In this paper, the authors propose a particle swarm optimization (PSO) for a discrete-time inverse optimal control scheme of a doubly fed induction generator (DFIG). For the inverse optimal scheme, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to achieve trajectory tracking. A posteriori, it is established that this control law minimizes a meaningful cost function. The CLFs depend on matrix selection in order to achieve the control objectives; this matrix is determined by two mechanisms: initially, fixed parameters are proposed for this matrix by a trial-and-error method and then by using the PSO algorithm. The inverse optimal control scheme is illustrated via simulations for the DFIG, including the comparison between both mechanisms.

This paper presents a discrete-time inverse optimal control scheme using a neural network for a doubly fed induction generator (DFIG). The DFIG generation scheme has a voltage-source converter connected between the rotor and the electrical grid, which is composed by a two insulate-gate bipolar transistors (IGBT) power converters connected in back-to-back configuration. These converters are known as rotor side converter (RSC) and grid side converter (GSC), respectively. The RSC is used to control the electric torque and reactive power of the DFIG, and the GSC is used to control the DC Link voltage in the IGBT connection and the stator terminals reactive power. To take into account possible parameter variations, a recurrent high order neural network (RHONN) is used to approximate the DFIG model in an identification process; after that, based on the neural model obtained, a discrete-time inverse optimal control scheme for the RSC is developed. Using a similar approach, a DC Link neural identifier and a controller are proposed for the GSC. The proposed control scheme applicability is validated by means simulations, which includes a comparison with proportional-integral controllers; then this control scheme is implemented in real-time. The paper novelty consists on the synthesis and real-time implementation of a DFIG inverse optimal control based on a RHONN.

On this paper, a robust velocity controller applied to a three-phase squirrel-cage induction motor under variable load conditions is designed. The induction motor drives an induction generator representing the load which freely delivers the generated power to the utility grid. The closed-loop scheme is designed at αβ coordinate frame and is based on the linearization block control technique in combination with the super-twisting algorithm. The controlled output variables are the angular mechanical velocity and the square modulus of rotor flux linkages. The motor reference velocity is set up by a pulse train above the synchronous velocity and, consequently, the impelled induction machine operates in generator mode; meanwhile, the reference of rotor flux square modulus varies according to load condition of the induction motor. In order to estimate the non-measurable variables, both a rotor flux linkages observer and a load torque observer are designed. For the first one, a non-linear state observer using first order sliding modes is applied at αβ coordinate frame and, for the second one, a Luenberger reduced asymptotic observer is used. The validation of the robustness for the proposed velocity controller is performed in a real-time experiment using a work-bench.

The authors propose robust non‐linear controllers for a wind system. The proposed controllers are based on a combination of the block control linearisation and super‐twisting algorithm, as a second‐order sliding mode technique. The main control loop is designed to regulate the electromagnetic torque and stator power factor of a doubly‐fed induction generator which is connected to the grid. The generator is mechanically coupled with a DC motor, which can emulate the wind turbine operation. Therefore, an auxiliary control loop is designed to control the DC motor velocity. Additionally, a grid‐side converter controller is proposed to regulate the DC‐link voltage into AC/DC/AC converter, and displacement factor of the energy flow between the rotor and the grid. A robust stability analysis of the complete closed‐loop system under external disturbances is presented. The robustness of the proposed control schemes is validated in real time using a workbench, which consists of a motor–generator group, AC/DC/AC electronic drive and dSPACE DS 1104 controller boards.