A method for improving the accuracy of multi-physical field coupling modeling for digital twins of power grid equipment
电网设备数字孪生体的多物理场耦合建模精度提升方法

摘要

电网设备数字孪生体的多物理场耦合建模是实现设备运行状态精准映射与预测的重要基础。针对传统建模方法在多场协同计算中精度不足的问题，本文提出一种基于高精度物理约束与数据驱动融合的精度提升方法。该方法在有限元多物理场求解框架下，引入电磁、热、力等多领域耦合特征的多尺度建模策略，并结合传感器实时数据进行模型参数动态校正。通过多层次模型验证与误差反馈优化，显著降低了多场耦合计算中的累积误差，提高了仿真与实测的一致性。实验结果表明，该方法在典型电网设备如变压器、断路器的热-电-力耦合分析中均取得优异表现，为设备全寿命周期的智能监测与预防性维护提供了技术支撑，具有重要的工程应用价值与推广潜力。

Abstract

Multi-physical field coupling modeling for digital twins of power grid equipment is an important foundation for achieving accurate mapping and prediction of equipment operating states. Aiming at the problem of insufficient accuracy in multi-field collaborative calculation with traditional modeling methods, this paper proposes an accuracy improvement method based on the integration of high-precision physical constraints and data-driven approaches. Under the framework of finite element multi-physical field solution, this method introduces a multi-scale modeling strategy for coupling characteristics in multiple domains such as electromagnetism, heat, and force, and dynamically corrects model parameters by combining real-time sensor data. Through multi-level model verification and error feedback optimization, the cumulative error in multi-field coupling calculations is significantly reduced, and the consistency between simulation and actual measurement is improved. Experimental results show that this method achieves excellent performance in thermo-electro-mechanical coupling analysis of typical power grid equipment such as transformers and circuit breakers. It provides technical support for intelligent monitoring and preventive maintenance throughout the equipment's full life cycle, and has important engineering application value and promotion potential.