PID-Controller Tuning Optimization for Power Station ...

PID-Controller Tuning Optimization for Power Station Processes Y. Hain, R. Kulessky, G. Nudelman,

Abstract: This paper presents practical results of the new approach for PI/PID controller tuning of power station control loops. This tuning is fulfilled in the frame of the robust theory approach by using four object-oriented criteria for power station processes. High accurate identification of process and its uncertainties provide the robustness basis of the controller optimization. Based on this approach the Control System Tuning Package (CSTP) was developed and successfully applied for control loops tuning of power units at the Israel Electric Corporation (IEC). Key Words: PID-controller tuning, Process identification, Power system modeling.

model is described as a first order lag plus time delay (FOPTD-model). • Dissimilar power station processes require different optimization criteria. In the above packages the same optimization criterion is usually applied to these processes. • PID-controller usage is also limited by control valve speed constraint. The latter can significantly restrict forceful properties of PID-controller. The known tuning packages do not take into consideration this constraint. Ignoring these requirements causes unrealistic PIDcontroller calculation and PI-controller becomes more effective. To overcome the above problems a new approach to PIDcontrol optimization is proposed [5]. The two-stage procedure is also applied to this approach which is based on the following principles: • Four parameters of PID-controller, G, R, D, TD , are optimized simultaneously according to its transfer function

I. INTRODUCTION We consider SISO (Single Input Single Output) - control loops controlling temperature, pressure, level, flow, load and other parameters of power station units. Optimal control loop tuning of such loops is a way for increasing efficiency, robustness and survivability of the power unit. This tuning is obtained by adjusting of PI- or PIDcontroller parameters. A number of effective computer packages for tuning optimization of PID-control loops are already used in practice: ExperTune, LALTS02, WES-Tune, Tune Wizard, Protuner System Analyzer, INTUNE etc. [1-6]. They use the two-stage optimization procedure: the first stage is a process identification while the second one is controller parameters calculation using the identified model so obtained. However, in practice PI-controllers are usually used in the above control loops of power stations instead of more adaptable PID-controllers. This phenomenon can be explained by the fact that the above packages do not take into consideration requirements of power station processes on PID-controller optimization: • Optimal parameters of PID- controller have to include the fourth parameter, which is also optimized: the filter time constant TD of a differentiating part. This filter is needed not only to reduce noises influence but also to prevent mechanical impacts in a control valve (final execution element). In the above packages such filter either absent or its time constant is predetermined by the ratio D / TD where D is the •

C (s) = G +

1 D⋅s + R ⋅ s TD ⋅ s + 1

(1)

•

The optimization is fulfilled in the frame of the robust theory approach using four object-oriented criteria for power station processes considering also the control valve speed constraint. • High accurate identification of process and its uncertainties support the robustness basis of the controller optimization. Theoretical principles of this approach are successfully applied for PID-control loops tuning of IEC power units. II. RESULTS OF POWER STATION PROCESS IDENTIFICATION The CSTP identifies SISO- or SIMO- (Single Input Multi Output) processes in either closed or open loops. Test data file is the time responses of these processes to a deterministic input signal (test signal) among them the step function. Examples of five main processes, temperature, level, load, flow and pressure, are shown below. For further usage the following functional is introduced:

differentiating gain. Usually D / TD ≥ 8 − 10 . Both PI- and PID- controllers robustness has to be based on their optimization which employs the most accurate model of a thermal process. However, above packages are usually oriented on a very simple model which is often inadequate for thermal processes. This

Q = (1 −

1

M {(h(t ) − hˆ(t )) 2 } )100% M {h 2 (t )}

(2)

where h(t), hˆ(t ) are a process and its model outputs accordingly. Clearly, Q ∈ [0,100%] and the ideal identification corresponds to Q =100% .

load, steam flow, throttle pressure) to the test signal (the control valve motion deviation), see Fig.3. The identified processes are described by the following transfer functions: Turbine-generator load process (Fig3b)

A. SISO-process identification

WTG ( s ) =

Temperature and level SISO-processes identified by the CSTP are exemplified in Fig.1. They are described by the following transfer functions: Temperature superheated process (Fig.1a) 1.4e , Q=98.9% 7956s 2 + 112.6 s + 1

WTP ( s ) =

20 s (−0.15s + 1) , Q=91.6% 0.51s 3 + 101s 2 + 56 s + 1

(7)

(2) Haifa PS, 140MW unit, Superheater temperature control time responses 545 Temperature, degC

Condenser level process (Fig.1b) (39.8s + 1)e −3 s , Q=95.8% WCL (s ) = 59.5s (78.3s + 1)

(6)

Throttle pressure process (Fig.3d) in closed loop operation with PI-pressure controller

−3 s

WSH ( s ) =

0.7 s + 0.27 , Q=97.7% 2.36 s 2 + 0.43s + 1

(3)

540 Model temperature 535 530 Unit temperature

a)

525 0

100

200

300 Time, sec

400

500

600

Temperature, degC

545 540 Model temperature

535 530 Unit temperature 525

0

100

200

300 Time, sec

b) 400

500

600

Fig.2. Plot of time responses for the cases of the FOPTD model (4)- (a) and of model (5) - (b). C. Conclusion The following conclusions can be reached: • As one can see from Fig.1-3 and other examples [5-8], optimal identified models represent their processes with very high accuracy. Besides, their quality indices are usually in interval of (95-98)% while 100% corresponds to the ideal identification [6, 7]. So the CSTP provides the high accuracy identification. • Usually the order of optimal models is in excess of FOPTD-model. So the FOPTD-model is not always the optimal one (compare, for example, (4) and (5) and Fig,2,a,b. • The CSTP identifies not only self-regulating and integrating processes but also differentiating processes (see for example (7) and Fig.3d).

Fig.1. Time plot responses of processes and their models: a) SISO-open loop identification; b) SISO-closed loop identification. We note that the FOPTD-model does not perform well for this process. This can see from Fig.2 where responses of the FOPTD-model WTP ( s ) =

1.44e −36 s . Q=92.8% 114s + 1

(4)

and the optimal model WTP ( s ) =

1.44e −15 s , Q=98.8% 99080s 3 + 7980s 2 + 131s + 1

III. CONTROL LOOP OPTIMIZATION RESULTS

(5)

Parameter optimization of PI/PID controllers is based on the above high accuracy identified model of a process. In other words, this identification is the first stage of the PI/PID optimal tuning while the second stage is the controller parameter optimization. The CSTP calculates four sets of optimal parameters for PID-controller, G , R, D, TD , according to its transfer function (1) from four different criteria [2]: or Integral

are given. B. SIMO-process identification Load, flow and pressure processes of 350MW unit are identified in the frame of SIMO-model by the CSTP (Fig.3). The data file includes three time responses (unit

2

A. Superheater temperature control system of 140MW unit

Index Quality maximum or Overshoot minimum or Rise Time minimum, or Settling Time minimum. In parallel four sets of PI-controller optimal parameters are also calculated. After comparing these optimization results, the best sets of PI- or PID- parameters are recommended as the optimal ones. This optimization is fulfilled on account of: • Permissible standard deviation of control valve fluctuations sd max caused by noises. •

The initial PI-controller has parameters of G=0.5, R=120. PID-optimal parameters calculated by the CSTP are G = 0.9, R = 100, D = 43, TD = 17 . We note that D / TD ≈ 2.5 . Requirements for optimal loop operation are as follows: a) Maximum integral index quality of control loop step response b) ∆ϕ max = 50deg , sd max = 0.1%, vs max = 10% / s The superheater temperature control loop responses are shown in Fig.4. Because of relatively low level of noise and sufficiently high value of sd max , PID-controller provides the highest quality.

Constraint for control valve maximum speed vs max .

Requirement for phase margin ( ∆ϕ max ) of this optimal control loop (robustness requirement). Industrial examples given below illustrate this presented tuning procedure. •

REMARK: It is possible to assume that the Set Point Filter combined together with PI-controller may provide the optimal behavior of a control loop with an integrating process. So we use usually PI-controllers for integrating process control.

Fig.4. Plot time responses for superheater temperature control loop of 140MW unit. B. Main air flow control system of 140MW unit The initial PI-controller has parameters of G=0.35, R=30. PID-optimal parameters calculated by the CSTP are equal to G = 0.48, R = 13.3, D = 0.38, TD = 5.7 . The ratio D / TD ≈ 0.07 . Requirements for optimal loop operation are as follows: a) Maximum integral index quality of control loop step response b) ∆ϕ max = 60deg , sd max = 0.1%, vs max = 5% / s The main air flow control loop responses are shown in Fig.5. Because of relatively low level of noise PIDcontroller provides better quality. However, due to value of sd max , which is smaller, dynamics improvement is lesser than in the previous example.

Fig.3. Plot time responses of processes and their models: SIMO-closed loop identification where: a - test signal (control valve position), b - unit load, c - steam flow, d throttle pressure.

3

Rutenberg PS, 575MW unit, Deairator level control system 2740 2720 Level set point

2700

Level, mm

2680 Deairator level before optimal tuning 2660 2640

Deairator level after optimal tuning

2620 2600 2580 2560

100

200

300

400 500 Time, sec

600

700

800

Fig.5. Plot time responses for main air flow control loop of 140MW unit.

Fig.7. Tuning results for deairator level control system of 575MW-power unit

C. Drum level control system of 75MW unit

D. Fuel Temperature Control System of 228MW Unit

According to the remark above we use PI-level controller. The initial PI-controller has parameters of G=4, R=300. PI-optimal parameters calculated by the CSTP are G=6.2, R=450. Requirements for optimal loop operation are as follows: a) Minimum response overshoot to the set point step b) ∆ϕ max = 60deg , sd max = 0.1%, vs max = 10% / s The corresponding responses are shown in Fig.6.

The initial PI-controller has parameters of G=1, R=160. PI-optimal parameters calculated by the CSTP are equal to G=0.31, R=818. Requirements for optimal loop operation are as follows: a) Maximum integral index quality of control loop step response b) ∆ϕ max = 60deg , sd max = 0.1%, vs max = 10% / s The fuel temperature control loop responses are shown in Fig.8. Eshkol PS, 228MW unit #8, Fuel heater control system Temperature, degC

115 Temperature set point

Fuel temperature

a)

110

105 Initial controller parametetrs 100

0

200

400

600

Optimal controller parameters

800 1000 Time, sec

1200

1400

1600

20 Position, %

Initial controller parametetrs Optimal controller parameters 15 10 5

Control valve position b)

0

Fig.6. Tuning results for drum level control system of 75MW power unit

0

200

400

600

800 1000 Time, sec

1200

1400

1600

Fig.8. Plot time responses fuel temperature control loop of 228MW unit.

D. Deairator Level Control System of 575MW Unit The PI-level controller is used. The initial PI-controller has parameters of G=3, R=5min. PI-optimal parameters calculated by the CSTP are G=6, R=20min. Requirements for optimal loop operation are as follows: a) Settling time minimum of the control loop step response ∆ϕ max = 60deg , sd max = 0.1%, vs max = 10% / sec, b) ∆ϕ min = 40deg , ∆g min = 6dB The corresponding responses are shown in Fig.7.

E. Load-frequency control system of 228MW unit The control system includes two PID-control loops of the unit load and of the throttle pressure. By using the CSTP the 228MW unit model was created for purposes of a new control structures acceptance of the above load-frequency control. Dynamical features of this system are checked by using data of different net frequency abrupt deviations. In particular, Fig.9 illustrates response of 228MW unit to net frequency deviation caused by trip of another unit 228MW.

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[3] Bailey infi90, Loop Tuning System, Controlsoft, Inc. 1992. [4] http//www.westinghousepc.com [5] http//www.tunewizard.com [6] http//www.protuner.com [5] R. Kulessky, Y. Hain, G. Nudelman, Conception of PID-Robust Control for Power Station Processes, Conference of the 2000 IEEE/IAS Industry Application, Rome, Italy, 2000, 00CH37129, vol.2, pp.1073-1080. [6] R. Kulessky, G. Nudelman, Y. Hain, Conceptions of Computer Package for Optimization of Power Station Control Loops Tuning, Conference Record of the 1999 IEEE/IAS Industry Application Conference, Phoenix, USA, 1999, 99CH36370, vol.1, pp.331-338. [7] R. Kulessky, G. Nudelman, Y. Hain, Thermal Power Plant Dynamics Identification, Proceedings of 1999 American Control Conference, San Diego, USA, 1999, 99CH36251, vol.2, pp.843-847. [8] Y. Hain,R. Kulessky, G. Nudelman, Identification – Based Power Unit Model for Load-Frequency Control Purposes. IEEE Transactions on Power Systems, vol.15, No.4, November 2000. [9] http://www.ardan-pic.co.il/cstp/CSTP-Main.htm

As one can see, the unit model created by the CSTP represents this unit behavior sufficiently well.

Fig.9. Simulation results for load-frequency control system of 228MW power unit IV. CONCLUSION

V. BIOGRAPHIES

Practical results of using the new computer package CSTP for PI/PID-controller tuning optimization are presented. The CSTP based on a new approach of PID-robust control optimization for power station processes. This approach is defined by the following principles [5]. • Four parameters of PID-controller, G, R, D, TD , are optimized simultaneously according to its transfer function (1) • The optimization is fulfilled in the frame of the robust theory approach using four object-oriented criteria for power station processes considering also the control valve speed constraint. • High accurate identification of process and its additive and multiplicative uncertainties support the robustness basis of the controller optimization. The presented practical results exemplify CSTP package successful implementation at power units of the Israel Electric Corporation.

Yakov Hain was born in Latue in 1955. He received his M.S. degree in Electrical Engineering from the Riga Politechnic Institute. His research interests are power system protection, energy system modeling, dynamic features estimation, control parameters optimal adjustment. He is a Member of the CIGRE Study Committee, No. 34. Currently he is the Deputy VicePresident of the Israel Electric Corporation, Israel., [email protected]

Roland Kulessky was born in Russia in 1937. He received his M.S. and Ph.D. degrees, all in Electrical Engineering, from the Ural Politechnic Institute (UPI), Russia, in 1959 and 1967. He received his Dr.Sc degree in Electrical Engineering from the Moscow Energy Institute in 1989. Up to 1991 he had worked in Russia: the Ural Turbine Plant (1959-61), Electro-Project Institute (1961-67), the UPI (1967-91). From 1989 he is a Professor of Electrical Engineering Faculty at the UPI. Immigrating to Israel in 1991 he currently works at Control System Department of the Israel Electric Corporation and at Electrical Engineering Faculty of the Technion - Israel Institute of Technology. His research interests are digital control systems optimization, process identification, variable bandwidth control, electric drive digital control, the amplitude quantization theory. [email protected]

V. ACNOWLEDGMENT

Gregory Nudelman was born in Russia in 1958. He received his M.S. degree in Control System Engineering from the Moscow Institute of Railway Engineering in 1980. Up to 1991 he has been working in Russia as a Control Field Engineer at the Start Up Company(1981-1991). There he dealt mainly with optimization of power units control systems configuration and their performance optimization in their main regimes From 1992 he is in Israel working as a Principal Engineer at the Haifa Power Station of the Israel Electric Corporation. His research interests are thermal power process identification, load - frequency coordinated control, industrial control system tuning, auto-tuning problem. [email protected]

The authors wish to thank D. Kohn and M. Bachar for their help in planning and executing this work at the Israel Electric Corporation VI. REFERENCES [1] http//www.controlsoftinc.com [2] http//www.expertune.com

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