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

G. Angala Parameswari and G. Arunsankar

Energy, 2025, vol. 324, issue C

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.

Keywords: Wind turbine voltage sag; Golden eagle optimizer; Doubly fed induction generator; Low voltage ride through; And artificial neural network (ANN) (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225011831
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:324:y:2025:i:c:s0360544225011831

DOI: 10.1016/j.energy.2025.135541

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().