CSE algorithm: ‘canal survey estimation’ to evaluate the flow rate extractions and hydraulic state in irrigation canals

One of the main problems in water management of irrigation systems is the control of the equitable distribution of water among different orifice offtakes. The difficulty of managing a canal is partly caused by the lack of knowledge of the canal state because the scheduled demand is often not fulfilled, since farmers extract more water than is scheduled and it is impossible to determine the canal state by the watermaster. However, an innovative developed algorithm called CSE is proposed in this paper. This algorithm is able to estimate the real extracted flow and the hydrodynamic canal state (that is, the water level and velocity along the irrigation canal). The algorithm solves an inverse problem implemented as a nonlinear optimization problem using the Levenberg–Marquardt method. The algorithm is tested taking into account several numerical examples, and a practical implementation was made for a real case study in the PAC-UPC canal, a 220 m laboratory canal especially designed for research into irrigation canal control area and irrigation canal modelling. This useful algorithm evaluates the real extraction flow and the canal state and could be a useful tool for a feedback controller.

[1]  Carlos Sepúlveda Toepfer Instrumentation, model identification and control of an experimental irrigation canal , 2008 .

[2]  José Rodellar Benedé,et al.  Simplified modeling of a laboratory irrigation canal for control purposes , 2011 .

[3]  Philip E. Gill,et al.  Practical optimization , 1981 .

[4]  T. Strelkoff Numerical Solution of Saint-Venant Equations , 1970 .

[5]  A. J. Clemmens,et al.  Test Cases for Canal Control Algorithms , 1998 .

[6]  Pascal Kosuth,et al.  Development and Evaluation of Canal Automation Algorithm CLIS , 1998 .

[7]  Birgit Wirtz Engineering Analysis A Survey Of Numerical Procedures , 2016 .

[8]  José Rodellar,et al.  GoRoSo: Feedforward Control Algorithm for Irrigation Canals Based on Sequential Quadratic Programming , 2013 .

[9]  R. Fletcher Practical Methods of Optimization , 1988 .

[10]  null null,et al.  Unsteady‐Flow Modeling of Irrigation Canals , 1993 .

[11]  Michael Cantoni,et al.  Systems engineering for irrigation systems: Successes and challenges , 2005 .

[12]  José Rodellar,et al.  Multivariable Model Predictive Control of Water Levels on a Laboratory Canal , 2014 .

[13]  Peter-Jules van Overloop,et al.  Multiple Model Predictive Control on a drainage canal system , 2008 .

[14]  E. Benjamin Wylie Control of Transient Free - Surface Flow , 1969 .

[15]  William H. Press,et al.  Numerical Recipes: FORTRAN , 1988 .

[16]  Dilini Delgoda,et al.  Multiple Model Predictive Flood Control in Regulated River Systems with Uncertain Inflows , 2013, Water Resources Management.

[17]  Iven M. Y. Mareels,et al.  Systems engineering for irrigation systems: Successes and challenges , 2005, Annu. Rev. Control..

[18]  Enrique Bonet Gil Experimental design and verification of a centralized controller for irrigation canals , 2015 .

[19]  Gaylord V. Skogerboe,et al.  Surface Irrigation: Theory and Practice , 1987 .

[20]  D. C. Rogers,et al.  Teaching canal hydraulics and control using a computer game or a scale model canal , 2008 .

[21]  R. Schreiber Numerical Methods for Partial Differential Equations , 1999 .

[22]  G. Cannell,et al.  Surface Irrigation: Theory and Practice , 1988 .