US researchers use machine learning to detect hidden solar array defects

US researchers say a self-supervised machine-learning tool can identify long-term physical defects in solar assets weeks or years before conventional inspections, potentially reducing operations and maintenance costs.From pv magazine USA Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine-learning algorithm designed to identify physical anomalies in solar energy systems. The project aims to reduce operations and maintenance (O&M) costs, which remain a significant hurdle for project economics as the industry scales. ...

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