(587c) A Methodology for Leak Detection in Natural Gas Pipelines Under Different Consumer Usage Patterns

Authors: 
Pan, X., Texas A&M University
Karim, M. N., Texas A&M University

Model based fault detection is one of the most widely used approaches for industrial process fault diagnosis [1]. For leak detection in a natural gas pipeline, software methods such as model-based methods or statistics based methods have been applied [2-5]. The model-based leak detection strategy is based on the pressure and flow rate measurement at the inlet and outlet of the pipeline and an estimation algorithm from the natural gas flow model. Estimation algorithms proposed for a single natural gas pipeline leak detection include Unscented Kalman Filter, particle filter, and Extended Kalman Filter [6-9]. However, leak detection in natural gas pipeline networks involving different consumer usages has not been studied.

In this paper, we consider the effects of different consumer usages in a natural gas pipeline network. Two different consumer usages are considered which are constant industrial usage and household consumer. The household consumer usage varies based on temperature and the time of the day and can’t be predicted precisely. The effect of leak on the flow rate of the pipeline network is simulated using the non-isothermal modelling. An estimation algorithm is developed for natural gas pipeline fault detection (leak size and location) in the presence of different consumer usages. Furthermore, due to the length of the pipeline, there is a time delay of the effect of consumer usage at both ends of the pipeline. Based on the non-linear isothermal model of the natural gas flow in a pipeline and boundary condition of constant pressure at both ends, a simple model was derived showing the effect of consumer usage on the inlet and outlet flow rate at steady state.  With a random leak treated as an unknown input, an unknown input observer with time delay is designed based on the above model for leak detection. Two residuals are generated for the leak size and location identification. An alarm of leak is triggered according to the magnitude of the residuals.

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