An adaptive control application in a large thermal combined power plant

www.elsevier.com/locate/enconman

Energy Conversion and Management 48 (2007) 174–183

An adaptive control application in a largethermal combined power plant

_Ilhan Kocaarslan *, Ertugrul Cam

Kırıkkale University, Faculty of Engineering, Department of Electrical and Electronics, 71451, Campus, Kırıkkale, Turkey

Received 23 March 2005; received in revised form 27 October 2005; accepted 26 April 2006Available online 7 August 2006

Abstract

In this paper, an adaptive controller was applied to a 765 MW large thermal power plant to decrease operating costs, increase qualityof generated electricity and satisfy environmental concerns. Since power plants may present several operating problems such as distur-bances and severe effects at operating points, design of their controllers needs to be carried out adequately. For these reasons, first, areduced mathematical model was developed under Computer Aided Analysis and Design Package for Control, (CADACS), so thatthe results of the experimental model have briefly been discussed. Second, conventional PID and adaptive controllers were designedand implemented under the real-time environment of the CADACS software. Additionally, the design of the adaptive model-referenceand conventional PID controllers used in the power plant for real-time control were theoretically presented. All processes were realized inreal-time. Due to safety restrictions, a direct connection to the sensors and actuators of the plant was not allowed. Insted a coupling tothe control system was realized. This offers, in addition, the usage of the supervisiory functions of an industrial process computer system.Application of the controllers indicated that the proposed adaptive controller has better performances for rise and settling times of elec-trical power, and enthalpy outputs than the conventional PID controller does.� 2006 Elsevier Ltd. All rights reserved.

Keywords: Energy application; Power plant; Real-time application; Model reference adaptive control

1. Introduction

Power-plant control has been developed over severaldecades, thanks to the contribution of practical engineersworking for manufacturers, control suppliers and utilitycompanies. In recent years, because of enhanced environ-mental awareness and requirements for the most economi-cally possible operation of power plants, the contextconditions have drastically been changing for the energymarket, particularly in the electrical field [1]. Also, thestrategies concerning operation, control and managementof power generation are going to be influenced stronglyby these factors [2,3]. For these reasons, the use of modern

0196-8904/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.enconman.2006.04.014

* Corresponding author. Tel.: +90 318 357 35 71x1245; fax: +90 318 35724 59.

E-mail addresses: [email protected], [email protected](_I. Kocaarslan).

control concepts has improved the system control quality.The dynamic behaviour of power plants heavily dependson inner and outer disturbances at setpoint. This is espe-cially the case for large coal-fired power plants. Boilerdynamics exhibit dramatic changes at low loads [4]. Fromthe control engineering point of view, such a plant repre-sents a time-variant and nonlinear multivariable processwith strong interactions and, hence, is very difficult to con-trol [5–7]. The main input variables of a fossil power plantare the flow rates of fuel, feed water, injection water andair, while the main output variables are electrical power,steam enthalpy after evaporation which in turn is a func-tion of steam temperature and steam pressure and the com-position of the combustion gas (see Fig. 1).

Conventional fossil fired units of large size have in thelast few years seen dramatic changes of the context condi-tions. It is a fact that major control equipment vendors andresearch departments are devising solutions which pay

mailto:[email protected]

mailto:[email protected]

Combustion Gas

Steam Pressure

SteamTemperature

Electrical Power

PowerPlant

Fuel Feed

Feed Water Flow

Injection Water Flow

Air Flow

Fig. 1. Power plant as a multivariable dynamic system.

