JPS5840610A - 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, the initial trial point with the opening extent m2 of the parallel damper which maximizes the plant efficiency within a manipulated variable range where restricted requirements of plant conditions are met is determined as a function of a plant load.

Description

[Detailed Description of the Invention] The present invention relates to a control method for a thermal power plant, and particularly to a method for constantly maintaining the plant efficiency at the highest point even when ambient conditions such as seawater temperature and atmospheric temperature fluctuate during normal load operation. This invention relates to a preferred control method.

The issue of highly efficient operational control of thermal power plants has been a hot topic for some time, but a control system that aims to improve the efficiency of a plant from an overall perspective has not yet been put into practical use. Reports on the optimum ratio of boiler efficiency by slightly manipulating the air-fuel ratio (e.g., poMoran et al.
l, Developmeni andapplic
self-optimizing
control to coal-fired steam
m-generating plant.

proc, IEE, voz, 115.42 (x
96'5-2)). In these conventional control systems, as shown in FIG.
Determine ゛.

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 method in a control method for a thermal power plant, and also to improve the efficiency of the plant from an overall perspective, and in particular, to provide an appropriate initial trial point according to the plant condition. An object of the present invention is to provide an efficiency optimization control method for a thermal power plant, which has a maximum efficiency search means that can determine the initial trial point of the opening degree as a function of the load of the plant.

In the present invention, in order to overcome the problems of the conventional method, the second
By using the plant model 1±A for efficiency calculation built into the control system 200 shown in the figure, by performing model standard efficiency prediction calculation, 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 main 1 for this control system μ is that the plant model 2 is
This is a method of searching for the maximum efficiency point by repeating the procedure of outputting a trial operation amount 9 for 40 and finding the corresponding plant efficiency 10. In this case, the initial trial point is determined in consideration of the plant efficiency characteristics and plant state constraints determined in advance using the plant model 240. That is, the initial trial point of the parallel damper opening that maximizes the efficiency within the manipulated variable range that does not violate the constraint conditions is determined as a function of the plant load.

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 plant control systems to which conventional methods can be applied as is are omitted to avoid cluttering the diagram. In FIG. 3, each symbol represents the following.

9a is the trial 02 excess rate, 9b is the trial damper opening, 9C is the trial condenser vacuum, lla is the optimum 02 excess rate, 11b is the optimum damper opening, 11C is the optimum condenser vacuum, and 13a is carbon saving. container (A), 13b is the economizer (B), 14 is the water 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, 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 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
30 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 draft fan, 47 is a boiler, 50 is a turret, and 60 is a water supply system.

Although there are many operating parameters that affect efficiency, in the description of this embodiment, 02 excess rate m1. Parallel damper opening m3. 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 m 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 mj corresponding to trial point 1.

m; is given to the Pratt model 240- for efficiency calculation, and the efficiency η1 as a steady value is determined. Similarly for trial points 2 and 3, η2. Find η3. A new trial point 4 is set on the extension of the straight line connecting the point with the lowest efficiency (in this case, 1) and the center of gravity of the remaining points (in this case, on line segments 2 and 3). Then, calculate the efficiency η4 corresponding to the trial operation amount m7 and m times. Here, a new trial point 5 is found in the same way from the newly created triangle 234, excluding one trial point. At this time, if trial point 5 violates the constraint condition of the plant state quantity, it is returned to 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 variables corresponding to this point are the optimal values (mγ, m7 in this case) and become the actual manipulated outputs 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 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 according to an embodiment of the present invention. The control algorithm will be explained step by step below. However, the symbols are defined as follows.

m bird: Operation amount i = 1 OT excess rate (%) 1 = 2 Parallel damper opening degree (%) i = 3 Condenser vacuum degree (wm Hg, 1 where, m
2 may indicate the opening degree of the parallel damper, but here it is defined by the following equation.

-・=Once -X100 (%)...(1)G s
+ G R Here, Gel represents the gas flow rate in the gas passage where the primary reheater 199 economizer (A) 13a is arranged in FIG. B) 13b
represents the gas flow rate in the gas passage where is located.

m1□X + ml, 11: Upper and lower limit of operation amount l=
102 Excess ratio upper, lower limit plate = 2 Parallel da/pa opening upper, lower limit i = 3 Condenser vacuum upper, lower limit GCL mHz * GCL + mla + condenser circulating water flow rate upper.

