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State estimation for wind farms including the wind turbine generator models

Author

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  • Miranda-Blanco, Blanca Nieves
  • Díaz-Dorado, Eloy
  • Carrillo, Camilo
  • Cidrás, J.

Abstract

Wind farms can be analyzed using state estimation methods, which can be used to obtain its running state, including several aspects that cannot be easily obtained using other methods (e.g., capacitor bank aging) Using these methods on these types of networks is strongly affected by decoupling between active and reactive power and by a radial configuration, which is typical. For example, this decoupling affects its observability and robustness as well as the technical feasibility of the results. To overcome these drawbacks, an extended state estimation method is proposed in which the models for the different wind turbine technologies have been incorporated. These models have been mainly generated from measurement data using neural networks and polynomial fitting; these models do not require parameter values, which are rarely available from manufacturers. Furthermore, the resulting equations for modeling wind turbines are easily integrated into the state estimator due to their simplicity and derivatives.Thus, a method that guarantees feasible results, at least for wind turbines, was generated with increased observability robustness.

Suggested Citation

  • Miranda-Blanco, Blanca Nieves & Díaz-Dorado, Eloy & Carrillo, Camilo & Cidrás, J., 2014. "State estimation for wind farms including the wind turbine generator models," Renewable Energy, Elsevier, vol. 71(C), pages 453-465.
    • Handle: RePEc:eee:renene:v:71:y:2014:i:c:p:453-465
    DOI: 10.1016/j.renene.2014.05.029
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    References listed on IDEAS

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    1. Niknam, Taher & Firouzi, Bahman Bahmani, 2009. "A practical algorithm for distribution state estimation including renewable energy sources," Renewable Energy, Elsevier, vol. 34(11), pages 2309-2316.
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    Cited by:

    1. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    2. Ganesh Mayilsamy & Kumarasamy Palanimuthu & Raghul Venkateswaran & Ruban Periyanayagam Antonysamy & Seong Ryong Lee & Dongran Song & Young Hoon Joo, 2023. "A Review of State Estimation Techniques for Grid-Connected PMSG-Based Wind Turbine Systems," Energies, MDPI, vol. 16(2), pages 1-27, January.

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