JPH08128304A - Thermal power generation plant autonomous adaptive control system - Google Patents

Abstract

PURPOSE: To monitor and evaluate operation regulation factors without using total system dynamic characteric model of a power generation plant by providing an autonomous managing means which enlarges an adoption degree of a specified starting schedule in accordance with repetition of starting. CONSTITUTION: An actual starting schedule is prepared by the use of a first starting schedule obtained by a schedule correction rate calculation means 600 and a second starting schedule obtained by adaptive knowledge acquiring means 800. An autonomousity management means 200 enlarges an adoption degree of the second starting schedule, by the operation that an autonomous factor λ is increased in accordance with repetition of starting, which factor regulates an adoption ratio of the first and second starting schedules at the time of preparing the actual starting schedule. Automatic preparation of the starting schedule for reducing the starting time to minimum is executed while satisfying plural operation regulationconditions such as equipment protection reference and environmental regulation values at the same time.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a control system for a thermal power plant, and in particular, to create a start-up schedule suitable for starting up the plant in a short time while satisfying various operational restriction conditions. Control system for a thermal power plant in Japan.

[0002]

2. Description of the Related Art When a thermal power plant is to be started up rapidly, many problems are operating restriction conditions, nonlinear characteristics between input and output, and the presence of time delay. That is, the startup schedule to be created needs to satisfy not only the operation amount such as the fuel flow rate and the water supply flow rate, but also many process state values such as thermal stress generated in equipment and NOx discharged from the boiler to satisfy the operation restriction condition. . Further, since there is a large non-linearity and a time delay between the turbine acceleration pattern and the load increase pattern, that is, the operation amount that defines the startup schedule, that is, the turbine acceleration rate and the load increase rate, and the above process state value, there is a simple delay during startup. With feedback control, it was impossible to realize rapid start-up while satisfying the operation restriction conditions with high accuracy. For this reason, in the first conventional method, as described in Japanese Patent Laid-Open No. 63-94009, a dynamic system model of the entire system of the plant is built into the control system, which is The start-up schedule was optimized by predicting the start-up characteristics by repeating the start-up simulation using. That is, the above non-linearity was modeled as table information or a physical formula and used to create the activation schedule. Also, as a second conventional method, as described in Japanese Patent Laid-Open No. 3-164804, the starting characteristic is evaluated based on the actual driving result, and the amount of modification of the starting schedule is calculated from this evaluation result by fuzzy inference. Having a means for calculating, learning the correction amount obtained at each startup to the neural network, and correcting the schedule at the previous startup with the value output from the neural network when the driving target is input to the neural network. Was used to optimize the startup schedule.
In other words, the control system does not have a dynamic characteristic model of the entire plant system, and the start schedule is created by utilizing the qualitative knowledge of the expert and the learning ability of the neural network by fuzzy reasoning.

[0003]

However, according to the above-mentioned prior art, the following problems arise.

(1) According to the first conventional method, the entire system dynamic characteristic model used for predicting the starting characteristic takes into consideration operating conditions such as the temperature, humidity, water temperature surrounding the plant and the temperature condition of the plant equipment before starting. It is necessary to make it something that has been done, it takes a lot of time to make it, and it has to be made for each target plant. In addition, there are many uncertainties such as heat transfer in the boiler and denitration reaction in the denitration equipment, which limits the accuracy of dynamic characteristics prediction. Also, in the second conventional method, it is difficult to construct a knowledge base for fuzzy reasoning in consideration of the fluctuations of the operating conditions and the uncertain factors because of the complicated causal relationship, and it takes a lot of time. Not only does this require reliability of the knowledge base.

(2) According to the first conventional method, in order to obtain the optimum start schedule within the practical time by using the whole system dynamic characteristic model of the plant, a high processing capacity and a large memory capacity are provided. You need a computer system.

(3) If the operating conditions such as the initial state of the plant before starting and the temperature are different, it is difficult to utilize the starting schedule created in the past, and the optimum solution must be obtained each time. That is, since the control system does not have the adaptability to the change in the above operating conditions, it is necessary to fully operate the high performance computer each time it is started. Further, in the second conventional method, as described above, it is difficult to construct the knowledge base for fuzzy inference in consideration of the fluctuation of the operating conditions and the uncertain factors. Similarly, it has no ability to adapt to changes in operating conditions.

The present invention is an invention made in view of the above problems, and an object thereof is (1) to determine an operation restriction factor without using a complex large-scale dynamic system model of a power plant. Be able to monitor and evaluate. That is, the minimum required dynamic characteristic model for monitoring the operation restriction factors should be used. (2) Without obtaining the optimum start schedule from the first start,
A thermal power plant that can optimize the startup schedule in the process of repeating actual startup, and (3) can adaptively use the startup schedule created in the past even if the operating conditions such as the initial state of the plant and the temperature are different. It is to provide an autonomous adaptive control system.

[0008]

A thermal power plant autonomous adaptive control system according to the present invention for achieving the above object is driven by a boiler for generating steam and steam generated by the boiler. A control system for a thermal power plant including a steam turbine is characterized by including the following means.

(1) Basic schedule creating means for creating a start-up schedule from ignition of the boiler to completion of start-up based on a temperature state before starting the thermal power plant, (2) in a starting process of the thermal power plant Operation restriction factor monitoring means for monitoring predetermined operation restriction factors,
(3) margin value evaluation means for evaluating a margin value for the operation restriction condition of the operation restriction factor obtained by the operation restriction factor monitoring means, (4) based on the margin value obtained by the margin value evaluation means, Schedule modification amount calculation means for calculating the modification amount of the startup schedule using fuzzy inference,
(5) When the past startup schedule and operating conditions of the thermal power plant are given as input values to the neural network, the schedule correction amount obtained from the schedule correction amount calculating means as the output value is obtained, Adaptive knowledge acquisition means that stores the correspondence between input and output,
(6) A first activation schedule created based on the schedule modification amount obtained by the schedule modification amount calculation means, and a second startup schedule created based on the schedule modification amount obtained by the adaptive knowledge acquisition means. The start-up schedule is used to create an actual start-up schedule, and the autonomous coefficient λ that defines the adoption ratio of the first and second start-up schedules when creating the actual start-up schedule is increased as the start-up is repeated. By doing so, autonomy management means for increasing the adoption ratio of the second startup schedule, (7) schedule execution control means for startup control of the thermal power plant according to the startup schedule created by the autonomy management means.

Further, the schedule modification amount calculation means is based on a knowledge base that stores a plurality of fuzzy rules that define a schedule modification amount of the start schedule corresponding to the size of the margin value obtained by the margin value evaluation means. The schedule modification amount may be calculated by incorporating the fuzzy rule.

Further, learning management means for learning the input / output correspondence by determining the internal connection state of the neural network in the adaptive knowledge acquisition means may be added to the above (1) to (7). .

A combined cycle power plant having a gas turbine, an exhaust heat recovery boiler for generating steam using the exhaust gas of the gas turbine, and a steam turbine driven by the steam generated in the exhaust heat recovery boiler Even in the control system, the object of the present invention can be achieved by providing the means (1) to (7) or the learning management means.

Further, the predetermined operation limiting factor may be at least one or more of the stress of the steam header at the outlet of the superheater of the boiler, the stress of the steam turbine, and the NOx discharged from the boiler. In the combined cycle power plant, at least one or more of the stress of the steam header of the superheater outlet of the exhaust heat recovery boiler, the stress of the steam turbine, and the NOx discharged from the exhaust heat recovery boiler may be used. .

Further, the start-up schedule in the combined cycle power plant comprises a start-up schedule of the gas turbine and a start-up schedule of the steam turbine, and the start-up schedule of the gas turbine is a predetermined parameter defining the start-up schedule. The predetermined parameters are the acceleration rate, rated speed holding time, initial load, initial load holding time, load rising rate,
It is at least one or more of load holding time, and the steam turbine start-up schedule is configured by predetermined parameters that define the start-up schedule, and the predetermined parameters are the high-pressure turbine bypass valve operating speed and the intermediate-pressure turbine bypass. Valve operating speed, low pressure turbine bypass valve operating speed, high pressure adjusting valve operating speed, medium pressure adjusting valve operating speed,
At least one of the low-pressure control valve operation speeds may be used.

