ADSIG as Gen-Former providing three port network for soft coupling of distribution feeders in addition to wind energy harvesting

Verma, Vishal; Singh, Ramesh; Gour, Ritika

doi:10.1016/j.ijepes.2019.105573

Cited by 2 publications

(2 citation statements)

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“…Models of six-phase induction generators combine the rotor's turn motion with the electrical dynamics of the stator's and rotor's phases [13][14][15][16][17]. To integrate 6phase dual star induction generators in the main electricity grid, two three-phase AC/DC converters can be used, each connected with one of the stator's three-phase windings [18][19][20]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

“…Figure19 Tracking of setpoint 8 for the renewable energy system which is based on a sixphase induction generator (a) control inputs u1 to u4, (b) convergence to zero of the tracking error for the state variables of the six-phase induction generator.…”

mentioning

confidence: 99%