IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v40y2012i1p438-444.html
   My bibliography  Save this article

Optimal estimation and control of WECS via a Genetic Neuro Fuzzy Approach

Author

Listed:
  • Kasiri, H.
  • Abadeh, M. Saniee
  • Momeni, H.R.

Abstract

Megawatt class wind turbines generally turn at variable speed in wind farm. Thus turbine operation must be controlled in order to maximize the conversion efficiency below rated power and reduce loading on the drive train. In addition, researchers particularly employ pitch control of the blades to manage the energy captured throughout operation above and below rated wind speed. In this study, fuzzy rules have been successfully extracted from Neural Network (NN) using a new Genetic Fuzzy System (GFS). Fuzzy Rule Extraction from Neural network using Genetic Algorithm (FRENGA) rejects wind disturbance in Wind Energy Conversion Systems (WECS) input with pitch angel control generation. Consequently, our proposed approach has regulated output aerodynamic power and torque in the nominal range. Results indicate that the new proposed genetic fuzzy rule extraction system outperforms one of the best and earliest methods in controlling the output during wind fluctuation.

Suggested Citation

  • Kasiri, H. & Abadeh, M. Saniee & Momeni, H.R., 2012. "Optimal estimation and control of WECS via a Genetic Neuro Fuzzy Approach," Energy, Elsevier, vol. 40(1), pages 438-444.
    • Handle: RePEc:eee:energy:v:40:y:2012:i:1:p:438-444
    DOI: 10.1016/j.energy.2012.01.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544212000278
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2012.01.022?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. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    2. Boukhezzar, B. & Lupu, L. & Siguerdidjane, H. & Hand, M., 2007. "Multivariable control strategy for variable speed, variable pitch wind turbines," Renewable Energy, Elsevier, vol. 32(8), pages 1273-1287.
    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. Howlader, Abdul Motin & Izumi, Yuya & Uehara, Akie & Urasaki, Naomitsu & Senjyu, Tomonobu & Yona, Atsushi & Saber, Ahmed Yousuf, 2012. "A minimal order observer based frequency control strategy for an integrated wind-battery-diesel power system," Energy, Elsevier, vol. 46(1), pages 168-178.
    2. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    3. Lee, Kyungeun & Lee, Hyesu & Lee, Hyoseop & Yoon, Yoonjin & Lee, Eunjung & Rhee, Wonjong, 2018. "Assuring explainability on demand response targeting via credit scoring," Energy, Elsevier, vol. 161(C), pages 670-679.
    4. Saha, Apu Kumar & Majumder, Mrinmoy, 2015. "Median based conversion of SGPA into percentage by cognitive methods," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 1153-1162.
    5. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.

    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. Hoffmann, F. & Baesens, B. & Mues, C. & Van Gestel, T. & Vanthienen, J., 2007. "Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 177(1), pages 540-555, February.
    2. Mérida, Jován & Aguilar, Luis T. & Dávila, Jorge, 2014. "Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization," Renewable Energy, Elsevier, vol. 71(C), pages 715-728.
    3. Fan, Zhixin & Zhu, Caichao, 2019. "The optimization and the application for the wind turbine power-wind speed curve," Renewable Energy, Elsevier, vol. 140(C), pages 52-61.
    4. Kwon, He-Boong & Lee, Jooh, 2019. "Exploring the differential impact of environmental sustainability, operational efficiency, and corporate reputation on market valuation in high-tech-oriented firms," International Journal of Production Economics, Elsevier, vol. 211(C), pages 1-14.
    5. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    6. Guo, Mengzhuo & Zhang, Qingpeng & Liao, Xiuwu & Chen, Frank Youhua & Zeng, Daniel Dajun, 2021. "A hybrid machine learning framework for analyzing human decision-making through learning preferences," Omega, Elsevier, vol. 101(C).
    7. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 83-102, June.
    8. Yolanda Vidal & Leonardo Acho & Ningsu Luo & Mauricio Zapateiro & Francesc Pozo, 2012. "Power Control Design for Variable-Speed Wind Turbines," Energies, MDPI, vol. 5(8), pages 1-18, August.
    9. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
    10. Xiao Fang & Olivia R. Liu Sheng & Paulo Goes, 2013. "When Is the Right Time to Refresh Knowledge Discovered from Data?," Operations Research, INFORMS, vol. 61(1), pages 32-44, February.
    11. S. Balcaen & H. Ooghe, 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/249, Ghent University, Faculty of Economics and Business Administration.
    12. Tang, Lingxiao & Cai, Fei & Ouyang, Yao, 2019. "Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 563-572.
    13. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
    14. Cabrera Llanos Agustín Ignacio & Ortíz Arango Francisco, 2012. "Pronóstico del rendimiento del IPC (Índice de Precios y Cotizaciones)mediante el uso de redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 57(2), pages 63-81, abril-jun.
    15. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    16. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    17. Nikita Tomin, 2023. "Robust Reinforcement Learning-Based Multiple Inputs and Multiple Outputs Controller for Wind Turbines," Mathematics, MDPI, vol. 11(14), pages 1-19, July.
    18. Marchioni, Andrea & Magni, Carlo Alberto, 2018. "Investment decisions and sensitivity analysis: NPV-consistency of rates of return," European Journal of Operational Research, Elsevier, vol. 268(1), pages 361-372.
    19. Mo Leo S. F. & Yau Kelvin K. W., 2010. "Survival Mixture Model for Credit Risk Analysis," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 4(2), pages 1-20, July.
    20. Chen, Jiahao & Hu, Zhiqiang & Liu, Geliang & Wan, Decheng, 2019. "Coupled aero-hydro-servo-elastic methods for floating wind turbines," Renewable Energy, Elsevier, vol. 130(C), pages 139-153.

    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:eee:energy:v:40:y:2012:i:1:p:438-444. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.