Revue des matériaux et énergies renouvelables
Volume 2, Numéro 1, Pages 38-47
2017-03-01

Neuro-fuzzy Speed Controller For Dual Star Induction Machine

Authors : Meliani Bouziane . Yssaad Benyssaad . Bourdim Mokhtar .

Abstract

This paper presents the modeling, design, and simulation of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling the speed of the Double Star induction Machine, the machine is fed by a five-level inverter. Analytical solutions of Pulse Width Modulation (PWM) strategies for multilevel Neutral Point Clamped (NPC) are presented. Double star Induction motor is characterized by highly non-linear, complex and time-varying dynamics and inaccessibility of some of the states and outputs for measurements. Hence it can be considered as a challenging engineering problem in the industrial sector. Various advanced control techniques has been devised by various researchers across the world. Some of them are based on the neuro-fuzzy techniques (ANFIS). The main advantage of designing the ANFIS coordination scheme is to control the speed of the DSIM to increase the dynamic performance, to provide good stabilization. To show the effectiveness of our scheme, the proposed method was simulated on an electrical system composed of a 4.5 kW six-phase induction machine and its power inverter. Digital simulation results demonstrate that the deigned ANFIS speed controller realize a good dynamic of the DSIM, a perfect speed tracking with no overshoot, give better performance and high robustness

Keywords

Index Terms— Dual star; Field Oriented Control; Neural Network; Fuzzy Logic; Multilevel Inverter