Abstract
This paper proposes an accurate diagnosing tool that can predict the stator inter-turn size in line start permanent magnetic synchronous motor (LSPMSM). The proposed diagnosing approach is developed based on an experimentally validated mathematical model of the motor under inter-turn fault. The developed model has been tested using MATLAB® under different loading and fault size conditions. Since the stator currents and voltages are easily accessible, it is decided to use them as the key signatures for developing the diagnostic tool. Several time and frequency-based features have been extracted using motor current and voltage waveforms under different loading and fault size conditions. The developed tool has been designed to correlate the extracted features with its corresponding size of stator inter-turn fault. Finally, testing of the developed diagnosis tool shows a high accuracy of 96% in detecting the size. Moreover, the proposed diagnostic tool is examined against motor parameter variations. The results confirm the robustness of the proposed approach where the accuracy is slightly affected under a wide range of motor parameter variations.
Highlights
In industrial applications, especially in oil and gas plants, induction motors are being gradually replaced by line start permanent magnet synchronous motors (LSPMSMs)
LSPMSM operates in two modes; asynchronous mode during which the machine runs as an induction motor, and synchronous mode in which the motor runs at a synchronous speed
The results show that the developed diagnostic tool is capable of detecting the number of shorted turns in LSPMSM
Summary
Especially in oil and gas plants, induction motors are being gradually replaced by line start permanent magnet synchronous motors (LSPMSMs). In [22], a feed-forward neural network-based tool for detecting inter-turn faults in permanent magnet synchronous motors was proposed. In [24], a neural network-based diagnostic tool for detecting the location of an inter-turn fault in an induction motor was developed. In [27], a tool for detecting the size of inter-turn faults in permanent magnet synchronous motors was developed. In [28], a neural network-based tool for detecting the inter-turn fault level in an induction motor was proposed. To the best of authors’ knowledge, no diagnostic tools that can predict the size of stator winding shorted turns in LSPMSMs has been developed in the literature.
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