高压电器2024，Vol.60Issue(5)：92-98,7.DOI:10.13296/j.1001-1609.hva.2024.05.012
基于胶囊神经网络的电力变压器故障诊断方法研究
Research on Fault Diagnosis Method of Power Transformer Based on Capsule Networks
摘要
Abstract
The operation status of power transformers,the hub equipment in the operation of electrical power sys-tems,is directly related to the safe operation of the entire system.For a long time,dissolved gas analysis(DGA)has served as an effective means for diagnosing transformer faults mainly due to its immunity to complex electromagnetic fields inside the transformers and external noise.For solving the limitations of existing DGA-based fault diagnosis method and improve accuracy of fault diagnosis further,a kind of fault diagnosis method for the oil immersed power transformer based on the capsule network(CapsNet)is proposed.The proposed method,by combining the unique ca-pability of CapsNet to handle vectors and the characteristics of dissolved gases in transformer oil,can be able to accu-rately model the intricate nonlinear relationship between dissolved gases and types of faults,realizing the automatic extraction of key features of the realized training of the model through dynamic routing technique and back propaga-tion algorithm.The test result based on real DGA data shows that the proposed method achieves 90.48%、82.46%and 87.93%on the fault diagnosis accuracy,macro average and recall geometric mean,and it outperforms both SVM and DBN,demonstrating its advantages in real-world transformer fault diagnosis applications.关键词
电力变压器/DGA/故障诊断/胶囊神经网络/动态路由算法Key words
power transformer/DGA/fault diagnosis/capsule network/dynamic routing algorithm引用本文复制引用
罗文萱..基于胶囊神经网络的电力变压器故障诊断方法研究[J].高压电器,2024,60(5):92-98,7.基金项目
国家自然科学基金面上项目(62373148) （62373148）
中央高校基本科研业务费专项资金面上项目(2021MS018). Project Supported by National Natural Science Foundation of China(62373148),Fundamental Research Funds for the Central Universities(2021MS018). （2021MS018）
