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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
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    References listed on IDEAS

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    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.
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    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.

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