Simplified Representation of Degradation, Inspection and Maintenance in a Strategic Decision Support Tool for Offshore Wind Operation and Maintenance
The global demand for energy is increasing in the current scenario of industrial development and offshore wind energy has a great potential to become a key player specifically in Europe’s renewable energy future. Naturally the flow of wind in offshore environments is more consistent and also the average wind velocity is higher than onshore. However, the cost of electricity generated from offshore wind turbines is higher currently and the challenge of cost reduction is at the top priority. Operation and maintenance costs are the main contributor to the life cycle cost of the offshore wind energy farms. A great proportion of operation and maintenance costs has been assigned to corrective replacement of major components of the wind turbines. Mathematical optimization models are frequently used in the maintenance management to lower the cost of maintenance and failure. To overcome the overhead expenses, the strategic decision support tools for offshore wind operation and maintenance such as NOWIcob (Norwegian Offshore Wind cost benefit model) can be used to investigate strategies for major components. In the current situation, the NOWIcob model is not able to capture the degradation of components with time and also how the degradation can be detected by inspections or condition monitoring systems. To implement degradation and inspection in NOWIcob, a simple/loose integration technique has been employed by developing the so called translators from detailed degradation models to be used as input into the NOWIcob. For this purpose, the linear elastic fractures mechanics model based on Paris Law and Gamma process has been used for degradation modelling approach. Monte Carlo simulations were applied to simulate the degradation paths and subsequently obtain the failure time and prewarning time.
PublisherUiT The Arctic University of Norway
UiT Norges arktiske universitet
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Copyright 2016 The Author(s)
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