Probabilistic cost modeling as a basis for optimizing inspection and maintenance of turbine support structures in offshore wind farms

Abstract. The operational management of offshore wind farms includes inspection and maintenance (I&M) of the wind turbine support structures. These activities are complex and influenced by numerous uncertain factors that affect their costs. The uncertainty in the I&M costs should be considered in decision value analyses performed to optimize I&M strategies for the turbine support structures. In this paper, we formulate a probabilistic parametric model to describe I&M costs for the common case in which a wind farm is serviced and maintained using a workboat-based strategy. The model is developed based on (a) interviews with a wind farm operator, engineering consultants, and operation and maintenance engineers, as well as (b) scientific literature. Our methodology involves deriving the probabilistic models of the cost model parameters based on intervals representing a subjective expert opinion on the foreseeable ranges of the parameter values. The probabilistic cost model is applied to evaluate the total I&M costs, and a sensitivity analysis is conducted to identify the main cost drivers. The model can be utilized to optimize I&M strategies at the component, structural system, and wind farm level. To illustrate its potential use, we apply it in a numerical study in which we optimize I&M strategies at the structural system level and identify and demonstrate a simplified approach of capturing uncertain I&M costs in the optimization. The simplified approach is generalized and made available for maintenance cost optimization of offshore wind turbine structures.

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Publication Details

Journal
Wind energy science
Published
2025-02-21
DOI
https://doi.org/10.5194/wes-10-461-2025
Citations
4
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
5.59

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article

Probabilistic cost modeling as a basis for optimizing inspection and maintenance of turbine support structures in offshore wind farms

Muhammad Farhan, Sebastian Thöns, М. Gündel, Ronald M. Schneider
4 citations
Wind energy science
Probabilistic and Robust Engineering Design
5.59
article

Probabilistic cost modeling as a basis for optimizing inspection and maintenance of turbine support structures in offshore wind farms

Muhammad Farhan, Sebastian Thöns, М. Gündel, Ronald M. Schneider
article en
4 citations

Abstract

Abstract. The operational management of offshore wind farms includes inspection and maintenance (I&M) of the wind turbine support structures. These activities are complex and influenced by numerous uncertain factors that affect their costs. The uncertainty in the I&M costs should be considered in decision value analyses performed to optimize I&M strategies for the turbine support structures. In this paper, we formulate a probabilistic parametric model to describe I&M costs for the common case in which a wind farm is serviced and maintained using a workboat-based strategy. The model is developed based on (a) interviews with a wind farm operator, engineering consultants, and operation and maintenance engineers, as well as (b) scientific literature. Our methodology involves deriving the probabilistic models of the cost model parameters based on intervals representing a subjective expert opinion on the foreseeable ranges of the parameter values. The probabilistic cost model is applied to evaluate the total I&M costs, and a sensitivity analysis is conducted to identify the main cost drivers. The model can be utilized to optimize I&M strategies at the component, structural system, and wind farm level. To illustrate its potential use, we apply it in a numerical study in which we optimize I&M strategies at the structural system level and identify and demonstrate a simplified approach of capturing uncertain I&M costs in the optimization. The simplified approach is generalized and made available for maintenance cost optimization of offshore wind turbine structures.

Wind energy scienceVol. 10(2)
Federal Institute For Materials Research and Testing (DE), Lund University (SE), Helmut Schmidt University (DE)
Bundesministerium für Wirtschaft und Energie, Projektträger Jülich
Openalex Percentile: Top 4%
Probabilistic and Robust Engineering Design
5.59
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