Open AccessA new extension of hesitant fuzzy set: An application to an offshore wind turbine technology selection process
Author(s) -
Narayanamoorthy Samayan,
Ramya Lakshmanaraj,
Kang Daekook,
Baleanu Dumitru,
Kureethara Joseph Varghese,
Annapoorani Veerappan
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12168
Subject(s) - turbine , wind power , offshore wind power , computer science , fuzzy logic , multiple criteria decision analysis , process (computing) , set (abstract data type) , energy (signal processing) , reliability engineering , automotive engineering , marine engineering , engineering , operations research , artificial intelligence , mathematics , mechanical engineering , electrical engineering , statistics , programming language , operating system
Wind energy is an energy source that is naturally clean, safe and cheap. It comes from a variety of sources. The electric energy generated by a wind turbine manifests as kinetic energy throughout the earth. The energy received from the wind is clean and is permanently available and can be generated forever. Turbine characteristics also have an impact on wind energy production. The turbine properties within a wind farm are important in estimating the load on power generation and wind turbine energy. The amount of energy released is calculated according to the type of the turbine model applied. In many situations, the choices of turbine model can incur various vague and complicated hesitation situations. To manage this situation, a hesitant fuzzy set with the Multi Criteria Decision Making (MCDM) is used. In the present research, the newly proposed Normal Wiggly Hesitant Fuzzy‐Criteria Importance Through Intercriteria Correlation (NWHF‐CRITIC) and Normal Wiggly Hesitant Fuzzy‐Multi Attribute Utility Theory (NWHF‐MAUT) methods were employed to rank turbine models based on quality, power level, voltage, and capacity. As part of this process, the NWHF method was utilized to extract and gather deeper information from the decision‐makers.