摘要
针对配电变压器故障特征提取困难、故障识别难度大的问题,提出一种将振动信号、自适应噪声完备集合经验模态分解(CEEMDAN)与图卷积神经网络(GCN)三者有机结合的故障诊断方法。首先,采用CEEMDAN对来自加速度传感器的振动信号进行处理,获得一组固有模态分量(intrinsic modal function);其次求取边际谱信息作为特征向量;然后,对特征向量矩阵构造无向加权完全图,并使用改进灰狼优化算法对高斯核带宽进行寻优;最后,搭建一个具备多通道和多连通的改进GCN模型进行特征二次挖掘与故障分类。与此同时,还在模型中加入一种名叫“峰值因子”指标实现对未知类型故障的辨识。在实例分析中,分别对油浸式和干式变压器进行故障模拟,提取不同状态的样本进行测试。实验结果表明,所提方法对油浸式和干式变压器的故障识别准确率分别达到97.73%和95.6%,优于其他两种对比方法。在面对未知类型故障以及运行工况发生变化时,也具备较高是识别能力。
Aiming at the difficulty of fault feature extraction and fault identification of distribution transformers,a fault diagnosis method combining vibration signals,complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and graph convolution neural networks(GCN)was proposed.Firstly,the vibration signal from the acceleration sensor is processed by CEEMDAN to obtain a set of intrinsic modal functions.Secondly,its marginal spectrum information is taken as the feature vector.Then,an undirected weighted complete graph is constructed for the eigenvector matrix,and an improved gray wolf optimization algorithm is used to optimize the Gaussian kernel bandwidth.Finally,an improved GCN model with multi-channel and multi-connectivity is built for feature secondary mining and fault classification.At the same time,an index called peak factor is added to the model to realize the identification of unknown faults.In the case analysis,the fault simulation of oil-immersed transformer and dry transformer is carried out respectively,and samples of different states are extracted for testing.The experimental results show that the accuracy of the proposed method for oil-immersed transformer and dry transformer fault identification is 97.73%and 95.6%,respectively,which is better than the other two comparison methods.In the face of unknown types of faults and operating conditions change,it also has a high ability to identify.
作者
洪翠
邱仕达
高伟
Hong Cui;Qiu Shida;Gao Wei(College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2022年第12期86-96,共11页
Journal of Electronic Measurement and Instrumentation
基金
福建省自然科学基金(2021J01633)项目资助
关键词
配电变压器
故障诊断
振动信号
CEEMDAN
GCN
distribution transformer
fault diagnosis
vibration signal
complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)
graph convolution network(GCN)
作者简介
洪翠,分别在1994年、2000年和2014年于福州大学获得学士学位、硕士学位和博士学位,现为福州大学副教授,主要研究方向为配电网及其主设备故障诊断。E⁃mail:hongcui@fzu.edu.cn;通信作者:高伟,2005年于福州大学获得学士学位,2008年于福州大学获得硕士学位,2021年于中国台湾科技大学获得博士学位,现为福州大学副教授,主要研究方向为电力系统及设备故障诊断。E⁃mail:gaowei0203@fzu.edu.cn
