Title :
“A new remedial strategy for permanent magnet synchronous motor based on artificial neural network”
Author :
Refaat, S.S. ; Abu-Rub, Haitham ; Saad, M.S. ; Aboul-Zahab, Essam M. ; Iqbal, Azlan
Author_Institution :
Texas A&M Univ. at Qatar, Doha, Qatar
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.
Keywords :
electric machine analysis computing; fast Fourier transforms; fault diagnosis; finite element analysis; neural nets; permanent magnet motors; stators; synchronous motors; 2D FEA; ANN; FFT; PMSM; artificial neural network; controllable impedance; fast Fourier transform; fault detection; fault diagnosis; fault severity identification; finite element analysis; grounded neutral point; insulation failure percentage estimation; load conditions; magnitude components; permanent magnet synchronous motor; post fault operation; remedial strategy; stator currents; stator winding turn fault; third harmonic-to-fundamental waveform ratio analysis; Artificial neural networks; Circuit faults; Fault tolerance; Fault tolerant systems; Stator windings; Windings; Artificial Neural Network; Fault Tolerance; Permanent magnet motor;
Conference_Titel :
Power Electronics and Applications (EPE), 2013 15th European Conference on
Conference_Location :
Lille
DOI :
10.1109/EPE.2013.6631967