A Multilayer Perceptron Controller Applied to the Direct Power Control of a Doubly Fed Induction Generator

This paper presents a direct power control strategy for a doubly fed induction generator by using an artificial neural network controller with the multilayer perceptron structure. This controller generates the direct- and quadrature-axis rotor voltage signals from both the stator current and voltage that are measured by the Hall sensors. The input variables of the control system are the rotor speed, the active and reactive power references, and their respective errors. The proposed control strategy allows the converter connected to the rotor terminals to operate with constant switching frequency. Digital simulation and experimental tests are performed for a 2.25-kW doubly fed induction generator to validate the proposed control strategy.