LAPSE:2019.0186
Published Article
LAPSE:2019.0186
Fuzzy Logic Based Multi-Criteria Wind Turbine Selection Strategy—A Case Study of Qassim, Saudi Arabia
Shafiqur Rehman, Salman A. Khan
January 31, 2019
The emergence of wind energy as a potential alternative to traditional sources of fuel has prompted notable research in recent years. One primary factor contributing to efficient utilization of wind energy from a wind farm is the type of turbines used. However, selection of a specific wind turbine type is a difficult task due to several criteria involved in the selection process. Important criteria include turbine’s power rating, height of tower, energy output, rotor diameter, cut-in wind speed, and rated wind speed. The complexity of this selection process is further amplified by the presence of conflicts between the decision criteria. Therefore, a decision is desired that provides the best balance between all selection criteria. Considering the complexities involved in the decision-making process, this paper proposes a two-level decision turbine selection strategy based on fuzzy logic and multi-criteria decision-making (MCDM) approach. More specifically, the fuzzy arithmetic mean operator is used in the decision process. The proposed approach is applied to wind data collected from the site of Qassim, Saudi Arabia. Results indicate that the proposed approach was effective in finding the optimal turbine from a set of 20 turbines of various capacities.
Keywords
decision-making, fuzzy arithmetic mean operator, fuzzy logic, wind energy, wind turbine
Suggested Citation
Rehman S, Khan SA. Fuzzy Logic Based Multi-Criteria Wind Turbine Selection Strategy—A Case Study of Qassim, Saudi Arabia. (2019). LAPSE:2019.0186
Author Affiliations
Rehman S: Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Khan SA: Computer Science Department, University of Pretoria, Pretoria 0002, South Africa
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Journal Name
Energies
Volume
9
Issue
11
Article Number
E872
Year
2016
Publication Date
2016-10-26
Published Version
ISSN
1996-1073
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PII: en9110872, Publication Type: Journal Article
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LAPSE:2019.0186
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doi:10.3390/en9110872
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Jan 31, 2019
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