JPS5840606A - Efficiency optimization controlling method of thermal power plant - Google Patents

Abstract

PURPOSE:To optimize the efficiency of a plant speedily with high precision without any restriction of the response speed of the plant, by using a plant model for efficiency calculation which is incorporated in a control system, and then carrying out model standard type efficiency estimating calculation. CONSTITUTION:When a maximum efficiency searching means 230 has an O2 excess rate m1 and the opening extent m2 of a parallel damper as operation parameters, a trial manipulated variable which corresponds to a trial point 1 is supplied to a plant model 240 for efficiency calculation to find efficiency eta. At trial points 2 and 3, the efficiency eta is found similarly. On the prolonged line of the straight line which connects the point 1 with minimum efficiency to the center of the 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 operation of the means 230 is stopped throughout load variation or a switching sequence for the number of auxiliary operator cabs. This trying method is repeated until a maximum efficiency point 7, thereby obtaining the best manipulated variables m1<7> and m2<7>.

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a method for controlling a thermal power plant, and in particular, to a method for constantly maintaining the plant efficiency at the highest point even when ambient conditions such as sea temperature and atmospheric temperature fluctuate during normal load operation. The present invention relates to a control method suitable for.

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.

There are a few reports on optimizing boiler efficiency by manipulating the air-fuel ratio (e.g., p. Moran et a.
1+, 0eve+opment andapplica
tion of self-optimisirjg
contr61to coal-fired s
team-generating plant.

Pr0C, IEE, VOl, 115. No. 2
(1968-2) ) There is only power. In these conventional control systems, 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 to determine the optimum manipulated variable 5.

However, the first problem with the conventional method is that it takes a large amount of time to find the maximum efficiency point, that is, the optimal operating amount, because control is based on the state feedbank 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. It has low credibility. Furthermore, the third problem with the conventional method is that it does not use actual measured values and is easily influenced by noise and composition errors.

The purpose of the present invention is to eliminate the drawbacks of the conventional methods described above in a control method for a thermal power plant, and also to improve the efficiency of the plant from an overall perspective. The purpose of the present invention is to provide an efficiency optimization control method for a thermal power generation platform that has stable control characteristics after load fluctuations.

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 p-200- shown in the figure to perform model standard efficiency prediction calculation, it is possible to achieve high speed and high accuracy without being constrained by the response speed of the plant. Achieved efficiency optimization. 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, the operation of the maximum efficiency search means 230 is suspended from the start of load fluctuation or the start of the operation of the sequence for switching the number of operating auxiliary machines until the load fluctuation and the sequence for switching the number of operating auxiliary machines are completed and the plant is in a thermal equilibrium state.

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

9a is the trial 0□ 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, IIC is the optimum condenser vacuum degree, 1
3a is the economizer (A), tab is the economizer (B), 14 is the ice wall, 15 is the primary superheater, 16a is the secondary superheater, 17 is the main steam, 18 is the high pressure turbine, 19 is the Primary reheater, 20
is a secondary reheater, 21 is a reheat steam, 22 is a 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, 30 is a parallel da/ba, 3
1 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, and 50 is a turret/
, 60 is the water supply system.

There are many operating parameters that affect efficiency, but in the explanation of this example, 02 over i'tt+ has a relatively large effect.
rate mt, parallel dan; opening m2. Condenser vacuum degree m
, and choose them as an example for optimization.As a basic algorithm for maximum efficiency search,
The complex method, which is one of the extreme value search methods, is used. In the maximum efficiency search means 230 in FIG. 3, two cases where the operating parameters are m and m2 are shown in order to clearly explain the principle of maximum efficiency search using the complex method. Now, the trial operation amount m1゜m corresponding to trial point 1
l is given to the platform model 240 for calculating efficiency, and efficiency η1 as a steady value is determined. Similarly for trial point 2.3, 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). and obtain efficiency No. 9 corresponding to the trial operation amounts mi and mi. Here, a new trial point 5 is similarly obtained from the newly created triangle 234 excluding trial point 1.

At this time, if trial point 5 violates the constraints on the plant state quantity, return to trial point 6 within the domain and create a new triangle 3.
46 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 (m7, m7 in this case) and becomes the actual manipulated 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 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.

m,: Operation amount! =10□Excess rate (%) i =2 Parallel damper opening degree (%) 123 Condenser vacuum degree (simplified Hg) Here, m2 may indicate the parallel damper opening degree, but here, the following Define by formula.

