Abstract The presence of leaks is a prevalent issue for aging gas distribution systems across the globe. These events, if not detected in time, may bring about environmental and health hazards, besides economic losses. Therefore, the development of efficient detection, quantification, and localization methods is crucial to all gas companies worldwide. In this paper, we present a leak monitoring system, called Leak Analytics System (LAS) using a probabilistic approach to determine the location and the rate (severity) of leakage in low-pressure gas distribution networks. This work aims to develop a robust, cost-effective, and real-time online monitoring system for low-pressure gas distribution networks. The leakage events are estimated using pressure and flow data obtained from steady-state modeling of the gas network. The robustness of the methodology is illustrated by analyzing gas networks in the presence of measurement errors, which account for unavoidable sensor noise in flow and pressure data. The feasibility of the proposed method is demonstrated on a small artificial gas network. Moreover, the method is applied to a section of the Singapore gas distribution network for a single as well as multiple leak scenarios. It is also experimentally shown that the severity of the leak and the location for a single leak scenario can be determined within an accuracy of 95% and 80% respectively, even in the presence of strong noise.

中文翻译：

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用概率方法检测低压气体分配网络中的泄漏

摘要 泄漏的存在是全球老化的气体分配系统的普遍问题。这些事件如果不及时发现，除了经济损失外，还可能带来环境和健康危害。因此，开发高效的检测、量化和定位方法对全球所有天然气公司都至关重要。在本文中，我们提出了一种称为泄漏分析系统 (LAS) 的泄漏监测系统，该系统使用概率方法来确定低压气体分配网络中的泄漏位置和泄漏率（严重性）。这项工作旨在为低压气体分配网络开发一个强大的、具有成本效益的、实时的在线监测系统。使用从气体网络的稳态建模中获得的压力和流量数据来估计泄漏事件。该方法的稳健性通过在存在测量误差的情况下分析气体网络来说明，这些误差是流量和压力数据中不可避免的传感器噪声的原因。该方法的可行性在一个小型人工燃气网络上得到了证明。此外，该方法适用于新加坡天然气分配网络的一部分，用于单个和多个泄漏场景。实验还表明，即使在存在强噪声的情况下，泄漏的严重程度和单个泄漏场景的位置也可以分别在 95% 和 80% 的精度内确定。该方法的可行性在一个小型人工燃气网络上得到了证明。此外，该方法适用于新加坡天然气分配网络的一部分，用于单个和多个泄漏场景。实验还表明，即使在存在强噪声的情况下，泄漏的严重程度和单个泄漏场景的位置也可以分别在 95% 和 80% 的精度内确定。该方法的可行性在一个小型人工燃气网络上得到了证明。此外，该方法适用于新加坡天然气分配网络的一部分，用于单个和多个泄漏场景。实验还表明，即使在存在强噪声的情况下，泄漏的严重程度和单个泄漏场景的位置也可以分别在 95% 和 80% 的精度内确定。