Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex, costly, and often estimated from indirect measurements. Existing HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces a spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational accuracy of the PINN model is improved through the implementation of the Residual-Based Attention (PINN-RBA) scheme that accelerates the PINN model convergence. The PINN-RBA model is benchmarked against self-adaptive attention schemes and classical vanilla PINN configurations. For the first time, PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, validated through PDE numerical solution and fiber optic sensor measurements. Furthermore, the spatio-temporal transformer ageing model is inferred, which supports transformer health management decision-making. Results are validated with a distribution transformer operating on a floating photovoltaic power plant.

翻译：变压器对于电力系统可靠高效运行至关重要，尤其在支持可再生能源并网方面。有效的变压器健康状态监测对维持电网稳定与性能具有关键意义。绝缘热老化是变压器的主要失效模式，通常通过监测热点温度进行追踪。然而，HST测量过程复杂、成本高昂，常需通过间接测量进行估算。现有HST模型多采用空间无关的热模型，仅能提供最恶劣工况下的HST估计值。本文提出一种变压器绕组温度与老化状态的时空模型，该模型在物理信息神经网络框架下，将基于物理的偏微分方程与数据驱动的神经网络相结合，以提高预测精度并获取时空分辨率。通过实施基于残差的注意力机制方案，PINN-RBA模型加速了PINN模型的收敛过程，从而提升了计算精度。研究将PINN-RBA模型与自适应注意力方案及经典原始PINN配置进行了基准比较。首次采用基于PINN的油温预测来估算变压器绕组的时空温度分布，并通过PDE数值解与光纤传感器测量进行了验证。此外，研究推导了时空变压器老化模型，为变压器健康管理决策提供支持。所有结果均在浮式光伏电站运行的配电变压器上得到了验证。