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Publications
Yeter, B., Garbatov, Y. and Guedes Soares, C. (2022), “Life-extension certification of offshore wind assets using unsupervised machine learning”, Reliability Engineering and System Safety, Vol. 219, 108229.
The objective of the present study is to conduct a techno-economic life-extension analysis for fixed offshore wind turbines for the purpose of classification and certification. The methodology developed analyses the life-extension projects merges a structural integrity assessment of a monopile structure with a corrosion-induced crack development with an economic analysis of the return on assets accounting for the likelihood of obtaining the estimated return. The methodology pre-processes the structural health monitoring data using a Gaussian kernel for denoising, followed by a time-domain crack growth analysis on a cycle-by-cycle basis. The corrosion-related failure mechanisms are growing concerns for currently operating monopile offshore wind turbines. The study introduces a nonlinear corrosion model to address the emergent issue, which is developed considering the spatial and temporal changes in the environmental and operational parameters and the reinforcing effect of fracture on the corrosion. The failure assessment diagram identifies the threshold based on which the maintenance cost is calculated with a confidence level. The economic analysis combines revenue estimates and operational expenditures, considering the life-extension duration and appropriate discount rate. The results of the life-extension assessment, presented through a risk-return diagram, are used to classify the life-extension projects using unsupervised machine learning K-means clustering algorithm.
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