Identification and verification of the parameters for canal control model based on measured water level and flow

A canal control model can be considered as a mathematical expression to represent the dynamic relationship between the canal building-gate-flow. The accuracy of the model determines the design effect of the controller. In view of the traditional canal control model, it is difficult to solve the problems which are complicated and difficult to apply in the actual engineering. In this study, a small canal monitoring system was first developed to conduct model tests. A parameter identification method was used to identify the canal control model, and then to verify the accuracy of the identification model. The parameter identification method was extended to the prototype project of South-to-North Water Diversion Project to further verify the reliability of parameter identification. Two typical flow conditions were designed in the model test, including the forward step condition and the reverse step condition, in order to fully observe the response of water level in the canal pond to the flow change, and thereby obtain the input and output information of the model. During the test, water level gauges and flow meters were used to monitor the water level and flow information in the test canal in real time, and then the monitored information was transmitted to the central monitor for storage. In the prototype project, the observation data were provided by the Administration of the Middle Route of South-to-North Water Diversion Project. The Integrator Delay (ID) model and the Integrator Delay Zero (IDZ) model were used in the control model, while, the Recursive Least Square (RLS) method was used in the identification method to identify and analyze the measured data. The results showed that recursive least squares were feasible to identify the parameters of canal control models (ID, IDZ), according to the principle of system identification in a model test and prototype observation. In the model test, the identification error was basically distributed between (-1, 1) cm, and the mean square error was less than 1.492×10-5 m2. The identification results were highly consistent with the measured values, and the identification data of ID model and IDZ model were similar. This was mainly because the flow rate in the test canal was small, together with the single flow change and the low frequency flow without obviously high frequency. In the prototype project of South-to-North Water Diversion Project, the identification error was basically distributed between (-2, 2) cm, and the mean square error was less than 7.675×10-5 m2. The identification data was still in high agreement with the measured value. However, when the water flow in the canal changed dramatically, the IDZ model can capture this change trend, while the ID model cannot reflect it. This was mainly because, when the water level changed drastically, the vibration frequency of water wave was faster, and the water flow was in high-frequency motion, so the response accuracy of the IDZ model was higher, which also proved its applicability to high-frequency water flow. Therefore, the IDZ model has better identification accuracy and stability than the ID model, particularly for large-scale canal projects with drastic changes in water flow. The findings can provide a certain reference for the modeling theory in the canal control and controller design, thereby to the modeling of the water transmission and distribution canal system in irrigation areas or water diversion projects.