SIC software

Presented at SCADA and related technologies for irrigation district modernization, II: a USCID water management conference held on June 6-9, 2007 in Denver, Colorado.Includes bibliographical references.An increasing number of irrigation canals are following modernization projects to improve their hydraulic efficiency, their quality of service to users and to face new operational constraints. The Gignac Canal has been specifically modernized in order to be used during certain periods of the year as an experimental platform for several partners. A SCADA system has been installed with display screens at the manager's office and a SCADA interface has been developed into the SIC hydrodynamic software allowing testing of any type of control algorithm. This testing can be done first on the SIC hydraulic simulation model, and then switched onto the real canal without any code rewriting or parameters change. This SIC SCADA interface is communicating with the SCADA system developed by DSA Company exchanging data forth and back through simple ASCII files. The features of this approach are described in this paper. This SCADA module is now included into the standard library of the SIC software. This tool has been intensively tested on the Gignac canal that will be used for illustration

Hydraulic Modeling of a Mixed Water Level Control Hydromechanical Gate

This article describes the hydraulic behavior of a mixed water level control hydromechanical gate present in several irrigation canals. The automatic gate is termed "mixed" because it can hold either the upstream water level or the downstream water level constant according to the flow conditions. Such a complex behavior is obtained through a series of side tanks linked by orifices and weirs. No energy supply is needed in this regulation process. The mixed flow gate is analyzed and a mathematical model for its function is proposed, assuming the system is at equilibrium. The goal of the modeling was to better understand the mixed gate function and to help adjust their characteristics in the field or in a design process. The proposed model is analyzed and evaluated using real data collected on a canal in the south of France. The results show the ability of the model to reproduce the function of this complex hydromechanical system. The mathematical model is also implemented in software dedicated to hydraulic modeling of irrigation canals, which can be used to design and evaluate management strategies

Stability and performance analysis of classical decentralized control of irrigation canals

Irrigation canals have a series structure which is generally used to design multivariable controllers based on the aggregation of decentralized monovariable controllers. SISO controllers are designed for each canal pool, assuming that the interactions will not destabilize the overall system. It is shown that, when the canal pools are controlled using the discharge at one boundary, the multivariable decentralized control structure is stable if and only if the SISO controllers are stable. The performance of the multivariable system is also investigated, and it is shown that the interactions decrease the overall performance of the controlled system. This loss of performance can be reduced by using a feedforward controller. Experimental results show the effectiveness of the method

DATA ASSIMILATION ON A FLOOD WAVE PROPAGATION MODEL : EMULATION OF A KALMAN FILTER ALGORITHM

International audienceThis study describes the assimilation of synthetically-generated river water level observations in a flood wave propagation model. For this approach to be applied in the framework of real-time flood forecasting, the cost of the data assimilation procedure, mostly related to the estimation of the background error covariance matrix, should be bound. An Ensemble Kalman Filter (EnKF) algorithm is applied, with a steady observation network, to demonstrate how the assimilation modifies the background correlation function at the observation point. It is shown that an initially Gaussian correlation function turns into an anisotropic function at the observation point, with a shorter correlation length-scale downstream of the observation point than upstream, and that the variance of the error in the water level state is significantly reduced downstream of the observation point. The covariance matrix resulting from the EnKF is then used as an invariant background error covariance matrix for a series of successive Best Linear Unbiased Estimation (BLUE) algorithms which emulate an EnKF at a lower cost. This study shows how the background error covariance matrix can be computed off-line, with an advanced algorithm, and then used with a cheaper algorithm for real-time application

Estimation Of Offtake Discharge And Cross-Device Parameters Using Data Assimilation For The Automatic Control Of An Irrigation Canal

The automatic control of open surface hydraulic systems such as Rivers (with dams and/or hydropower plants) or irrigation canals (with gated cross-devices) almost always use hydraulic models. These models can be used in different manners, either just to test and validate controllers prior to implementation, to tune the controller parameters off-line, or used on-line in real-time. The control algorithm calculates the control action variables u, using measured variables z obtained from the real system, in order to achieve some objectives for some controlled variables y. These models have always limited precision due to unknown or wrong: parameters, input variables and internal states. Among the parameters we find cross-device discharge and bed friction coefficients. Among input variables we find the inflow or outflow discharges entering into the river, or taken by users from the canal. Indeed, they are rarely measured, or in the best cases with a limited precision. This is a problem since the tuning of the control parameters of the feedback loops depends a lot on the dynamics of the system and therefore on the previous listed parameters. A feedforward control component, very useful for this class of delayed systems, could benefit from the knowledge of the input variables. In this paper we will show how data assimilation technics can reconstruct these unknown parameters and variables. We will also focus on the required number and locations of the measurements, to be able to reconstruct this correctly. We will study the best or required configurations allowing to use this information, detect and isolate some problems, correct the model, and reconstruct the wrong or unknown variables, inputs or parameters of the model. The framework we will use for this study is the Kalman filtering one. We will see that this framework is very powerful to solve the above described problems

