capable of dynamically capturing opportunistic maintenance windows, removing restrictions of maintenance strategy planning introduced by low accessibility and insufficient maintenance experience. The project will provide the floating wind sector with new knowledge regarding operation and maintenance, coupled with the development of new datasets, simulation tools, and new maintenance concepts. Overall, the outcomes of the project will contribute to operational and maintenance cost reduction, maximizing energy production efficiency, and reducing the unplanned downtime of FOWTs and wind farms in general | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 29/05/24 | |
