Pattern recognition and diagnosis of short and open circuit faults inverter in induction motor drive using neural networks

Authors

  • Younes Tamissa Laboratoire du Génie Electrique, LAGE, Department of Electronics and Telecommunications, University of Kasdi Merbah-Ouargla, Algeria
  • Fella Charif Laboratoire du Génie Electrique, LAGE, Department of Electronics and Telecommunications, University of Kasdi Merbah-Ouargla, Algeria
  • Farid Kadri Laboratoire du Génie Electrique, LAGE, Department of Electronics and Telecommunications, University of Kasdi Merbah-Ouargla, Algeria
  • Abderrazak Benchabane Laboratoire du Génie Electrique, LAGE, Department of Electronics and Telecommunications, University of Kasdi Merbah-Ouargla, Algeria

Abstract

The operation and control induction motor and inverter drives under faulty condition is a big challenging task in the present day. For that reason, these electrical systems must have well considering to provide a relevant diagnosis of these elements. Consequently, detecting defects early will be very important in order to find methods to allow us to control the operation and protecting action to avoid regular failures. This study develops a technique based on pattern recognition in short and open circuit switch faults in three-phase inverter fed induction motor using neural networks technique, with a simple feature extractor technique Fast Fourier Transform (FFT) to make detection and diagnosis faults possible and straight forward with a neural network. Simulation results and classification performances improved by using a neural network for fault detection and pattern recognition.

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Published

2023-03-29

Issue

Engineering Review, Vol. 42, No. 3

Section

Articles

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