Radial basis function network control with improved particle swarm optimization for induction generator system

This study proposes a radial basis function network (RBFN) controlled three-phase induction generator (IG) system using ac-dc and dc-ac power converters. In this study, first, the indirect field-oriented mechanism is implemented for the control of the IG. The electric frequency of the IG is controlled using the indirect field-oriented control mechanism. Then, an ac-dc power converter and a dc-ac power inverter are adopted to convert the electric power generated by a three-phase IG from variable-frequency and variable-voltage to constant-frequency and constant-voltage. Moreover, two on-line trained RBFNs using backpropagation learning algorithm with improved particle swarm optimization (IPSO) are introduced as the regulating controllers for both the dc-link voltage and the ac line voltage of the dc-ac power inverter. The IPSO is adopted in this study to adapt the learning rates in the backpropagation process of the RBFNs to improve the learning capability. By using the proposed RBFN controller with IPSO, the IG system can employ for stand-alone power application effectively. Finally, some experimental results are provided to show the effectiveness of the proposed IG system.