Dynamic Signal State Estimation of Electromagnetic Flowmeter Measurement Based on Variance Compensation Kalman Filter

Electromagnetic flowmeter is widely applied in single-phase flow, solid-liquid two-phase flow and solid-liquid-gas three-phase flow measurement due to the advantages of high accuracy and high stability. However, existing electromagnetic flowmeter inherit the shortcomings of limited applicability and slow dynamic response speed. In order to solve the problem above, this paper make several modifications on the traditional Kalman filter to form an adaptive algorithm. The traditional Kalman filter method is difficult to track the changing flow signal. In order to solve the problem that the flow state changes with the flow state in electromagnetic flowmeter, the adaptive estimation considering the change of real-time flow state is introduced, which provides a solution to remove invalid signal more effectively, make rapid changes according to the change of flow state, and reduce the actual error of filtering. The adaptive Kalman estimation model under continuous flow condition is established, which makes the scope of application wider, improves the signal-to-noise ratio and greatly improves the signal accuracy. Through simulation, the results obtained by Kalman filter are more close to the real value than the measured value, indicating that the variance compensation adaptive Kalman filter has the effect of eliminating noise. At the same time, with the passage of time, the filtering gain
$K$
slowly decreases until it becomes stable to a constant. When the adaptive Kalman gain is stable, the adaptive Kalman filtering is equivalent to low-pass filtering, and the system reaches steady state. The adaptive Kalman filtering solves the problem that the traditional Kalman filtering method is difficult to track the changing flow signal.