JPS5840616A - 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 polonged 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 trail manipulated variable is found. Then, a new trial point 5 is also found similarly from the triangle 234. In this case, the operation of the maximum efficiency searching means 230 is stopped throughout load variation until the plant is balanced thermally. This trying method is repeated until a maximum efficiency point 7, obtaining the best manipulated variables m1<7> and m2<7>.

Description

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

The issue of highly efficient operational control of thermal power plants has been a hot topic for some time, but control systems that aim to improve the efficiency of plants from an overall perspective have yet to be put into practical use.

Few reports on optimizing boiler efficiency by manipulating the air-fuel ratio (e.g. F, M or an et
of,, l) eveLopment andappt
cation of setf-optimisin
g control to coot-fired st
eam-generating plant.

Proc, IEE, Vo/:, 115. A2
(1968-2)). In these conventional control systems, as shown in FIG.
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 method is that it takes a lot of time to find the maximum efficiency point, that is, the optimal operating position, because control is based on the state field bank from the plant with a large thermal time constant. .

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

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 to improve the efficiency of the plant from an overall perspective, and in particular to provide a method for controlling a thermal power plant that has stable control characteristics after load fluctuations. The purpose of the present invention is to provide an efficiency optimization control method for a power generation plant.In order to overcome the problems of the conventional method,
By performing model standard efficiency prediction calculation using the plant model ζ±A for efficiency calculation built into the control system 200 shown in the figure, rapid and highly accurate efficiency can be achieved without being constrained by the response speed of the plant. Achieved optimization. The basic method of the maximum efficiency search means 230 in the υ type water control system is to
This is a method of searching for the maximum efficiency point by repeating the procedure of outputting the trial operation amount 10 for 40 and finding the corresponding plant efficiency 10. In this case, the operation of the maximum efficiency search means 230 is suspended from the start of the load change until the load change is completed and the plant is in a thermal equilibrium state.

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 complicating the diagram. In FIG. 3, each symbol represents the following.

9a is trial 02 excess rate, 9b is trial damper opening degree, 9C
is the trial condenser vacuum degree, lla is the optimum 02 excess rate, ll
b is the optimum damper opening degree, llc is the optimum condenser vacuum degree, 13
a is economizer prisoner, 13b is economizer (to), 14 is 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, 20 is a secondary reheater, 21 is reheated steam, 22 is a medium/low pressure turbine, 23 is a generator, 24 is a condenser, 25 is condensate, 26 is a low-pressure feed water heater, 27 is a 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, 1 Liao is a boiler, Σ'' turbine, 60 is a water supply system.

There are many operating parameters that affect efficiency, but in the description of this embodiment, 02 excess ratio m3. 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 clearly explain the principle of maximum efficiency search using the complex method. now,
The trial operation amount m11°m2J corresponding to the trial point 1 is given to the plant model 240 for efficiency calculation, and the efficiency η1 as a steady value is determined. Similarly, η2 for trial point 2.3
, η3 is determined. Among them, the point with the lowest efficiency (in this case, it is taken as l) and the center of gravity of the remaining points (in this case, the line segment 2
．． A new trial point 4 is placed on the extension of the straight line connecting
Select trial operation amount mI4. Efficiency η corresponding to m7
Find 4. Here, a new trial point 5 is obtained in the same way from the newly created triangle 234 excluding the trial point 1. At this time, if trial point 5 violates the constraint condition of the plant state quantity, return to trial point 6 within the domain and create a new triangle 346.
Determine the trial direction using . 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 (m, 7.m,' 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,: Manipulated amount i: 102 Excess ratio (%) i22 Parallel damper opening degree (%) l23 Condenser vacuum degree (wHg) Here, m2 may indicate the parallel damper opening degree, 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. 3, and GR represents the primary reheater 19 and the economizer (A) 13b represents the gas flow rate in the gas passage where it is arranged.

ml , , , ml ff11. : Operation amount upper, lower limit i-10 □ Excess ratio upper, lower limit i-2 Parallel damper opening upper, lower limit i=3 Condenser vacuum upper, lower limit Gct, , aax g Gct, ,, l+: Condenser Upper and lower limits of circulating water flow rate (kg/5ee) Dr Wall ; Upper limit of condenser circulating water temperature rise width (tr
)Y++ax: Low pressure turbine exhaust humidity upper limit (%)
GGReast, GGRm1m: Upper and lower limits of gas recirculation flow rate (kg/5ec) Reset I Initial simplex formation initial trial point m1 (i=1 to 3) shall satisfy all of the above constraint conditions, m, , m2 ．． m3 makes 3
A polygon (called a simplex) with angles (in the case of FIG. 4, -6) is formed in the dimensional space, and this is set as the initial simplex. As for this formation method, one point is set as the initial trial point m-, and the remaining (k-1) points are determined by the following equation using, for example, a uniform random number r' (j=2 to k).

