Automated detection and diagnosis of leak fault considering volatility by graph deep probability learning

Leak fault significantly affects the reliable and sustainable green hydrogen energy supply by renewable Power-to-Hydrogen (P2H2) system. Deep learning has been widely applied to automated leak fault detection and diagnosis of hydrogen systems. However, due to the intermittency of wind/solar-power-generation, existing approaches developed by monitored signals have the bad generalization to leak scenario under large volatility of power-to‑hydrogen production. This study introduces graph deep probability learning approach, in which an attention-based graph neural network (GNN) learns the dependency between installed sensors. Variational inference is integrated to model posterior distribution of sensor dependencies, by using which the leak fault under large volatility of power-to‑hydrogen production is detected and localized by using normal time-series signals under different timeslots of one day as training data. Experiment of hydrogen leak faults from P2H2 system is conducted to verify the more accuracy of proposed approach compared to 6 state-of-the-art approaches. Results demonstrated our proposed approach detects the leak fault more accurately with a higher AUC of 0.96 and successfully localizes all the leak faults. This study supports more reliable and efficient safety monitoring for upcoming renewable P2H2 system in future.