Squirrel-cage induction generator system using probabilistic fuzzy neural network for wind power applications

An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected power application using probabilistic fuzzy neural network (PFNN) is proposed in this study. First, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase SCIG to power grid. Then, the characteristics of wind turbine emulator are described in detail. Moreover, in order to improve the transient and steady-state responses of the DC-link voltage of the SCIG system, the intelligent PFNN controller is proposed for DC/AC power inverter to replace the conventional proportional-integral (PI) controller. The online trained PFNN using back propagation learning algorithm is implemented as the tracking controller for the DC-link voltage of the DC/AC power inverter. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the SCIG system for grid-connected wind power applications is verified with experimental results.