基于多层特征融合CNN的变压器PRPD图谱识别

Transformer PRPD pattern recognition based on multi-layer feature fusion CNN

智能化的分类算法在局部放电模式识别中应用良好,但是需要人工提取特征,因而存在特征丢失和识别效率低的问题。文中对传统的卷积神经网络进行多层特征融合的改进,并用于局部放电模式识别,以预处理后的PRPD图谱为输入,自动提取图谱特征,并进行深层和浅层的特征融合以防止特征丢失,最后输出分类结果。此外文中算法还对传统CNN的池化策略进行改进,使用最大二均值池化,进一步保留了图谱的有效特征。实验结果表明,相比于传统的人工提取统计特征再输入分类器的模式,特征融合CNN的识别正确率更高,达91.21%,且耗时更少。

The intelligent classification algorithm is applied well in partial discharge pattern recognition,but it needs to extract features manually,so there are problems of feature loss and low recognition efficiency.In this paper,the traditional convolutional neural network was improved by multi-layer feature fusion,and it was used for partial discharge pattern recognition.The pre-processed PRPD map was used as input to automatically extract the map features and performed deep and shallow features fusion to prevent features lost,and finally output the classification result.In addition,the algorithm also improved the pooling strategy of traditional CNN,and used the maximum two-mean pooling to further preserve the effective features of the graph.The experimental results show that compared with the traditional mode of extracting statistical features manually and then input into the classifier,feature fusion CNN has a higher recognition accuracy rate of 91.21%and less time consuming.