Adaptive UKF-based model predictive control of a Fresnel collector field

Abstract

One of the ways to improve the efficiency of solar energy plants is by using advanced control and optimization algorithms. In particular, model predictive control strategies have been applied successfully in their control.

The control objective of this kind of plant is to regulate the solar field outlet temperature around a desired set-point. Due to the highly nonlinear dynamics of these plants, a simple linear controller with fixed parameters is not able to cope with the changing dynamics and the multiple disturbance sources affecting the field.

In this paper, an adaptative model predictive control strategy is designed for a Fresnel collector field belonging to the solar cooling plant installed at the Escuela Superior de Ingenieros in Sevilla. The controller changes the linear model used to predict the future evolution of the system with respect to the operating point.

Since only the inlet and outlet temperatures of the heat transfer fluid are measurable, the intermediate temperatures have to be estimated. An unscented Kalman filter is used as a state estimator. It estimates metal-fluid temperature profiles and effective solar radiation.

Simulation results are provided comparing the proposed strategy with a PID + feedforward series controller showing better performance. The controller is also compared to a gain scheduling generalized predictive controller (GS-GPC) which has previously been tested at the actual plant with a very good performance. The proposed strategy outperforms these two strategies.

Furthermore, two real tests are presented. These tests show that the proposed controller achieves adequate set-point tracking in spite of strong disturbances.