JPS5840605A - Efficiency optimization controlling method of thermal power plant - Google Patents

Abstract

PURPOSE:To optimize the efficiency of a plant speedily with high precision without any restriction of the response speed of the plant, by using a plant model for efficiency calculation which is incorporated in a control system, and then carrying out model standard type efficiency estimating calculation. CONSTITUTION:When a maximum efficiency searching means 230 has an O2 excess rate m1 and the opening extent m2 of a parallel damper as operation parameters, a trial manipulated variable which corresponds to a trial point 1 is supplied to a plant model 240 for efficiency calculation to find efficiency eta. At trial points 2 and 3, the efficiency eta is found similarly. On the prolonged line of the straight line which connects the point 1 with minimum efficiency to the center of gravity between the remaining points 2 and 3, a new trial point 4 is set, and the efficiency which corresponds to its trial manipulated variable is found. Then, a new trial point 5 is found similarly from the triangle 234. In this case, a new trial point is determined on the basis of the difference between efficiency at each trial point and prescribed standard efficiency. This trying method is repeated until a maximum efficiency point 7, thereby obtaining the best manipulated variables m1<7> and m2<7>.

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a method for controlling a thermal power plant, and is particularly suitable for maintaining the plant efficiency at the highest level even when ambient conditions such as seawater temperature and atmospheric temperature fluctuate during normal load operation. related to control methods.

The issue of highly efficient operation and control of thermal power plants has been a hot topic for a while, but the control/system that aims to improve the efficiency of the plant from a comprehensive perspective has yet to be put into practical use. Few reports on optimizing boiler efficiency by manipulating the air-fuel ratio (e.g. plMoran et a
l,, Development andapplic
self-optimizing
control to coal-fired steam
m-generating plant.

PrOc, IEE, VOL, 115. A2
(1968-2)). In these conventional control/systems, as shown in FIG.
Based on the state feedback 4 from 0, the search means 3 in the control device 2 performs a maximum efficiency search to determine the optimum operation amount 5.

However, the first problem with the conventional system is that it takes a lot of time to find the maximum efficiency point, that is, the optimum operating amount, because the control is based on state feedback from the plant with a large thermal time constant.

In addition, the second problem with the conventional method is that it is difficult to actually measure efficiency with high precision, so the efficiency i/denox method is used, which uses behavior such as main steam pressure as a substitute for efficiency evaluation. Its credibility is low. Furthermore, the third problem with the conventional method is that it is easily influenced by noise and detection errors because it uses actually measured values.

It is an object of the present invention to eliminate the drawbacks of the conventional methods described above in a control method for a thermal power plant, and to improve the efficiency of the plant from an overall perspective. To provide an optimization control method.

In the present invention, in order to overcome the problems of the conventional method, the second
By using the plant model 240 for efficiency calculation built into the control system shown in the figure to perform model standard efficiency prediction calculations, it is possible to achieve rapid and highly accurate efficiency without being constrained by the response speed of the plant. Achieved optimization. The basic method of the maximum efficiency search means 230 in this control/system 200 is to output a trial operation amount 1 to the plant model before determining the optimum operation amount 11, and calculate the corresponding plant efficiency 1o. This is a method of searching for the maximum efficiency point by repeating the procedure of finding.

In this case, a new trial point is determined based on the difference value between the efficiency at each trial point and a predetermined standard efficiency.

FIG. 3 shows the basic configuration of an efficiency optimization control/system according to an embodiment of the present invention. However, this diagram only shows functions specific to efficiency optimization, and various minor control systems and plant control systems to which conventional methods can be applied as is are omitted to avoid cluttering the diagram. still,
In FIG. 3, each symbol represents the following.

9a is the trial 02 excess rate, 9b is the trial damper opening degree, 9C
is the trial condenser vacuum degree, lla is the optimum O2 excess rate, 11b
is the optimum damper opening degree, 11c is the optimum condenser vacuum degree, 13a
is the economizer prisoner, 13b is the economizer CB), 14 is the water wall, 15
is the primary superheater, 16 is the secondary superheater, 17 is the main steam, 18
is a high pressure turbine, 19 is a primary reheater, 20 is a secondary reheater, 21 is reheated steam, 22 is a medium/low pressure turbine, 23 is a generator, 24 is a condenser, 25 is condensate, 26 is a low pressure feed water heater, 27 is a feed water pump, 28 is a high pressure feed water heater, 29 is a circulating water pump, 30 is a parallel damper, 31 is a pulverized coal mill, 32 is an air preheater, 33 is a forced draft fan, 34 is a suction Ventilator, boiler, turbia, 60... water supply system.

There are many operating parameters that affect efficiency, but in the explanation of this example, we selected three that have a relatively large effect: 02 excess ratio m1, parallel damper opening m2, and condenser vacuum degree m. -I decided to optimize it as an example. As a basic algorithm for maximum efficiency search, the complex method, which is one of the extreme value search methods, is used. In the maximum efficiency search means 230 of the z00a diagram, in order to clearly explain the principle of maximum efficiency search using the complex method, we will explain the operation Two cases where the parameters are ml and m2 are shown. now,
The trial operation amount m1゜m↓ corresponding to the trial point 1 is given to the plant model 240 for efficiency calculation, and the efficiency η1 as a steady value is determined. Similarly for trial points 2 and 3, η2,1
Find 3. Among these, the point with the lowest efficiency (in this case, it is set as 1) and the center of gravity of the remaining points (in this case, the line segment 2.3
) above), select a new trial point 4 on the extension of the straight line connecting them, and calculate the efficiency η4 corresponding to the trial operation amounts m4 and m4.
seek. Here, a new trial point 5 is obtained in the same way from the newly created triangle 234 excluding the trial point 1. At this time, if the trial point 5 violates the constraint conditions of the plant state quantity, the process returns to the trial point 6 within the defined domain, and a new triangle 346 is used to determine the trial direction. By repeating this trial method, the maximum efficiency point (7 in this case) can be reached. The operating lid corresponding to this point is the optimum value (in this case m
7, m7), which is the actual operating output for the plant.

By continuing such control operations, even if the maximum efficiency point moves due to changes in ambient conditions such as seawater temperature and atmospheric temperature, it is possible to follow this and determine the optimal operation k.

The above has been limited to explaining the principle of efficiency optimization control applying the complex method, but next, an outline of the control algorithm in the embodiment will be explained.

FIG. 4 shows the basic processing procedure of efficiency optimization control in the embodiment of the present invention. The control algorithm will be explained step by step below. However, the symbols are defined as follows.

m,: Manipulated variable i-102 excess rate (%) I-2 parallel damper opening degree (%) i=3 Condenser vacuum degree (wHg) Here, m2 may indicate the parallel damper opening degree. , here defined by the following equation.

Here, Gs represents the gas flow rate in the gas passage where the primary superheater 15 and the economizer 13a are arranged in FIG. represents the gas flow rate in the gas passage.

” I rrax Hml 1ml'n: On the amount of operation,
Lower limit i-10□ Excess rate upper, lower limit 1==2 Parallel damper opening upper, lower limit i = 3
Condenser vacuum upper and lower limits Gct, mat I Gct, mln" 4 Condenser circulating water flow rate upper and lower limits (Kg/5ec) DT max 'Condenser circulating water temperature rise upper limit (C) Y
Yu, x: Low pressure turbine exhaust humidity upper limit (%) GGR□
x + GGRmla: Upper and lower limits of gas recirculation flow rate (K
g / 歎) STEP-1 Initial non-plex formation initial trial point m'
, (i=l~3) satisfy all of the above constraints, and ml, m2. In the three-dimensional space created by m3, a polygon (called a simplex) with an angle of (-6 in the case of Fig. 4) is formed, and this is set as an initial simplex. As for this formation method, one point is the initial trial point m'1, and the remaining (k-13 points are, for example, uniform random numbers rJ(j-2~
k) using the following formula.

m'-mI111111+r'("+max"+mtn
l...=...-(21 However, 0(rj<1) ml determined in this way always satisfies the constraint condition as a manipulated variable (this is called an explicit constraint condition), but as a process state variable. The constraint condition (this is called the implicit constraint condition) may not be satisfied.In that case, the trial point is moved in the direction of the center of gravity of the already determined point to the midpoint.In this way, ultimately all Then, the plant efficiency ηI (' = 1 to 6) corresponding to each point determined through the above trial is expressed as the plant model moxibustion ± and '
It can be obtained using

Calculation of the center of gravity of I-Ding F1 Here, we exclude the point with the lowest efficiency among the points of the simplex (calculate the center of gravity mGI of the simplex defined by k-11 points. Now, the efficiency -2 The distance ΔmG from a point to the center of gravity is expressed by the following equation.

ΔmQ , ==mQ l -m', ...
・・・・・・・・・・・・・・・・・・・・・(4) Determination of new trial point The new trial point is a point extended from the center of gravity by α1 times the distance between the points ΔmQ, and this is m, then mI SmGI ten α1ΔmGI ・・・・・・・・・・・・
It is represented by (5).

In this case, if an explicit constraint condition is violated, the trial point is set on the constraint condition.

That is, if the upper limit m, m, , is violated, then k◆
1 m, :=m, mI−−−−−°°−°−゛−
−−−−−°°−°, °, −, (51, lower limit m, f
If fl, , is violated, mk+11=m1min...
………………………(7).

Pressure i efficiency calculation Using the plant model 240 for efficiency calculation, the efficiency η corresponding to the new trial point m, is determined.

Here, the simulation range as a plant model covers all systems where energy balance is a problem among systems required for normal load operation.

In the turbine system, not only the extraction system but also the /-le steam is considered. In addition, in the boiler system, not only heat loss due to exhaust gas but also heat radiation from the boiler wall is considered. Furthermore, the minor control system and plant control system for each machine are, of course, targeted to the side wall. In addition, actual measured values are used for seawater temperature, atmospheric temperature, and wind speed as ambient conditions.

If the process state calculated by the plant model 240 for monitoring efficiency calculation violates the implicit constraint condition, the trial point m1
All related information is invalidated, and the process returns to 5TEP-3 to determine a new trial point. In this case, considering the causal relationship between the operation and the process state, α1 is corrected according to the following formula, and 5TEP
- Return to 3.

・・・・・・・・・・・・・・・・・・・・・・・・
...(8) U (DT>DT ,,, ) U (
Y>Y lTl-, ) --(91 Here, α1 for the 0□ overbite rate is not corrected.

The reason is that as long as the 02 excess rate is operated within the limit value, the implicit constraint condition will not be violated.

Among the efficiencies corresponding to the new trial point for indenter IF country convergence judgment and each point constituting the original non-plex, the maximum and minimum efficiencies were determined by '7, respectively.
mat and η1°, and it is determined whether the maximum efficiency point has been reached according to the following equation.

Here, ε is the criterion for reaching the maximum efficiency point.

If the above formula is satisfied, it can be said that the practical maximum efficiency point has been reached. When the maximum point is reached, the manipulated variable corresponding to η, 8 is output, and the process returns to 5TEP-1 to form an initial simplex. If the maximum score is not reached, proceed to the next 5TEP-7.

[MyoI Efficiency Improvement Direction Judgment If the new trial point yields a higher efficiency than any of the efficiencies of the points that make up the original songplex, the operation output is performed even if the maximum efficiency point has not been reached, and the next Proceed to 5TEP-8. If there is no operation output, continue with the next 5TEP-8
Proceed to. The reason for providing this function is that if the trial point reliably improves the efficiency compared to the current operating state, then the actual operation output will be performed without waiting for the maximum efficiency point to be reached.

Among the points composing the simplex from which the M new simplex is formed, exclude the operating point showing the lowest efficiency, add new trial points, and form a new nonplex from the resulting points. 5TEP
- Return to 2.

Next, an example of the efficiency optimization control process according to the present invention is shown in FIG. This figure shows the operating parameters 0□overbite rate m,
In the three-dimensional space defined by , Haralel damper opening 2 and condenser vacuum m, each locus moves toward its optimum value as 3.
It is projected onto two planes. In this figure, 1 to 6 are the vertices of the initial simplex, 7 to 11 are trial points, the double circle is the maximum efficiency point, and the dotted line connecting points 7 to 11 is the trial operation process, 7
The solid line connecting the points ~11 is the optimal operation process.

For example, looking at the relationship between "1" and m2, the optimal point is reached in the 11th trial. However, for m3,
Although it has come close to the optimal point, it has not yet reached the optimal point.

Although the above has been limited to an overview of the 415 control algorithm in the embodiment, the specific method will be additionally explained below.

How to determine the initial trial point m11 and the size of the simplex in the initial simplex formation step 1 is an important issue that affects the convergence of the maximum efficiency search.

In order to improve convergence, the initial trial point m11 should be selected at a point with as high efficiency as possible, and this m11
We thought that the size of the initial simplex should be determined according to the distance from the distance to the allowable operating limit, and adopted the following method.

FIG. 6 shows the relationship between the plant efficiency and the 0□ overcutting rate m1, and also shows the influence of atmospheric temperature on the efficiency characteristics. In other words, the solid line represents the efficiency characteristic when the atmospheric temperature is 30 C, and the dotted line represents the efficiency characteristic when the atmospheric temperature is 1 Or. The upper and lower limits of the allowable range for m are based on boiler exhaust gas regulation values, combustion stability,
It is determined by fan capacity, etc., but varies depending on the load level. From this characteristic, the upper limit of the manipulated variable m as a function of the load,
. □(G), lower limit m1. .

Therefore, as shown in FIG. 7, the initial trial point determination unit 96 selects the initial trial point m, which is the point within the operation permissible range where the maximum efficiency can be expected. Next, the size of the initial 7 complex is m
: In order to determine the distance from to the operational permissible limit, m and the coordinates m, , (j=2 to k) of the vertices of the initial non-plex excluding ml are determined by the following equation.

・・・・・・・・・・・・・・・・・・・・・・・・
...0υHowever, mr = ("+ mar +"
i 1a )/2, r'' is -disturbance (15) number (0(r'<t) % KLI is constant (0<K
tt<,11.

FIG. 9 shows the relationship between the plant efficiency and the gas recirculation flow rate with respect to the parallel damper opening m2. The upper limit of the allowable range of m2 is the capacity limit characteristic of the waste recirculation fan a
and primary superheater 15 and energy saver ■13a using high-speed gas flow
The limit characteristics for preventing heat/burning are already determined to prevent the heat from being cut. In addition, the lower limit is the NOx countermeasure lower limit characteristic for suppressing the increase in NOx # degree due to the decrease in the gas recirculation flow rate. d and the reheat steam temperature stabilization lower limit characteristic e. ffL2U29m2UI
Im, L3. m2L2. m2L, each has the characteristic aT
This is the limit point determined by b, c, and '1e, and in the case of this figure, the range between m2L3 and m2υ1 is the allowable operation range. The physical basis for the characteristics shown in Figure 8 is that when m2 is decreased, the amount of gas diverted to the primary reheater increases (as is clear from the definition of Equation 11), but the reheat steam temperature In the control system,
This is to keep the reheated steam temperature constant by lowering the gas recirculation flow rate. Since the five operating permissible limits shown in Fig. 9 change depending on the load level, the final permissible limits m2□ and "2+1nVi" are determined as shown in Fig. 10. That is, m2□, Upper limit m2U2 obtained by circulation fan capacity limit calculation means 102 and upper limit m2U1 obtained by Anon Yukanoto upper limit calculation means 103
The lower value among them is determined by the low value selection means 107. In addition, m2ff,... is the lower limit "2 L3, N Q obtained by the reed/cut prevention lower limit calculation means 104.
The maximum value selection means 108 selects the largest value of the lower limit m2L2 obtained by the X countermeasure lower limit calculation means 105 and the lower limit m2L2 obtained by the reheat steam temperature stabilization lower limit calculation means 106.
Determined by. Here, m2[,, m2L29m2L
, is calculated as a function of load L+7), m2υ,, m2
13 is calculated as a function of the J@Dawn gas volumetric flow rate VGAS. Further, VGAS is the combustion gas volumetric flow rate calculation means 10.
1 as a function of load. As shown in FIG. 9, as m2 increases, the waste recirculation flow rate increases, and the power of the gas recirculation fan increases accordingly, so that the plant efficiency has a drooping characteristic. Therefore, assuming that the maximum efficiency point is always at the lower limit m2m1n of the operational permissible limit, the initial trial point m4 is selected as this point. In addition, as in the case of m, in order to determine the size of the initial nonplex according to the distance from m; to the operational permissible limit, m2 coordinates m2J(J -2
~k) is determined by the following formula.

・・・・・・・・・・・・・・・・・・・・・α2 However, m2-("2ea'x +"2'm+o)/2,
fj is a uniform random number (0(r'<1), and Kt and 2 are constants (0<KL2<1).

FIG. 10 shows the relationship between the plant efficiency, the circulating water flow rate, and the circulating water temperature rise width with respect to the condenser vacuum degree m3. The physical basis for this characteristic is that increasing m3 increases the turbine internal efficiency, but on the other hand, as the circulating water flow rate increases, the pump power increases, and as a result,
By having a point where the plant efficiency is maximum. Also,
Since the flow rate of circulating water decreases as m3 decreases, the range of increase in temperature of the circulating water increases. Here, the allowable operation range is defined by the circulating water flow rate upper limit characteristic f1 determined by the circulating water pump capacity, the circulating water flow rate lower limit characteristic g1 for limiting the scale deposition rate in the cooling pipe, and the protection of plankton contained in the circulating water. This is the upper limit characteristic of the circulating water temperature rise range. The above limits are determined at the plant planning stage and do not change during operation, but the allowable operating range defined by these limits is greatly influenced by the load level and seawater temperature, as shown in Figure 11. Ru. In the figure, the solid line is the seawater temperature of 21t.
The dotted line shows the efficiency at 18C. Therefore, in order to determine the initial trial point m~, an initial trial point determining means 109 as shown in FIG. 12 is used. In this means, - based on the characteristics shown in FIG. 11, the upper limit m3 of the manipulated variable is determined as a function of the load. ,,． 8 (g), lower limit m. 1j represents the manipulated variable m8M (g) corresponding to the most efficient point, and Tc ”T+ with seawater temperature Tc as a parameter.
, T2 , . . . , characteristic functions corresponding to T are prepared. Therefore, as shown in FIG. 12, the initial trial point determining means 109 selects the initial trial point m~ at the point where maximum efficiency can be expected within the operation permissible range. Next, in order to determine the size of the initial simplex according to the distance from m's to the operational tolerance limit, m and the coordinates ms'' (j = 2 to k) of the vertices of the initial nonplex excluding ml are Determine by formula.

・・・・・・・・・・・・・・・・・・・・・・・・
...Q3) However, "3= (m3max +ms
rn + n I/2, rJ is a uniform random number (0<r
'<,1 ), Kt, 3 is a constant (0<KL3<,1
).

The plant characteristics shown in FIGS. 6, 8, and 11 can be known using the plant model 240. In addition, the upper limit of the operation amount m1 determined by each means shown in FIGS. 7, 9, and 12. ,．． 1! (i=1 to 31 and lower limit "1m1n (i-1 to 31 are all taken into consideration implicit constraints, and have different meanings from the explicit constraints used in equation (2). Therefore, Old 103 Q3)
Each vertex coordinate m, (i
=l~a, j=2~k) violates the explicit constraint, the coordinates of each vertex are set above the explicit constraint using the same idea as in formula +61 (7). Also, II 111210
The value of the constant KLI = 1 to 3) in equation 3) may be determined to be an appropriate value suitable for the plant to be controlled by analyzing the control characteristics using a model.

In the above description of the embodiments of the present invention, handling of transient states of the plant due to load fluctuations and switching of the number of auxiliary machines in operation has not been mentioned, but this will be explained below.

Because plants have a large heat capacity, it is difficult to understand their true efficiency during transients. Therefore, in this embodiment, as shown in FIG. 13, the maximum efficiency search means 230 stops operating during the load change and immediately after the load change is completed. In order to detect the presence or absence of load fluctuation, the current time is set to and the load L (to
l, L(t.

- month), ...... L (to-nΔt), the maximum value is L cnax, and the minimum value is L m +. Binding, L m a x L m lゎ〉ε, ・・・・・・
If it is I, it is assumed that there has been a load fluctuation, that is, it is assumed that there is a steady load according to the Oa formula until time t2 in Fig. 13. , t2, after detecting the load fluctuation, the maximum efficiency searching means main unit is stopped. Time t when the plant is in a thermal transient state even after the load change is completed
4, the maximum efficiency search means 230 is inactive. [
After 4, it is regarded as a steady state and starts operating again. In this case, in the maximum efficiency search means 230, the initial 7/
The operation will start from plex formation step 1. Here, as the value of nΔt, a necessary and sufficient value is used in consideration of the transient characteristics of the plant to be controlled.

When there is a large load change that involves a change in the number of auxiliary machines in operation, such as a pulverized coal mill or water pump, the maximum efficiency search means 230 is activated or deactivated in consideration of the thermal transient state caused by the change in the number of auxiliary machines. let

In other words, as shown in FIG. 14, when the example shown in FIG. 13 involves switching the number of auxiliary machines in operation, the number of auxiliary machines in operation is in operation and the heat equilibration time ts required as a result of the number switching has elapsed. The maximum efficiency search means 230 is operated from.

If the number of auxiliary machines in operation is to be changed even in a steady load state, the operation will be stopped for 1 second during the switching/-can operation.

FIG. 15 shows the processing procedure of the embodiment for the above purpose. This process operates at a cycle Δt, and the /-can operation determination means 111 determines whether or not the no-ken for switching the number of auxiliary machines in operation is in operation, and if it is in operation,
The timer reset means 112 resets a timer for counting the time tA after the completion of the switching no-dance, and the operation of the maximum efficiency search means 230 is stopped by the stop command means 1°13. On the other hand, if the switching 7-tance is completed,
The elapsed time t^ thereafter is measured by the clock means 114. Next, the load fluctuation state determination means 115 determines whether or not the load is fluctuating according to equation 04). If the load is changing, the operation of the maximum efficiency searching means 230 is stopped, and if the load changing is completed, the thermal equilibrium state determining means 116 determines the thermal equilibrium state by comparing LA and ISO magnitude. If the state is in thermal equilibrium, the operation command means 117 causes the maximum efficiency search means 230 to operate.

In this example, when determining the coordinates of each vertex of the initial simplex, the initial trial point mi regarding the 02 excess rate is
is determined by considering both the load and the atmospheric temperature T1. However, since the influence of atmospheric temperature on plant characteristics is relatively small, it is not necessarily necessary to take atmospheric temperature into account, and even if the method determines the initial trial point m1 by simply considering the load, there is a risk that the control characteristics will deteriorate significantly. There isn't. In addition, as shown in Fig. 4, when the manipulated variable converges to the maximum efficiency point, the initial simplex is formed again by the method shown in Figs. 6 to 12. As long as the operation of the maximum efficiency search means 230 is not interrupted due to switching of the number of operating vehicles, etc., the control characteristics will not be impaired even if the method forms an initial simplex near the convergence point.

Furthermore, in this embodiment, the direction of the new trial point is determined from the vertex of the simplex with the lowest efficiency to the direction of the center of gravity expressed by equation (3), but it is not necessarily necessary to set it in such a direction. Stable efficiency optimization is also possible by the following method. The first method is to place a new trial point on a straight line that passes through the centroids of the p points with the lowest efficiency and the centroids of the remaining (k-91) points among the vertices forming the simplex. This is a method of determining.The second method is
Divide the k vertices into group A consisting of p vertices with lower efficiency and group B consisting of the remaining q vertices, and from the lowest efficiency point of both groups t<
In this method, one new trial point is determined in the straight line direction passing through the points excluding the points t=1 to p). Also, the third
When calculating the center of gravity mGI of a simplex, the method of
Instead of using equation (3) to directly use the efficiency η11 of each m point as a weighting coefficient, this method uses the following equation to calculate the difference η;-7 with respect to the standard value η as a weighting coefficient.

However, it is desirable to modify 7 according to the load level. In the first and second methods described above, stable convergence can be expected without being affected by the singularity of the lowest efficiency point. Also, 0
Compared to formula (3), the third method based on formula 5) more accurately determines the direction of maximum slope of the efficiency characteristic with respect to the manipulated variable and determines a new trial point, so good convergence can be expected. .

In addition, in this example, if the new trial point has a higher efficiency than any of the efficiencies of the points that make up the original / complex, the operation output can be performed even if the maximum efficiency point has not been reached. Avoid sudden changes in volume. Furthermore, in order to increase this effect, it is also effective to select n vertices from the ones with the highest efficiency at the vertices of the //plex and use their center of gravity as the actual manipulated variable. In this case, it is effective to select the value of n in the range of 2 x n (k/2).

Further, in this embodiment, when the process state at the new trial point violates the implicit constraint condition, only the manipulated variable directly related to the violated constraint condition is retreated according to formula 18) (9+). However, since the causal relationship between the process state to which the constraint is imposed and the manipulated variable does not necessarily correspond one-to-one, formula (9) (+8) can be used as is for the manipulated variable that is directly related to the violated constraint. For manipulated variables that are not directly related, it is desirable to correct the α amount according to α 1-α l (1-β)...t+61. However, in the above equation, β=0. It may be about 2. Another method is to estimate the operation amount limit based on the degree of violation of the implicit constraint,
A method of retreating to the limit value is also effective. This method is
It is effective for optimizing the manipulated variable whose efficiency characteristic with respect to the manipulated variable shows a monotonic function. Now, if the state quantity at the lowest efficiency point in the non-plex is xl, the state quantity at the new trial point is x2, and the constraint condition is XL, then in order to correct α and retreat the manipulated variable to the limit value, for example, α' can be determined by linear interpolation using the following equation.

In reality, the correction coefficient ξ (0 × ξ × 1) takes into account nonlinearity.
The new trial point will be determined using the following formula.

In addition, in this embodiment, the maximum efficiency search means 230 is operated or paused depending on the load fluctuation state and the switching of the number of auxiliary machines in operation, but in this case, the load fluctuation monitoring section shown in FIGS. nΔt does not necessarily have to be constant, and in short, it is sufficient as long as it is the minimum necessary time for the plant to reach a state of thermal equilibrium. Therefore, by sequentially correcting the load level and the load fluctuation width, the maximum efficiency search means 230 can have many chances to operate.

In a thermal power plant, the thermal time constant is generally smaller during high-load operation than during low-load operation, so the thermal equilibrium time is shortened during high-load operation, and nΔt can be reduced. Also,
For the above-mentioned reasons, it is desirable to modify the time ts required for heat equilibrium due to switching the number of operating auxiliary machines according to the load level. This 1. Furthermore, since it differs depending on the type of auxiliary equipment whose number is to be changed, it is desirable to modify it by considering the load level and the type of auxiliary equipment. Thereby, it is possible to increase the operating rate of the efficiency optimization control means as much as possible, and the degree of contribution to highly efficient operation of the plant can be improved.

The first effect of the present invention is that because predictive control is performed using a plant model built into the control system, efficiency optimization can be achieved quickly within 5 minutes without being constrained by the response speed of a plant with a large thermal time constant. This makes it possible to significantly improve the operating rate of the optimization function. The conventional method requires a time of 3'0 to 60 minutes for optimization, which means that the optimization function cannot be achieved unless the steady load condition continues for any longer. It can be said that this method cannot be practically used for load operation needs.

The second effect of the present invention is that by optimizing the manipulated variables of multiple operating parameters that affect plant efficiency, efficiency can be improved from a comprehensive perspective of the plant, and the conventional method targeted at improving the efficiency of individual equipment. The point is that the efficiency can be significantly improved compared to the conventional method.

The third effect of the present invention is that when optimizing efficiency, the plant efficiency directly calculated using the plant model built into the control system is used, so the conventional method indirectly detects efficiency based on the efficiency index method. compared to
The point is that the convergence accuracy to the optimum value is high.

The fourth effect of the present invention is that since efficiency is optimized using a plant model built into the control system, the influence of noise and detection errors is greater than in the conventional method, which optimizes efficiency based on actual measured values. The point is that stable and highly accurate optimization is possible without being affected by

The fifth effect of the present invention is that stable convergence to the maximum efficiency point is possible.

[Brief explanation of the drawing]

FIG. 1 is for explaining the structural difference between the conventional control/stem and the control system of the present invention shown in FIG. 2. FIG. 2 is for explaining the structural features of the control system of the present invention in comparison with the conventional control system shown in FIG. FIG. 3 shows the basic configuration of the efficiency optimization control system. FIG. 4 shows the basic processing procedure of efficiency optimization control. FIG. 5 shows an example of the efficiency optimization control process. FIG. 6 shows the characteristics of plant efficiency versus 02 excess rate. FIG. 7 shows a method for determining the initial trial point and operating amount limit for the 02 excess rate based on the characteristics shown in FIG. FIG. 8 shows the characteristics of plant efficiency and gas recirculation flow rate with respect to parallel damper opening. FIG. 9 shows a method for determining the initial trial point and operation amount limit for the parallel damper opening based on the characteristics shown in FIG. 10th
The figure shows the characteristics of plant efficiency, circulating water flow rate, and circulating water temperature rise width with respect to condenser vacuum degree. FIG. 11 shows the characteristics of plant efficiency with respect to condenser vacuum degree using load level and seawater temperature as parameters. The twelfth cause is the first
Based on the characteristics shown in Figure 1, the method for determining the initial trial point and operating amount limit for the degree of vacuum in the condenser is shown. FIG. 13 shows the operating section and the stopping section of the maximum efficiency search means due to load fluctuations. FIG. 14 shows the operation section and the stop section of the maximum efficiency search means due to load fluctuations and switching of the number of operating auxiliary machines. FIG. 15 shows processing means for realizing the purpose shown in FIG. 14. 100...Plant, 2...Control system, 3...
・Maximum efficiency search means, 4... State filled pack, 5
...Optimum operation amount, 200...Control/Stem, 230
... Maximum efficiency search means, 240 ... Plant model, engineering ... Trial operation amount, 10 ... Plant efficiency, 11
...Optimal Manipulating Agent Patent Attorney Akiaki Takahashi -,,, Good, f7'' - Figure 14 S Mouth (hBa body JMU 6th day 7th 6' II p13 Figure 14- Figure 15)

Claims (1)

[Claims] 1.02 Determining plant efficiency as a function of the manipulated variable of a thermal power plant in which at least one of the excess ratio, parallel damper opening degree, and condenser vacuum degree is an operational parameter for operational control; In a control method, the control method includes determining the manipulated variable of the parameter using the plant efficiency, and determines the manipulated variable that maximizes the efficiency through a trial operation using the d-plat efficiency, which corresponds to a plurality of trial points. An efficiency optimization control method for a thermal power plant, characterized by determining a new trial point based on efficiency. 2. Efficiency of a thermal power plant, characterized in that in determining a new trial point as set forth in claim 1, the new trial point is determined based on the difference value between the efficiency at each trial point and a predetermined standard efficiency. Optimized control method.