@ARTICLE{Kumarb_Ram_Krishan_Design_2024,
 author={Kumarb, Ram Krishan and Choudhary, Jayanti},
 volume={vol. 73},
 number={No 2},
 journal={Archives of Electrical Engineering},
 pages={373-392},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences},
 abstract={The advancement of ocean renewable energy through Tidal Stream Turbines (TSTs) necessitates the use of a variety of computer models to properly evaluate TST efficiency. The Doubly Fed Induction Generator (DFIG) is the most widely utilized Wind Turbine (WT) in the expanding global wind sector. Grid-tied wind energy systems often use the DFIG to meet conventional grid needs including power quality enhancement, grid stability, grid synchronization, power regulation, and fault ride-through. This paper demonstrates the design of a novel control scheme for the operation of the DFIG. The suggested control scheme consisted of an Improved Recurrent Fuzzy Neural Network (IRFNN) and Ant Colony Optimization with Genetic Algorithms (GACOs). A global control system is created and executed to monitor the changeover between the two operating modes. The plant enters a variable speed mode when the tidal speed is low enough, where the system is controlled to ensure that the turbo-generator module functions at peak power extraction efficiency for any specific tidal velocity. The findings demonstrate the system’s superior efficiency, with the highest power extraction provided despite variations in tidal stream input.},
 type={Article},
 title={Design of a novel control scheme for the operation of the doubly fed induction generator},
 URL={http://journals.pan.pl/Content/131501/07.pdf},
 doi={10.24425/aee.2024.149922},
 keywords={DFIG, GACOS, improved recurrent fuzzy neural network (IRFNN), modelling, TST},
}