JPS5840609A - 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 an O2 excess rate m1 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 and atmospheric temperature.

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 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 a single point in time 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, pJoran et al.
al,,l)development andappli
carion of self-optimizing
control to coal-fired ste
am-generating plant.

proc, IEE, VOl, 115. No,
2 (1968-2)) There is only one. In these conventional control/systems, as shown in FIG. 1, a maximum efficiency search is performed in a single search stage 3 in a control device 2 based on state feedback 4 from a plant 100 to determine an optimum manipulated variable 5.

However, the first problem with the conventional system is that it takes a lot of time to find the maximum efficiency point, that is, the optimum operating amount, because the control is based on state feedback from the plant with a large thermal time constant.

The second problem with the conventional method is that it is difficult to actually measure efficiency with high precision, so an efficiency index method is used that uses behavior such as main steam pressure as a substitute for efficiency evaluation. It is a matter of low gender. Furthermore, the third problem with the conventional method is that it is easily influenced by noise and detection errors because it uses actually measured values.

An object of the present invention is to eliminate the 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 to provide a method for controlling a thermal power plant by setting 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 maximum efficiency, and can determine the initial trial point of the 02 excess rate as a function of the plant load and atmospheric temperature.

In the present invention, in order to overcome the problems of the conventional method, the second
By using the plant model l↓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 fast 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 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 a corresponding plant efficiency of 1!2. This is a method of searching for the maximum efficiency point by returning the procedure kW. in this case,
The initial trial point is determined by considering the efficiency characteristics of the plant and the constraints on the plant condition, which were determined in advance using the Flint Model L Tsuchibe. That is, the initial trial point of the Ot excess rate 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 and atmospheric temperature.

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 Figure 3, each symbol represents the following.

9a is the trial 0□excess rate, 9b is the trial open degree, 9C
is the trial condenser vacuum degree, 11a is the optimum 02 excess rate, 11b
is the optimum damper opening degree, 11C is the optimum condenser vacuum degree, 13a
is the economizer ■13b is the economizer [F]), 14 is the ice wall, 15
is the primary superheater, 16 is the secondary superheater, 17 is the main steam, 18
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 power generation d, 24 is a condenser, 25 is condensate, 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 damper, 31 is a pulverized coal mill, 32 is an air preheater, 33 is a forced draft fan, 34 is a suction 40 is the boiler, 50 is the turbine, and 60 is the water supply system.

Does it affect efficiency? Although there are many operating parameters, in the explanation of this embodiment, we selected three that have a relatively large effect: 02 excess ratio m1, parallel opening degree m, and water container vacuum degree m3. I decided to do it. 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 m2' corresponding to trial point 1 is given to the 7-lint model L machine for efficiency calculation, and the efficiency η as a steady value is calculated.
Find 1. Similarly for trial points 2 and 3, ηmi, ηS
seek. Among these, the point where the efficiency is the lowest (in this case 1
) and the center of gravity of the remaining point (in this case, on line segments 2 and 3), select a new trial point 4 on the extension of the straight line, and set the trial operation amount m7. Find the efficiency η4 corresponding to m,'. Here, a new triangle 234 is created excluding trial point 1.
Similarly, a new trial point 5 is determined from . At this time, if the trial point 5 violates the constraint conditions of the plant state quantity, the process returns to the trial point 6 within the defined domain, and a new triangle 346 is used to determine the trial direction. By repeating this trial method, the maximum efficiency point (7 in this case) can be reached.

The manipulated variable corresponding to this point is the optimal value (in this case m,',
, m2'), which is the actual operating output for the plant.

By continuing such control operations, even if the maximum efficiency point moves due to changes in ambient conditions such as seawater temperature and atmospheric temperature, it is possible to follow this and determine the optimum operation amount.

The above has been limited to explaining the principle of efficiency optimization control applying the complex method, but next, an outline of the control algorithm in the embodiment will be explained.

FIG. 4 shows the basic processing procedure of efficiency optimization control in the embodiment of the present invention. The control algorithm will be explained step by step below. However, the symbols are defined as follows.

mI: Manipulated amount l = 10□ Excess rate (%) i-2 parallel damper opening degree (%) Summer-3 condenser vacuum degree (mmHg) Here, m2 may indicate the parallel damper opening degree. , here defined by the following equation.

