a new coordinated control strategy for boiler-turbine system of coal-fired power plant

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 13, NO. 6, NOVEMBER 2005 943

A New Coordinated Control Strategyfor Boiler-Turbine System of

Coal-Fired Power PlantShaoyuan Li, Senior Member, IEEE, Hongbo Liu, Wen-Jian Cai, Yeng-Chai Soh, and Li-Hua Xie

Abstract—This paper presents the new development of theboiler-turbine coordinated control strategy using fuzzy rea-soning and autotuning techniques. The boiler-turbine system isa very complex process that is a multivariable, nonlinear, slowlytime-varying plant with large settling time and a lot of uncer-tainties. As there exist strong couplings between the main steampressure control loop and the power output control loop in theboiler-turbine unit with large time-delay and uncertainties, auto-matic coordinated control of the two loops is a very challengingproblem. This paper presents a new coordinated control strategy(CCS) which is organized into two levels: a basic control level anda high supervision level. Proportional-integral derivative (PID)type controllers are used in the basic level to perform basic controlfunctions while the decoupling between two control loops can berealized in the high level. A special subclass of fuzzy inferencesystems, called the Gaussian partition with evenly (GPE) spacedmidpoints systems, is used to self-tune the main steam pressurePID controller’s parameters online based on the error signal andits first difference, aimed at overcoming the uncertainties due tochanging fuel calorific value, machine wear, contamination of theboiler heating surfaces and plant modeling errors. For the largevariation of operating condition, a supervisory control level hasbeen developed by autotuning technique. The developed CCS hasbeen implemented in a power plant in China, and satisfactoryindustrial operation results demonstrate that the proposed controlstrategy has enhanced the adaptability and robustness of theprocess. Indeed, better control performance and economic benefithave been achieved.

Index Terms—Boiler-turbine coordinated control strategy,decoupling control, industrial application, multivariable systems,power plant.

I. NOMENCLATURE

Critical gain in power control loop.Critical gain in pressure control loop.Proportional parameter in proportional-integralderivative (PID) controller.Integral parameter in PID controller.

Manuscript received December 1, 2003. Manuscript received in final formJune 8, 2005. Recommended by Associate Editor V. Gopal. This work wassupported in part by the National Natural Science Foundation of China underGrant 60474051, in part by the Key Technology and Development Program ofShanghai Science and Technology Department under Grant 04DZ11008, and inpart by the program for New Century Excellent Talents in University of China(NCET).

S. Li and H. Liu are with the Institute of Automation, Shanghai Jiaotong Uni-versity, Shanghai 200030, China (e-mail: syli@ sjtu.edu.cn).

W.-J. Cai, Y.-C. Soh, and L.-H. Xie are with the School of Electrical andElectronic Engineering, Nanyang Technological University, Singapore 639798,Singapore (e-mail: [email protected]).

Digital Object Identifier 10.1109/TCST.2005.854319

Differential parameter in PID controller.Transfer function output to input .Decoupling compensator output matrix.Decoupling compensator matrix.Error between set-point and current value.Change value of the .First-order lag filter time constant.System sampling period.Fuzzy membership of variable .System static gain.System critical period.Amplitude margin.Phase margin.Boiler firing rate.Governor value position.Main steam pressure.Power output.Main steam flow.Main steam temperature.

AbbreviationsDCS Distributed control systems.CCS Coordinated control system.GPE Gaussian partition with evenly space.TPE Triangle partition with evenly space.CARMA Controlled autoregressive moving average.AGC Automatic generation control.

II. INTRODUCTION

THE majority of coal-fired power plants in China built morethan a decade ago were once expected to operate at near

full capacity but are now operating in a load following modedue to the rapid development of the power industry. Conse-quently, AGC of power networks becomes necessary in orderto meet varying load demands at different time periods. As akey component of power network AGC, the coordinated con-trol of fossil-fueled generating units plays a vital role in safeand economic operation of the system. Since the performanceof the multi-input–multi-output (MIMO) boiler-turbine systemcan vary significantly due to the complex nonlinearity in dif-ferent operating regions, conventional linear control methodsmay not be sufficient for the whole operation range [1]–[5].

For the boiler-turbine control system, the central task is toadjust the output power to meet system demand while min-imizing unwanted pressure and temperature variations. Theturbine speed is controlled by the main steam pressure to drive

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944 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 13, NO. 6, NOVEMBER 2005

the power generator [1]. However, as the electricity demandswings from minute to minute, the turbine speed has to bevaried to meet the different load demands. The variation ofturbine speed causes a chain reaction of change in firing rate,demand on the coal, grinding and feeding coal, and eventuallythe steam generated. The time scale of the steam pressureprocess is naturally quite slow at about 8 to 15 min for thesystem under study. In contrast, by opening the governorvalve, different amount of steam can be supplied immediately[2], but this is at the expense of depleting stored energy inthe evaporator of the boiler leading to main steam pressurevariations, and it takes a long time to recover it to its referencevalue. Thus, for a rapidly changing power demand, controllingthe governor valve will be more effective, but a sustainedchange can only be achieved via changing the firing rate. Asa compromise, a master–slave control strategy [3] has beenadopted in most power plants in conventional control mode,i.e., the turbine speed control loop works as the master loop totrack the main steam pressure, and the combustion control loopas the slave loop to track the varying turbine speed. In such ascheme, a proportional derivative (PD) controller is needed tocoordinate the relationship between the two control loops [3],the controller parameters, however, are very difficult to adjusteven within a very narrow operation range. In practice, it ismostly done by a trial-and-error method. As a result, the mainsteam pressure and the power output of the generating units, inmost systems, are still being controlled manually. It is difficultto avoid excessive stresses on the process components and tomeet the economic and quality requirement.

Even though several advanced control structures for theboiler-turbine CCS have been proposed to tackle the problemin the literature, the problem still remains unsolved for a largeoperating range [6]–[9]. In this paper, an advanced controlstrategy is proposed to solve this particular coordinated controlproblem. By considering the complex nature of the process, asupervisory control structure is proposed and it consists of thefollowing components.

1) Proportional–integral derivative (PID)-type controllersare used as basic control units due to their simple struc-ture and the concept is well understood by field engineersand operators.

2) A steady-state triangular decoupler is designed which si-multaneously decouples the strongly coupled main steampressure and power output loops for both set-point and un-measured pulverized coal disturbance.

3) A supervisory control level via an autotuning techniqueis used to tune the parameters of the controller at dif-ferent operating conditions as system parameters can varysignificantly due to the complex nonlinearity in load fol-lowing mode.

4) The PID type controllers and decouplers are gain-sched-uled according to the actual load to take account of theload-dependent nonlinear characteristics of the boiler-tur-bine process.

5) A special subclass of fuzzy inference systems, i.e.,Gaussian partition system with evenly spaced midpoints(GPE) [17], is employed to autotune the PID parameters

Fig. 1. Schematic diagram of the 300 MW boiler-turbine unit.

Fig. 2. Conventional CCS.

of the main steam pressure online, and it overcomesuncertainties caused by changing fuel calorific value,machine wear and plant modeling errors.

The control strategy has been realized in the FOXBORO I/ASeries DCS and implemented on a 300 MW boiler-turbine unit,i.e., Unit 1 of Yuanbaoshan Power Plant in China for two years.

The remaining sections of this paper are organized as follows.Section II describes the process and plant characteristics. Thesteady-state triangular decoupler and autotuning supervisorycontrol techniques are given in Section III. Section IV discussesthe gain-scheduling of the PID type controllers and decouplersand the autotuning main steam pressure PID controller bya fuzzy mechanism. Section V presents the implementationand the operating results of the proposed control strategy onthe boiler-turbine system. Finally, conclusions are drawn inSection VI.

III. PROCESS DESCRIPTION

The schematic diagram of the investigated 300 MW boiler-turbine unit is shown in Fig. 1. The boiler is a compound cir-culation tower boiler with low circulation ratio that produces947 tons of steam per hour at maximum continuous rating. Therated main steam pressure and the super-heater outlet temper-ature are 18.5 Mpa and 545 C, respectively. Electric power isgenerated by the feeding steam from the boiler to the turbine,and the steam from the turbine condenses to water through thecondenser, which is sent to the boiler again by the feedwaterpump.

LI et al.: A NEW COORDINATED CONTROL STRATEGY FOR BOILER-TURBINE SYSTEM 945

Fig. 3. New CCS.