_I . Kocaarslan, E. Cam / Energy Conversion and Management 48 (2007) 174–183 175

much more attention to issues like management, cost cut-ting, fuel conservation and economic optimization in thefield of power generation [2]. Due to changes of powerdemands, quality differences of the coal types used andthe contamination of the boiler heating surfaces, conven-tional three-term PID-control schemes will not reach a highdegree of control performance [3]. Since the dynamicbehaviour of a power plant is usually nonlinear, time-var-iant and governed by strong cross coupling of the inputvariables even for a reduced mathematical model, specialcare has to be taken for the design of the correspondingcontrollers. Power plant engineers tried to cover theseproblems by introducing decoupling networks, severaltypes of disturbances and setpoint feed-forward controlschemes [4] as well as parametric and structural changesin the control algorithms, but in practice especially theentalphy value in boiler control exhibits a poor sluggishdynamic behaviour. On the other hand, the growing needsof complex, huge, modern and combined power plantsrequire optimum and flexible operation. Due to the effectsdiscussed above and the expected economical benefits, animprovement in once-through boiler control is necessary.To utilize the heat energy released by burning coal withvery little loss and to meet variations in energy outputrequirements, modern adaptive control concepts shouldbe applied [8]. During the past decades, much researchon improving the theory of adaptive control with respectto practical needs has been carried out. Robustification[9–13] and simplification in the operating [10] of model ref-erence adaptive control algorithms rendered possibility oftheir implementation and application in a industrial envi-ronment. Especially in Refs. [12,13], outputs of the systemwere investigated taking stepwise increase of the electricoutput setpoint w1 by 100–130 MW and 150 MW, respec-tively. However, in this study a stepwise decrease of theelectric output setpoint w1 by 150 MW was considered. Itwas seen that with decreasing the setpoint, the outputs ofthe system have more rapid responses than the others have.Boiler dynamics is just the basis of control-system structur-ing, since it explains why the fundamental physical pro-cesses that develop in power-generation plants determinethe relevant interactions among input and output variables,and the nature of the basic dynamic relationships and ofthe principal nonlinearities [4]. In the case of boiler control,starting from the experimental modelling [9], several adap-

tive control schemes have been evaluated using simulationstudies [10].

In this study, because of the reasons mentioned above, anadaptive reference control technique was applied to a765 MW large combined thermal power plant in real-timeto reduce cost of electricity generation and increase eco-nomical life of machines and parts in power plants. Thecontrol algorithm was improved under real-time environ-ment of the CADACS software [14]. Since there were somesafety restrictions to connect the controllers and systemdirectly, a coupling to the process control system wasrealized to offer an additional usage of the supervisory func-tions of an industrial process computer system. Moreover, acomparative study was carried out using the conventionalPID controller and the proposed adaptive controller. Basedon the study results the control scheme described here wasproposed and implemented for real-time control.

2. Power plant

In coal-fired units, the furnace needs to be operatedslightly below the atmospheric pressure to minimize thesoot dispersion to the environment; in those cases, furnacepressure requires careful control, integrated properly withcombustion control [4]. The investigated plant representsa 765 MW combinational block consisting of a generator/steam turbine unit providing 652.5 MW electrical powerdue to a coal fired once-through boiler with live steam at195 bar and 535 �C and another generator/gas turbine unitproviding 112.5 MW electrical power. Pulverized coal is fedto 32 burners which are arranged in four layers. It is neces-sary that air for the combustion is supplied by ventilators.The outlet gases of the turbine are used as heat and oxygencarrier for the succeeding steam boiler. In order to avoidexcess air within the furnace for working points between30% and 55% of the full power, the gas turbine outlet gasesare deviated and added finally before the intermediatesuperheater. The power plant consists of a boiler, a turbineand a generator. The boiler can be modelled by a stronglycoupled multivariable system. This makes it very interestingfrom a control engineering point of view. In the boiler, thechemical energy is converted to thermal energy (i.e. steam).The dynamic behaviour of a boiler is heavily dependent onmany different operating conditions, as follows:

• changes of the quality and thus calorific value of thecoal, which results in changes in the enthalpy and pres-sure of the live steam as well as that of the powergenerator,

• decreasing efficiency of the coal feeder in time,• changes in the system dynamic due to the drying of heat-

ing surfaces, burners, feeders etc. causes,• changes in the operating point because of the changes in

the fence variables and load changes,• variable boiler dynamics due to the climatic situations

such as changes in the outlet temperature of the gas tur-bine in a combinational power station block

Wmu•

≡2 hy ≡2

electrical power coal feed

Feedwater flow

mathematical model

LPy ≡1

enthalpy

Bmu•

≡1

LPy ≡1

5

34

hy ≡2

12

Gas turbine input

Bmu•

≡

1u2u

Wmu•

≡

1: Generator; 2: Turbine; 3: Valve; 4: Superheater; 5: Boiler

Fig. 2. Schematic diagram (a) of a combi-power plant and (b) thecorresponding mathematical model.

176 _I. Kocaarslan, E. Cam / Energy Conversion and Management 48 (2007) 174–183

Therefore, the dynamic and static properties of the sys-tem must be well known to design an efficient controller.On the other hand, to handle such a complex system withseveral inputs and outputs is complicated. Therefore, themost important input and output variables will be usedfor model building as shown in Fig. 2. For the investigatedpower plant two input and output variables are sufficientfor describing the desired process behaviour. As shown inFig. 2, the coal feed and feedwater flow are chosen as inputvariables. The output variables are electrical power andenthalpy.

