A decision support system for strategic maintenance planning in offshore wind farms

Li, Xiaodong; Ouelhadj, Djamila; Song, Xiang; Jones, Dylan; Wall, Graham; Howell, Kerry E.; Igwe, Paul Agu; Martin, Simon; Song, Dongping; Pertin, Emmanuel

doi:10.1016/j.renene.2016.07.037

Cited by 42 publications

(24 citation statements)

References 24 publications

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“…Differences are mainly located in the diverse ingredients of the maintenance cost in different models. The amount of literature in this direction is vast, as many researches have introduced different economic criteria, [12,23,25,31,40], which have discussed in the offshore wind energy sector. In representative works summarized in Table 1, one can see that there are basically 8 kinds of costs related to the maintenance of power systems: power production, maintenance, start-up, fixed, variable, opportunity, compensation, and failure costs.…”

Section: Economic Criterionmentioning

confidence: 99%

Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms

Zhong

Pantelous

Beer

2018

Mechanical Systems and Signal Processing

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© 2017 Elsevier Ltd Offshore wind farm is an emerging source of renewable energy, which has been shown to have tremendous potential in recent years. In this blooming area, a key challenge is that the preventive maintenance of offshore turbines should be scheduled reasonably to satisfy the power supply without failure. In this direction, two significant goals should be considered simultaneously as a trade-off. One is to maximise the system reliability and the other is to minimise the maintenance related cost. Thus, a non-linear multi-objective programming model is proposed including two newly defined objectives with thirteen families of constraints suitable for the preventive maintenance of offshore wind farms. In order to solve our model effectively, the nondominated sorting genetic algorithm II, especially for the multi-objective optimisation is utilised and Pareto-optimal solutions of schedules can be obtained to offer adequate support to decision-makers. Finally, an example is given to illustrate the performances of the devised model and algorithm, and explore the relationships of the two targets with the help of a contrast model

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Section: Economic Criterionmentioning

confidence: 99%

Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms

Zhong

Pantelous

Beer

2018

Mechanical Systems and Signal Processing

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“…Decision support systems have the ability to integrate diverse aspects of the planning process, and may take into account the spatial dimension. As such, they have been broadly used in the management of natural resources, including water [44,45], fire [46,47], or wind [48]. Decision support systems may be used to produce scenarios or forecasts [49] and may encompass a large number of qualitative or quantitative variables.…”

Section: Methodological Precedents Research Objectives and Noveltiesmentioning

confidence: 99%

Mapping the Viability, Time, and Cost of Manual Borehole Drilling in Developing Regions

Martínez‐Santos

Martín-Loeches

Solera

et al. 2017

Water

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While access to water remains an issue in arid and semiarid regions across the world, aquifers have the potential to help millions of people out of poverty by providing a reliable source of drinking and irrigation water. Manual boreholes are increasingly advocated as a safe and cost-effective substitute to mechanized drilling, as well as to traditional excavation methods. This research banks on the assumption that field and remote sensing data can be integrated within a geospatial database in order to map the viability of manual boreholes based on factors such as rock type, water table depth, landforms, or water quality. The approach presents three main novelties in relation to methodological precedents: (1) outcomes are not only expressed in terms of technical feasibility, but also as a function of drilling time and cost; (2) maps refer to a specific drilling technique; and (3) results take into account borehole diameter, as this constrains both drilling time and cost. The method provides univocal outcomes that can be immediately useful for non-experts, donors, planners, or practitioners and that can be readily exported to other catchment-scale settings. Results were validated against geophysical data.

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“…To our knowledge, the models developed in NOWITECH and related projects are the first addressing this problem. Other research communities have recently addressed the same or similar problems (Li et al, 2016). However, there exist several studies on other problems related to determining optimal fleet size and mix in the operations research community.…”

Section: State-of-the-art In Modellingmentioning

confidence: 99%

Operation and Maintenance Modelling

Welte¹,

Sperstad²,

Halvorsen-Weare³

et al. 2018

Offshore Wind Energy Technology

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One of the biggest challenges in the development of offshore wind farms is the cost and limited opportunities for maintenance and access. This calls for new solutions when it comes to operation and maintenance (O&M) strategies, condition monitoring and logistics, just to mention some examples. To assess different solutions and to select the best ones, good models for maintenance and logistics decision support are required. This chapter provides an overview of, and a brief introduction to, O&M modelling for offshore wind farms, including transport and logistics for O&M. The main focus of the chapter is on strategic O&M modelling. An O&M simulation model and a model for O&M vessel fleet optimization are presented. The models can be used for analysis and optimization of different aspects of maintenance and logistics and the influence on the costs and availability during the operational phase of an offshore wind farm. Applications of the models are illustrated by examples, among others for cost-benefit evaluation for a new solution for remote inspection of the turbine nacelle. Furthermore, the influence

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A decision support system for strategic maintenance planning in offshore wind farms

Cited by 42 publications

References 24 publications

Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms

Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms

Mapping the Viability, Time, and Cost of Manual Borehole Drilling in Developing Regions

Operation and Maintenance Modelling

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