An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology

Abstract

In order to find transformer (reactor) faults in time, a transformer working condition detection method and verification system based on voiceprint recognition technology was proposed. In this system, 73 groups of transformer audio were collected by the voice sensor on-site, with a total of about 1800 min. The recognition pattern based on a deep learning convolutional neural network was established. Through experiments, it was found that aiming at the additive superposition problem of transformer sound generated by a stable working condition and unstable instantaneous noise, a new method based on the cosine similarity algorithm was proposed to realize the separation detection of sound pattern superposition. The acoustic signals of the iron core under sinusoidal excitation were mainly frequency components of 100 Hz and 200 Hz. Harmonic excitation would aggravate the noise in this frequency band, and the third harmonic excitation had the greatest influence. Due to DC magnetic bias, the hysteresis loop of the iron core was distorted to a certain pole. In addition to the 100 Hz component, the odd harmonics of 150 Hz, 250 Hz, and 350 Hz and even harmonics of 200 Hz, 300 Hz, and 400 Hz also increased obviously. With the increase of direct current content, the performance of the noise signal became more prominent. A transformer working condition detection and verification analysis system was established.