Gas pipeline parameter estimation method based on improved ARUKF

Abstract

The invention discloses a gas pipeline parameter estimation method based on an improved ARUKF, and the method comprises the steps: building a state space model of a natural gas pipeline according to the mass and momentum balance conditions of fluid dynamics in the pipeline; applying the fading factor and the maximum correlation entropy criterion to a cost function of a state equation and a cost function of a measurement equation, and after a filtering equation is established, using an improved ARUKF algorithm estimating and updating the pressure and the flow value of the gas pipeline until theset parameter estimation precision or the maximum number of iterations is reached.The problem of influence of abnormal values in measurement information is effectively solved, the detection and estimation precision of the pressure and flow of the gas pipeline is improved, and the frequency of false alarm in the gas pipeline network is reduced, so that the cost loss and harm of parameter estimation errors to the environment and personal health are reduced.

Description

technical field [0001] The invention belongs to the technical field of navigation, guidance and control, and in particular relates to a gas pipeline parameter estimation method based on an improved ARUKF (adaptive robust unscented Kalman filter). Background technique [0002] With the large-scale exploitation of natural gas and the urgent demand for energy, the gas pipeline presents the characteristics of high pressure and large transportation volume, which puts forward higher requirements for the safe transportation of natural gas pipeline. In practice, the pipeline flow system is a nonlinear system, which is easily affected by irrelevant interference in the external environment and random errors in sensor measurement. In the process of data acquisition of natural gas pipelines, there are often disturbances of abnormal values, which affect Estimation of pressure and flow. Therefore, the application of filtering algorithm is reasonable and feasible, and this method provides...

AI Technical Summary

Problems solved by technology

On the basis of the robustness research, the fading factor can be introduced into the filtering algorithm, but when the gas pipeline network system is subject to external instantaneous interference, the fading factor cannot guarantee the optimal performance of the filtering algorithm under the condition of uncertain noise distribution. Optimality, leading to filter performance degradation or even divergence

Using the robust unscented Kalman filter of the maximum correlation entropy criterion, when the environment of the gas pipeline network is harsh and the state and measurement outliers exist at the same time, the filtering algorithm will have a large state estimation error, which will lead to a decrease in algorithm performance , and even diverge

Images