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Fuzzy Logic Based Multi-Criteria Wind Turbine Selection Strategy—A Case Study of Qassim, Saudi Arabia

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  • Shafiqur Rehman

    (Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Salman A. Khan

    (Computer Science Department, University of Pretoria, Pretoria 0002, South Africa)

Abstract

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.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:872-:d:81436
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    2. 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.
    3. Supciller, Aliye Ayca & Toprak, Fatih, 2020. "Selection of wind turbines with multi-criteria decision making techniques involving neutrosophic numbers: A case from Turkey," Energy, Elsevier, vol. 207(C).
    4. M. Mujahid Rafique & Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems, 2018. "Feasibility of a 100 MW Installed Capacity Wind Farm for Different Climatic Conditions," Energies, MDPI, vol. 11(8), pages 1-18, August.
    5. Xue, Jie & Yip, Tsz Leung & Wu, Bing & Wu, Chaozhong & van Gelder, P.H.A.J.M., 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renewable Energy, Elsevier, vol. 172(C), pages 897-917.
    6. Khadijah Barashid & Amr Munshi & Ahmad Alhindi, 2023. "Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia," Energies, MDPI, vol. 16(2), pages 1-22, January.
    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. Dhiman, Harsh S. & Deb, Dipankar, 2020. "Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms," Energy, Elsevier, vol. 202(C).
    9. Paweł Ziemba & Jarosław Wątróbski & Magdalena Zioło & Artur Karczmarczyk, 2017. "Using the PROSA Method in Offshore Wind Farm Location Problems," Energies, MDPI, vol. 10(11), pages 1-20, November.
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