A 24-sectors direct power control-feedforward neural network method of DFIG integrated to dual-rotor wind turbine

Abstract

In this work, a 24-sector direct power control (24-sector DPC) of a doubly-fed induction generator (DFIG) based dual-rotor wind turbine (DRWT) is studied. The major disadvantage of the 24-DPC control is the steady-state ripples in reactive and active powers. The use of 24 sectors of rotor flux, a feedforward neural network (FNN) algorithm is proposed to improve traditional 24-sector DPC performance and minimize significantly harmonic distortion (THD) of stator current and reactive/active power ripple. The proposed method is modeled and simulated by using MATLAB/Simulink software under different tests and compared with conventional 24-sector DPC.