Feedback linearization control for a distributed solar collector field

Abstract

This article describes the application of a feedback linearization technique for control of a distributed solar collector field using the energy from solar radiation to heat a fluid. The control target is to track an outlet temperature reference by manipulating the fluid flow rate through the solar field, while attenuating the effect of disturbances (mainly radiation and inlet temperature). The proposed control scheme is very easy to implement, as it uses a numerical approximation of the transport delay and a modification of the classical control scheme to improve startup in such a way that results compared with other control structures under similar conditions are improved while preserving short commissioning times. Experiments in the real plant are also described, demonstrating how operation can be started up efficiently. r

APPLICATION of a SMITH PREDICTOR based NONLINEAR PREDICTIVE CONTROLLER to a SOLAR POWER PLANT

“…In (Silva et al, 2003), a change in time scale which linearizes the plant is implemented, allowing very high and sudden changes in the reference variable. A feedforward linearization control strategy where both feedforward and feedback can be achieved is given in (Cirre et al, 2005). Starting from nonlinear PDE, a lumped parameter model is obtained by applying Orthogonal Collocation and an Adaptive Receding Horizon Control in (Igreja et al, 2005).…”

Nonlinear Adaptive Control of Heat Transfer Fluid Temperature in a Parabolic Trough Solar Power Plant

“…In the absence of noise, the projection algorithm shown in Equation (6) has been proven to reduce, on every step, the norm of the model parametric identification error ||θ a − θ(k)||, θ a being the actual process parameter set. For noisy environments, a minimum absolute value of the estimation…”

Numerical analysis of the inlet channel and basin geometries for vortex generation in a gravitational water vortex power plant

“…Some of these control techniques are in the form of: i) an adaptive (proportional-integral) PI controller based on a pole assignment approach [26]; ii) a robust PI controller with reset action on its integral term [27]; iii) a PID controller complemented with a filter to counteract the resonance dynamics effects [28]; iv) a nonlinear PID controller with time varying gain [29]; v) a robust PID controller with fixed parameters based on the quantitative feedback theory (QFT) [30]; vi) a feedback linearization [31]; vii) an adaptive nonlinear control using feedback exact linearization together with a lyapunov's approach [32]; viii) an indirect adaptive nonlinear control based on a recurrent neural network and the output regulation theory [33]; ix) an internal model control [34], and x) a fuzzy logic control [35]. A feedforward term is a fundamental element in most of these control frameworks in order to mitigate the effect of the measured disturbances on the plant dynamics.…”