电力系统保护与控制2024，Vol.52Issue(9)：99-110,12.DOI:10.19783/j.cnki.pspc.231278
基于漏磁场和ICOA-ResNet的变压器绕组早期故障诊断
Transformer windings based on leakage field and ICOA-ResNet early fault diagnosis
摘要
Abstract
There is low diagnostic accuracy of transformer winding deformation and slight inter-turn short circuit fault diagnosis.Thus an early fault diagnosis method for transformer windings is proposed.First,ANSYS simulation software is used to establish a finite element model consistent with the relevant parameters of the experimental transformer.It also analyzes the distribution law of the leakage magnetic field of the transformer in the winding faults,and selects the appropriate fault characteristics and the installation position of the optical fiber leakage magnetic field sensor according to these laws.Then,the optimal hyperparameters of residual networks(ResNet)are found by improved coati optimization algorithm(ICOA),and the ICOA-ResNet model is constructed based on these parameters.The fault characteristics are input into the model for fault diagnosis.Finally,the feasibility of the proposed early fault diagnosis model for transformer windings is verified by simulation data and dynamic model experiments.Compared with four other models such as the support vector machine,the proposed model has higher accuracy in early fault diagnosis.This shows that the proposed method is effective in transformer winding deformation and inter-turn short circuit fault diagnosis.关键词
变压器早期故障诊断/绕组变形/漏磁场/长鼻浣熊优化算法/残差神经网络/超参数优化Key words
transformer early fault diagnosis/winding deformation/leakage magnetic field/coati optimization algorithm/residual networks/hyperparameter optimization引用本文复制引用
刘建锋,李志远,周亚茹..基于漏磁场和ICOA-ResNet的变压器绕组早期故障诊断[J].电力系统保护与控制,2024,52(9):99-110,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.51777119). 国家自然科学基金项目资助(51777119) （No.51777119）
