Transformers are vital assets for the reliable and efficient operation of
power and energy systems. They support the integration of renewables to the
grid through improved grid stability and operation efficiency. Monitoring the
health of transformers is essential to ensure grid reliability and efficiency.
Thermal insulation ageing is a key transformer failure mode, which is generally
tracked by monitoring the hotspot temperature (HST). However, HST measurement
is complex and expensive and often estimated from indirect measurements.
Existing computationally-efficient HST models focus on space-agnostic thermal
models, providing worst-case HST estimates. This article introduces an
efficient 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 efficiency of the PINN model is
improved through the implementation of the Residual-Based Attention scheme that
accelerates the PINN model convergence. PINN based oil temperature predictions
are used to estimate spatio-temporal transformer winding temperature values,
which are validated through PDE resolution models and fiber optic sensor
measurements, respectively. Furthermore, the spatio-temporal transformer ageing
model is inferred, aiding transformer health management decision-making and
providing insights into localized thermal ageing phenomena in the transformer
insulation. Results are validated with a distribution transformer operated on a
floating photovoltaic power plant.

通过物理学信息神经网络配置的部分微分方程与数据驱动神经网络相结合的高效时空模型，改进了对变压器绕组温度和老化估计的预测准确性和获得时空分辨率，进而推断了时空变压器老化模型，帮助变压器健康管理决策，并提供了关于变压器绝缘体的局部热老化现象的见解。

基于残差注意力物理网络的可再生能源电厂中变压器的高效时空寿命评估

Residual-based Attention Physics-informed Neural Networks for Efficient  Spatio-Temporal Lifetime Assessment of Transformers Operated in Renewable  Power Plants