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

authored by: Shuya Zhong, Athanasios A. Pantelous, Michael Beer, Jian Zhou
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.
Organisation(s): Institute for Risk and Reliability
External Organisation(s): University of Liverpool
Tongji University
National University of Singapore
Shanghai University
Monash University
Type: Article
Journal: Mechanical Systems and Signal Processing
Volume: 104
Pages: 347-369
No. of pages: 23
ISSN: 0888-3270
Publication date: 01.05.2018
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Control and Systems Engineering, Signal Processing, Civil and Structural Engineering, Aerospace Engineering, Mechanical Engineering, Computer Science Applications
Sustainable Development Goals: SDG 7 - Affordable and Clean Energy
Electronic version(s): https://www.repository.cam.ac.uk/bitstream/1810/288941/4/MSSP16-1379R2.pdf (Access: Open)
https://doi.org/10.1016/j.ymssp.2017.10.035 (Access: Closed)