基于MFCC和随机森林的GIS动作声纹特征辨识和操作机构异常分类OACSTPCD

Aiming at the problem of abnormal or faulty operation mechanism of gas-insulated switchgear(GIS),which leads to faults or inability to trip when operating its switches,an abnormal classification model of the operation mechanism of GIS equipment based on the Mel-frequency cepstrum coefficient(MFCC)and random forest is proposed.Firstly,according to the preprocessing of the collected voiceprint signal,MFCC is used to extract the features of the voiceprint signal.Then,a random forest is constructed to identify the voiceprint feature,and the classification results of GIS action anomalies are obtained.Finally,taking a 110 kV GIS equipment as an example,the voiceprint signals of the energy storage mechanism and transmission mechanism of the circuit breaker and the isolating switch are collected when they are abnormal or faulty,and the audio sample library is constructed.The classification model proposed in this paper is compared with a variety of classical models.The results show that MFCC can effectively extract the features of voiceprint signals under different working conditions of GIS actions,and random forest performs best among many classification and recognition models,which can effectively improve the accuracy of abnormal working conditions recognition of GIS actions.