Thesis
Development of a system level post prognostics reasoner for FRP turbine blades
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- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2024
- Thesis identifier
- T16939
- Person Identifier (Local)
- 202063179
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- The surge in offshore wind energy amplifies the urgency to optimise O&M lifecycle costs, a pivotal endeavor for bolstering affordability. These costs are anticipated to constitute a significant portion of overall lifecycle expenditures. A crucial strategy in achieving these cost reductions involves transitioning from traditional maintenance models, such as calendar-based repairs, to more sophisticated approaches like Predictive Maintenance. This paradigm shift poses a formidable challenge, as uncertainties related to damage propagation, weather dynamics, and maintenance planning exert considerable pressure on O&M practitioners. The objective of this thesis is to delineate steps illustrating the design of an autonomous decision-making system for wind turbine blades. The initial phase involves identifying the most consequential failure modes through a comprehensive Failure Modes, Effects and Criticality Analysis (FMECA). Subsequently, a degradation function is proposed for a primary failure mode, namely leading edge erosion, furnishing the groundwork for approaching the O&M optimisation challenge.In the progression toward an autonomous system, an essential tool is introduced to facilitate the selection of baseline calendar-based maintenance strategies for leading edge erosion at the wind farm level. This tool serves as a precursor to the ultimate design of a RL-based autonomous decision-making agent, incorporating prognostics information specifically for leading edge erosion. The obtained results showcase the efficacy of the proposed agent, demonstrating a noteworthy reduction in expected costs ranging from 12% to 21% when compared to condition-based maintenance. Furthermore, the agent contributes to a diminished risk of blade failure, highlighting the promising impact of autonomous decision-making in the realm of wind turbine O&M.
- Advisor / supervisor
- Kolios, Athanasios
- Brennan, Feargal
- Resource Type
- DOI
- Funder
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Articoli
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PDF of thesis T16939 | 2024-06-05 | Pubblico | Scaricare |