S. E. Hosseini

Machine Learning-Based Anomaly Detection in Hydrogen Fuel Cells: A Path Toward Sustainable and Reliable Energy Systems

A two-stage semi-supervised framework for anomaly pattern discovery and codification for hydrogen fuel cells is developed, with Gini importance identifying power difference, capacity, load power, load current, measured power, and load resistance as the principal predictive variables.