Role of grouping in the development of an overall maintenance optimization framework for offshore wind turbines

Zafar Hameed and Jørn Vatn

Journal of Risk and Reliability, 2012, vol. 226, issue 6, 584-601

Abstract: The reliability and maintenance of offshore wind turbines present new challenges that demand novel, optimal, but at the same time cost effective, strategies to handle them. Wind turbines are installed in arrays and the combination of these arrays is called a wind farm. Owing to the wind loads and atmospheric conditions it is expected that certain wind turbines on the wind farm will behave similarly as they are located in close proximity. These similarities in the wind turbines need to be exploited at the wind farm level. To address this issue, a method is proposed to group different activities at both the intra and inter wind turbines at a wind farm level. Different kinds of tasks could be grouped together, such as inspection, preventive replacement at the wind farm level to share the access and travel costs from one wind turbine to another. To undertake these tasks, a hybrid approach of using the block replacement policy and condition-based model has been adopted to exploit the strengths of each approach. Moreover, using such a hybrid approach at the wind farm level, which groups different activities together, will act as a decision support tool that favors having an optimal strategy. By grouping different tasks at the wind farm level in conjunction with the hybrid approach will be advantageous by addressing the issues of access, logistics, transportation and weather in an efficient way. Moreover, this grouping of activities will to help optimize the frequency of visits to the wind farm and reduce the hazards related with risks and safety.

Keywords: Grouping; condition; maintenance; inspection; optimization (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:226:y:2012:i:6:p:584-601

DOI: 10.1177/1748006X12464616

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