Here, G8 represents the gas flow rate in the gas passage where the primary superheater 15 and the economizer (A) 13a are arranged in FIG. ) 13
b represents the gas flow in the gas passage where it is located.

m, ... m, -, ・: Operation amount upper, lower limit I-10 □ Excess ratio upper, lower limit 1:2 /: Rarel damper opening upper, lower limit 1 = 3 Condenser vacuum upper, lower limit GCLma, HGCL mla: Upper and lower limits of condenser circulating water flow rate (K9/5ec) D □,: Upper limit of condenser circulating water temperature rise width (r) Y rm
mx: Low pressure turbine exhaust humidity upper limit (%) Gca
1la In the three-dimensional space created by m3, a polygon (called a simplex) with an angle of (-6 in the case of Fig. 4) is formed, and this is set as an initial simplex. As for this formation method, one point is the initial trial point m
:, and the remaining (k-1) points are, for example, -random number r'
It is determined by the following equation using (j=2 to k).

”+=”+ mtn +r(mt mat ”ra
rin) +++++++++++ (2) However, θ<
r'<, 1 ml determined in this way always satisfies the constraints as a manipulated variable (this is called an explicit constraint), but it also satisfies the constraints as a process state quantity (this is called an implicit constraint) 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 η5 (j=1 to 6) corresponding to each point determined through the above trial is obtained using the plant model 240.

[Takumi Nao Tesan] Calculation of center of gravity Here, the center of gravity mG1 of a simplex defined by (k-1) points excluding the point with the lowest efficiency among the points of the simplex is calculated. Now, assuming that the lowest point of efficiency is j-1, mQ is expressed by the following equation.

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

4'mG, = mGI-m i =--
−−−−−−−−−−−(4) ``0〒Yoro Determination of new trial point Set the new trial direction from the lowest efficiency point to the direction of the center of gravity, and move from the center of gravity by α1 times the distance ΔmQ between the two If the extended point is the new trial point and this is mk◆1, then ``';'' = mc++ αtΔmG+・song・song・song(5
). 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 m1□ is violated, m1◆ is set to m,
□8 Song, song...Song (6), lower limit m,
1. If it violates mPI-m11o...Song (7).

JIA3A Efficiency calculation Using the plant model 240 for efficiency calculation, calculate +1 for the efficiency η corresponding to cliff 1 new trial point m1+1. 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.

Takumi = l If the process state calculated by the plant model 240 for constraint monitoring efficiency calculation violates the implicit constraint, the trial point m11
Discard all information regarding 1 and return to 5TEP-3.
Determine a new trial point□. In this case, considering the causal relationship between the operation and the process state, the α-fish is modified according to the following formula, and 5T
Return to EP-3.

・・・・・・・・・・・・・・・ (8) U (DT
>DT ,,,,) U (Y>Y-,,)-・-・-
(9) Here, α1 for the 0□ 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.

组5A=B Among the efficiencies corresponding to the new trial point for convergence judgment and each point constituting the original simplex, the maximum and minimum efficiencies are respectively ηwa
Assuming that x and η are 1ゎ, it is determined whether the maximum efficiency point has been reached or not according to the following formula.

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 the maximum point is reached, the manipulated variable corresponding to ηtahx 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.

F0A3j Judgment of 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 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.

ffi Among the points that make up the simplex from which the new nonplex is formed, exclude the operating point that shows the lowest efficiency, add new trial points, and form a new hornplex from the resulting points. 5TEP
- Return to 2.

Next, an example of the efficiency optimization control process according to the present invention is shown in FIG. This figure shows the operating parameters Q2 excess rate m,
, /: Larel damper opening m2. This is a projection of trajectories each moving toward an optimum value onto three planes in a three-dimensional space defined by the condenser vacuum degree m3. 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, 7 to 11 are the trial points.
The solid line connecting the 11 points 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 m) should be selected at a point with the highest possible efficiency, and the size of the initial simplex should be determined according to this m: and the distance to the operational tolerance limit. I thought that it should be done, and decided on the following method.

