Advanced Control Strategy of DFIG based Wind Turbine using combined Artificial Neural Network and PSO Algorithm

This paper proposes an advanced control strategy for DFIG based Wind Turbine. The designed method is based on a combination of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN). PSO combined ANN is suggested to track the available maximum power point (MPPT) at varying wind speeds...

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Bibliographic Details
Published in2020 International Conference on Electrical and Information Technologies (ICEIT) pp. 1 - 7
Main Authors Ali, Youssef Ait, Ouassaid, Mohammed
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2020
Subjects
Artificial Neural Network (ANN)
Artificial neural networks
Doubly Fed Induction Generator (DFIG)
Doubly fed induction generators
MPPT
Particle Swarm Optimization (PSO)
PI control
Transient response
Wind power generation
Wind speed
Wind Turbine
Wind turbines
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Summary:This paper proposes an advanced control strategy for DFIG based Wind Turbine. The designed method is based on a combination of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN). PSO combined ANN is suggested to track the available maximum power point (MPPT) at varying wind speeds. Thus, PSO is used to improve the dynamic performance of Doubly Fed Induction Generator (DFIG) by optimizing its Proportional Integral (PI) controller gains. This work highlights the performance of the PSO optimized PI controller against classical one. The proposed control strategy is verified via 2 MW DFIG-WT using the MATLAB/Simulink environment. The obtained results validate the proposed PSO-PI as an effective tool for improving the dynamic behavior of DFIGWT, it reveals that the overshoots are reduced by 50 % compared to the classical PI. In addition, a faster transient response is achieved using the proposed control strategy.
DOI:10.1109/ICEIT48248.2020.9113163