To enhance the computation efficiency and accuracy of three-dimensional steady temperature field of transformer windings, we propose a new non-invasive Reduced Order Model (ROM) based on a mechanism-embedded cascade network. Initially, a snapshot matrix is formed from the Full Order Model (FOM) and then combined with Proper Orthogonal Decomposition (POD) to extract key modal features that characterize the temperature field. Subsequently, a cascade network architecture, integrating Multilayer Perceptron (MLP) and Radial Basis Function Neural Network (RBFNN), is devised to swiftly map working condition parameters to modal coefficients. Additionally, the cascade network is embedded with condition sensitivity and modal contribution mechanisms to further enhance prediction accuracy. Finally, by linearly weighting the modes with predicted modal coefficients, a rapid reconstruction of the steady temperature field in transformer windings is achieved. Validation against Fluent software simulations and experimental measurements demonstrate a close agreement, with computational errors of less than 4K and an impressive single solution time of only 0.0087 s, which is 48760 times faster compared to Fluent software.

中文翻译：

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基于机构嵌入级联网络的变压器绕组三维稳态温度场实时求解方法

为了提高变压器绕组三维稳态温度场的计算效率和精度，我们提出了一种基于机制嵌入级联网络的新型非侵入性降阶模型（ROM）。最初，由全阶模型 (FOM) 形成快照矩阵，然后与本征正交分解 (POD) 相结合，提取表征温度场的关键模态特征。随后，设计了集成多层感知器（MLP）和径向基函数神经网络（RBFNN）的级联网络架构，以快速将工况参数映射到模态系数。此外，级联网络嵌入了条件敏感性和模态贡献机制，以进一步提高预测精度。最后，通过使用预测模态系数对模态进行线性加权，实现了变压器绕组中稳态温度场的快速重建。针对 Fluent 软件模拟和实验测量的验证显示出非常接近的一致性，计算误差小于 4K，单次求解时间仅为 0.0087 秒，令人印象深刻，比 Fluent 软件快了 48760 倍。