Particle Swarm Optimization Recurrent Neural Network Based Z-source Inverter Fed Induction Motor Drive

摘要

In this study, the proposal is made for Particle Swarm Optimization (PSO) Recurrent Neural Network (RNN) based Z-Source Inverter Fed Induction Motor Drive. The proposed method is used to enhance the performance of the induction motor while reducing the Total Harmonic Distortion (THD), eliminating the oscillation period of the stator current, torque and speed. Here, the PSO technique uses the induction motor speed and reference speed as the input parameters. From the input parameters, it optimizes the gain of the PI controller and generates the reference quadrature axis current. By using 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 corresponding target reference three phase current. The training process utilized the supervised learning process. Then the proposed technique is implemented in the MATLAB/SIMULINK platform and the effectiveness is analyzed by comparing with the other techniques such as PSO-Radial Biased Neural Network (RBNN) and PSO-Artificial Neural Network (ANN). The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem.