Abstract

In this chapter, we will compare the performance of a multilevel direct torque control (DTC) control for the double-star induction machine (DSIM) based on artificial neural network (ANN). The application of DTC control brings a very interesting solution to the problems of robustness and dynamics. However, this control has some disadvantages such as variable switching frequency, size, and complexity of the switching tables and the strong ripple torque. A solution to this problem is to increase the output voltage level of the inverter and associate the DTC control with modern control techniques such as artificial neural networks. Theoretical elements and simulation results are presented and discussed. As results, the flux and torque ripple of the five-level DTC-ANN control significantly reduces compared to the flux and torque ripple of the three-level DTC-ANN control. By viewing the simulation results using MATLAB/Simulink for both controls, the results obtained showed a very satisfactory behavior of this machine.

Highlights

  • With the appearance of the structures of the multilevel inverters proposed for the first time by [2], the research was able to face the handicaps presented by the classical structure

  • Simulation results of speed, stator flux, torque, stator current, and stator voltage show the good performance of the three- and five-level direct torque control (DTC)-artificial neural network (ANN) control of double-star induction machine (DSIM)

  • The good reference speed tracking is ensured, with advantages brought by the use of five-level DTC based on artificial neural networks (DTC-ANN) control, the minimization of torque ripple, and stator flux, which is confirmed by the simulation results

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Summary

Introduction

The use of a conventional two-level inverter in the field of high power applications is not appropriate because it requires electronic components capable of withstanding high reverse voltage and high current. Direct Torque Control Strategies of Electrical Machines we do not need to use capacities for each phase, which eliminates the risks of parasitic resonances [4] In this structure, diodes called floating diodes are associated with each phase, which serves to apply the different voltage levels of the DC source. The use of hysteresis tapes is the cause of electromagnetic torque ripples and noise in the machine To solve these drawbacks, in the framework of this work, we try to apply the multilevel direct torque control for DSIM and to develop a new control method such as artificial neural networks that replaces the switching tables [8].

DSIM model
Modeling of three-level inverter
Modeling of five-level inverter
Direct torque control based on neural networks
Neural network strategy
Simulation results
Conclusion
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