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A methodology for impact evaluation of alternative control strategies in a large-scale power plant
Control Eng. Practice, Vol. 5, No. 3, pp. 325-335, 1997 Copyright © 1997 Elsevi~ Science Lid Printed in Great Britain. All rights resm-ved 0967-0661/97 $17.00 + 0.00
Pergamon PII:S0967-0661 (97)000(~-9
A METHODOLOGY FOR IMPACT EVALUATION OF ALTERNATIVE CONTROL STRATEGIES IN A LARGE-SCALE POWER PLANT A.O. Soares*, A. Gon~aives**, R.N. Silva** and J.M. Lemos** *CPPE - Companhia Portuguesa de Produf,&~de Electricidade, S.A. Rua Mouzinho da Silveira 10, 1250 Lisboa, Portugal **INESC - lnstituw de Engenharia de Sistemas • Computadores, Rua Aires Redol 9, 1000 Lisboa, Portugal ([email protected])
(Received November 1995; in final form January 1997)
A b s t r a c t : This work is concerned with a methodology for impact evaluation of alternative industrial process-control strategies applied to the steam generator of a 125 MW fuel-fired thermoelectric power-plant unit. The methodology employed is described. It relies on a "systems" approach and embodies nonlinear plant modeling and simulation. The technique used to build the model is presented, as well as some comparisons between different control structures based on classical and predictive/adaptive control strategies. A case study is presented which is distinguished by the availability of a rich set of experimental data, allowing detailed model tuning and validation. The methodology followed forms a paradigm which may be applied to other types of industrial processes. Copyright © 1997 Elsevier Science Ltd
K e y w o r d s : Power plants, steam generators, modelling, PID control, adaptive control, predictive control, simulation.
The first stages of this kind of project comprise the impact evaluation of alternative control strategies. More specifically, questions such as the following need to be answered:
1. I N T R O D U C T I O N
The rehabilitation of large scale power-plant units has become an issue of the utmost importance. The reasons for developing rehabilitation projects include recovering the original productivity, and extending the operating time of the equipment. A rehabilitation project takes less time than building a new unit, and may be economically advantageous (Absenger, 1994).
• Is it worthwhile to improve/renew the control system for a given rehabilitation cost? • May better regulation be achieved if an additional measure of a variable is made? • What are the performance limits of the dynamic process? • If changes can be made in the process so that its dynamic response is altered, would this be beneficial for control or not?
Control and instrumentation are important aspects of rehabilitation. The fast evolution of microelectronics and computers, providing more and more reliable, accurate and powerful information and process control systems, motivates the use of these technologies to increase the efficiency, reliability under load changes, and availability of the plant, while environmental concerns are also met.
This work is concerned with this type of question. A methodology relying on a "systems" approach for evaluating the impact of alternative control strategies in industrial process-plant units is presented and thoroughly illustrated using the results
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of a study performed on a large-scale power-plant unit. The work presented refers to a phase in which the rehabilitation project is being performed. The aim is to provide the team performing the final engineering design, with information which anticipates the dynamic behaviour of the controlled plant. Thus, at this stage, in which the new instrumentation and control system has not yet been implemented, experimental validation may refer only to the model upon which the study relies. Experimenting with industrial plants is a difficult and costly task. The result is that published works seldom report experimental data, especially if this has to be obtained under unusual operating conditions (e.g. with the automatic control loop of a critical variable turned off). It is thus stressed that the case study presented here is based on a rich set of experimental data, which allows detailed model tuning and validation, as well as a deep insight into the dynamic behavior of the plant. The paper is organized as follows: in Section 2 the steps of a general methodology for the impact evaluation of alternative control strategies in industrial process-plant units is presented. This methodology is then applied in Section 3 to a steam generator of a 125 M W fuel fired thermoelectric power-plant unit. In Section 4, conclusions are drawn.
2. M E T H O D O L O G Y The methodology presented relies on the following steps: • Formulation of the problem, • Planning the experimental work, • Performing experiments for data collection and analysis, • Development, calibration, and validation of the plant model, • Computer simulations for the evaluation of alternative control strategies.
2.1 Formulation of the problem Formulation of the problem is the initial step. The questions to be answered by the study are formulated. Besides those already mentioned in the introduction, examples of questions include: • Is a classical control structure based on a network of PI(D) loops enough, or should more advanced features (such as predictive control) be considered?
• What is the best regime of operation (as measured by the region of the characteristic curves) for a given pump? • What is the impact on the overall plant performance of using sensors with a better signal-to-noise ratio? • Can a nett improvement in the dynamic response of the plant be achieved by using sensors with less measurement delay, or faster actuators? All the information that is relevant to the problem is collected. This might include, for instance, data available from previously performed experiments (e.g. during plant commissioning) or design values of the variables for the equilibrium. The general structure of the model to be developed is established, including a definition of which parts of the plant are to be considered (e.g. if the main concern is the boiler of a thermoelectric power plant, the condenser need not be modeled).
2.2 Planning the experimental work Based on the information collected in the first step, the experimental work that is needed to acquire the data corresponding to the relevant situations considered is planned. For instance, if the recovery capacity of a boiler after a feedwater pump shut-down is to be evaluated, this situation must be considered in the experimental work. To calibrate the models, experimental tests must be done in order to define the static and dynamic characteristics of the system being modeled. To determine the static characteristics, the system must be driven to several operating points, and in order to determine the dynamic characteristics small changes around each stabilized operating point are to be made, taking care to inject sufficient excitation in the relevant frequency ranges.
2.3 Performing experiments for data collection
and analysis Data collection is carried out by a data-acquisition system, with an adequate sampling frequency to allow the observation of the important dynamics of the system. Usually, it is better to work at a sampling rate that is higher than needed, and then to decimate or filter the data. Figure 1 shows a picture of the data-acquisition system installed at the plant site. The plant was a 125 MW thermoelectric power unit with a 24 year-old control system which was to be renewed. The electrical signals, coming from the sensors coded 0 volt to 5 volt, were collected in a contact ruler (white board in the left of the picture). The
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results being compared with known experimental data in order to validate the model, which is then revised as the work progresses. Several types of model may be considered. For the sake of the present study, the model should be a compromise between capturing the global nonlinear behavior of the plant, and its ability to test different control strategies. In the work performed, a nonlinear lumped-parameter model consisting of the interconnection of algebraic blocks and linear transfer functions was used. Further details are given below.
2.5 Computer simulations of alternative control
strategies In this step, the model is assumed to be available for use in a set of computer simulations with several control strategies, in order to analyze their impact. The aim is to clarify the answers to the questions formulated in Step 1. A sensitivity analysis with respect to the plant model and the control parameters is also performed. Fig. 1. The data-acquisition system installed in the plant during an experimental session. contacts on this ruler are connected to two dataacquisition boards (total of 32 signals with 12-bit resolution) installed on a portable personal computer. In order to prevent high-frequency noise corrupting the low-frequency signals due to aliasing effects, low-pass analog filters are inserted at the input of the data-acquisition channels. In the case study considered, a sampling rate of 3 Hz was used for all signals recorded. For the signals for which this was too high, decimation was performed. Data collection is a critical step of the work. On the one hand, if the data is not correctly registered, or if it does not cover the situations needed for model calibration and validation, costly experiments may be wasted. On the other hand, careful analysis of acquired data may reveal, for instance, damage to sensors, valves with leakage, and other defect conditions. During the experiments, all the events (such as pump start-up or shut down, altering the modes of operation or special maneuvers) were carefully flagged in order to allow proper interpretation of the data.
3. APPLICATION TO THE STEAM GENERATOR OF A 125 MW POWER-PLANT UNIT Some of the steps presented in the methodology are illustrated below by an application to the steam generator of the 125 MW power plant unit previously referred to. Further details are available in (Soares, 1995; Gon~alves, 1993). A number of studies of impact evaluation of alternative control strategies in steam generators and power-plant units based on modelling and simulation are described in the literature (Coito, et al., 1988; Gibbs, et al., 1991; Katebi, et al., 1995; Maffezzoni, et al., 1983; Thumm and Welfonder, 1983; Thysso, 1981; Usoro, 1977). The case study presented here is, however, distinguished by the availability of a rich set of experimental data, allowing detailed model-tuning and validation. Indeed, and as shown below, experiments were conducted in such hazardous situations as open-loop drum-level control and feedwater pump commutation due to sudden shut-down of one of the pumps.
3.1 Modelling and validation 2.4 Development, calibration, and validation of
the system model After the data has been collected and analyzed, an initial model is developed, and its parametrization is performed. A set of simulations are run, the
Figure 2 depicts a simplified scheme of the water, steam, and flue gas circuits of the plant, between the feedwater tank and the turbine valve. The notation is given in the Appendix. The water coming from the tank is pumped by the feed pumps, through the high-pressure heaters
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(AAP6, AAP7), the feedwater valve (VAL) and the economizer, until it arrives at the drum. In the drum there is a natural circulation of water through the downcomers to pipes in the furnace walls, where the water is vaporized by the effect of the energy liberated by fuel combustion in the furnace. The remaining water and the steam thus generated return to the drum. The steam flows out of the drum, passing through the superheaters and the turbine valve. The drum water level is kept constant by controlling the feedwater flow. For the purposes of building the model, drum steam and water are assumed to be in a thermodynamic state of saturated equilibrium. The system is a complex one. In order to build the model, it is decomposed according to its physical elements, as shown in Fig. 2, taking care of the interactions. The model developed is characterized by the following features: • It is based on physical laws (mass, energy, and momentum conservation equations), • Parameters are determined from geometry, material properties, manufacturers' data (e.g. pump characteristics) and experimental data, • System nonlinearities are included so that the model remains valid over a wide operating range, • Steam-water and air-gas table property polynomial fits are used to provide realistic physical thermodynamic properties over a wide operating range, • The time constants expressing delays in interaction between variables are determined using system-identification procedures. The complete model is lengthy and beyond the scope of this paper; only selected, illustrative parts are thus described here.
Fig. 3. Feedpump static relationships. Continuous lines represent model results. Dots represent experimental points.
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Fig. 4. Dynamic relationship between the command signal and the pump velocity As a first example, consider Fig. 3, which depicts the static relationships between pressure, flow, and velocity in a feedwater pump. Dots represent experimental points to which the model given by eq. (1) has been fitted. The following model has been used: Cm = [ l ( p _ t:)o)] ~ _ Com. P
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In eq. (1): P is the discharge pressure. C TM is the mass flow ~, ~, t:)o, a n d Com are parameters depending on the pump velocity, whose numerical values have been found using least-squares. The dynamic relationship between the command signal and the pump velocity was estimated, based on experimental data, as a first-order ARX model connected to a static non-linearity as in the block diagram of Fig. 4. Figure 5 compares the experimental results with the simulated ones, showing a good agreement between them (error less than ].5% in modulus). The feedwater valve model is given by a static functional relationship, (eq. (2)), for which results can be seen in Fig. 6: A P , at = ¢(a,at, C ~ l ) ,
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polynomial approximations. In order to compute AP~sht, a simplified superheater model was used. Equation 8 gives the percentage of oxygen in the combustion gases. 02vsec o --
where: a.~l is the valve opening area C~'~aI is the water flow ¢(., .) denotes the algebraic relationship between variables For the subsystem made up of the drum, waterwall, and downcomers, as depicted in Fig. 7, it is possible to compute the rates of change of mass and energy inside the system using the balance equations:
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The equations developed were written in the SystemBuild//MATRiXx 1 environment, resulting in a set of block diagrams. This type of description allows both simulation and linearization of the model around an operating point, as well as easy introduction of modifications in the model. After being developed and calibrated, the model was validated by comparing an extensive set of experimental data for stationary and dynamic points of operation with the results of simulation in the same points. Figure 8 refers to one of the test situations considered, in which the level of the drum, in an open loop and starting from one equilibrium point at 125 MW, is disturbed by changes in the feedwater flow. Figure 9 shows the drum level when the steam flow-rate falls down suddenly. It is possible to observe non-minimumphase effects, a typical drum boiler characteristic. 3.2 Control structure being used in the plant The control structure being used in the plant when this work began is shown in Fig. 10. As can be seen, only the drum level and the water pressure drop in the feedwater valve, VAL, were being regulated. 3.3 Alternative control strategies In this work, two alternative control structures are considered. One is based on classical control, i.e., 1 MATRIXx is a trade mark of Integrated Systems Inc.
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Fig. 10. Control structure being used in the plant when this work began. an architecture formed by the interconnection of PIs and PIDs, and the other is based on predictive and adaptive control. It is important to set off the introduction of the excess oxygen regulation structure because, as is well known, there is a balance between the NOx emissions and a good burnout of combustion products.
3.3.1. Classical control. Figures 11 to 14 show the proposed classical control structure. Figures 15 to 18 illustrate a typical analysis departing from the structure shown. The advantage of using a PID, as compared with a PI, in the external loop of the drum-level regulation system, Fig. 11, is considered. From these figures the following conclusions are drawn:
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Fig. 14. Regulation structure of superheated steam pressure. • With a PI, a good step response is obtained at a load of 125 MW (Fig. 15), but the overshoot becomes almost 60% when the working point is changed to 40 MW (Fig. 16). • Instead, with a PID, it is possible to find a tuning yielding a good step response both near 125 MW (Fig. 17) and 40 MW (Fig.
is). This example, obtained using a trial-and-error method in the nonlinear model, is only meant to call attention to the potential of including a derivative action in the outer loop of the drumlevel cascade-regulation structure (see Fig. 11). This means that the control system being specified should include this possibility and, perhaps more importantly, the software tools needed for its tuning and evaluation.
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Fig. 16. Drum-level simulation with a PI controller: step response at 40 MW. Horizontal scale is in [s]. Vertical scale is in [ram]. The system at hand presents a strong interaction between its variables. Many examples were found in which one variable was apparently well regulated, but this caused unacceptable trips in other variables. Thus, a balance should be sought. For small perturbations,, linear techniques may be employed but, for larger perturbations, e.g. due to load changes, the nonlinear character of the plant prevents their use, and the results should be checked using the nonlinear model. 3.3.2. Predictive and adaptive control. Predictive control is now finding increasing acceptance in industry. In (Gibbs, et al., 1991) a feasi-
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Fig. 18. Drum-level simulation with PID controller: step response at 40 MW. Horizontal scale is in Is]. Vertical scale is in [mm]. bility study on the application of nonlinear modelbased predictive control to fossil fueled powerplants is reported. As stated there, a serious disadvantage of the approach is the effort required to develop a simplified model of the plant. Indeed, due to several factors, this model may even be slowly time-varying. In this paper, this is circumvented by the use of adaptive techniques. In predictive control, the decision about the numeric value of the manipulated variable is taken at each sampling time, based on a system predictive model. In adaptive control the controller gains are adjusted on-line. MUSMAR (Mosca, et al., 1989) is an algorithm that combines these two aspects. In this work, MUSMAR was tested in
In order to analyse the impact of the alternative control strategies, a performance comparative study between the three architectures considered (original control system, proposed "classical" control and predictive adaptive control) was carried out. This study was made up of a set of experiments simulating significant disturbances to the normal operating point, at 125 M W and 40 M W loads: • Experiment 1 - The steam flow-rate is subject to an abrupt reduction of 60 ton/h, • Experiment 2 - The steam flow-rate is subject to an abrupt rise of 60 t o n / h at 40 MW, • Experiment 3 - The fuel flow-rate is subject to abrupt changes of ± 5 ton/h, • Experiment 4 - The feedwater flow-rate is subject to abrupt changes of + 30 ton/h. The restrictions are: if the drum level exceeds ± 200 mm, and the superheated steam pressure exceeds 135 bar, the security system is activated, and the plant stops working. The results obtained with the control structure currently being used in the plant, Experiment 1, are shown in Fig. 19. As can be seen, the superheated steam pressure exceeds the m a x i m u m limit.
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Fig. 21. Proposed. classical control structure with feedforward. Simulation of a 60 ton/h reduction in the superheat steam flow-rate. Horizontal scale is in [s]. With respect to the proposed classical control .structure, the result obtained with the regulation structure of superheated steam pressure without feedforward, Fig. 20, is not satisfactory, because the superheated steam pressure reaches values outside the allowed operating range. To solve this problem, feedforward action was introduced. As can be seen in Fig. 21, the superheated steam pressure remains inside its allowed operating range. Nevertheless there is a strong interaction between this variable and the others represented in the figure.