Multi-variable approach for the command of Canal de Provence Aix nord water supply subsystem, A

Presented during the USCID water management conference held on October 13-16, 2004 in Salt Lake City, Utah. The theme of the conference was "Water rights and related water supply issues."Includes bibliographical references.The Canal de Provence is fully user oriented. Water users can take the water freely without respecting neither rotation nor any sort of priority allocation. Its structure. consisting of main free flow canals and pressurized distribution networks. is well adapted to this strategy. The main canal must be able to face the regime variations coming from this kind of distribution. The current regulation conception first split the whole system into a series of assumed independent sub-systems. The multi-variable aspect is then taken into account by a coordination of the sub-systems adjustment, carrying the discharge correction from downstream to upstream. The Aix nord branch control presents interesting characteristics such as many different hydraulic entities (free surface canals. reservoirs. pumping stations) and operating constraints (levels in reservoirs. optimization of pumping costs). A real multi-variable approach will allow managing all gate and pump operations and all constraints at the same time. leading to a global optimisation of the whole system. The MIMO (Multi Input - Multi Output) model is established from transfer functions. the coefficients of which are deduced from the physical and geometrical characteristics of the system. A Linear Quadratic Regulator is computed and tested on a complete non-linear numerical model of the hydraulic system. The system to be controlled includes many discrete commands (pump operations) that are not managed by a classical optimal control. These commands are treated apart, leading to calculated perturbations that are introduced in the regulator.Proceedings sponsored by the U.S. Department of the Interior, Central Utah Project Completion Act Office and the U.S. Committee on Irrigation and Drainage

Implementation of a Differential Flatness Based Controller on an Open Channel Using a SCADA System

International audienceWith a population of more than 6 billion people, food production from agriculture must be raised to meet increasing demand. While irrigated agriculture provides 40% of the total food production, it represents 80% of the freshwater consumption worldwide. In summer and drought conditions, efficient management of scarce water resources becomes crucial. The majority of irrigation canals are managed manually, however, with large water losses leading to low water efficiency. This article focuses on the development of algorithms that could contribute to more efficient management of irrigation canals that convey water from a source, generally a dam or reservoir located upstream, to water users. We also describe the implementation of an algorithm for real-time irrigation operation using a supervision, control, and data acquisition (SCADA) system with an automatic centralized controller. Irrigation canals can be viewed and modeled as delay systems since it takes time for the water released at the upstream end to reach the user located downstream. We thus present an open-loop controller that can deliver water at a given location at a specified time. The development of this controller requires a method for inverting the equations that describe the dynamics of the canal in order to parameterize the controlled input as a function of the desired output. The Saint-Venant equations [1] are widely used to describe water discharge in a canal. Since these equations are not easy to invert, we consider a simplified model, called the Hayami model. We then use differential flatness to invert the dynamics of the system and to design an open-loop controller

Reservoir Management For Flood And Drought Protection Using Infinite Horizon Model Predictive Control

Model Predictive Control (MPC) is a control method that solves in real time an optimal control problem over a finite horizon. The finiteness of the horizon is both the reason of MPC\u27s success and its main limitation. In operational water resources management, MPC has been in fact successfully employed for controlling systems with a relatively short memory, such as canals, where the horizon length is not an issue. For reservoirs, which have generally a longer memory, MPC applications are presently limited to short term management only. Short term reservoir management can be effectively used to deal with fast process, such as floods, but it is not capable of looking sufficiently ahead to handle long term issues, such as drought. To overcome this limitation, we propose an Infinite Horizon MPC (IH-MPC) solution that is particularly suitable for reservoir management. We propose to structure the input signal by use of orthogonal basis functions, therefore reducing the optimization argument to a finite number of variables, and making the control problem solvable in a reasonable time. We applied this solution for the management of the Manantali Reservoir. Manantali is a yearly reservoir located in Mali, on the Senegal river, affecting water systems of Mali, Senegal, and Mauritania. The long term horizon offered by IH-MPC is necessary to deal with the strongly seasonal climate of the region

Short Term Reservoirs Operation On The Seine River: Performance Analysis Of Tree-Based Model Predictive Control

The Seine River, in France, flows through territories of large economic value, among which the metropolitan area of Paris. A system of four reservoirs operates upstream to regulate the river flows in order to protect the area against extreme events, such as floods and droughts. Current reservoirs management is based on reactive filling curves, designed from an analysis of historical hydrological regimes. The efficiency of this management strategy is jeopardized when inflows are significantly different from their seasonal average. To improve the current management strategy, we investigated the use of Tree-Based Model Predictive Control (TB-MPC). TB-MPC is a proactive and centralized method that uses information available in real-time, as ensemble weather forecasts. Reservoir management is tested under past hydro-climatic conditions using time series of ensemble weather forecasts produced by ECMWF (European Centre for Medium-Range Weather Forecasts) and weather observations. The performance of TB-MPC is compared to that of deterministic Model Predictive Control (MPC), showing the benefits of considering forecasts uncertainty by using ensemble forecasts