Asynchronous motor stator turn-to-turn short circuit fault diagnosis based on d-q transform and WOA-LSTM
In order to realize reliable online diagnosis of inter-turn short-circuit faults in asynchronous mo-tor stator windings,a fault diagnosis method based on d-q transform and whale optimization algorithm(WOA)optimized long-short-term memory network(LSTM)was proposed.It is known through theoreti-cal derivation that the d-q transform can effectively extract the characteristic spectral data in the stator current.The whale optimization algorithm was used to optimize the three key parameters in the long short-term memory network and the WOA-LSTM fault classification model was established.In order to verify the effectiveness of the fault diagnosis method based on d-q transform and WOA-LSTM,wavelet trans-form,fast Fourier transform and d-q transform were used to extract the current spectrum data as the in-put data set,and a YE2-100L1-4 asynchronous motor was used as the experimental object for experi-mental verification.The results show that compared with wavelet transform and fast Fourier transform,the d-q transform can more accurately extract the fault features in the stator current,more accurately reflect the fault state of the motor,and help to improve the fault classification accuracy.Compared with the tra-ditional LSTM algorithm,the classification accuracy of LSTM algorithm optimized by WOA can reach 98.3%,which can reliably realize the diagnosis of inter-turn short-circuit faults of different degrees.
asynchronous motorfault diagnosisstator winding turn-to-turn short circuitd-q transform theorywhale optimization algorithmlong and short-term memory neural networks
NETL
NSTL
万方数据
