Leak diagnostics in natural gas pipelines using fault signatures
Most of the oil and natural gas resources are transported via pipelines. However, due to unavoidable factors such as corrosion and earthquakes, these pipelines frequently experience faults such as leaks. In the past, undetected leaks in pipelines resulted in massive human and material losses. Though...
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Elsevier Ltd
2022
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130777874&doi=10.1016%2fj.ijpvp.2022.104698&partnerID=40&md5=18ce1ebb33ee201a046bde621e6ba4bf http://eprints.utp.edu.my/33010/ |
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| Summary: | Most of the oil and natural gas resources are transported via pipelines. However, due to unavoidable factors such as corrosion and earthquakes, these pipelines frequently experience faults such as leaks. In the past, undetected leaks in pipelines resulted in massive human and material losses. Though, it is possible to timely and accurately detect leaks or other faults in pipelines by improvising existing fault detection and diagnostics (FDD) methodologies. In this study, fault signatures are used to identify a leakage as well as a leaking section in a natural gas pipeline. A long transportation pipeline (up to 150 km) is simulated under transient conditions for the leak detection and diagnostics (LDD) study. Under normal operating conditions, mass flow rate measurements are used to estimate pipeline models based on autoregressive exogenous (ARX) model. Mass flow rate limits under leak-free conditions are defined by calculating adaptive thresholds. The models are tested for leakage at several locations; a minimum detectable leak with zero false alarm was 0.084 m in diameter (around 6 of the total diameter). Finally, the indicated leakage started the algorithm to identify the leaking section. Identification of a leaking section is based on a fault signature from three locations in a pipeline. The leaking section was detected by comparing a specific fault signature with a defined diagnostics matrix in the presence of 0.5 white noise. © 2022 Elsevier Ltd |
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