Transformer sound anomaly detection method based on improved wavelet packet and deep learning

Abstract

The invention relates to the technical field of computers, in particular to a transformer sound anomaly detection method based on an improved wavelet packet and deep learning. The method comprises thefollowing steps: A) collecting audio signals of N transformers in different running states; B) performing wavelet packet transformation\ on each audio signal, determining a threshold lambda of the sample entropy by adopting an improved sample entropy, recalculating a wavelet coefficient eta of each component, reconstructing component signals, and obtaining reconstructed audio signals; C) performing short-time Fourier transform to generate a feature image; D) classifying the extracted feature images according to the running state of the transformer; and E) establishing a convolutional neural network model, using the classified feature images for training, and using the trained convolutional neural network model for transformer sound anomaly detection. The transformer sound anomaly detection method has the advantages that noise signals in collected transformer signals can be effectively eliminated, abnormal fault characteristics of the transformer are extracted, and engineers are assisted in transformer fault diagnosis.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a transformer sound anomaly detection method based on improved wavelet packets and deep learning. Background technique [0002] During the operation of the transformer, under the influence of alternating current, a periodically changing alternating magnetic flux will be generated in the iron core, which will further cause the iron core to emit a uniform "humming" sound. The size of the "hum" sound is proportional to the voltage and current installed on the transformer, and the "hum" sound in normal operation is uniform. If the transformer is abnormal, it will produce abnormal sound. By detecting the sound of the transformer, it is possible to diagnose the fault of the transformer. [0003] During the operation of the transformer, the collected audio signal of the transformer contains a lot of noise due to on-site noise and interference from other transformers. Moreover, voic...

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Problems solved by technology

[0005] The technical problem to be solved by the present invention is: the current technical problem of monitoring the abnormal sound effect of the transformer is poor

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