The choice of these variables may be justified as follows:the power plant operates in natural balanced pressuremode. By this operation the heat storage of the boilercan not be used, because the speed of changing powerdepends only on the steam generator. This means that

electrical power

reference

-

PT2

1

10.25

+

–

PID

PI

Decouplingcontroller

controller

2 DT +

PT2

-

enthalpy

reference

Fig. 3. Structure of the conventional c

the steam generation influences immediately the generatedelectrical power, which is important for the user. Theenthalpy of the steam at the outlet of the evaporator seemsto be the best measure for steam quality, due to its fastreaction to heating disturbances. Further it is not affectedby injection water. After deciding the input and outputvariables, measurements were made in the power plant.Due to lack of physical properties the dynamic behaviourcannot be determined by theoretical calculations [15]. Mea-surements were made such that the effect of each input onthe electrical power and enthalpy was determined. When astep change is made for one input, the other is kept con-stant. The results of parameter estimation and a discussionof the different models have been in depth covered byUnbehauen and Kocaarslan [12].

3. Controllers

Developments in the control area have been increasedby three major needs: the need to deal with increasinglycomplex systems, the need to accomplish increasinglydemanding design requirements, and the need to attainthese requirements with less precise advanced knowledgeof the plant and environment [16]. Increasingly complexdynamic systems with significant uncertainity have led toa surge in the development of new control strategies [17].In this study, various controllers were applied for compar-ison with the proposed power plant.

3.1. Conventional controller

The conventional PI-PID control scheme is illustrated inFig. 3.

In this control structure, the electrical power is con-trolled by the PIDT controller. This is affected via the fuelsupply, the controller at the same time influencing the feed-water supply through a PT2 delay element. This can be con-sidered as a decoupling element. Further, the setpoint ofthe electrical power is feed forwarded via a 2DT + DT2

s

s

120

60

network

DT2

power

plant

-

electrical power

enthalpy

ontroller with decoupling network.

T(z) +

-

1/R(z)UI(z)

1/(1-z-1)j U(z) G(z)

Z(z)

S(z)

W(z)

Controller

Y(z)

Fig. 4. Block diagram of the basic control system.


element to the fuel supply; these elements serve to optimizethe control. The enthalpy is controlled by the PI controlleris again feedforwarded to the fuel supply.

3.2. The adaptive control algorithm

In the phase of planning the pilot installation of theadaptive control scheme for the boiler, several simulationstudies have been carried out [9] for different types of SISOand MIMO control algorithms. As a result of these studies,the plant could be handled well enough by two SISO adap-tive controllers, one for power control using the coal feedas manipulated value and the other for controlling theenthalpy value by actuating on the feed water. The choicemeets the demands of the operating personal and fits wellinto the structure of the existing conventional controlequipment.

3.2.1. The linear control law

Neglecting any disturbance and in the case of a dead-time Tt = dT (T: sampling time) the plant input-outputbehaviour may be described by the discrete transferfunction

GðzÞ ¼ Y ðzÞUðzÞ ¼

BðzÞAðzÞ z

�d ð1Þ

where A and B are polynomials in z�1.The control law in z-domain is represented by

UðzÞ ¼ ½T ðzÞW ðzÞ � SðzÞY ðzÞ� 1

RðzÞ ð2Þ

-

+

cS

cR

1mB UW +

+

+

-

Fig. 5. Block diagram of t

where w is the reference value and T, S and R are controllerfeedforward and feedback polynomials (Fig. 4). For modelreference;

GwðzÞ ¼Y ðzÞW ðzÞ ¼

Y mðzÞW ðzÞ ¼

BmðzÞAmðzÞ

z�d ¼ GmðzÞ ð3Þ

where Gm(z) represents the transfer function of the refer-ence model, the control rule equation (2) leads to the closedloop setpoint transfer function

GwðzÞ ¼T ðzÞ

RðzÞB�1AðzÞ þ SðzÞz�1ð4Þ

Substituting P(z) = R(z)B(z) into Eq. (4) and comparingnumerator and denominator polynomials with Eq. (3)yields the design equations