The predetermined operation limiting factors are the superheater outlet steam header stress of the exhaust heat recovery boiler, the steam turbine stress, and NOx exhausted from the exhaust heat recovery boiler, and the margin value evaluating means is For the superheater outlet steam header stress of the exhaust heat recovery boiler and the steam turbine stress, a minimum margin value for the operation limit value is obtained for each section obtained by dividing the starting process of the thermal power plant into a plurality of sections, and the exhaust For NOx discharged from the heat recovery boiler, an instantaneous value and a moving average value are obtained to obtain a minimum margin value for the operation limit value for each section obtained by dividing the starting process of the thermal power plant into a plurality of sections. May be

Furthermore, the schedule correction amount calculation means is a steam turbine main plan based on the minimum margin value of the exhaust heat recovery boiler superheater outlet steam header stress and the minimum margin value of the steam turbine stress obtained from the margin value evaluation means. (ST
Fuzzy inference for PS) and gas turbine global adjustment (GTG)
T) fuzzy inference, fuzzy inference for gas turbine main plan (GTPS) and fuzzy inference for steam turbine local adjustment (STLT) based on the minimum margin value of the exhaust heat recovery boiler exhaust NOx, and the STPS performs the fuzzy inference. By comparison, a wider range of schedule parameters are targeted for correction, the GTPS performs more precise correction as compared with the GTGT, and the GTGT performs more global correction as compared with the GTPS. It may be.

Furthermore, the schedule correction amount calculation means is for the STPS, STLT, GTPS, GT
From the plurality of correction amounts for the same schedule parameter obtained from each fuzzy inference for GT, the priority value determining means selects low values for the speed-up rate, the load change rate, and the valve operation speed, and the speed holding time, A high value may be selected as the parameter related to the load holding time.

Furthermore, the neural network used in the adaptive knowledge acquisition means has a multi-layer structure composed of at least an input layer and an output layer by collecting a plurality of units having a signal conversion function, and the output unit of the preceding layer unit. It is possible to set the internal connection strength provided in the connection between the input unit and the input unit of the next layer unit, the input layer is provided with a number of units corresponding to the startup schedule parameters and operating conditions, and the output layer has the startup unit. The number of units corresponding to the schedule parameter may be arranged, and the signal input to the input layer may be preset by the learning management means, converted according to the internal connection strength, and output from the output layer.

Furthermore, the learning management means includes cumulative correction amount calculation means for cumulatively calculating a new correction amount obtained from the schedule correction amount calculation means each time the thermal power plant is repeatedly started. A cumulative correction amount storage means for storing the cumulative correction amount obtained from the cumulative correction amount calculation means, and a schedule storage means for storing the activation schedule applied to the actual activation in the past,
The past operating schedule and operating conditions are respectively taken out from the operating condition storing means for storing the operating conditions at the time of actual start-up in the past, the schedule storing means and the operating condition storing means, and the adaptive knowledge acquiring means obtains them. The internal connection strength of the neural network is determined so that the output value obtained from the output unit when inputting to the input unit of the neural network in step 3) matches the cumulative correction amount stored in the cumulative correction amount storage means. And a learning means for that.

Furthermore, the operating conditions stored in the operating condition storage means are the shutdown period of the thermal power plant, plant equipment temperature, pressure, condenser cooling water temperature, atmospheric temperature, humidity, and plant operation restriction conditions. It may be at least one of the above.

Further, the autonomy managing means sets the start schedule X ₁ determined by the correction amount obtained from the schedule correction amount calculating means as the first start schedule, and sets the autonomic coefficient λ in the first start schedule X _0. The value λX ₀ obtained by the multiplication and the first startup schedule X ₁
Is multiplied by the complement of the autonomous coefficient (1-λ) to obtain the value (1-
λ) X ₁ is added to the activation schedule X _IN, which is input to the neural network together with the operating conditions, and the correction amount ΔX _OUT obtained from the output unit and the initial activation schedule X ₀ are added. when the power-on schedule X ₂ to be set to the second power-on schedule, complement of the first start the autonomous factor Schedule X ₁ (1
1-?) Value obtained by multiplying the _(1-λ) X 1 and the second start scheduled X ₂ in the autonomous factor activation is obtained by adding the value .LAMBDA.x ₂ obtained by multiplying the lambda schedule X _R may be adopted as an actual activation schedule and transferred to the schedule execution control means.

Furthermore, the autonomy managing means has an autonomic coefficient generating means for varying the autonomic coefficient λ,
At the time of initial activation, λ = 0, and 0 ≦ λ ≦ as the number of activations is increased in correspondence with the number of activations i or the schedule modification amount obtained by the schedule modification amount calculation means.
The value of λ may be increased in the range of 1. Furthermore, the operation limiting factor may be at least the difference in expansion between the rotor and the casing of the steam turbine, the stress generated in the drum of the exhaust heat recovery boiler, and the SO or CO discharged from the exhaust heat recovery boiler. Good.

Furthermore, the thermal power generation plant autonomous adaptive control system having the above configuration may be used as a start control system for a gasification combined cycle power generation plant, an atmospheric pressure and a pressurized fluidized bed boiler power generation plant.

Furthermore, the series of means for creating the start schedule in the thermal power plant autonomous adaptive control system according to the present invention may be used as guidance presenting means for creating or modifying the start schedule in the plant operation training simulator. .

Furthermore, the thermal power plant autonomous adaptive control system according to the present invention may be provided with means for displaying the actual starting schedule.

[0026]

According to the present invention, based on the first activation schedule created based on the schedule modification amount obtained by the schedule modification amount calculation means, and the schedule modification amount obtained by the adaptive knowledge acquisition means. An actual startup schedule is created using the created second startup schedule, and an autonomous coefficient λ that defines the adoption ratio of the first and second startup schedules when creating the actual startup schedule is set. Since the autonomous management means is provided which increases the adoption ratio of the second startup schedule by increasing it as the startup is repeated, when the actual startup schedule is determined, the fuzzy inference is performed as the startup is repeated. To the conclusion from the neural network obtained by the adaptive knowledge acquisition means Lies will be intensified the degree, it is possible to create a suitable start-up schedule in response to changes in the operating conditions of the plant.

That is, the adaptive knowledge acquisition means has a neural network for storing the correspondence between the schedule and the operating conditions of the plant every time the plant is started, and the correction amount obtained by the schedule correction amount determination means. Therefore, when the actual activation schedule is created, the first schedule created by the modification amount from the schedule modification amount determining means by the fuzzy inference and the first schedule created by the modification amount from the adaptive knowledge acquisition means. By increasing the ratio of the second schedule adopted (autonomous coefficient λ) as the start-up is repeated, the actual start-up schedule is the ratio of the second schedule created corresponding to the operating conditions of the plant. The higher the start-up frequency and the greater the number of times the plant is started, the more suitable the start-up schedule that responds to changes in plant operating conditions. It is possible to create a rule.

The functions of the above-mentioned means (1) to (7), the knowledge base and the learning management means of the present invention will be described below.

(1) In the basic schedule creating means, focusing on the stop period having a relatively high correlation with the temperature state of the plant, the central power feeding command is selected from a plurality of schedules prepared in advance as table information for each stop period. The corresponding schedule is selected according to the start time commanded by the office, and this is used as the basic schedule. This creates a schedule that, although not necessarily optimal, can safely start the plant.

(2) In the operation limiting factor monitoring means, the process state in which direct measurement is possible is monitored by the signal obtained from the measuring instrument during the process of starting the plant by the schedule execution control means, and direct measurement is difficult. For various process states, the behavior of the operation restriction factors is estimated and monitored by inputting measurable process states into the dynamic characteristic model.

(3) The allowance value evaluation means evaluates the allowance value with respect to the limit value for the operation limit factor obtained by the operation limit factor monitoring means in the entire starting process.

(4) The schedule correction amount calculation means calculates the correction amount of the schedule according to the size of the margin value obtained by the margin value evaluation means. At this time, it is calculated by fuzzy inference using a knowledge base described later.

(5) The adaptive knowledge acquisition means acquires adaptive knowledge by using a neural network. Here, the adaptive knowledge is not only the schedule correction measure represented by the fuzzy rule, but also the knowledge having the adaptability capable of coping with changes in plant operating conditions. In order to acquire this knowledge, the neural network stores the correspondence relationship between the schedule and the plant operating conditions and the correction amount obtained by the schedule correction amount determining means each time the plant is started.

