A Method for Fault Diagnosis of Turn-to-turn Short Circuit of Permanent Magnet Synchronous Motor

Abstract

The invention discloses a PMSM (permanent magnet synchronous motor) turn-to-turn short circuit fault diagnosis method, and the method comprises the following steps: S1, estimating a short-circuit current of a phase and the number of short-circuit turns through employing an extended Kalman filter, and estimating three short-circuit currents and three numbers of short-circuit turns through employing the extended Kalman filter; S2, selecting the maximum amplitude of the three short-circuit currents and the numbers of short-circuit turns to be compared with a set threshold value, and judging that there is a turn-to-turn short circuit fault if the maximum amplitude of the selected short-circuit current is greater than the set threshold value, or else, judging that there is no turn-to-turn short circuit fault; S3, judging a fault phase: judging that the phase where the maximum amplitude of the three short-circuit currents is located is the fault phase under the condition that there is the turn-to-turn short circuit fault.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a method for diagnosing inter-turn short circuit faults of permanent magnet synchronous motors. Background technique [0002] Among motor faults, inter-turn short circuit of stator winding is the most common and destructive fault. Therefore, in order to ensure safe operation, an effective fault diagnosis method is necessary. [0003] At present, there are mainly three methods for diagnosing inter-turn short circuit faults of stator windings. The first method is based on signal analysis, such as stator current, flux linkage, voltage, symmetrical component and wavelet transform, etc.; these methods use corresponding tools to analyze the characteristic frequency components related to inter-turn short circuit faults, and these methods require a certain amount of time To analyze the sampled signal, it is not suitable for real-time diagnosis of faults; the second is knowledge...

AI Technical Summary

Problems solved by technology

The first method is based on signal analysis, such as stator current, flux linkage, voltage, symmetrical component and wavelet transform, etc.; these methods use corresponding tools to analyze the characteristic frequency components related to inter-turn short circuit faults, and these methods require a certain amount of time To analyze the sampled signal, it is not suitable for real-time diagnosis of faults; the second is knowledge-based methods, such as fuzzy logic systems, neural networks, support vector machines, etc.; these methods require a large number of training samples and training time, often not suitable for real-time The need for diagnosis; the third is the method of fault estimation based on state or parameters, which requires a mathematical model of the system under study; but this method currently used cannot judge the phase of the fault

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