声纹的变压器放电与机械故障诊断研究  

Research on transformer discharge and mechanical fault diagnosis based on voiceprint

作　　者：吴国鑫 詹花茂[1] 李敏[1] WU Guoxin;ZHAN Huamao;LI Min(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学新能源电力系统国家重点实验室,北京102206

出　　处：《应用声学》2021年第4期602-610,共9页Journal of Applied Acoustics

基　　金：北京市自然科学基金资助项目(3202032)。

摘　　要：变压器中的一些放电和机械故障会产生异常声,可用于故障检测。据此,该文提出基于可听声的变压器放电和机械故障诊断方法。针对机械故障声与变压器本体噪声特征相似易混淆的问题,提出改进小波包-BP神经网络算法,与传统小波包-BP神经网络算法相比声音识别率提高了5.7%。为提高声音识别系统的泛化性,提出基于梅尔对数频谱和卷积神经网络的声音识别算法。两种算法相互验证,提高了系统的可靠性。在真实变压器油箱中模拟了不同类型放电和机械故障。实验结果表明,该文提出的两种方法能成功识别放电声和机械故障声,声音识别率分别为99.6%和97.57%。Some discharges and mechanical faults in the transformer will produce abnormal sounds,which can be used for fault detection.Accordingly,a transformer fault diagnosis method based on audible sound is proposed.Aiming at the problem that the sound of mechanical faults is similar to the background noise of the transformer itself and is easy to be confused,an improved wavelet packet-BP neural network algorithm is proposed.Compared with the traditional wavelet packet-BP neural network algorithm,the sound recognition rate is increased by 5.7%.In order to improve the generalization of the sound recognition system,a sound recognition algorithm based on log-Mel spectrum and convolutional neural network is proposed.The two algorithms are mutually verified,which improves the reliability of the system.Different types of discharges and mechanical failures are simulated in the real transformer tank.The test results show that the two methods proposed in this paper can successfully identify the sounds of discharge and mechanical failure,and the sound recognition rates are 99.6%and 97.57%,respectively.

关 键 词：变压器 可听声 小波包 BP神经网络 卷积神经网络 

分 类 号：TM411[电气工程—电器]