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This paper presents a declarative model of maintenance logistics for offshore wind farms. Its implementation in decision-making tools supporting wind turbine maintenance enables online prototyping of alternative scenarios and variants of wind turbine servicing, including weather-related operation vessel movement and routing of unmanned aerial vehic...
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... to difficult weather conditions, the time for the maintenance and repair mission was reduced to 7 h. There are three service teams, whose parameters are listed in Table 3 (all teams have the same competencies). For these data, the answer to the following question is sought: Do the available vessels and teams W 1 , W 2 , and W 3 allow for the servicing of a given set of T = {T 10 , T 28 , T 50 , T 52 , T 68 , T 90 , T 47 } in the assumed time horizon H = 420 (7 h)? ...Similar publications
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Citations
... Another set of models allows for stochastic formulations of vessel fleet composition and optimization of maintenance operations encompass opportunistic maintenance , i.e. models taking into account energy production and wind forecast data while implementing anticipated weather conditions and failures in the WT during planning a rolling horizon (Bødal, 2022;Stehly & Duffy, 2021). Models implemented in this are based on AI methods including: fuzzy set formalisms (Huang, 2021;Imani et al., 2021;Zhong, 2019), constraints programming (Banaszak et al., 2023;Froger et al., 2018), ontologies (Mohamed, 2023;Strack et al., 2021), deep learning (Xiao et al., 2021), etc. ...
By integrating preventive, proactive, and reactive maintenance strategies, offshore wind farm (OWF) operators can ensure that wind turbines (WT) can operate as much as possible, thereby maximizing energy production while only slightly increasing maintenance costs. This three-stage driven approach aims to reduce the number of unplanned maintenance activities that crews must undertake in potentially challenging offshore conditions, extend the lifespan of equipment, and minimize operational costs while ensuring a safe and sustainable energy production process. To enable the analysis of these mutually conditioned expectations, a reference model is proposed that integrates the components of the wind farm maintenance chain in their life cycle. This model allows for a precise formulation of the problems that constitute the above-mentioned challenges. The adopted declarative representation guarantees both the openness of the model structure and the use of commercially available constraints programming environments for implementation and solution generation. The possibilities of using the model to evaluate selected variants of the wind farm maintenance strategy are illustrated by the results of the attached computer experiments.
... The primary contribution of this work is a methodology for evaluating specific maintenance operations for Offshore Wind Farms (OWFs) concerning the implementation of predictive maintenance. This continues previous research by the authors, which focused on developing specific reference models for predictive maintenance [11,12], where service mission planning was the focus of the work and not the monetary impact. ...
... In [43], a spare parts inventory is optimized in tandem with an O&M strategy, and a strategic level preventive maintenance and planning system is described considering several objectives in [44,45], where elements such as costs and reliability are optimized by considering asset degradation over time and failure rates. When planning the schedule for a specific maintenance campaign, several methods and approaches are available [46], and examples can be found in [11,47]. While the methods are well described, the impact and benefits they can provide are evaluated from a strategic life-cycle perspective or are not considered at all. ...
The successful implementation of predictive maintenance for offshore wind farms suffers from a poor understanding of the consequential short-term impacts and a lack of research on how to evaluate the cost-efficiency of such efforts. This paper aims to develop a methodology to explore the short-term marginal impacts of predictive maintenance applied to an already existing preventive maintenance strategy. This method will be based on an analysis of the performance of the underlying predictive model and the costs considered under specific maintenance services. To support this analysis, we develop a maintenance efficiency measure able to estimate the efficiency of both the underlying prediction model used for predictive maintenance and the resulting maintenance efficiency. This distinction between the efficiency of the model and the service results will help point out insufficiencies in the predictive maintenance strategy, as well as facilitate calculations on the cost–benefits of the predictive maintenance implementation. This methodology is validated on a realistic case study of an annual service mission for an offshore wind farm and finds that the efficiency metrics described in this paper successfully support cost–benefit estimates.
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