PROJECT TITLE :

A Random Forest Regression Based Space Vector PWM Inverter

ABSTRACT:

Using a random forest (RF) regression based implementation of space vector pulse width modulation (SVPWM) for a two-level inverter, this research shows an improved three-phase induction motor (TIM) drive performance improvement. To enhance the performance of a traditional space vector modulation system, the RF approach offers the advantages of rapid installation and improved prediction. In order to demonstrate the superiority of the proposed RF technique to other techniques, an adaptive neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) based SVPWM schemes are employed and compared. Searching for the ideal controller parameters using a backtracking technique is used in this proposed speed controller. ANFIS and ANN controllers fail to match the RF-based SVPWM in terms of damping, settling time, steady state error, and transient responsiveness when tested in a variety of operating environments. SVPWM inverter controller for induction motor drive prototype is built and tested. The experimental results reveal that the proposed RF-based SVPWM inverter controller has a good agreement with the modelling results in terms of speed response and stator current.