The undesired and unexpected closure of valves in pipelines is the most frequent failure that causes interruptions in the transport of natural gas. This work aims at the detection of valve closures by solving a state estimation problem with the Particle Filter method. The gas flow problem in the duct is solved with a Weighted Average Flux – Total Variation Diminishing scheme, while state variables are estimated with simulated measurements of pressure, velocity and temperature at different points along the pipeline. Two versions of the particle filter method are implemented in this work for the solution of the state estimation problem, namely, the Sampling Importance Resampling (SIR) and the Auxiliary Sampling Importance Resampling (ASIR) algorithms. Accurate estimations were obtained with both algorithms for configurations involving pipelines with one or three valves. On the other hand, the SIR algorithm required a larger number of particles than the ASIR algorithm for the same solution accuracy.

管道中阀门的意外关闭是导致天然气运输中断的最常见故障。这项工作旨在通过使用粒子过滤器方法解决状态估计问题来检测阀门关闭。管道中的气流问题通过加权平均通量 - 总变化递减方案解决，而状态变量则通过管道沿线不同点的压力、速度和温度的模拟测量来估计。为了解决状态估计问题，本文实现了粒子滤波方法的两个版本，即采样重要性重采样 (SIR) 和辅助采样重要性重采样 (ASIR) 算法。对于涉及具有一个或三个阀门的管道的配置，这两种算法都获得了准确的估计。另一方面，对于相同的求解精度，SIR 算法需要比 ASIR 算法更多的粒子数。