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

Shuya Zhong; Athanasios A. Pantelous; Michael Beer; Jian Zhou

doi:10.1016/j.ymssp.2017.10.035

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

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

Management

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53 Citations (SciVal)

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.

Original language	English
Pages (from-to)	347-369
Number of pages	23
Journal	Mechanical Systems and Signal Processing
Volume	104
DOIs	https://doi.org/10.1016/j.ymssp.2017.10.035
Publication status	Published - 1 May 2018

Bibliographical note

Funding Information:
The authors would like to thank the anonymous reviewers for their insightful comments that significantly improved the quality of this paper. Moreover, the gracious supports of the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems and Environment (EP/L015927/1), the Recruitment Program of High-end Foreign Experts (Grant No. GDW20163100009), and the China Scholarship Council ([2014]3026) should be acknowledged.

Publisher Copyright:
© 2017 Elsevier Ltd

Funding

The authors would like to thank the anonymous reviewers for their insightful comments that significantly improved the quality of this paper. Moreover, the gracious supports of the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems and Environment (EP/L015927/1), the Recruitment Program of High-end Foreign Experts (Grant No. GDW20163100009), and the China Scholarship Council ([2014]3026) should be acknowledged.

Keywords

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

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Civil and Structural Engineering
Aerospace Engineering
Mechanical Engineering
Computer Science Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.ymssp.2017.10.035

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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N2 - 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

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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