A characteristic of the boiler system is highly complex andnonlinear. The main nonlinearity is related to the property ofgain and time constant in the boiler process, and the propertyvariation is dependent on the plant load. In order to control thecomplex system, the boiler-turbine CCS is usually adopted inthe industry which can be regarded as a two-input–two-output(TITO) multivariable control system. The conventional struc-ture of CCS is shown in Fig. 2, where the two inputs are boilerfiring rate (coal feeder speed) and turbine governor valve po-sition ; two outputs are the main steam pressure and thereal power output , respectively.

The boiler-turbine unit is a time-varying and nonlinear systemwith strong interactions and uncertainties. Through careful the-oretical analysis [5], a linearized system model for a given op-erating point can be obtained as

(1)

where the and denotes the steady-state values of and, is the transfer function between the main steam flowand the power output which represents the dynamics of

the turbine and reheater.Although the boiler-turbine unit transfer function can be ap-

proximated by (1) for a given operating point, the boiler-tur-bine system remains very complex, and conventional control ap-proaches [1], [5] have encountered great difficulties due to thefollowing factors.

1) Strong Coupling. There are strong couplings betweenthe main steam pressure control loop and the poweroutput control loop. If there exists frequent unmeasured

pulverized coal disturbances caused by uncertain coalmill working conditions [3], the strong couplings couldcreate severe problems for system stability and control,particularly when the unit is operating at high load.

2) Nonlinearity. The system exhibits highly nonlinear char-acteristics when the power output changes over a widerange, as pressure increases/decreases and heat transferrates do not vary in proportion to the fuel inputs.

3) Long Settling Time with Time-delay. There is a longsettling time in the change of steam pressure or poweroutput while changing the coal feeder speed due to mas-sive storages of mass and energy. The results of real-timedynamic experiments [5] show that the time constant andtime-delay from the coal feeder speed to the main steampressure or the power output can vary within 6 15 minwith varying load.

4) Uncertainty. In coal-fired units, the fuel is supplied bycoal mills which have uncertain dynamics due to varyingtime-delay of the grinding process and the uncertain be-havior of the mills caused by coal quality variation andmachine wear.

IV. COORDINATED CONTROL

A hierarchical coordinated control scheme for the main steampressure and the power output consists of two levels: a basiccontrol level and a supervisory control level as shown in Fig. 3.

The supervisory control level is referred to as the autotunerof CCS controller/decoupler, its functions include:

• monitoring the control performance online and iden-tifying the mathematical model of the boiler-turbinesystem;


Fig. 4. Multivariable feedback structure of CCS.

• retuning CCS controller/decoupler parameters automati-cally to enhance the adaptability of the control system dueto large variation of operating conditions.

The basic control level consists of three conventional feedfor-ward/feedback PID-type controllers:

• A unilateral PD-type decoupler adjusts the power outputto follow load demand changes while maintaining themain steam pressure within the permitted range and com-pensates the load varying caused by the uncertain coalmill working condition.

• A PI controller for the electric power output loop.• A fuzzy autotuning PID controller in the main steam pres-

sure control loop which provides good performance withthe ability to overcome uncertainties due to changing fuelcalorific values, machine wear and plant modeling errors.

A. Steady-State Triangular Decoupler

As the time-delay of the turbine and reheater is muchsmaller than that of the boiler , the time-delay in can beignored and the following approximation in (1) can be obtained:

(2)

where is the static gain of . Fig. 4 shows the block di-agram of the multivariable feedback control structure of CCS,

is the matrix containing the PIDcontrollers, is the input compensator matrix and is theoutput compensator matrix.

Denote , If and are chosen,

respectively, as

and (3)

and substituting (2) into (1), the transfer function matrix of CCSfor the controlled plant becomes

(4)

The strong interaction between the two control loops is reduced,and the process is now approximately a triangular decoupledsystem. The parameter of the decoupling compensator matrix

can be adapted by the CCS autotuner.

B. Autotuning CCS Parameters

The autotuner for CCS parameters monitors the control per-formance of the CCS online. If the absolute value of the error

between the set-point and the current value of the mainsteam pressure exceed a prescribed threshold for a predeter-mined period of time, the autotuner automatically retunes theCCS parameters. Otherwise, the CCS parameters remain un-changed. The autotuning algorithm is given as follows.

Fig. 5. Z-N identification scheme of CCS.

1) Closed-loop identification of the controlled plant by usingthe decentralized step test and least-squares algorithmaround an operating condition based on the input–outputplant data (see the Appendix).

2) Determine the static gain in (2) according to the con-trolled plant model to adapt the decoupling compensatormatrix .

3) Perform multivariable Ziegler–Nichols identificationexperiments [16] according to Fig. 5 using the dynamicmodel obtained from Step 1) to determine the ultimateperiod , the ultimate gains and of the con-trolled plant (see the Appendix) corresponding to thepower output control loop and the main steam pressurecontrol loop, respectively.

4) Based on and , determine prescribed ranges( , ) and ( , ) for themain steam pressure fuzzy autotuning controller.In this application, based on the experiments, wechoose , ,

, .5) From the values of , and , use the tuning

method proposed in [16] to tune the PI parameters of thepower output control loop.

V. BASIC CONTROL LEVEL

The boiler-turbine process includes fairly strong nonlinear-ities due to the difference between the stored energy at eachplant load. To cope with the load-dependent nonlinear dynamiccharacteristics, in the basic control level the local PID type con-trollers and decouplers are designed at several load levels basedon linear control theory firstly according to methods describedin Section III-B, and then gain-scheduling technique accordingto the actual load described in Section IV-B is used to obtain theglobal controllers/decouplers parameters.

There are three PID-type controllers in the basic control levelas shown in Fig. 3, each has different functions.

1) PI controller Form and obtained from Step 3) ofthe autotuning algorithm, the parameters and canbe obtained by Ziegler–Nichols method as follows [16]:

(5)

2) PD controller A PD controller as shown in Fig. 3 pro-vides a feedforward compensation to the given load


under the Voltage-constant running mode. From Figs. 2or 3, the equivalent transfer function of the system withan integral filter in the generator is given by

(6)

With a feedforward controller to compensate the action ofto , we have

(7)

where is the controller in the first control loop, andfeedforward controller can be obtained as

(8)

This feedforward controller may not be realizable. Inpractice, a PD controller is usually adopted to satisfy thisneed with the following form:

(9)

where the parameters and can be calculated bythe Ziegler–Nichols method in [16], as follows:

(10)

and the filter time constant can be determined fromexperiments, the engineering rule of thumb value is twoto three times the process time constant.

3) PID controller The PID controller used in this paper isgiven as follows:

(11)

where the time constant of the first-order lag filter, and , .

Through onsite observation and theoretical analysis [5],the main contributing factors for uncertain system perfor-mance of the main steam pressure loop are as follows.

• Disturbance caused by frequent unmeasured pulverizedcoal disturbance under the uncertain coal mill workingconditions.

• Parameter uncertainty and variation caused by the uncer-tainties due to the changing fuel calorific value, machinewear and the contamination of the boiler heating surfaces.As the main steam pressure control loop plays a crucialrole in rejecting various disturbances and tracking thepower output, high-quality control performance is veryimportant since the change of main steam pressure repre-sents the energy balance between boiler steam productionand grid load demand or stored energy in the evaporator.If the main steam pressure is too high, the stress on theplant increases and the plant’s life span will decrease. If itis too low, the efficiency will decrease. If there exist per-sistent large main steam pressure variations, the safe op-eration of the unit cannot be guaranteed. When the boileris operated at constant pressure, the main steam pressuredynamics changes with unit load, a better control strategy

is to use autotuning PID controller to implement real-timecontrol.

A. Fuzzy Autotuning PID Controller

In order to enhance the robustness and control performance ofthe main steam pressure loop, a fuzzy autotuning PID controlleris adopted for better performance [9]. By using fuzzy rules basedon expert knowledge to adjust PID parameters which are ini-tially determined by classical tuning rules, high-quality controlperformance can be expected than that of the PID controllerswith fixed parameters.

1) Fuzzy Rules for Tuning PID Parameters: Based on thestep response analysis, human expertise for the process and ex-tensive simulation studies, a set of autotuning rules for the PIDparameters of the following form are proposed:

if is and is

then is is is (12)

where and are the current error and its first differ-ence of the main steam pressure, , , , and representa member of fuzzy sets for , , , and , respec-tively, .

For the given prescribed minimum/maximumof and of in Step 4) of the

autotuning algorithm, the tuning coefficients , and of, and , are given, respectively, by

(13)

(14)

(15)

According to the Ziegler–Nichols PID tuning rule, when 4,the integral action of PID controller is moderate. Therefore, bychosen smaller or bigger than 4, a stronger or weaker integralaction can be obtained.

The fuzzy sets and may be either big (B) or small (S)and are characterized by the membership functions of naturallogarithm. The grade of the membership functions and thevariable ( or ) has the following relation:

or for big (16)

or for small (17)

Based on the processes characteristics, four singleton member-ship functions denoting the linguistic variable small (S), middlesmall (MS), middle (M) and big (B) for the fuzzy sets are de-signed. They are defined as 2.8, 3.4, 4 and

5.For the system performance to be robust and to achieve fast

response with small overshoot, the PID controller must be tunedsuch that the three control actions coordinate with each other asthe process is affected by multiple time-delays and large settlingtimes. For example, to obtain a desired step response, a big con-trol signal is needed at the beginning to achieve a fast rise time,which requires a big proportional gain and a small derivative


TABLE IFUZZY AUTO TUNING RULES OF K

gain. Also, since processes have large time-delay, a small inte-gral gain is desired to reduce the overshoot. When the outputresponse is near the set-point, the proportional gain and integralgain should be changed from large to small and from small tolarge, respectively, to make the controlled output converge tothe set-point quickly. The autotuning rules for , andare given in Tables I–III, respectively.

2) Fuzzification Strategy and Fuzzy Inference: In fuzzifica-tion, the shape of membership function characterizes the intu-ition of converting the crisp value into linguistic value to fit thehuman thinking process. Gaussian-shaped membership functionis chosen as the membership function of the antecedent part

or in (12) as it better fits human intuition. Similarto the triangle partition system with evenly space (TPE) [18],Gaussian-shaped membership function have the following fea-tures: 1) the membership function is symmetrical about its cen-tral value, 2) all membership functions have the same shape, and3) the space between the central values of two adjacent membersare equal and these constitute the GPE used in our system.

The Gaussian membership function is defined as

others(18)

where is the center value of the membership function of. Point is a unique element that has membership value

1 in , this guarantees four rules without zero contribution atany one time.

According to the fuzzy autotuning rule given in (12),and Tables I–III, the crisp sampling value oris first fuzzified into linguistic value based on the previousfuzzification strategy by seven reference membership functionsdenoting the linguistic variables: negative big (NB), negativemiddle (NM), negative small (NS), zero (Z), positive small(PS), positive middle (PM) and positive big (PB), respectively.Then, the crisp value of the th rule given in (12) isobtained by the product of the membership function values of

and

(19)

where is the membership function value of the refer-ence fuzzy set given a value of , and is themembership function value of the reference fuzzy set givena value of . Based on , the values of and foreach rule are determined from their corresponding membership

TABLE IIFUZZY AUTO TUNING RULES OF K

TABLE IIIFUZZY AUTO TUNING RULES OF �

functions given in (8) and (9). Then the defuzzification yieldsthe following results:

(20)

(21)

(22)

where or is the value of or corresponding to thegrade for the th rule . Once , and are obtained,the PID controller parameters , and can be autotunedonline based on (13)–(15).

B. Gain-Scheduling of the PID Type Controllers andDecouplers

If it is known how the dynamics of a process change with theoperating conditions of the process, it is possible to change thecontroller parameters accordingly, known as gain-scheduling.A measurable process variable, which is descriptive of the oper-ating condition and used to adjust the controller parameters, isknown as a scheduling variable. For the boiler-turbine process,the actual load is chosen as the scheduling variable.

A set , containing m values ofthe scheduling variable is chosen and arranged according to:

for ). For each value of


in the set a linear model (, 2) is identified by using identification methods described in

Section III-B and for each model parameters of the local PIDtype controllers and decouplers are tuned according to methodsdescribed in Section III-B.

By assuming that the parameters of the PID type controllersand decouplers change linearly between the two load levels,the parameters can be gain-scheduled between the frozen oper-ating points by linear interpolation

(23)according to the actual load , where .

VI. INDUSTRIAL IMPLEMENTATION AND APPLICATION RESULTS

The proposed coordinated control strategy described in thispaper is realized by using the software development system ofthe FOXBORO I/A Series DCS and has been developed andsuccessfully applied in the control of a 300 MW boiler-turbineunit, i.e., Unit 1 of Yuanbaoshan Power Plant in China.

A. Identifying the Plant Models and Autotuning ControllerParameters

To design an efficient control system for such a complicatedplant reliably, open-loop identification of the control plant wasperformed under different coal quality and load levels by usingidentification methods described in Section III-B. For example,parameters at two typical operating conditions of 210 MW(70% of full load) and 270 MW (90% of full load) and withcalorific value of the coal in the range of 3300 to 3600 kcal/kgare obtained and listed in Tables IV and V. The form of theselinear models is given in (1) where( 1, 2).

Remark 1: The identified plant models have been exten-sively validated, including field dynamic experiments carriedout before the control system have been designed, the open-loopidentification by using the methods described in Section III-Band through comparisons between simulation results and actualplant data. From the validations, it has been concluded thatthe plant models reflect the process dynamics in a satisfactorymanner, so that the initial design of the control system can beperformed on the plant models.

Remark 2: The identified plant models are only accurate atthe typical operating conditions. In order to avoid the disadvan-tages of self-tuning control based on rational transfer functionparameter estimation directly, increase the robustness and facil-itate commissioning, self-tuning control method from the fre-quency domain approach [19] is adopted by the estimation of theultimate point of the controlled plant. Simulation studies showthat the changes of the ultimate point parameters of the con-trolled plant are small when there are large changes in the pa-rameters of the plant transfer function model. The results pointto the robustness of the proposed self-tuning control method.

To illustrate the design procedure of the proposed CCS, thelocal decoupler/controllers initial parameters of the two typical

TABLE IVMODEL PARAMETERS OF 210 MW

TABLE VMODEL PARAMETERS OF 270 MW

operating conditions described previously were designed for in-stance as follows.

• The steady triangular decoupler parameter was tunedfrom the steady-state plant model parameters as 0.936 and0.962 corresponding to the 210 MW and 270 MW oper-ating conditions, respectively.

• 1 of (6) was selected.• The desired critical period and critical gains ( , and

) were determined as (727 s, 0.269 and 2.304) and(556 s, 0.192 and 2.046) corresponding to the 210 MWand 270 MW operating conditions, respectively.

• The PI controller parameters ( , ) of the power outputcontrol loop were tuned as (1.019, 60.58 s) and (0.598,49.86 s), respectively, for the two operating conditions ac-cording to the values of , and based on spec-ified phase margin and specified amplitudemargin 3.

• Based on simulation studies and real-time experiments,the ranges for ( , ) and ( , )are obtained as , ,

, ,respectively.


Fig. 6. CCS at load change rate of 3 MW/min.

After the local PID type controllers/decouplers are designedat several typical load levels based on linear control models, thecontroller/decouplers parameters of other operating conditionsare gain-scheduled according to method given in Section IV-B.

B. Real-Time Application and Discussion

After commissioning, the proposed CCS has been put intoservice in the 300 MW commercial power-generating unit formore than three years. Here, several real-time operation resultsare recorded and used as examples to demonstrate the effective-ness of control strategy.

Case 1: An essential experiment for the boiler-turbine unitis increasing the load at the rate of 3 MW/min to test the controlperformance, before the CCS can be put in operation. Fig. 6shows the operation results for a 180-min period, where themain steam pressure and the power output increasefrom 13.56 MPa to 18.06 MPa and from 185 MW to 285 MW,respectively. The controlled variables showed fast response withsmall tracking error and good steady-state performance for boththe main steam pressure and the power output, while the mainsteam temperature still maintains the set-point of 545 C.

Case 2: Load changes over a wide range between 185 MWand 285 MW at the allowable maximum rate of 10 MW/min. Inthis case, the main steam temperature will be severely affectedunder conventional control strategy. Fig. 7 shows three typicaloperating conditions within the specified operation range;1) decreasing the set-point of two control loops andduring 20–40 min; 2) setting and as pulse signals during90–100 min; and 3) increasing and during 150–180 min.The controlled variables and , as well as main steamtemperature are all kept within acceptable limits neartheir set-points and good tracking performance is obtained.Since the maximum rate of load changes in the power plant is6 MW/min and the system has good tracking performance evenat is 10 MW/min (150–160 min.), the proposed control strategysatisfies the load following requirement.



Case 3: Fig. 8 shows the operation result under coordinatedcontrol when the unit is decreasing the load over a wide rangeat the rate of 9 MW/min from 287 MW to 197 MW. As can beseen, the principal controlled variables of the unit are all keptnear their setpoints and good load following is achieved, so theefficiency of the unit is enhanced.

Case 4: The 4-h operation results of the proposed CCSand manual operation under high load condition are comparedduring commissioning to validate the proposed control scheme.As shown in Figs. 9 and 10, the main steam pressure and thepower output under the proposed control strategy are runningmuch smoother than those of manual operation under the fullload 300 MW. In manual control mode, the frequent strongunmeasured pulverized coal disturbance results in the mainsteam pressure exceeding the prescribed upper limit for safeoperation and cause the high-pressure bypass valve to openresulting in wasted energy. Under automatic control mode, thesystem has a very good disturbance rejection property, safe andeconomic operation of the unit is guaranteed.

Remark 3: This paper is based on the practical applicationproject which had been carried out at Yuanbaoshan PowerPlant, China. While many of the control loops were regulated


Fig. 9. CCS under manual control.

Fig. 10. CCS under automatic control.

in automatic mode, the main steam pressure loop and the poweroutput loop have had to be controlled by operators because ofthe limitations of the conventional CCS. As it is difficult toguarantee safe operation of the plant using a conventional CCS,such studies were not conducted, and, hence, no comparativeresults are available between the proposed approach and theconventional CSS. Hence, the results of the proposed approachand manual operation of the plant is presented, to demonstratethe improvement in performance with the proposed CSS.

VII. CONCLUSION

A new coordinated control strategy for the boiler-turbine unitin a power plant was proposed in this paper to improve thesystem performance under the load following mode. The systemconsists of two levels with a fuzzy inference system for auto-tuning the PID controller. The CCS and fuzzy autotuning PIDcontrollers have been implemented in a 300 MW boiler-turbineunit in China for more than two years. The system has been per-forming very well after three years of fine-tuning. In summary,we have adverted the following:

1) the CCS had successfully replaced the manual operationin loop coordinate control and the performance has beenvery robust;

Fig. 11. Closed-loop TITO control system.

2) both the main steam pressure and the electric poweroutput have fast responses with low overshoot and goodsteady-state performance;

3) the main variables of the unit including the main steamtemperature are all kept within acceptable limits neartheir set-points and good tracking performance evenunder load-variation over a wide range.

The CCS proposed can be easily implemented in other coal-fired boiler-turbine unit of the power plants without much mod-ification. The research work on the extension of the technologyand an adaptive predictive control for the CCS of power plant iscurrently under investigation.

APPENDIX

RECURSIVE LEAST SQUARES IDENTIFICATION ALGORITHM

The simple identification approach is on the basis of a MIMOprocess under decentralized control. To simplify our derivation,we adopted two inputs and two outputs control system (shownin Fig. 11).

Where , and controllers, noises and process transferfunctions, respectively, the notation , , 1, 2, areused in both and domain. In general, and couldbe any type of controllers that make the closed-loop systemstable. Without loss of generality, assumed that the controllers

and are proportional type. The fundamental relation-ship between error signals and transfer function outputs for thesystem are expressed as

and

Assumed that the process initially test at a steady state withinitial set point, error, and output variables. Notes that , , ,

, and , respectively, and then , .To identify the process parameters, the test involves the fol-

lowing two steps.

1) When kept fixed, make a step change from to ,record the error signals for the two loops, until the newsteady state is reached at . is definedas the maximum settling time of all loops (See Fig. 2).The incremental equation from the second steady state tothe new state becomes

(A1a)


(A1b)

2) Make a step change in from to , while keepingas before, record the error signals for the two

loops, until the new steady state is arrived at. Again, the incremental equation from the third

steady state to the new state can be written as

(A2a)

(A2b)

Combine (A1a), (A1b), (A2a) and (A2b) into matrix form

(A3a)

where

and

(A3b)

Because of ,thus, the matrix is nonsingular if

. From (A1) and (A2), it can be proved that

Therefore, for a bound-input–bound-output (BIBO)stable system, , in Laplace domain.

