A novel decision-making system for selecting offshore wind turbines with PCA and D numbers. (1st November 2022)

• Record Type: Journal Article
• Authors: Xu, Li; Wang, Jin; Ou, Yanxia; Fu, Yang; Bian, Xiaoyan
• Is Part Of: Energy. Volume 258(2022)
• Publication Date: 2022-11-01
• Subjects: Offshore wind turbine -- Decision-making system -- D numbers -- PCA -- TOPSIS; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124818
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: A new decision-making system for selecting offshore wind turbine selection is established. The affecting factors and the corresponding weights are determined by PCA. D-number theory transforms fuzzy and linguistic assessment into reliable quantitative form. MADM fused with D numbers addresses the uncertainty cause by expert judgment.