m, j=m, , , II+rj, (m, , , -ml,
1. ) ----・42) However, O<r' S 1 m- determined in this way always satisfies the constraint condition as a manipulated variable (this is called an explicit constraint condition), but as a process state variable The constraint condition (this is called the implicit constraint condition) may not be satisfied. In that case, the trial point is moved in the direction of the center of gravity of the already determined point to the midpoint. In this way, ultimately all points are determined.

Then, the plant efficiency η1j=1 to 6) corresponding to each point determined through the above trial is obtained using the equation.

Fdo Ayo ■ Calculation of the center of gravity This calculates the center of gravity mQ of a simplex defined by (k-1) points excluding the point with the lowest efficiency among the points of the simplex. Now, assuming that the lowest point of efficiency is j=l, mGI is expressed by the following formula.

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

ΔrnGl :l: mat-m+' ・・---
(4) Determining the new trial point under -F sub-two causes The new trial direction is taken from the lowest efficiency point to the direction of the center of gravity, and the point extended from the center of gravity by α1 times the distance Δmcl between both points is set as the new trial point, If this is fm, then ml”' ::m(H4+ minus ΔmQl −−−−
−−−−+(represented by +51. In this case, if an explicit constraint condition is violated, the trial point is set on the constraint condition.

That is, the upper limit mlff1. If it infringes □, m,
=m, ffi,, −−−−−−−−(6)
Let k◆1 and lower limit m1ff11. m? ′＋
mlff1+, ...... (force).

Efficiency calculation Using the plant model 240 for efficiency calculation, calculate +1 for the efficiency η corresponding to the new trial point mIk+1. here,
The range of simulation as a plant model covers all systems required for normal load operation where energy balance is an issue. In the turbine system, not only the extraction system but also the /-le steam is considered.

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

Tadashi Kuchiishi 1) If the process state calculated by the plant model for constraint monitoring efficiency calculation 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, α1 is corrected according to the following formula, and 5TEp
' Return to -3.

α2 go 4...Condition (G GR> GGRo-) U
(GGRml.)・・・・・・・・・(8) u (DT >DT ,,,)U (Y >Y,,,,
, ) -・---・(9) Here, 02 This is because as long as the excess rate is operated within the limit value, the implicit constraint condition will not be violated.

[Giant] Kazushi Convergence Judgment Among the efficiencies corresponding to the new trial point and each point constituting the original simplex, the maximum and minimum efficiencies are η, respectively.
,8 and η,n, ,! : L, determine whether the maximum efficiency point has been reached 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. When the maximum point is reached, the operation amount corresponding to ηmatK is output, and the process returns to 5TEP-1 to form the initial simplex.If the maximum point is not reached, the next (12) new trial points form the original simplex. If an efficiency higher than any of the efficiencies at each point is obtained, the operation output is used even if the maximum efficiency point has not been reached, and the process proceeds 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 efficiency compared to the current operating state, it will actually output the operation without waiting for the electric power to reach the high efficiency point. .

Lower Wata = Field Among the points that make up the simplex from which a new simplex is formed, exclude the operating point that shows the lowest efficiency, add new trial points, and create a new simplex from the resulting points. form and return to 5TEP-2.

Next, an example of the efficiency optimization control process according to the present invention is shown in FIG. This figure shows the operating parameters 0□excess rate m,
, Haralel damper opening degree m2 + a water burial vacuum degree m
3 are projections of trajectories each moving toward an optimal value onto three planes in a three-dimensional space. In this diagram 1~
6 is the vertex of the initial simplex, 7 to 11 are trial points, the double circle is the maximum efficiency point, the dotted line connecting points 7 to 11 is the trial operation process, and the solid line connecting points 7 to 11 is the optimal operation process.

For example, looking at the relationship between m 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 rQI' and the size of the simplex in the initial simplex formation step 1 is an important issue that affects the convergence of the maximum efficiency search. In order to improve convergence, the initial trial point ml' should be selected at a point with the highest possible efficiency, and this m
We thought that the size of the initial simplex should be determined according to the distance between / and the allowable operating limit, and adopted the following method.

FIG. 6 shows the relationship between the plant efficiency and the 02 excess ratio m1, and also shows the influence of atmospheric temperature on the efficiency characteristics. The solid line is the efficiency characteristic when the atmospheric temperature is 30 r, and the dotted line is the efficiency characteristic when the atmospheric temperature is 1 orr. The upper and lower limits of the allowable range for m, L/J are determined by boiler exhaust gas regulation values, combustion stability, fan capacity, etc., and 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. . .

Therefore, as shown in FIG. 7, the initial trial point determining section 96 selects the initial trial point m1' at a point within the operational allowable range at which maximum efficiency can be expected. Next, in order to determine the size of the initial/amplenox according to the distance from ml to the operational tolerance limit, m, the coordinates Tll,j of the vertices of the initial/umprenex excluding m,' (j = 2~k ) Determined by the k-th equation.

・・・・・・・・・(II) However, ml = (m 1+++az + m1+m1.
)/2, rj is a uniform random number (o(r'<1
), KLI is a constant (o<KLl<1).

FIG. 9 shows the relationship between the plant efficiency and the gas recirculation flow rate with respect to the parallel damper opening m2. The upper limit of the allowable range of m2 is the gas recirculation fan capacity limit characteristic a
and primary superheater 15 and economizer (A) 1 using high-speed gas flow.
The ash cut prevention limit characteristic of 3a if ash cut should be prevented is already determined. Also, the lower limit is the primary reheater 19
and the excessive cut prevention limit characteristic C for the economizer (G) 13b.

It is determined by the NOx countermeasure lower limit characteristic d, which suppresses the increase in NOX concentration due to the decrease in the gas recirculation flow rate, and the reheat steam temperature stabilization lower limit characteristic e. m2112. m2U, .

m2L, , m, L2. m2L, characteristic characteristics a, b
, c, d, and e, and in the case of this figure, m
The range between 2L3 and m2u is the allowable operation range. The physical basis for the characteristics shown in Figure 8 is that when m and i are reduced, (
1) As is clear from the definition of equation, the amount of gas diverted to the primary reheater side increases, but in the reheat steam temperature control system, the reheat steam temperature can be kept constant by lowering the gas recirculation flow rate. This is to keep it safe.

The five operating permissible limits shown in Figure 9 change depending on the load level, so the final permissible limits m2+yaz and m2m1
° is determined as shown in FIG. That is, m2. ,
, is the upper limit m2υ2 obtained by the gas recirculation fan capacity limit calculation means 102 and the ash cut prevention upper limit calculation means 1
The lower value of the upper limits m2υ1 obtained in step 03 is determined by the low value selection means 107.

Also, m2. ．． One is the lower limit m2L8 obtained by the ash cut prevention lower limit calculation means 104, 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. The largest value among them is determined by the maximum value selection means 108. Here, m2U2. m2L2. m2L, is calculated as a function of the load, m2U, , m2L3 is calculated as a function of the combustion gas volumetric flow rate VaAs. Also,
It is calculated by the vGAsVi combustion gas volumetric flow rate calculation means 101 as a function of load. As shown in Figure 9, m2
The plant efficiency has a drooping characteristic because the gas recirculation flow rate increases as the gas recirculation flow rate 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 m2. Assuming that it is at ml7, select this as the initial trial point mj. In addition, as in the case of ml, in order to determine the size of the initial amplifier according to the distance from m21 to the operational allowable limit, the initial /
m of the vertex of Ninrex, coordinate m2' (j = 2 ~ k)
eDetermined by the following formula.

・・・・・・・・・(12) However, m 2 ”” (m2 max + m2 m
1 s )/2, rj is a uniform random number (0<”, 1), K
- is a constant (0<KLl<, 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 m3 increases the turbine internal efficiency, but on the other hand, as the circulating water flow rate increases, the pump power increases, and as a result, the plant efficiency reaches its maximum. It depends. Also, m
3, the flow rate of circulating water decreases, and therefore the range of increase in temperature of circulating water increases. Here, the allowable operation range is defined by the circulating water flow rate upper limit characteristic 5 determined by the circulating water pump capacity, the circulating water flow rate lower limit characteristic g1 for limiting the scale deposition rate in the cooling pipe, and the protection of plankton contained in the circulating water. This is the upper limit characteristic of the circulating water temperature rise range. The above limits are determined at the plant planning stage and do not change during operation, but the allowable operating range defined by these limits is greatly influenced by the load level and seawater temperature, as shown in Figure 11. Ru. In the figure, the solid line 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 m31, an initial trial point determining means 109 as shown in FIG. 12 is used.

