JPS5840612A - 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 found similarly from the triangle 234. In this case, if the efficiency of the new trial point is higher than those of trial points after trial, a manipulated variable which corresponds to the new trial point is regarded as an operation command value for the actual operation of the plant even before a maximum efficiency point.

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.

Although the issue of highly efficient operational control of thermal power plants has been a hot topic for some time, a control/system that aims to improve efficiency from a comprehensive plant perspective has not yet been put into practical use.

Few reports on optimizing boiler efficiency by manipulating the air-fuel ratio (e.g. FlMOr”net al
, Development and application
ion of self-optimizing co
ntrolto coal-fired steam-
generating plant.

Pr0C, IEE, VOl, 115, A2
(1968-2)). In these conventional control systems, as shown in Figure 1, the plant 1
The controller 2 based on the status feedback 4 from 00
The search means 3 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.

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.

The purpose of the present invention is to eliminate the drawbacks of the conventional methods as described above in a method of controlling a thermal power plant, and to improve the efficiency of the plant from an overall viewpoint. The purpose of the present invention is to provide an efficiency-optimized 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 200 shown in the figure to perform model standard efficiency prediction calculations, efficient efficiency optimization can be achieved quickly and with high accuracy without being constrained by the response speed of the plant. realized. The basic method of the maximum efficiency search means 230 in this control/system 200 is a procedure in which a trial operation amount 9 is output to the plant model 240 to determine the optimum operation amount 11, and the corresponding plant efficiency 10 is determined. This method searches for the maximum efficiency point by repeating the steps. In this case, if the efficiency at the new trial point is higher than the efficiency at the already tried trial point, even if the maximum efficiency point has not been reached,
The operation amount corresponding to this new trial point is set as the operation command value for actually operating the plant.

FIG. 3 shows the basic configuration of an efficiency optimization control system that is an embodiment of the present invention. However, this diagram only shows functions specific to efficiency optimization, and various minor control systems and graft 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 trial 02 excess rate, 9b is trial damper opening degree, 9C is trial condenser vacuum degree, 11a is optimum 02 excess rate, 11b is optimum damper opening degree, 11C is optimum condenser vacuum degree, 13a is carbon saving container (A), 13b is the economizer (B), 14 is the ice wall, 1
5 is the primary superheater, 16 is the secondary superheater, 17 is the main steam, 1
8 is a high-pressure turbine/, 19 is a primary reheater, 2o 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 a condensate , 26 is a low pressure feed water heater, 27 is a feed water pump, 28 is a high pressure feed water heater, 29
3o is a circulating water pump, 3o 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, 47 is a boiler, dan is a turbine, and μμ is a water supply system.

Although there are many operating parameters that affect efficiency, in the description of this embodiment, the o2 excess rate m1. 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 m and m2 are shown in order to easily explain the principle of maximum efficiency search using the complex method. now,
The trial operation amount m1°mj corresponding to the trial point l is given to the plant model 240 for efficiency calculation, and the efficiency η1 as a steady value is determined. Similarly for trial point 2.3, 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, the line segment 2.3
A new trial point 4 is selected on the extension of the straight line connecting the above), and the efficiency η4 corresponding to the trial operation amounts m7 and m4 is determined. Here, excluding trial point 1, a new triangle 2 is created.
Similarly, a new trial point 5 is obtained from 34. 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 m7.
m:), 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 optimum operation needle.

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 a definite example of the present invention. The control algorithm will be explained step by step below. However, the symbols are defined as follows.

ml, operation amount 1-10□excess rate (%) 1=2 Parallel damper opening degree (%) i=3 Condenser vacuum degree (Hg) Here, m2 may indicate the opening degree of the parallel damper 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.

mI a+ax r m1m1n Two operation amount upper, lower limit l2 102 Excess rate upper, lower limit 1-2 Parallel damper opening upper, lower limit 1-3 Condenser vacuum upper, lower limit GCL +naz + GcL m1m: Condenser circulating water Upper and lower flow limits (kg/!1lec) DT□E: Upper limit of condenser circulating water temperature rise (C) Y□X
:Low pressure turbine exhaust humidity upper limit (%) GGR□x HG
GRml++: Gas recirculation flow rate upper and lower limit initial trial point 5
(i=1 to 3) satisfy all of the above constraints, m,, rr+2. In the three-dimensional space created by m3, there is an angle (
In the case of FIG. 4, a -6) polygon (this is called a 7-noplex) is formed, and this is taken as the initial simplex. As for this formation method, one point is the initial trial point m ~, and the remaining (k-11 points are, for example, − perturbation number rj (J
-2 to k) is determined by the following formula.

m (= rn I −1, 10 r (ml
□, -ml 1, )...
f21 However, m determined in this way always satisfies the constraint as a manipulated variable (this is called an explicit constraint), but it also satisfies the constraint as a process state quantity (this is called an implicit constraint). constraints) 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. Then, the plant efficiency η (''=1 to 6) corresponding to each point determined through the above trial is obtained using the plant model 240.

Here, the center of gravity mGI of the simplex defined by the point (k-1)(rlA) excluding the point with the lowest efficiency among the points of the simplex is calculated.Now, the lowest efficiency point is J
21, mGI is expressed by the following formula.

jη7 mi jso2 Moreover, the distance ΔmQ from the lowest efficiency point to the center of gravity is expressed by the following equation.

ΔmQ, -mQ, -m'i...
...... (4) The new trial direction is taken from the lowest efficiency point to the direction of the center of gravity, and the point extending from the center of gravity by α1 times the distance between image points ΔmG1 is set as a new trial point, and this is m,
Then, it is expressed as m, ": mG, 10 (Z, ΔmGI--(51). 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, □ is violated, m,
=m, □8 ・・・・・・・・・・・・ (
6) and lower limit m, 1. m:”
=m, mim・・・・・・・・・
(Using the plant model 240 for force efficiency calculation, find the efficiency ηl(+l) corresponding to the new trial point m, "'.

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 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.

d If the process state calculated by the plant model 240 for monitoring efficiency calculation violates the implicit constraint, the trial point m,
Discard all information regarding ' and return to 8TEP-3.
Determine a new trial point. In this case, considering the causal relationship between the operation and the process state, αl is modified according to the following formula to obtain 5TE.
Return to P-3.

α2 α2 meeting - Condition (GaR>GcR, -riU(GaR
<GaR,, +11) 2 ...... (8) α3 needle...condition (Ocr, >0cL---)U (
Gcz<GcL−+−) 2 U (D t > D t□,)U(Y>Y□8) ・
(9) Here, α for the 02 excess 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 and each point constituting the original simplex, the maximum and minimum efficiencies are calculated by η11, respectively.
, and η, 1°, and it is determined whether the maximum efficiency point has been reached according to the following equation.

Here, e 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, a manipulated variable corresponding to η, 8 is output, and the process returns to 8T'EP-1 to form an initial simplex. If the maximum score is not reached, proceed to the next 5TEP-7.

[M5 A ■ Mouth Efficiency Improvement Direction Judgment If the new trial point yields a higher efficiency than any of the efficiencies of each point constituting the original simplex, the operation output is performed even if the maximum efficiency point has not been reached. Proceed to the next 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 original simplex, exclude the operating point showing the lowest efficiency, add new trial points, form a new simplex from the resulting points, and perform 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 02 excess rate m9
．． Parallel damper opening m2. Condenser vacuum level m, 3
It is a projection of trajectories each moving toward an optimal value in a dimensional space onto three planes. In this figure, 1 to 6 are the vertices of the initial 7 noplexes, 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, 7 to 1
The solid line connecting -at===r in 1 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 m 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 7-complex formation step 1 is as follows.
This is an important problem that affects the convergence of maximum efficiency search. In order to improve the convergence, the initial trial point m11 should be selected at a point with the highest possible efficiency, and the size of the initial 7 emblems should be determined according to this m'l and the distance to the operational tolerance limit. I thought that it should be done, and decided on the following method.

FIG. 6 shows the relationship between Plan 1 efficiency and 0□excess ratio m, and also shows the influence of atmospheric temperature on the efficiency characteristics. That is, the solid line represents the efficiency characteristics when the atmospheric temperature is 30C, and the dotted line represents the efficiency characteristics when the atmospheric temperature is 10C. 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 ml as a function of the load
maw (L) + lower limit m, 1° (L) can be expressed, and the manipulated variable m1M (L, Ta) corresponding to the maximum efficiency point can be expressed as a function of load and atmospheric temperature Ta. Therefore, as shown in FIG. 7, the initial trial point determination unit 96 selects the initial trial point m1 at a point within the operation permissible range where maximum efficiency can be expected. Next, in order to determine the size of the initial simplex according to the distance from mj to the operational limit, m
One coordinate m11 (''=2~k) is determined by the following equation.

However, m, −(m, ,,n+”1 mla)/
2 + rJ is a uniform random number (0 x 1), and Ll is a constant (0 (KL 1 x 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 m is the gas recirculation fan capacity limit characteristic a
and primary superheater 15 and economizer (A) 1 using high-speed gas flow.
The limit characteristic for preventing ash cuts in 3a has already been determined. Also, the lower limit is the primary reheater 19
and cut-off prevention limit characteristic C for the coal burner (B) Determined.

m212, m2. , , m2L3. m2L2.
m2L, are the limit points determined by the characteristics a, b, c, d, and e, respectively, and in the case of this figure, m2L3 and m2UI
The area between them is the permissible operating range. The physical basis for the characteristics shown in Figure 8 is that as m2 is decreased, 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 operating permissible limits shown in FIG. 9 change depending on the load level, the final permissible limits m2□, m2. ,．．
11 is determined as shown in FIG. That is, m2.
, , is determined by the lower value selection means 107 as the lower of the upper limit m2[2 obtained by the gas recirculation fan capacity limit calculation means 102 and the upper limit m2U obtained by the ash cut prevention upper limit calculation means 103. do. Also, m2□,0 is
The lower limit m2. obtained by the ash cut prevention lower limit calculation means 104. , , The maximum value selection means 108 determines the largest value among the lower limit m2L2 obtained by the NOx countermeasure lower limit calculation means 105 and the lower limit m2L obtained by the reheat steam temperature stabilization lower limit calculation means 106. . Here, m2[2
, m2.2. m, ), is calculated as a function of the load, m2UI9m2L3 is the combustion gas volumetric flow rate V GAI
Calculated as a function of +. Further, VGA8 is calculated by the combustion gas volumetric flow rate calculation means 101 as a function of the load. 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, the maximum efficiency point is always the lower limit of the operational tolerance m2w1n
Assuming that this will be the case, I choose this as the initial trial point.

In addition, as in the case of ml, in order to determine the size of the initial //plex according to the distance from m↓ to the operational permissible limit, m2 of the vertices of the initial 7 noplexes excluding rJ
Coordinates m2 (J-2 to k) are determined by the following equation.

・・・・・・ (13 However, m2=('m2□x+ ”2 +elj/ 2
+r is a − disturbance number (0×1), and KL2 is a constant (0<KL2×1).

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 m 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. The permissible operating range for this iron is determined by the circulating water flow rate upper limit characteristic f1 determined by the circulating water pump capacity.
The lower limit characteristic g1 of the circulating water flow rate is for limiting the rate of scale deposition in the cooling pipe, and the upper limit characteristic of the circulating water temperature rise width is to protect 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 operational tolerance range defined by these limits depends on the load level and seawater temperature, as shown in Figure 11. It depends greatly on the situation. In the figure, the solid line is the seawater temperature 21
The dotted line shows the efficiency at 18C. Therefore, in order to determine the initial trial point mA, 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 of the manipulated variable is set as a function of the load: ``3 ra*x (L) + lower limit m3 m+
a(Ll sOperation amount corresponding to the maximum efficiency point "3M (L
) and using the seawater temperature Tc as a parameter, T c = T t , T 2 .

...A characteristic function corresponding to To is being 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 m7 to the operational tolerance limit, m of the vertices of the initial 7/plex excluding m11,
The coordinate m3 (J=2 to k) is determined by the following formula.

...... (131 However, m, = (m, k,, +m,,,.)/2.r
is - random number (o<r<, 1), +, 3 is constant (0<
KLSRI1).

The plant characteristics shown in FIGS. 6, 8, and 11 can be known using the plant model 240. Further, the upper limit of the operation amount m, -, (I-1 to I-3) and the lower limit m, which are determined by each means shown in FIGS. 7, 9, and 12. (I-1 to I-3) all take implicit constraints into consideration, and have different meanings from the explicit constraints used in equation (2).

Therefore, 0υ, 13. (Each vertex coordinate ml of the initial simplex determined by formula 131 (1=1~3.''=2~
If f61.k) violates the explicit constraint, then f61. Using the same idea as the method, each vertex coordinate is set under explicit constraints. In addition, 0υ, [121, The value of the constant K L 1 (l = 1 to 3) in equation 03 is calculated by simulation/yo/
By analyzing the control (property) characteristics, appropriate values suitable for the plant to be controlled can be determined.

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 t. q load L(to
+, L('o-Δt), =-L(to-11Δ
t), the maximum value is L □ and the minimum value is L m l m, and L , x Lmlm > εr, −3
” If it is Q41, it is assumed that there has been a load change. That is, the load is considered to be steady according to the αΦ formula until time t2 in FIG. 13, and after detecting the load change at t2, the maximum efficiency search means
30 will be suspended. At time t4'' when the plant is in a thermal transient state even after the load change is completed, the maximum efficiency search means 2
'30 is on hiatus. t, and thereafter, it is considered to be a steady state,
works again. In this case, maximum efficiency search means-230
In the initial non-breathing step 1 shown in FIG.
The operation will start from. 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 a change in the number of auxiliary machines in operation, the heat equilibration time ts required during the number change/-can operation and as a result of the number change has elapsed. Then, the maximum efficiency search means 230 is operated.

If the number of auxiliary machines in operation is to be changed even in a steady load state, it will be stopped only during the switching no-kense operation and 18 hours.

FIG. 15 shows the processing procedure of the embodiment for the above purpose. This process operates at a period of Δt, and the sequence operation determining means 111 determines whether the sequence 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 measuring the time LA 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 timer 114 measures the subsequent elapsed time LA. Next, the load fluctuation state determination means 115 determines whether or not the load is fluctuating according to the α4 formula. If the load is changing,
The operation of the maximum efficiency search means 230 is stopped, and if the load change is completed, the thermal equilibrium state determination means 116 determines that IA and 1s.
The thermal equilibrium state is determined by comparing the magnitude of . 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 nonplex, the initial trial point m regarding the 02 excess rate:
is determined by considering both the load and the atmospheric temperature Ta. 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 initial trial point m is determined by simply considering the load, the control characteristics will be significantly deteriorated. No worries. Also,
As shown in Figure 4, when the manipulated variable converges to the maximum efficiency point, the initial non-plex is formed again by the method shown in Figures 6 to 12, but load fluctuations or auxiliary equipment operation As long as the operation of the maximum efficiency search means 230 is not interrupted due to a change in the number of units, the control characteristics will not be impaired even if the initial non-plex is formed near the convergence point.

In addition, in this example, the direction of the new trial point is determined from the vertex of the non-plex 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 create a new trial on a straight line that passes through the centroids of the 9 points with the lowest efficiency and the centroids of the remaining (k-p) points among the vertices Ω that form a nonplex. This is a method of determining points. Also, the second method is
k vertices are connected to the less efficient one ((A consisting of lil
and group B consisting of the remaining nine, and determine a new trial point t(B) in the straight line direction passing through the center of gravity excluding the t(t=1-p)th point from the lowest efficiency point of both groups. The third method is to calculate the nonplex center of gravity mc+((-) by using the difference with respect to the standard value This method is calculated using the following equation using η1-η as a weighting coefficient.

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
Compared to equation (3), the third method based on equation (3) more accurately determines the maximum slope direction 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 embodiment, if the new trial point yields a higher efficiency than any of the efficiencies of the points constituting the original seven-point press, the operation output is 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 among the non-plex vertices with higher efficiency and use their center of gravity as the actual manipulated variable. In this case, it is effective to select the value of n within the range of 2/2.

In addition, in this example, if the process state at a new trial point violates the implicit constraint, only the manipulated variable directly related to the violated constraint is set back according to formulas (81, (91). 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, f81.Equation (9) can be used as is for the manipulated variable that is directly related to the violated constraint. Assuming that, for manipulated variables that are not directly related, it is desirable to correct αi according to αI - α + (1 - β) mountain... αQ. However, in the above equation, it is sufficient to set β = about 0.2. Also, as another method,
It is also effective to estimate the operation amount limit based on the degree of violation of the implicit constraint condition and retreat to the limit value. This method is effective for optimizing the operating amount for which the effectiveness and drivability of the operating lid are a monotonous function. Now, if the state quantity at the lowest efficiency point in non-plex is XI, and the state quantity at the new trial point is x2 + the constraint condition is xL, then in order to correct α and retreat the manipulated variable to the limit value, for example, the following α' can be found by linear interpolation according to the equation.

In reality, the correction coefficient ξ(0'<ξ<1
) to determine the new trial point 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 shorter during high-load operation, and I) nΔt can be made smaller. Further, 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 for the above-mentioned reason. This 1s also differs depending on the type of auxiliary equipment whose number is to be changed, so 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 side (goods) 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 efficiency can be improved from a comprehensive perspective of Sibrand by optimizing multiple operating parameters that affect plant efficiency. Compared to the conventional method, efficiency can be significantly improved.

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 optimal value is high.

The fourth effect of the present invention is that since the plant model built into the control system is used to optimize efficiency, noise and detection errors are reduced compared to the conventional method, which optimizes efficiency based on actual measured values. It is possible to perform stable and highly accurate optimization without being affected by

A fifth effect of the present invention is that by preventing sudden changes in the manipulated variable, highly safe and stable plant operation is possible.

[Brief explanation of the drawing]

FIG. 1 is for explaining the structural differences of the conventional control system in comparison with the control system of the present invention shown in FIG. 2. FIG. 3 shows the basic configuration of an efficiency-optimizing control system for explaining the structural features of the control system of the present invention in comparison with the control system of FIG. FIG. 4 shows the basic processing 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. Figure 7 shows 0 based on the characteristics of Figure 7.
2 shows a method for determining the initial trial point and operating amount limit for the excess rate. FIG. 8 shows the characteristics of plant efficiency and gas recirculation flow rate with respect to parallel damper opening. Figure 9 shows
A method for determining the initial trial point and operation amount limit for the parallel damper opening degree will be shown based on the characteristics shown in FIG. Figure 10 shows plant efficiency and circulating water flow rate versus condenser vacuum degree. and the characteristics of the circulating water temperature rise range. FIG. 11 shows the characteristics of plant efficiency with respect to condenser vacuum degree using load level and seawater temperature as parameters. 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 system, 3...
・Maximum efficiency search means, 4... State feedback, 5
...Latest manipulated variable, lO...Plant efficiency, 11...
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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 the 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 plant efficiency. If the efficiency at a point is higher than the efficiency at a trial point that has already been tried, even if the maximum efficiency point has not been reached, the operation amount corresponding to this new trial point is used as the operation command value for actually operating the plant. An efficiency optimization control method for a thermal power plant, characterized by: