Simulation and experimental control of six-phase induction generator for wind turbines

Pantea, Alin; Yazidi, A.; Bétin, F.; Carriere, S.; Capolino, Gérard‐André

doi:10.1109/icelmach.2016.7732674

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…Due to the nonlinearities and multivariable structure of the associated state-space model the control and stabilization problem of 6-phase DSIGs is nontrivial [21][22][23][24]. Several attempts have been made to develop model-based control schemes for 6-phase DSIGs and to demonstrate robustness of the associated control loops to model uncertainty and perturbations [25][26][27][28]. In particular results on sliding-mode-type and fault-tolerant control of six-phase induction generators can be found in [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Optimal Control for Six-Phase Induction Generator-Based Renewable Energy Systems

Rigatos¹,

Abbaszadeh²,

Sari³

et al. 2023

J Energ Power Technol

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The article aims at optimizing six-phase induction generator-based renewable energy systems (6-phase IGs or dual star induction generators) through a novel nonlinear optimal control method. Six-phase induction generators appear to be advantageous compared to three-phase synchronous or asynchronous power generators, in terms of fault tolerance and improved power generation rates. The dynamic model of the six-phase induction generator is first written in a nonlinear and multivariable state-space form. It is proven that this model is differentially flat. The 6-phase IG is approximately linearized around a temporary operating point recomputed at each sampling interval to design the optimal controller. The linearization is based on first-order Taylor series expansion and the Jacobian matrices of the state-space model of the 6-phase IG. A stabilizing optimal (H-infinity) feedback controller is designed for the linearized state-space description of the six-phase IG. The feedback gains of the controller are computed by solving an algebraic Riccati equation at each iteration of the control method. Lyapunov analysis is used to demonstrate global stability for the control loop. The H-infinity Kalman Filter is also used as a robust state estimator, which allows for implementing sensorless control for 6-phase IG-based renewable energy systems. The nonlinear optimal control method achieves fast and accurate tracking of setpoints by the state variables of the 6-phase IG, under moderate variations of the control inputs.

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