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A Rule-Based Fuzzy Logic Methodology for Multi-Criteria Selection of Wind Turbines

Author

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  • Shafiqur Rehman

    (Center for Engineering Research, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran 34464, Saudi Arabia)

  • Salman A. Khan

    (College of Computing and Information Sciences, Karachi Institute of Economics and Technology, Karachi 75190, Pakistan)

  • Luai M. Alhems

    (Center for Engineering Research, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran 34464, Saudi Arabia)

Abstract

The domain of renewable energy has seen tremendous growth in the past many years. This growth has shown optimism for a sustainable future and promises to lead the human race towards a cleaner and healthier environment. Wind energy, which is a vital part of this clean energy revolution, has received significant attention globally. To get benefit from wind energy, wind farms need to be developed with the highest efficiency so that the maximum energy can be harnessed. A key decision in this development process is selection of a turbine type that shows highest compatibility with the geographical and topographical features of the site where the turbines are to be installed. In practical terms, the turbine selection mechanism should consider several decision criteria. In many cases, these criteria are conflicting with each other. Furthermore, the choice and aspirations of the decision-maker who selects these turbines should be considered in the selection process and should be flexible. This paper presents a preliminary study on a rule-based turbine selection methodology which is based on the concepts of fuzzy logic. The proposed methodology analyzes several scenarios in conjunction with the turbine selection model. The applicability of the methodology is demonstrated via two test scenarios. Data from a real potential site in Saudi Arabia were used, and 17 turbines from different manufacturers and with rated capacities in range of 1.5–3 MW were evaluated. The results indicate that the proposed scheme is able to incorporate decision-maker’s aspirations and effectively reflects these aspirations in the turbine selection process.

Suggested Citation

  • Shafiqur Rehman & Salman A. Khan & Luai M. Alhems, 2020. "A Rule-Based Fuzzy Logic Methodology for Multi-Criteria Selection of Wind Turbines," Sustainability, MDPI, vol. 12(20), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8467-:d:427893
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    References listed on IDEAS

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    1. Jung-Tae Lee & Hyun-Goo Kim & Yong-Heack Kang & Jin-Young Kim, 2019. "Determining the Optimized Hub Height of Wind Turbine Using the Wind Resource Map of South Korea," Energies, MDPI, vol. 12(15), pages 1-13, July.
    2. Perkin, Samuel & Garrett, Deon & Jensson, Pall, 2015. "Optimal wind turbine selection methodology: A case-study for Búrfell, Iceland," Renewable Energy, Elsevier, vol. 75(C), pages 165-172.
    3. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    4. Saul Gass & Thomas Saaty, 1955. "The computational algorithm for the parametric objective function," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 39-45, March.
    5. A. Charnes & W. W. Cooper & R. O. Ferguson, 1955. "Optimal Estimation of Executive Compensation by Linear Programming," Management Science, INFORMS, vol. 1(2), pages 138-151, January.
    6. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    7. A. Dinmohammadi & M. Shafiee, 2017. "Determination of the Most Suitable Technology Transfer Strategy for Wind Turbines Using an Integrated AHP-TOPSIS Decision Model," Energies, MDPI, vol. 10(5), pages 1-17, May.
    8. EL-Shimy, M., 2010. "Optimal site matching of wind turbine generator: Case study of the Gulf of Suez region in Egypt," Renewable Energy, Elsevier, vol. 35(8), pages 1870-1878.
    9. Shafiqur Rehman & Salman A. Khan, 2016. "Fuzzy Logic Based Multi-Criteria Wind Turbine Selection Strategy—A Case Study of Qassim, Saudi Arabia," Energies, MDPI, vol. 9(11), pages 1-26, October.
    10. Bertrand Mareschal & Jean Pierre Brans, 1990. "The PROMETHEE methods for MCDM: the PROMCALC, GAIA and BANK ADVISER software," ULB Institutional Repository 2013/9337, ULB -- Universite Libre de Bruxelles.
    11. Dong, Yao & Wang, Jianzhou & Jiang, He & Shi, Xiaomeng, 2013. "Intelligent optimized wind resource assessment and wind turbines selection in Huitengxile of Inner Mongolia, China," Applied Energy, Elsevier, vol. 109(C), pages 239-253.
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    2. Alphan, H., 2021. "Modelling potential visibility of wind turbines: A geospatial approach for planning and impact mitigation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).

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