A new remedial strategy for permanent magnet synchronous motor based on artificial neural network

Shady S. Refaat, Haitham Abu-Rub, M. S. Saad, E. M. Aboul-Zahab, Atif Iqbal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper proposes an effective approach to detect, isolate, and identify fault severity and post fault operation of permanent magnet synchronous motors (PMSM) in the presence of stator winding turn fault. The paper proposes fault tolerant operation of PMSM under post condition with stator winding turn fault by using grounded neutral point through controllable impedance using artificial neural network (ANN). The fault detection and diagnosis is achieved by using a strategy based on the analysis of the ratio of third harmonic to fundamental waveform obtained from Fast Fourier Transform (FFT) of magnitude components of the stator currents. The strategy helps to detect stator turn fault, isolate the faulty components, and estimate different insulation failure percentages and remedial operation of PMSM in the presence of stator winding turn fault. The model of PMSM with stator winding turn fault is simulated at different load conditions using a (2-D) Finite Element Analysis (FEA). Experimental results demonstrate the validity of the proposed technique.

Original languageEnglish
Title of host publication2013 15th European Conference on Power Electronics and Applications, EPE 2013
DOIs
Publication statusPublished - 17 Dec 2013
Event2013 15th European Conference on Power Electronics and Applications, EPE 2013 - Lille, France
Duration: 2 Sep 20136 Sep 2013

Publication series

Name2013 15th European Conference on Power Electronics and Applications, EPE 2013

Other

Other2013 15th European Conference on Power Electronics and Applications, EPE 2013
CountryFrance
CityLille
Period2/9/136/9/13

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Keywords

  • Artificial Neural Network
  • Fault Tolerance
  • Permanent magnet motor

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology
  • Electrical and Electronic Engineering

Cite this

Refaat, S. S., Abu-Rub, H., Saad, M. S., Aboul-Zahab, E. M., & Iqbal, A. (2013). A new remedial strategy for permanent magnet synchronous motor based on artificial neural network. In 2013 15th European Conference on Power Electronics and Applications, EPE 2013 [6631967] (2013 15th European Conference on Power Electronics and Applications, EPE 2013). https://doi.org/10.1109/EPE.2013.6631967