A survey on control schemes for distributed solar collector fields ...

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Solar Energy 81 (2007) 1240–1251

A survey on control schemes for distributed solar collector fields.Part I: Modeling and basic control approaches

E.F. Camacho a, F.R. Rubio a, M. Berenguel b,*, L. Valenzuela c

a Universidad de Sevilla, Escuela Superior de Ingenieros, Departamento de Ingenierıa de Sistemas y Automatica,

Camino de Los Descubrimientos s/n, E-41092, Sevilla, Spainb Universidad de Almerıa, Departamento de Lenguajes y Computacion, Area de Ingenierıa de Sistemas y Automatica,

Carretera Sacramento s/n, E-04120 La Canada, Almerıa, Spainc Plataforma Solar de Almerıa – CIEMAT, Carretera Senes s/n, P.O. Box 22, E-04200 Tabernas, Almerıa, Spain

Received 9 August 2006; received in revised form 20 December 2006; accepted 8 January 2007Available online 7 February 2007

Communicated by: Associate Editor B. Norton

Abstract

This article presents a survey of the different automatic control techniques that have been applied to control the outlet temperature ofsolar plants with distributed collectors during the last 25 years. Different aspects of the control problem involved in this kind of plants aretreated, from modeling and simulation approaches to the different basic control schemes developed and successfully applied in real solarplants. A classification of the modeling and control approaches is used to explain the main features of each strategy.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Solar thermal energy; Temperature control; Automatic control

1. Introduction

When the crisis of 1973, during which oil prices rosesdramatically, real interest in renewable sources of energywas rekindled. Attention turned to application of solarpower for the generation of electricity and really interestinginitiatives appeared. The programs initiated included a200 kWe rated plant constructed at Coolidge, Arizona in1979 (Larsen, 1987) and 500 kWe plant built in 1981 atthe Plataforma Solar de Almerıa (PSA), Spain (Schrauband Dehne, 1983). The plant constructed in Spain was partof the International Energy Agency (IEA) project entitledSmall Solar Power Systems (SSPS). In this plant two typesof collecting systems were considered. One was a centralreceiver system (CRS) and other was a distributed collectorsystem (DCS) using parabolic troughs.

0038-092X/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.solener.2007.01.002

* Corresponding author. Tel.: +34 950 015683; fax: +34 950 015129.E-mail address: [email protected] (M. Berenguel).

Parabolic trough systems concentrate sunlight onto areceiver pipe located along the focal line of a trough collec-tor. A heat transfer fluid (HTF), typically synthetic oil orwater, is heated as it flows along the receiver pipe and isrouted either to a heat exchanger, when this fluid is oil,to produce steam that feeds an industrial process (forinstance a turbine), to a flash tank, when the fluid is pres-surized water, to produce steam up to 200 �C for an indus-trial process, or to a turbine when superheated andpressurized steam is produced directly in the solar field(Zarza et al., 2001, 2002a,b). In order to provide viablepower production they have to achieve their task despitefluctuations in energy input, i.e. the sunlight. An effectivecontrol scheme is needed to provide operating require-ments of a solar power plant. Most of the plants that areoperational currently, such as the SEGS plants in Califor-nia (Price et al., 1990), provide this energy in the form of oilheated by a field of parabolic trough collectors. How-ever, the processes usually connected to such fields forelectricity generation (Wettermark, 1988; Price et al.,

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Nomenclature

AC adaptive controlANN artificial neural networkCC cascade controlCRS central receiver systemDCS distributed collector systemDISS direct solar steamDSG direct steam generationFF feedforwardFLC fuzzy logic controlGS gain schedulingHTF heat transfer fluidIEA International Energy AgencyIHP Improving Human Potential EU ProgrammeIMC internal model controlLQG linear quadratic Gaussian controlMLP multilayer perceptronMPC model (based) predictive controlMUSMAR multivariable self-tuning multipredictor

adaptive regulator

NARX nonlinear autoregressive models with exogenousinputs

NC nonlinear controlNNC neural network controllersPDE partial differential equationPID proportional-integral-derivativePRBS pseudo random binary sequencePSA Plataforma Solar de Almerıa (Spain)RBF radial basis functionRC robust controlSEGS solar electricity generating systemSISO single input single outputSSPS small solar power systemsTDC time delay compensationTMR Training and Mobility of Researchers EU Pro-

gramme

E.F. Camacho et al. / Solar Energy 81 (2007) 1240–1251 1241

1990) or seawater desalination (Zarza, 1991) are most effi-cient when operated continuously. To do this they must beprovided with a constant supply of hot oil at some pre-specified temperature despite variations in the ambienttemperature, the inlet temperature and the direct solarradiation. This requirement prompted the use of a storagetank as a buffer between solar collection and the industrialprocess on early plants such as the SSPS system at the PSA,Spain (Wettermark, 1988) and SEGS I in California. Forthis purpose, later plants, SEGS II–IX, operated a gas firedboiler running in parallel to the solar field in order to makeup any shortfalls in the solar produced steam (Price et al.,1990). Whilst these facilities enable the overall plant poweroutput to be maintained during shortfalls, they do notremove the requirement for a fixed quality energy outputfrom the field, in the form of tight outlet temperature con-trol (Meaburn and Hughes, 1996). The purpose of this con-trol is not to maintain a constant supply of solar producedthermal energy in the face of disturbances because this isnot a cost effective strategy; in theory, oversized collectorfields could be used with parts only operating during peri-ods of low solar radiation. Rather, the aim of a controlscheme should be to regulate the outlet temperature ofthe collector field by suitably adjusting the oil flow rate(Wettermark, 1988). This is beneficial in a number of ways.Firstly, it furnishes any available thermal energy in a usableform, i.e., at the desired operating temperature, whichimproves the overall systems efficiency and reduces thedemands placed on auxiliary equipment such as the storagetank. Secondly, the solar field is maintained in a state ofreadiness for the resumption of full scale operation whenthe intensity of sunlight rises once again; the alternativeis unnecessary shutdowns and startup procedures which

are both wasteful and time consuming. Finally, if the con-trol is good, i.e., fast and well damped, the plant can beoperated close to design limits, thereby improving the pro-ductivity (Meaburn and Hughes, 1996).

During the last 25 years considerable effort has beendevoted by many researchers to improve the efficiency ofsolar thermal power plants with distributed collectors fromthe control and optimization viewpoints. Most of the workdone and summarized in this paper has been devoted toimprove the operation of the Acurex field of the SSPS plantlocated in the PSA, Spain, which uses a parabolic troughsystems using oil as heat transfer medium because commer-cial plants for electricity production (Pilkington SolarInternational, 1996) and facilities available to tests auto-matic controllers are using this fluid. But there are alsosome recent experiences controlling parabolic trough sys-tems using water/steam as heat transfer fluid (Zarzaet al., 2001, 2004; Leon and Valenzuela, 2002; Leonet al., 2002; Eck et al., 2003; Valenzuela et al., 2003, 2004).

Currently the SSPS plant is composed of a distributedcollector field, a thermal storage system and the powerblock (Fig. 1). The distributed collector field has consti-tuted an ideal test-bed plant for control schema implemen-tation as it presents complex dynamics and strongdisturbances acting on the plant during the daily operation.The distributed collector field consists of 480 east–westaligned single axis tracking collectors made by the AcurexCorporation in the United States forming 10 parallel loopswith a total aperture mirrors area of 2672 m2. Each of theloops is formed by four twelve-module collectors, suitablyconnected in series. The loop is 172 m long, the active partof the loop measuring 142 m and the passive part 30 m.The heat transfer fluid used is Santotherm 55 thermal oil,

OILSTORAGE

TANKSOLAR COLLECTORS

FIELD

T

TIC

T

T

F T

TOSTEAM

GENERATOROR

DESALINATIONPLANT

FIC

T Temperature measurement

TIC Temperature control

F Flow measurement

FIC Flow control

Fig. 1. Schematic diagram of the Acurex solar collector field.

