图信号与图卷积网络相结合的局部放电模式识别方法

A Partial Discharge Pattern Recognition Method Combining Graph Signal and Graph Convolutional Network

为充分利用局部放电(partial discharge,PD)信号的特征信息和特征与特征之间的关联性,来提高局部放电诊断准确性,该文提出一种基于图信号和图卷积网络(graph convolutional network,GCN)的局部放电诊断方法。首先,选取局部放电脉冲的时频谱构建图信号,将时频谱灰度矩阵的子矩阵(即局部特征)作为图节点,并考虑节点的空间相邻和特征相似性为每个节点匹配邻居,以形成局部特征区域间的拓扑关联,丰富局部放电时频谱的数据信息。然后,采用GCN融合图信号的节点特征和拓扑结构以自主学习局部放电特征、识别放电类型。结果表明,所提方法可以有效地诊断局部放电类型,相较于传统的深度学习方法,对于局部放电时频谱的信息利用更为全面,识别准确率更高,且随着样本规模减小,识别优势更加明显。

In order to improve the accuracy of partial discharge(PD) diagnosis by making full use of the local features of PD signal and the correlation between those local features, a novel PD diagnosis method based on graph signal and graph convolutional network(GCN) was proposed in this paper. Firstly, this method selected the time-frequency(T-F) spectrum of PD pulse to construct the graph signal. In detail, it took the sub-matrixes of the T-F spectrum gray matrix as the graph nodes namely local features while the spatial adjacency and the features similarity of nodes were regarded as the principles of matching the neighbors for each node, which formed the topology between different nodes and enriched the data information of T-F spectrum. Then, the GCN, which adopting the nodes’ features and graph topology together as inputs, was arranged to learn the PD features and identify the PD types. The experimental results show that the proposed method can effectively diagnose PD types. Compared with the traditional deep learning methods, it has richer information of PD T-F spectrum and higher recognition accuracy. Moreover, with the reduction of sample size, the advantage is more obvious.