In this means, based on the characteristics shown in FIG. 11, the upper limit m3□8 (g), the lower limit m3m1 .
(2) A characteristic function representing the manipulated variable m3M (g) corresponding to the maximum efficiency point and corresponding to Tc2Tl s T2 i . . . T with the seawater temperature Tc as a parameter is prepared. Therefore, as shown in FIG. 12, the initial trial point determining means 109 selects the initial trial point m,' at the point where maximum efficiency can be expected within the operational tolerance range.

Next, in order to determine the size of the initial nonplex according to the distance from m3' to the operational tolerance limit, m of the vertices of the initial simplex excluding m1', coordinates m3J (j = 2
~k) is determined by the following formula.

・・・・・・・・・03) However, m, = (m3mm, "m3m1n)/2
, rj is a uniform random number (0 < rj < 1), K Ll is 7
It is a constant (0<KL3<1).

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 m1ffl is determined by each means shown in FIGS. 7, 9, and 12.

(i=1~3) and the lower limit m1m1゜(i=1~3) are
Both of them take implicit constraints into consideration, and have different meanings from the explicit constraints used in equation (2). , Therefore, if each vertex coordinate mt' (i=1~3.j=2~k) of the initial simplex determined by (IJ)H(Equation 13) violates the explicit constraint, then Using the same idea, each vertex coordinate is set under explicit constraints.

Ma&, the constant KL in the HQZ(+3) formula, (i-1~
As for the value of 3), an appropriate value suitable for the plant to be controlled may be determined by analyzing the control characteristics through simulation.

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

Because the plant has a large heat capacity, it is difficult to know the 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 fluctuations, the current time is
Assume that the load L (
to), L(to-Δt), ・L(to-n
ot ), the maximum value is Lmmx, and the minimum value is L ail
m, L, −−L−1−>iL −+++
+ma4), it is assumed that there has been a load change. That is, the first
Until time t in FIG. 3, the load is regarded as steady according to the α4 formula, 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 t4 when the plant is in a thermally transient state. After t4, it is regarded as a steady state and operates again. In this case, the maximum efficiency search means 230
In the initial simplex formation step 1 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 switching the number of auxiliary machines in operation, during the number switching sequence and after 1 s of heat equilibration time required as a result of the number switching has elapsed. Activate "Genyu, the most efficient search method." If the number of operating auxiliary machines is to be changed even in a steady load state, the operation will be stopped only during the switching operation and during the period ts.

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 determination means 111 determines whether the 7-can for switching the number of auxiliary machines in operation is in operation, and if it is in operation,
The timer reset means 112 resets a timer for counting the time tA after the completion of the switching sequence, and 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 time measuring means 114 measures the elapsed time 1A thereafter. Next, the load fluctuation state determination means 115 determines whether or not the load is fluctuating according to equation 04). If the load is changing, the operation of the maximum efficiency searching means main unit 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 tA and ts. 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 m-
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 account, and even if the initial trial points m and I are determined by simply considering the load, the control characteristics will deteriorate significantly. There's no need to worry about it. In addition, as shown in Fig. 4, when the manipulated variable converges to the maximum efficiency point, the initial simplex is formed again by the method shown in Figs. 6 to 12. As long as the operation of the maximum efficiency search means is not interrupted due to switching of the number of operating vehicles, etc., the control characteristics will not be impaired even if the method forms an initial simplex near the convergence point.

Furthermore, in this embodiment, the direction of the new trial point is determined from the vertex of the simplex with the lowest efficiency to the direction of the center of gravity expressed by equation (3), but it is not necessarily necessary to set it in such a direction. Stable efficiency optimization is also possible by the following method. The first method is to locate a new trial point on a straight line that passes through the centroids of the p points with the lowest efficiency and the centroids of the remaining (k=P) points among the vertices forming the simplex. This is a method to determine. Also, the second method is
Divide the k vertices into a group A consisting of p vertices with lower efficiency and 8 groups consisting of the remaining g vertices, and calculate tct from the lowest efficiency point of both groups.
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 to calculate the center of gravity mc+'t'' of the simplex by (
3) Instead of directly using the efficiency η2 of each vertex as a weighting coefficient, this method uses the following equation to determine the difference η−−vk with respect to the standard value r.

However, it is desirable to modify it 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 51 can be expected to have better convergence because it more accurately determines the maximum slope direction of the efficiency characteristic with respect to the manipulated variable and determines a new trial point. .

In addition, in this example, if the new trial point has a higher efficiency than any of the efficiencies of the points constituting the original simplex, the manipulated variable is Avoid sudden changes in Furthermore, in order to increase this effect, it is also effective to select n vertices from the ones with the highest efficiency at the vertices of the //plex and use their center of gravity as the actual manipulated variable. In this case, it is effective to select the value of n within the range of 2/2.

In addition, in this example, if the process state at the new trial point violates the implicit constraint, only the manipulated variable directly related to the violated constraint is Since causal relationships do not necessarily have a one-to-one correspondence, equations (8) and (9) should be used as is for the manipulated variables that are directly related to the violated constraint, and αI should be used for the manipulated variables that are not directly related. Notice αI (1-β) ・・・・・・・・・
・It is desirable to correct α1 according to Oo. However, in the above equation, β2 may be approximately 0.2. 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 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, assuming that the state quantity at the lowest efficiency point in the simplex is Xl, the state quantity at the new trial point is X2, and the constraint condition is XL, in order to correct α and retreat the manipulated variable to the limit value, for example, the following formula is used. α' can be found by linear interpolation. In reality, a new trial point is determined using the following equation using a correction coefficient ξ (0<ξ<1) in consideration of nonlinearity.

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 range, 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 not short during high-load operation, and pnΔtf'i can be reduced. In addition, the time required for heat equilibrium due to switching the number of auxiliary machines in operation t8
For the above reasons, it is also desirable to modify the load level according to the load level. This 1. Furthermore, since it differs depending on the type of auxiliary equipment whose number is to be changed, it is desirable to modify it by considering the load level and the type of auxiliary equipment. Thereby, it is possible to greatly increase the operating rate of the efficiency optimization control means, 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 the plant by optimizing the manipulated variables of multiple operating parameters that affect plant efficiency, which is compared to conventional techniques that target efficiency improvement of individual equipment. The point is that efficiency can be significantly improved compared to the conventional method.

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

The fourth effect of the present invention is that since efficiency is optimized using a plant model built into the control/system, noise and detection errors are reduced compared to conventional methods that optimize efficiency based on actual measured values. The point is that it is possible to perform stable and highly accurate optimization without being affected.

The fifth effect of the present invention is that by suspending the maximum efficiency search means during load fluctuations and until the plant reaches a thermal equilibrium state after the completion of load fluctuations, stable efficiency optimization control characteristics can be achieved even in plants with load fluctuations. That's what you get.

[Brief explanation of the drawing]

FIG. 1 is for explaining the structural differences of a conventional control system in comparison with the control system of the present invention shown in FIG. FIG. 2 is for explaining the structural features of the control stem of the present invention in comparison with the conventional control system shown in FIG. FIG. 3 shows the basic configuration of the efficiency optimization control system. FIG. 4 shows the basic processing procedure of efficiency optimization control. FIG. 5 shows an example of the efficiency optimization control process. FIG. 6 shows the characteristics of plant efficiency versus 02 excess rate. FIG. 7 shows a method for determining the initial trial point and operating amount limit for the 02 excess rate based on the characteristics shown in FIG. FIG. 8 shows the characteristics of plant efficiency and gas recirculation flow rate with respect to parallel damper opening. FIG. 9 shows a method for determining the initial trial point and operation amount limit for the parallel damper opening based on the characteristics shown in FIG. FIG. 10 shows the characteristics of plant efficiency, circulating water flow rate, and σi ring water temperature rise width with respect to the condenser vacuum degree. Figure 11 shows the characteristics of plant efficiency with respect to condenser vacuum degree using load level and seawater temperature as parameters. Figure 12 shows initial trial points and operations for condenser vacuum degree based on the characteristics of Figure 11. We show how to determine the quantity limit. FIG. 13 shows the operating section and the stopping section of the maximum efficiency searching means due to load fluctuations. FIG. 14 shows operating sections and stopping sections of the maximum efficiency search means due to load fluctuations and switching of the number of auxiliary machines in operation. FIG. 15 a1 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
...Optimum operation amount, 200...7 control stems, 230
... Maximum efficiency search means, 240 ... Plant model, Dan ... Trial operation amount, 10 ... 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 a control method, the control method includes determining a manipulated variable of the parameter using a plant, and determines a manipulated variable that maximizes efficiency through a trial operation using the plant efficiency, wherein load fluctuation of a predetermined width or more is detected; After the load change is completed, it is determined that the plant has reached a thermal equilibrium state, and the maximum efficiency search is suspended from when the load change is detected until it is determined that the plant has reached a thermal equilibrium state. Efficiency optimization control method for thermal power plants.