广东电力2023，Vol.36Issue(11)：146-156,11.DOI:10.3969/j.issn.1007-290X.2023.11.016
基于DKG和IGCN的电力变压器故障诊断方法
Fault Diagnosis Method of Power Transformer Based on DKG and IGCN
摘要
Abstract
In order to improve the accuracy of fault diagnosis for power transformers,this paper proposes a fault diagnosis method for power transformers based on domain specific knowledge graph(DKG)and improved graph convolutional neural network(IGCN).Firstly,it uses DKG to construct a knowledge graph of power transformers composed of nodes and edges,and establishes a mapping relationship between the knowledge graph and fault samples to form a transformer fault knowledge graph,providing data support for subsequent fault diagnosis.Secondly,on the basis of the principle of spectral domain GCN,it introduces the adaptive graph pooling based on sparse attention to improve the spectral domain GCN network.Finally,by using different diagnostic methods,it carries out fault diagnosis experiments on 3 000 transformer fault samples collected to verify the superiority of the proposed method.The experimental results show that the proposed method can effectively diagnose transformer fault types.Compared with traditional fault diagnosis methods such as CNN,DBN,and DNN,the recognition rate increases by 0.7％,4.7％,and 9.2％for 2 400 sample sizes respectively.As the number of samples decreases,the recognition advantage becomes more apparent.At 400 sample sizes,the recognition rates increase by 4.2％,7.4％,and 14.3％respectively.It can be seen that the proposed method can utilize more comprehensive information on transformer fault samples,has better diagnostic performance,and the recognition advantage becomes more obvious as the number of samples decreases.关键词
电力变压器/领域知识图谱/改进图卷积网络/稀疏注意力/自适应池化Key words
power transformer/domain knowledge graph(DKG)/improved graph convolutional network(IGCN)/sparse attention/adaptive pooling分类
信息技术与安全科学引用本文复制引用
黄欣,郇嘉嘉,赵敏彤,吴伟杰,张伊宁..基于DKG和IGCN的电力变压器故障诊断方法[J].广东电力,2023,36(11):146-156,11.基金项目
中国南方电网有限责任公司科技项目(GDKJXM20190387) （GDKJXM20190387）
