  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.
