Abstract

Inter-turn short-circuit faults can lead to further faults in motors. This makes monitoring and identifying such faults particularly important. However, because of interference in their working environment, fault signals can be weak and difficult to detect in permanent magnet synchronous motors. This paper proposes a method for overcoming this by extracting the inverter harmonics as an excitation source and then extracting characteristic of fault measurements from the negative sequence voltage. First of all, a model of permanent magnet synchronous motor faults is established and a fault negative sequence voltage is introduced to calculate the fault indicators. Then the high frequency harmonic excitation in the voltage is extracted. This is injected into the original voltage signal and the high frequency negative sequence component is separated and detected by a second-order generalized integrator. Simulation results show that the proposed method can effectively identify inter-turn short-circuit faults in permanent magnet synchronous motors while remaining highly resistant to interference. The method is especially effective when the severity of the fault is relatively small and the torque is relatively large.

Highlights

  • Permanent magnet synchronous motors (PMSM) are widely used in modern industrial settings.the long-term use of such motors can lead to faults [1], the faults of permanent magnet motors are divided into electrical faults, mechanical faults and magnetic faults

  • This paper looks at how to solve the problem of inter-turn short-circuit fault signals in permanent magnet synchronous motors being weak and difficult to detect

  • As the for by the normal negative sequence voltage, it does not have a serious effect on the fault indicator

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Summary

Introduction

Permanent magnet synchronous motors (PMSM) are widely used in modern industrial settings. Existing inter-turn short-circuit fault detection methods include using: stator current Park vectors [5]; negative sequence currents [6]; current harmonics [7]; the back electromotive force [8]; motor parameters [9]; high-frequency injection [10]; a zero sequence voltage [11]; and artificial intelligence [12,13]. Each of these methods offer certain advantages. At the end of the paper the results of a simulation are presented that confirm the effectiveness of the proposed method

Mathematical Model of an Inter-Turn Short-Circuit Fault
Effective
Fault Signal
Fault Indicator
Fault Signal Measurement and Diagnosis
Conclusions
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