(6) In determining the actual activation schedule, the autonomy management means has the ability to acquire the adaptive knowledge rather than the conclusion from the fuzzy inference as the activation is repeated. It works to strengthen the degree of dependence on. That is, the adoption ratio (autonomous coefficient λ) of the schedule created by the modification amount from the schedule modification amount determining means by the fuzzy reasoning and the schedule created by the modification amount from the adaptive knowledge acquisition means is made variable, and the activation is repeated. By increasing the ratio of the latter in accordance with the above, the function of enhancing the adaptability and autonomy with respect to changes in the operating conditions is exerted.

(7) The schedule execution control means controls the process states necessary for starting the plant, such as turbine acceleration control, load increase control, steam pressure control, etc., in accordance with a startup schedule created by the autonomous management means described later. To do.

The knowledge base is an aggregate of a plurality of rules that define a schedule correction policy in a causal relationship with the margin value obtained by the margin value evaluation means, and each rule is dependent on the size of the margin value. It functions as a piece of knowledge that is a computer-processable expression of the expert's knowledge about the method of modifying the schedule.

Further, the learning management means causes the adaptive knowledge acquisition means to learn the correspondence. For this purpose, the correspondence between the schedule and plant operating conditions for each start of the plant and the correction amount obtained by the schedule correction amount determining means is sequentially presented as a learning sample to the neural network for learning. That is, the learning management means functions to determine the internal connection state of the neural network so that the latter is output when the former is input to the neural network.

[0038]

Embodiments of the present invention will be described below.

FIG. 1 shows a basic configuration of a thermal power plant autonomous adaptive control system 1000 of the present invention. The present system operates upon receipt of a start time T _Ri (i indicates the next start, for example, i = 1 indicates the first start) which is a start command from the central power feeding command station 2000, and schedule execution control means in the system. The thermal power plant 3000 is actually started by 300.

This system 1000 includes a basic schedule creating means 100, a schedule modifying means 150, a schedule execution control means 300, and an operation restriction factor monitoring means 40.
0, margin value evaluation means 500, schedule correction amount calculation means 600, knowledge base 700, correction amount storage means 650,
It comprises adaptive knowledge acquisition means 800, learning management means 900, and autonomy management means 200.

The basic schedule creating means 100 selects a corresponding schedule from a plurality of schedules prepared in advance for each stop period as table information, corresponding to the start time T _Ri instructed by the central power feeding command station,
This is the basic schedule X _0i . Where X
Is a vector quantity consisting of a plurality of parameter values that define the schedule, and details will be described later. This creates a schedule that is not optimal, but safe and reliable for plant startup.

In the schedule correction means 150, the schedule correction amount calculation means 60 using fuzzy inference described later.
A new schedule X _i is obtained by using the correction amount ΔX _i-1 obtained from 0 and stored in the correction amount storage means 650 and the previous activation schedule X _i-1 stored in the learning management means 900 described later. create. Here, at the first startup (i = 1)
, ΔX ₀ = 0 and X ₀ = X ₀₁ .

The schedule execution control means 300 controls the process state associated with the start-up such as turbine speed, load, steam pressure in the thermal power plant 3000 according to the start-up schedule X _Ri created by the autonomy management means 200 described later. .

The operation restriction factor monitoring means 400 operates by inputting a measurable process state into the dynamic characteristic model for a process state which is difficult to directly measure while the plant is started by the schedule execution control means 300. Estimate and monitor the behavior of limiting factors. In addition, the process state where direct measurement is possible is monitored by the signal obtained from the measuring instrument. The operation restriction factors to be monitored here will be specifically described later.

The allowance value evaluation means 500 evaluates the allowance value for the operation restriction factor obtained by the operation restriction factor monitoring means 400 in the entire starting process. The definition of this margin value will also be specifically described later.

In the schedule correction amount calculation means 600,
The correction amount ΔX _{i of the} schedule is calculated according to the size of the margin value obtained by the margin value evaluation means 500. At this time, it is calculated by fuzzy inference using the knowledge base 700 described below.

The knowledge base 700 is a set of a plurality of rules that define a schedule correction policy in a causal relationship with the margin value obtained by the margin value evaluation means 500, and each rule is dependent on the size of the margin value. It works as a piece of knowledge that is a computer-processable representation of the expert's knowledge about how to modify the schedule.

In the correction amount storage means 650, the correction amounts ΔX ₁ to
ΔX _i is stored in association with each past plant activation.

The adaptive knowledge acquisition means 800 acquires adaptive knowledge using a neural network. Here, the adaptive knowledge is not only the schedule correction measure represented by the fuzzy rule, but also the knowledge having the adaptability capable of coping with changes in plant operating conditions. In order to acquire this knowledge, the neural network has a schedule and plant operating conditions each time the plant is started, and a correction amount ΔX ₁ obtained by the schedule correction amount calculation means 600.
The correspondence with ~ ΔX _i is stored. Here, as the plant operation condition, in the embodiment of the present invention, the stop period T _Si (which means the elapsed time between the previous stop time and the command start time T _Ri ) that most affects the start-up temperature state of the plant is used.

The learning management means 900 causes the adaptive knowledge acquisition means 800 to learn the correspondence. Therefore, the schedule X _{Ri for} each plant startup, the stop period T _Si which is a plant operation condition, and the correction amount ΔX obtained by the schedule correction amount calculation means 600.
The correspondence with the cumulative value of _i is sequentially presented as a learning sample and learned. That is, the learning management means 900 uses X _Rk (k = 1 to i−1) and T _Sk (k = 1 to 1) in the input layer of the neural network used as the adaptive knowledge acquisition means 800.
When i-1) is input, the output value from the output layer ΔX _OUTk
Has a function of determining the connection state in the neural network so as to match the cumulative correction amount ΔX _Qk (k = 1 to i−1) of ΔX _i . The cumulative correction amount ΔX _Qk will be specifically described later.

In determining the actual activation schedule, the autonomy control means 200 increases the degree of dependence on the conclusion ΔX _OUTi from the neural network rather than the conclusion ΔX _i from the fuzzy inference as the activation is repeated. Work. That is, the adoption ratio of the schedule created by the modification amount obtained from the schedule modification amount calculation means 600 based on the fuzzy inference and the schedule created by the modification amount obtained from the adaptive knowledge acquisition means 800 is represented by an autonomous coefficient λ. Is made variable, and the ratio of the latter is increased as the activation is repeated, thereby increasing the adaptability and autonomy to the change in the operating condition. The above-mentioned autonomous coefficient λ will be specifically described separately.

The outline of the present invention has been described above.
As a thermal power plant to which the present invention is applied, an embodiment will be described by taking a combined cycle power plant including a gas turbine, an exhaust heat recovery boiler, and a steam turbine as an example.

FIG. 2 shows a combined cycle power plant 300.
The relationship between the device configuration of 0 and the schedule execution control means 300 is shown. The schedule execution control means 300 further comprises a gas turbine control system 330 and a steam turbine control system 360. In the gas turbine equipment 3200,
The energy generated by combustion by injecting the fuel 22 into the combustor 23 and pressurizing the combustion air 24 by the compressor 20 is converted into mechanical energy by the gas turbine 26, and as a result, power generation connected to the common shaft 28 is performed. Machine 3
5 is converted into electric energy and a part of the electric power is used as the driving force of the compressor 20. At startup, the fuel control valve opening command 311 from the gas turbine control system 330
By operating the fuel control valve 21 to adjust the fuel flow rate, the gas turbine equipment 3200 and the steam turbine equipment 3300 connected to the common shaft 28 and the generator 35 are accelerated.

Exhaust gas 27 from the gas turbine 26
Is guided to the exhaust heat recovery boiler facility 3100, and the thermal energy of the exhaust gas 27 is recovered. At this time, the exhaust gas 27
As a result, the fluid in the various heat exchangers arranged in the flue 14 of the exhaust heat recovery boiler facility 3100 receives heat, evaporates, and is overheated. The exhaust heat recovery boiler equipment 3100 of this embodiment is composed of a steam system having three pressure levels, and the steam generated from each is a high pressure steam 341, an intermediate pressure steam 342, and a low pressure steam 343. In the steam turbine equipment 3300, the high-pressure turbine 31, the intermediate-pressure turbine 32, and the low-pressure turbine 33 are driven by the thermal energy of these steams, and play a role in power generation by the generator 35 connected to the common shaft 28.

