TY - JOUR
T1 - A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways
T2 - An application to a case in China
AU - Xue, Jie
AU - Yip, Tsz Leung
AU - Wu, Bing
AU - Wu, Chaozhong
AU - van Gelder, P. H.A.J.M.
N1 - Funding Information:
This work was sponsored in part by the National Natural Science Foundation of China (Grant No.: 51809206 ); International Cooperation and Exchange of the National Natural Science Foundation of China (Grant No.: 51920105014 ); China Scholarship Council (Grant No.: 201706950088 ); National Key Technologies Research & Development Program (Grant No.: 2019YFB1600603 ); Shenzhen Science and Technology Innovation Committee (Grant No.: CJGJZD20200617102602006 ), and the Major Project of Technological Innovation of Hubei Province (Grant No.: 2017CFA008 ). Additionally, grateful acknowledgment is made to the anonymous reviewers and the editor for their valuable contribution and constructive suggestions to this paper.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/7
Y1 - 2021/7
N2 - Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection.
AB - Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection.
KW - Fuzzy bayesian network
KW - Marine traffic safety
KW - Multiple-attribute decision-making (MADM)
KW - Offshore wind turbine (OWT)
KW - Principal component analysis (PCA)
KW - Wind energy
UR - http://www.scopus.com/inward/record.url?scp=85104433780&partnerID=8YFLogxK
U2 - 10.1016/j.renene.2021.03.084
DO - 10.1016/j.renene.2021.03.084
M3 - Journal article
AN - SCOPUS:85104433780
VL - 172
SP - 897
EP - 917
JO - Renewable Energy
JF - Renewable Energy
SN - 0960-1481
ER -
