Abstract
Three-phase line-start permanent magnet synchronous motors are considered among the most promising types of motors in industrial applications. However, these motors experience several faults, which may cause significant financial losses. This paper proposed a feed-forward neural network-based diagnostic tool for accurate and fast detection of the location and severity of stator inter-turn faults. The input to the neural network is a group of representative statistical and frequency-based features extracted from the steady-state three-phase stator current signals. The current signals with different numbers of shorted turns and loading conditions are captured using the developed finite element JMAG ™ model for interior mount LSPMSM. In addition, an experimental set-up was built to validate the finite element model and the proposed diagnostics tool. The simulation and experimental test results showed an overall accuracy of 93.125% in detecting the location and the size of inter-turn, whereas, the accuracy in detecting the location of the fault is 100%.
Highlights
In recent decades, faults in electric motors have been considered a major issue in industry
The results demonstrated the success of the artificial neural network (ANN) at fault diagnosis
JMAGTM MODEL TESTING AND EXPERIMENTAL VALIDATION Using the developed JMAGTM, the performance of the interior-mount line-start permanent magnet synchronous motors (LSPMSMs) with a stator inter-turn fault was investigated for different numbers of shorted turns and loads
Summary
Faults in electric motors have been considered a major issue in industry. In [38], a feed-forward neural network-based tool for detecting inter-turn faults in permanent magnet synchronous motors was proposed. In [39], a neural network-based diagnostic tool for detecting the location of an inter-turn fault in an induction motor was developed.
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