At startup, the exhaust heat recovery boiler equipment 3100
High pressure main steam 341, medium pressure main steam 342, generated from the above
By bypassing the low-pressure main steam 343 via the high-pressure bypass valve 334, the intermediate-pressure bypass valve 335, and the low-pressure bypass valve 336, the individual pressures are controlled to predetermined values, and the high-pressure control valve 331, the medium-pressure control valve 332, and the like. By operating the low pressure regulator valve 333, the high pressure turbine 31,
The output of the intermediate pressure turbine 32 and the low pressure turbine 33 is increased. Therefore, the flow rates of the high-pressure bypass steam 344, the medium-pressure bypass steam 345, and the low-pressure bypass steam 346 increase as the opening degrees of the respective bypass valves 334, 335, 336 increase, and the opening degrees of the regulator valves 331, 332, 333 increase. When is increased, the amount of steam flowing into the steam turbine increases, and the amount decreases. Opening degree command 32 to these bypass valves
4, 325, 326 and opening degree commands 321 and 3 to the regulator valve
22 and 323 are both steam turbine control system 36
Output from 0. Further, when the temperature deviation between the medium-pressure superheated steam 1 and the high-pressure turbine exhaust 2 is within a predetermined value during the startup of the plant, the medium-pressure stop valve 337 is opened. The operation signal 327 at this time is also instructed by the steam turbine control system 360. Condensed water 37 from the condenser 34 is supplied to the low-pressure water supply pump 16, the medium-pressure water supply pump 17, and the high-pressure water supply pump 18.
Thus, the flow rates of the low-pressure feed water 3, the medium-pressure feed water 4, and the high-pressure feed water 5 are controlled so that the water levels of the low-pressure drum 6, the medium-pressure drum 7, and the high-pressure drum 8 are maintained within predetermined values.

Here, the factors that limit the operation are the stress generated in the rotors of the high-pressure turbine 31 and the intermediate-pressure turbine 32, and the stress generated in the headers at the outlets of the high-pressure superheater 11 and the intermediate-pressure superheater 12. , NOx emissions from the flue outlet 15 of the exhaust heat recovery boiler facility 3100 to the atmosphere. Each of the stresses described above is a dynamic process with a large time delay, that is, heat transfer from the gas turbine exhaust gas 27 to the metal of the heat exchanger, heat transfer from the metal to the internal fluid, and heat transfer from the internal fluid to the metal of interest. Appears as a result. Further, the centrifugal force is added to the rotor, and the mechanical stress corresponding to the steam pressure is added to the header. Hereinafter, unless otherwise specified, these are collectively referred to as stress. The NOx emission amount from the boiler also largely depends on the NOx emission characteristic of the gas turbine itself and the temperature characteristic of the denitration device 13 installed in the flue. Therefore, in order to accurately manage these driving restriction factors,
Coordination and consistency of the above control operations are required. Therefore, as described above, in the thermal power plant autonomous adaptive control system 1000 of the present invention, the operation restriction factor monitoring means 400 and the margin value evaluation means 500 for monitoring and evaluating these dynamic behaviors are provided.

FIG. 3 shows the relationship between the starting process and the starting schedule parameter of the combined cycle power plant to which the present invention is applied.

[0058] As shown in the figure, as the scheduling parameters of the gas turbine relationships, speed increasing ratio (DN), rated speed holding time (DT _NL), first load (L _I), first load retention time (DT _LI), The first load increase rate (DL ₁ ), the load holding time (DT _HL ), the second load increase rate (DL ₂ ), and the third load increase rate (DL ₃ ). The fuel control valve 2 which is the operating end has a start-up schedule defined by these as a control target.
The gas turbine is started by adjusting the opening degree of 1. Further, as the operating end relating to the steam turbine, as described above, the high pressure bypass valve (HPBV) 334, the intermediate pressure bypass valve (IPBV) 335, the low pressure bypass valve (LPB).
V) 336, high pressure control valve (HPCV) 331, medium pressure control valve (IPCV) 332, low pressure control valve (LPCV) 33
3. There is a medium pressure stop valve (ISHV) 337, and high pressure bypass valve operating speed (DA _HBV ), medium pressure bypass valve operating speed (DA _IBV ), low pressure bypass valve operating speed (DA _LBV ), low pressure bypass as schedule parameters. Valve operation waiting time (D
T _LBV ), high pressure control valve first operating speed (DA _HCV1 ), high pressure control valve second operating speed (DA _HCV2 ), low pressure control valve operating speed
(DA _LCV ). The control targets and operation timings other than these schedule parameters are as illustrated. Here, as steam conditions, T _MS is high-pressure main steam temperature, P _MS is high-pressure main steam pressure, P _CRP is high-pressure turbine exhaust pressure, P _IS is medium-pressure main steam pressure, P _LS is low-pressure main steam pressure, and ΔT is This is the deviation between the medium pressure superheater steam temperature and the high pressure turbine exhaust temperature. Further, in the figure, the% display of each valve indicates the opening.

Next, the detailed configuration of the control system and each means will be described step by step with reference to FIG. 4 and the subsequent figures.

FIG. 4 shows a detailed configuration of a thermal power plant autonomous adaptive control system for a combined cycle power plant which is an embodiment of the present invention.

The present system operates in response to a start time T _Ri (i indicates the next start-up, for example, i = 1 indicates the first start-up) which is a start-up command from the central power feeding command station 2000,
The combined cycle power plant 3000 is actually started by the schedule execution control means 300 in the system. The system 1000 includes a basic schedule creating means 100,
Schedule correction means 150, schedule execution control means 300, operation restriction factor monitoring means 400, margin value evaluation means 500, schedule correction amount calculation means 600, knowledge base 700, correction amount storage means 650, adaptive knowledge acquisition means 800, learning management means. 900 and an autonomy management means 200. Hereinafter, each of the above means will be described step by step.

FIG. 5 shows the basic schedule creating means 100.
Shows the basic schedule creation method in. here,
When the start time T _Ri is commanded from the central power feeding command station 2000, first, the stop period T _Si , which is the elapsed time from the previous plant stop time T _STPi ,

[0063]

## EQU1 ## T _Si = T _Ri −T _STPi (1) Next, a table corresponding to the suspension period T _Si is selected from a plurality of schedules prepared in advance for each suspension period as table information, and this is set as the basic schedule X _0i . Therefore, X _0i
Is a vector quantity consisting of multiple parameter values that define the schedule.

In the schedule correction means 150, the schedule correction amount calculation means 60 using fuzzy inference described later is used.
A new schedule X _i is obtained by using the correction amount ΔX _i-1 obtained from 0 and stored in the correction amount storage means 650 and the previous activation schedule X _i-1 stored in the learning management means 900 described later.

[0065]

[Formula 2] X _i = X _i-1 + ΔX _i-1 (2) Here, at the first startup (i = 1), Δ
X ₀ = 0 and X ₀ = X ₀₁ .

In the schedule execution control means 300, the combined cycle power plant 300 is _operated according to the start schedule X _Ri created by the autonomy management means 200 described later.
The control of the process state associated with startup such as turbine speed, load, steam pressure at 0 is carried out by the above-mentioned method.

In the operation restriction factor monitoring means 400, during the process in which the plant is started by the schedule execution control means 300, the steam turbine stress and the boiler stress, which are the process states that are difficult to directly measure, move in the measurable process state. Estimate and monitor dynamic behavior by inputting into the characteristic model. Further, NOx emitted from the exhaust heat recovery boiler, which is a process state in which direct measurement is possible, is monitored by a signal obtained from a measuring instrument.

FIG. 6 shows a method of monitoring the steam turbine stress in the operation limiting factor monitoring means 400, which occurs on the rotor surface and rotor bore of the high-pressure turbine and the intermediate-pressure turbine, which are points to be focused and monitored as the operation limiting factor. It was constructed as a dynamic characteristic model for stress. That is, the steam condition (temperature, pressure) inside the turbine and the heat transfer coefficient on the rotor surface are estimated from the turbine inlet steam condition, speed and load, and the unsteady temperature distribution inside the rotor metal is calculated to determine the rotor surface and bore. In addition to calculating thermal stress, centrifugal stress was added to obtain total stress. The stress generated in the exhaust heat recovery boiler is also monitored by the same method. That is, the internal heat transfer coefficient of the high-pressure superheater outlet header and the medium-pressure superheater outlet header, which are the points to be monitored as an operation limiting factor, are estimated from the steam conditions (temperature, pressure) and the flow rate,
As in the case of the steam turbine, the unsteady temperature distribution inside the metal is calculated to calculate the thermal stress inside and outside the header metal, and the stress due to steam pressure is added to obtain the total stress. Next, the margin value evaluation means 500 for evaluating the margin value with respect to the limit value of the operation restriction factor obtained by the operation restriction factor monitoring means 400 will be described.

FIG. 7 shows a margin value evaluation method for the limiting value of the steam turbine stress. First, a time period (t ₁ ~
t ₆ ) is divided into a plurality of sections (in this example, the case of five divisions is shown), and the minimum stress margin value m (i) in the i-th section is obtained. Here, the stress margin value m is defined by the following equation, where the limit value is S _L and the value obtained by the operation limit factor monitoring means 400 is S.

[0070]

Equation 3] _{m = S L -S ... (3} ) In addition, the stress value is positive when the tensile stress, if negative means compressive stress. As described above, the stress points are actually the rotor surface and the bore of the high-pressure turbine and the rotor surface and the bore of the medium-pressure turbine, so there are four minimum stress margin values to be obtained for each section. These are described below.

M _HS (i): High pressure turbine rotor surface minimum stress margin value in section i m _HB (i): High pressure turbine rotor bore minimum stress margin value in section i m _IS (i): Medium pressure turbine rotor in section i Minimum surface stress margin m _IB (i): Medium pressure turbine rotor bore minimum stress margin in section i The exhaust heat boiler stress margin evaluation method is basically the same as the turbine stress margin evaluation method. . However, two points to be noted are the header outer surface at the high pressure superheater outlet and the medium pressure superheater outlet. This is because the stress generated on the outer surface of the header is larger than that on the inner surface.
Therefore, there are two minimum stress allowance values to be obtained for each section, and they are set as follows.

_{M HHD} (i): Minimum stress margin value of outer surface of high pressure superheater header in section i m _IHD (i): Minimum stress margin value of outer surface of medium pressure superheater header in section i FIG. The margin value evaluation method for Similarly to the case of turbine stress, this method first divides the time zone (t _{1 to} t ₇ ) from the start of gas turbine startup to the elapse of a predetermined time after the completion of plant startup into a plurality of sections (in this example, 6 sections). Then, the minimum emission NOx instantaneous value margin value m _PS (i) and the minimum emission N in the i-th section are shown.
The Ox average value margin value m _PA (i) is calculated. Here, discharge NO
x Instantaneous value allowance m _PS and NOx emission mean value allowance m
_{The PA} has respective limit values P _SL , P _AL , and measured values P _S ,
Let P _A be defined by the following equation.

[0073]

(4) m _PS = P _SL −P _S (4)

[0074]

## _EQU00005 ## m _PA = P _AL −P _A (5) FIG. 9 shows the evaluation results obtained from the margin value evaluation means 500 m _HS , m _HB , m _IS , m _IB , m _HHD , and m _IHD. , M _PS , m
Upon receipt of the _PA , an overall outline of the schedule correction amount calculation method in the schedule correction amount calculation means 600 will be shown. The schedule correction amount calculation means 600 calculates the schedule correction amount ΔX _i according to the size of the margin value obtained by the margin value evaluation means 500. The schedule correction amount calculation means 600 further includes the steam turbine stress adjustment means 61.
0, boiler stress adjusting means 620, exhaust NOx adjusting means 6
30 and priority value determining means 640. Each of the adjusting means has a low value selecting means 611, 621, respectively.
631 and the low value selecting means 611 has a margin value m _HS ,
From among m _HB , m _IS , and m _IB , the low value selection means 621 determines the margin value m _HHD . From m _IHD , the low value selection means 631 selects the minimum margin values from the margin values m _PS and m _PA , and sets these as m _T , m _B and m _P , respectively. That is, m _T ,
m _B and m _P are defined by the following equations.

[0075]

M _T = Min (−m _HS , m _HB , −m _IS , m _IB ) ... (6)

[0076]

[ _Equation 7] m _B = Min (m _HHD , m _IHD ) (7)

[0077]

[Equation 8] m _P = Min (m _PS , m _PA ) ... (8) Next, fuzzy inference 612, using the above m _T , m _B , and m _P.
Schedule correction amounts ΔX _T (i), ΔX _B (i), and ΔX _P (i) are calculated by 613, 622, 623, 632, and 633. Here, the subscripts T, B, and P indicate that the correction amounts are determined according to the steam turbine stress, the boiler stress, and the margin value of exhausted NOx, respectively.

FIG. 10 shows the membership function used in the fuzzy rule at this time. The membership function shown in FIG. 10 (1) is a steam turbine main plan for adjusting the stress by modifying the schedule of the steam system from the turbine stress allowance m _T and the boiler stress allowance m _B.
The correction coefficient k _S for (STPS) and the schedule correction coefficient k _G for gas turbine wide area adjustment (GTGT) for adjusting stress by correcting the schedule of the gas turbine system are defined. Also, FIG.
The membership function shown in 0 (2) is a correction coefficient k _G for the gas turbine main plan (GTPS) for adjusting the emission NOx by adjusting the schedule of the gas turbine system from the emission NOx margin value m _P. And a schedule correction coefficient k _S for local adjustment (STLT) of steam turbine for adjusting exhaust NOx by correcting the schedule of the steam system. More specifically, FIG. 10 (1) (a) shows a membership function used in the conditional part of the fuzzy rule for stress evaluation, and shows four functions (NS, ZO, PS, PB).
(B) shows the membership function for determining the schedule correction coefficient used in the conclusion part, and the five functions (NB,
NS, ZO, PS, PB). Here, the meaning of each membership function is NB: Negative Big, N
S: Negative Small, ZO: Zero, PS: Positive Sma
ll, PB: Positive Big. FIG. 10 (2) (a) shows a membership function used in the conditional part of the fuzzy rule for evaluating emission NOx, and shows four functions (NS, ZO,
PS, PB), and (b) shows the membership function for determining the schedule modification coefficient used in the conclusion part, which is composed of five functions (NB, NS, ZO, PS, PB). Here, the meaning of each membership function is shown in FIG. 9 (1) above.
Is the same as in.

Next, a fuzzy rule using the above membership function will be described.

FIG. 11 shows a steam turbine main plan (STPS)
Shows the turbine stress adjustment rule used in. Here, 4
High pressure bypass valve operating speed (DA _HBV ) and medium pressure bypass valve operating speed (DA
_IBV ), high pressure control valve first operating speed (DA _HCV1 ), high pressure control valve second operating speed (DA _HCV2 ), turbines in the second, third, fourth and fifth sections among the five _target sections shown in FIG. A fuzzy rule for correction in relation to the stress margin value m _T is shown. That is, the margin value m in the second and third sections
A rule table defining the correction amounts of DA _HCV1 and DA _HCV2 in the relationship between _T (2) and m _T (3), and the margin values m _T (3) and m _T (4) in the third and fourth sections DA _{HCV1 in a} relationship
And the rule table that defines the amount of modification of DA _HCV2 , and
And a rule table defining correction amounts of DA _HBV , DA _IBV , DA _HCV1 and DA _HCV2 in the relation between the margin values m _T (4) and m _T (5) in the fifth section. However, the blank portion of the rule table means that the causal relationship between each stress margin value and the schedule parameter is small or almost nonexistent.

FIG. 12 shows the gas turbine wide area adjustment (GTG).
The turbine stress adjustment rule used in T) is shown. Here, as the six correction schedule parameters, the acceleration rate
(DN), initial load holding time (DT _LI ), first load rise rate (D
L ₁ ), load holding time (DT _HL ), second load increase rate (D
A fuzzy rule for correcting L ₂ ), the third load increase rate (DL ₃ ) in relation to the turbine stress margin value m _T in the five target sections shown in FIG. 7 is shown. That is, the first
When a rule table that defines DN, the correction amount of DT _LI and DL ₁ in relation allowance m _T (1) and m _T (2) in the second interval, margin value in the second and third sections m
_In the relationship between _T (2) and m _T (3), DT _LI , DL ₁ , DT _HL
And a rule table that defines the modification amount of DL ₂ , and
And a rule table defining the correction amounts of DL ₁ , DT _HL , DL ₂ and DL ₃ in the relation between the margin values m _T (3) and m _T (4) in the fourth and fifth intervals, It is composed of a rule table that defines the correction amounts of DT _HL , DL ₂ and DL ₃ in the relationship between the margin values m _T (4) and m _T (5). Also in this rule table, the blank portion means that the causal relationship between each stress margin value and the schedule parameter is small or almost nonexistent.