1242 E.F. Camacho et al. / Solar Energy 81 (2007) 1240–1251

able to support temperatures up to 300 �C, which ispumped from the bottom of a storage tank through thesolar field, where picks up the heat transferred throughthe receiver tube walls, till the top of the tank. The heatedoil stored in the tank is used to boil water that is utilized ina steam turbine to drive an electricity generator or to feed aheat exchanger of a desalination plant. The storage tankwas included in order to allow flexible electricity produc-tion and to provide a buffer between the electricity genera-tion and the fluctuating solar input (Kalt et al., 1982).

For the initial startup of the plant, the system is pro-vided with a three-way valve which allows the oil to be cir-culated in the field until the outlet temperature is adequateto enter the storage tank. The operation limits for the oilpump are between 2.0 and 12.0 l s�1. The minimum valueis there for safety and mainly to reduce the risk of the oilbeing decomposed, which happens when the oil tempera-ture exceeds 305 �C. The consequence of exceeding themaximum oil temperature is that all the oil may have tobe changed. Another important restricting element in thissystem is the difference between the field’s inlet and outletoil temperatures. A suitable or normal difference is aroundor less than 70 �C. If the difference is higher than 100 �C,then there is a significant risk of causing oil leakage dueto high oil pressure in the pipe system.

The paper is organized as follows: in Section 2, the mainfeatures of the DCS from the control point of view are out-lined. Section 3 summarizes the fundamental modeling andsimulation approaches taken by most of the authors, whileSection 4 is devoted to explain the basic control strategiesused to control DCS during the last 25 years. Finally, someconclusions are included.

2. Main features of the DCS from the control point of view

The main difference between a conventional power plantand a solar plant is that the primary energy source, whilebeing variable, cannot be manipulated. The intensity ofthe solar radiation, in addition to its seasonal and dailycyclical variations, also depends on atmospheric conditions

such as cloud cover, humidity and air transparency. Due tothis fact, a solar plant is required to cope with some prob-lems that are not encountered in other thermal powerplants. The objective of the control system in a distributedcollector field is to maintain the outlet oil temperature ofthe loop (or the highest outlet oil temperature reached byone of the collectors each sampling time) at a desired levelin spite of disturbances such as changes in the solar irradi-ance level (caused by clouds), mirror reflectivity or inlet oiltemperature. The means available for achieving this is viathe adjustment of the fluid flow and the daily solar powercycle characteristics are such that the oil flow has to changesubstantially during operation. This leads to significantvariations in the dynamic characteristics of the field, suchas the response rate and the dead time, which cause difficul-ties in obtaining adequate performance over the operatingrange with a fixed parameter controller. Thus this plantpresents some characteristics that make it difficult tocontrol:

• Nonlinearity, complexity, requiring modeling simplifica-tions, changing dynamics and changing environmentalconditions: (i) The solar radiation acts as a fast distur-bance with respect to the dominant time constant ofthe process; (ii) time varying input/output transportdelay, since the delay in action depends on the manipu-lated variable (oil flow rate); this type of delay appearsboth in the field and in the pipe connecting the loopsto the storage tank; (iii) when modeling simplificationsare done, there are strong unmodeled dynamics andthe linearized dynamics vary with the operating point;indeed, the plant is best modeled as a distributed param-eter system and, further, there are antiresonance modes(frequencies at which the magnitude of the frequencyresponse changes abruptly) in the frequency responseof the collector field within the control bandwidth, insuch a way that when the system is excited with a signal(oil flow or solar radiation) with principal frequencycomponents corresponding to those of the antiresonancemodes, variations at the system output are very small.

Table 1DCS model variables and parameters

Symbol Description Units

t Time sx Space mq Density kg m�3

c Specific heat capacity J K�1 kg�1

A Cross-sectional area m2

T(t,x) Temperature K, �Cq(t) Oil pump volumetric flow rate m3 s�1

I(t) Solar radiation W m�2

g0 Mirror optical efficiencyG Mirror optical aperture mTa(t) Ambient temperature K, �CHl Global coefficient of thermal losses W m�1 �C�1,

W m�1 K�1

Ht Coefficient of metal–fluidtransmission

W m�2 �C�1,W m�2 K�1

Di Inner diameter of the pipe line ml Tube length mTin(t) Inlet fluid temperature K, �CTout(t) Outlet fluid temperature K, �C


• Solar systems are in general expensive in terms of theenergy produced and so any improvements in perfor-mance that could be gained through the use of advancedcontrol techniques would help to present them as a via-ble alternative to conventional energy sources.

• A solar collector is essentially a very large heat exchan-ger and these types of systems are quite common in pro-cess industry, then most of the experience gained withthe control of solar collector fields can be used for other,more common, industrial processes.

These aspects render the control problem at hand a dif-ficult one and call for the use of carefully designed controlalgorithms, presenting enough robustness to cope with thehigh levels of uncertainty present in the plant. The activitiesperformed by the control groups related to this field covermodeling, identification and simulation, classical propor-tional-integral-derivative control (PID), feedforward con-trol (FF), model based predictive control (MPC),adaptive control (AC), gain-scheduled control (GS), cas-cade control (CC), internal model control (IMC), timedelay compensation (TDC), optimal control (LQG), non-linear control (NC), robust control (RC), fuzzy logic con-trol (FLC) and neural network controllers (NNC). Thebasic control approaches (PID, CC and FF) are brieflycommented in this paper within the scope of the controlof DCS, while the rest are described in the second part ofthis survey.

3. Modeling and simulation approaches

Several classifications of modeling approaches can befound in the literature, having a wide acceptance as pre-sented by Brosilow and Joseph (2002). The hierarchy ofprocess models has been used for different purposes in thistype of solar plants: control models, simulation models,setpoint optimization models, fault tolerance, etc. Modelsfor control purposes range from the simplest ones, basedon steady-state relationships or in linear low-orderapproaches, to nonlinear empirical or first principles-basedones.

In practice, the DCS has been modeled both by usingfirst principles or empirically by conducted practical tests.In this second case, when introducing a step input signalin the oil flow in an open loop configuration (reaction curvemethod) while the rest of disturbances do not experiencechanges, the response can be approximated by that of afirst order system or overdamped second order system witha delay depending on the fluid velocity (Camacho et al.,1997). This kind of step response suggests the use of loworder linear descriptions of the plant (as is usually donein the process industry) to model the system and to designdiverse control strategies. When using persistent excitationsignals (e.g. random binary sequences) or by analyticallyexamining the dynamics of the system (Meaburn andHugues, 1993b, 1995) it can be seen that the plant exhibitsa number of antiresonance modes (frequencies at which the

magnitude of the frequency response changes abruptly)within the control bandwidth. Thus, nonlinear models(both mechanistic and empirical ones) or high order linearmodels around different operating points should have to beused (Camacho et al., 1997).