BmðzÞ ¼ T ðzÞ ð5aÞAmðzÞ ¼ P ðzÞAðzÞ þ SðzÞz�d ð5bÞ

which serve for synthesis of the controller. For P(z) to bepolynomial R(z) must contain B(z) as factor. Such that in

Y

Adaption

mG

cG

G

Z

E

Y

Ym

+ + +

+

+

-

he adaptive controller.


case of a nonminimum phase system (B(z) contains rootsoutside the unit circle of the complexes z-plane) an unstablecancellation of hidden modes will occur in Eq. (4). To beapplicable to systems with nonminimum phase characteris-tics, which is the case for the boiler transfer functions, thecontrol structure has to be augmented by introducing acorrection network (CNW), originally proposed by [8,9].

As depicted in Fig. 5 the CNW is bypass to the planthaving transfer function

GcðzÞ ¼Y cðzÞUðzÞ ¼

BcðzÞAcðzÞ

z�d ð6Þ

with Bc(z) = (1 � z�1)Bc(z) and

RaðzÞ ¼ RðzÞ þ RcðzÞ ¼ PðzÞBðzÞ þ P cðzÞBcðzÞ ð7ÞThus as illustrated in Fig. 5, the signals y(k) and yc(k) arefed back seperately using polynomials S(z) and Sc(z). Sothe control law

UðzÞ ¼ 1

RaðzÞ½T ðzÞW ðzÞ � SðzÞY ðzÞ � ScðzÞY cðzÞ� ð8Þ

Enthalphy w2

AdaptiveController

ConventionelPID-Controller

uA

uK

1

1

+

+

Conventional PID-Controller

AdaptiveController

uA

u

1

1

-

+

ElectricalPower

w1

+

Central PowerDistributor(LoadDispatching)and\ or Control Room

1: Boiler 2: Superheater3: Valve 4: Turbine 5: Generator

-

-

-

Fig. 6. Schematic diagram for the coupling of the adaptive con

will generate the setpoint transfer function

GwaðzÞ ¼Y aðzÞW ðzÞ ¼

½BcAþ AcB�Tz�d

AðAcRa þ ScBcz�dÞ þ SAcBz�d ð9Þ

Taking into account Ra from Eq. (7) the design equationsmay be derived from

AmðBcAþ AcBÞ ¼ AAcPBþ AAcP cBc þ AScBcz�d þ SAcBz�d

¼ BcAðP cAc þ Scz�dÞ þ AcBðPAþ Sz�dÞð10Þ

In addition to Eqs. (5a) and (5b) the solution of a thirdidentity

Am ¼ P cAc þ Scz�d ð11Þ

is necessary to synthesize the controller for the augmentedplant.

0.05

0.05

Δ

Δ

uD

uK +

+ u2= wm•

K

uD

u1= cm•

+

+

1

Feed Water Flow

Coal Feed

y2=hEnthalphy

Electrical Power

5

4

3

2

trol real-time system to the equipment of the power plant.


3.2.2. The adaptive control law

Up to now no disturbances have been considered,because they will not be handled by the linear controllaw. No integral action or incremental model has beenintroduced there. Due to the safety demands in power plantcontrol, the additional phase lag of integral control actionshould be avoided. Instead, disturbance will be rejectedusing adaptive feed-forward. For a non-measurable deter-ministic disturbance Z(z) acting on the output of the plant,the controlled value is given by

Y ¼ BA

z�dU þ Z ð12Þ

Introducing the filtered error

Em ¼ AmE ¼ AmY m � AmY � AmY c

¼ Bmz�dW � ½PA� Sz�d �Y � ½P cAc þ Scz�d �Y c ð13Þ

whose d-step prediction may be written as

zdEm ¼ BmW � ðRþ RcÞU � SY � ScY c � PAZzd ð14Þ

Fig. 7a. Shape of the curves of signals uDi(t), wi(t), yi(t) and YMi(t) of the adapby 150 MW.

The control law may be derived by setting Eq. (13) equal tozero for vanishing filtered error. The control law includingdisturbance rejection is then given by

U ¼ 1

r0 þ rc0

BmW � R� þ R�c� �

z�1U � SY � ScY c � PAUzd� �

ð15Þ

with R = rQ + Rz�1 and Rc = rc0 + Rcz�1.

For estimation of the controler parameters a differentfrequency domain representation of the model error, asderived by Kocaarslan [8].

Em ¼ R_

z�dU þ S_

z�dY þ c_�AmY ð16Þ

has to be introduced, which may be represented in time do-main as

emðkÞ ¼ aTmY ðkÞ � P

_T1 ðk � 1Þm1ðk � dÞ ð17Þ

tive control circuit with stepwise increase of the electric output setpoint w1


with

aTm¼�½am0 � � �amnam� ð18aÞ

p_T

1 ¼� r_

0 � � � r_

mr s_

0 � � � s_

ms

�� c_

h ið18bÞ

Y ðkÞ¼ ½Y ðkÞ � � �Y ðk�namÞ�T ð18cÞ

m1ðk�dÞ¼ ½uðk�dÞ � � �uðk�d�nrÞjY ðk�dÞ � � �Y ðk�d�nsÞj1�T:ð18dÞ

The update of the controller parameters in

p_

1ðkÞ ¼ p_

1ðk � 1Þ þ qðkÞemðkÞ ð19Þmay be calculated for example by a recursive least-squaresalgorithm. In the actual implementation a square root filteralgorithm with constant trace of the covariance matrix wasused [11].

3.2.3. Design of the correction network

To guarantee the minimum-phase characteristics of theaugmented plant, the roots of the numerator polynomialof the Ga(z)

Fig. 7b. Shape of the curves of signals uKi(t), and uAi(t) of the adaptive control

Ba ¼ AcBþ ABc ð20Þhave to be located within the unit circle of the z-plane. For-mer approaches [8] used a fixed correction network, whichwas tuned off line according to a priori knowledge aboutthe plant using root-locus plots as design tool. This methodis simple for the skilled design engineer, but turned out tobe quite difficult for the non-experienced user, which is alsothe case for the operating personal of the power plant.Therefore, an automatic tuning procedure, based on esti-mated plant parameters A and B had been built into thecontrol algorithm, which requests on line solution of thelinear diophantine polynomial equation (20). To makeapparent the role of the free design parameters by specify-ing Ba(z), which has to be a Hurwitz polynomial due to sta-bility reasons with well adjusted CNW, given by

Y ðzÞ ¼ BðzÞAcðzÞBaðzÞ

BmðzÞAmðzÞ

z�dW ðzÞ ð21Þ

will be considered. The closed loop poles are defined by theroots of Am(z) in combination with the numerator roots of

circuit with stepwise increase of the electric output setpoint w1 by 150 MW.


the augmented plant. The synthesis of the correction net-work thus results in a pole placement problem, which isin case of the implemented control algorithm further sim-plified by specifying closed loop behaviour in terms ofthe well-understood parameters damping d and bandwidthw.

This leads to

BaðzÞ ¼ 1þ ba1z�1 þ ba2z�2 ð22Þwith ba1 ¼ 2edwT cos wT

ffiffiffiffiffiffiffiffiffiffiffiffiffi1� d2p�

, ba2 ¼ e�2dwT so that bychoosing d and w the minimum phase assumption for theaugmented plant is always fulfilled, additionally the closedloop dynamics may be shaped.

4. Experimental setup and results

In this paper, the adaptive control algorithm was imple-mented under the real-time environment of the CADACSsoftware [14]. Due to safety restrictions a direct connection

Fig. 8. Shape of the curves of signals ui(t), wi(t) and yi(t) of the convention150 MW.

to the sensors and actuators of the power plant was notallowed. Instead, a coupling to the process control systemas illustrated in Fig. 6 was carried out. This offers in addi-tion the usage of the supervisory functions of an industrialprocess computer system. During adaptive control themanipulated values were supervised and compared to thecorresponding values of the conventional controller, oper-ating in parallel, but not acting on the plant. The outputvalues of the adaptive controllers were limited, if they aredifferent by more than 10% in magnitude from the signalsof the conventional control scheme.

The adaptive controllers had been under operation forabout three months at the Gerstein power plant. Due tolack of space, in this paper only one measurement forcomparison of adaptive and conventional control willbe presented. Figs. 7a, 7b and 8 illustrate normal operat-ing of the power plant, which is used for secondarycontrol and supports frequency control in the UEW(United Electricity Plants Westfalia) network, thus the

al controller with stepwise increase of the electric output setpoint w1 by


power reference value generated by the central power dis-tributor changes permanently. In Figs. 7a, 7b and 8 thesignals:

uDi(t): ‘‘Controlled variable difference’’: Difference of thecontrolled variables of the adaptive and conven-tional controller

