Modern Control E

7/27/2019 Modern Control E

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Power Station and Process Control Systems

Modern Control Algorithmsin Process Control Systems

Observer- based State Control


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Observer-based State Control 3

Contents Page

Modern Control Algorithms in Steam Generator andAuxiliary Plant Control

Calculation-guided Closed-loop Control Systems 4


Unit Model (Steam Generator and Turbine) 5

Fuzzy Control Algorithms 5

Practical Utilization 5

PID Controller versus State Controller with Observer 6

Performance Comparison using an Application Example 6

Conventional State Controller with Observer 7

Mauell State Controller with Observer (MZR) 8

Tools for the Calculation of the Controlled System Parameters 9

Measuring Value Acquisition of the Controlled SystemResponse 10

Identification of the Controlled System and Indication of theCharacteristic Parameters 10

Load Level Balancing of the Superheater Time Response 11

Comparison Between PID and MZR 12

PID Controller 12

Mauell State Controller (MZR) 14

Retrofitting Projects 15

Conclusions 15


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4 Observer-based State Control

Calculation-guided Closed-loop Control Systems

Fields of application and characteristics

Condensate stop control

District heating supply from cogeneration

Calculation-guided closed-loop controls are primarily used in

complex power station heat circuits where often contrasting taskdefinitions have to be taken into account:

Fast and opposite load variations at the consumer end,e.g., turbines / generator and district heating system

Slow increase of the steam generator output

Using conventional closed-loop control circuits responding tosystem deviations, these tasks can only be solved with a maximumeffort in the planning and commissioning phases. They also hindera dynamic operation of the plant.

The block model assigns parameters on the basis of thetechnological design specifications, supplies reference inputvariables for all control loops of the meshed system - right up to the

furnace operation setpoint control. Model errors are corrected bythe secondary setpoint controllers of all major process areas, suchas FD floating pressure, turbine extraction pressure, heatingcondenser pressure.

Observer-based State Control

Field of application and characteristics

Live steam temperature control

Intermediate superheater temperature control

Control loops with highly load-dependent time constants and ofhigher system order

The state controller with observer is used for higher-level processeswith self-regulation, but can also be used for processes with deadtime. The process/plant-specific disturbance analysis is carried outby an internal state observer and the analysis result is taken intoaccount when the manipulated variable is generated. Actuatornon-linearity, system dead time, etc. are taken into account byinternal compensation circuits so that a persisting system deviationis avoided. An online parameter adaptation derives the optimal

Modern Control Algorithms for Steam Generator and Auxiliary Plant Control

1

4

5

6

6

3

- Brown coal

2

GHeat valuecorrection

- Refuse

District heating

1 Unit model (smooth load control)2 Condensate stop control3 Calculation-guided control

4 Fuzzy control5 Turbine speed and power control6 Observer-based state control

Unit control concept and use of modern control algorithms


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state controller parameters from the controlled systemcharacteristics.

The use of a state controller results in an aperiodic and asymptoticdamping of the controlled variable characteristics which leads to a

smooth actuator operation and therefore a smooth response of thelinked variables.

Unit Model (Steam Generator and Turbine)


Anticipation of the boiler and turbine output development, gradientlimiting for the load balancing through the block capacity control(to avoid irregular block operation).

Large-scale power station components only allow certain loadcharacteristics. A setpoint control that is not optimally adjusted to

the possible actual conditions will lead to unnecessary fluctuationsin the control response and thus to unauthorized controller actions.This puts unnecessary strain on the materials and reduces plantefficiency. If the time characteristics for the setpoint control of theessential components is derived on the basis of plant-specificparameters (load level setpoint gradient, storage capacity, etc.), theblock model can be used to implement a specific setpoint control ofall large-scale components which takes into account the actualprocess conditions. This will result in an optimal utilization of thedynamic performance and a higher overall efficiency at loadvariations. Untimely correction of the control process through thesecondary setpoint control systems can be avoided.

Fuzzy Control Algorithms


FGD plant optimization of the deposition degree of the SO2content in the raw gas by selecting and specifying the number ofthe spraying levels in operation

Correction of the furnace operation control due to fluctuations inthe heat value and mass current measurement errors, e.g., solidwaste or refuse-fired furnace.

Plant areas producing inconsistent process variable fluctuations

which cannot be measured are difficult to control satisfactorily, evenif complex control loops with a high degree of intermeshing andcomplex feedforward control are employed. The calorific value ofrefuse or brown coal, for example, cannot be determinedsatisfactorily due to the fluctuations in the composition of thesefuels. The operation of incinerators in particular requires variousfuelling parameters to be taken into account in order to achieve ahigh energy utilization factor. Such parameters would be, forinstance, the furnace temperature range, the minimum retentiontime of the gaseous products at specific temperatures, air excessor starvation in the various burning zones, the quality of the steamparameters.

The method of multi-variable control based on a Fuzzy algorithmcan be used for non-linear and linear processes. Referencesetpoints are derived from the various actual values for all furnace-relevant control loops (e.g., control of the roller/ travelling gratespeed, primary/secondary/tertiary air quantities, refuse feedingspeed, backup firing).

Practical Utilization

The described control strategies are implemented on the standardprocessors of the ME 4012 process control system so that thepractical utilization of these modern control concepts is greatlyfacilitated.


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Example of a steam generator with a two-line superheater, displayed on the ME-VIEW operator control and monitoring system. Line 1 isequipped with a PID controller, line 2 with state controller.


PID Controller versus State Controller with Observer


Analog, standard algorithm PID control on a pneumatic, hydraulicor electronic basis has proved its worth over many years, and is stillfirst choice in daily control circuit design even in the days of digitalprocess control systems. Despite the fact that the PID controller

structure is not adapted to the controlled process and its settingparameters do not allow to draw direct conclusions on the controlquality, points in favour of its use are the detailed knowledge onhow the PID controller works, and the many years of experiencethat provide plant engineers with the intuitive feel for tuning.

In this report, we confront the well-established technique (PIDcontrol) with the new technique (observer-based state control) andshow that an alternative exists for the PID controller that is equallypractical but more robust to unpredictable changes in the behaviourof the controlled system (e.g., superheater processes).

Based on the state controller described in technical literature and

many application reports, Mauell extended the observer concept ofthe state controller by implementing essential features that have notbeen fully documented in the past.

Essential modules of the observer concept:

State observer

Disturbance observer

Actuator observer

Dead time compensation

User-friendly and objective parameter setting and optimizationconcept

Performance Comparison using an Application Example

In this section we compare the performance of a Mauell statecontroller with that of a PID controller. In our example thecontrollers are used for the live steam temperature control of asteam generator, which is characterized by large and highly load-dependent time constants, a higher system order (n = 3....5 typ.),external disturbances due to fluctuations in the heating of thesuperheater, and high demands on the control quality.

The application example will also demonstrate the versatility of theMauell state controller with respect to the fields of application andits suitability for all aperiodic systems with compensation or deadtimes. It will show that the state controller offers clear advantageswhen used in higher-order systems and control loops with longdead times and actuator-dependent non-linearities.

The effect of the two alternative control concepts is illustrated for asteam generator with a two-line superheater (see figure below).


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In the following we will only look at the structure of the Mauell statecontroller as the structure and principle of operation of the PIDcontroller is common knowledge.

We start with a summary of the concept of conventional observer-based state control so that you will be able to compare it with the

Mauell state controller and easily recognize its advantages andinnovative features.

Conventional State Controller with Observer

The figure below shows the basic structure of a conventional statecontroller with state observer and secondary injection controller

implemented in a 3rd order controlled system.

From a complete set of state variables that clearly describe thesystem state, the conventional state controller generates amanipulated variable by means of linear combination.

Owing to the fact that only the state variable can be measured, thestate observer derives the other required state variables T1 and T2and transmits them to the state controller. The variable T3 is

derived by the state observer and is the equivalent to the systemdeviation Ta = Ta - Ta, soll.

From the observer deviation Ta -T3 and the input variable Te, the

state observer then tries to determine the external disturbances(manipulated-variable independent changes of the output variable)on the controlled system.

However, this conventional state control has the following short-comings:

The state observer cannot fully compensate for the observationerror Ta - T3 and is thus not able to completely reproduce the

required state variables.

In the steady state, the state controller produces a permanentcontrol deviation.

Adaptation of the internal parameters to the load is practically notpossible.

The state controller is adapted to the structure of the controlledsystem. However, as secondary control loops and actuators withtheir mostly non-linear behavior are also part of the system to becontrolled, they too must be taken into account. Adaptation istherefore more difficult to achieve and the controller adjustment isless exact.

Dead times of the controlled system cannot be taken into account.

There is no tool for user-friendly and objective parameter setting

and optimization.

The commissioning of the conventional state controller thereforerequires extensive knowledge of the state space representation.

We see that the practical use of the conventional controller islimited because of the drawbacks described above. Also, plantdesigners and commissioning engineers, even the servicepersonnel, must have special knowledge of state space systems.

Conventional state controller with observer

D

Te Ta

State observer

x h2

x k1

h3

k2 x k3

x h1 x

+ + +

++

+

-

x

-PI

- Ta soll

Ta

T1 T2 T3

Te, soll

State controller


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Mauell State Controller with Observer (MZR)

The Mauell state controller has inherited the basic structure of theconventional state controller which has been extended byadditional structural features. These new features are described inthe following.

Elimination of the Steady-state Observer Error

In order to eliminate the steady-state observer error (Ta - T3), thebasic structure of the observer concept is extended by adisturbance observer so that the observer error Ta - T3 convergestowards zero.

Elimination of the Steady-state System Deviation

For known disturbances on the controlled system, the steady-statedeviation of a state controller can be determined from the transferfunction of the controlled system and the parameters of the state

controller.

As the disturbances are statically mapped by the state observer, itis possible to evaluate the contents of the integrator and as a resultcompute the change of the state controllers state variable inorder to eliminate the steady-state system deviation.

In this context, the reference input variable can also be consideredas a disturbance in the sense of a shift of the neutral position.These observations formed the basis for the development of theMauell state controller.

The additive connection of the state observers integrator output forthe elimination of the steady-state deviation is expressed by thefactor k0. As the calculation of the factor k0 takes the transferfunction of the controlled system into account, inaccuracies in theelimination of the system deviation must be expected.

Due to the fact that deviations in the steady state between thecontrolled system and the model in the state observer are takeninto account by the disturbance observer, model errors, especiallygain factor deviations, have no influence on the control precision ofthe steady-state controlled system.

Compensation of the Non-linearities of Secondary FollowerControllers and Actuators

To make the state controller robust to non-linear variations of thefollower controller and actuator, a so-called actuator observer hasbeen implemented.

The actuator observer has the task of varying the state variablesT1 to T3 so that they can be used for the determination of the statecontrollers manipulated variable without showing any signs of thesecondary control loops dynamic behavior.

Therefore, within the framework of the overall cascade, thesecondary control loop has only a delaying effect. It does not affectthe stability of the state controller.

Mauell state controller (implemented in a 3rdorder controlled system) with its internal structure adapted to the order of the controlledsystem

X

PI

XX

XX XX

X XXh1

h2

h3

Xh0

k1 k2 k3k0

l1

l2

l3

T2 T3T1

Te Ta

Ta,soll

Disturbance observer State observer

Actuator observer

System parameters:

n Order

ks Gain

T Denominatortime constant

Tt

Tz Numerator

from the systemidentificationmodel

Speed

v

-

Load balancing

Dead time

time constant

State controller


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D

A

MU

A

D

analog

Y

X

W

tvtnKp

M

t

z

n

- Gain factor ks

- System order n

Controlled system

Step change

inquiry

Controlled system model

- Denominator time constant T- Numerator time constant T- Dead time T

System parameters:

Controller model

SetpointgeneratorController in PLS

Structure of the controlled system and controller model generation for the optimization of the setting parameters

Dead Time Compensation of the Controlled System

The actuator observer divides the entire feedback system into thetwo decoupled subsystems PI follow-up control and State setpointcontrol.

If the controlled member of the setpoint control (in our example thisis the superheater) has a dead time (from the superheater inlettemperature Te to the outlet temperature Tt), this cannot be takeninto account by the state observer.

This will also be compensated for by the actuator observer. Theactuator observer represents the third decoupled subsystem, theso-called Dead time system. Like the secondary PI follow-upcontrol, it has only a delaying effect on the feedback control(corresponding to the dead time) and does not in any way affectthe stability of the state controller.

Tools for the Calculation of the Controlled System Parametersand the Optimization of the Mauell State ControllerParameters

A concept had to be developed that allows to determine thecontrolled system parameters n, k, T and Tt and to set up andoptimize the controller parameters in a convenient and practicalway. This concept was implemented in the Mauell controller setuptool AE 4012BS-EH.

A special algorithm based on online computing of the numerousparameters of the disturbance quantities, actuator and stateobservers and state controller and its integration in the Mauell state

controller, obtains the following controlled system parameters byapplying a step change and recording the response to this stepchange:

n = Order

k = Gain

T = Time constant

Tt = Dead time

Function generators adapt these parameters to the currentoperating point as a function of load.

The only parameter of the Mauell state controller that remains to beset is the speed factor V. This parameter defines by what factorthe feedback control loop is to be faster than the controlled system.

Next, we shall briefly discuss the steps to go through to determinethe controlled system parameters.


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Measuring Value Acquisition ofthe Controlled SystemResponse

The measuring value acquisitionrecords the response of the

controlled system to a stepchange of the manipulatedvariable. Before the step changeis applied the controlled systemmust be in the steady-statecondition, or at master controlleroptimization, the follow-up control(e.g., inlet temperature control)must operate in automatic mode.

The amplitude of the step input tothe manipulated variable shoulddiffer clearly from that of any

superimposed disturbances.While the measuring values arebeing recorded, the diagram axesare automatically zoomed andscaled to offer a good overview ofthe measured data.

The transfer function of thecontrolled system is obtainedwhen the controlled system has settled and returned to the steady-state following the application of the step change. The data recordof the measured values can now be saved together with acomment (e.g., the corresponding load point or operating point).

Identification of the Controlled System and Indication of theCharacteristic Parameters

A mathematical model is created from the measuring data set ofthe controlled system step response that clearly describes the

dynamic behavior of the controlled system.

Via menu the stored measuring values are loaded for theidentification of the controlledsystem together with their timespecification and recordinginformation. The actualidentification starts with themarking of the point of timewhen the step change wasapplied (change of themanipulated variable).The automatic determination ofthe model parameters iscompleted as soon as the optimalmodel quality is reached, i.e., ifthe error area between thesimulated behavior of thecontrolled system on the basis ofthe mathematical model and themeasuring data set of the stepresponse is at a minimum.The parameters of the controlledsystem are also displayed.

After the identification of thecontrolled system, the parameter

set of the controlled systemmodel can be stored togetherwith a comment fordocumentation purposes.

System identification during the computing of the step response with mathematical description

Measuring value acquisition of a step response


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Load Level Balancing of theSuperheater Time Response

The derived values can be useddirectly for adjusting the Mauellstate controller. Only the timeconstant T is varied load-dependently, as shown in thefigure, based on the assumptionthat the time constant T changesinversely proportional to the steamquantity. This behavior resultsfrom the higher steam flowvelocities at a high steam quantity.

The parameters for the Mauellstate controller can be set onlineby directly transmitting the derivedvalues.

Load level balancing

Graphical configuration interface with online process data for entering the controlled system parameters as well as the speed factor V.


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Comparison between PID and MZR

PID Controller

The alternative control loop with PID controller has also beenoptimized on the basis of the data of the controlled systemidentification. Supplementary to the procedure for the statecontroller, the program module Controller Design has beenemployed.

The program module Controller Design allows you determineoptimal parameters for P, I, PI, PD and PID controllers on the basisof the system model from the controlled system identification.

The first step is to select the system model of the Controlledsystem identification process. The program loads a graphicalrepresentation of the system model.

The desired control algorithm is specified by enabling the

parameters (kp, Tn, Tv).

The control quality can be specified by entering the maximumovershoot and adapting the dynamic behavior of the actuator signalto that of the actuator. Also, only moderate actuator speeds shouldbe selected to avoid unnecessary wear of the servo drive andgland.

The optimization procedure starts with the simulation of the closedcontrol loop as a response to a step change of the referencevariable. The program provides you with a continuous graphicalrepresentation of the optimization sequence.

The optimization procedure is completed as soon as the specifiedquality criteria are reached, supplying the optimal controller settingparameters (see diagram below):

P component Proportion. coefficient kPI component Reset time Tn [s]

D component Setup time TV [s]

Deriving the optimal setting parameters kp, Tn, Tv for a PID controller with the help of the Controller Setup Tool


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Optimization Complexity

The PID controller tuning is based on the characteristic quantities n,k, T and the parameter T of the controlled system. Theseparameters can be determined in a convenient and straightforwardway using the AE4012BS-EH controller setup tool, and adjusted

load-dependently.

Using the program module Controller Design, the PID controllercan be optimized by deriving optimal kp, Tn and Tv parameters.

Assessment of the PID Controller

The controller results can be characterized as follows:

Rise time Longer than for the MZR

Settling time Longer than for the MZR

Overshoot Depending on the parameteradaptation

Error area Larger than for the MZR

Actuator movement Uneven, highly intervening

Commissioning Similar to the MZR, morecomplex for highly nonlinearsystems

Operating point dependency Partially controllable throughcontrolled adaptation of theparameters kp, Tn, Tv (verycomplex)

Detail diagram of the PID controller in the ME-VIEW operator control and monitoring system (line 1 of the superheater)


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Mauell State Controller (MZR)

The analysis of the model parameters n and k and the load-dependent parameter T specified for the state controller revealed avery good model adaptation.

The advantages of the actuator observer have been proved true asonly the superheater behavior from the superheater inlettemperature to the superheater outlet temperature had to be takeninto account for the parameter assignment of the state controller.

Possible variations from the secondary injection control, theinjection cooler and the actuator, are not taken into account in thismodel. This increases the model quality considerably as difficult tomodel nonlinearities can be neglected.

The initially selected presetting for the speed factor V (initial value1.5), that corresponds to the maximum value of the concurrent PIDcontroller, could be increased to the speed factor V = 3.5.

Optimization complexity

The Mauell state controller tuning is based on the characteristicquantities n, k, T and the parameter Tt of the controlled system.

These parameters can also be determined using the AE4012BS-EH controller setup tool, and adjusted load-dependently.

The speed factor V is the only setting parameter that affects thedynamic behavior of the control loop. The adaptation of thisparameter does not require any specific knowledge.

It was not necessary to implement feed forward control.

Assessment of the Mauell State Controller

The controller results can be characterized as follows:

Rise time Shorter than for the PID

Setting time Much shorter than for the PID

Overshoot 0%, always with asymptoticdamping and thus a goodcondition for a smooth process

Error area Much smaller than for the PID

Actuator movements Accurate, even, allowingtemperature adjustment withminimum feed water quantity.

Commissioning Similar to the PID

Operating point independence Can be compensated for byadjusting the load-dependent

system time constant T.

Detail diagram of the MZR controller in the ME-VIEW operator control and monitoring system (line 2 of the superheater)


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Qualitative comparison of the controller results TA -Tr / ZR(state controller) and TA -Tr PID(PID controller)

Retrofitting Projects

The state controller with observer can be retrofitted to boilers withconventional process control system (hard-wired control andanalog feedback system with control console operating elements)as well as to newer plants with digital process control system and

video terminals, simply by integrating a module of the ME 4012digital process control system in the existing control systems. Theexisting connections between the control room, controller modulesand servo drives remain unchanged. The integration can becarried out while the system is operating provided the necessarypreparations have been made.

It is also possible to integrate the Mauell state controller on thebasis of a Mauell hardware platform so that the plant operator isprovided with the familiar operator control and signalizing concept.The communication with the third-party system is established overa serial or parallel-wire connection. The parameters can beassigned online.

Conclusions

The above comparison between a PID controller and a Mauellstate controller showed that the Mauell state controller withobserver has clear advantages when used in complex systems thatwould be difficult to control using standard algorithms.

The control behavior illustrated in the example has been verified byconcrete industrial applications and proven its worth. Mauellobserver-based control is for example implemented since October1996 in block 5 (coal-/oil-fueled, 420t/h steam capacity) of theHafen power plant, Stadtwerke Bremen AG.

Other applications, like for example the four boilers and the 1000t/hsteam generator of the waste-fueled power station of AWGWuppertal, confirm the successful employment of the Mauell statecontroller with observer.

Momentarily superposed control(interfering) command. The commands are

set off with a time delay to get clearreadings.

FEW: Injection water quantity

FEW/SC

FEW/PID

TSetpoint

[C] TA - Tr/SC TA - Tr/PID TWA - Tr: Outlet temperature

[t/h]

Measured data monitoring

Time

Time


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Representatives

Helmut Mauell GmbHAm Rosenhgel 1-7D-42553 VelbertGermanyTel. + 49 (0)20 53 1 30Fax + 49 (0)20 53 1 34 03E-Mail [email protected]

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Mauell Corporation31 Old C ab in Hollow RoadDillsburg P A 17019US ATel. + 1717 4 32 86 86Fax + 1717 4 32 86 88E-Mail [email protected]

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Tel. + 55 (0)11 79 47-17 77Fax + 55 (0)11 79 47-17 74E-Mail [email protected]

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P. O. Box 3486Abu Dhabi U.A.E.Tel. + 9712 6 22 44 11Fax + 9712 6 22 44 55E-Mail [email protected]

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