3.1. Fundamental models

A distributed solar collector field, under generalassumptions and hypotheses, may be described by a dis-tributed parameter model of the temperature (Kleinet al., 1974; Rorres et al., 1980; Orbach et al., 1981; Caro-tenuto et al., 1985, 1986; Carmona, 1985; Camacho et al.,1988, 1997; Berenguel et al., 1994). The dynamics of thedistributed solar collector field are described by the follow-ing system of partial differential equations (PDE) describ-ing the energy balance:

qmcmAmoT m

otðt;xÞ¼ g0GIðtÞ�P rc�DipH tðT mðt;xÞ�T fðt;xÞÞ

qf cf Af

oT f

otðt;xÞþqfcfqðtÞ

oT f

oxðt;xÞ¼DipH tðT mðt;xÞ�T fðt;xÞÞ

ð1Þ

where the subindex m refers to the metal and that of f tothe fluid and all the parameters and variables are describedin Table 1. Prc represents the sum of radiative and conduc-tive thermal losses, that usually are modelled as a linearconductive relation of the form Hl(Tm(t,x) � Ta(t)). A sim-plified energy balance neglecting heat losses has been alsoused by several authors (e.g. Johansen and Storaa,2002a,b; Farkas and Vajk, 2002a,b,c, 2003; Silva et al.,2003a,b; Willigenburg et al., 2004a,b; etc.), described by:

AoTotðt; xÞ þ qðtÞ oT

oxðt; xÞ ¼ g0G

qcIðtÞ ð2Þ


where T(t,x) is the oil temperature at position x along thetube, with boundary condition T(t, 0) = Tin(t), Tin(t) beingthe inlet oil temperature to the distributed solar collectorfield. The objective is to control the variable Tout(t) = T(t, l)to its specified setpoint. The incoming energy depends onthe peak optical efficiency of the collectors, on the mirrorsreflectivity, on the effective reflecting surface and on theeffective irradiance onto the collector. These two last vari-ables depend on the incidence angle between solar rays andthe vector normal to the collector surface, this angle beinga function of the solar hour and date.

Both lumped and distributed parameter versions of themodels obtained from Eqs. (1) and (2) have been used bothfor control and simulation purposes, based in generalhypotheses. Depending on the applications the propertiesof the oil are considered constant or functions of the tem-perature. The development of numerical simulation modelsof the plant has played an important role in the design ofdifferent control strategies avoiding a number of expensiveand time consuming controller tuning tests at the solarpower plant.

Based on Eq. (1), a distributed parameter model of theAcurex field was developed (Carmona, 1985; Camachoet al., 1988) and implemented (Berenguel et al., 1994; Cam-acho et al., 1997) and has been used for simulation pur-poses by many researchers. Some authors have modifiedthis original simulation model or performed differentimplementations to use other numerical methods or toaccount for the dynamics of the tubes connecting the outletof the DCS with the storage tank. As shown in Rato et al.(1997a), the dynamic characteristics of a tube joining theoutput of the loops with the top of the storage tank aregiven by a gain less than one, a time constant and a vari-able delay. This approximation has been adopted in orderto modify the basic formulation of the nonlinear model toaccount for dynamic characteristics introduced by the tube.The modified model has been validated with data obtainedat the plant in closed-loop operation (Rato et al., 1997a).In Normey-Rico et al. (1998) a modification was performedto this nonlinear model of parabolic trough collectors inBerenguel et al. (1994) and Camacho et al. (1997) toinclude varying transport delay. In Meaburn (1995), amodification of the original model was also developed, asit suffered from the limitation of not being able to ade-quately represent transport delay effects and the inconve-nience of not having a steady state finder. When usingthe model for transient studies, the initial conditions arefound by simply running the model over a time to permitinitial transients to decay. To overcome this, the discretemodel equations were reformulated to provide the capabil-ity of direct calculation of steady-state conditions using animplicit trapezoidal approximation, instead of a 2-stepEuler approximation as that used by Berenguel et al.(1994) and Camacho et al. (1997).

All the mentioned models are based on standard fluidflow and thermodynamic considerations, but consideringuncompressible fluid. The effort is nowadays placed in

modeling DCS with direct steam generation. In Yebraet al. (2001, 2005) and Yebra (2006) a model is being devel-oped using the Modelica thermohydraulic libraryThermofluid.

The dynamic validation of the models has been done invarious ways. Most of the authors have used typical step-response test performed at the plant. In Meaburn andHughes (1997) dynamic validation was conducted by mak-ing a comparison between the plant and model in the fre-quency domain. The frequency response of the plant wasobtained by a Fourier analysis of measured input and out-put data during transients. The method of excitation usedwas the simple pulse test. This was chosen in preferenceto periodic signals such as the common pseudo randombinary sequence (PRBS) simply because it extracts thedynamic information very quickly. In comparison, a PRBSsignal needs to be well over an hour long to extract the rel-evant data with sufficient accuracy, suffering from the influ-ence of solar radiation drifts. In order to use PRBS typesignals, computer models have to be used, as done in Cam-acho et al. (1994b).

Eqs. (1) and (2) have also been used for control purposes(Camacho et al., 1997), in the development of feedforwardcontrollers (Rorres et al., 1980; Carotenuto et al., 1986;Rubio, 1985; Rubio et al., 1986, 2006; Camacho et al.,1992, 1997; Berenguel et al., 1994; Meaburn and Hughes,1997; Silva et al., 1998; Valenzuela and Balsa, 1998; Johan-sen and Storaa, 2002a,b), nonlinear PID controllers includ-ing a real-time numerical integration of the distributedplant model (Johansen and Storaa, 2002a,b), nonlinearmodel-based predictive controllers (Camacho and Bereng-uel, 1994b; Berenguel, 1996; Arahal et al., 1997, 1998a,b;Berenguel et al., 1997b, 1998, Pickhardt and Silva, 1998;Berenguel, 1998; Pickhardt, 1999, 2000a), internal modelcontrol (Farkas and Vajk, 2002a,b,c, 2003), time delaycompensation (Normey-Rico et al., 1998), feedback linear-izing controllers (Carotenuto et al., 1985; Barao et al.,2002; Silva et al., 2002b; Igreja et al., 2003; Cirre et al.,2005), multirate controllers (Silva et al., 2002a, 2003b)etc. (all these strategies treated in other sections) and forsetpoint optimization purposes. Rorres et al. (1980) andOrbach et al. (1981) suggested an optimal control formula-tion where the objective is to maximize net produced powerwhen the pumping power is taken into consideration. InCirre et al. (2004a,b) a compensator was introduced toautomatically compute setpoints for the whole range ofoperating conditions of the Acurex distributed solar collec-tor field, looking for the maximum achievable temperaturetaking into account operational constraints, such as themaximum constructive temperature (305 �C), the satura-tion of the control signal (oil flow between 2 and12 l s�1), the maximum temperature gradient between theinlet and outlet oil temperature (80 �C) and accountingfor the actual values of the disturbances (mainly in solarradiation, inlet oil temperature and mirrors reflectivity).An enthalpy balance is used for setpoint optimization pur-poses taking into account the mentioned aspects. The


advantages of using this kind of setpoint optimizationstrategy are evident in the starting phase of the operation,when the largest variations in the inlet oil temperatureoccurs due to the existence of cold oil within the tubesand the recirculation using the three-way valve till reachingthe minimum temperature to be entered at the top of thestorage tank.

3.2. Data-driven models

Linear black-box models have been obtained fromparameter identification by many authors for control pur-poses. Low order linear models have been commonly usedfor adaptive control purposes (Camacho et al., 1992,1994a; Camacho and Berenguel, 1997; Normey-Ricoet al., 1998; Perez de la Parte et al., 2007; Rubio et al.,2006), while high order linear models are employed for gainscheduled controllers (Camacho and Berenguel, 1993,1994a,b; Camacho et al., 1994b, 1997; Pickhardt, 1998,2000b; Rato et al., 1997b; Nenciari and Mosca, 1998;Johansen et al., 2000), all these treated in part II of thissurvey.

Regarding nonlinear models, several methodologies,among which numerous types of artificial neural networks(ANN), have been proposed for building a nonlinearmodel of the solar power plant, which consequently wasused for simulation purposes or as a core element in vari-ous model based prediction schemes. In Kalogirou (2000,2001) a comprehensive review of applications of ANN inrenewable energy systems is performed. Within the scopeof solar plants with distributed collectors, in Arahal et al.(1997, 1998b) the application of the general identificationmethodology to obtain neural predictors for use in a non-linear predictive control scheme is shown. Every step of themethodology is explained. Nonlinear autoregressive mod-els with exogenous inputs (NARX) models are used in thiswork, where several algorithms for selecting past signal val-ues as inputs are developed. Multilayer perceptrons (MLP)and radial basis functions (RBF) networks are used in thiswork, while in Arahal et al. (1998a) a comparison is donebetween different types of RBF neural networks for thesame plant. Berenguel et al. (1998) used a static neural net-work in an autoregressive configuration and proposed aselection method based on the reduction of the estimatedgradient for determining the past values that the networkneeds to construct the prediction. Pereira and Dourado(2002a,b) suggested a neuro-fuzzy system based on a radialbasis function network with support vector learning, whileHenriques et al. (2002c) used a recurrent network in com-bination with an on-line learning strategy to update boththe weights of the network and the current state. In Sbarc-iog et al. (2004) and Wyns et al. (2004), the identification ofa DCS is performed both using neural networks and phys-ical models. The nonlinear identification problem is tackledby decomposing the complex system in two main compo-nents: an active part and a passive part. For the active partof the solar power plant a model based on the parallel con-

nection of ten neural networks is built, while for the passivepart a white box model and a neural network black boxmodel are developed. All models are identified and vali-dated using measurement data. In Ionescu et al. (2004) amodel of the overall solar power plant is also developedusing neural networks, to avoid overhead generated bytraining each one of the networks presented in the workof Sbarciog et al. (2004) and Wyns et al. (2004).

4. Basic control algorithms

The control theory for linear processes has for sometime been considered a well established scientific disciplinewith powerful techniques for analyzing and designing con-trollers. The main problems in process control when apply-ing the linear control theory are caused by the fact that(Seborg, 1994, 1999): (i) a linear mathematical model ofthe plant is needed and finding one is not a trivial problemin many cases; (ii) mathematical models of real processescannot take all aspect of reality into account and simplify-ing assumptions have to be made where models are onlyapproximations of reality, (iii) most processes are nonlinearand (iv) because of changing environmental conditionsmost processes are not time invariant.

While in other power generating processes, the mainsource of energy (the fuel) can be manipulated as it is usedas the main control variable, in solar energy systems, themain source of power which is solar radiation cannot bemanipulated, acting as a disturbance when considering itfrom a control point of view. Although these types ofplants have all the characteristics needed for usingadvanced control strategies able to cope with changingdynamics, (nonlinearities and uncertainties) most of themare controlled by traditional PID controllers. As fixedPID controllers cannot cope with some of the mentionedproblems, they have to be detuned with low gain, produc-ing sluggish responses or if they are tightly tuned they mayproduce high oscillations when the dynamics of the processvary, due to environmental and/or operating conditionschanges. This is the case of distributed solar collector fields,where the use of more efficient control strategies resultingin better responses would increase the number of opera-tional hours of the field. Thus, when the control specifica-tions are very tight and the control system makes theprocess work at high frequencies, where uncertainties arehigher, or for some systems with complex dynamics thatcannot be approximated by simple linear low order models,more sophisticated or advanced control techniques areneeded, as those included in Table 2 proposed by Seborg(1994, 1999) according to their use in industry, where mostof the techniques are addressed in this article. Category I,treated in the first part of this survey, consists of standardcontrol strategies that have been widely used for severaldecades. The vast majority of automatic control loops inthe process industries (about 90%) still relay on variousforms of the ubiquitous PID controller, which has been

Table 2Classification of process control strategies according to the degree of usein industry

Category I: conventional control strategies Acronym

Manual controlPID control PIDRatio controlCascade control CCFeedforward control FF

Category II: advanced control: classical techniques

Gain scheduling GSTime delay compensation TDCDecoupling controlSelective/override controllers

Category III: advanced control: widely used techniques

Model predictive control MPCStatistical quality controlInternal model control IMCAdaptive control AC

Category IV: advanced control: newer techniques with some

industrial applications

Optimal control LQGNonlinear control NCRobust control RCNeural network controllers NNCFuzzy logic control FLCExpert systems

Category V: advanced control: proposed strategies with few (if

any) industrial applications


commercially available for over 60 years. The other catego-ries are treated in part II of this survey.

4.1. PID control (PID)

Due to the significant variations in the dynamic charac-teristics of DCS mentioned in Section 2, it is difficult toobtain a satisfactory performance over the total operatingrange with a static controller, mostly if well dampedresponses are required, due to the existence of resonancedynamics. The use of PID controllers (Fig. 2) with fixedparameters has been restricted to safe operation conditions(backup controllers) (Carmona et al., 1987; Camacho et al.,1997), but they cannot cope with nominal operation of theplant without including additional compensators in thecontrol loop (Barao, 2000; Barao et al., 2002). Even inthose cases, performance is restricted by the excitation of

Kp

Kp/Ti 1/s

1/Tt

+

-

+

+

+

+r(s) e(s)

Fig. 2. Basic PID + antiw

resonance modes, but good results have been achieved inthe reported literature both in terms of setpoint trackingand disturbance response when restricting the bandwidthof such controllers. Practically all the tested PID-basedcontrol schemes incorporate a feedforward term in the con-trol loop to account for the effect of measurable distur-bances (Camacho et al., 1992, 1997; Berenguel, 1996;Rubio et al., 2006). In Cirre et al. (2004a,b) a class ofPID structure combined with a feedforward term and ablock for automatic generation of setpoint has been satis-factory tested at the plant. Adaptive or gain schedulingPI controllers (Camacho et al., 1992, 1997; Vaz et al.,1998), switching fuzzy logic or neural network based PIDcontrollers (Cardoso et al., 1999; Henriques et al.,1999a,b; Markou and Petropoulakis, 1998), fuzzy logicPID controllers (Berenguel et al., 1997a, 1999; Stirrupet al., 2001) and robust PID controllers (Cirre et al.,2003) are good examples of this philosophy of includinga feedforward action and some kind of adaptation to plantdynamics when using PID controllers. In Vaz et al. (1998) aPID controller with gain interpolation is developed, whilein Johansen and Storaa (2002a,b) a mixed feedback/feed-forward energy based control using PID control is imple-mented in the form of a PID feedback with time-varying/nonlinear gain. These control schemes will be explainedin the part II of this survey.

