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

verfasst von

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.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

The University of Liverpool
Tongji University
National University of Singapore
Shanghai University
Monash University

Typ

Artikel

Journal

Mechanical Systems and Signal Processing

Band

104

Seiten

347-369

Anzahl der Seiten

23

ISSN

0888-3270

Publikationsdatum

01.05.2018

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik, Signalverarbeitung, Tief- und Ingenieurbau, Luft- und Raumfahrttechnik, Maschinenbau, Angewandte Informatik

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://www.repository.cam.ac.uk/bitstream/1810/288941/4/MSSP16-1379R2.pdf (Zugang: Offen)
https://doi.org/10.1016/j.ymssp.2017.10.035 (Zugang: Geschlossen)