Fig. 22. Adaptive control. Simulation of a 60 ton/h reduction in the superheat steam flowrate. Horizontal scale is in Is]. The results obtained with the adaptive control structure in Experiment 1 are depicted in Figs 22 and 23. Figure 22 shows the most important variables that affect the performance of the drumlevel and steam-pressure controllers. Figure 23 shows the drum-level controller gains. The maximum value of the superheated steam pressure is less than 130 bar. This is better than in the previous case, but the drum-level performance is worse. Figure 22 also shows the adaptation stage of the drum-level controller, from 20 to 100 seconds. During this period of time the controller gains, Fig. 23, were being adjusted in response to a set-point change. After t=300 seconds the gains changed, because the superheated flow-rate reduction drove the system to another operating point. It is interesting to observe that, in spite of the fact that the perturbation is a reduction in the superheated steam flow-rate, the result is a small drum-level increase, followed by an abrupt reduction. Observing Fig.19, it is possible to conclude that this is imposed by the steam pressure control.
4. CONCLUSIONS In this work, a methodology for performing impact studies for alternative control strategies in process-plant industrial units undergoing rehabilitation studies has been presented. The methodology relies on a system approach, and includes the steps of problem formulation, planning the experimental work, performing experiments for data collection and analysis, development, calibration, and validation of the system model, and computer simulation of alternative control strategies.
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Fig. 23. Adaptive control. Simulation of a 60 ton/h reduction in the superheat steam flowrate. Drum level controller gains. Horizontal scale is in [s]. The methodology is illustrated by its application to a large-scale power plant unit. A nonlinear model of the steam generator unit was developed and validated, and then used to evaluate the impact of classical, predictive and adaptive control-based structures. A distinguishing feature of the work presented is the availability of a rich set of experimental data, allowing detailed model tuning and validation. Classical, predictive, and adaptive control-based structures were tested on the simulation model. The methodology illustrated by the case study presented here forms a paradigm that may be applied to other types of industrial processes undergoing rehabilitation projects.
REFERENCES Absenger, A. (1994). Rehabilitation of Industrial power-plants. A B B Review, 6/7, 10-16. Coito, F., F. Garcia, J. M. Lemos and A. Mano (1988). Modelling and control of a drum boiler. Preprints of the IFA C International Symposium on Power Systems Modelling and Applications, Brussels, Belgium. Gibbs, Bruce P., David S. Weber and David W. Porter (1991). Application of nonlinear modelbased predictive control to fossil power plants. Proc. 30th I E E E C D C 1850 - 1856. Brighton, U.K. Gonqalves, A. J. (1993). A Thermoelectric powerplant Unit Model for coordinated furnaceturboalternator control. M. Sc. Thesis, Instituto Superior T6cnico. The Technical University of Lisbon. Portugal.
Katebi, M.R., M.A. Johnson, A.W. Pike, A.W. Ordys, M. Grimble, C. Cloughley and R. Farnham. (1995). New Software Tools for powerplant Modelling, Simulation and Optimization. Proc. Euraco Workshop. Florence, Italy. Maffezzoni, C., G. Magnani and L. Marcocci (1983). Computer aided modelling of Large power-plants. In Modelling and Control of Electric Power-Plants. C. Maffezzoni ed., Pergamon Press. Mosca, E., G. Zappa and J. M. Lemos (1989). Robustness of Multipredictor Adaptive Regulators: MUSMAR, Automatica, 25, 4, 521-529. Soares A. (1995). Modelling and Control of the Steam Generator Unit in a Thermoelectric power-plant. M. Sc. Thesis, Instituto Superior T~cnico. The Technical University of Lisbon. Portugal. Thumm, T. and E. Welfonder (1983). Modular steam generator and turbine model for on line process control and power system simulation. In Modelling and Control of Electric PowerPlants, C. Maffezoni ed., Pergamon Press. Thysso, A. (1981). Modelling and Parameter Estimation of a Ship Boiler. Automatica, 17, 1, 175-166. Usoro, P. B. (1977). Modelling and simulation of a drum-boiler- turbine power-plant under emergency state control. M. Sc. Thesis, MIT.
