Residual-aware health prediction of power transformers via spatiotemporal graph neural networks.

ABSTRACT: Accurate health state prediction and timely fault detection of power transformers are critical for ensuring the reliability and resilience of modern power systems. This paper proposes a residual-aware spatiotemporal graph neural network (STGNN) framework that jointly models dynamic topological dependencies among multivariate Supervisory Control and Data Acquisition (SCADA) signals and their temporal evolution. The proposed approach constructs time-varying sensor graphs via attention mechanisms to capture evolving inter-variable relationships and applies Chebyshev spectral graph convolution for localized spatial feature aggregation. Temporal dependencies are modeled using gated recurrent units (GRUs) enhanced with residual connections, enabling robust forecasting under nonstationary operati

SUBMITTER: Zeng P 

PROVIDER: S-EPMC12599914 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature