Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501884
Title: Techno-economic evaluation of condition monitoring and its utilisation for operation and maintenance of wind turbines using probabilistic simulation modelling
Author: McMillan, David
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2008
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Abstract:
Condition monitoring systems are installed in wind turbines with the goal of providing component-specific information to wind farm operators, which is the key prerequisite of a condition-based maintenance policy. Theoretically, adoption of a condition based maintenance policy will increase equipment availability, operational efficiency and economic yield: this is achieved via maintenance and operating actions based on the condition monitoring information. As with many good theoretical ideas, condition monitoring for wind turbines is imperfect. This fact has inhibited widespread utilisation of the technology and associated maintenance policies until now. Electricity generation companies' experience of such systems is mixed: the most widely-held view being that onshore wind turbine condition monitoring systems are not cost-effective (or marginally so), whereas in the offshore case, economic and technical benefits of CM systems will be substantial - closer to the theoretical case. These views, however, are based on anecdotal evidence and extrapolation rather than any kind of analytic approach, and such perceptions cannot take account of all the relevant factors. It can be concluded that the economic case for condition monitoring applied to wind turbines is currently not well quantified and the factors involved are not fully understood. In order to make more informed decisions regarding whether deployment of condition monitoring for wind turbines is economically justified, a methodology for capturing the processes involved is proposed in this thesis. The specific form of the methodology is quantitative analysis comprising probabilistic methods: discrete-time Markov Chains, Monte Carlo methods and time series modelling. The flexibility and insight provided by this framework captures the operational nuances of this complex problem, thus enabling quantitative evaluation of wind turbine condition monitoring systems and condition based maintenance in a variety of operational scenarios. The proposed methodology therefore tackles a problem which has not been addressed in literature or by industry until now.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.501884  DOI: Not available
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