State estimation plays a key role in the development of advanced control strategies for Concentrating Solar Thermal Power (CSP) systems, by providing an estimate of process variables that are otherwise infeasible to measure. The present study proposes a state estimation scheme for a once-through direct steam generation plant, the SG4 steam generation system at the Australian National University. The state estimation scheme is a modified Extended Kalman Filter that computes an estimate of the internal variables of the mono-tube cavity receiver in the SG4 system, from a dynamic non-linear model of the receiver. The proposed scheme augments the capabilities of a Continuous-Direct Extended Kalman Filter to deal with the switched nature of the receiver, in order to produce estimates during system start-up, cloud transients and operation of the plant. The estimation process runs at regular sample intervals and happens in two stages, a prediction and a correction stage. The prediction stage uses the receiver model to calculate the evolution of the system and the correction stage modifies the predicted estimate from measurements of the SG4 system. The resulting estimate is a set of internal variables describing the current state of the receiver, termed the state vector. This paper presents a description of the modified Extended Kalman Filter and an evaluation of the scheme using computer simulations and experimental runs in the SG4 system. Simulations and experimental results in this paper show that the filtering scheme improves a receiver state vector estimation purely based on the receiver model and provides estimates of a quality sufficient for closed loop control. (C) 2015 Elsevier Ltd. All rights reserved.