APPENDIX A. NOTATION The notation for the signals used in this paper is as follows: A A P , High-pressure heater B m b , Water pump B r r , Drum boiler C.Ar , Air circuit C~alrn, , Atomization air flow-rate Ca'~fr,,i , Combustion air flow-rate C~cy~,~i , Combustion air flow-rate, by unit of fuel, at furnace inlet C~'~p~c , Combustion air flow-rate by, unit of fuel, necessary for a stoichiometric combustion Cb~tl , Feedwater pump 1 control signal Ccm~f~,~ , Combustion air control signal C c ' ~ i , Fuel flow-rate at the furnace inlet C.Fuel , Fuel circuit C~al , Feedwater flow-rate m Cgec o , Water flow-rate at the drum inlet C~im~j , Injection water flow-rate Err ?T~ C~pgb or C~urgas , Purge water flow-rate C~'~ l , Water flow-rate through the feedwater valve Cvat , Valve command signal Cv~brro , Steam flow-rate in the drum outlet Cv~fz o , Steam flow-rate at the superheater outlet Dp , Pressure drop between the feedwater valve and the drum
Impact Evaluation of Alternative Control Strategies E c o , Economizer Egb~r , D r u m boiler water enthalpy Egecoo , W a t e r enthalpy at the economizer outlet Evbrr , S t e a m d r u m boiler enthalpy Egt& ,Downcomers water enthalpy / ~ a , Fluid enthalpy at the waterwalls F r n , Furnace F r n + B r r , System composed by furnace and drum Ktrb , Steam valve position K s m ~ g , Waterwalls metal specific heat /k/mg~g , Waterwalls equivalent (metal-water) mass Nbal , Feedwater p u m p velocity Ngbr~ , D r u m boiler level O2vseco , Percentage of oxygen in the combustion gases PvsIlo , Steam pressure at the superheater outlet Pvbrr , S t e a m pressure in the drum QImpag , Gases-to-metal heat transfer rate at the waterwalls
335
Q~r~gpag , Metal-to-water heat transfer rate at the waterwalls T a l , Water t a n k Tmpag , Downcomers' metal t e m p e r a t u r e Val , Feedwater valve Vfep~c , Volume of gases resulting from a complete combustion of 1 kg of fuel, in stoichiometric conditions Vfsp~cl~n , Volume of dry gases resulting from a complete combustion of 1 kg of fuel, in real conditions Vgbr~ , D r u m boiler water volume Vo2 , Volume of Oxygen in gases Vpag , Waterwalls volume Vtde, Downcomers volume Vvbr~ , Drum boiler steam volume Pgbrr , D r u m boiler water density Pvbrr , D r u m boiler steam density Pgtde , Downcomers water density fipag , Fluid density at the waterwalls
Pergamon PII:S0967-0661 (97)000(~-9
A METHODOLOGY FOR IMPACT EVALUATION OF ALTERNATIVE CONTROL STRATEGIES IN A LARGE-SCALE POWER PLANT A.O. Soares*, A. Gon~aives**, R.N. Silva** and J.M. Lemos** *CPPE - Companhia Portuguesa de Produf,&~de Electricidade, S.A. Rua Mouzinho da Silveira 10, 1250 Lisboa, Portugal **INESC - lnstituw de Engenharia de Sistemas • Computadores, Rua Aires Redol 9, 1000 Lisboa, Portugal ([email protected])
(Received November 1995; in final form January 1997)
A b s t r a c t : This work is concerned with a methodology for impact evaluation of alternative industrial process-control strategies applied to the steam generator of a 125 MW fuel-fired thermoelectric power-plant unit. The methodology employed is described. It relies on a "systems" approach and embodies nonlinear plant modeling and simulation. The technique used to build the model is presented, as well as some comparisons between different control structures based on classical and predictive/adaptive control strategies. A case study is presented which is distinguished by the availability of a rich set of experimental data, allowing detailed model tuning and validation. The methodology followed forms a paradigm which may be applied to other types of industrial processes. Copyright © 1997 Elsevier Science Ltd
K e y w o r d s : Power plants, steam generators, modelling, PID control, adaptive control, predictive control, simulation.
The first stages of this kind of project comprise the impact evaluation of alternative control strategies. More specifically, questions such as the following need to be answered:
1. I N T R O D U C T I O N
The rehabilitation of large scale power-plant units has become an issue of the utmost importance. The reasons for developing rehabilitation projects include recovering the original productivity, and extending the operating time of the equipment. A rehabilitation project takes less time than building a new unit, and may be economically advantageous (Absenger, 1994).
• Is it worthwhile to improve/renew the control system for a given rehabilitation cost? • May better regulation be achieved if an additional measure of a variable is made? • What are the performance limits of the dynamic process? • If changes can be made in the process so that its dynamic response is altered, would this be beneficial for control or not?
Control and instrumentation are important aspects of rehabilitation. The fast evolution of microelectronics and computers, providing more and more reliable, accurate and powerful information and process control systems, motivates the use of these technologies to increase the efficiency, reliability under load changes, and availability of the plant, while environmental concerns are also met.
This work is concerned with this type of question. A methodology relying on a "systems" approach for evaluating the impact of alternative control strategies in industrial process-plant units is presented and thoroughly illustrated using the results
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of a study performed on a large-scale power-plant unit. The work presented refers to a phase in which the rehabilitation project is being performed. The aim is to provide the team performing the final engineering design, with information which anticipates the dynamic behaviour of the controlled plant. Thus, at this stage, in which the new instrumentation and control system has not yet been implemented, experimental validation may refer only to the model upon which the study relies. Experimenting with industrial plants is a difficult and costly task. The result is that published works seldom report experimental data, especially if this has to be obtained under unusual operating conditions (e.g. with the automatic control loop of a critical variable turned off). It is thus stressed that the case study presented here is based on a rich set of experimental data, which allows detailed model tuning and validation, as well as a deep insight into the dynamic behavior of the plant. The paper is organized as follows: in Section 2 the steps of a general methodology for the impact evaluation of alternative control strategies in industrial process-plant units is presented. This methodology is then applied in Section 3 to a steam generator of a 125 M W fuel fired thermoelectric power-plant unit. In Section 4, conclusions are drawn.
2. M E T H O D O L O G Y The methodology presented relies on the following steps: • Formulation of the problem, • Planning the experimental work, • Performing experiments for data collection and analysis, • Development, calibration, and validation of the plant model, • Computer simulations for the evaluation of alternative control strategies.
2.1 Formulation of the problem Formulation of the problem is the initial step. The questions to be answered by the study are formulated. Besides those already mentioned in the introduction, examples of questions include: • Is a classical control structure based on a network of PI(D) loops enough, or should more advanced features (such as predictive control) be considered?
• What is the best regime of operation (as measured by the region of the characteristic curves) for a given pump? • What is the impact on the overall plant performance of using sensors with a better signal-to-noise ratio? • Can a nett improvement in the dynamic response of the plant be achieved by using sensors with less measurement delay, or faster actuators? All the information that is relevant to the problem is collected. This might include, for instance, data available from previously performed experiments (e.g. during plant commissioning) or design values of the variables for the equilibrium. The general structure of the model to be developed is established, including a definition of which parts of the plant are to be considered (e.g. if the main concern is the boiler of a thermoelectric power plant, the condenser need not be modeled).
2.2 Planning the experimental work Based on the information collected in the first step, the experimental work that is needed to acquire the data corresponding to the relevant situations considered is planned. For instance, if the recovery capacity of a boiler after a feedwater pump shut-down is to be evaluated, this situation must be considered in the experimental work. To calibrate the models, experimental tests must be done in order to define the static and dynamic characteristics of the system being modeled. To determine the static characteristics, the system must be driven to several operating points, and in order to determine the dynamic characteristics small changes around each stabilized operating point are to be made, taking care to inject sufficient excitation in the relevant frequency ranges.
2.3 Performing experiments for data collection
and analysis Data collection is carried out by a data-acquisition system, with an adequate sampling frequency to allow the observation of the important dynamics of the system. Usually, it is better to work at a sampling rate that is higher than needed, and then to decimate or filter the data. Figure 1 shows a picture of the data-acquisition system installed at the plant site. The plant was a 125 MW thermoelectric power unit with a 24 year-old control system which was to be renewed. The electrical signals, coming from the sensors coded 0 volt to 5 volt, were collected in a contact ruler (white board in the left of the picture). The
Impact Evaluation of Alternative Control Strategies
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results being compared with known experimental data in order to validate the model, which is then revised as the work progresses. Several types of model may be considered. For the sake of the present study, the model should be a compromise between capturing the global nonlinear behavior of the plant, and its ability to test different control strategies. In the work performed, a nonlinear lumped-parameter model consisting of the interconnection of algebraic blocks and linear transfer functions was used. Further details are given below.
2.5 Computer simulations of alternative control
strategies In this step, the model is assumed to be available for use in a set of computer simulations with several control strategies, in order to analyze their impact. The aim is to clarify the answers to the questions formulated in Step 1. A sensitivity analysis with respect to the plant model and the control parameters is also performed. Fig. 1. The data-acquisition system installed in the plant during an experimental session. contacts on this ruler are connected to two dataacquisition boards (total of 32 signals with 12-bit resolution) installed on a portable personal computer. In order to prevent high-frequency noise corrupting the low-frequency signals due to aliasing effects, low-pass analog filters are inserted at the input of the data-acquisition channels. In the case study considered, a sampling rate of 3 Hz was used for all signals recorded. For the signals for which this was too high, decimation was performed. Data collection is a critical step of the work. On the one hand, if the data is not correctly registered, or if it does not cover the situations needed for model calibration and validation, costly experiments may be wasted. On the other hand, careful analysis of acquired data may reveal, for instance, damage to sensors, valves with leakage, and other defect conditions. During the experiments, all the events (such as pump start-up or shut down, altering the modes of operation or special maneuvers) were carefully flagged in order to allow proper interpretation of the data.
3. APPLICATION TO THE STEAM GENERATOR OF A 125 MW POWER-PLANT UNIT Some of the steps presented in the methodology are illustrated below by an application to the steam generator of the 125 MW power plant unit previously referred to. Further details are available in (Soares, 1995; Gon~alves, 1993). A number of studies of impact evaluation of alternative control strategies in steam generators and power-plant units based on modelling and simulation are described in the literature (Coito, et al., 1988; Gibbs, et al., 1991; Katebi, et al., 1995; Maffezzoni, et al., 1983; Thumm and Welfonder, 1983; Thysso, 1981; Usoro, 1977). The case study presented here is, however, distinguished by the availability of a rich set of experimental data, allowing detailed model-tuning and validation. Indeed, and as shown below, experiments were conducted in such hazardous situations as open-loop drum-level control and feedwater pump commutation due to sudden shut-down of one of the pumps.
3.1 Modelling and validation 2.4 Development, calibration, and validation of
the system model After the data has been collected and analyzed, an initial model is developed, and its parametrization is performed. A set of simulations are run, the
Figure 2 depicts a simplified scheme of the water, steam, and flue gas circuits of the plant, between the feedwater tank and the turbine valve. The notation is given in the Appendix. The water coming from the tank is pumped by the feed pumps, through the high-pressure heaters
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(AAP6, AAP7), the feedwater valve (VAL) and the economizer, until it arrives at the drum. In the drum there is a natural circulation of water through the downcomers to pipes in the furnace walls, where the water is vaporized by the effect of the energy liberated by fuel combustion in the furnace. The remaining water and the steam thus generated return to the drum. The steam flows out of the drum, passing through the superheaters and the turbine valve. The drum water level is kept constant by controlling the feedwater flow. For the purposes of building the model, drum steam and water are assumed to be in a thermodynamic state of saturated equilibrium. The system is a complex one. In order to build the model, it is decomposed according to its physical elements, as shown in Fig. 2, taking care of the interactions. The model developed is characterized by the following features: • It is based on physical laws (mass, energy, and momentum conservation equations), • Parameters are determined from geometry, material properties, manufacturers' data (e.g. pump characteristics) and experimental data, • System nonlinearities are included so that the model remains valid over a wide operating range, • Steam-water and air-gas table property polynomial fits are used to provide realistic physical thermodynamic properties over a wide operating range, • The time constants expressing delays in interaction between variables are determined using system-identification procedures. The complete model is lengthy and beyond the scope of this paper; only selected, illustrative parts are thus described here.
Fig. 3. Feedpump static relationships. Continuous lines represent model results. Dots represent experimental points.
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Fig. 4. Dynamic relationship between the command signal and the pump velocity As a first example, consider Fig. 3, which depicts the static relationships between pressure, flow, and velocity in a feedwater pump. Dots represent experimental points to which the model given by eq. (1) has been fitted. The following model has been used: Cm = [ l ( p _ t:)o)] ~ _ Com. P
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In eq. (1): P is the discharge pressure. C TM is the mass flow ~, ~, t:)o, a n d Com are parameters depending on the pump velocity, whose numerical values have been found using least-squares. The dynamic relationship between the command signal and the pump velocity was estimated, based on experimental data, as a first-order ARX model connected to a static non-linearity as in the block diagram of Fig. 4. Figure 5 compares the experimental results with the simulated ones, showing a good agreement between them (error less than ].5% in modulus). The feedwater valve model is given by a static functional relationship, (eq. (2)), for which results can be seen in Fig. 6: A P , at = ¢(a,at, C ~ l ) ,
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polynomial approximations. In order to compute AP~sht, a simplified superheater model was used. Equation 8 gives the percentage of oxygen in the combustion gases. 02vsec o --
where: a.~l is the valve opening area C~'~aI is the water flow ¢(., .) denotes the algebraic relationship between variables For the subsystem made up of the drum, waterwall, and downcomers, as depicted in Fig. 7, it is possible to compute the rates of change of mass and energy inside the system using the balance equations:
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The equations developed were written in the SystemBuild//MATRiXx 1 environment, resulting in a set of block diagrams. This type of description allows both simulation and linearization of the model around an operating point, as well as easy introduction of modifications in the model. After being developed and calibrated, the model was validated by comparing an extensive set of experimental data for stationary and dynamic points of operation with the results of simulation in the same points. Figure 8 refers to one of the test situations considered, in which the level of the drum, in an open loop and starting from one equilibrium point at 125 MW, is disturbed by changes in the feedwater flow. Figure 9 shows the drum level when the steam flow-rate falls down suddenly. It is possible to observe non-minimumphase effects, a typical drum boiler characteristic. 3.2 Control structure being used in the plant The control structure being used in the plant when this work began is shown in Fig. 10. As can be seen, only the drum level and the water pressure drop in the feedwater valve, VAL, were being regulated. 3.3 Alternative control strategies In this work, two alternative control structures are considered. One is based on classical control, i.e., 1 MATRIXx is a trade mark of Integrated Systems Inc.
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3.3.1. Classical control. Figures 11 to 14 show the proposed classical control structure. Figures 15 to 18 illustrate a typical analysis departing from the structure shown. The advantage of using a PID, as compared with a PI, in the external loop of the drum-level regulation system, Fig. 11, is considered. From these figures the following conclusions are drawn:
Impact Evaluation of Alternative Control Strategies
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is). This example, obtained using a trial-and-error method in the nonlinear model, is only meant to call attention to the potential of including a derivative action in the outer loop of the drumlevel cascade-regulation structure (see Fig. 11). This means that the control system being specified should include this possibility and, perhaps more importantly, the software tools needed for its tuning and evaluation.
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In order to analyse the impact of the alternative control strategies, a performance comparative study between the three architectures considered (original control system, proposed "classical" control and predictive adaptive control) was carried out. This study was made up of a set of experiments simulating significant disturbances to the normal operating point, at 125 M W and 40 M W loads: • Experiment 1 - The steam flow-rate is subject to an abrupt reduction of 60 ton/h, • Experiment 2 - The steam flow-rate is subject to an abrupt rise of 60 t o n / h at 40 MW, • Experiment 3 - The fuel flow-rate is subject to abrupt changes of ± 5 ton/h, • Experiment 4 - The feedwater flow-rate is subject to abrupt changes of + 30 ton/h. The restrictions are: if the drum level exceeds ± 200 mm, and the superheated steam pressure exceeds 135 bar, the security system is activated, and the plant stops working. The results obtained with the control structure currently being used in the plant, Experiment 1, are shown in Fig. 19. As can be seen, the superheated steam pressure exceeds the m a x i m u m limit.
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Fig. 22. Adaptive control. Simulation of a 60 ton/h reduction in the superheat steam flowrate. Horizontal scale is in Is]. The results obtained with the adaptive control structure in Experiment 1 are depicted in Figs 22 and 23. Figure 22 shows the most important variables that affect the performance of the drumlevel and steam-pressure controllers. Figure 23 shows the drum-level controller gains. The maximum value of the superheated steam pressure is less than 130 bar. This is better than in the previous case, but the drum-level performance is worse. Figure 22 also shows the adaptation stage of the drum-level controller, from 20 to 100 seconds. During this period of time the controller gains, Fig. 23, were being adjusted in response to a set-point change. After t=300 seconds the gains changed, because the superheated flow-rate reduction drove the system to another operating point. It is interesting to observe that, in spite of the fact that the perturbation is a reduction in the superheated steam flow-rate, the result is a small drum-level increase, followed by an abrupt reduction. Observing Fig.19, it is possible to conclude that this is imposed by the steam pressure control.
4. CONCLUSIONS In this work, a methodology for performing impact studies for alternative control strategies in process-plant industrial units undergoing rehabilitation studies has been presented. The methodology relies on a system approach, and includes the steps of problem formulation, planning the experimental work, performing experiments for data collection and analysis, development, calibration, and validation of the system model, and computer simulation of alternative control strategies.
