Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling

Yan, Rundong; Dunnett, Sarah

Abstract

In order to improve the operation and maintenance (O&M) of offshore wind turbines, a new Petri net (PN)-based offshore wind turbine maintenance model is developed in this paper to simulate the O&M activities in an offshore wind farm. With the aid of the PN model developed, three new potential wind turbine maintenance strategies are studied. They are (1) carrying out periodic maintenance of the wind turbine components at different frequencies according to their specific reliability features; (2) conducting a full inspection of the entire wind turbine system following a major repair; and (3) equipping the wind turbine with a condition monitoring system (CMS) that has powerful fault detection capability. From the research results, it is found that periodic maintenance is essential, but in order to ensure that the turbine is operated economically, this maintenance needs to be carried out at an optimal frequency. Conducting a full inspection of the entire wind turbine system following a major repair enables efficient utilisation of the maintenance resources. If periodic maintenance is performed infrequently, this measure leads to less unexpected shutdowns, lower downtime, and lower maintenance costs. It has been shown that to install the wind turbine with a CMS is helpful to relieve the burden of periodic maintenance. Moreover, the higher the quality of the CMS, the more the downtime and maintenance costs can be reduced. However, the cost of the CMS needs to be considered, as a high cost may make the operation of the offshore wind turbine uneconomical.

Citation

Yan, R., & Dunnett, S. (2021). Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling. Applied Sciences, 11(2), Article 574. https://doi.org/10.3390/app11020574

• Journal Article Type: Article
• Acceptance Date: Jan 6, 2021
• Online Publication Date: Jan 8, 2021
• Publication Date: Jan 2, 2021
• Deposit Date: Jul 5, 2023
• Publicly Available Date: Jul 7, 2023
• Journal: Applied Sciences
• Electronic ISSN: 2076-3417
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 2
• Article Number: 574
• DOI: https://doi.org/10.3390/app11020574
• Keywords: Fluid Flow and Transfer Processes; Computer Science Applications; Process Chemistry and Technology; General Engineering; Instrumentation; General Materials Science
• Public URL: https://nottingham-repository.worktribe.com/output/22455104
• Publisher URL: https://www.mdpi.com/2076-3417/11/2/574