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

Shuya Zhong, Athanasios A Pantelous, Michael Beer, Jian Zhou

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.

LanguageEnglish
Pages347-369
Number of pages23
JournalMechanical Systems and Signal Processing
Volume104
DOIs
StatePublished - 1 May 2018

Keywords

  • Cost parameters
  • Maintenance
  • Multi-objective Programming
  • Offshore wind farms
  • Reliability
  • Scheduling

Cite this

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abstract = "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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Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms. / Zhong, Shuya; Pantelous, Athanasios A; Beer, Michael; Zhou, Jian.

In: Mechanical Systems and Signal Processing, Vol. 104, 01.05.2018, p. 347-369.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

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AU - Zhong,Shuya

AU - Pantelous,Athanasios A

AU - Beer,Michael

AU - Zhou,Jian

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AB - 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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