Researchers in Slovakia have demonstrated a machine-learning framework that predicts PV inverter output and detects anomalies using only electrical and temporal data, achieving 100% accuracy in ...
The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.
When most of us think about artificial intelligence (AI), we envision algorithms, learning loops, and vast data models ...
TotalEnergies' deployment of machine learning at its Port Arthur, Texas, refinery demonstrates how predictive AI can ...
Discover Taiwo Feyijimi's work at the crossroads of AI, engineering, and learning. Explore his innovative frameworks shaping ...
Abstract: Power transformers are critical components in ensuring the continuous and stable operation of power systems. Failures in these units can lead to significant power outages and costly downtime ...
Abstract: Cascading failure studies help assess and enhance the robustness of power systems against severe power outages. Onset time is a critical parameter in the analysis and management of power ...
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