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@article{170125,
        author = {K. S.Ramanjaneyulu and S.Yogendra varma and V.Sai and L.SAMBAMURTHY and S.SIVARAMKRISHNA and A.SATEESH},
        title = {DFIG-based wind turbine advanced control strategy employing a combination of PSO and artificial neural networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3190-3197},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170125},
        abstract = {An enhanced control approach for wind turbines based on DFIG is proposed in this research. The proposed approach is predicated on a hybrid of Artificial Neural Network (ANN) and Particle Swarm Optimisation (PSO). To track the available maximum power point (MPPT) at different wind speeds, PSO combined ANN is recommended. PSO is therefore employed to optimise the Proportional Integral (PI) controller gains of the Doubly Fed Induction Generator (DFIG), so improving its dynamic performance. The performance of the PSO-optimized PI controller in comparison to the conventional one is highlighted in this paper. Via a 2 MW DFIG-WT, the suggested control technique is validated using the MATLAB/Simulink environment. The acquired findings confirm that the suggested PSO-PI is a useful technique for enhancing DFIG WT's dynamic behaviour. It shows that, in comparison to the traditional PI, the overshoots are 50% lower. Additionally, the suggested control method produces a quicker transient reaction.},
        keywords = {Particle Swarm Optimisation (PSO), Artificial Neural Network (ANN), Double Fed Induction Generator (DFIG), Wind Turbine, MPPT.},
        month = {November},
        }