A novel decision-making system for selecting offshore wind turbines with PCA and D numbers

Li Xu, Jin Wang, Yanxia Ou, Yang Fu and Xiaoyan Bian

Energy, 2022, vol. 258, issue C

Abstract: Offshore wind turbine selection is a complex multi-attribute decision-making (MADM) problem with multiple variables and schemes. As a result of the intervention of expert judgment and linguistic assessment, various uncertainties arise in the process of wind turbine selection. This work presents a novel decision-making system for selecting offshore wind turbine by combining D numbers with principal component analysis (PCA). Firstly, we build five main attribute indexes involving technology, matching with wind resources, economy, historical performance of wind turbine and after-sales service of manufacturer through historical experience and expert advice. Then, to reduce the subjectivity of experts in selecting decision variables, PCA is employed to select twelve secondary indicators and determine the corresponding weights. Secondly, experts evaluate the performance of schemes according to the language set, and give the confidence of judgment. We propose to quantify the evaluation results in the form of D numbers, which can directly express the incomplete information of experts and realize the integration of expert opinions. Finally, the optimal scheme is obtained through the technique for order preference by similarity to ideal solution (TOPSIS). The selection results from an actual case show that the proposed model can effectively realize offshore wind turbine selection.

Keywords: Offshore wind turbine; Decision-making system; D numbers; PCA; TOPSIS (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222017212
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:energy:v:258:y:2022:i:c:s0360544222017212

DOI: 10.1016/j.energy.2022.124818

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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