用物理神经网络优化变压器温度传感器布局，提升监测效率。
Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
- 融合物理约束的神经网络建模热扩散过程
- 在有限传感器数量下实现高精度温度场重建
- 适用于1D/2D场景，适合电力系统监控优化
本研究利用物理信息神经网络（PINNs）模拟和预测电力变压器内部的温度分布，基于预测结果确定在传感器数量受限条件下的最优布置方案，以实现高效运行监测。方法结合了PINNs与混合整数优化编程，构建了变压器内部最优传感器布局模型。首先将原有的1维变压器热建模PINN扩展至2维空间，求解二维热扩散方程；最终建立的模型可应用于1维和2维场景中的传感器最优配置问题。
原文摘要 · Abstract (English)
Our work aims at simulating and predicting the temperature conditions inside a power transformer using Physics-Informed Neural Networks (PINNs). The predictions obtained are then used to determine the optimal placement for temperature sensors inside the transformer under the constraint of a limited number of sensors, enabling efficient performance monitoring. The method consists of combining PINNs with Mixed Integer Optimization Programming to obtain the optimal temperature reconstruction inside the transformer. First, we extend our PINN model for the thermal modeling of power transformers to solve the heat diffusion equation from 1D to 2D space. Finally, we construct an optimal sensor placement model inside the transformer that can be applied to problems in 1D and 2D.
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