Application of Artificial Intelligence to Real-Time Fault Detection in Permanent-Magnet Synchronous Machines

Author

Nyanteh, Yaw ; Edrington, Chris ; Srivastava, Sanjeev ; Cartes, David

Author_Institution

Center for Adv. Power Syst., Florida State Univ., Tallahassee, FL, USA

Volume

49

Issue

3

fYear

2013

fDate

May-June 2013

Firstpage

1205

Lastpage

1214

Abstract

This paper discusses faults in rotating electrical machines in general and describes a fault detection technique using artificial neural network (ANN) which is an expert system to detect short-circuit fault currents in the stator windings of a permanent-magnet synchronous machine (PMSM). The experimental setup consists of PMSM coupled mechanically to a dc motor configured to run in torque mode. Particle swarm optimization is used to adjust the weights of the ANN. All simulations are carried out in MATLAB/SIMULINK environment. The technique is shown to be effective and can be applied to real-time fault detection.

Keywords

artificial intelligence; electric machine analysis computing; fault diagnosis; mathematics computing; neural nets; particle swarm optimisation; permanent magnet motors; stators; synchronous motors; ANN; Matlab-Simulink environment; artificial intelligence; artificial neural network; dc motor; expert system; particle swarm optimization; permanent-magnet synchronous machines; real-time fault detection; rotating electrical machines; short-circuit fault current detection; stator windings; Artificial neural networks; Insulation; Rotors; Stator windings; Testing; Windings; Expert system; neural network; particle swarm optimization (PSO); permanent-magnet synchronous machine (PMSM);

fLanguage

English

Journal_Title

Industry Applications, IEEE Transactions on

Publisher

ieee

ISSN

0093-9994

Type

jour

DOI

10.1109/TIA.2013.2253081

Filename

6480827