A Modified Extended Kalman Filter to Estimate the State of the SG4 Receiver at the Australian National University

The temperature control of direct steam generation depends on information about the dynamic heat transfer behaviour of the receiver during operation. This information may come from sensors or dynamic mathematical models of the heat transfer process taking place at the receiver. In practice, sensors are susceptible to noise and calibration errors and models suffer from uncertainty due to inexact parameter tuning and model simplification. As a result, the information generated by sensors or models contains uncertainty and may be inadequate for control purposes. This paper presents simulations and experimental runs of a modified Extended Kalman Filtering scheme that estimates the state of the mono-tube steam cavity receiver inside the SG4 steam generation system at the Australian National University. The filtering scheme combines the available measurements in the SG4 steam generation system with a dynamic model of the receiver, to compute an estimate of the receiver state suitable for closed loop temperature control. Simulations of the filtering scheme run with pre-recorded data, to evaluate and tune its performance. Experimental results demonstrate how the filtering scheme runs concurrently with the SG4 system and computes estimates of the receiver state in real time. Experimental results show that the filtering scheme performs well under the effect of noisy measurements and poor parameter calibration, and provides an estimation of the SG4 receiver state suitable for control purposes.