4.2. Feedforward control (FF)

Feedforward controllers are extensively used in industryto correct the effect caused by external and measurable dis-turbances. The disturbances are sensed and used to calcu-late the value of the manipulated variable required tomaintain control at the setpoint (using a model of howthe disturbances affect the process). The offset resultingfrom modeling errors can be eliminated by adding feed-back. DCS suffer from changes in the received energywhich can be slow, as daily radiation variations, mirrorreflectivity changes due to accumulation of dust, etc.; orfast, mainly due to passing clouds and changes in the inletoil temperature at the starting phase of the power conver-sion system. These disturbances force the oil flow to changeproducing a variable residence time of the fluid within thefield. Feedforward has been widely used in the control ofDCS (Rorres et al., 1980; Carotenuto et al., 1986; Camachoet al., 1992, 1997; Berenguel et al., 1994; Meaburn and

ACTUATOR PLANT

+

-

v(s) u(s) y(s)

indup control scheme.

PLANTr(t)

CONTROLLER

FEEDFORWARD

CONTROLLER

+

-

u(t)

uFF(t)

++

y(t)

Input fluidtemperature

Solarradiation

PLANTr(t) CONTROLLERFEEDFORWARD

CONTROLLER

+

-

uC(t) u FF(t) y(t)

Input fluidtemperature

Solarradiation

Fig. 3. Basic feedforward control schemes (FF). (a) Parallel configuration, (b) series configuration.

Fig. 4. Solar plant output using parallel feedforward compensation.


Hughes, 1997; Silva et al., 1998; Valenzuela and Balsa,1998). Both dynamic and static feedforward terms (andalso white/black box models) have been developed in thisscope. The steady state gain of the plant, although a func-tion of the irradiance, ambient temperature, the inlet tem-perature and the volumetric flow rate, can be predictedusing simple static models of the plant (Carmona et al.,1987; Espana and Rodrıguez, 1987; Camacho et al.,1992). The most extensively used feedforward compensa-tion, both in parallel (Fig. 3(a)) and series (Fig. 3(b)) con-figurations, uses a steady-state energy balance from Eq. (1)and experimental data, derived from a correlation for theoil flow as function of the inlet and outlet oil temperatures,solar radiation, mirror reflectivity and ambient tempera-ture (Camacho et al., 1992; Valenzuela and Balsa, 1998).In both cases, the radiation and inlet oil temperature serveto directly adjust the oil flow to the values calculated tomaintain the outlet temperature at the desired level. Thisrestricts the outlet temperature excursions, which is desir-able from the control viewpoint and ensures that the outlettemperature is predominantly a function of the oil flow,which is the manipulated variable. These feedforward con-trollers have proved to be effective in many of the tests per-formed at the plant and have been used by many of thecontrol algorithms tested at the plant (Camacho et al.,1994a,b; Rubio et al., 1995; Camacho and Berenguel,1997; Ke et al., 1998; Luk et al., 1999; Cardoso et al.,1999; Stirrup et al., 2001; Johansen and Storaa, 2002a,b;etc.). Figs. 4 and 5 show experimental results obtained byCamacho et al. (1997). PID controllers combined withfeedforward controllers are also the basis of the new gener-ation of solar plants with direct steam generation (Valenzu-ela et al., 2004, 2005, 2006).

Fig. 5. Solar plant output using series feedforward compensation.


4.3. Cascade control

Cascade control is a traditional control technique aimedat cancelling the effects of the disturbances on the con-trolled output by splitting the control problem in two timescales and two control loops: an inner control loop (slave)devoted to compensate for disturbances and the outer con-trol loop (master) controlling the process output (Fig. 6).Few applications of cascade control are reported in the lit-erature and are mainly developed in the scope of the cas-cade control of a DCS for controlling the average of thetemperatures at the outlet of the loops and the temperatureof the oil entering the storage tank (Silva et al., 1997; Ratoet al., 1997a). In the inner loop an adaptive model basedpredictive controller exploiting the information conveyed

MASTER

CONTROLLER

SLAVE

CONTROLLER

r2(s) r1(s)

TS

TS2(s)

TS2(s)

Fig. 6. Multirate casc

by accessible disturbances (radiation changes and inlet oiltemperature) is used, while in the outer loop a PID isemployed. The difference in the dominant time constantsof the inner (faster) and outer (slower) control loops isexplored by employing different sampling rates in each ofthem. Cascade control has been recently used in the scopeof controlling solar plants with distributed collectors withdirect steam generation (Valenzuela et al., 2004).

5. Concluding remarks

The main features of the different modeling and basiccontrol approaches used during the last 25 years to controlDCS have been outlined. The DCS may be described by adistributed parameter model of the temperature. It iswidely recognized that the performance of PI and PID typecontrollers will be inferior to model based approaches(Camacho et al., 1997; Meaburn and Hughes, 1995). Evenwhen the plant is linearized about some operating pointand approximated by a finite dimensional model, the fre-quency response contains anti-resonance modes near thebandwidth that must be taken into consideration in thecontroller in order to achieve high performance (Meaburnand Hughes, 1993a, 1995). Thus, the ‘‘ideal’’ controllershould be high-order and nonlinear. The control tech-niques outlined in this paper range from the simplest onestreated in the first part of the survey to others with highcomplexity studied in the second part, trying to find atrade-off between commissioning time and performance.

Acknowledgements

The authors thank CICYT and FEDER for partiallyfunding this work under grants DPI2001-2380-CO2,DPI2002-04375, DPI2004-07444-C04-01/04, DPI2004-06419 and by the Consejerıa de Innovacion, Ciencia y Em-presa de la Junta de Andalucıa. The experiments describedin this paper were also performed within the projects‘‘Enhancement and Development of Industrial Applica-tions of Solar Energy Technologies’’, supported by EECProgram ‘‘Human Capital and Mobility – Large Installa-tions Program’’, EC-DGS XII Program ‘‘Training andMobility of Researchers’’ and EC-DGS XII program‘‘Improving Human Potential’’ and promoted by CIEMAT– PSA, Spain. This work has been also performed withinthe scope of the specific collaboration agreement betweenthe PSA and the Automatic Control, Electronics andRobotics (TEP-197) research group of the Universidad de

COLLECTOR

FIELD

y2(t)STORAGE TANK

INLET

y1(t)

1(s)

TS1(s)

ade control (CC).


Almerıa titled ‘‘Development of control systems and toolsfor thermosolar plants’’.

References

Arahal, M.R., Berenguel, M., Camacho, E.F., 1997. Nonlinear neuralmodel-based predictive control of a solar plant. In: Proceedings of theEuropean Control Conference ECC’97. Brussels, Belgium, TH-E I2,Paper 264.

Arahal, M.R., Berenguel, M., Camacho, E.F., 1998a. Comparison of RBFalgorithms for output temperature prediction of a solar plant. In:Proceedings of the CONTROLO’98, Coimbra, Portugal, 9–11September.

Arahal, M.R., Berenguel, M., Camacho, E.F., 1998b. Neural identifica-tion applied to predictive control of a solar plant. Control Eng.Practice 6, 333–344.

Barao, M., 2000. Dynamics and nonlinear control of a solar collector field.PhD Thesis, Universidade Tecnica de Lisboa, Instituto SuperiorTecnico, Lisboa, Portugal (in Portuguese).

Barao, M., Lemos, J.M., Silva, R.N., 2002. Reduced complexity adaptivenonlinear control of a distributed collector solar field. J. ProcessControl 12 (1), 131–141.

Berenguel, M., Camacho, E.F., Rubio, F.R., 1994. Simulation softwarepackage for the Acurex field. Dpto. de Ingenierıa de Sistemas yAutomatica, ESII Sevilla, Spain, Internal Report. www.esi2.us.es/~rubio/libro2.html.

Berenguel, M., 1996. Contributions to the Control of Distributed SolarCollectors. PhD Thesis, Universidad de Sevilla, Spain (in Spanish).

