Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ

Chen Zhang and Tao Yang

Renewable Energy, 2021, vol. 164, issue C, 1540-1549

Abstract: The complex structure and harsh working environment of wind turbines cause frequent failures and unavailability of these turbines in wind farms. To promote the long-term stable development of wind power and enhance its market competitiveness, the reduction of operation and maintenance costs is particularly important, which are estimated to account for approximately 1/3 of the total life cycle cost. With the continuous increase in the size and number of wind turbines, wind farm maintenance tasks and resources are increasing and becoming unpredictable. The realization of the dynamic scheduling of maintenance tasks and resources under various constraints has become vital. In this study, an optimal multi-objective model of maintenance planning and resource allocation for wind farms is established. The maintenance tasks are obtained according to the preset maintenance strategy and current operating status of the wind turbine components. The dynamic requirements of maintenance planning and resource allocation for different wind farms in adjacent areas are periodically generated, and the Non-dominated sorting genetic algorithm-ΙΙ (NSGA-ΙΙ) is adopted to conduct a combinatorial optimization process. The validity of the proposed model are verified by a corresponding case study, along with a comparative analysis with other optimization algorithms and a sensitivity study of different parameters.

Keywords: Wind farm; Maintenance planning; Resource allocation; Optimization; Non-dominated sorting genetic algorithm-ΙΙ (NSGA-ΙΙ) (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148120316979
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:164:y:2021:i:c:p:1540-1549

DOI: 10.1016/j.renene.2020.10.125

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().