Deep learning-based hydrogen leakage localization prediction considering sensor layout optimization in hydrogen refueling stations

Abstract

To address the challenge and reliance on subjective experience in monitoring hydrogen leaks at hydrogen refueling stations (HRS), a regression prediction model for leakage localization based on Temporal convolutional network (TCN) and multimodal fusion technology is proposed for the first time. This model can accurately predict the spatial coordinates where the leakage source occurs in HRS. This study constructed a baseline dataset of hydrogen leakage, assisted by CFD simulation. Genetic Algorithm (GA) optimized the sensor layout to lower application costs while increasing monitoring effectiveness. The wind speed, wind direction, and hydrogen concentration data collected are multimodally fused to help the model mine more potential features. We compared the proposed model with current classical algorithms such as LSTM, GRU. The results demonstrate that our model achieves higher accuracy, delivering a localization model with an average error of just 0.54 m. The proposed method can provide guidance for the layout of monitoring sensors in the large-scale HRS and provide more accurate diagnostic results for hydrogen leakage behaviors to ensure the safe operation of HRS compared to other methods.