초록 ▼

There is provided a method for detecting and locating leaks in a pipeline network in real-time. A flow model is provided that characterizes flow behavior for at least one of steady and unsteady states respectively corresponding to an absence and a presence of model leaks in the pipeline network, the

There is provided a method for detecting and locating leaks in a pipeline network in real-time. A flow model is provided that characterizes flow behavior for at least one of steady and unsteady states respectively corresponding to an absence and a presence of model leaks in the pipeline network, the flow model including a leaking factor kL. A deterministic model is provided to evaluate at least one of a leak status and a no leak status relating to the pipeline network using deterministic criteria. The deterministic criteria is based on a Liapunov Stability Theory. A deviation matrix is constructed based on the flow model and the deterministic model to generate eigenvalues. A leak alarm is generated when at least one of the eigenvalues is less than a predetermined value.

대표청구항 ▼

1. A method for detecting and locating leaks in a pipeline network in real-time, comprising the steps of:providing a flow model that characterizes flow behavior for at least one of steady and unsteady states respectively corresponding to an absence and a presence of model leaks in the pipeline netwo

1. A method for detecting and locating leaks in a pipeline network in real-time, comprising the steps of:providing a flow model that characterizes flow behavior for at least one of steady and unsteady states respectively corresponding to an absence and a presence of model leaks in the pipeline network, the flow model including a leaking factor kL;providing a deterministic model to evaluate at least one of a leak status and a no leak status relating to the pipeline network using deterministic criteria, the deterministic criteria being based on a Liapunov Stability Theory; andconstructing a deviation matrix based on the flow model and the deterministic model to generate eigenvalues, and generating a leak alarm when at least one of the eigenvalues is less than a predetermined value.2. The method of claim 1, further comprising the step of providing a probability and statistical model based on a Bayesian Probability Model to construct a random table that certifies the presence of a detected leak, and wherein said generating step generates the leak alarm when the at least one of the eigenvalues is less than the predetermined value and the presence of the detected leak is certified.3. The method of claim 2, wherein leak coordinates are located as points in z-coordinates in a curvilinear (rθz) cylindrical mesh grid.4. The method of claim 1, further comprising the step of tracking leak locations using the deterministic model, fluid sonic velocity and a time lag between leak and no leak detection measurements.5. The method of claim 1, further comprising the steps of, subsequent to said constructing step:generating a standard deviation model for assigning a value and classifying a disturbance in the pipeline network;calculating another standard deviation model to evaluate a width of deviation of a typical flow vector point at time i=0 . . . n; wherein a standard deviation close to zero indicates a small leak, and as the standard deviation increases a larger leak is indicated, andwherein |λ1ij|, |λ2ij|, |λ3ij| respectively represent an absolute eigenvalue of velocity, mass and pressure at a particular time and pipeline node point.6. The method of claim 1, wherein the pipeline network includes a plurality of pipelines, and the method further comprises the steps of:decomposing the pipeline network into a mesh of networks; andanalyzing the mesh of networks using nodal analysis and Kirchoff's Laws by modifying Hardy Cross program codes for an unsteady state to analyze the plurality of pipelines for leaks.7. The method of claim 1, wherein the pipeline network includes a plurality of pipelines, and the method further comprises the steps of:identifying a plurality of loops and a plurality of nodes within the pipeline network;locating, from among the plurality of nodes, a central node from which each of the plurality of loops emanates;identifying, from among the plurality of loops, a minimum number of loops that are capable of being constructed from the central node;determining if all of the plurality of nodes are included within any one loops from among the minimum number of loops;respectively drawing arbitrary lines that connect a given one of the nodes to the central node, when the given one of the nodes is not included within the any one loops from among the minimum number of loops, the arbitrary lines and the minimum number of loops forming a plurality of sub-networks; andanalyzing each of the plurality of sub-networks,wherein said analyzing step comprises generating a pressure and velocity profile for evaluating a pressure drop and leak profile with respect to the plurality of sub-networks, based on a Hardy Cross algorithm.8. The method of claim 7, further comprising the steps of: identifying a plurality of code coverage tasks for analyzing fluid flow in the unsteady states, using the Hardy Cross algorithm;generating a persistent unique subprogram code for each of the plurality of code coverage tasks;incorporating a coverage program task model for different fluid and network systems into a modified format of the persistent unique subprogram code for each of the plurality of code coverage tasks to produce an instrumented version of the Hardy Cross algorithm;compiling and linking the instrumented version of the Hardy Cross algorithm into an executable program that identifies a new set of test cases from a plurality of test cases, the plurality of test cases to be run for code coverage data collection purposes of the plurality of code coverage tasks;altering a code coverage database to accommodate the new set of test cases and at least one code coverage task that is one of new, modified and expanded;clearing any code coverage data for the plurality of code coverage tasks from the code coverage database;running the executable program with a test case from the new set of test cases and collecting code coverage data for the plurality of code coverage tasks, until all new test cases from the new set of test cases have been run; andupdating the code coverage database with the collected code coverage data for non-affected code coverage tasks in the code coverage database so as to eliminate a need to run all of the executable program.9. The method of claim 8, wherein said step of generating the persistent unique subprogram code for each of the plurality of code coverage tasks comprises the step of generating a persistent unique name for each of the plurality of code coverage tasks by changing version indicators in code names of the plurality of code coverage tasks.