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

Town...m ,,,...: Operation amount upper limit, lower limit i-10, excess rate upper limit, lower limit I-2 parallel damper opening upper limit, lower limit! -3 Condenser vacuum level upper limit, lower limit Gc L wax HαL1 construction: Condenser circulating water flow rate upper limit,
Lower limit (Kg / 5ec) 9 pieces...: Upper limit of condenser circulating water temperature rise 1) Y, E
: Low pressure turbine exhaust humidity upper limit (%) Gan mat
@ G ORlal m: Gas recirculation flow rate upper and lower limits (
Kg / sec) Giant U Ayoro Initial trial point for formation of initial simplex m,
'(j=1 to 3) satisfy all of the above constraints, and m,, m2. In the three-dimensional space created by m, a polygon (called a simplex) with angles (-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 set as an initial trial point m-, and the remaining (k-1) points are set as one random number rj (j=2 to
k) using the following formula.

m, j=m, , nl, +r 1(m, , ,, -
m, ffl,,,) −(2) However, 0<rj
<1 Although m and j determined in this way always satisfy the constraints as manipulated variables (this is called an explicit constraint),
Constraints as process state quantities (referred to as implicit constraints) may not be satisfied. In that case, the trial point is moved in the direction of the center of gravity of the already determined point to the midpoint. In this way, ultimately all points are determined. Then, the plant efficiency η' (j=1 to 6) corresponding to each point determined through the above trial is calculated as the plant model 240.
obtained using

F Artist] B Calculation of center of gravity Here, the center of gravity mol 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 efficiency point is 1, mQ is expressed by the following equation.

Ση11 j+2 Further, the distance ΔmQ from the lowest efficiency point to the center of gravity is expressed by the following equation.

Δm(, 1= mQ I −m,'
-... (4) ``A E■ Determination of new bow trial point The new trial direction is taken from the lowest efficiency point to the direction of the center of gravity, and the point extending from the center of gravity by α1 times the distance ΔmQl between the two points is the new trial point. And if this is m and 1, then m, "' = mQ, +a quantity ΔmQ l
・=・It is expressed as (5). In this case, if an explicit constraint condition is violated, the trial point is set on the constraint condition.

That is, the upper limit m1. ,,,! m.
, =m, ,e,,...
・If (5) violates the lower limit mlff11°,
m,” ' = m 1 ,,,
...-(7).

组蓮■三I Efficiency Calculation Using the plant model L earthwork for efficiency calculation, calculate +1 for 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 seal 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.

If the process state calculated by "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, αN is corrected according to the following formula, and 5TEP
- Return to 3.

...(8) GCIF-1-)U(DT>DT--riU(Y>Y,,
, )...(9) Therefore, α for the 02 excess rate is not corrected.

The reason is that as long as the 0□excess rate is operated within the limit value,
This is because the implicit constraint conditions are not violated.

The maximum and minimum efficiencies among the efficiencies corresponding to the new trial point and each point constituting the original simplex are determined by η1.
! and η, 1. Then, it is determined whether the maximum efficiency point has been reached or not according to the following equation.

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, η,,! The operation amount corresponding to is output, and the process returns to 5TEP-1 to form an initial simplex. If the maximum score is not reached, proceed to the next 5TEP-7.

(b) Pruning - (b) Efficiency improvement direction determination 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. Proceed to the next 5TEP-8. If you do not want to output the operation, continue with the next 5 TEP-
Proceed to step 8. 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.

20 Among the points that make up the 77 plexes from which the new simplex is formed, the operating point showing the lowest efficiency is excluded, new trial points are added, and a new simplex is formed 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 02 excess rate m1
, parallel damper opening m2, condenser vacuum degree m, tension 3
It is a projection of trajectories each moving toward an optimal value in a dimensional space onto three planes. In this figure, 1 to 6 are the vertices of the initial 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 to 1
The solid line connecting the 1 points is the optimal operation process. For example, m,
Looking at the relationship between 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 ml11 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 mIi should be selected at a point with the highest possible efficiency, and this ml
We thought that the size of the initial nonplex 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. In other words, the solid line represents the efficiency characteristics when the atmospheric temperature is 30C, and the dotted line represents the efficiency characteristics when the atmospheric temperature is 10C. The upper and lower limits of the permissible range of m are determined by boiler exhaust gas regulation values, fuel stability, fan capacity, etc., and vary depending on the load level. From this characteristic, the upper limit of the manipulated variable is determined as a function of the load.
The lower limit m, ffi, , (g) can be expressed, and the manipulated variable mr M (L + T a ) corresponding to the maximum efficiency point can be expressed as a function of the load and the atmospheric temperature T. be able to. Therefore, as shown in FIG. 7, the initial trial point determination unit 96 calculates m1 coordinates m+' (j = 2 to k) are determined by the following formula.

...(11) However, rl'%: (m, , , +1"", ,,,)
/2. rj is - random number (0<r-'ku1), K
LI is constant ff(0(I(Lt<1).

FIG. 9 shows the relationship between the plant efficiency and the gas recirculation flow rate with respect to the parallel damper opening m2. The upper limit of the allowable range of m is the gas recirculation fan capacity limit characteristic a
and primary superheater 15 and economizer (A) 1 using high-speed gas flow.
The ash cut prevention limit characteristics of the device that prevents assonyu cut in 3 a are already determined. Also, the lower limit is the primary reheater 1
9 and economizer G3) 13b, ash cut prevention limit characteristic c1, NOx countermeasure lower limit characteristic d1 for suppressing the increase in NOx concentration due to a decrease in gas recirculation flow rate, and reheat steam temperature stabilization lower limit characteristic e It is determined by m, U2゜m
24), , m213. m, L, , m2L are characteristics a and b, respectively.

It is the limit point determined by c, d, and e, and in the case of this figure, m
The range between 2L3 and m, U 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,
This is to keep 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 m2. , t and m2 peaks are determined as shown in FIG. That is, m2. ,! teeth,
The lower value of the upper limit m, Q2 obtained by the gas recirculation fan permissible limit calculation means 102 and the upper limit m, , U obtained by the ash cut prevention upper limit calculation means 103 is determined by the low value selection means 107. . Further, m, w, , are the lower limits m determined by the ash cut prevention lower limit calculation means 104.
2L8, the lower limit m2L2 obtained by the NOx countermeasure lower limit calculation means 105, and the reheat steam temperature stabilization lower limit calculation means 106
The maximum value selection means 108 determines the largest value among the lower limits m and L obtained in the above. Here, m2U21m2
L2゜m2L, is calculated as a function of load, m2g
, .

m2L, is calculated as a function of the combustion gas volumetric flow rate VGAS. In addition, VGAs are the combustion gas volumetric flow rate calculation means 1
01 as a function of load.

As shown in FIG. 9, as m2 increases, the gas recirculation flow rate increases and the gas recirculation fan power increases accordingly, so the plant efficiency has a drooping characteristic.
The point of maximum efficiency is always the lower limit of the operating tolerance limit m2. . , and select the initial trial point m2' as this. Also, m,
As in the case of , the initial non-breech size is m21
The m2 coordinates of the vertices of the initial nonplex excluding m21 are determined according to the distance from
2J (J=2~k) is determined by the following formula.

...(12) However, m2-(m, , ,, +m2...)/2.
rJ is a uniform random number (0<r'kl), KL is a constant (
0<K Lt<1 ). - Figure 10 shows the relationship between plant efficiency, circulating water flow rate, and circulating water temperature rise width with respect to water coverr vacuum degree m. The physical basis for this characteristic is that increasing m increases the turbine internal efficiency, but on the other hand, as the circulating water flow rate increases, the pump power increases, and as a result,
By having a point where the plant efficiency is maximum. Also,
Since the circulating water flow rate decreases as m, decreases, the circulating water temperature rise increases. Here, the operating permissible range is defined by the circulating water flow rate upper limit characteristic f determined by the circulating water pump capacity, the circulating water flow rate lower limit characteristic g1 that limits the scale deposition rate in the cooling pipe, and the protection of blank tons contained in the circulating water. This is the upper limit characteristic of the circulating water temperature rise. 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 is the seawater temperature 21
The dotted line shows the efficiency at 18C. Therefore, an initial trial point m is determined.

For this purpose, an initial trial point determining means 109 as shown in FIG.
Use. In this means, based on the characteristics shown in FIG.
Upper limit of manipulated variable as a function of load・m 3 B * x
(IJ), lower limit "3+zljυ, represents the manipulated variable m3M(L) corresponding to the maximum efficiency point, and seawater temperature Tc
With parameters as Tc = T, , T2 , 4・T
We have prepared characteristic functions corresponding to . Therefore, the 12th
As shown in the figure, the initial trial point determination means 109 sets the initial trial point m, 1
Choose. Next, in order to determine the initial simplex size f, depending on the distance from m3' to the operational tolerance limit, '
m, coordinates of the vertices of the initial simplex except m,'
a'(j-2~k) is determined by the following equation.

...(13) However, F6 s= (" 3 mat 10" 3 mJ /
2, r'' is a -random number (0<r'ku1), KL3 is a constant (
o < KL3ku1).

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. ,,f(l=1~3) and the lower limit "1m1a ('~1~3)" are all taken into account the implicit constraint, and are the same as the explicit constraint used in equation (2). have different meanings.

Therefore, each vertex coordinate m+'('=1~3.J
If =2~t<) violates the explicit constraint, then (6)
Using the same concept as equation (7), each vertex coordinate is set under explicit constraint conditions. In addition, the value of the constant KLI (1=1 to 3) in equation (11), (12), and (1a) is
By analyzing the control characteristics using the method, appropriate values suitable for the plant to be controlled can be determined.

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

Because plants have large heat capacities, it is difficult to know 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 assumed to be 1°, and the load L (to
), L(to-Δt),...L(to-nΔt
), the maximum value is Lyu□ and the minimum value is Lmtm, Lrmax LInte >εL ”・
- If (14), it is assumed that there was a load fluctuation. That is, the first
Until time t2 in FIG. 3, the load is regarded as a steady load according to equation (14), and after detecting load fluctuation at t2, the maximum efficiency search means 230 is stopped. Maximum efficiency search means 2 is used until time t, when the plant is in a thermally transient state even after the load change is completed.
30 is on hiatus. From t onwards, it is assumed to be in a steady state and the operation resumes. 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 nΔ[, 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

That is, as shown in Figure 14, if the example shown in Figure 13 is accompanied by a change in the number of auxiliary machines in operation, during the sequence of changing the number of auxiliary machines and as a result of the change in the number of machines, the required heat equilibration time t8 has elapsed. The maximum efficiency search means 230 is operated from.

If the number of auxiliary machines in operation is to be changed even in a steady load state, the operation will be stopped only during the switching sequence and 18 hours.

FIG. 15 shows the processing procedure of the embodiment for the above purpose. This process operates at a period of Δt, and the 7-can operation determination means 111 determines whether the sequence for switching the number of auxiliary machines in operation is in operation, and if it is in operation, the timer reset means 112 Then, the timer for measuring the time tA1 after the switching sequence photon is reset, and the operation of the maximum efficiency search means 230 is stopped by the stop command means 113. On the other hand, when the switching sequence is completed, the elapsed time after that is measured by the clocking means 114.

Next, the load fluctuation state determination means 115 determines whether the load is fluctuating or not according to equation (14).

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 ts. If the state is in thermal equilibrium, the operation command means 1
17. The maximum efficiency search means LLQ is operated.

In this example, when determining the coordinates of each vertex of the initial simplex, the initial trial point m1 regarding the o2 excess rate is
is determined by considering both the load and the atmospheric temperature T. However, since the influence of atmospheric temperature on plant characteristics is relatively small, it is not necessarily necessary to take atmospheric temperature into account, and even if the initial trial points m and t are determined by simply considering the load, the control characteristics will deteriorate significantly. There is no need to do so. 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 230 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.

Further, in this embodiment, the direction of the new trial point is determined to be the direction of the center of gravity expressed by equation (3), which is the vertex of the simplex where the efficiency is the lowest, but it is not necessarily necessary to set it in such a direction. Stable efficiency optimization is also possible by the following method. The first method is to create a new line on a straight line that passes through the center of gravity of p points with the lowest efficiency and the center of gravity of the remaining (g)-p) points among the vertices forming the simplex. This is a method of determining trial points. In addition, the second method divides the k vertices into group A, which consists of p vertices with lower efficiency, and group B, which consists of the remaining q vertices, and calculates t<t= from the lowest efficiency point of both groups. Al. Also, the third method is simplex C7) 1ir4□ rr
When calculating l 'G 1, instead of using equation (3) and using the efficiency η,j of each vertex directly as a weighting coefficient, use the following equation to use the difference η11-η with respect to the standard value η as a weighting coefficient. It's a method.

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. It's Kade (
Compared to equation (3), the third method based on equation (15) more accurately determines the effect on the amount of operation and the direction of maximum inclination for driving performance, and determines a new trial point, resulting in better convergence. can be expected.

Still, based on this example, if the new trial point yields a higher efficiency than any of the efficiencies of the points constituting the original simplex, even if the maximum efficiency point has not been reached, the operation output can be applied. , avoiding sudden changes in the amount of operation. 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 simplex 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 variables directly related to the violated constraint are retreated according to equations (8) and (9). There is. 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, equations (8) and (9) can be used as is for the manipulated variable that is directly related to the violated constraint. For manipulated variables that are not directly related, αI minute αI (1-β) ... (16
) is desirable. However, in the above equation, β may be approximately 0.2. Another effective method is to estimate the operation amount limit based on the degree of violation of the clearance 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, the state quantity at the lowest efficiency point in the simplex is xl, and the state quantity at the new trial point is x
2. Assuming that the constraint condition is X, in order to correct α and retreat the manipulated variable to the limit value, α' can be obtained by linear interpolation using the following equation, for example.

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

Furthermore, in this embodiment, the maximum efficiency searching means 2307 is activated or deactivated depending on the load fluctuation state and the switching of the number of auxiliary machines in operation. The load fluctuation monitoring interval nΔt shown 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, it is recommended to make adjustments sequentially depending on the load level and load fluctuation range.
It is possible to increase the chances that the maximum efficiency search means 23 operates.

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. Furthermore, it is desirable that the time required for heat equilibrium, 1 s, associated with switching the number of auxiliary machines in operation be modified in accordance with the load level for the above-mentioned reasons. Since this number 18 also varies depending on the type of auxiliary equipment whose number is to be changed, it is desirable to modify the number of auxiliary equipment 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; this means that if the steady load condition continues any longer, the optimization function will not function. 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 directly calculated using the plant model built into the control system is used, so the efficiency can be indirectly detected based on the efficiency index method. Compared to the conventional method,
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, the effects of noise and detection errors are greater than with conventional methods that optimize 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 since the initial trial point of the Ot excess rate can always be set near the maximum efficiency point, the convergence to the maximum efficiency point is extremely good under any plant load and atmospheric temperature. It is.

[Brief explanation of the drawing]

FIG. 1 is for explaining the structural differences of the conventional control system in comparison with the control system of the present invention shown in FIG. FIG. 2 is for explaining the structural features of the control system of the present invention in comparison with the conventional control system shown in FIG. FIG. 3 shows the basic 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 a method for determining the initial trial point and operating amount limit for the O8 excess rate based on the characteristics of FIG. 7. FIG. 8 shows the characteristics of plant efficiency and gas recirculation flow rate with respect to parallel damper opening. 9th
The figure shows a method for determining the initial trial point and operating amount limit for the parallel damper opening based on the characteristics shown in FIG. 1st
Figure 0 shows the characteristics of plant efficiency, circulating water flow rate, and circulating water temperature rise width with respect to condenser vacuum degree. Figure 11 shows
The characteristics of plant efficiency with respect to condenser vacuum degree are shown 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 system, 3...
・Maximum efficiency search means, 4... State feedback, 5
...optimum operation amount, moxibustion 1 time...control system, ;30
...Maximum efficiency search means, equipment...Plant model, U...Trial operation amount, 10...Plant efficiency, 11
...Optimum operation amount 10 20th island 3rd ward j4- 32 23 ml
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Claims (1)

[Claims] 102 Overjudgment: Determining plant efficiency as a function of the manipulated variable of a parameter of a thermal power plant in which the II rate is an operating parameter for operation control, and determining the manipulated variable of the parameter using this plant efficiency. and determining the amount of operation that maximizes the efficiency through trial operations of plant efficiency calculations. In the control method to be determined, 0 as a function of the plant load.
2. An efficiency optimization control method for a thermal power plant, the main feature of which is determining an initial trial point for excess ratio. 2. In the control system according to claim 1,
An efficiency optimization control method for a thermal power plant, characterized in that an initial trial point of 02 excess rate is determined as a function of plant load and atmospheric temperature.