The fuzzy rules shown in FIGS. 11 and 12 are for STPS and GTG based on the boiler stress margin value.
It is also used for T.

FIG. 13 shows the gas turbine main plan (GTPS).
The emission NOx adjustment rule used in is shown below. Here, the speedup rate (DN) is set as eight correction schedule parameters,
Rated speed holding time (DT _NL), first load (L _I), first load retention time (DT _LI), the first load increase rate (DL _1), the load holding time
(DT _HL ), second load increase rate (DL ₂ ), third load increase rate (D
L ₃ ) is the emission NO in the six target sections shown in FIG.
A fuzzy rule for correction in relation to the x margin value m _P is shown.

That is, the margin value m in the first and second sections
A rule table defining the correction amounts of DN, DT _NL , L _I and DT _LI in the relationship between _P (1) and m _P (2), and the margin values m _P (2) and m in the second and third sections A rule table defining the correction amounts of DT _NL , L _I , DT _LI, and DL ₁ in the relationship of _P (3), and a margin value m in the third and fourth sections
_In the relationship between _P (3) and m _P (4), DT _LI , DL ₁ , D
A rule table defining correction amounts of T _HL , DL ₂ and DL ₃ , and margin values m _P (4) and m in the fourth and fifth sections
DL ₁ , DT _HL , DL ₂ and DL _{3 in} the relationship of _P (5)
It consists of a rule table that defines the modification amount of. Also in this rule table, the blank portion means that the causal relationship between each stress margin value and the schedule parameter is small or almost nonexistent.

FIG. 14 shows the steam turbine local adjustment (STL
The emission NOx adjustment rule used in T) is shown. here,
High pressure bypass valve operating speed (DA _HBV ) and medium pressure bypass valve operating speed (DA
_IBV ), the high pressure control valve first operating speed (DA _HCV1 ) in relation to the emission NOx margin value m _{P in the first, second} and third sections among the six sections of interest shown in FIG. Show the rules. That is, in the relationship between the margin values m _P (1) and m _P (2) in the first and second sections, DA _HBV , DA
A rule table defining the correction amounts of _IBV and DA _HCV1 , and margin values m _P (2) and m _P (3) in the second and third sections
In the above relation, it comprises a rule table defining the correction amounts of DA _HBV , DA _IBV and DA _HCV1 .

Compared to STLT, the above-mentioned STPS targets a wider range of schedule parameters, GTPS performs more precise correction than GTGT, and GTGT has a broader perspective than GTPS. It works to make various corrections.

FIG. 15 shows a schedule modification amount calculation method by fuzzy inference. This figure shows calculation of the steam system schedule correction coefficient k _S as an example. In this example, for a certain schedule parameter, the membership function and membership value as conclusions from the four rules are (NS, 0.6), (ZO, 0.8),
The case where (PS, 0.4) and (PB, 0.2) are obtained is shown.
The comprehensive evaluation value is defined by the weight W (i) of the trapezoidal portion determined by each membership value and the center of gravity k _SG of the correction coefficient k _S (i). That is, W (1) = 0.168, W (2) = 0.096,
W (3) = 0.096, W (4) = 0.09 and k _S (1)
= -0.2, k _S (2) = 0, k _S (3) = 0.15, k _S (4)
= 0.35, k _SG is calculated as follows.

[0088]

[Equation 9]

Therefore, the modification amount k _SG for this activation schedule parameter is 0.0273. Using this result, the schedule correction amount ΔX _Ti is calculated according to the following equation.

[0090]

ΔX _T = k _SG (X _Max −X _Min ) (10) Here, X _Max and X _Min represent upper and lower limit values of X.

According to the method described above, the ST
Plural correction amounts are calculated for the same schedule parameter by fuzzy inference of PS, GTGT, STPS, STLT. Therefore, in order to surely keep all the operation limiting factors within the limit values, the priority value determining means 64 shown in FIG.
At 0, the final correction amount ΔX (i) is determined. That is, the rate of speed increase,
The low value on the safe side is selected by the low value selection means LVG in the figure for the parameters relating to the load change rate and the valve operation speed.
The high value selecting means HVG selects a high value on the safe side for the parameters relating to the speed holding time and the load holding time.

In the correction amount storage means 650, the correction amounts ΔX ₁ to
ΔX _i is stored in association with each past plant activation.

In the learning management means 900, the schedule X _Rk (k = 1 to i-) for each plant start is applied to the adaptive knowledge acquisition means 800 composed of a neural network.
1) and the stop period T _Sk (k = 1 to 1) which is a plant operation condition.
i-1) and the cumulative correction amount ΔX _Qk (k = 1 to i−1) which is the cumulative value of the schedule correction amount ΔX _i are sequentially presented as learning samples and learned.

FIG. 16 shows a learning sample presented to the neural network. If the start is completed up to the nth time, the cumulative correction amount ΔX as the teacher data
_Qi is a value defined by the following equation.

[0095]

[Equation 11]

That is, ΔX _Qi is obtained by cumulatively calculating a new correction amount obtained each time the activation is repeated according to the above equation (11) by the cumulative correction amount calculating means 910. The cumulative correction amount ΔX _Qi is stored in the cumulative correction amount storage means 920.

The learning management means 900 uses X _Rk (k = 1 to i-1) and T _Sk (k) in the input layer of the neural network.
= 1 to i-1), the output value Δ from the output layer
X _OUTk (k = 1 to i-1) is the cumulative correction amount ΔX _Qk (k = 1 to 1)
It functions to determine the connection state in the neural network so as to match i-1).

Next, the neural network and its learning method will be described.

This neural network has an input layer,
It is composed of a three-layer structure consisting of an intermediate layer and an output layer. Regarding this basic structure, the MIT Press and Neurocomputing Foundations of
Research, 1988, pages 318-362 (The M
IT Press, Neurocomputing Foundations of research,
1988, pp318-362). Further, in this paper, when a certain input signal pattern is given to the input layer, the output layer pattern is matched with the desired signal pattern, that is, the teacher data. A learning algorithm (referred to as backpropagation) that modifies the connection strength of the input section to each unit is shown. Also in the embodiment of the present invention,
The structure of the neural network and the learning algorithm itself use those shown in the above paper.

FIG. 17 shows the relationship between the structure of the neural network and the input / output variables. The input variables are the activation schedule parameter X and the suspension period T _S. As X, X _Rk (k = 1 to i−) read from the schedule storage unit 950 that stores past activation schedules during learning.
1) is a schedule parameter X _INi which is determined by the autonomy management means 200 described later when the activation schedule is created.
Is entered. Further, as T _S , the stop period storage means 94 storing the past plant stop period at the time of learning
T _Sk (k = 1 to i−1) read from 0 is input to T _Si when the activation schedule is created. When X _INi is input to the neural network, ΔX _OUTi determined by the internal connection strength determined by learning is output as an output variable. At the time of learning, ΔX _OUTk (k = 1 to i
−1) is learned so as to match ΔX _Qk (k = 1 to i−1).

Next, the autonomy management means 200 will be specifically described. When determining the actual activation schedule X _Ri , the autonomy management means 200 functions to strengthen the degree of dependence on the conclusion ΔX _OUTi from the neural network rather than the conclusion ΔX _i from the fuzzy inference as the activation is repeated. do. That is, the schedule created by the correction amount ΔX _i obtained from the schedule correction amount calculation means 600 by the fuzzy inference and the adaptive knowledge acquisition means 8
The adaptability and autonomy to the change in the operating conditions are made variable by changing the autonomous factor λ, which is the adoption ratio of the schedule created by the correction amount ΔX _OUTi obtained from 00, and expanding the latter ratio as the activation is repeated. Function to increase This autonomous coefficient λ is the autonomous coefficient generating means 210.
Is determined. Specifically, the activation schedule X _Ri defined by the following equation is adopted for the actual activation.

[0102]

X _Ri = λ (X _0i + ΔX _OUTi ) + (1−λ) X _i (12) Further, in the above equation, necessary input X _INi for obtaining ΔX _OUTi which is the output from the neural network. Is defined by the following equation.

[0103]

Equation 13] _{X INi = λX 0i + (1} + λ) X i ... (13) where the autonomous factor lambda, When 0 ≦ λ ≦ 1, when lambda = 0, a X _Ri = X _i, λ = 1, X _Ri = X _0i +
ΔX _OUTi . That is, the former is a startup schedule in which only the conclusions from the fuzzy inference are adopted and the output from the neural network is not used. The latter is a startup schedule in which the basic schedule is corrected by the output from the neural network, and as described above, the adaptability to changes in operating conditions is fully utilized.
Therefore, as the value of λ increases, the control system becomes more autonomous. However, if the autonomic coefficient λ is increased before the neural network has sufficient start-up experience (before providing sufficient adaptive ability by learning),
The optimality and high reliability of the created startup schedule cannot be expected. Therefore, how to select this autonomous coefficient λ is important. Autonomy management means 20 in the present embodiment
In 0, the following method is applied.

FIG. 18 shows an autonomous management system for determining the autonomous coefficient λ in the autonomous management means 200. In this embodiment, the three methods can be applied depending on how to determine the autonomous coefficient λ as shown in the figure. The first method is the leap-growth type autonomy management method shown in (1) of this figure, in which λ = 0 is set until the number of activations i reaches a predetermined value i _A , and the neural network continuously learns and adapts. Is set higher and λ = 1 with i ≧ i _A , so that the autonomy is increased at once. The second method is a linear continuous growth Autonomous management scheme shown in the figure (2), i <the i _B lambda =
0, the neural network enhances the adaptive ability to some extent by learning, and in i _B ≤i≤i _C , the autonomous ability is gradually increased while increasing the adaptive ability, and i ≥i
_This is a method of setting λ = 1 in _C. The third method is the nonlinear continuous growth type autonomy management method shown in (3) of this figure.
λ is a schedule correction amount calculation means 6 by fuzzy inference
This is a function of the correction amount ΔX _i obtained from 00. This method is a method in which λ is increased in accordance with a decrease in the correction amount ΔX _i . The function that defines λ is specifically the following expression.

[0105]

[Equation 14]

Alternatively, the same function can be achieved by the following equation.

[0107]

(Equation 15)

FIG. 19 shows the effect of improving the optimal value convergence by the autonomous adaptive control system of the present invention. This figure (1)
This is the case when not using autonomous control, that is, when optimizing the startup schedule only by fuzzy reasoning.
In the case of autonomous adaptive control, that is, in the case where a fuzzy inference and a neural network are combined to give the system adaptive capacity to optimize the activation schedule. All of them show how the initial schedule converges toward the optimum value as the plant is repeatedly started. However, in the case of (1), since there is no adaptability to changes in operating conditions as described above, there is a non-convergence region, and convergence beyond the convergence limit cannot be expected. On the other hand, in the case of (2), since the neural network has the adaptive ability to acquire the schedule correction method including the causality of the operating conditions and the activation schedule, the neural network can converge to an optimum value without limit. This is evaluated from another angle as follows.

FIG. 20 shows the effect of improving the starting characteristic of the plant by the autonomous adaptive control system of the present invention, which is shown in comparison with the case where the autonomous adaptive control is not used. As described above, the non-convergence region exists when the autonomous adaptive control is not used, and thus the created activation schedule has a range. Therefore, the stress and exhausted NOx that are the operation limiting factors also have a range. Therefore, in order to satisfy all the limiting conditions, the schedule to be created needs to have a sufficient margin in advance, and the start-up time becomes longer accordingly. On the other hand, in the case of the autonomous adaptive control, it is possible to flexibly adapt to changes in operating conditions as described above, so that it is possible to always create an optimum schedule. Therefore, it is possible to start in the shortest necessary time by the start schedule that can satisfy the operation restriction condition with high accuracy.

Although the embodiments of the present invention described above have been specifically described for the combined cycle power generation plant, the present invention is applicable to other plants such as a normal power generation plant including a boiler, a steam turbine, and a generator. Of course, it can be applied to a coal gasification power generation plant and an atmospheric pressure or pressurized fluidized bed boiler power generation plant. It is also clear that the fuel used is not limited to coal, petroleum, LNG and the like.

Further, in the embodiment of the present invention, the stress of the steam turbine and the exhaust heat recovery boiler header and the exhausted NOx are treated as the operation limiting factors, but depending on the characteristics of the applied plant,
It is also possible to adopt a method that takes into consideration other factors, for example, the difference in expansion between the rotor of the steam turbine and the casing, the stress of other parts such as the drum of the exhaust heat recovery boiler, and the exhaust SOx and CO. Further, it is clear that even if the stress is not necessarily estimated as a dynamic characteristic model, indirect limit value management such as the rate of change or the range of change of the steam temperature or the metal temperature can be performed without changing the essence of the present invention. Is.

Further, in the embodiment of the present invention, only the plant stop period is shown as the operating condition, but in addition to this, the plant starting characteristics such as equipment temperature, pressure, water temperature, atmospheric humidity, temperature and pressure are related. It is also possible to add various state quantities to implement the essence of the present invention without changing the basic principle. Further, when the plant is operated while changing the operation restriction condition at each startup, the operation restriction condition can be handled as a part of the operation condition without changing the basic principle of the present invention.

Further, in the embodiment of the present invention, the starting schedule parameters to be modified are eight gas turbine-related and four steam turbine-related, but the invention is not necessarily limited to these, and FIG. It is obvious that the present invention can be implemented without changing the basic principle as long as it is a parameter that defines the plant starting pattern such as the other parameters shown, the opening / closing timing condition of the regulator valve, the set value for steam pressure control, and the like. In the embodiment of the present invention, the start start time commanded by the central power feeding command station is exactly followed,
In addition, it is possible to create a start-up schedule of the shortest time, but instead of the start-up completion time instructed by the central power supply command center, the reference time is shifted even if it is gas turbine ignition time, load combination time, target load arrival time, etc. It is obvious that the present invention can be implemented without changing the principle of the present invention.

[0114]

The first effect of the present invention is that, in a start control system for a thermal power plant, a plurality of operation restrictions such as equipment life standards and equipment protection standards such as NOx emissions, which are not possible with the conventional methods, and environmental regulation values. It is to enable automatic creation and execution of a startup schedule that minimizes the startup time while satisfying the conditions at the same time. As a result, the burden on the operator is significantly reduced, and the energy loss accompanying the shortened start-up time can be reduced, so that the plant operation cost can be significantly reduced.

The second effect of the present invention is that, in a start control system for a thermal power plant, a central power supply command station is provided while simultaneously satisfying a plurality of operation restriction conditions such as equipment protection standards such as equipment life and NOx emissions and environmental regulation values. It is possible to complete the startup at the time specified by. This enables stable and accurate power supply to the power system, which requires frequent start and stop of the plant due to fluctuations in power demand.

The third effect of the present invention is to re-create the start schedule even when the operation limit value is changed or the start command time is changed from the central power supply command station in the start control system of the thermal power plant. And this is possible. This enables flexible and safe plant operation and power system operation.

The fourth effect of the present invention is that, in the startup control system for a thermal power plant, since it has the ability to adapt to changes in operating conditions, a large-scale complex dynamic characteristic model for predicting the startup characteristics of the plant is constructed. , It does not have to be built into the startup control system. As a result, the system design and construction time can be significantly reduced, the computer load can be reduced, and the startup schedule can be created faster.

The fifth effect of the present invention is to realize a start schedule optimization function having adaptability to changes in operating conditions in a start control system for a thermal power plant, by utilizing the features of fuzzy inference and neural networks. It is possible to simplify the knowledge base used in fuzzy inference. This makes it possible to significantly reduce the time required to build the knowledge base.

[Brief description of drawings]

FIG. 1 shows a basic configuration of a thermal power plant autonomous adaptive control system of the present invention.

FIG. 2 shows the relationship between the equipment configuration of the combined cycle power plant to which the present invention is applied and the startup schedule execution control.

FIG. 3 shows a relationship between a start process and a start schedule parameter of a combined cycle power plant to which the present invention is applied.

FIG. 4 shows a detailed configuration of a thermal power plant autonomous adaptive control system for a combined cycle power plant that is an embodiment of the present invention.

