IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i11p872-d81436.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/11/872/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/11/872/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jowder, Fawzi A.L., 2009. "Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain," Applied Energy, Elsevier, vol. 86(4), pages 538-545, April.
    2. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    3. Cavallaro, Fausto & Ciraolo, Luigi, 2005. "A multicriteria approach to evaluate wind energy plants on an Italian island," Energy Policy, Elsevier, vol. 33(2), pages 235-244, January.
    4. Souma Chowdhury & Ali Mehmani & Jie Zhang & Achille Messac, 2016. "Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes," Energies, MDPI, vol. 9(5), pages 1-31, May.
    5. Lee, Amy H.I. & Chen, Hsing Hung & Kang, He-Yau, 2009. "Multi-criteria decision making on strategic selection of wind farms," Renewable Energy, Elsevier, vol. 34(1), pages 120-126.
    6. 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.
    7. Al-Yahyai, Sultan & Charabi, Yassine & Gastli, Adel & Al-Badi, Abdullah, 2012. "Wind farm land suitability indexing using multi-criteria analysis," Renewable Energy, Elsevier, vol. 44(C), pages 80-87.
    8. 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.
    9. 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.
    10. Dombi, J., 1982. "Basic concepts for a theory of evaluation: The aggregative operator," European Journal of Operational Research, Elsevier, vol. 10(3), pages 282-293, July.
    11. Höfer, Tim & Sunak, Yasin & Siddique, Hafiz & Madlener, Reinhard, 2016. "Wind farm siting using a spatial Analytic Hierarchy Process approach: A case study of the Städteregion Aachen," Applied Energy, Elsevier, vol. 163(C), pages 222-243.
    12. Baseer, M.A. & Meyer, J.P. & Rehman, S. & Md. Mahbub Alam, & Al-Hadhrami, L.M. & Lashin, A., 2016. "Performance evaluation of cup-anemometers and wind speed characteristics analysis," Renewable Energy, Elsevier, vol. 86(C), pages 733-744.
    13. 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.
    14. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
    15. José F. Herbert-Acero & Oliver Probst & Pierre-Elouan Réthoré & Gunner Chr. Larsen & Krystel K. Castillo-Villar, 2014. "A Review of Methodological Approaches for the Design and Optimization of Wind Farms," Energies, MDPI, vol. 7(11), pages 1-87, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Xia & Xu, Li & Cai, Jingjing & Peng, Cheng & Bian, Xiaoyan, 2024. "Offshore wind turbine selection with multi-criteria decision-making techniques involving D numbers and squeeze adversarial interpretive structural modeling method," Applied Energy, Elsevier, vol. 368(C).
    2. Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems & M. Mujahid Rafique, 2018. "Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review," Energies, MDPI, vol. 11(3), pages 1-34, February.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. Khan, Meer A.M. & Rehman, S. & Al-Sulaiman, Fahad A., 2018. "A hybrid renewable energy system as a potential energy source for water desalination using reverse osmosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 456-477.
    12. Nansheng Pang & Mengfan Nan & Qichen Meng & Siyang Zhao, 2021. "Selection of Wind Turbine Based on Fuzzy Analytic Network Process: A Case Study in China," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    13. Mamdouh Abdulrahman & David Wood, 2019. "Wind Farm Layout Upgrade Optimization," Energies, MDPI, vol. 12(13), pages 1-25, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. A. G. Olabi & Tabbi Wilberforce & Khaled Elsaid & Tareq Salameh & Enas Taha Sayed & Khaled Saleh Husain & Mohammad Ali Abdelkareem, 2021. "Selection Guidelines for Wind Energy Technologies," Energies, MDPI, vol. 14(11), pages 1-34, June.
    2. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.
    3. Rodrigues, S. & Bauer, P. & Bosman, Peter A.N., 2016. "Multi-objective optimization of wind farm layouts – Complexity, constraint handling and scalability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 587-609.
    4. Jarosław Wątróbski & Paweł Ziemba & Jarosław Jankowski & Magdalena Zioło, 2016. "Green Energy for a Green City—A Multi-Perspective Model Approach," Sustainability, MDPI, vol. 8(8), pages 1-23, July.
    5. 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.
    6. Pollini, Nicolò, 2022. "Topology optimization of wind farm layouts," Renewable Energy, Elsevier, vol. 195(C), pages 1015-1027.
    7. Souma Chowdhury & Ali Mehmani & Jie Zhang & Achille Messac, 2016. "Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes," Energies, MDPI, vol. 9(5), pages 1-31, May.
    8. Guirguis, David & Romero, David A. & Amon, Cristina H., 2017. "Gradient-based multidisciplinary design of wind farms with continuous-variable formulations," Applied Energy, Elsevier, vol. 197(C), pages 279-291.
    9. Park, Jinkyoo & Law, Kincho H., 2015. "Layout optimization for maximizing wind farm power production using sequential convex programming," Applied Energy, Elsevier, vol. 151(C), pages 320-334.
    10. Sofia Spyridonidou & Dimitra G. Vagiona, 2020. "Systematic Review of Site-Selection Processes in Onshore and Offshore Wind Energy Research," Energies, MDPI, vol. 13(22), pages 1-26, November.
    11. Guirguis, David & Romero, David A. & Amon, Cristina H., 2016. "Toward efficient optimization of wind farm layouts: Utilizing exact gradient information," Applied Energy, Elsevier, vol. 179(C), pages 110-123.
    12. Paweł Ziemba, 2019. "Inter-Criteria Dependencies-Based Decision Support in the Sustainable wind Energy Management," Energies, MDPI, vol. 12(4), pages 1-29, February.
    13. Nansheng Pang & Mengfan Nan & Qichen Meng & Siyang Zhao, 2021. "Selection of Wind Turbine Based on Fuzzy Analytic Network Process: A Case Study in China," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    14. Chen, K. & Song, M.X. & Zhang, X. & Wang, S.F., 2016. "Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm," Renewable Energy, Elsevier, vol. 96(PA), pages 676-686.
    15. Yildiz, S.S., 2024. "Spatial multi-criteria decision making approach for wind farm site selection: A case study in Balıkesir, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    16. Xu, Ye & Li, Ye & Zheng, Lijun & Cui, Liang & Li, Sha & Li, Wei & Cai, Yanpeng, 2020. "Site selection of wind farms using GIS and multi-criteria decision making method in Wafangdian, China," Energy, Elsevier, vol. 207(C).
    17. Jangid, Jayant & Bera, Apurba Kumar & Joseph, Manoj & Singh, Vishal & Singh, T.P. & Pradhan, B.K. & Das, Sandipan, 2016. "Potential zones identification for harvesting wind energy resources in desert region of India – A multi criteria evaluation approach using remote sensing and GIS," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1-10.
    18. Giallanza, A. & Porretto, M. & Cannizzaro, L. & Marannano, G., 2017. "Analysis of the maximization of wind turbine energy yield using a continuously variable transmission system," Renewable Energy, Elsevier, vol. 102(PB), pages 481-486.
    19. Asadi, Meysam & Pourhossein, Kazem, 2021. "Wind farm site selection considering turbulence intensity," Energy, Elsevier, vol. 236(C).
    20. Peri, Erez & Tal, Alon, 2020. "A sustainable way forward for wind power: Assessing turbines’ environmental impacts using a holistic GIS analysis," Applied Energy, Elsevier, vol. 279(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:872-:d:81436. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.