Z-Source Inverter Fed Induction Motor Drive control using Particle Swarm Optimization Recurrent Neural Network

The suggestion is prepared for Particle Swarm Optimization (PSO) Recurrent Neural Network (RNN) based Z-Source Inverter Fed Induction Motor Drive in this document. The proposed method is employed to develop the presentation of the induction motor while decreasing the Total Harmonic Distortion (THD), eliminating the oscillation period of the stator current, torque and speed. Currently, as the input parameters, the PSO technique uses the induction motor speed and reference speed. It optimizes the raise of the PI controller and produces the reference quadrature axis current from the input parameters. By employing the RNN the reference three phase current for accurate control pulses of the voltage source inverter is predicted. The RNN is trained by the input motor actual quadrature axis current and the reference quadrature axis current with the associated target reference three phase current. The training process used the supervised learning process. Next the proposed technique is implemented in the MATLAB/simulink platform and the competence is scrutinized by comparing with the other techniques such as PSO-Radial Biased Neural Network (RBNN) and PSO-Artificial Neural Network (ANN). The comparison results show the superiority of the proposed approach and confirm its potential to effort out the problem.