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Grey Predictor reference model for assisting particle swarm optimization for wind turbine control

Author

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  • Hodzic, Migdat
  • Tai, Li-Chou

Abstract

This paper proposes an approach of forming the average performance by Grey Modeling, and use an average performance as reference model for performing evolutionary computation with error type control performance index. The idea of the approach is to construct the reference model based on the performance of unknown systems when users apply evolutionary computation to fine-tune the control systems with error type performance index. We apply this approach to particle swarm optimization for searching the optimal gains of baseline PI controller of wind turbines operating at the certain set point in Region 3. In the numerical simulation part, the corresponding results demonstrate the effectiveness of Grey Modeling.

Suggested Citation

  • Hodzic, Migdat & Tai, Li-Chou, 2016. "Grey Predictor reference model for assisting particle swarm optimization for wind turbine control," Renewable Energy, Elsevier, vol. 86(C), pages 251-256.
    • Handle: RePEc:eee:renene:v:86:y:2016:i:c:p:251-256
    DOI: 10.1016/j.renene.2015.08.001
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    Cited by:

    1. Ernesto Chavero-Navarrete & Mario Trejo-Perea & Juan-Carlos Jáuregui-Correa & Roberto-Valentín Carrillo-Serrano & José-Gabriel Rios-Moreno, 2019. "Pitch Angle Optimization by Intelligent Adjusting the Gains of a PI Controller for Small Wind Turbines in Areas with Drastic Wind Speed Changes," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    2. Baniassadi, Amir & Shirinbakhsh, Mehrdad & Torabi, Farschad, 2017. "Multivariate optimization of off-grid wind turbines with variable demand - Case study of a remote commercial building," Renewable Energy, Elsevier, vol. 101(C), pages 1021-1029.

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