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Hybrid approach based optimal low voltage ride through capability in DFIG-based wind energy systems

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  • Angala Parameswari, G.
  • Arunsankar, G.

Abstract

The doubly fed induction generator (DFIG), the foundation of the large-scale wind energy conversion system (WECS), has become more and more popular recently because of its numerous technological and financial benefits. Significant over current in the DFIG rotor circuit is caused by grid voltage sags. This paper suggests a hybrid strategy for a DFIG-based WECS's low voltage ride-through (LVRT) capabilities. The proposed hybrid approach is the combination of Golden Eagle optimizer (GEO) and Artificial Neural Network (ANN) commonly it is named as GEO-ANN approach. Its main goal is to assure that WECS and capacity of LVRT is maintained throughout voltage drops and failure stages. GEO is used to optimize the DFIG mechanical power, ANN is used to predict the fault and stator voltage dip. The proposed model is put into practice using the MATLAB platform and contrasted with several existing methods, like Deep Q-Network (DQN), Recurrent Neural Network (RNN), and Mountain Gazelle optimization (MGO) algorithm. The proposed method's accuracy is 97 % which is higher and the total harmonic distortion (THD) is 1.78 % which is lower than the existing methods. This suggests that adopting the proposed methodology could lead to improved performance.

Suggested Citation

  • Angala Parameswari, G. & Arunsankar, G., 2025. "Hybrid approach based optimal low voltage ride through capability in DFIG-based wind energy systems," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225011831
    DOI: 10.1016/j.energy.2025.135541
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