JPS5840614A - 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 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 also found similarly from the triangle 234. In this case, if plant conditions at the new trial point do not meet restricted requirements, only manipulated variables which concern the restricted requirements directly are corrected and a new trial point is determined.

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 a control system that fully aims to improve efficiency from a comprehensive Plan If perspective has not yet reached North Jishu.

Few reports on optimizing boiler efficiency by manipulating the air-fuel ratio (e.g. p., MOran et a.
l,, Development and appl.
cation of self-optimism
gcontrol to coal-f-ireds
team-generating plant, pro
c, ■EE, VO1, 155, 42 (1968-2)
). With these conventional control 7 stems,
As shown in FIG. 1, the search means 3 in the control device 2 performs a maximum efficiency search based on the state feedback 4 from the plant 100, and determines the optimum manipulated variable 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.

The second problem with the conventional method is that it is difficult to actually measure efficiency with high precision, so an efficiency index method is used that uses behavior such as main steam pressure as a substitute for efficiency evaluation. It is a matter of low gender. 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.

An object of the present invention is to eliminate the disadvantages of the conventional method in a method for controlling a thermal power generation jet, and also to improve the efficiency of a plant from an overall viewpoint, and in particular, to provide a thermal power generation plant with stable control characteristics. The purpose of this invention is to provide an efficiency-optimized control method.

In the present invention, in order to solve the problems of the conventional method, the second
By using the plant model 240i for efficiency calculation built into the control system 200 shown in the figure and performing model standard efficiency prediction calculation, rapid and highly accurate efficiency optimization is possible without being constrained by the response speed of the plant. It was realized. The basic method of the maximum efficiency search means 230 in this control system 200 is to output a trial operation amount 9 to the plant model 240 before determining the optimum operation amount 11, and to obtain the corresponding plant efficiency 10. This method searches for the maximum efficiency point by repeating the process. In this case, if the plant state at the new trial point violates the constraint, only the manipulated variables directly related to the violated constraint are corrected,
Determine a new trial point.

FIG. 3 shows the basic configuration of seven efficiency optimization control systems according to an embodiment of the present invention. However, this diagram only shows functions specific to efficiency optimization, and the 6-scale minor control system and plant control system to which the conventional method can be directly applied are omitted to avoid complication of the diagram. still,
In FIG. 3, each symbol represents the following.

9a is the trial 02 excess rate, and 9b is the trial damper opening degree.

9C is the trial condenser vacuum degree, 11a is the optimum 02 excess rate, 1
1b is the optimum damper opening degree, 11C is the optimum condenser vacuum degree, 1
3a is the economizer (A), 13b is the economizer (B), 14 is the water wall, 15 is the primary superheater, 16 is the secondary superheater, 17 is the main steam, 18 is the high pressure turbine, 19 is the 1 Next reheater, 20 is 2
Secondary reheater, 21 is reheat steam, 22 is medium/low pressure turbine,
23 is a generator, 24 is a condenser, 25 is a condensate water, 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, 3 (l 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 draft fan, 40 is a boiler, 50 is a turbine,
60 is the water supply system.

There are many operating parameters that affect efficiency, but in the description of this example, the 62 excess ratio m1, which has a relatively large effect,
Parallel damper opening m2. We selected three condenser vacuum degrees, m, and decided to optimize them as an example. The complex method, which is one of the extreme value search methods, is used as the basic algorithm for maximum efficiency search. In the maximum efficiency search means 230 in FIG. 3, two cases where the operating parameters are ml and m2 are shown in order to explain the principle of maximum efficiency search using the complex method in an easy-to-understand manner. Now, the trial operation amount m1°ml corresponding to trial point 1 is given to the plant mosil 240 for efficiency calculation, and the total efficiency η1 as a steady value is determined. Similarly for trial points 2 and 3, η2. η
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, line segments 2 and 3
A new trial point 4t- is placed on the extension of the straight line connecting the
Efficiency η4 corresponding to the selected and trial operation amount m4, m:
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 manipulated variable corresponding to this point is the optimal value (in this case m
7, m: ), which is the actual operating output for the plant.