Now that is nonsingular, can be solved by

(A4)

Substituting (A1b), (A2b) and (A3b) into (A4), we obtain theequation shown at the bottom of the page.

Consequently, the problem of identification of coupledclosed-loop MIMO system is transformed into the identi-fication of four single open-loop problems. Suppose that

decentralized controllers are proportional controllers, that isboth and are constants, the relation between the originalsystem input–output and the decentralized identification systemare given as.

Consider a first-order plus time delay and a second-order plustime delay system, the solutions for 1 and 2 are givenas follows.

1) 1, each loop output results becomes

(A5)which can be written into the compact form

where

2) 2, (A5) becomes

(A6)

It is again expressed as

where

Equations (A5) and (A6) can be solved by the LeastSquares methods for each transfer functions 1, 2and 1, 2, to form the regression form


where ,.

Its Least Squares estimation for are

(A7)

Once are found from (A7), , and can be recov-ered from

for , and , , , and from

for 2, respectively.

APPENDIX

ZIEGLER–NICHOLS FREQUENCY RESPONSE METHOD [16]

The Ziegler–Nichols frequency method is based on using thecontroller connected as a proportional controller, the experimentis carried out in the following procedures.

1) Connect a controller to the process, set the parametersso that control action is proportional, i.e., , and

0.2) Increase the control gain slowly until the process starts to

oscillate.3) The gain when this occurs is , and the period of the

oscillation is .4) Determine the PID controller parameters according to the

Ziegler–Nichols method as shown in the figure at the topof the page.

ACKNOWLEDGMENT

The authors would like to thank the anonymous reviewersfor their helpful comments and constructive suggestions withregard to this paper.

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Shaoyuan Li (SM’05) was born in 1965. He receivedthe B.S. and M.S. degrees from Hebei University ofTechnology, Tangshan, China, in 1987 and 1992, re-spectively, and the Ph.D. degree from the Departmentof Computer and System Science, Nankai University,Tianjin, Beijing, China in 1997.

He is currently a Professor at the Institute of Au-tomation, Shanghai Jiao Tong University, Shanghai,China. His research interests include fuzzy systemsand nonlinear system control.


Hongbo Liu was born in 1964. He receivedthe Ph.D. degree from the Research Center ofAutomation, Northeastern University, China, in2000. He is currently working toward the Ph.D.degree at the Institute of Automation, Shanghai JiaoTong University, Shanghai, China.

His research interests include complex thermalprocess modeling and control, fuzzy control, andadaptive control.

Wen-Jian Cai was born in 1957. He received theB.S. and M.S. degrees from Harbin Institute ofTechnology, Harbin, China, in 1980 and 1983,respectively, and the Ph.D. degree in systems engi-neering, Oakland University, Rochester, MI, in 1992.

He is currently an Associate Professor in theSchool of Electrical and Electronic Engineering,Nanyang Technological University, Singapore. Hisresearch interest includes advanced process control,fuzzy logic control, robust control, and estimationtechniques.

Yeng-Chai Soh received the B.Eng. degree in elec-trical and electronic engineering from the Universityof Canterbury, New Zealand, in 1983 and the Ph.D.degree in electronic engineering from the Universityof Newcastle, Australia, in 1987.

He is currently a Professor in the School ofElectrical and Electronic Engineering, NanyangTechnological University, Singapore. Since 1995, hehas been the Head of the Control and InstrumentationDivision. His current research interests are in theareas of robust system theory and applications.

Li-Hua Xie received the B.E. and M.E. degree inelectrical engineering from the Nanjing Universityof Science and Technology, Nanjing, China, in1983 and 1986, respectively, and the Ph.D. degreein electronic engineering from the University ofNewcastle, Australia, in 1992.

He is currently an Associate Professor in theSchool of Electrical and Electronic Engineering,Nanyang Technological University, Singapore.His current research interests include optimal androbust control.

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