Lower limit (kg/『) D□,: Upper limit of condenser circulating water temperature rise range (C) Y□t:
Low pressure turbine exhaust humidity upper limit (%) GGm□x + G
GRml++ a Gas recirculation flow rate upper and lower limits (kg/s
ec) The initial trial points m, (1:1 to 3) for forming the initial plex are those that satisfy all of the above constraints, m, , m2. , m, form a polygon (called a simplex) with angles (-6 in the case of Fig. 4), and use this as an initial simplex. As for this formation method, one point is set as an initial trial point m-, and the remaining (k-1) points are set as -a random number r
, (1=2~k) and is determined by the following equation.

m, = m1111-1-r (m, lIl, 8-m,
1111...(2) However, ml determined in this way always satisfies the constraint as a manipulated variable (this is called an explicit constraint), but it does not satisfy the constraint as a process state quantity. 3 (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. Then, the plant efficiency η (J=1 to 6) corresponding to each point determined through the above trial is determined by the plant model 240.
obtained using

-W stone = Yo calculation of the center of gravity Here, we exclude the point with the lowest efficiency and rate among the points of the 7/plex (calculate the center of gravity mQ of the simplex defined by k-11 points. Now, Minimum efficiency point is j-1
Then, m(II is expressed by the following formula, and the distance from the lowest efficiency point to the center of gravity ΔmQ.

is expressed by the following formula.

ΔmG, = mGI−mX −, , −・−(4
)7 Determining the new trial point Set the direction of the new trial from the lowest efficiency point to the center of gravity, and define the new trial point as the point extended from the center of gravity by α1 times the distance between the two points, ΔmQ, and set this as m: ", then m,"' wm(,, -f-a, ΔmG+ "
- (Represented by 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,,-〇 ・・・・・・・・・
(6) and lower limit m 1.1. If f infringes, m
,+1=m,,,,...
・・・・・・ (As a force.

ETI outsourcing efficiency calculation Using the plant model 240 for efficiency calculation, find l in the efficiency η 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.

〒〒〒〒〒〒〒Plant model for constraint monitoring efficiency calculation 〒〒〒〒〒〒〒〒Plant model for constraint monitoring efficiency calculation] - If the calculated process state violates the implicit constraint, the trial point m,
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, α5 is corrected according to the following formula, and 5T
Return to EP=3.

U(DT>DT---IU(Y>Y---ri...1
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.

5TEP-6 Convergence Judgment Among the efficiencies corresponding to the new trial point and each point constituting the original simplex, the maximum and minimum efficiencies are calculated as η□8, respectively.
and η1. It is determined whether the maximum efficiency point has been reached or not according to the following formula.

Here, 1 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 operation t corresponding to η3.8 is output, and the process returns to 5TEP-1 to form 9 initial amplifiers. If the maximum score is not reached, proceed to the next step.

8TEP-7 Efficiency Improvement Direction Judgment If the new trial point yields a higher efficiency than any of the efficiencies of the points constituting the original 77 plexes, 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.

b〒i=1 Among the points that make up the simplex of the new simplex, exclude the operating point that shows the lowest efficiency, add new trial points, and form a new simplex 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 operation parameter (O7 excess rate m)
1. Parallel opening degree m2. In the three-dimensional space defined by the condenser vacuum degree m, the trajectories of each moving toward the optimum value are expressed 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 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 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 m'' should be selected at the point with the highest possible efficiency, and the size of the initial simplex should be determined according to the distance between this mI and the operational tolerance limit. With this in mind, we decided on the following method.

FIG. 6 shows the relationship between plant efficiency and 02 excess ratio m, 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 30C, and the dotted line is the efficiency characteristic when the atmospheric temperature is 10C. The upper and lower limits of the allowable range for ml 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 as a function of load is ``1''.
mat (L) + lower limit "t-+- (Ll) can be expressed, and the manipulated variable mLM (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 mj at the point where maximum efficiency can be expected within the operational permissible range.Next, the initial trial point mj is determined from the initial 7/Grex size using ml as the operational permissible limit. In order to determine the m1 coordinates m,' (j=2 to k) of the vertices of the initial simplex excluding ml, the following equation is used to determine the m1 coordinates according to the distance to ml.

・・・・・・・・・ αυ However, m, = (m, aha, + ml min
) / 2 p ” is - random number (o<r ri 1)
, K, is a constant (0<KLI<.

l).

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.
3a, the ash cut prevention and prevention limit characteristics for preventing ash cut have already been determined. Also, the lower limit is the primary reheater 1
9 and the ash cut prevention limit characteristic for the economizer (B) 13b C1 NOx countermeasure lower limit characteristic d and reheat steam temperature stabilization lower limit characteristic e for suppressing the increase in NoXp degree due to the decrease in gas recirculation flow rate Determined.

m2at e mtu+ + “tLs snow m2L21
mtLt II'i k This is the limit point determined by the characteristics a, b, c, d, and e. In this figure, it is m21. and m2g
The range between is the permissible operating range. The physical basis for the characteristics shown in Figure 8 is that as m2 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 operating permissible limits shown in FIG. 9 change depending on the load level, the final permissible limit m21. Yo and m2
1. is determined as shown in FIG. That is, for m2, the lower value of the upper limit m2υ obtained by the gas recirculation fan capacity limit calculation means 102 and the upper limit m, υ1 obtained by the ash cut prevention upper limit calculation means 103 is selected as the low value selection means. 107. Also, m...
Lower limit m 2 r, & , NO OX countermeasure lower limit calculation means 105 obtained by the ash cut prevention lower limit calculation means 104
The lower limit m, L obtained by , and the lower limit m, L obtained by the reheat steam temperature stabilization lower limit calculation means 106 ．． The largest value among them is determined by the maximum value selection means 108. Here, m, , , mB, 2. m, L is load L+7)
m, Ut, m, L, are calculated as a function of the combustion gas volumetric flow rate Va^8. Also,
VCAll is calculated as a function of the combustion gas volumetric flow rate calculation means 1019 load. As shown in Figure 9, m2
The plant efficiency has a drooping characteristic because the gas recirculation flow rate increases as the value increases, and the gas recirculation fan power increases accordingly. Therefore, the point of maximum efficiency is always the lower limit of the operating tolerance limit m21. Assuming that it is, select this for the initial trial point. In addition, as in the case of ml, in order to determine the size of the initial song plex according to the distance from ml to the operational permissible limit, m2 coordinates m2 (J = 2 ~ k) is determined by the following formula.

・・・・・・・・・ αδ However, m2 = (rr12 was x + m2 m
1 o 1/2 + r' is - random number (oa
r >, KLI is a constant (0(KLI ri 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. Well,
As the temperature decreases by ~3, the range of increase in temperature of the circulating water increases due to the decrease in the flow rate of the circulating water. Here, the permissible operation range is defined 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 restrictions are determined at the plant planning stage and do not change during operation; however, the allowable operating range defined by these limits is greatly affected by the load level and seawater temperature, as shown in Figure 11. , will be done. In the figure, the solid line shows the efficiency when the seawater temperature is 21C, and the dotted line shows the efficiency when the seawater temperature is 18C. Therefore, in order to determine the initial trial point mA, an initial trial point informal determination means 109 as shown in FIG. 12 is used. In this means, based on the characteristics shown in FIG. 11, the upper limit m3 s+ax (L) + lower limit m5 of the manipulated variable is determined as a function of the load.
．． ! . (L), the manipulated variable m corresponding to the maximum efficiency point, M(L
) and the seawater temperature Tc as a parameter, T c ,,” T + −, T,2 .

....A characteristic function corresponding to T is prepared.

Therefore, as shown in FIG.
In 09, the initial trial point mA is selected at the point where maximum efficiency can be expected within the operational tolerance range. Next, the initial simplex size (k) from ml to the operational tolerance limit is determined by the following equation.

・・・・・・・・・ a'5 However, ~4 = ("s, ramy + m3 ml.)
/2. r' is a uniform random number (o<r, 1), and KL is a constant (o, KL, 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 m, ... (i = 1 to 3) and the lower limit "l + m1m (' = 1 to 3) determined by each means shown in Figs. 7, 9, and 12 are as follows. This takes implicit constraints into consideration, and has a different meaning from the explicit constraints used in equation (2).

Therefore, the initial non-prenox noxno each vertex coordinate m'+ (j=1~3.j=:
2~k) violates the explicit constraint, then '(6
) , (Using the same idea as Equation 71, each vertex coordinate is taken on the explicit constraint condition. Also, 0υ, , (121, (
The value of the constant KLL (i = l~3) in equation 13 is 7
By analyzing control characteristics through simulation,
What is necessary is to determine an appropriate value that suits the plant to be controlled.

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.

The load L(t
o), L('o-Δj), )-・-L(to-nΔt)
The maximum value is L@aXj and the minimum value is Ll. Binding, L--
-L-+->at--Q41, it is assumed that there was a load change in voice. That is, until time t2 in FIG. 13, the load is assumed to be steady according to formula 04, and after detecting load fluctuation at t2, the maximum efficiency search means 230 is stopped. Even after the load change is completed, the maximum efficiency search means 230 remains inactive until time t, when the plant is in a thermally transient state. t
After 4, it is regarded as a steady state and starts operating again. In this case, the maximum efficiency search means 230 starts its operation from the initial wavelength formation step 1 shown in FIG. Here, the value of nΔt is the transient of the controlled plant,
Use necessary and sufficient values taking into consideration the characteristics.

As long as the operation of the maximum efficiency search means 230 is not interrupted due to load fluctuations or switching of the number of operating auxiliary machines, 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 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
The k vertices are divided into group A, which consists of p vertices with lower efficiency, and group B, which consists of the remaining q vertices, and t(t
In this method, t new trial points are determined in a straight line direction passing through the center of gravity excluding the =1 to p)th points. In addition, the third method is (3) when determining the center of gravity mG1 of the simplex.
Instead of using the formula to directly use the efficiency η1 of each vertex as a weighting coefficient, this method uses the following formula to calculate the difference ηl−η with respect to the standard value η as a weighting coefficient.

1=Correct, but 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,(
Compared to equation (3), the third method based on equation (c) more accurately determines the maximum slope direction of the efficiency characteristic with respect to the manipulated variable and determines a new trial point, so it is expected to have better convergence. can.

In addition, in this example, if the new trial point has an efficiency higher than any of the efficiencies of the original simplex points, 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 vertices of the simplex with the highest 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 variables directly related to the violated constraint are retreated according to equations (8) and (9). ing. However, since the causal relationship between the process state where the constraint is imposed and the manipulated variable does not necessarily have a one-to-one correspondence, the formula +8L (9) should 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 αl according to ＼αI:α+(1−β)... (g). However, in the above equation, β may be approximately 062. Another effective method is to estimate the operation amount limit based on the degree of violation of the implicit constraint condition and to retreat to the -5 limit value. This method is effective for optimizing the manipulated variable whose efficiency characteristic with respect to the manipulated variable is a monotonic function. Now) If the state quantity at the lowest efficiency point in the simplex is Xl, and the state quantity at the new trial point is X2 + the constraint condition is XL, then to correct α and retreat the manipulated variable to the limit value, for example, use the following formula. α' can be found by linear interpolation using the % formula.

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

α'=((1+αl--11ξ... α mallet x2-
x.

In addition, in this embodiment, the maximum efficiency search means 23013 and 1
The load fluctuation monitoring interval nΔt shown in 4A does not necessarily have to be constant, and may just be the minimum necessary time for the plant to reach a thermal equilibrium state. Therefore, by sequentially making corrections according to the load level and the load fluctuation range, the maximum efficiency search means 230 can have more opportunities 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 is directly calculated using the plant model built into the control system, so efficiency is indirectly detected based on the efficiency index method. Compared to conventional methods, the convergence accuracy to the optimal 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 the initial trial point of the parallel damper opening can always be set near the maximum efficiency point.
This is the point where the convergence to the maximum efficiency point is extremely good under any plant load.

BRIEF DESCRIPTION OF THE DRAWINGS 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 structure A and characteristics 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. Figure 6 shows
0. The characteristics of plant efficiency with respect to excess rate are shown. FIG. 7 shows 0.0. The method for determining the initial trial point and operating amount limit for the excess rate is shown. 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. 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...blunt, 2...control system, 3...
・Maximum efficiency search means, 4... State feedback, 5
...optimal operation amount, 200...control system, plexiuni...maximum efficiency search means, 1''...plant model, 9...trial operation amount, 10...plant efficiency, 11
---Optimal operation rod 5 figures, etc., (θ234ttsu *'-) Medical Nagikin, 0 Yokoto ll Zumasu J (IW・k Shu 1 counterfeit) ￥150 1itq figure

Claims (1)

[Claims] 1. Obtaining plant efficiency as a function of the manipulated variable of the parameter of a thermal power plant in which the opening degree of the parallel damper is used as an operating parameter for operational control; and determining the manipulated variable of the parameter using the plant efficiency. , a control method comprising determining an operation amount that maximizes the efficiency through the trial operation for deriving the efficiency, and determining an initial trial point in consideration of predetermined efficiency characteristics of the plant and constraints on the plant state. An efficiency optimization control method for a thermal power plant, characterized in that an initial trial point of a parallel damper opening is determined as a function of the load of the plant.