uAi(t): Controlled variables of the adaptive controlleruKi(t): Controlled variables of the conventional controller

yi(t): System outputswi(t): Reference inputs

uMi(t): Parallel model outputs of the adaptive controller

were documented with i = 1 for the electrical power controlloop and i = 2 for the enthalpy control loop. Since thepower plant unit used in the test has a highly big poweroutput (765 MW) changes will constantly occur. Therefore,an experiment with a stepwise increase in the electric out-put is presented in the following. Figs. 7a and 7b showthe dynamic behaviour of the input and output signals ofthe power plant unit with adaptive control according toFig. 6 with a stepwise increase of the setpoint by150 MW. The two output signals Y1 and Y2 immediatelyfollow the setpoint variations with a very slight deviation.The controlled variable of the adaptive controller changesvery rapidly as required and so that only small overshootsand slight control deviations occur. In the process, the sec-ond controlled variable (enthalpy) exhibits an overshoot ofonly 10%. This is much smaller than that in a conventionalcontrol.

Further, tests were again carried out with the conven-tional controller under the same conditions. Fig. 8 illus-trates the dynamic behaviour of the input and outputsignals of the power plant unit of the adaptive and conven-tional control with stepwise increase of the setpoint. Here,again the overshoot of the two controlled variables and thesustained deviations are shown for comparison in Figs. 7aand 7b; this will be especially noted in the case of con-trolled variable y2 (enthalpy).

To evaluate the results of the adaptive and conventionalcontrol the rise time, settling time and loss function forboth control concepts were compiled in Table 1. The lossfunction is defined as

fl ¼1

N

XN

k¼1

e2ðkÞ ð23Þ

Table 1Comparison of the adaptive and conventional controls according to Figs.7a, 7b, 8

Adaptive control Conventional control

Electricalpower

Enthalpy Electricalpower

Enthalpy

Rise time [tR (s)] – 300–400 500–700 500–600Settling time [te3% (s)] 400–500 500–600 500–700 1000–1200Loss function [f1] 70.56 324 94.10 484

where e is control deviation and N is the number of samplesin the measurement.

In Table 1, it should be noted that during dynamic oper-ation the adaptive controller achieves a steady-state condi-tion with regard to the setpoint control as quickly aspossible both in the heat balance of the steam generator(i.e. enthalpy) and in the electric output. It can be seenfrom the values of the indicated control times of the adap-tive controller used in the power plant for control of theelectrical power and enthalpy are considerably faster thanthose of the conventional controller.

In this context, it should be noted that the rise and set-tling times for the adaptive control are equal. Moreover,there is only a slight overshoot. Thus, a lower loss functionis obtained than that with the conventional controller.Thiswill be especially noted in the case of the enthalpy control.During dynamic operation the adaptive controller achievesa steady-state condition with regard to the setpoint controlas quickly as possible both in the heat balance of the steamgenerator (enthalpy) and in the electric output. It will beseen from the values of the indicated control times thatthe adaptive controller used in the power plant for controlof the electrical power and enthalpy is considerably fasterthat the conventional controller.

5. Conclusion

For the improvement of the installed conventional con-trol equipment of a 765 MW power station, a new conceptusing an adaptive control scheme for enthalpy and electri-cal power control of a once-through boiler has been pro-posed and implemented. The results of real-timeexperiments listed in Table 1 and shown Figs. 7a, 7band 8 indicate that the new control strategy exhibitspromising results under all operating conditions. FromTable 1, rise times of the enthalpy range from 300 to400 s and from 500 to 600 s for adaptive and conventionalcontrollers, respectively. It is shown that the proposedadaptive controller is about 57% better than the conven-tional controller in rise time. The rise time of the adaptivecontroller in power is about setpoint. Therefore, it hasalso highly better performance in rise times. As for set-tling times, the proposed adaptive controller has about33% better performance in electrical power and twice bet-ter performance in enthalpy than the conventional con-troller. Also shown in the table, the loss function of theadaptive controller is 33% less in electrical power and49% less in enthalpy than that of the conventional con-troller. The study results suggest that the adaptive boilercontrol saves fuel, increases efficiency and guarantees auniform steam quality. Additionally, economical life ofdevices is increased, the cost of generating electricity isdecreased with the proposed adaptive controller. In con-clusion, the adaptive controller can be proposed to con-trol power and enthalpy outputs of such large thermalpower plants.


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An adaptive control application in a large thermal combined power plant