Robust cascade controller for the power factor of the three-phase supply and the induction motor velocity.

It is well known that induction motors consume active and reactive energy from the utility grid to operate; additionally, when a power converter drives the motor, a high content of current harmonics is produced, and both circumstances decrease the utility grid power factor, which later requires to be improved. To this end, this paper presents a novel complete solution through a robust control system employed in a back-to-back topology power converter to deliver, instead of consuming, regulated reactive power toward the main grid, which comes from a capacitor bank in a DC-bus. This salient feature of delivering reactive power, and simultaneously, regulating the speed for an induction motor, becomes one of the contributions of this work to enhance the power factor.

Real-time Neural Inverse Optimal Control for Low-Voltage Rid-Through enhancement of Double Fed Induction Generator based Wind Turbines.

The Low-Voltage Ride-Through (LVRT) capacity of the Doubly Fed Induction Generator (DFIG) is one of the important requirements to ensure power systems stability, incorporating wind energy. While traditional control schemes present inappropriate performances under disturbances, this paper introduces a novel Neural Inverse Optimal Control (N-IOC) scheme for LVRT capacity enhancing. The developed controller is synthesized using recurrent high order neural network, which is utilized to build-up the DFIG and the DC-link dynamics.

Particle swarm optimization for discrete-time inverse optimal control of a doubly fed induction generator.

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.

Discrete-time neural inverse optimal control for nonlinear systems via passivation.

This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot.