Control and Stability Analysis of a Doubly Fed Induction Generator

Bouaouiche, T.; Machmoum, Mohamed

doi:10.1109/epepemc.2006.4778633

2006 12th International Power Electronics and Motion Control Conference 2006

DOI: 10.1109/epepemc.2006.4778633

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Control and Stability Analysis of a Doubly Fed Induction Generator

T. Bouaouiche¹,

Mohamed Machmoum²

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2015

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Modeling and Control of DFIG Through Back-to-Back Five Levels Converters Based on Neuro-Fuzzy Controller

Dida

Benattous²

2015

J Control Autom Electr Syst

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This paper deals with the power generation control in variable speed wind turbine. In this context, the wind energy conversion system (WECS) is equipped with a doubly fed induction generator (DFIG) and back-to-back five-level neutral-point-clamped converters in the rotor circuit. The modeling and the control of the five-level converter is presented. A vector control of the rotor side converter allows independent control of the stator active and reactive power and optimal speed tracking for maximum power capture from the wind. An adaptive neuro-fuzzy inference system is proposed as alternative of Mamdani type fuzzy controller to improve the robustness and reject any disturbance in the system. Three neuro-fuzzy controllers (NFCs) are used to control the rotational speed, and the stator active and reactive power. Another fuzzy logic system is proposed as a PI gain tuner in the DC-link voltage control loop to improve the dynamic response and robustness of the DC-link voltage control. In purpose to prove the performances of the global system, simulation was carried out in Matlab-Simulink software environment with 1.5MW DFIG-WECS.

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Modeling and Control of DFIG Through Back-to-Back Five Levels Converters Based on Neuro-Fuzzy Controller

Dida

Benattous²

2015

J Control Autom Electr Syst

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Control and Stability Analysis of a Doubly Fed Induction Generator

Cited by 1 publication

References 4 publications

Modeling and Control of DFIG Through Back-to-Back Five Levels Converters Based on Neuro-Fuzzy Controller

Modeling and Control of DFIG Through Back-to-Back Five Levels Converters Based on Neuro-Fuzzy Controller

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