Berenguel, M., Camacho, E.F., Rubio, F.R., Luk, P.C.K., 1997a.Incremental fuzzy PI control of a solar power plant. IEE Proc.Control Theory Appl. (Part D) 144 (6), 596–604.

Berenguel, M., Arahal, M.R., Camacho, E.F., 1997b. Modeling freeresponse of a solar plant for predictive control. In: Proceedings of the11th IFAC Symposium on Systems Identification SYSID’97.Fukuoka, Japan, vol. 3, pp. 1291–1296.

Berenguel, M., 1998. Some control applications to solar plants. In:Proceedings of the International Workshop on Constrained ControlSystems. DAS-CTC-UFSC-NECCOSYDE, Florianopolis, SC, Brasil.

Berenguel, M., Arahal, M.R., Camacho, E.F., 1998. Modeling freeresponse of a solar plant for predictive control. Control Eng. Practice6, 1257–1266.

Berenguel, M., Rubio, F.R., Camacho, E.F., Gordillo, F., 1999.Techniques and applications of fuzzy logic control of solar powerplants. In: Leondes, C.T. (Ed.), . In: Fuzzy Theory Systems Techniquesand Applications, vol. 2. Academic Press (25).

Brosilow, C., Joseph, B., 2002. Techniques of Model-Based Control.Prentice Hall.

Camacho, E.F., Rubio, F.R., Gutierrez, J.A., 1988. Modeling andsimulation of a solar power plant with a distributed collector system.In: Proceedings of the International IFAC Symposium on PowerSystems Modeling and Control, Brussels, Belgium.

Camacho, E.F., Rubio, F.R., Hugues, F.M., 1992. Self-tuning control of asolar power plant with a distributed collector field. IEEE ControlSystems Magazine, 72–78.

Camacho, E.F., Berenguel, M., 1993. Application of generalized predictivecontrol to a solar power plant. In: Proceedings of the EC Esprit/CIMCIDIC Conferences on Advances in Model-Based Predictive Control,Oxford, UK, vol. 2, pp. 182–188.

Camacho, E.F., Berenguel, M., 1994a. Application of generalized predic-tive control to a solar power plant. In: Clarke, D.W. (Ed.), Advancesin Model-Based Predictive Control. Oxford University press.

Camacho, E.F., Berenguel, M., 1994b. Application of generalized predic-tive control to a solar power plant. In: The Third IEEE Conference onControl Applications, vol. 3, Glasgow, UK, pp. 1657–1662.

Camacho, E.F., Berenguel, M., Bordons, C., 1994a. Adaptive generalizedpredictive control of a distributed collector field. IEEE Trans. ControlSystems Technol. 2 (4), 462–467.

Camacho, E.F., Berenguel, M., Rubio, F.R., 1994b. Application of a gainscheduling generalized predictive controller to a solar power plant.Control Eng. Practice 2 (2), 227–238.

Camacho, E.F., Berenguel, M., 1997. Robust adaptive model predictivecontrol of a solar plant with bounded uncertainties. Int. J. AdaptiveControl Signal Process. 11 (4), 311–325.

Camacho, E.F., Berenguel, M., Rubio, F.R., 1997. Advanced Control ofSolar Plants. Springer-Verlag.

Cardoso, A.L., Henriques, J., Dourado, A., 1999. Fuzzy supervisor andfeedforward control of a solar power plant using accessible disturbances.In: European Control Conference ECC99, Karlsruhe, Germany.

Carmona, R., 1985. Analysis, Modeling and Control of a DistributedSolar Collector Field with a One-axis Tracking System. PhD Thesis,University of Seville, Spain (in Spanish).

Carmona, R., Aranda, J.M., Silva, M., Andujar, J.M., 1987. Regulationand automation of the SSPS-DCS ACUREX field of the PSA. ReportNo. R-15/87, PSA.

Carotenuto, L., La Cava, M., Raiconi, G., 1985. Regular design for thebilinear distributed parameter of a solar power plant. Int. J. SystemsSci. 16, 885–900.

Carotenuto, L., La Cava, M., Muraca, P., Raiconi, G., 1986. Feedforwardcontrol for the distributed parameter model of a solar power plant.Large Scale Systems 11, 233–241.

Cirre, C.M, Moreno, J.C., Berenguel, M., 2003. Robust QFT control of asolar collectors field. In: Martınez, D. (Ed.), IHP Programme.Research Results at PSA within the Year 2002 Access Campaign,CIEMAT.

Cirre, C.M., Valenzuela, L., Berenguel, M., Camacho, E.F., 2004a. Acontrol strategy integrating automatic setpoint generation and feed-forward control for a distributed solar collector field. In: Proceedingsof the XIV Jornadas de Automatica, Leon, Spain (in Spanish).

Cirre, C.M., Valenzuela, L., Berenguel, M., Camacho, E.F., 2004b. Controlof solar plants with automatic setpoint computation. RIAI: RevistaIberoamericana de Automatica e Informatica Industrial 1, 56–66.

Cirre, C.M., Valenzuela, L., Berenguel, M., Camacho, E.F, 2005.Feedback linearization control for a distributed solar collector field.16th IFAC World Congress, Prague.

Eck, M., Zarza, E., Eickhoff, M., Rheilander, J., Valenzuela, L., 2003.Applied research concerning the direct steam generation in parabolictroughs. Solar Energy 74, 341–351.

Espana, M.D., Rodrıguez, V.L., 1987. Approximate steady-state modelingof a solar trough collector. Solar Energy 37 (6), 447–545.

Farkas, I., Vajk, I., 2002a. Experiments with internal model-basedcontroller for Acurex field. In: Martınez, D. (Ed.), Proceedings ofthe 2nd Users Workshop IHP Programme, CIEMAT.

Farkas, I., Vajk, I., 2002b. Internal model-based controller for a solarplant. In: Proceedings of the 15th IFAC World Congress, Barcelona,Spain.

Farkas, I., Vajk, I., 2002c. Modeling and control of a distributed solarcollector field. In: Proceedings of the Energy and EnvironmentCongress, Opatija, Croatia.

Farkas, I., Vajk, I., 2003. Experiments with robust internal model-basedcontroller for Acurex field. In: Martınez, D. (Ed.), IHP Programme.Research Results at PSA within the Year 2002 Access Campaign,CIEMAT.

Henriques, J., Cardoso, A., Dourado, A., 1999a. Supervision and c-meansclustering of PID controllers for a solar power plant. Int. J.Approximate Reasoning 22 (1–2), 73–91.

Henriques, J., Cardoso, A., Dourado, A., 1999b. Fuzzy supervisor of PIDcontrollers for the Acurex Field at PSA In: Martınez, D. (Ed.),Proceedings of the 2nd Users Group TMR Programme at PSA,CIEMAT.

Henriques, J., Gil, P., Dourado, A., 2002c. Neural output regulation for asolar power plant. In: Proceedings of the 15th IFAC World Congress,Barcelona, Spain.

Igreja, J.M., Lemos, J.M., Barao, M., Silva, R.N., 2003. Adaptivenonlinear control of a distributed collector solar field. In: Proceedingsof the European Control Conference ECC03, Cambridge, UK.

http://www.esi2.us.es/~rubio/libro2.html.

http://www.esi2.us.es/~rubio/libro2.html.


Ionescu, C., Wyns, B., Sbarciog, M., Boullart, L., De Keyser, R., 2004.Comparison between physical modeling and neural network modelingof a solar power plant. In: Proceedings of the IASTED InternationalConference on Applied Simulation and Modeling ASM’04, Rhodes,Greece, June 28–30.

