Offshore wind turbine selection with multi-criteria decision-making techniques involving D numbers and squeeze adversarial interpretive structural modeling method

In the design of wind farms, the selection of wind turbines is crucial for maximizing energy efficiency and economic gain. The conventional approach in offshore wind turbine research has often relied on expert scoring, which can overlook objective data and involve subjective judgment uncertainties. To address these issues, this paper introduces an innovative hybrid multi-criteria decision-making (MCDM) framework, D-SAISM (squeeze adversarial interpretive structural modeling based on D numbers). This framework aims to improve the evaluation process by adding clarity and rectifying prevalent issues. It utilizes the D numbers method, which helps convert qualitative expert evaluations into numerical data, thus reducing uncertainties. Additionally, the framework integrates the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with an adversarial approach into the traditional interpretive structural modeling (ISM). Through three dimensions squeeze, the transformation of the system to be evaluated from a topologically active system to a more definitive and rigid one, achieving a comparison of the advantages and disadvantages of alternative solutions. This enhancement aids in better visualization and understanding of the selection process. The effectiveness and practical utility of the newly developed D-SAISM model were confirmed when applied to a real offshore wind power project. The model's recommendations closely matched the actual turbine selection, demonstrating its value in decision-making for wind turbine selection in wind farms.