Water supply pipe network pipeline roughness coefficient and joint water demand synchronous self-adaptive checking method

Abstract

The invention relates to a water supply pipe network pipeline roughness coefficient and joint water demand synchronous self-adaptive checking method. The water supply pipe network pipeline roughness coefficient and joint water demand synchronous self-adaptive checking method comprises the following steps that (1), water supply pipe network monitoring data are collected, and a self-adaptive checking process is initialized; (2), an inference observation value of the water demand of the joint at the current moment is calculated based on the water supply network monitoring data, and an inference observation Kalman filter is substituted to calculate an optimal estimation value of the water demand of the joint at the current moment; (3), an inference observation value of the pipeline roughness coefficient at the current moment is calculated based on the water supply network monitoring data, and the inference observation Kalman filter is substituted to calculate an optimal estimation value ofthe pipeline roughness coefficient at the current moment; and (4), the optimal estimation value of the water demand of the joint at the current moment and the optimal estimation value of the pipelineroughness coefficient at the current moment are substituted into the next moment, and the steps (2) to (3) are repeated to complete the check at the next moment. Compared with the prior art, the pipeline roughness coefficient and the joint water demand in the pipe network can be checked at the same time, and the checking precision is high.

Description

technical field [0001] The invention relates to a method for checking parameters of a water supply pipe network model, in particular to a method for synchronously self-adaptive checking of the roughness coefficient of the water supply pipe network and the water demand of nodes. Background technique [0002] The mathematical model of the water supply pipeline network is widely used to simulate the operating conditions of the real pipeline network system such as node pressure, pipeline flow, and water quality changes. Reasonable use of the pipeline network model can significantly improve the operational efficiency and economic benefits of the pipeline network, and facilitate business operations such as pipeline maintenance and customer service. However, because the accuracy of the pipeline network model often cannot meet the needs of use, the practical application of the pipeline network model such as real-time simulation and state prediction of the pipeline network is still g...

AI Technical Summary

Problems solved by technology

[0009] Advantages and disadvantages: The theoretical basis of this method is relatively simple, but it is only suitable for small-scale pipe networks. When using it, the pipe network needs to be simplified first, and the convergence rate is very low.

[0015] Advantages and disadvantages: The display calibration method puts forward a series of basic theories that should be followed in the pipeline network calibration process, and lays the foundation for the mathematical theory of the calibration problem, but it still has many shortcomings, including: (1) usually requires waiting The number of parameters to be calibrated is equal to the number of monitoring values, that is, the calibration must be a positive definite problem, which limits the scope of application of this method; (2) the error of the monitoring value cannot be measured, and it is usually assumed that the measured value of the node water head or pipeline flow is completely accurate. Unable to estimate the uncertainty of the calibrated parameters; (3) Requires high mathematical skills and complex equation solving tools

[0022] Advantages and disadvantages: This type of method is highly scalable and is currently the most widely used method. In recent years, most of the methods used in research can be classified into this type

However, there are still the following deficiencies in the existing implicit calibration method research: (1) Most of the research only checks a single parameter of the pipeline roughness coefficient or the node water demand, and checks the two parameters at the same time. Relatively little research has been done on the parameters

(2) Few studies focus on how to reduce the impact of the errors in the monitoring values ​​on the calibration process, and evaluate the uncertainty of the parameters after calibration; (3) Existing studies usually only consider a few Monitoring data under typical operating conditions, the calibrated hydraulic model is difficult to maintain a high accuracy under different operating conditions of the pipeline network

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