Figure 6 shows the relationship between plant efficiency and 02 excess rate m1, and also shows the influence of atmospheric temperature on efficiency characteristics. In other words, the solid line is the efficiency characteristic when the point score is 100 when the atmospheric temperature is 30 r. The upper limit of the measurement range of m and TPA are determined by boiler exhaust gas regulation values, combustion stability, fan capacity, etc., but vary depending on the load level. From this characteristic, the upper limit m1 of the manipulated variable is determined as a function of the load. ,! (L), lower limit m, ml. (g), and the manipulated variables m and M(L, Ta) corresponding to the maximum efficiency point can be expressed as a function of the load and the atmospheric temperature Ta. Therefore, as shown in FIG. 7, the initial trial point determining section 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 complex according to the distance from ml to the operational tolerance limit, the m1 coordinates mj (J=2~k) of the vertices of the initial nonplex excluding ml are determined using the following formula. do.

・・・・・・・・・・・・・・・0υ However, “I = (ml wax +”1 ml)
/2, r' is a uniform random number (0 less than 1), K L
l is a constant (0<K Ll <1).

FIG. 9 shows the relationship between the plant efficiency and the gas recirculation flow rate with respect to the parallel door/bar opening m2. The upper limit of the allowable range of m2 is the gas recirculation fan capacity limit characteristic a
and primary superheater 15 and economizer (A) with high-speed gas flow
13 A's cut prevention limit characteristics for preventing the cut/cut are already determined. Further, the lower limit is the cut prevention limit characteristic C for the primary reheater 19 and the economizer 13b.

It is determined by the NOx countermeasure lower limit characteristic d and the reheat steam temperature stabilization lower limit characteristic e for suppressing the increase in NOX concentration due to a decrease in the gas recirculation flow rate. m2U2. m2[,.

m2L5. m2L2. m2L, have characteristics a and b, respectively
, c, d, and e, and in the case of this figure, the range between m2L3 and m2111 is the allowable operation 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. In the temperature control system, (16) this is to keep the reheat 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 m2. , x and m2 i,. is determined as shown in FIG. That is, m2゜, . Still, m2゜1゜ is the ash cut prevention lower limit calculation means 1
Lower limit m2L31 met in 04 Lower limit m2L2 obtained by NOX countermeasure lower limit calculation means 105. and the lower limit m2L obtained by the reheat steam temperature stabilization lower limit calculation means 106, the largest value is determined by the maximum value selection means 108. Here, m2U29m2L21m2L1 is calculated as a function of load, and m2uI9m2L3 is calculated as a function of combustion gas volumetric flow rate ■GAS. Also, VGAS
is calculated by the combustion gas volumetric flow calculation means 101 as a function of load. As shown in FIG. 9, as m2 increases, the gas recirculation flux increases and the gas recirculation fan power increases accordingly, so that the plant efficiency has a drooping characteristic. Therefore, assuming that the maximum efficiency point is always at the lower limit m2 of the operating allowable limit, the initial trial point m+ is selected as this point. In addition, as in the case of ml, in order to determine the size of the initial simplex according to the distance from m4 to the operational permissible limit, m2 coordinates m2 (J=2~k ) is determined by the following formula.

・・・・・・・・・・・・・・・(6) However, m2=
(m2.,,+m2゜,.)/2. γJ is a uniform random number (0
γ′<,1), Kl2 is a constant (0<Kl, 2<
, 1).

FIG. 10 shows the relationship between the plant efficiency, the circulating water flow rate, and the circulating water temperature rise width with respect to the condenser vacuum degree m3. The physical basis for this characteristic is that increasing 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,
As the circulating water flow rate decreases as m3 decreases, the circulating water temperature rise increases. Here, the allowable operation range is defined by the upper limit characteristic of the circulating water flow rate f1 determined by the circulating water pump capacity, the lower limit characteristic g1 of the circulating water flow rate that limits the scale deposition rate in the cooling pipe, and the lower limit characteristic g1 of the circulating water flow rate, which is determined by the circulating water pump capacity and the protection of plankton contained in the circulating water. This is the upper limit characteristic of the circulating water temperature rise range. The above restrictions are determined at the plant planning stage and do not change during operation, but the allowable operating range defined by these is greatly influenced by the load level and seawater temperature, as shown in Figure 11. Ru. In the figure, the solid line is the seawater temperature 21
The point - indicates the efficiency when the temperature is 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 m3°, 8 (g), the lower limit m, 1° (υ) of the manipulated variable as a function of the load, the manipulated variable m3M (t) corresponding to the maximum efficiency point. and using the seawater temperature Tc as a parameter, Tc = T+,
Characteristic functions corresponding to T2, . . . T, are prepared. Therefore, as shown in FIG. 12, the initial trial point determining means 109 selects the initial trial point m4 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/amplenox according to the distance from mA to the operational tolerance limit, the m3 coordinate arch (J-2~k) of the apex of the initial simplex excluding m4 is determined using the following formula. .

・・・・・・・・・・・・・・・ Q3 However, city a-(
m3゜a
K L3 ri 1).

The plant characteristics shown in FIGS. 6, 8, and 11 can be known using the plant model 240. In addition, the upper limit of the operation amount m1 determined by each means shown in FIGS. 7, 9, and 12. ,,． 8 (I = 1 to 3) and the lower limit m1w+n (' = 1 to 3) are both taken into consideration implicit constraints, and have different meanings from the explicit constraints used in equation (2). . Therefore 0υ, (6), 03
The coordinates of each vertex of the initial 7 noglenox determined by the formula m, (-
=1~3. ”=2~k) violates the explicit constraint, then +61. (Using the same idea as Equation 71, each vertex coordinate is set on the explicit constraint condition. Also, 0υ, shout, 0
The value of the constant Kt+ (1=1 to 3) in Equation 3 may be determined to be an appropriate value suitable for the plant to be controlled by analyzing the control characteristics through simulation.

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

Plants have a large heat capacity, so it is difficult to understand the true efficiency during transient periods. 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 as to, and the load t(to
), t(t.

−Δt)I'''L(tO−nΔt), the maximum value is Lf
Let fiaK+minimum value be Llm, Laa, -L,i, >εt,
Q4), it is assumed that there was a load fluctuation. That is, until time t2 in FIG.
After detecting the load fluctuation in step 2, 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 t4 when the plant is in a thermally transient state. After t, it is considered to be in a steady state and it starts operating again. In this case, the maximum efficiency search means 230 starts its operation from initial simplex 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 system will be stopped only for 1 second while the switching cycle is in operation.

FIG. 15 shows the processing procedure of the embodiment for the above purpose. This process operates at a cycle Δt, and the /-can operation determining means 111 determines whether the sequence for switching the number of auxiliary equipment in operation is in operation, and if it is in operation,
The timer reset means 112 resets the timer that measures the time t^ at which the switching sequence is completed; 1. The operation of the maximum efficiency search means 230 is stopped by the stop command means 113. On the other hand, if the switching sequence is completed,
The elapsed time tA thereafter is measured by the clock means 114. Next, the load fluctuation state determining means 115 determines whether or not the load is fluctuating according to formula 0411. If the load is changing, the operation of the maximum efficiency searching means 230 is stopped, and if the load changing is completed, the thermal equilibrium state determining means 116 determines the thermal equilibrium state by comparing the magnitudes of t and t6. 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 m1 regarding the 0□excess rate is
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. There is no need to worry. 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 fluctuations or switching of the number of operating auxiliary machines, the control characteristics will of course not be impaired even if the system 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 can also be achieved by the following method. The first method is to create a new line on a straight line that passes through the centroids of the nine least efficient points and the centroids of the remaining (k-p) points among the vertices forming the /noblex. This is a method of determining trial points. Also, the second method is
Divide the k vertices into group A, which consists of 9 vertices with the lowest efficiency, and group B, which consists of the remaining q vertices, and calculate tct from the lowest efficiency point of both groups.
In this method, four new trial points are determined in a straight line direction passing through the center of gravity excluding the =l-p)th point. In addition, the third method is (3) when determining the center of gravity mGI of /Noblex.
Instead of using the equation to directly use the efficiency η of each vertex as a weighting coefficient, this method uses the following equation to calculate the weighting coefficient using the difference η-η 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. Still, QF
Compared to Equation (3), the third method based on Equation J determines a new trial point by more accurately determining the maximum slope direction of the efficiency characteristic with respect to the manipulated variable, so good convergence can be expected.

In this example, if the efficiency at the new trial point is higher than any of the efficiencies of the points constituting the original simplex, the operation amount can be reduced by outputting the operation even if the maximum efficiency point has not been reached. Avoid sudden changes in Furthermore, in order to increase this effect, it is also effective to select n vertices from those with the highest efficiency at the vertices of /Noblex and use their center of gravity as the actual manipulated variable. In this case, it is effective to select the value of n in the range of 2 x n<,/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 where the constraint is imposed and the amount of operation does not necessarily correspond one-to-one, for the amount of operation that is directly related to the violated constraint, (s), (9! Formula) It shall be used as is, and α-qat (t-β) shall be used for manipulated variables that are not directly related.
...It is desirable to modify the α chant according to σQ.

However, in the above equation, β may be approximately 0.2. However, 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 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 7-plex is xl + the state quantity at the new trial point x2 + the constraint condition 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 using the formula.

In fact, in consideration of nonlinearity, 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 shortened during high-load operation, and nΔt can be reduced. Also,
For the above-mentioned reason, it is also desirable to modify the time t8 required for heat equilibrium due to switching of the number of auxiliary machines in operation according to the load level. Since this IB also differs depending on the type of auxiliary equipment whose number is to be changed, it is desirable to modify the IB in consideration of the load level and the type of auxiliary equipment. Thereby, it is possible to reduce 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 system. In the conventional method, it takes 30 to 60 minutes for MA, which means that the optimization function cannot be achieved unless the steady load state continues for any longer, and in recent years intermediate load operation for thermal power plants has been difficult. 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, plant efficiency directly calculated using a plant model built into the control system is used, so compared to the conventional method that indirectly detects efficiency based on the efficiency index method. Therefore, the accuracy of convergence to the optimum value is high.

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

A fifth effect of the present invention is that stable efficiency optimization control characteristics can be obtained even in a plant that undergoes large load fluctuations accompanied by switching of the number of operating auxiliary machines.

[Brief explanation of the drawing]

FIG. 1 is for explaining the structural differences between the conventional control system and the control system of the present invention shown in FIG. FIG. 2 is for explaining the mechanical features of the control/stem of the present invention in comparison with the conventional control/stem shown in FIG. FIG. 3 shows the basic configuration of the efficiency optimization control system. FIG. 4 shows the basic processing procedure of efficiency optimization control. FIG. 5 shows an example of the efficiency optimization control process. FIG. 6 shows the relationship between plant efficiency and 02 excess rate. FIG. 7 shows a method for determining the initial trial point and operating amount limit for the 0□ excess rate based on the characteristics shown in FIG. FIG. 8 shows the characteristics of plant efficiency and gas circulation flow rate with respect to parallel damper opening degree. 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. FIG. 10 shows the characteristics of granoid efficiency, circulating water flow rate, and circulating water temperature rise width with respect to the degree of vacuum of the condenser. FIG. 11 shows the characteristics of plant efficiency with respect to condenser vacuum degree using load level and seawater temperature as parameters. FIG. 12 shows a method for determining the initial trial point and operating amount limit for the condenser vacuum degree based on the characteristics shown in FIG. 11. FIG. 13 shows the operating section and the stopping section of the maximum efficiency search means due to load fluctuations. FIG. 14 shows the operation section and the stop section of the maximum efficiency search means due to load fluctuations and switching of the number of operating auxiliary machines. FIG. 15 shows processing means for realizing the purpose shown in FIG. 14. 100...Plant, 2...Control/Stem, 3...
・Maximum efficiency search means, 4... State feedback, 5
...optimum operation amount, L standing J...control system, 23.
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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 the excess rate, parallel damper opening, and condenser vacuum is an operating parameter for operational control; In a control method, the control method includes determining an operation amount of the parameter using a plant, and determining an operation amount that maximizes efficiency through a trial operation using the platform efficiency, wherein load fluctuation of a predetermined width or more is detected; Switching the number of auxiliary machines in operation/- Determine that the engine is in operation, determine that the plant has reached a thermal equilibrium state, and change the plant to a thermal equilibrium state from the time the load change is detected or from the time the engine starts operating. An efficiency optimization control method for a thermal power plant, characterized in that the maximum efficiency search operation is suspended until it is determined that the maximum efficiency has been reached.