Voiceprint Recognition of Discharge Aliasing Signals from 750 kV Transformer and Pin-Plate Based on Sparse Representation Theory and Convolutional Neural Network
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摘要:
变压器绝缘水平和健康状态对电网的安全稳定至关重要，为研究750 kV变压器内部存在放电故障时，箱体外采集的可听声信号中可能混杂有电晕声、鸟鸣等其他干扰信号的工程实际问题，提出一种基于稀疏表示理论（SBSS）与卷积神经网络（CNN）的750 kV变压器与尖板放电混叠信号的声纹识别方法. 首先，采集武胜750 kV变电站变压器正常运行声信号作为背景声，构建针-板放电模型得到放电声信号和现场常见干扰声作为前景声，通过添加不同信噪比的前景声到背景声中构造混叠声信号；然后，利用基于稀疏表示理论的盲分离算法实现目标前景声纹图谱和冗余背景声纹图谱的分离；最后，对CNN模型超参数进行优化，以提高模型对分离后的各类前景声纹谱图的分类性能. 研究结果表明：通过盲源分离算法可以剔除冗余背景声干扰，使神经网络聚焦于前景声的分类识别；本文方法可实现混叠声信号中前景声纹的分离，分离后，CNN、支持向量机（SVM）和反向传播神经网络（BPNN）的识别准确率分别提高7.6%、17.2%和14.3%.
Abstract:Transformer insulation level and health state are crucial to the safety and stability of the power grid. In order to study the practical engineering problem that the audible acoustic signals collected outside the box may be mixed with other interference signals, such as corona sound and bird song when there is a discharge fault inside the 750 kV transformer, a voiceprint recognition of 750 kV transformer and pin-plate discharge aliasing signals based on sparse representation theory (SBSS) and convolutional neural network (CNN) was proposed. Firstly, the normal operation sound signal of Wusheng 750 kV Substation was collected as the background sound, and the discharge sound signal and the common interference sound in the field were used as the foreground sound by constructing the pin-plate discharge model. The aliasing sound signal was constructed by adding the foreground sound with different signal-to-noise ratios to the background sound. Secondly, the blind separation algorithm based on SBSS was used to realize the separation of target foreground and redundant background voiceprint spectra. Finally, the hyperparameters of the CNN model were optimized to improve the classification performance of the model on the separated various types of foreground voiceprint spectra. The results show that the blind source separation algorithm can eliminate the redundant background sound interference so that the neural network can focus on the classification and recognition of foreground sound. The proposed method can separate foreground voiceprint in the aliasing sound signals, and the recognition accuracies of the CNN, the support vector machine (SVM), and the back-propagation neural network (BPNN) after separation are improved by 7.6%, 17.2%, and 14.3%, respectively.
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图 3 变压器声纹谱图稀疏盲分离方法流程
Figure 3. Process of sparse blind separation method for transformer voiceprint spectra
图 9 不同批尺寸识别准确率对比
Figure 9. Comparison of recognition accuracies of different batch sizes
图 10 不同学习率识别准确率对比
Figure 10. Comparison of recognition accuracies of different learning rates
图 11 不同优化器识别准确率对比
Figure 11. Comparison of recognition accuracies of different optimizers
表 1 声纹样本
Table 1. Voiceprint samples
前景声 背景声 混叠信号 样本数/个 时长/s 放电声 武胜变 放电声 + 武胜变 135 2 说话声 武胜变 说话声 + 武胜变 135 2 鸟鸣声 武胜变 鸟鸣声 + 武胜变 135 2 
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表 2 分离背景与参考谱图声纹相似度对比
Table 2. Voiceprint similarity comparison of separated background and reference spectra
谱图类型 放电声 说话声 鸟鸣声 J 0.87 0.89 0.87
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表 3 CNN网络结构参数
Table 3. Parameters of CNN structure
结构层 通道数/个 核尺寸 激活函数/池化类型 输入层 1 卷积层 16 5 × 5 ReLU 池化层 16 3 × 3 Max Pooling 卷积层 32 5 × 5 ReLU 池化层 32 3 × 3 Max Pooling 卷积层 64 5 × 5 ReLU 池化层 64 3 × 3 Max Pooling 卷积层 128 5 × 5 ReLU 全连接层 1024 Softmax 输出层 3
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表 4 不同方法的声纹识别准确率对比
Table 4. Comparison of voiceprint recognition effects of different methods
% 谱图类型 CNN SVM BP 混叠声纹谱 91.43 65.71 43.81 前景声纹谱 99.05 82.86 58.10
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