IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v74y2014i2d10.1007_s11069-014-1189-1.html
   My bibliography  Save this article

RETRACTED ARTICLE: Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect

Author

Listed:
  • Dalibor Petković

    (University of Niš)

  • Siti Hafizah Ab Hamid

    (University of Malaya)

  • Žarko Ćojbašić

    (University of Niš)

  • Nenad T. Pavlović

    (University of Niš)

Abstract

We present a project management methodology designed for the selection of wind turbines wake effect most influential parameters, who need to run wind farm project for large energy conversion. Very frequently, the managers of these projects are not project management professionals, so they need guidance to have autonomy, using minimal time and documentation resources. Therefore, agile method is adapted to assist the project management. Wind energy poses challenges such as the reduction in the wind speed due to the wake effect by other turbines. If a turbine is within the area of turbulence caused by another turbine, or the area behind another turbine, the wind speed suffers a reduction and, therefore, there is a decrease in the production of electricity. In order to increase the efficiency of a wind farm, analyzing the parameters, which have influence on the wake effect, is one of the focal research areas. To maximize the power produced in a wind farm, it is important to determine and analyze the most influential factors on the wake effects or wake wind speeds since the effect has most influence on the produced power. This procedure is typically called variable selection, and it corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. In this study, architecture for modeling complex systems in function approximation and regression was used, based on using adaptive neuro-fuzzy inference system (ANFIS). Variable searching using the ANFIS network was performed to determine how the five parameters affect the wake wind speed. Our article answers the call for renewing the theoretical bases of wind farm project management in order to overcome the problems that stem from the application of methods based on decision-rationality norms, which bracket the complexity of action and interactions in projects.

Suggested Citation

  • Dalibor Petković & Siti Hafizah Ab Hamid & Žarko Ćojbašić & Nenad T. Pavlović, 2014. "RETRACTED ARTICLE: Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.
    • Handle: RePEc:spr:nathaz:v:74:y:2014:i:2:d:10.1007_s11069-014-1189-1
    DOI: 10.1007/s11069-014-1189-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-014-1189-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-014-1189-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Mustakerov, Ivan & Borissova, Daniela, 2010. "Wind turbines type and number choice using combinatorial optimization," Renewable Energy, Elsevier, vol. 35(9), pages 1887-1894.
    3. Yin, Peng-Yeng & Wang, Tai-Yuan, 2012. "A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms," Renewable Energy, Elsevier, vol. 48(C), pages 489-498.
    4. Saavedra-Moreno, B. & Salcedo-Sanz, S. & Paniagua-Tineo, A. & Prieto, L. & Portilla-Figueras, A., 2011. "Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms," Renewable Energy, Elsevier, vol. 36(11), pages 2838-2844.
    5. González, Javier Serrano & Gonzalez Rodriguez, Angel G. & Mora, José Castro & Santos, Jesús Riquelme & Payan, Manuel Burgos, 2010. "Optimization of wind farm turbines layout using an evolutive algorithm," Renewable Energy, Elsevier, vol. 35(8), pages 1671-1681.
    6. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    7. Nagai, Baku M. & Ameku, Kazumasa & Roy, Jitendro Nath, 2009. "Performance of a 3Â kW wind turbine generator with variable pitch control system," Applied Energy, Elsevier, vol. 86(9), pages 1774-1782, September.
    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. Sebestyén, Viktor, 2021. "Renewable and Sustainable Energy Reviews: Environmental impact networks of renewable energy power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Ahuja, Anjali & Jain, Anamika & Jain, Madhu, 2022. "Transient analysis and ANFIS computing of unreliable single server queueing model with multiple stage service and functioning vacation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 464-490.
    3. Asadi, Shahla & Nilashi, Mehrbakhsh & Iranmanesh, Mohammad & Hyun, Sunghyup Sean & Rezvani, Azadeh, 2022. "Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach," Technovation, Elsevier, vol. 118(C).
    4. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
    5. Nazarimehr, Fahimeh & Sheikh, Javad & Ahmadi, Mohammad Mahdi & Pham, Viet–Thanh & Jafari, Sajad, 2018. "Fuzzy predictive controller for chaotic flows based on continuous signals," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 349-354.

    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. Dalibor Petković & Siti Ab Hamid & Žarko Ćojbašić & Nenad Pavlović, 2014. "Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.
    2. Serrano González, Javier & Burgos Payán, Manuel & Santos, Jesús Manuel Riquelme & González-Longatt, Francisco, 2014. "A review and recent developments in the optimal wind-turbine micro-siting problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 133-144.
    3. Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
    4. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. DuPont, Bryony & Cagan, Jonathan & Moriarty, Patrick, 2016. "An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm," Energy, Elsevier, vol. 106(C), pages 802-814.
    6. Khan, Salman A. & Rehman, Shafiqur, 2013. "Iterative non-deterministic algorithms in on-shore wind farm design: A brief survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 370-384.
    7. Gu, Huajie & Wang, Jun, 2013. "Irregular-shape wind farm micro-siting optimization," Energy, Elsevier, vol. 57(C), pages 535-544.
    8. Cuadra, L. & Ocampo-Estrella, I. & Alexandre, E. & Salcedo-Sanz, S., 2019. "A study on the impact of easements in the deployment of wind farms near airport facilities," Renewable Energy, Elsevier, vol. 135(C), pages 566-588.
    9. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.
    10. 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.
    11. Salcedo-Sanz, S. & Gallo-Marazuela, D. & Pastor-Sánchez, A. & Carro-Calvo, L. & Portilla-Figueras, A. & Prieto, L., 2014. "Offshore wind farm design with the Coral Reefs Optimization algorithm," Renewable Energy, Elsevier, vol. 63(C), pages 109-115.
    12. Yin, Peng-Yeng & Cheng, Chun-Ying & Chen, Hsin-Min & Wu, Tsai-Hung, 2020. "Risk-aware optimal planning for a hybrid wind-solar farm," Renewable Energy, Elsevier, vol. 157(C), pages 290-302.
    13. Saavedra-Moreno, B. & Salcedo-Sanz, S. & Paniagua-Tineo, A. & Prieto, L. & Portilla-Figueras, A., 2011. "Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms," Renewable Energy, Elsevier, vol. 36(11), pages 2838-2844.
    14. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    15. 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.
    16. Dhoot, Aditya & Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2021. "Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability," Energy, Elsevier, vol. 223(C).
    17. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    18. Mittal, Prateek & Kulkarni, Kedar & Mitra, Kishalay, 2016. "A novel hybrid optimization methodology to optimize the total number and placement of wind turbines," Renewable Energy, Elsevier, vol. 86(C), pages 133-147.
    19. Bansal, Jagdish Chand & Farswan, Pushpa, 2017. "Wind farm layout using biogeography based optimization," Renewable Energy, Elsevier, vol. 107(C), pages 386-402.
    20. Dhunny, A.Z. & Timmons, D.S. & Allam, Z. & Lollchund, M.R. & Cunden, T.S.M., 2020. "An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model," Energy, Elsevier, vol. 201(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:spr:nathaz:v:74:y:2014:i:2:d:10.1007_s11069-014-1189-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.