Hydrogen leakage localization technology in hydrogen refueling stations combining RL and hidden Markov models - Jun Wang; Lei Wang; Ding Wang; Xiang Yu (Sustainable Energy Research)
Abstract With the global energy structure shifting towards clean and efficient hydrogen energy, the safety management issues of hydrogen refueling stations are becoming increasingly prominent. To address these issues, a hydrogen leak localization algorithm for hydrogen refueling stations based on a combination of reinforcement learning and hidden Markov models is proposed. This method combines hidden Markov model to construct a probability distribution model for hydrogen leakage and diffusion, simulates the propagation probability of hydrogen in different grid cells, and uses reinforcement learning to achieve fast and accurate localization of hydrogen leakage events. The outcomes denoted that the training accuracy reached 95.2%, with an F1 value of 0.961, indicating its high accuracy in hydrogen leak localization. When the wind speed was 0.8 m/s, the mean square error of the raised method was 0.03, and when the wind speed was 1.0 m/s, the mean square error of the raised method was 0.04, proving its good robustness. After 50 localization experiments, the proposed algorithm achieves a localization success rate of 93.7% and an average computation time of 42.8 s, further demonstrating its high accuracy and computational efficiency. The proposed hydrogen leakage location algorithm has improved the accuracy and efficiency of hydrogen leakage location, providing scientific basis and technical guarantee for the safe operation of future hydrogen refueling stations.