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

arXiv:2405.06443v1 Announce Type: new
Abstract: 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 …

abstract arxiv assessment attention cs.lg cs.sy eess.sy efficiency energy grid health integration monitoring networks neural networks physics physics-informed plants power renewable renewables residual stability support systems temporal through transformers type vital