Data Reconciliation on the Complex Hydraulic System of Canal de Provence
Publication: Journal of Irrigation and Drainage Engineering
Volume 131, Issue 3
Abstract
A data reconciliation module, based on the measurements from the hydraulic network, has been recently developed and implemented in the supervisory system of the Société du Canal de Provence (SCP). The software has initially been used daily to check the measured flow on the main canal. The data reconciliation occurs just after the measurement process. The measurement network on the hydraulic system includes many sensors subject to failure or deviation and is spread over a huge area. In addition, discharge and volume measurements in open-channel hydraulic networks are characterized by large uncertainties. The objective of the data reconciliation is to take advantage of information redundancy on a system to make a cross-check of real-time measurements. By using this information redundancy, a data reconciliation module allows detection of inconsistent measurements and measurement deviations and provides corrected values whether the initial measurements are valid, biased, or invalid. A derived consequence is better scheduling of the maintenance of sensors. The results are corrected values for measured variables and proposed values for nonmeasured quantities. A statistical analysis of the results is performed. This analysis allows evaluation of the uncertainties attached to the estimated flows and volume values. It allows also detecting invalid measurements and drift of sensors and making decisions about which maintenance operations to perform.
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References
Canivet, E. (2002). “Réconciliation et validation des données sur un système hydraulique complexe, le Canal de Provence.” PhD thesis, Université Lyon 1.
Chui, C. K., and Chen, G. (1998). Kalman filtering with real time applications, Springer-Verlag, Berlin.
Freund, R., and Wilson, W. (2003). Statistical methods, Elsevier, New York.
Gill, E., Murray, W., and Wright, M. H. (1981). Practical optimization, Academic Press, New York.
Judge, G. G., Hill, R. C., Griffiths, W. E., Lutkepohl, H., and Lee, T.-C. (1998). Introduction to the theory and practice of econometrics, Wiley, New York.
Kratz-Bousghiri, S., Nuninger, W., and Kratz, F. (1996). “Fault detection in stochastic dynamic systems by data reconciliation.” Engineering Simulation, 13, 837.
Lawson, C. L., and Hanson, R. J. (1995). Solving least-square problems, SIAM, Philadelphia.
Maquin, D., and Ragot, J. (2000). Diagnostic des systèmes linéaires, Hermès, Paris.
Mathworks. (2002). Statistical toolbox user guide, Mathworks, Natick, Mass.
Narasimham, S., and Jordache, C. (2000). Data reconciliation and gross error detection: An intelligent use of process data, Gulf Professional Publishing, New York.
Ragot, J., Darouach, M., Maquin, D., and Bloch, G. (1992). Validation de données et diagnostic, Hermès, Paris.
Wonnacott, T., and Wonacott, R. (1990). Introductory statistics, Wiley, New York.
Zwingelstein, G. (1995). Diagnostic des defaillances théorie et pratique pour les systèmes industriels, Hermès, Paris.
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© 2005 ASCE.
History
Received: Jan 16, 2004
Accepted: Jun 29, 2004
Published online: Jun 1, 2005
Published in print: Jun 2005
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