By continuing all of these 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 re-optimal operation amount.

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.

mI: Manipulated amount i=1 o, Excess rate (%) i-2 Parallel damper opening degree (%) i=3 Condenser vacuum degree (Hg) Here, m2 indicates the parallel damper opening degree. However, here it is defined by the following formula.

Here, Gs represents the gas flow rate in the gas passage where the primary superheater 15 and the economizer (A) 13a are arranged in FIG. ) 13b represents the gas flow rate in the gas passage where it is arranged.

m,...m,,,, operation amount upper, lower limit i-10 □ Excess rate upper, lower limit 1 = = 2 /< Rarel damper opening upper, lower limit i = 3 Condenser vacuum upper, lower limit GCLma , I GCL m l ll: Upper and lower limits of condenser circulating water flow rate (Kg/bu) D T ++ax ; Upper limit of condenser circulating water temperature rise width (
r) Y wax: Low pressure turbine exhaust humidity upper limit (
%) GGRn+axlGGRml+: Upper and lower limits of gas recirculation flow rate (Kg/formula) [The initial trial point m'I (i=1 to 3) for forming the initial simplex shall satisfy all of the above constraints, m, , m2. In the three-dimensional space created by m, a polygon (this is called a simplex) with angles (in the case of Figure 4, 26) (+- is formed and this is the initial simplex.) The method for forming is as follows. One point is set as the initial trial point m11, and the remaining (k-1) points are determined by the following equation using, for example, a uniform random number r(''-2~k)t-.

m1=m11. -1-r (m, ,, -m11.)
−・・−・・(21 However, O(r <1) ml determined in this way always satisfies the constraint condition as a manipulated variable (this is called an explicit constraint condition), but it does not satisfy the constraint condition as a gross state quantity. The 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 points are determined. And the plant efficiency η' corresponding to each point determined through the above trials
(j=x~6) is obtained using the Gra/Gt model 240.

Calculation of the center of gravity of the simplex defined by (k-1) points excluding the point with the lowest efficiency among the points in the 7/brecs. lowest point ij
When =1, mGI is expressed by the following formula.

Further, the distance ΔmQ from the lowest efficiency point to the center of gravity is expressed by the following equation.

3m at = mG, -m', ・
−−−−−−・(410 匝TE=1 Determination of new trial point The direction of the new trial is taken from the lowest efficiency point to the direction of the center of gravity, and the distance between the two points ΔmQ is extended by α times the center of gravity. Let the point be a new trial point and let it be m', '', then m', ``' = m G, +a, Δ'' a
+ −−96, f51. In this case, if an explicit constraint condition is violated, the trial point is set on the constraint condition.

In other words, if the upper limit m 1 Bat 'l'' is violated, then
If the lower limit m, =, · is violated, k+( rrll:mIIwI1190980013.(force).

5TEP-4 Efficiency Calculation Using the plant model 240 for efficiency calculation, the efficiency η corresponding to the new test point mτ゛1 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 nord 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 aircraft are, of course, simulated. In addition, actual measured values are used for seawater temperature, atmospheric temperature, and wind speed as ambient conditions.

Yoko Kuchino In the plant model 240 for constraint monitoring efficiency calculation, if the process state violates the implicit constraint, the trial point m':
” will be invalidated and return to 5TEP-3.
Determine a new trial point. In this case, considering the causal relationship between the operation and the process state, α1 is modified according to the following formula to obtain 5TE.
Return to P-3.

・・・・・・・・・(8) U (DT > DT---) U (Y>Y---)...
- Old... (9) Here, α for the 02 excess rate is not corrected.

The reason is that as long as the 0□ excess rate is operated within the limit value, the implicit constraint condition will not be violated.

1 large = b Among the efficiencies corresponding to the new trial point for convergence judgment and each point constituting the original 7 complexes, the maximum and minimum efficiencies are calculated as η71, respectively.
, and η+m1m, and it is determined whether the maximum efficiency point has been reached according to the following formula.

ηIIIax 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. If you reach the maximum point, ηyu! The operation amount corresponding to 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.

4U■Horizontal Efficiency Improvement Direction If the new trial point yields a higher efficiency than any of the efficiencies of the points that make up the original simplex, 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 why this function was provided is that if the trial point can definitely improve efficiency even in the current operating state, then it will be possible to actually output the operation without having to spend money to reach the maximum efficiency point. .

A new simplex is created by excluding the operating point that shows the lowest efficiency among the points that make up the seven complexes from which the new simplex is formed, and adding new trial points. form 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 parameter 02 excess rate m
+, Halal damper opening m2. The trajectories each move toward the optimum value in the three-dimensional space defined by the condenser vacuum degree m3 are projected onto all three planes. In this figure, 1 to 6 are the vertices of the initial simplex, 7 to 11 are the trial points, the double circle is the maximum efficiency point, and the dotted line connecting points 7 to 11 is the trial operation process.
The solid line connecting points 7 to 11 is the optimal operation process.

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

Although the above has been limited to a general explanation of the control algorithm in the embodiment, the specific method will be additionally explained below.

How to determine the initial trial point m ~ and the size of the simplex in the initial simplex formation step 1 is as follows.
This is an important problem that affects the convergence of maximum efficiency search. In order to improve convergence, the initial trial point mII should be selected at a point with the highest possible efficiency, and this m'l
We thought that the size of the initial simplex should be determined according to the distance at the operating tolerance limit 1, and adopted the following method.

FIG. 6 shows the relationship between plant efficiency and O1 excess rate m1, and also shows the influence of atmospheric temperature on efficiency characteristics. That is, the solid line is the efficiency characteristic when the atmospheric temperature is 301Z', and the dotted line is the efficiency characteristic when it is 100. The upper and lower limits of the permissible range of m are determined by boiler exhaust gas regulation values, combustion stability, fan capacity, etc., and vary depending on the load level. From this characteristic, as a function of the load, the upper limit of the manipulated variable is 4m, a+, (L), and the lower limit is m, -1° (L)'! i = the manipulated variable mI M (L, T-) which can be expressed and corresponds to the maximum efficiency point as a function of the load and the atmospheric temperature T.
k can be expressed. Therefore, as shown in Figure 7,
The initial trial point determination unit 96 selects the initial trial point mif at a point within the operation permissible range at which maximum efficiency can be expected.

Next, in order to determine the size of the initial simplex according to the distance from m to the inner boundary of the operation permissible area, m of the vertices of the initial simplex excluding Ik, coordinates mIN:l:2
~k) is determined by the following formula.

・・・・・・・・・0]) However, m I= (m , ,, +m, ,,, )/
2, r is a uniform random number (Oar kuI), and KLI is a foot count (0 (KL, <1).

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 ash cut prevention limit characteristic to prevent gamma cut of the primary superheater 15 and economizer (A) 13a due to gas recirculation and fan capacity limit characteristic 1-speed gas flow. Determined. In addition, the lower limit is the ash cut prevention limit characteristic C1 for the primary reheater 19 and the economizer (B) 13b, the NOx countermeasure lower limit characteristic d for suppressing the increase in NOX concentration due to the decrease in gas recirculation flow rate, and the It is determined by the thermal steam temperature stabilization lower limit characteristic e. m2
(12゜rn2[,, m2L3 + TJ2L2 +
``tL+ is each characteristic a.

These are the limit points determined by b, c, d, and e, and in the case of this figure, the range between m2L and m2L1 is the allowable operation range.

The physical basis for the characteristics shown in Figure 8 is that when m2 k is reduced, the amount of gas diverted to the primary reheater side increases, as is clear from the definition of equation (1), but the amount of gas diverted to the primary reheater side increases. This is because the temperature control system maintains the reheated steam temperature constant by lowering the gas recirculation flow rate. Since the five operational permissible limits shown in FIG. 9 change depending on the load level, the final permissible limits m2·, 8 and m, 14 are determined as shown in FIG. 10.

That is, m2□ is the gas recirculation fan capacity limit calculation means 1
The upper limit m2 obtained in 02. , and the upper limit m2U obtained by the hot cut prevention upper limit calculation means 103.

The lower value of these is determined by the lower value selection means 107.
Add one. In addition, m2111IIl is the lower limit m2 L3 obtained by the lower limit calculation means 104 for preventing ann Yukasoto,
N Q x Lower limit m obtained by countermeasure lower limit calculation means 105
2L2 and the lower limit m2L obtained by the reheat steam temperature stabilization lower limit calculation means 106, the maximum value selection means 108 determines the largest value. Here, m2, 2゜m2L
2. m2L, is calculated as a function of loading, m2Ul
9m2L3 is calculated as a combustion gas volumetric flow rate VGAS(7) function. Further, VGA8 is calculated as a function of load by combustion gas volumetric flow rate calculation means 101. As shown in FIG. 9, as m2 increases, the gas 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 m211 of the operational allowable limit, the initial trial point m↓ is selected as this point. Similarly to the case of ml, in order to determine the distance from the initial simplex size 7 m to the operational tolerance limit, we calculate the ``m2 coordinates of the vertices of the initial simplex excluding It'' 2 (J = 2 ~
k) is determined by the following formula.

・・・・・・・・・α2 However, In, : (In2.,,+m2...t,
) / 2. r is a uniform random number (0 ku 1), L
L2 is a constant (0 (KL!<1).

FIG. 10 shows the relationship between the condenser vacuum degree m, plant efficiency, circulating water flow rate, and circulating water temperature rise width. The physical basis for this characteristic is that increasing "s" 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, the plant efficiency is maximized. In addition, as the m3 decreases, the circulating water flow rate decreases, so the temperature rise of the circulating water increases.The allowable operation range is defined by the upper limit characteristic of the circulating water flow rate determined by the circulating water pump capacity. f1
There is a lower limit characteristic g1 of the circulating water flow rate for limiting the rate of scale deposition in the cooling pipe, and an upper limit characteristic of the circulating water altitude rise width for protecting the plankton contained in the circulating water. 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 varies depending on the load level and seawater temperature, as shown in Figure 11. Depends on it. In the figure, the solid line is the seawater temperature 21
The dotted line shows the efficiency when r is 18tll'. Therefore, in order to determine the initial trial point, an initial trial point determining means 109t as shown in FIG. 12 is used. In this means, based on the characteristics shown in FIG. 11, the upper limit m3+aax(L)+lower limit m3. of the manipulated variable is determined as a function of the load. ．． ．． ．． (L), represents the manipulated variable m8M (L)'r corresponding to the maximum efficiency point, and Tc=T as seawater a degree Tc as all parameters.

Characteristic functions corresponding to T, I...T are prepared.

Therefore, as shown in FIG.
In step 09, the initial trial point mA is selected at the point where maximum efficiency can be expected within the operational tolerance range. Next, in order to determine the size of the initial simplex according to the distance from the river to the operational limit, the m3 coordinates m3 (22~k) of the vertices of the initial simplex excluding "l" are determined using the following formula. .

・・・・・・・・・03 However, "3 = ("3mmx +m3-tm)/2+
' is a uniform random number (o<'<1), KL
3 is a constant (0<KL3(1).

The plant characteristics shown in FIGS. 6, 8, and 11 can be known using the plant model 24 (l). The upper limit of the manipulated variable rri determined by , , , .

(i=1~3) and lower limit mt rain (1=l~
3) all take into account all implicit constraints, and have different meanings from the explicit constraints used in equation (2). .

Therefore, if each vertex coordinate m1 (I-1~3.J=2~k) of the initial simplex determined by the 0υα2αy formula violates the explicit constraint, each vertex coordinate can be Taken above explicit constraints. Also,
(2) The value of the constant Kt, + (i=1 to 3) in the O2O2 formula may be determined to be an appropriate value suitable for the plant to be controlled by analyzing the control characteristics through simulation.

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 completely 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 It. Assume that the load L(to
).

Let the maximum value of L(tO-Δt), ・b(to-nΔt) be L +sa□, and the minimum value tL-+・~L vaax
' L lll11>εL ・・・・・・
If (14), then it is assumed that there has been a load change, that is, the load is considered to be steady according to the α4 formula until time t2 in Fig. 13, and after detecting the load change at t2, the maximum efficiency search means 230 is suspended. Even after the load change is completed, the maximum efficiency search means 230 remains inactive until time t4 when the plant is in a thermally transient state. After t4, it is considered to be a steady state,
works again. In this case, the maximum efficiency search means 230 starts its operation from the initial amplifier formation step 1 in FIG. 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 must be changed during the number switching sequence and after the required thermal equilibration time ts has elapsed as a result of the number switching. 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 only during the switching sequence operation and 18 hours.

FIG. 15 shows the processing procedure of the embodiment for the above purpose. This process operates at a cycle Δt, and the sequence operation determining means 111 determines whether the sequence for switching the number of operating auxiliary equipment is in operation, and if it is in operation,
The timer reset means 112 sets the timer kIJ for counting the time tA1 after the completion of the switching sequence, and the operation of the maximum efficiency search means 230 is stopped by the stop command means 113. On the other hand, when the switching sequence is completed, the subsequent elapsed time tA1 is measured by the timer 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 search means 230 is stopped, and if the load change has been completed, the thermal equilibrium state determining means 116 determines the thermal equilibrium state by comparing the magnitude of one person and 1 s. 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
It is determined by considering both the Mira load and the atmospheric temperature T. However, since the influence of atmospheric temperature on plant characteristics is relatively small, it is not necessarily necessary to take atmospheric temperature into consideration, and even if the method determines the initial trial point m) by simply considering only the load, the control characteristics will deteriorate significantly. No worries. Also,
As shown in Figure 4, when the manipulated variable converges to the maximum efficiency point, the initial simplex is formed again using the method shown in Figures 6 to 12. As long as the operation of the maximum efficiency search means 230 is not interrupted due to switching or the like, the control characteristics will of course not be impaired even if the initial simplex is completely formed near the convergence point.

Further, in this embodiment, the direction of the new trial point is determined to be the direction of the center of gravity expressed by equation (3), which is the vertex of the simplex where the efficiency is the lowest, 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 locate 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-p) points among the vertices forming the simplex. This is a method to determine. Also, the second method is
Divide the k vertices into group A, which consists of p vertices with lower efficiency, and group B, which consists of the remaining q vertices, and from the lowest efficiency point of both groups, t<t
In this method, one new trial point is determined in a straight line direction passing through the center of gravity excluding the points from =1 to p). In addition, the third method is to calculate the center of gravity mGlk of the / complex (
3) Instead of setting the efficiency ηIk of each vertex as a direct weighting coefficient using equation 3), this method uses the following equation to obtain the difference ηl−η total weighting coefficient with respect to the standard value η.

However, it is desirable to modify η 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
The third method based on equation 5) has better convergence than equation (3) because it determines the maximum slope direction of the efficiency characteristic with respect to the manipulated variable more accurately and determines the new trial point. can be expected.

In addition, in this embodiment, if the new trial point has a higher efficiency than any of the efficiencies of the points constituting the original simplex, 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 move the money to n vertices from the one with the highest efficiency at the vertices of the simplex, and set the center of gravity as the total actual operation amount. In this case, it is effective to select the value of n within the range of 2 x n<k/2.

Furthermore, 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 retracted according to formula +81 (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, Equation 81 (9) 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, α amount - αI (1 - β) ...
It is desirable to correct α- according to ...+161.

However, in the above equation, β may be approximately 0.2. Another effective method is to estimate the manipulated variable limit based on the degree of violation of the implicit constraint condition and regress to the limit ([. It is effective in optimizing the quantity. Now, if the state quantity at the lowest efficiency point in the simplex is eX++ + the state quantity at the new trial point is X2 + the constraint condition is xL, α is fully corrected and the manipulated variable is retreated to the limit llL. For example, α' can be obtained by linear interpolation using the following formula.

In reality, the correction coefficient ξ(0〈ξ〈1)
The new trial point will be determined using the following formula.

XL-X.

α'=((1+α)−−11ξ ・・・・・・・・・
...aex2-x.

Further, 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 shown in FIGS. 13 and 14 The interval nΔt does not necessarily have to be constant, and in short, it may be 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. Since this tS also differs depending on the type of auxiliary equipment whose number is to be changed, it is desirable to modify it in consideration of 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. In the conventional method, optimization takes 30 to 60 minutes, which means that the optimization function cannot be achieved unless the steady load condition continues for any longer. It can be said that it can hardly be put to practical use for the needs of

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 that is directly calculated using the plant model built into the control system is used. The point is that the accuracy of convergence to the optimal value is higher than that of the method.

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 differences of a conventional control system in comparison with the control system of the present invention shown in FIG. 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 structure 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 the opening degree of the Clarel damper. 9th
The figure shows a method for determining the initial trial point and operating amount limit for the parallel damper opening based on the characteristics shown in FIG. 1st
Figure 0 shows plant efficiency and circulating water flow rate versus condenser vacuum level. and the characteristics of the circulating water temperature rise range. FIG. 11 shows the characteristics of the plant efficiency with respect to the condenser vacuum level as a full load level and seawater level as a full parameter. Figure 12 shows the 11th
The method for determining the initial trial point and operating amount limit for the condenser vacuum degree is shown based on the characteristics shown in the figure. 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. 1st
FIG. 5 shows processing means for realizing the purpose shown in FIG. 14. 100...Plant, 2...Control/Stem, 3...
・Maximum efficiency search means, 4... State feedback, 5
...Optimization device, 200...Control system, 23
0...Maximum efficiency search means, 24o...Plant model, 9...Trial operation amount, IO...Plant efficiency, 1
1... Optimum operation 1, j ma'no' Figure 1 20 'IIfJ30 YJ+口0z通切KI th-■ m+ C0zA sashiko no $qa口9muv;8 mouthweight 2 (,X@Larelda Shinoma (open cliff) Figure 1 Tada 10th 3rd (Banshinken cliff on the 7th) No. 11 Exit 1. (I y'g#'L"-jL) % 12 Figure 13 III 14th ω 'fJrsrn

Claims (1)

[Claims] 1.0. Determining plant efficiency as a function of the manipulated variable of a thermal power plant in which at least one of an excess rate, a parallel damper opening degree, and a condenser vacuum degree is an operational parameter for operation control; determining the manipulated variable of the parameter using the plant efficiency, and determining the manipulated variable that maximizes the efficiency through a trial operation using the plant efficiency, based on the effect soak corresponding to a plurality of trial points. In a control method that decides a new trial point using An efficiency optimization control method for a thermal power plant, characterized by: 2. In the control method according to claim 1, the gas recirculation fan capacity limit, the hot cut prevention limit, and the NO.
Efficiency optimization control for a thermal power plant, characterized in that when at least one of the X countermeasure limit and the reheat steam temperature stabilization limit is violated, the parallel damper opening degree is corrected and this is set as a new trial point. Method. 3. In the control method described in claim 1, if at least one of the upper limit of the circulating water flow rate, the lower limit of the circulating water flow rate, and the upper limit of the circulating water temperature rise range is violated, the condenser vacuum degree is corrected, An efficiency optimization control method for a thermal power plant characterized by using this as a new trial point.