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Sensorless Direct Field-Oriented Control of Induction Motor Drive Using Artificial Neural Network-Based Reactive

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  • 1Department of Applied Electronics, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708 00 Ostrava, Czech Republic.

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Summary

This study introduces an advanced sensorless control for induction machines (IM) using a novel reactive power (Q-MRAS) estimator with artificial neural networks. This method offers improved robustness and stable regenerative operation compared to traditional approaches.

Keywords:
ANN-Q-MRASDFOCMRASQ-MRASartificial neural networkinduction machinesensorless control

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Sensorless control of induction machines (IM) is crucial for efficient electric drives.
  • Conventional model reference adaptive system (MRAS) estimators face challenges with parameter variations and regenerative modes.
  • Artificial neural networks (ANNs) offer potential for enhancing adaptive system performance.

Purpose of the Study:

  • To present an advanced sensorless control method for IM using a Q-MRAS estimator integrated with a feedforward artificial neural network.
  • To demonstrate the improved robustness and parameter independence of the proposed control strategy.
  • To validate stable operation of the proposed method, especially in the regenerative mode.

Main Methods:

  • Development of a Q-MRAS estimator incorporating a feedforward artificial neural network for IM speed estimation.
  • Simulation studies using MATLAB/Simulink to validate the proposed control algorithm.
  • Real-time implementation on a TMS320F28335 Digital Signal Controller with a LabVIEW interface.

Main Results:

  • The proposed Q-MRAS with ANN demonstrated superior robustness and reduced dependence on IM parameters compared to conventional Q-MRAS.
  • Stable operation was achieved in the regenerative mode, a common challenge for sensorless IM drives.
  • Experimental validation on a 2.2 kW three-phase IM drive confirmed the simulation findings.

Conclusions:

  • The integration of ANNs into the Q-MRAS estimator significantly enhances the performance of sensorless IM control.
  • The proposed method provides a robust and reliable solution for IM drives, particularly in applications involving regenerative braking.
  • This advanced control strategy holds promise for improving the efficiency and stability of electric motor systems.