Research on Fault Diagnosis of On-load Tap Changer Based on Transformer
In response to the insufficient correlation between traditional network captured acoustic features of On-load tap chang-er(OLTC),leading to low accuracy in fault diagnosis,this study proposes a fault diagnosis method for OLTC based on Transformer neural network.Firstly,Mel-frequency cepstral coefficients(MFCC)are utilized to extract acoustic features from OLTC sound sam-ples,reducing the data dimensionality of the samples.Subsequently,the Transformer is employed to comprehensively capture the re-lationships among acoustic features and achieve fault diagnosis for OLTC.Experimental results demonstrate that the Transformer-based approach achieves a high accuracy rate of 97.5％in diagnosing faults such as transmission shaft looseness,contact wear,stick-ing,and gear engagement issues in OLTC.Moreover,it contributes to a certain extent in shortening the diagnostic time.
on-load tap changeracoustic featuresfault diagnosismel-frequency cepstral coefficientstransformer
万方数据
维普
