基于参数优化变分模态分解的交流变频电机转子断条故障识别方法

Fault Identification of Broken Rotor Bars for the Variable Frequency AC Motor Based on Parameter Optimized Variational Mode Decomposition

该文提出一种基于变分模态分解(VMD)和Park变换的交流变频电机早期转子断条故障识别方法。重点对低速状态下电机早期断条故障特征频率识别展开研究,基于电流信号特点结合萤火虫优化算法(FA)设定变分模态分解参数,进一步强化其自适应分解能力,并在此基础上获取三相电流的基频分量信息,通过Park变换检测早期故障特征。仿真与实验结果表明,该方法检测电机早期转子断条故障特征优于经验模态分解(EMD)方法,并可实现故障特征频率的早期定位,这对电机的故障辨识和预警具有重要支撑意义。

This paper presents a novel detection method for broken rotor bar fault in variable frequency AC motor based on variational mode decomposition(VMD)and Park transformation.The identification method of the fault characteristic frequency under the low-speed state was further studied.Combined with the characteristics of current signals and the firefly algorithm(FA),the adaptive decomposition ability of VMD was strengthened.On this basis,the supply frequency components of the three-phase current were extracted and the fault features were detected through Park transformation.Finally,simulation and experimental results show that the proposed method is superior to the empirical mode decomposition(EMD)for detecting early fault features and can realize the identification of the early fault frequencies,which is a great significance for the early fault detection and forecast of the variable frequency AC motor.