Availability Assessment of Offshore Wind Turbines with Condition-based Opportunistic Maintenance Using GSPN

Jiang, G, Chen, Y, Huang, P, Yan, R, Li, H ORCID: 0000-0001-6429-9097, Loughney, S ORCID: 0000-0003-0217-5739, Yang, Z ORCID: 0000-0003-1385-493X and Wang, J ORCID: 0000-0003-4646-9106 (2025) Availability Assessment of Offshore Wind Turbines with Condition-based Opportunistic Maintenance Using GSPN. In: IFAC Papersonline , 59 (22). pp. 740-745. (16th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles CAMS 2025, 25th Aug - 28th Aug 2025, Wuhan, China).

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Abstract

This paper assesses the availability of an offshore wind turbine under condition-based opportunistic maintenance strategies. Generalized stochastic Petri nets (GSPN) are adopted to analyze the impact of implementing the maintenance strategy, with the consideration of factors such as vessel hiring and weather windows. A 25-year operational period is simulated using a Monte-Carlo simulation to evaluate availability and total cost of the wind turbine. By examining the availability and total cost under various opportunistic maintenance efficiencies, it is concluded that an opportunistic maintenance efficiency in a certain range offers a cost-availability trade-off. Overall, the method and the results of this paper contribute to the effective operation and maintenance of offshore wind turbines.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	offshore wind turbine; availability; condition -based opportunistic maintenance; GSPN; 4015 Maritime Engineering; 40 Engineering; 4007 Control Engineering, Mechatronics and Robotics; 7 Affordable and Clean Energy; 4007 Control engineering, mechatronics and robotics; 4008 Electrical engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Elsevier
Date of acceptance:	1 August 2025
Date of first compliant Open Access:	30 April 2026
Date Deposited:	30 Apr 2026 14:52
Last Modified:	30 Apr 2026 14:52
DOI or ID number:	10.1016/j.ifacol.2025.11.723
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28465

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