Leak detection in pipelines using the extended kalman filter and the extended boundary approach
| dc.contributor.advisor | Habibi, Saeid R. | en_US |
| dc.contributor.advisor | Burton, Richard T. | en_US |
| dc.contributor.committeeMember | Noble, Scott | en_US |
| dc.contributor.committeeMember | Bugg, James D. | en_US |
| dc.contributor.committeeMember | Shi, Yang | en_US |
| dc.creator | Doney, Kurtis | en_US |
| dc.date.accessioned | 2007-10-09T14:22:46Z | en_US |
| dc.date.accessioned | 2013-01-04T05:00:54Z | |
| dc.date.available | 2007-10-10T08:00:00Z | en_US |
| dc.date.available | 2013-01-04T05:00:54Z | |
| dc.date.created | 2007-10 | en_US |
| dc.date.issued | 2007-10-10 | en_US |
| dc.date.submitted | October 2007 | en_US |
| dc.description.abstract | A model based algorithm of pipeline flow is developed and tested to determine if the model is capable of detecting a leak in a pipeline. The overall objective of this research is to determine the feasibility of applying the Extended Kalman Filter and a new technique defined as the Extended Boundary Approach to the detection of leakages in a physical water distribution system. The demands on the water supply system increase as the human population grows and expands throughout the world. Water conservation is required to ensure an adequate supply of water remains for future generations. One way to conserve this water is by reducing the leakages in underground water distribution systems. Currently between 10 to 50 percent of the pumped water is lost due to unrecognized leakages. This results in a huge revenue loss of water, chemicals and energy that is required for transporting the water. The detection of underground leakages is a very complex problem because many leakages are small and go unnoticed by today’s leak detection technology. A model based leak detection technique is developed and tested in this thesis. The Method of Characteristics is used to develop a model of a single pipeline. This method is extensively used and provides the most accurate results of the two partial differential equations of continuity and momentum that describe pipe flow. The Extended Kalman Filter is used to estimate two “fictitious” leakages at known locations along the pipeline. In order to ensure the model is observable four pressure measurements are needed at equally spaced nodes along the pipeline. With the development of the Extended Boundary Approach only the upstream and downstream pressure measurements are required, however; the upstream and downstream flow measurements are also required. Using the information from the two “fictitious” leaks the actual leak location and magnitude can be determined. This method is only capable of detecting one leak in a single pipeline. The results of the developed model show that the approach is capable of theoretically determining the leak location and magnitude in a pipeline. However, at this time, the feasibility of implementing the proposed leak detection method is limited by the required level of accuracy of the sensors which is beyond that found in today’s technology. It was also found that the EKF used primarily steady state information to predict the leakage. It is recommended that further research explore alternate models which might better enhance the EKF approach using transient information from the pipeline. This may allow implementation on a real pipeline. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10388/etd-10092007-142246 | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Extended Kalman Filter | en_US |
| dc.subject | Pipelines | en_US |
| dc.subject | Leak Detection | en_US |
| dc.subject | Water | en_US |
| dc.title | Leak detection in pipelines using the extended kalman filter and the extended boundary approach | en_US |
| dc.type.genre | Thesis | en_US |
| dc.type.material | text | en_US |
| thesis.degree.department | Mechanical Engineering | en_US |
| thesis.degree.discipline | Mechanical Engineering | en_US |
| thesis.degree.grantor | University of Saskatchewan | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science (M.Sc.) | en_US |