Fault diagnosis method and system for a permanent magnet synchronous motor

Abstract

The present disclosure provides a fault diagnosis method of a permanent magnet synchronous motor, comprising the following steps: Step1: extract input parameters, the input parameters include the rotor position, angular velocity, and counter electromotive force of the synchronous motor; Step2: output according to the input parameters Fault eigenvalues, the output fault eigenvalues ​​include negative sequence components, d-axis flux linkage, and angular position difference; Step3: The neural network performs fault diagnosis according to the fault eigenvalues. The present disclosure also provides a fault diagnosis system for a permanent magnet synchronous motor. The disclosure only needs to build an observer at the motor end or in the detection system, which can effectively simplify the fault diagnosis system. And compared with the traditional third harmonic component extraction, the negative sequence component used in this disclosure is simpler and more stable and reliable. At the same time, a neural network is used for fault diagnosis, which has the characteristics of simple algorithm and low application cost, and can be applied to low-end small three Phase permanent magnet synchronous motor realizes online fault diagnosis.

Description

technical field [0001] The present disclosure relates to the technical field of permanent magnet synchronous motor control, in particular to a fault diagnosis method of a permanent magnet synchronous motor. Background technique [0002] Permanent Magnet Synchronous Motor (PMSM) has the characteristics of simple structure, reliable operation, small size, light weight, low loss and high efficiency, and is widely used in industry, aviation, military and other fields. In the application process, due to the high power density of PMSM, the thermal load of the motor is high; at the same time, the motor is in poor working conditions, and it is often subjected to frequent instantaneous overload and shock, which makes the motor prone to inter-turn short-circuit faults and demagnetization faults. The position detection of the position sensor (commonly used encoder) is the key link of the motor. Once the position signal cannot be obtained or the position signal is inaccurate, the motor ...

AI Technical Summary

Problems solved by technology

[0006] However, in the existing technical solution, for different fault types, the corresponding fault diagnosis modules are relatively independent, and each diagnosis module needs to be separately configured with different state observers, which leads to high complexity of the entire fault diagnosis system

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