Pattern Recognition of Partial Discharge using Acoustic Array Data Based on DCNN

Microphone arrays have the potential to detect partial discharge phenomena in electric equipment non-contact and flexibly. However, the existing methods do not fully consider the data characteristics of microphone arrays, and there is a paucity of research on the identification of local discharge types. to address the varying acoustic characteristics of different fault types, the microphone array data is initially processed to obtain the respective Mel spectrograms. Subsequently, the features are extracted using a deep convolutional neural network (DCNN), and the local discharge types are classified. Based on this approach, a local discharge type recognition model for acoustic array data is proposed, which is founded on the extraction of Mel spectrogram features.