Digital Twin and Machine Learning-Based Diagnostics for PEM Electrolyzer

The degradation of the health state of Proton Exchange Membrane (PEM) water electrolyzer, caused by power supply variability, operating temperature changes, and other chemical factors, represents a major challenge for green hydrogen production efficiency. This paper presents an advanced hybrid system combining a digital twin and machine learning, enabling real-time anomaly detection of a PEM electrolyzer. This intelligent approach allows for the real-time prediction of operating parameters, name