7 papers
Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
Ibai Ramirez, Jokin Alcibar, Joel Pino +3
Scientific Machine Learning (SciML) integrates physics and data into the learning process, offering improved generalization compared with purely data-driven models. Despite its pot…
Reinforcement Learning-Based Controlled Switching Approach for Inrush Current Minimization in Power Transformers
Jone Ugarte Valdivielso, Jose I. Aizpurua, Manex Barrenetxea +1
Transformers are essential components for the reliable operation of power grids. The transformer core is constituted by a ferromagnetic material, and accordingly, depending on the…
Comparative analysis and evaluation of ageing forecasting methods for semiconductor devices in online health monitoring
Adrian Villalobos, Iban Barrutia, Rafael Pena-Alzola +2
Semiconductor devices, especially MOSFETs (Metal-oxide-semiconductor field-effect transistor), are crucial in power electronics, but their reliability is affected by aging processe…
A Hybrid Probabilistic Battery Health Management Approach for Robust Inspection Drone Operations
Jokin Alcibar, Jose I. Aizpurua, Ekhi Zugastia +1
Health monitoring of remote critical infrastructure is a complex and expensive activity due to the limited infrastructure accessibility. Inspection drones are ubiquitous assets tha…
Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants
Ibai Ramirez, Joel Pino, David Pardo +5
Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer hea…
Towards a Probabilistic Fusion Approach for Robust Battery Prognostics
Jokin Alcibar, Jose I. Aizpurua, Ekhi Zugasti
Batteries are a key enabling technology for the decarbonization of transport and energy sectors. The safe and reliable operation of batteries is crucial for battery-powered systems…