Real-Time Neural Inverse Optimal Control for a Wind Generator

Abstract

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 two insulated-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.