Feedback/feedforward dual adaptive control of a solar collector field

Silva, R.N.; Filatov, N.M.; Lemos, J.H.; Unbehauen, H.

doi:10.1109/cca.1998.728431

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…A feedforward action is required in most cases to mitigate the effect of disturbances in the signal to be controlled (Berenguel, Camacho, & Rubio, 1993;Camacho, Rubio, & Hughes, 1992;Camacho et al, 1997;Carotenuto, LaCava, Muraca, & Raiconi, 1986;Meaburn & Hughes, 1996;Rorres, Orbach, & Fischl, 1980;Silva, Filatov, Lemos, & Unbehauen, 1998). In fact, the required system response cannot be guaranteed with a simple linear control technique because of the strong nonlinearities of the plant.…”

Section: Introductionmentioning

confidence: 96%

Feedback linearization control for a distributed solar collector field

Cirre

Berenguel

Valenzuela

et al. 2007

Control Engineering Practice

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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

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Section: Introductionmentioning

confidence: 96%

Feedback linearization control for a distributed solar collector field

Cirre

Berenguel

Valenzuela

et al. 2007

Control Engineering Practice

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“…As it was mentioned, all previously described techniques were focused on controlling the solar collector outlet temperature by varying the heat transfer fluid (HTF) flow rate (the manipulated variable) through the collector field [11] but no auxiliary backup was considered. On the other hand, modeling the solar collector field with an auxiliary fossil fuel-fired heater creates an additional manipulated variable that could increase the performance of traditional control schemes.…”

Section: Introductionmentioning

confidence: 99%

Automatic control strategies for hybrid solar-fossil fuel power plants

et al. 2014

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a b s t r a c t Solar electrical generating systems are a class of solar energy systems which use parabolic trough collectors (PTC) to produce electricity from sunlight. In order to provide power production, one of the major challenges is to held the collector outlet temperature or steam temperature around of a specified set point by adjusting the flow rate of the heat transfer fluid (HTF) within upper and lower bounds. In some cases, an auxiliary heater can be used to provide heat in absence of solar radiation or during cloudy days. This paper presents a comprehensive study of three control schemes proposed to keep the steam temperature around its set point by adjusting the fuel (propane) and air mass flow rate of the auxiliary fossil fuel-fired heater. A non-linear dynamic model was developed in SIMULINK Ò to study the performance of each control scheme. Variation of controlled and manipulated variables along with the valve signals is presented for a period of a cloudy day. The results showed that the combination of feedforward and three level cascade control is the best alternative to track the temperature set point. It was also found that a single three level cascade control without feedforward had less oscillations and low fuel consumption compared to the others control strategies.

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“…Due to the non-linearity the adaptive controllers (Coito et al, 1997 andSilva et al, 1998) could be used to overcome this the unpredictable change in the plant. However a problem may appear with the identification which can be long due to the fast changes in the system.…”

Section: Introductionmentioning

confidence: 99%