Johansen, T.A., Hunt, K.J., Petersen, I., 2000. Gain-scheduled control ofa solar power plant. Control Eng. Practice 8 (9), 1011–1022.

Johansen, T.A., Storaa, C., 2002a. Energy-based control of a distributedsolar collector field. Automatica 38 (7), 1191–1199.

Johansen, T.A., Storaa, C., 2002b. An internal energy controller fordistributed solar collector fields. In: Martınez, D. (Ed.), Proceedings ofthe 2nd Users Workshop IHP Programme, CIEMAT.

Kalogirou, S.A., 2000. Applications of artificial neural-networks forenergy systems. Appl. Energy 67, 17–35.

Kalogirou, S.A., 2001. Artificial neural networks in renewable energysystems applications: a review. Renew. Sustain. Energy Rev. 5, 373–401.

Kalt, A., Loosme, M., Dehne, H., 1982. Distributed Collector SystemPlant Construction Report, IEA/SSPS Operating Agent DFVLR,Cologne.

Ke, J.Y., Tang, K.S., Man, K.F., Luk, P.C.K., 1998. Hierarchical geneticfuzzy controller for a solar power plant. In: Proceedings of the IEEEInternational Symposium on Industrial Electronics ISIE’98, SouthAfrica, vol. 2, pp. 584–588.

Klein, A.A., Duffie, J.A., Beckman, W.A., 1974. Transient considerationsof flat-plate solar collectors. Trans. ASME J. Eng. Power 96A, 109.

Larsen, M., 1987. Performance of the coolidge solar thermal electricpower plant. J. Solar Energy Eng. 109, 2–8.

Leon, J., Valenzuela, L., 2002. DISS Project. Results of three yearsoperating a thermal solar plant with parabolic collectors for directsteam production. In: Proceedings XI Congreso Iberico-VI CongresoIberoamericano de Energıa Solar, Vilamoura-Algarve, Portugal,September 29–October 2 (in Spanish).

Leon, J., Zarza, E., Valenzuela, L., Hennecke, K., Weyers, H.D., Eickhoff,M., 2002. Direct steam generation. Three years of operation of DISSproject. In: Proceedings of the International Symposium on Concen-trated Solar Power and Chemical Energy Technologies, Zurich,September 4–6.

Luk, P.C.K., Low, K.C., Sayiah, A., 1999. GA-based fuzzy logic controlof a solar power plant using distributed collector fields. Renew. Energy16 (1–4), 765–768.

Markou, H., Petropoulakis, L, 1998. PID-Type Fuzzy control of theAcurex solar collector field. In: Martınez D. (Ed.) Proceedings of the2nd users Workshop. TMR Programme at PSA, Ed. CIEMAT, pp.37–42.

Meaburn, A., Hughes, F.M., 1993a. A control technique for resonancecancellation. In: Proceedings of the IEE Colloquium on NonlinearControl using Structural Knowledge of System Models, London.

Meaburn, A., Hugues, F.M., 1993b. Resonance characteristics of distrib-uted solar collector fields. Solar Energy 51 (3), 215–221.

Meaburn, A., Hughes, F.M., 1995. A pre-scheduled adaptive controlscheme based upon system knowledge. In: Proceedings of the IEEColloquium on Adaptive Controllers in Practice – Part One, pp. 6/1–6/5.

Meaburn, A., 1995. Modeling and control of a distributed solar collectorfield. PhD Thesis, The Department of Electrical Engineering andElectronics, UMIST, UK.

Meaburn, A., Hughes, F.M., 1996. A simple predictive controller for useon large scale arrays of parabolic trough collectors. Solar Energy 56(6), 583–595.

Meaburn, A., Hughes, F.M., 1997. Feedforward control of solar thermalpower plants. J. Solar Energy Eng. 119 (1), 52–60.

Nenciari, G., Mosca, E., 1998. Supervised multicontrollers for tempera-ture regulation of a distributed collector field. In: Martınez, D. (Ed.),Proceedings of the 1st Users Group TMR Programme at PSA,CIEMAT.

Normey-Rico, J., Bordons, C., Berenguel, M., Camacho, E.F., 1998. Arobust adaptive dead-time compensator with application to a solar

collector field. In: Proceedings of the IFAC Linear Time DelaySystems Workshop, Grenoble, France, pp. 105–110.

Orbach, A., Rorres, C., Fischl, R., 1981. Optimal control of a solarcollector loop using a distributed-lumped model. Automatica 27 (3),535–539.

Pereira, C., Dourado, A., 2002a. Application of a neuro-fuzzy networkwith support vector learning to a solar power plant. In: Proceedings ofthe 15th IFAC World Congress, Barcelona, Spain.

Pereira, C., Dourado, A., 2002b. Application of a neuro-fuzzy networkwith support vector learning to a solar power plant. In: Martınez, D.(Ed.), Proceedings of the 2nd users workshop IHP Programme,CIEMAT.

Perez de la Parte, M., Cirre, C.M., Camacho, E.F., Berenguel, M., 2007.Application of predictive sliding mode controllers to a solar plant.IEEE Transactions on Control Systems Technology (submitted forpublication).

Pickhardt, R., 1998. Application of adaptive controllers to a solar powerplant using a multi-model description. In: Proceedings of the AmericanControl Conference, Albuquerque, NM.

Pickhardt, R., Silva, R.N., 1998. Application of a nonlinear predictivecontroller to a solar power plant. In: Proceedings of the 1998 IEEEInternational Conference on Control Applications, Glasgow, UK, vol.1, pp. 6–10.

Pickhardt, R., 1999. Results of the application of adaptive controllers tothe Acurex field. In: Martınez, D. (Ed.), Proceedings of the 2nd usersgroup TMR Programme at PSA, CIEMAT.

Pickhardt, R., 2000a. Nonlinear modeling and adaptive predictive controlof a solar power plant. Control Eng. Practice 8 (8), 937–947.

Pickhardt, R., 2000b. Adaptive control of a solar power plant using amulti-model control. IEE Proc. Theory Appl. 147 (5), 493–500.

Pilkington Solar International, 1996. Status Report on Solar ThermalPower Plants, Cologne, Germany.

Price, H., Kearney, D., Replogle, I., 1990. Update on the performance andoperation of SEGS III–VII. In: ASME International Solar EnergyConference, Miami.

Rato, L., Silva, R.N., Lemos, J.M., Coito, F., 1997a. MultirateMUSMAR cascade control of a distributed solar field. In: Proceedingsof the European Control Conference ECC’97, Brussels, Belgium.

Rato, L., Borelli, D., Mosca, E., Lemos, J.M., Balsa, P., 1997. MUSMARbased switching control of a solar collector field. In: Proceedings of theEuropean Control Conference ECC’97, Brussels, Belgium.

Rorres, C., Orbach, A., Fischl, R., 1980. Optimal and suboptimal controlpolicies for a solar collector system. IEEE Trans. Automatic ControlAC-25, 1085–1091.

Rubio, F.R., 1985. Adaptive control of industrial processes. Applicationto a Solar Plant. PhD Thesis, University of Seville, Spain (in Spanish).

Rubio, F.R., Carmona, R., Camacho, E.F., 1986. Adaptive control of theACUREX field. In: Kesserlring, P., Selvage, C.S. (Eds.), . In: The IEA/SSPS Solar Thermal Power Plants, vol. 2. Springer-Verlag, pp. 5.2.1–5.2.18.

