Squirrel-Cage Induction Generator System Using Wavelet Petri Fuzzy Neural Network Control for Wind Power Applications

A wavelet Petri fuzzy neural network (WPFNN) controller is proposed to control squirrel-cage induction generator (SCIG) system with an ac/dc power converter and a dc/ac power inverter for grid-connected wind power applications. First, the ac/dc power converter and the dc/ac power inverter are developed to deliver the electric power generated by a three-phase SCIG to power grid. Moreover, the ac/dc power converter and the dc/ac power inverter are mainly designed to control the mechanical rotor speed, dc-link voltage, and reactive power output of the SCIG system, respectively. Furthermore, since the varying active power outputs of the dc/ac power inverter seriously affect the tracking control of the dc-link voltage, a novel intelligent WPFNN controller is proposed to replace the traditional proportional-integral controller for the tracking control of the dc-link voltage in this study. In addition, the network structure and the online learning algorithm of the proposed WPFNN are described in detail. Finally, some experimental results are provided to show the effectiveness of the intelligent controlled-SCIG system using the proposed WPFNN controller for grid-connected wind power applications.