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Energies 2019, 12(6), 998; https://doi.org/10.3390/en12060998

Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades

Department of Civil Engineering, Aalborg University, 9220 Aalborg, Denmark
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Received: 8 January 2019 / Revised: 13 February 2019 / Accepted: 8 March 2019 / Published: 14 March 2019
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Abstract

Due to the considerable increase in clean energy demand, there is a significant trend of increased wind turbine sizes, resulting in much higher loads on the blades. The high loads can cause significant out-of-plane deformations of the blades, especially in the area nearby the maximum chord. This paper briefly presents a discrete Markov chain model as a simplified probabilistic model for damages in wind turbine blades, based on a six-level damage categorization scheme applied by the wind industry, with the aim of providing decision makers with cost-optimal inspection intervals and maintenance strategies for the aforementioned challenges facing wind turbine blades. The in-history inspection information extracted from a database with inspection information was used to calibrate transition probabilities in the discrete Markov chain model. With the calibrated transition probabilities, the damage evolution can be statistically simulated. The classical Bayesian pre-posterior decision theory, as well as condition-based maintenance strategy, was used as a basis for the decision-making. An illustrative example with transverse cracks is presented using a reference wind turbine. View Full-Text
Keywords: discrete Markov chain model; transverse cracks; condition-based maintenance; maintenance strategy discrete Markov chain model; transverse cracks; condition-based maintenance; maintenance strategy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yang, Y.; Sørensen, J.D. Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades. Energies 2019, 12, 998.

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