Abstract
Condition diagnosis of power transformers using acoustic signals is a nonstop, contactless method of equipment maintenance that can diagnose the transformer’s type of abnormal condition. To heighten the accuracy and efficiency of the abnormal method of diagnosing abnormalities by sound, a method for abnormal diagnosis of power transformers based on the Attention-CNN-LSTM hybrid model is proposed. This collects the sound signals emitted by the real power transformer in the normal state, overload, and the discharge condition. It preprocesses the sound signals to obtain the MFCC characteristics of the sound signals. It is then grouped into a set of sound feature vectors by the first- and second-order differences, and enters the Attention-CNN-LSTM hybrid model for training. The training results show that the Attention-CNN-LSTM hybrid model can be used for the status sound detection of power transformers, and the recognition of the three states can achieve an accuracy rate of more than 99%.