摘要：This study proposes a method based on Bayesian networks (BNs) to optimize the reliability of gas supply in natural gas pipeline networks. The method integrates probabilistic safety analysis with preventive maintenance to achieve the targets of minimizing gas shortage risk and reducing maintenance costs. For this, the tasks of unit failure probability calculation, system maximum supply capacity analysis, gas supply reliability assessment and system maintenance planning are performed. A stochastic capacity network model is coupled with a Markov model and graph theory to generate the state space of the pipeline network system. BN, is then, proposed as the modeling framework to describe the stochastic behavior of unit failures and customer gas shortage. The system maintenance problem is converted into a Markov decision process (MDP), and solved by using deep reinforcement learning (DRL). The effectiveness of the proposed method is validated on a case study of a European gas pipeline network. The results show that the proposed method outperforms others in identifying optimal maintenance strategies. The DRL-optimized maintenance strategy is capable of responding to a dynamic environment through continuous online learning, considering the randomness of the unit failures and the uncertainty in gas demand profiles.

关键字：Natural gas pipeline network; Gas supply reliability; Preventive maintenance; Bayesian network; Reinforcement learning

ISSN号：0951-8320

卷、期、页：卷: 225

发表日期：2022-09-01

影响因子：6.187900

期刊分区（SCI为中科院分区）：二区

收录情况：SCIE（科学引文索引网络版）,EI（工程索引）

发表期刊名称：RELIABILITY ENGINEERING & SYSTEM SAFETY

参与作者：王伟,恩里克.齐奥,虞维超

通讯作者：范霖,杨兆铭,彭世亮

第一作者：苏怀,张良,左丽丽,张劲军

论文类型：期刊论文

论文概要：范霖,苏怀,王伟,恩里克.齐奥,张良,杨兆铭,彭世亮,虞维超,左丽丽,张劲军，A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning，RELIABILITY ENGINEERING & SYSTEM SAFETY，2022，卷: 225

论文题目：A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning