Small Leak Location for Intelligent Pipeline System via Action-Dependent Heuristic Dynamic Programming
Xuguang Hu, Huaguang Zhang, Dazhong Ma, Rui Wang, Pengfei Tu
Abstract
In the construction process of intelligent pipeline system, pipeline monitoring is an important content to improve the safety of pipeline operation. Small leak location, in particular, is the primary focus of pipeline monitoring due to the unclear pressure drop point. To solve this, in this article, a data driven-based method of small leak location is proposed. First, through the collected pipeline parameters, empirical flow variables, and historical pressure values in pipeline system, a pipeline model based on pressure along pipeline is presented by a three-layer neural network to close to the industrial scenarios. Then, on the basis of the analyzed propagation process of negative pressure wave, an action-dependent heuristic dynamic programming with pressure–distance physical constraints is proposed to obtain the small leak location result. The proposed method is suitable for the collection of only pressure data scenario, which expands the application range. Finally, different cases of small leak location results indicate that the proposed method can locate the leak point, and the field tests further show that the proposed method has satisfactory performances in pipeline leak analysis.