为了解决变压器铁心松动故障的识别与诊断，提出基于完全自适应噪声集合经验模态分解(CEEMDAN)-小波阈值的环境噪声去除方法,并提出使用三维卷积神经网络（3D-CNN）去识别基于声纹的变压器铁心松动故障诊断方法。搭建变压器铁心松动故障试验平台，采集铁心在不同松动程度下的噪声信号；将采集的用于故障识别的声纹信号经过CEEMDAN-小波阈值算法，利用变压器本体噪声和环境噪声在峭度上的差异滤波，得到信噪比较高的变压器声纹信号；再将滤波后的声纹信号经过短时傅里叶变化生成时频矩阵，并用Mel滤波器降维得到Mel-语谱图，制作成适合3D-CNN输入格式的数据集；搭建好网络的各层，利用3D-CNN对变压器铁心松动故障进行分类和识别。试验结果表明：所提方法在考虑环境噪声的条件下，变压器铁心松动故障的识别率达到90%以上，可用于变压器铁心松动故障的识别和诊断。

In order to solve the identification and diagnosis of transformer iron core looseness faulta method of removing environmental noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-wavelet-threshold is proposedand a method of identifying transformer iron core looseness fault based on voiceprint using three-dimensional convolutional neural network (3D-CNN) is proposed. A transformer iron core looseness fault test platform is built to collect the noise signals of the iron core under different degrees of looseness. The voiceprint signal collected for fault identification is filtered by CEEMDAN-wavelet-threshold algorithmand the transformer voiceprint signal with high signal-to-noise ratio is obtained by using the difference between transformer body noise and environmental noise in kurtosis. Thenthe time-frequency matrix is generated by short-time Fourier transform of the filtered voiceprint signalthe Mel spectrogram is obtained by dimensionality reduction of Mel filterand the data set suitable for the input format of 3D-CNN is made. Each layer of the network is builtand 3D-CNN is used to classify and identify the transformer iron core looseness fault. The experimental results show that the recognition rate of transformer iron core looseness fault is more than 90% under the condition of considering environmental noiseand can be used for the recognition and diagnosis of transformer iron core looseness fault.

国家自然科学基金项目(51577050)；江苏省电力有限公司重点科技项目(J2021053)