Real-Time Neural Inverse Optimal Control for a Wind Generator

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 voltagesource 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.

I. Introduction

Wind power is the harvest of the wind mechanical energy converting it into electrical energy by means of wind turbines. The wind energy utilization has grown in greater proportion because it is clean, renewable, and does not pollute as compared with traditional energy sources. In June 2016, the worldwide wind capacity has reached 456,486 GigaWatts (GW), which presents world electricity demand [1]. The conversion of wind to electrical energy is done by using two types of generators [2], such as: Synchronous and asynchronous; the last one is known as doubly fed induction generator (DFIG).