A Power Plant Application

Transcription

1 Workshop-Young RMIT Melbourne Dec 009 p. Simulation Model Emulation in Control System Design A Power Plant Application C. Lu, N.W. Rees and P.C. Young c.lu@unsw.edu.au, n.rees@unsw.edu.au School of EET, UNSW, Australia. CRESS, Lancaster University, UK

2 Workshop-Young RMIT Melbourne Dec 009 p. Quotation - Profos 1959 The only remedy is increased teamwork: The control theorist should make it his obligation to discuss the assumptions on which he is going to base his calculation with a experienced control or power plant engineer before erecting a great theoretical building upon an unfit foundation, whereas the practical engineer should take time and pains for such discussions. This would enhance the practical value of corresponding studies and promote the mutual understanding between theoreticians and practician. And this is certainly one of the most important conditions that control theory will be of practical help.

3 Workshop-Young RMIT Melbourne Dec 009 p. Power plant control is very important Modern power stations have excellent computer control systems but the control techniques used are still mainly SISO/PID Attempts have been made at MIMO control (especially LQR) but they have not caught on This study proposes a new advanced control scheme that seems to have potential for large and fast rate load changes in wide range of power plant

4 Power Plant Layout Workshop-Young RMIT Melbourne Dec 009 p.

5 Workshop-Young RMIT Melbourne Dec 009 p. Control problems Power plants need to produce MW as efficiently as possible without violating temperature, pressure and water level constraints. Plant is highly nonlinear with strong interactions, between MW, steam pressure P, and water level DL. Schematic shows the three major inputs and outputs.

6 Workshop-Young RMIT Melbourne Dec 009 p. Major inputs/outputs freq sync u1 - MWsp u - DLsp u3 - Psp G(s) (highly coupled) y1 - MW y - DL y3 - TSVP pump trans mill trans

7 Workshop-Young RMIT Melbourne Dec 009 p. Step input interactions Step Response 1.5 From: In(1) From: In() From: In(3) 1 To: Out(1) Amplitude To: Out() To: Out(3) Time (sec)

8 Workshop-Young RMIT Melbourne Dec 009 p. Control problems Stiff system - 10 time s difference in time delays between the major variables The existing control scheme cannot meet the market demand on fast load change to power industry, causing instability even trips to the plant One of the key control issue is the drum boiler, where the steam is generated. Water level shrink and swell caused by load change.

9 Workshop-Young RMIT Melbourne Dec 009 p. Drum boiler DRUM VALVE Ws - steam flow L - drum water level Steam Pd - drum pressure TVSP - steam pressure Wf - feedwater flow Saturated Mix RISER Q - heat input DOWNCOMER Natrual/Forced Circulation

10 Workshop-Young RMIT Melbourne Dec 009 p. 1 Unit Controller UNIT CONTROLLER UC Load/Pressure Control Drum Level Control Temperature Control Combustion Control PRC SC SC SC SC SC SC SC SC SC SC SC SC

11 Workshop-Young RMIT Melbourne Dec 009 p. 1 Control loops of power plant MW manual MW demand with rate limiter MW demand K Kmw FF PID MW manual setpoint 1 Sum 9 MW controller Sensor Kyt 30 s 15 s +1 Sensor Sensor DL FB MW FB Trans Sliding Pressure Sliding Pressure Mannual TSVP setpoint 1 Kpff 1 K Kp FF Sum 10 L_init Drum Level setpoint PID Pressure Controller PID Level Controller 1 Kup Sum 5 FW Flow FB qs _init PID Feedwater Controller Q_init 1 fuel demand 1 Feedwater Pump Fuel System tf Sensor qs qf tf Q Drum L Drum P Drum Boiler Yt Pd DL Steam Flow TSVP Throttle Valve & Superheater Turbine / Generator qs MW Out TSVP MW output Kfqs Steam Flow FB Sensor TSVP FB

12 Workshop-Young RMIT Melbourne Dec 009 p. 1 UNSW Simulator 8 8 MANUAL SETUP UNIT COORDINATOR DEAERATOR LEVEL CONTROLLER RESERVE TANK MW OUTPUT DEAERATOR (LP) CLOSED FEEDWATER HEATER GENERATOR FUEL CONTROLLER FEEDWATER FEED PUMP FEEDPUMP CONTROLLER (HP) CLOSED FEEDWATER HEATER ECONOMISER DRUM LEVEL CONTROLLER DRUM BOILER 3 3 SUPERHEATER TEMPERATURE CONTROLLER SUPERHEATER & DESUPERHEATER Superheater THROTTLE VALVE CONTROLLER TV Governor Valve TURBINE & REHEATER FEEDER MILLS OR COMPRESSOR FURNACE FORCED DRAUGHT FAN FAN SPEED CONTROLLER AIR HEATER CONDENSATE PUMP CONTROLLER CONDENSATE PUMP CONDENSER

13 Workshop-Young RMIT Melbourne Dec 009 p. 1 UNSW Simulator A nonlinear UNSW Simulator is established to simulate the complex nonlinear power plant. A mixture of Knowledge based and Interpretation models are created in Matlab/Simulink. The extension of key section model - Åström and Bell nonlinear drum boiler model together with other models for all key power plant sections makes the simulator truly a complex, physically based simulation system.

14 Workshop-Young RMIT Melbourne Dec 009 p. 1 UNSW simulator The simulator also includes the existing plant control systems (PID loops) for MW, pressure and level plus the feedforward of unit demand, sliding pressure and overfiring/underfiring signals. All major actuator nonlinearity and constrains are implemented With all above features UNSW simulator can be configured and tuned against a real plant data. The simulator needs to be benchmark tuned against real plant data, before it can be used for control study.

15 Workshop-Young RMIT Melbourne Dec 009 p. 1 Simulator benchmark tuning Set-up real plant operational parameters - MW, Pressure, etc. Tune individual PID control to match the plant key sections dynamic benchmark test data. Internal saturations (such as PID control outputs) need to be removed under small (10 per cent) perturbation and normal operation ramp inputs. Validate the simulator against plant inputs outputs data. One this is done the simulator represent the complex nonlinear power plant.

16 Benchmark data - Drum level Workshop-Young RMIT Melbourne Dec 009 p. 1

17 Workshop-Young RMIT Melbourne Dec 009 p. 1 Benchmark Model response 0.64 water level

18 Workshop-Young RMIT Melbourne Dec 009 p. 1 Advanced control implementation Add-on structure (keep conventional control inside) Trim actions to unit control setpoints 1 Fall back 0 Adv control 1 Unit Demand Sum ADVANCED CONTROL Switch U_adv Sum 1 CONVENTIONAL CONTROL 1 U_con G(s) POWER PLANT PROCESS 3 Y Y

19 Workshop-Young RMIT Melbourne Dec 009 p. 1 Advanced control implementation 3 6 Boiler Demand 3 mux S & Y MW Demand Sliding P SP Unit Coordinater MWd MW BP TSVPd 3 TSVP BP Demand Bypass 3 setpoints 3 sfp To Workspace1 3 Out Set Out FB Advanced Controller 0 Constant FBK Fallback 3 3 Switch 3 Out 1 In1 Out Out 3 Input Scaling mw demand Sliding Pressure boiler demand In1 In In3 Non linear Power Plant Subsystem with PID control loops MW Output Drum level Pressure at TV In1 In Out1 In3 Output Scaling outputs Y e Manual Operation uu 3 3 To Workspace uu 3 ycl To Workspace

20 Workshop-Young RMIT Melbourne Dec 009 p. Control design - Why Captain It has paid special attention to the Transfer Function (TF) models using robust unbiased Refined Instrumental Variable (RIV) and Simplified Refined Instrumental Variable (SRIV) algorithms It can establish both DT and CT well fit TF models. It can identify the TF models in a multiple-input, single output (MISO) manner. It handles multivariable NMSS formation and control design in a streamlined way. It has multivariable PIP control implementation.

21 Workshop-Young RMIT Melbourne Dec 009 p. Control design - Why Captain It has a specialised function rivid which allows the automatic search for a group of the best fit models over a user defined range of different model structures and time delays which makes the determination of the TF models very efficient. Its model fitness statistical criteria includes the Coefficient of Determination (RT ), Akaike Information Criterion (AIC) and especially the Young Information Criterion (YIC) which give very clear indication of how well the model describes the data.

22 Workshop-Young RMIT Melbourne Dec 009 p. Dominant Mode Analysis(DMA) The response of high order linear dynamic models is always dominated by a small number of modes. If these modes can be detected, then they form the basis of an accurate reduced order model. Through Dominant Mode Analysis (DMA) [Young, 1999] such a dominant mode model can be obtained from the real data, as a reduced order emulation of the high order simulation model. Often such dominant mode model can mimic the larger perturbation responses.

23 Workshop-Young RMIT Melbourne Dec 009 p. Dominant Mode Analysis(DMA) High Order Model: (e.g. 17th order and circa 180 parameters) Reduced Order Model: (e.g. 3rd order and 7 parameters) High Dynamic Order Model Estimated Parameters Parameterized State-Dependent Parameter Regression (SDR) Relationships..... Reduced Dynamic Order Model Parameters

24 Workshop-Young RMIT Melbourne Dec 009 p. Dominant Mode Analysis(DMA) The resulting nominal emulation model can produce continuous and discrete SISO and MISO transfer function models. Further CAPTAIN functions are used to form NMSS models which can be used to design feedback controllers. In particular the multivariable LQ-PIP controls can be formed.

25 Workshop-Young RMIT Melbourne Dec 009 p. Control design Define 3 x 3 input/output discrete-time (DT) transfer function (TF) model, a linear model of the above nonlinear process, as the suitable model for this application Refined Instrumental Variable (RIV) algorithms from Captain is used for the identification of nominal TF models.

26 Workshop-Young RMIT Melbourne Dec 009 p. Identification of nominal TF models The process excitation and data logging, in order to be able to apply to a real plant, is in SIMO form, as it is impossible to perturb 3 major power plant setpoints simultaneously. The model is then established in MISO way, after all data collected, provided that the plant s operating points are steady and not time varying during the logging.

27 Workshop-Young RMIT Melbourne Dec 009 p. Identification of nominal TF models Two different sets of models are necessary in our control design procedure. They are design model DM and process model PM. The idea is that the DM used for control design cannot be used to verify the controller (ideal result). Yet at design stage, it is difficult to tune control on an nonlinear simulator, or the real process. So PM is used to verify control result first before further tests on a nonlinear model.

28 Workshop-Young RMIT Melbourne Dec 009 p. Identification of nominal TF models It is desirable that DM is as simple as possible, yet it is necessary to capture the major process dynamics with correct phase. With quantitative analysis procedure DM and PM design method gives the designer much better chances to identify and judge the right DM model efficiently. DM s model structure requires common denominator, while PM does not have such restrictions.

29 Workshop-Young RMIT Melbourne Dec 009 p. Identification of nominal TF models DM - as low order as possible with common denominator TFs in a row of TF matrix. [ 3 1] and [3 3 1] models have been established as the lower order models - for control design. PM - no order or common denominator restrictions on TF. [4 4 1] model is the highest order structure fits the process very well - for control tuning. Through DMA two reduced order linear dynamic models are estimated. (Both have lower order than that of nonlinear simulator)

30 Workshop-Young RMIT Melbourne Dec 009 p. 3 NMSS and PIP control design NMSS presentation of MIMO TF model for control design is extremely relevant to industrial applications, for its explicit use of only measurable variables and their past values that are available from DCS system. The non-minimal states as past measurable values in the state space matrix makes the resulting control with inherent model predictive control action, that is significant feature for problems with long time delay, such as power plant control.

31 Workshop-Young RMIT Melbourne Dec 009 p. 3 NMSS and PIP control design In DT LQR design, system output vector Y in the cost function has horizontal history values from the DM model (dynamics of the process) it is interesting to note the non-adaptive model predictive action of the resulting state feedback control law. PIP control implementation, with introduced integral of error in NMSS, is a multivariable controller with extra integral action to eliminate the static error in the system. It is important feature for real control applications.

32 Workshop-Young RMIT Melbourne Dec 009 p. 3 PIP control The block diagram for such a PIP control system is shown. The negative sign associated with is introduced to allow the integral states to take on the same structural form as multivariable PI/PID. In this manner, the PIP controller can be interpreted as a logical extension of conventional PI and PID controllers, with additional dynamic feedback and input compensator introduced automatically when the process has second order or higher dynamics or more than a single sample pure time delay.

33 Workshop-Young RMIT Melbourne Dec 009 p. 3 PIP forward path control y (k) d + - z(k) K(I) + - I u(k) y(k) Process M(z)+I L(z) ^ -1 S(z )

34 Workshop-Young RMIT Melbourne Dec 009 p. 3 Design flowchart Captain Tool Box To establish models Data Collected from Power Plant System Identification DM Lower order model with common denominators sets of models PIP-LQR Design Young, et al DM Design Model PM The most fit model with/without common denominators NO PIP-LQR Design NO Implement control structure with LQR gains. Either FB or FP structure Control Simulation Using DM PM Process Model Choose Weights Control Simulation Using PM NO Better Control - Advanced controller manipulates all 3 set points to achieve much reduced DL and P errors while the MW is least disturbed Desired control results? YES Non-linear System Simulation and Real Plant Test Benchmark Tuned Non-linear Simulator Desired control results? YES Real Issues A. Add-on structure B. Scenario tests C. Disturbance tests D. Starting up... DCS Implementation Program DCS with Controller Structure

35 Workshop-Young RMIT Melbourne Dec 009 p. 3 Advanced controller tuning Follow the design procedure DM, PM, and nonlinear simulator are utilized to carry design, initial control test and nonlinear control test LQ-PIP forward path (FP) control structure is found to be able to stabilise the process and to improve the performance. While standard feedback structure (FB) gives identical results when DM is used as process, but failed the test when PM or nonlinear models is used.

36 Workshop-Young RMIT Melbourne Dec 009 p. 3 Advanced controller tuning Extensive tuning is carried out at each stage of the design and test, through diagonal weight vectors w y,w u and w z. NMSS representation gives a sense of physical meaning on weights but the link to the control performance is still not direct due to LQ s nature. A numerical procedure with quantitative error indicators is created to tune the weights one at a time, which makes the tuning process more efficient.

37 Workshop-Young RMIT Melbourne Dec 009 p. 3 Control results This multivariable linear control solution, when applied to a nonlinear process, cannot totally decouple the interactions, rather it is a optimal manipulation of the interaction to reduce the interaction so the errors can be significantly reduced. Control results show very good error deduction, especially drum water level is well within the alarm lines. Shrink and swell has be reduced more than 5 times. Pressure delay has been much reduced.

38 Workshop-Young RMIT Melbourne Dec 009 p. 3 Tuning results - 31DM The weights are tuned directly in favour of MW and DL, so we have good tracking performance, P is tuned to its derivation because pressure derivation has the immediate effect on water level, as shown in both nonlinear and linear models. The resulting steam pressure is moving around setpoint during the transient. In fact in practice the pressure is never tuned too tight.

39 Workshop-Young RMIT Melbourne Dec 009 p. 3 Tuning results - 31DM Tuning is progressive - from on PM and on NS (nonlinear simulator) Results shown are from the tuning on NS. w y = ; w u = ; w z = ;

40 Workshop-Young RMIT Melbourne Dec 009 p. 4 Tuning results MW step Blue - open loop, Red-dotted - unity weights, Black - weights tuned MW-unity MW -tuned MW-PID DL-unity DL -tuned DL-PID TSVP-unity TSVP-tuned TSVP-PID

41 -0.05 Workshop-Young RMIT Melbourne Dec 009 p. 4 Tuning results MW ramp Blue - open loop, Red-dotted - unity weights, Black - weights tuned MW-unity MW -tuned MW-PID DL-unity DL -tuned DL-PID TSVP-unity TSVP-tuned TSVP-PID 0

42 -0.05 Workshop-Young RMIT Melbourne Dec 009 p. 4 Tuning results TSVP step Blue - open loop, Red-dotted - unity weights, Black - weights tuned MW-unity MW -tuned MW-PID DL-unity DL -tuned DL-PID TSVP-unity TSVP-tuned TSVP-PID 0

43 -0.05 Workshop-Young RMIT Melbourne Dec 009 p. 4 Tuning results TSVP ramp Blue - open loop, Red-dotted - unity weights, Black - weights tuned MW-unity MW -tuned MW-PID DL-unity DL -tuned DL-PID TSVP-unity TSVP-tuned TSVP-PID 0

44 Workshop-Young RMIT Melbourne Dec 009 p. 4 Original control performance y1 MW - Red, y Drum level - Green, y3 TSVP - Blue

45 Workshop-Young RMIT Melbourne Dec 009 p. 4 Closed loop wide range load - 31DM y1 MW - Red, y Drum level - Green, y3 TSVP - Blue

46 Workshop-Young RMIT Melbourne Dec 009 p. 4 Control signals - 31DM u1 for MW - light blue, u for Drum level - magenta, u3 for TSVP - yellow

47 Closed loop wide range load - 331DM y1 MW - Red, y Drum level - Green, y3 TSVP - Blue Workshop-Young RMIT Melbourne Dec 009 p. 4

48 Control signals - 331DM u1 for MW - light blue, u for Drum level - magenta, u3 for TSVP - yellow Workshop-Young RMIT Melbourne Dec 009 p. 4

49 Workshop-Young RMIT Melbourne Dec 009 p. 4 Discussion and Future Works Multiple range control designs Nominal TF models from the data collected at different load range, design and tune controller against each model Test each controller on wide range operation Compare the performances of each controller over wide range load condition Use the best performed control or if none of them can cover the full load range of power plant - gain scheduling?

50 Workshop-Young RMIT Melbourne Dec 009 p. 4 Discussion and Future Works Multiple range control designs Nominal TF models from the data collected at different load range, design and tune controller against each model Test each controller on wide range operation Compare the performances of each controller over wide range load condition Use the best performed control or if none of them can cover the full load range of power plant - gain scheduling? Control sensitivity test - Varying plant parameters (Monte Carlo Simulation)

51 Workshop-Young RMIT Melbourne Dec 009 p. 5 Discussion and Future Works Develop a complete nonlinear State-Dependant Regression (SDR) TF model for this nonlinear system.

52 Workshop-Young RMIT Melbourne Dec 009 p. 5 Discussion and Future Works Develop a complete nonlinear State-Dependant Regression (SDR) TF model for this nonlinear system. Control design issues

53 Workshop-Young RMIT Melbourne Dec 009 p. 5 Discussion and Future Works Develop a complete nonlinear State-Dependant Regression (SDR) TF model for this nonlinear system. Control design issues Mapping the relationship between PM and DM. (PM and DM are all nominal models obtained from the nonlinear data.) To judge how well DM represents the dominant dynamics of the original system.

54 Conclusions Workshop-Young RMIT Melbourne Dec 009 p. 5

55 Conclusions Workshop-Young RMIT Melbourne Dec 009 p. 5