Fuzzy Control of a Supercritical Once-Through Boiler

1

Fuzzy Control of a Supercritical Once-Through Boiler

S. Ali A. Moosavian

¹

Ali Ghaffari

²

Ali Chaibakhsh

³

Department of Mechanical Engineering

K. N. Toosi Univ. of Technology, Tehran, Iran, P.O. Box 16315-996, Fax: (+98) 21-733-4338

Email:

moosavian@kntu.ac.ir

1- Assistant Professor.

2- Associate Professor.

3- Graduate Student.

Abstract

Increasing the use of electricity and need for more and safer power generation has motivated investigation into new control methods resulting in better performance.

Better system performance means increase in power generation efficiency, also decrease in the maintenance costs. To design suitable controllers, adequate information about the system dynamics is required. In this paper, for the supercritical once-through boiler of Neka power plant, a multilayer neuro fuzzy model is used to design P²ID controllers. Based on boiler real performance, these controllers are designed to control superheaters and re- heaters temperature. Simulation results show very good performance of these controllers in terms of more accurate and less fluctuation in the temperature of corresponding subsystems.

Keywords: Control, Power Plants, Fuzzy Logic.

I. Introduction.

In power generation, an essential requirement is to achieve optimal operation in terms of variant objectives, such as minimization of load tracking errors, minimization of fuel consumption and heat rate, maximization of duty life, and minimization of pollutant emissions. Load tracking, voltage and frequency stability have been the basic issues of more effective control design, [1]. General duties for the power plant control systems are expressed below.

• Capability of generating a desired load for a wide range of operation commanded by the dispatching center;

• Having a fast response to the set point changes;

• Accurate performance even in the presence of disturbances;

• Safe and smooth operation of power plant;

• Reduction of thermal stresses due to steam temperature and pressure fluctuations, that in turn minimizes operational and maintenance costs;

• Reduction of the fuel consumption, due to higher efficiency of combustion;

• Reduction of the environmental pollutions;

• Reduction of the operators supervisory efforts;

• Fault diagnostics capabilities to minimize the maintenance costs.

Control of fossil fuel boilers has been extensively addressed, using classic and modern control approaches, [2- 4], linear or non-linear model of boiler, [5-6]. Despite the significant developments in the control theories, PID controllers are still the major controllers used in the industry.

In the last three decades, fuzzy logic control has evolved as an alternative or complementary to the conventional control strategies in various applications, [7-8]. The fuzzy logic controllers are recommended for systems with nonlinearity and uncertainty. Unlike conventional controls, designing a fuzzy logic controller does not require precise knowledge of the system model such as the poles and zeroes of the system transfer function, [9]. It has been shown a hybrid scheme of fuzzy control and PID control has a better performance compared to the conventional classic and fuzzy control algorithms, [10-12].

To implement different control laws and strategies, having models of system dynamics is necessary. These models for steam power plants can be obtained based on fundamental laws of Physics such as conversion of mass,

momentum and energy, semi-empirical laws for Heat transfer and Thermodynamics, [13-14]. Such models can be also developed using mathematical identification procedures based on measured data during real working state of the plant, [15-16]. The procedure to determine a test-data-based model from input-output boiler data, involves four following steps, [17]:

• Experiments and collect input-output data

• Elect and define a model structure

• Estimate the parameters of model

• Model validation In this approach

In this paper, two fuzzy P²ID controllers are designed and implemented for the superheater and reheater temperature control of Neka power plant. This new controller has benefits of both fuzzy PD and PI controllers. To this end, an Adaptive Neuro Fuzzy Interface System (ANFIS) is used to prepare the plant model based on real data measured at the power plant. These fuzzy models are developed to cope with nonlinearity and uncertainty. Also, these nonlinear fuzzy models are able to cover a wide range of operational conditions. Simulation results show the effect of using these new fuzzy P²ID temperature controllers on the cycle processes.

II. System Description

A 440 MW unit (Unit #2) at Neka Power Plant is studied for identification, modeling, and new controllers design.

Neka power plant is located at 25 km far from Neka, a city in Northern Iran. The plant consists four fossil fueled generating units with the rated capacity of 440 MW. The steam generator of each unit is a supercritical once through Benson boiler, Fig. 1. A single furnace with 14 bottom burners serves to heat all parts of boiler. The hot water is converted to steam in evaporator, and gets superheated by passing through superheaters. The evaporator lies in the area of the higher flue gas temperature and is supplied as a spiral belt. The evaporator outlet temperature is about 365^oC. Neka superheater consists of 4 sections built in the boiler second pass. The heating surface of superheater #1 is located in the boiler walls. The steam leaves superheater #1 outlet header, and goes toward superheater #2. The outlet steam temperature from these sections should be constant at 407^oC.

These sections have an emergency temperature control that works just when the boiler is overhauling. The steam leaves superheater #2 toward superheater #3. For rapid steam temperature control, four spray attemperators have been considered between the two sections. In the de-superheater mixing path, atomized feedwater is injected by nozzles into the steam path, thereby, reducing the steam temperature. The spray flow is taken from feed water pumps and should not normally exceed approximately 6% of the boiler load at the time. The mean water spray flow from each nozzle is 10 ton per hour. In this case, the outlet temperature should be kept

constant at 470^oC. The steam temperature control of superheater #3 is shown in Fig. 2. Its control system is a cascade control method that can improve system performance compared to single closed-loop control by limiting the effect of the disturbances of the secondary variable (inlet steam temperature) on the primary output (outlet steam temperature). In outer control loop, the superheater #3 outlet temperature is compared with its set point (470^oC) to yield an error signal for a PI controller. This controller adjusts the set point for inner control loop to be compared with superheater #3 inlet temperature. For a better performance, the rate of fuel consumption signal (as a prediction signal) and high pressure steam flow signal are considered in the control loop. This signal goes to a P-Action controller to adjust the water spray valve stem position. The superheater #4 temperature control is same as that of #3. The steam temperature at outlet header of boiler should be constant at 535^oC. At full load condition, the outlet superheated steam pressure at boiler outlet header is 18.1 MPas.

The superheated steam from main steam header is fed toward the high pressure (HP) turbine, and from high pressure turbine is discharged into the cold reheat header, Fig. 3. The steam temperature in cold reheat line is 351^oC.

The reheater consists of two sections. There are four attemperators between these sections. The reheater outlet temperature is controlled in the same way to superheater sections with small differences (i.e. prediction signals are generated by the rate of steam temperature and feed water flow signals), Fig. 4. The spray flow is taken from an low pressure (LP) stage of the feed pump. The outlet reheated steam temperature after RH-A should be constant at 452^oC and for RH-B should be constant at 535^oC. Also, at the full load condition, the outlet reheated steam pressure is 4.35 MPas. The reheated steam is used to feed the intermediate pressure (IP) turbine. Exhaust steam from IP turbine is fed into the low pressure turbine. Also the extraction steam from IP turbine is fed into the feedwater turbo pump and feedwater storage tank (de-aerator). Four low pressure heaters are fed by the extraction steam from LP turbine. The low quality low pressure steam from all parts is discharged into the main condenser. This condenser is an open loop condenser using the sea water to cool up the hot water and steam. The condensate water is pumped into the feedwater storage tank via a train of LP heaters. A feedwater turbo pump with 13.2 MW power (or two electrical pumps with 9MW power when the turbo pump fails) prepares compressed feedwater at 28 MPas Pressure for boiler. Then, the feedwater is fed into the high pressure heaters and its temperature is increased about 80^oC. The hot feedwater inlet into the economizer header and the power generation cycle is repeated. A concise description of boiler HP sections, and IP parts is summarized in Tables I and II, respectively.

III. Fuzzy Model Development.

The fuzzy logic modeling is best suited for systems with nonlinearity and uncertainty such as steam power plants, [18- 19]. Most of processes in a steam boiler have multi input and multi output (e.g., temperature, pressure). The TSK fuzzy models can be expressed by a set of following typical rules, [1] and [20]:

n j n j

j j j

n

n j

x c ...

x c c , THEN y LX

is nd x and ... a is LX

IF x

+ + +

=

_{0 1 1}

1 1

(1) whereLX₁^j is a membership function associated with the input variable

x

_n. Neural network learning techniques facilitate these fuzzy model parameter tuning. The neural networks to prepare these models are the general-purpose adaptive neuro fuzzy inference system (ANFIS) technique. In the ANFIS method, a fuzzy system with n inputs and N rules is represented by a five-layer feed forward network structure with N neural processing units in layers L1, L2, L3, and L4, and a single unit in L5, [14], as follows.

- Layer 1 (L₁): Each neuron in layer L1 fuzzifies the incoming input signal using Gaussian membership functions:

)2

(

) 1 (

β

µ

_{r i}

=

_x−α

e

x

(2)

- Layer 2 (L₂): At this layer by multiplying all incoming values, each node calculates the degree of fulfillment of the corresponding rule.

) (

n

1 i

r

r

∏

=

= µ x

_i

τ

(3)

- Layer 3 (L3): Each unit calculates a relative degree of fulfillment of the corresponding rule by normalizing its degree of fulfillment with respect to the degrees of fulfillment of all the rules.

∑

=

=

_n

i i r r

1

τ

τ τ

(4)

- Layer 4 (L4): Each node calculates the consequent of the corresponding rule weighted by its relative degree of fulfillment.

) (

₀^r ₁^r _{1 n}^r _n

r r r

r

y c c x ... c x

y = τ = τ + + +

(5)

- Layer 5 (L5): The only neural unit in L5 is connected to all units in L4. The node calculates the final output, y, of the fuzzy system by adding all the incoming weighted consequents.

∑

=

=

^N

i

y

i

y

1

(6) All input-output patents can be defined as follows:

1 ) 1 ( ) 1 (

1 × + + ×

×

=

_{M n N n N}

M

X C

Y

(7)

Usually

M > (n + 1)N

and there are more patterns than parameters to calculate. In this case, there is no exact solution for equation (7). To estimate C, the squared error

||

2

Y - XC

||

is minimized. So, C is obtained as below.

XY X X

C

_lse

= (

^T

)

⁻¹ (8)

Also C can be computed recursively by Goodwin and Sin adapting method.

1 ,..., 2 , 1 , 0 , 1

) (

1 1

1 1 1

1 1 1 1 1

⎪⎩

⎪⎨

⎧

− + =

−

=

− +

=

+ +

+ + +

+ + + + +

M x i

x x x

C x y x C

C

i i T i

i T i i i i i

i T i T i i i i i

ψ ψ ψ ψ

ψ

ψ

(9)

The parameters of membership function are determined by backpropagation. For any parameters of membership functions z, the change in z for a single rule after a pattern has been propagated, i.e.

∆ z

, is obtained as:.

z z E

∂

− ∂

=

∆ σ

(10)

where

σ

is an arbitrary learning rate factor and E is the usual error given by the sum of squared difference between the target output y* and actual output y:

∑

=

−

=

^P

p

p

p

y

y W

E

1

2 )

* (

) 2 (

) 1

(

(11)

By applying chain rule for each parameter we have.

y z y y

z E

y z E

r r r

r r r r

∂

− ∂

−

=

∂

∂

∂

∂

∂

∂

∂

∂

∂

− ∂

=

∆

τ µ µ τ

σ

µ µ τ τ τ σ τ

) 1 ( ) (

^*

(12)

To implement the above procedure, each boiler section is assumed as a fuzzy system with 3 inputs and one output. For instance, fuel flow, the prior section output temperature and the steam flow are considered as system inputs and the temperature of outlet is used as system output. These fuzzy systems are adapted with hybrid learning algorithm. First, the input patterns are propagated keeping the antecedent parameters constant, and then the optimal consequent parameters are estimated recursively using the least square estimation procedure in (9). Next, the input patterns are propagated again while keeping the consequent parameters constant, then the antecedent parameters are modified by back propagation using (12).

The first step for preparing the model is gathering the boiler subsystems data from power plant units. The unit data are recorded in Units Data Access System (UDAS) at the power plant control room that are easily accessible.

These experimental real data are recorded for 60 hours with a 5 second sampling time. About 20,000 steps of the collected data are used in the learning procedure. The fuzzy models are adapted for the full range of variation. So, when the load is ramped down from 100 to 50 percent, the models are trained and when load is ramped up from 50 to

100 percent, the models are checked. Following this procedure, the obtained models for different boiler sections have been validated based on comparisons with real measured data, [21]. Next, two fuzzy P²ID controllers are designed and implemented for the superheater and reheater temperature control of Neka power plant.

IV. Controller Design and Simulations.

The control system at Neka power plant is a conventional system having electronic P and PI cards. This power plant has been built in 1978, and since then the control system has lost its desire efficiency. To replace this old control system with new PC-based one, a new Fuzzy P²ID Controller may be suggested as explained in this section. The aim of the new suggested control system is to achieve simultaneously a fast rise time and a minimum steady-state error with robustness within an acceptable tolerance. It is known that the fuzzy PD controller can handle the former goal, while the fuzzy PI controller can deal with the latter, [22-23].

The Fuzzy PI controllers used for nonlinear systems is shown in Fig. 5. The output of the digital fuzzy PI controller in the discrete time-domain is as follows:

( ) ( ) ( )

( ) ( ) ( )

PI PI uPI PI

PI P v I p

u nT u nT T K u nT

u nT K e nT K e nT

= − + ∆

∆ = +

⁽¹³⁾

where T>0 is the sampling time, K_P and K_I are the proportional and integral gains, respectively, and KuIP is a fuzzy control gain is determined following the design procedure, and:

T T nT e nT nT e

e

T

T nT u nT nT u

u

v

PI PI

PI

) (

) ) (

(

) (

) ) (

(

−

= −

−

= −

∆

(14)

Here, ∆u_PIis the incremental control output,

e

_P the error signal, and

e

_I the rate of change of the error signal.

The Fuzzy PD controllers are the most conventional used for nonlinear systems, Fig. 6. The output of the digital fuzzy PD controller in the discrete time-domain is as follows:

( ) ( ) ( )

( ) ( ) ( )

PD PD uPD PD

PD d p p i

u nT u nT T K u nT

u nT K e nT K e nT

= − − + ∆

∆ = +

⁽¹⁵⁾

The proposed Fuzzy P²ID controller is an integrated scheme that combines the fuzzy PI and fuzzy PD control actions, where the advantages of both control actions are utilized to compensate and complement each individual shortcoming. The structure of this new controller is shown in Fig. 7. Therefore, it is obtained:

) ( ) ( )

( nT u nT u nT

u =

_PD

+

_PI (16)

The fuzzy membership functions and rules are the same for both Fuzzy PI and PD Controllers. For each input, there are three membership functions, while for the output there are seven ones. In the FPI, the former input isK_ie_pand the latter isK_Pe_v. In the FPD, the former input isK_de_pand the

latter isK_Pe_i. The controllers inputs,

e

_v^,

e

_i^and

e

_pare normalized to lie in the interval (0,1), and output is normalized to lie in the interval (0,20), Fig. 8.

The dynamic behavior of the boiler subsystems varies with the load condition. So, for improving the control performance, during a range of operation, the controller gains are changed according to load variations. To this aim a TSK fuzzy system is used to change the gains. The fuzzy P²ID controller gains for different loads are presented in Table III.

These sets of gains were obtained by fine-tuning of several trials to get the best possible results for a fair comparison.

These controllers are applied to control the superheater and reheater temperature, while other subsystems utilize their old controllers. The effect of these Fuzzy P²ID controllers is observed on all parts temperature. The improvement in steam temperature and pressure fluctuations increases the generated power [21]. Some sample results at steady state phase and 97% load (427 MW) are shown in figures 9 to 13. As it is seen, with the new controllers the temperature fluctuations in boiler parts are decreasing and the water spray flow used to de-superheat the hot steam in the superheater and reheater is clearly less than the conventional P and PI controllers, figures 9 and 10. Also, the implementation of new controllers affects the outlet steam pressure, and increases the generated power while the fuel consumption is reduced, figures 11 to 13.

V. Conclusions.

In this paper, two fuzzy P²ID controllers were designed and implemented for the superheater and reheater temperature control of Neka power plant. This new controller has benefits of both fuzzy PD and PI controllers. An Adaptive Neuro Fuzzy Interface System (ANFIS) was used to prepare the plant model based on real data measured at the power plant. These fuzzy models have been developed to cope with nonlinearity and uncertainty. Also, these nonlinear fuzzy models are able to cover a wide range of operational conditions. The purpose of the controller design is to have higher and safer power generation. The benefits of new controller are faster rise time and lower steady-state error with considerable robustness within an acceptable tolerance.

Simulation results were discussed,, and revealed that using these new fuzzy P²ID temperature controllers has significant effect on the cycle processes.

References

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#88-0417, pp. 109-112. 1988.

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Elektron. February 1988.

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An overview”, International Journal of Intelligent Control System, 1996.

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Table I: Description of steam generator (HP parts) M.C.R. Unit Fuel Gas Fuel Oil Electrical Rate MW 440 440

Steam rate t/h 1408 1408 Acceptance Pr. MPas 21 21

Feedwater Temp. ^oC 264 264

Inlet Pr. MPas 25 26.1

Outlet Pr. MPas 19 19.6

Pressure Loss MPas 6 6.5

Temperature ^oC 535 535

Table II: Description of steam generator (IP parts) M.C.R. Unit Fuel Gas Fuel Oil Steam Flow rate t/h 1267 1267

Acceptance Pr. MPas 6.5 6.4 Reheater Inlet Tem. ^oC 351 342 Reheater Outlet Tem. ^oC 535 525

Inlet Pressure MPas 5.1 5.08 Outlet Pressure MPas 4.87 4.85 Pressure Loss MPas 0.23 0.23 Fuel Flow Kg/s 21.95 25.73

Cold Air Tem. ^oC 35 35

Air Tem. Before AH ^oC 40 90

Excess Com. Air - 1.1-1.2 1.1-1.4

Exhaust Gas Tem. ^oC 120 162

Efficiency % 94.4 92.8

Table III: Fuzzy P²ID Controller Gain Load

(MW)

K

^p

K

_i

K

_d

uPI

K K

uPD

440-420 1 0.1 0.1 0.8 0.01 420-400 1 0.1 0.1 1 0.01 400-380 1 0.1 0.1 1.1 0.01 380-360 1 0.1 0.1 1.2 0.01 360-340 1 0.01 0.1 1.3 0.01 340-320 0.5 0.01 0.1 1.45 0.01 320-300 0.2 0.01 0.1 1.2 0.01 300-280 0.2 0.01 0.1 1.2 0.01 280-260 0.2 0.01 0.1 1.15 0.01 260-240 0.2 0.01 0.1 1.15 0.01 240-220 0.2 0.01 0.1 1 0.01

Fig. 1: Boiler Cross Section

Fig. 2: Superheater Temperature Control

Fig. 3: Subsystems of Power Unit

Fig. 4: Reheater Temperature Control

Fig. 5: Fuzzy PI Controller

Fig. 6: Fuzzy PD Controller

Fig. 7: Fuzzy P²ID Controller

Fig. 8: Fuzzy Interface Surface

Fig. 9: Superheater #4 outlet Temperature

Fig. 10: Reheater-B outlet Temperature

Fig. 11: Boiler outlet Pressure

Fig. 12: Generated Power

Fig. 13: Fuel consumption