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Fig. 23. Adaptive control. Simulation of a 60 ton/h reduction in the superheat steam flowrate. Drum level controller gains. Horizontal scale is in [s]. The methodology is illustrated by its application to a large-scale power plant unit. A nonlinear model of the steam generator unit was developed and validated, and then used to evaluate the impact of classical, predictive and adaptive control-based structures. A distinguishing feature of the work presented is the availability of a rich set of experimental data, allowing detailed model tuning and validation. Classical, predictive, and adaptive control-based structures were tested on the simulation model. The methodology illustrated by the case study presented here forms a paradigm that may be applied to other types of industrial processes undergoing rehabilitation projects.
REFERENCES Absenger, A. (1994). Rehabilitation of Industrial power-plants. A B B Review, 6/7, 10-16. Coito, F., F. Garcia, J. M. Lemos and A. Mano (1988). Modelling and control of a drum boiler. Preprints of the IFA C International Symposium on Power Systems Modelling and Applications, Brussels, Belgium. Gibbs, Bruce P., David S. Weber and David W. Porter (1991). Application of nonlinear modelbased predictive control to fossil power plants. Proc. 30th I E E E C D C 1850 - 1856. Brighton, U.K. Gonqalves, A. J. (1993). A Thermoelectric powerplant Unit Model for coordinated furnaceturboalternator control. M. Sc. Thesis, Instituto Superior T6cnico. The Technical University of Lisbon. Portugal.
Katebi, M.R., M.A. Johnson, A.W. Pike, A.W. Ordys, M. Grimble, C. Cloughley and R. Farnham. (1995). New Software Tools for powerplant Modelling, Simulation and Optimization. Proc. Euraco Workshop. Florence, Italy. Maffezzoni, C., G. Magnani and L. Marcocci (1983). Computer aided modelling of Large power-plants. In Modelling and Control of Electric Power-Plants. C. Maffezzoni ed., Pergamon Press. Mosca, E., G. Zappa and J. M. Lemos (1989). Robustness of Multipredictor Adaptive Regulators: MUSMAR, Automatica, 25, 4, 521-529. Soares A. (1995). Modelling and Control of the Steam Generator Unit in a Thermoelectric power-plant. M. Sc. Thesis, Instituto Superior T~cnico. The Technical University of Lisbon. Portugal. Thumm, T. and E. Welfonder (1983). Modular steam generator and turbine model for on line process control and power system simulation. In Modelling and Control of Electric PowerPlants, C. Maffezoni ed., Pergamon Press. Thysso, A. (1981). Modelling and Parameter Estimation of a Ship Boiler. Automatica, 17, 1, 175-166. Usoro, P. B. (1977). Modelling and simulation of a drum-boiler- turbine power-plant under emergency state control. M. Sc. Thesis, MIT.
APPENDIX A. NOTATION The notation for the signals used in this paper is as follows: A A P , High-pressure heater B m b , Water pump B r r , Drum boiler C.Ar , Air circuit C~alrn, , Atomization air flow-rate Ca'~fr,,i , Combustion air flow-rate C~cy~,~i , Combustion air flow-rate, by unit of fuel, at furnace inlet C~'~p~c , Combustion air flow-rate by, unit of fuel, necessary for a stoichiometric combustion Cb~tl , Feedwater pump 1 control signal Ccm~f~,~ , Combustion air control signal C c ' ~ i , Fuel flow-rate at the furnace inlet C.Fuel , Fuel circuit C~al , Feedwater flow-rate m Cgec o , Water flow-rate at the drum inlet C~im~j , Injection water flow-rate Err ?T~ C~pgb or C~urgas , Purge water flow-rate C~'~ l , Water flow-rate through the feedwater valve Cvat , Valve command signal Cv~brro , Steam flow-rate in the drum outlet Cv~fz o , Steam flow-rate at the superheater outlet Dp , Pressure drop between the feedwater valve and the drum
Impact Evaluation of Alternative Control Strategies E c o , Economizer Egb~r , D r u m boiler water enthalpy Egecoo , W a t e r enthalpy at the economizer outlet Evbrr , S t e a m d r u m boiler enthalpy Egt& ,Downcomers water enthalpy / ~ a , Fluid enthalpy at the waterwalls F r n , Furnace F r n + B r r , System composed by furnace and drum Ktrb , Steam valve position K s m ~ g , Waterwalls metal specific heat /k/mg~g , Waterwalls equivalent (metal-water) mass Nbal , Feedwater p u m p velocity Ngbr~ , D r u m boiler level O2vseco , Percentage of oxygen in the combustion gases PvsIlo , Steam pressure at the superheater outlet Pvbrr , S t e a m pressure in the drum QImpag , Gases-to-metal heat transfer rate at the waterwalls
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Q~r~gpag , Metal-to-water heat transfer rate at the waterwalls T a l , Water t a n k Tmpag , Downcomers' metal t e m p e r a t u r e Val , Feedwater valve Vfep~c , Volume of gases resulting from a complete combustion of 1 kg of fuel, in stoichiometric conditions Vfsp~cl~n , Volume of dry gases resulting from a complete combustion of 1 kg of fuel, in real conditions Vgbr~ , D r u m boiler water volume Vo2 , Volume of Oxygen in gases Vpag , Waterwalls volume Vtde, Downcomers volume Vvbr~ , Drum boiler steam volume Pgbrr , D r u m boiler water density Pvbrr , D r u m boiler steam density Pgtde , Downcomers water density fipag , Fluid density at the waterwalls