Neural networks based power flow control of the doubly fed induction generator

This paper describes the models of a wind power system, such as the turbine, generator, power electronics converters and controllers, with the aim to control the generation of wind power in order to maximize the generated power with the lowest possible impact in the grid voltage and frequency during normal operation and under the occurrence of faults. The presented work considers a wind power system equipped with the doubly fed induction generator and a vector-controlled converter connected between the rotor and the grid. The paper presents comparative results between proportional-integral controllers and neural networks based controllers, showing that better dynamic characteristics can be obtained using neural networks based controllers.