Analysis of opening/closing coil current and fault diagnosis of high-voltage circuit breakers based on physics-informed neural networks

JIN Lijun; ZHANG Zhiwei; ZHENG Hanbo; LI Jinheng; WANG Jinyu; YAO Siyu

doi:10.19783/j.cnki.pspc.251210

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Analysis of opening/closing coil current and fault diagnosis of high-voltage circuit breakers based on physics-informed neural networks

更新时间：2026-05-14

- Analysis of opening/closing coil current and fault diagnosis of high-voltage circuit breakers based on physics-informed neural networks
- Power System Protection and Control Vol. 54, Issue 10, (2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.251210
  CLC：
- Published：2026
- 稿件说明：
移动端阅览
JIN Lijun, ZHANG Zhiwei, ZHENG Hanbo, et al. Analysis of opening/closing coil current and fault diagnosis of high-voltage circuit breakers based on physics-informed neural networks[J]. Power System Protection and Control, 2026, 54(10).
DOI：

JIN Lijun, ZHANG Zhiwei, ZHENG Hanbo, et al. Analysis of opening/closing coil current and fault diagnosis of high-voltage circuit breakers based on physics-informed neural networks[J]. Power System Protection and Control, 2026, 54(10). DOI： 10.19783/j.cnki.pspc.251210.

摘要

针对传统的高压断路器故障诊断模型存在机理缺失与数据依赖等问题，提出一种基于多输出物理信息网络的分合闸线圈电流动态特性建模及故障诊断方法。首先，依据分合闸线圈动作机理搭建电气 - 机械物理场耦合的数学模型，建立物理先验约束函数。然后，考虑线圈电流与动铁芯运动存在强耦合关系，将微分方程以残差损失形式嵌入相应的分支网络。并引入自适应权重调整策略平衡各项梯度流，实现分合闸电流动态特性快速准确地正向推演与分析。最后，提出结合迁移学习思想的故障诊断方法，将先验约束模型向物理实体迁移。试验结果表明，所提方法具有较高的物理一致性与求解精度，即便在较小实测数据规模下故障诊断准确率仍达到 96.86%。

Abstract

To address the issues of mechanism deficiency and heavy data dependency in traditional high-voltage circuit breaker fault diagnosis models

a method for dynamic characteristic modeling and fault diagnosis of opening/closing coil currents based on a multiple-output physics-informed neural network is proposed. First

an electromechanical coupled mathematical model is constructed based on the operating mechanism of the opening/closing coils

and corresponding physical prior constraint functions are established. Then

considering the strong coupling relationship between coil current and the motion of the moving iron core

the differential equations are embedded into the corresponding branch networks in the form of residual loss. An adaptive weight adjustment strategy is introduced to balance the gradient flows among different loss terms

enabling fast and accurate forward inference and analysis of the dynamic characteristics of opening/closing currents. Finally

a fault diagnosis method integrated with transfer learning is proposed

allowing the prior-constrained model to be transferred to physical systems. Experimental results demonstrate that the proposed method achieves high physical consistency and solution accuracy

attaining a fault diagnosis accuracy of up to 96.86% even under limited measured data.

关键词

Keywords

references

RAISSI M, PERDIKARIS P, KARNIADAKIS G E, et al. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707.

HUANG B, WANG J. Applications of physics-informed neural networks in power systems-a review[J]. IEEE Transactions on Power Systems, 2022, 38(1): 572-588.

GU L, QIN S, XU L, et al. Physics-informed neural networks with domain decomposition for the incompressible Navier-Stokes equations[J]. Physics of Fluids, 2024, 36(2).

LIANG R, LIU W, XU L, et al. Solving elastodynamics via physics-informed neural network frequency domain method[J]. International Journal of Mechanical Sciences, 2023, 258.

ZHANG H, SUN H, KANG L, et al. Prediction of health level of multiform lithium sulfur batteries based on incremental capacity analysis and an improved LSTM[J]. Protection and Control of Modern Power Systems, 2024, 9(2): 21-31.

XIA S, ZHANG C, LI Y, et al. GCN-LSTM based transient angle stability assessment method for future power systems considering spatial-temporal disturbance response characteristics[J]. Protection and Control of Modern Power Systems, 2024, 9(6): 108-121.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

基于SO-PAA-GAF和AdaBoost集成学习的高压断路器故障诊断

Transformer partial discharge fault diagnosis based on ring area graph entropy features

Fault diagnosis of grid-connected inverters based on symmetric-point pattern and ECA-ConvNeXt

Adaptive power system transient stability assessment based on conditional denoising diffusion models and active transfer learning

基于条件变分自编码器与改进ConvXGB模型的二次设备小样本故障诊断方法

Related Author

SI Jiangkuan

TUSONGJIANG·Kari

FAN Xiang

GAO Wensheng

Related Institution

ZHU Wei(1. School of Electrical Engineering, Xinjiang University

Hami Power Supply Company, Xinjiang Electrical Power Corporation of SGCC

State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment, Department of Electrical Engineering, Tsinghua University

河北省分布式储能与微网重点实验室(华北电力大学)

河南省煤矿装备智能检测与控制重点实验室

AI问答

⁰