Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141666
Title: Leak detection in low-pressure gas distribution networks by probabilistic methods
Authors: Gupta, Payal
Zan, Thaw Tar Thein
Wang, Mengmeng
Dauwels, Justin
Ukil, Abhisek
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Gupta, P., Zan, T. T. T., Wang, M., Dauwels, J., & Ukil, A. (2018). Leak detection in low-pressure gas distribution networks by probabilistic methods. Journal of Natural Gas Science and Engineering, 58, 69-79. doi:10.1016/j.jngse.2018.07.012
Journal: Journal of Natural Gas Science and Engineering
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.
URI: https://hdl.handle.net/10356/141666
ISSN: 1875-5100
DOI: 10.1016/j.jngse.2018.07.012
Rights: © 2018 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:ERI@N Journal Articles

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