Improved Direct Torque Control Based on Neural Network of the Double-Star Induction Machine Using Deferent Multilevel Inverter

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

A comparative study between a simplified fuzzy PI and classic PI input-output linearizing controller for the wind-turbine doubly fed induction generator

International audienceThe paper presents a comparative study of a linearizing control with classic PI and fuzzy PI controllers of the active and reactive stator power of a doubly fed induction generator (DFIG) applied to a wind-energy conversion systems (WECS). The paper discusses the operating principles of the power-generation scheme. Simulation results show that the preented input-output linearizing control provides a decoupled control, perfect tracking of the generated active and reactive power and robustness the active- and reactive-power variations. Keywords: Doubly-Fed Induction Generator (DFIG), Input-Output linearization, Fuzzy Logic Controller (FLC)