基于物理信息神经网络的套管电容芯体等效热导率反演方法

doi:10.19595/j.cnki.1000-6753.tces.251735

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摘要

干式胶浸纸（RIP）套管在直流输电和换流变压器中得到了广泛应用。然而其内部无油冷却,热量易在电容芯体等关键结构中聚集,导致局部过热,进而加速绝缘老化甚至诱发故障。准确识别套管关键部件的热导率,可显著提升热仿真模型的准确性,对设备运行的安全性具有重要意义。为此,该文提出一种基于物理信息神经网络（PINN）的套管电容芯体等效热导率反演方法。首先,建立RIP套管二维轴对称热传导模型,采用区域划分与材料等效简化策略,构造嵌入热传导偏微分方程、边界条件与稀疏温度数据的PINN结构,搭建110 kV干式套管温升试验平台,模拟不同负载工况,获取中心导杆、油温和空气温度用于网络训练。然后,将反演得到的热导率引入有限元仿真模型,对比热导率更新前后仿真温度与试验实测温度的差异。结果表明：热导率更新后各典型位置温度预测误差均小于2 K,平均绝对误差低于热导率更新前的结果。该方法在数据稀疏、结构复杂、参数不可直接测量的实际工程条件下表现出良好的适应性与准确性。

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关键词 ：物理信息神经网络, 热导率反演, 胶浸纸套管, 参数识别, 温升试验

Abstract：

Dry-type resin-impregnated paper (RIP) bushings have been widely applied in converter transformers and high-voltage direct-current (HVDC) systems owing to their oil-free insulation, strong moisture resistance, and low maintenance requirements. However, the absence of internal oil circulation causes heat to accumulate within the bushing core, which increases the thermal stress on the composite insulation structure and accelerates insulation aging. Temperature-field modeling of RIP bushing are performed by finite element methods (FEM) and the results of temperature distribution simulations are highly dependent on the selected model parameters. Because the thermal conductivity of the multilayer capacitive screen cannot be directly characterized using conventional measurement techniques, determining its equivalent thermal properties remains a major challenge for accurate thermal simulation and temperature-rise analysis. This study addresses this problem by proposing a data-efficient inversion method, identifying the thermal conductivity of the capacitive screen using sparse temperature information.
A physics-informed neural network (PINN) based inversion framework was developed to estimate the equivalent radial and axial thermal conductivities of the capacitive screen. A two-dimensional axisymmetric heat-conduction model was constructed using domain partitioning to represent the conductor, capacitive core, bottom flange and outer sheath. The multilayer screen region was homogenized into an anisotropic medium, enabling the model to retain directional thermal-transfer characteristics while reducing geometric and computational complexity. The steady-state heat-conduction partial differential equation (PDE) and interface conduction conditions across different materials were embedded directly into the total loss function of PINN method. This formulation restricts the solution space to physically consistent states and enables the network to infer internal parameters directly from sparse temperature measurements. To enhance PINN’s training stability, an adaptive gradient-balancing strategy was employed, which dynamically regulated the weights of the PDE, boundary, and observation losses and strengthen convergence robustness.
To evaluate the effectiveness of the proposed PINN method, a 110 kV RIP bushing temperature-rise platform was established. Under the consistency of other variables such as ambient temperature and humidity, steady-state temperature data of four different operating current levels (60%I_n, 80%I_n, I_n, and 110%I_n) at several typical positions were collected, including conductor rod, the connecting seat, bottom flange and the outer sheath. After reversion, the identified thermal conductivity parameters were imported into a FEM model for independent verification. The simulations replicated the measured temperature-rise characteristics with deviations held within ±2 K across all monitored points, indicating that the inferred parameters accurately reflect the thermal behavior of the composite capacitive screen. These results demonstrate that the proposed PINN-based inversion framework provides a reliable and data-efficient means for identifying effective thermal properties in multilayer insulation systems, offering a practical tool for improving thermal-field modeling and enhancing the design and evaluation of power-equipment insulation structures.
The results show that the proposed PINN inversion framework can effectively recover the anisotropic equivalent thermal conductivity of multilayer composite insulation from sparse obtainable temperature data. The method relies on only a small set of training data and retains high physical consistency and interpretability, making it possible to infer otherwise unmeasurable thermal conductivity from discrete temperature measurements. The framework can be extended to other power equipment, including transformer windings, reactor insulation cylinders, and dielectric components in gas-insulated switchgear (GIS). The proposed approach provides an effective pathway for improving thermal-field modeling accuracy, refining temperature-rise evaluation, and supporting future digital-twin applications for the thermal management of power equipment.

Key words： Physics-informed neural network thermal conductivity inversion resin impregnated paper bushing parameter identification temperature rise experiment

收稿日期: 2025-10-13

PACS:

TM835.4

基金资助:

南方电网公司科技项目资助（031300KC23120020）

通讯作者: 刘亚东男,1982年生,研究员,博士生导师,研究方向为输电线路早期故障检测与电力设备数值孪生及故障反演。E-mail：lyd@sjtu.edu.cn

作者简介: 吕昱辉男,1998年生,博士研究生,研究方向为人工智能技术在输变电设备热状态监测方面的应用。E-mail：yuhuilv98@sjtu.edu.cn

引用本文:

吕昱辉, 刘亚东, 严英杰, 蔡建峰, 黄晓波. 基于物理信息神经网络的套管电容芯体等效热导率反演方法[J]. 电工技术学报, 2026, 41(19): 6753-6766. Lü Yuhui, Liu Yadong, Yan Yingjie, Cai Jianfeng, Huang Xiaobo. Inversion Method for Equivalent Thermal Conductivity of Bushing Capacitive Screen Based on Physics-Informed Neural Network. Transactions of China Electrotechnical Society, 2026, 41(19): 6753-6766.

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https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.251735 https://dgjsxb.ces-transaction.com/CN/Y2026/V41/I19/6753

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