Rubio, F.R., Berenguel, M., Camacho, E.F., 1995. Fuzzy logic control ofa solar power plant. IEEE Trans. Fuzzy Systems 3 (4), 459–468.

Rubio, F.R., Camacho, E.F., Berenguel, M., 2006. Control de campos decolectores solares. RIAI – Revista Iberoamericana de Automatica eInformatica Industrial 3 (4), 26–45.

Sbarciog, M., Wyns, B., Ionescu, C., De Keyser, R., Boullart, L., 2004.Neural networks models for a solar plant. In: Proceedings of 2ndIASTED Conference on Neural Networks and Computational Intel-ligence (NCI2004), Grindelwald, Switzerland.

Schraub, F.A., Dehne, H., 1983. Electric generation system design:management, startup and operation of IEA distributed collector solarsystem in Almerıa, Spain. Solar Energy 31 (4), 351–354.

Seborg, D.E., 1994. A perspective on advanced strategies for processcontrol, In: Balchen, J.G. (Ed.), Modeling, identification and Control.A Norwegian Research Bulletin, 15(3), 1994.

Seborg, D.E., 1999. A perspective on advanced strategies for processcontrol (Revisited). In: Frank, P.M. (Ed.), Advances in Control-Highlights of ECC’99. Springer, 1999.


Silva, R.N., Rato, L.M., Lemos, J.M., Coito, F., 1997. Cascade control ofa distributed collector solar field. J. Process Control 7 (2), 111–117.

Silva, R.N., Filatov, N., Lemos, J.M., Unbehauen, H., 1998. Feedback/feedforward dual adaptive control of a solar collector field. In:Proceedings of the IEEE International Conference on ControlApplications, Glasgow, UK, 1998. vol. 1, pp. 309 –313.

Silva, R.N., Rato, L.M., Lemos, J.M., 2002a. Observer based time warpedcontrol of distributed collector solar fields. In: Martınez, D. (Ed.),Proceedings of the 2nd Users Workshop IHP Programme, CIEMAT.

Silva, R.N., Rato, L.M., Barao, L.M., Lemos, J.M., 2002b. A physicalmodel based approach to distributed collector solar field control. In:Proceedings of the American Control Conference, Anchorage, AKMay 8–19, pp. 3817–3822.

Silva, R.N., Rato, L.M., Lemos, J.M., 2003a. Time scaling internal statepredictive control of a solar plant. Control Eng. Practice 11 (12), 1459–1467.

Silva, R.N., Lemos, J.M., Rato, L.M., 2003b. Variable sampling adaptivecontrol of a distributed collector solar field. IEEE Control. SystemsTechnol. 11 (5), 765–772.

Stirrup, R., Loebis, D., Chipperfield, A.J., Tang, K.S., Kwong, S., Man,K.F., 2001. Gain-scheduled control of a solar power plant using ahierarchical MOGA-tuned fuzzy PI-controller. In: Proceedings of theISIE 2001, the IEEE International Symposium on Industrial Elec-tronics, vol. 1, pp. 25–29.

Valenzuela, L., Balsa, P., 1998. Series and parallel feedforward controlschemes to regulate the operation of a solar collector field. In:Martınez, D. (Ed.), Proceedings of the 2nd Users Workshop. TMRProgramme at PSA, CIEMAT, Almerıa (Espana), pp. 19–24.

Valenzuela, L., Zarza, E., Berenguel, M., Camacho, E.F., 2003. Controlconcepts for direct steam generation process in parabolic troughs. In:Proceedings of the ISES Solar World Congress, Goteborg, Sweden,June 14–19.

Valenzuela, L., Zarza, E., Berenguel, M., Camacho, E.F., 2004. Directsteam generation in solar boilers. IEEE Control Systems Magazine 24(2), 15–29.

Valenzuela, L., Zarza, E., Berenguel, M., Camacho, E.F., 2005. Controlconcepts for direct steam generation in parabolic troughs. SolarEnergy 78, 301–311.

Valenzuela, L., Zarza, E., Berenguel, M., Camacho, E.F., 2006. Controlschemes for direct steam generation in parabolic solar collectors underrecirculation operation mode. Solar Energy 80, 1–17.

Vaz, F., Oliveira, R., Silva, R.N., 1998. PID control of a solar plant withgain interpolation. In: Martınez, D. (Ed.), Proceedings of the 2ndUsers Group TMR Programme at PSA, CIEMAT.

Wettermark, G., 1988. Performance of the SSPS solar power plants atAlmerıa. J. Solar Energy Eng. 110, 235–246.

Willigenburg, L.G., van, Bontsema, J., De Koning, W.L., Valenzuela, L.,Cirre, C.M., 2004a. Direct reduced-order digital control of a solarcollector field. In Martınez D. (Ed.), IHP Programme. ResearchResults at PSA within the Year 2003 Access Campaign, CIEMAT.

Willigenburg, L.G. van Bontsema, J., De Koning, W.L., Valenzuela, L.,Cirre, C.M., 2004b. Digital optimal reduced-order control of a solarpower plant. In: Proceedings of the UKACC-IEE CONTROL 2004,University of Bath, UK.

Wyns, B., Sbarciog, M., Ionescu, C., Boullart, L., De Keyser, R., 2004.Neural network modeling versus physical modeling application to asolar power plant. In: Martınez D (Ed.), IHP Programme. ResearchResults at PSA within the Year 2003 Access Campaign, CIEMAT.

Yebra, L.J., Dormido, S., Berenguel, M., Zarza, E., 2001. Modeling andsimulation of a parabolic collector field. In: Workshop en Metod-ologıas de Modelado y Simulacion de Sistemas, Barcelona (inSpanish).

Yebra, L.J., Berenguel, M., Dormido, S., 2005. Extended MovingBoundary Models for Two-Phase Flows, 16th IFAC World Congress,Praha, 2005.

Yebra, L.J., 2006. Object-oriented modelling of parabolic DCS usingModelica. PhD Thesis, National University of Distance Education(UNED), Madrid, Spain (in Spanish).

Zarza, E., 1991. Solar Thermal Desalination Project. First phase results &second phase description. Coleccion Documentos CIEMAT.

Zarza, E., Valenzuela, L., Leon, J., Hennecke, K., Eck, M., Weyers, H.D.,Eickhoff, M., 2001. The DISS project: Direct steam generation inparabolic troughs. Operation and maintenance experience & Updateon project status. In: Proceedings of ASME International Solar EnergyConference: Forum 2001. Washington, DC, USA, 21–25 April.

Zarza, E., Valenzuela, L., Leon, J., Hennecke, K., Weyers, H.D., Eickhoff,M., 2002a. Direct steam generation in parabolic troughs. Final resultsand conclusions of the DISS project. In: Proceedings of the 11thSolarPaces International Symposium on Concentrated Solar Powerand Chemical Energy Technologies, Zurich, September 4–6.

Zarza, E., Valenzuela, L., Leon, J., Weyers, H.D., Eickhoff, M., Eck, M.,Hennecke, K., 2002b. The DISS project: direct steam generation inparabolic trough systems. Operation & Maintenance experience andupdate on project status. J. Solar Energy Eng.-Trans. ASME 124, 126–133.

Zarza, E., Valenzuela, L., Leon, J., Hennecke, K., Eck, M., Weyers, H.D.,Eickhoff, M., 2004. Direct steam generation in parabolic troughs: finalresults and conclusions of the DISS project. Energy 29, 635–644.

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