FIG. 5 shows a basic schedule creation method in the embodiment of the present invention.

FIG. 6 shows a method of monitoring steam turbine stress as an operation limiting factor of a combined cycle power plant that is an embodiment of the present invention.

FIG. 7 shows a steam turbine stress margin value evaluation method as an operation restriction factor of the combined cycle power plant according to the embodiment of the present invention.

FIG. 8 shows an exhaust NOx margin value evaluation method as an operation limiting factor of the combined cycle power plant according to the embodiment of the present invention.

FIG. 9 shows an overall outline of a schedule modification amount calculation method in consideration of all operation restriction factors in the embodiment of the present invention.

FIG. 10 shows a membership function used in a fuzzy rule according to an embodiment of the present invention.

FIG. 11 shows a steam turbine stress adjustment rule used for a steam turbine main plan (STPS) by the schedule correction amount calculation means in the embodiment of the present invention.

FIG. 12 shows a steam turbine stress adjustment rule used for gas turbine wide area adjustment (GTGT) in the schedule correction amount calculation means in the embodiment of the present invention.

FIG. 13 shows an emission NOx adjustment rule used for a gas turbine main plan (GTPS) by the schedule correction amount calculation means in the embodiment of the present invention.

FIG. 14 shows an exhaust NOx adjustment rule used for steam turbine local adjustment (STLT) in the schedule correction amount calculation means in the embodiment of the present invention.

FIG. 15 shows a schedule correction amount calculation method by fuzzy inference according to an embodiment of the present invention.

FIG. 16 shows a learning sample for a neural network in the embodiment of the present invention.

FIG. 17 shows a structure of a neural network and definitions of input / output variables in the embodiment of the present invention.

FIG. 18 shows an autonomy management system according to an embodiment of the present invention.

FIG. 19 shows the effect of improving the optimal value convergence by the autonomous adaptive control system of the present invention.

FIG. 20 shows the effect of improving the starting characteristics of the combined cycle power plant by the autonomous adaptive control system of the present invention.

[Explanation of symbols]

100 ... Basic schedule creation means, 150 ... Schedule correction means, 200 ... Autonomy management means, 300 ... Schedule execution control means, 400 ... Operation restriction factor monitoring means, 500 ... Margin value evaluation means, 600 ... Schedule correction amount calculation means, 650 ... Correction amount storage means, 700 ... Knowledge base, 800 ... Adaptive knowledge acquisition means, 900 ... Learning management means, 1000 ... Autonomous adaptive control system for thermal power plant, 2000 ... Central power supply command station.

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification code Internal reference number FI Technical display location G05B 13/02 N 9131-3H L 9131-3H 13/04 9131-3H 23/02 X 7618-3H 301 J 7618-3H G06F 17/60 (72) Inventor Masae Takahashi 5-2-1 Omika-cho, Hitachi-shi, Ibaraki Hitachi Ltd. Omika factory (72) Incorporator Takao Akiyama Miyuki-cho, Hitachi-shi, Ibaraki No. 1-1 No. 1 Hitachi Ltd. Hitachi factory

Claims (20)

[Claims] 1. A control system for a thermal power plant having a boiler for generating steam, and a steam turbine driven by the steam generated by the boiler, the start-up from ignition to completion of start-up of the boiler. A basic schedule creating means for creating a schedule based on a temperature condition before starting the thermal power plant; an operating restriction factor monitoring means for monitoring a predetermined operating restriction factor in the startup process of the thermal power plant; A margin value evaluation means for evaluating a margin value with respect to the operation restriction condition of the operation restriction factor obtained by the factor monitoring means, and a fuzzy inference of the correction amount of the starting schedule based on the margin value obtained by the margin value evaluation means. And a schedule correction amount calculating means for calculating the past correction schedule of the thermal power plant. Adaptive learning to store the correspondence between the input and output so that the schedule modification amount calculated by the schedule modification amount calculating means can be obtained as an output value when the rule and the operating condition are given as input values to the neural network. Means, a first activation schedule created based on the schedule modification amount obtained by the schedule modification amount calculation means, and a second startup schedule created based on the schedule modification amount obtained by the adaptive knowledge acquisition means. An actual startup schedule is created using the startup schedule, and the first method for creating the actual startup schedule
And an autonomy management means for increasing the adoption ratio of the second startup schedule by increasing the autonomic coefficient λ that defines the adoption ratio of the second startup schedule as the startup is repeated, and the autonomy management means A schedule execution control means for starting and controlling the thermal power plant according to the created startup schedule, and an autonomous adaptive control system for a thermal power plant. 2. A control system for a thermal power plant having a boiler for generating steam and a steam turbine driven by the steam generated by the boiler, wherein the boiler is activated from ignition to completion of activation. A basic schedule creating means for creating a schedule based on a temperature condition before starting the thermal power plant; an operating restriction factor monitoring means for monitoring a predetermined operating restriction factor in the startup process of the thermal power plant; A margin value evaluating means for evaluating a margin value for the operation limiting condition of the operation limiting factor obtained by the factor monitoring means, and a schedule correction amount of the starting schedule corresponding to the size of the margin value obtained by the margin value evaluating means. A knowledge base that stores a plurality of fuzzy rules that define A schedule correction amount calculation means for calculating the schedule correction amount by fuzzy inference using a fuzzy rule, and a past start schedule and operating conditions of the thermal power plant, when given as input values to a neural network, the schedule as an output value. The adaptive knowledge acquisition means for storing the input / output correspondence and the internal connection state of the neural network in the adaptive knowledge acquisition means are determined so that the schedule modification amount obtained by the correction amount calculation means is obtained. The learning management means for learning the output correspondence, the first start schedule created based on the schedule correction amount obtained by the schedule correction amount calculation means, and the schedule correction amount obtained by the adaptive knowledge acquisition means. The second startup schedule created based on Module and an actual boot schedule are created, and at the time of creating the actual boot schedule, the first
And an autonomy management means for increasing the adoption ratio of the second startup schedule by increasing the autonomous coefficient λ that defines the adoption ratio of the second startup schedule as the startup is repeated, and created by the autonomy management means. Schedule execution control means for starting and controlling the thermal power plant according to the activated start schedule, the autonomous adaptive control system for the thermal power plant. 3. A thermal power plant having a gas turbine, an exhaust heat recovery boiler for generating steam using exhaust gas of the gas turbine, and a steam turbine driven by the steam generated by the exhaust heat recovery boiler. In the control system, a starting schedule from ignition of the gas turbine to completion of starting, a basic schedule creating means for creating based on a temperature state before starting the thermal power plant, and in a starting process of the thermal power plant An operation restriction factor monitoring unit for monitoring a predetermined operation restriction factor, a margin value evaluation unit for evaluating a margin value of the operation restriction factor obtained by the operation restriction factor monitoring unit with respect to the operation restriction condition, and a margin value evaluation unit. A schedule for calculating the modification amount of the startup schedule by using fuzzy inference based on the obtained margin value. And a past correction schedule of the thermal power plant and operating conditions are given as input values to the neural network, the schedule correction amount calculated by the schedule correction amount calculation unit is obtained as an output value. As described above, the adaptive knowledge acquisition means for storing the correspondence relationship between the input and output, the first activation schedule created based on the schedule correction amount calculated by the schedule correction amount calculation means, and the adaptive knowledge acquisition means An actual boot schedule is created using the second boot schedule created based on the obtained schedule modification amount, and the first boot schedule is created when creating the actual boot schedule.
And an autonomy management means for increasing the adoption ratio of the second startup schedule by increasing the autonomous coefficient λ that defines the adoption ratio of the second startup schedule as the startup is repeated, and created by the autonomy management means. Schedule execution control means for starting and controlling the thermal power plant according to the activated start schedule, the autonomous adaptive control system for the thermal power plant. 4. A thermal power plant having a gas turbine, an exhaust heat recovery boiler for generating steam using exhaust gas from the gas turbine, and a steam turbine driven by the steam generated in the exhaust heat recovery boiler. In the control system, a starting schedule from ignition of the gas turbine to completion of starting, a basic schedule creating means for creating based on a temperature state before starting the thermal power plant, and in a starting process of the thermal power plant An operation restriction factor monitoring unit for monitoring a predetermined operation restriction factor, a margin value evaluation unit for evaluating a margin value of the operation restriction factor obtained by the operation restriction factor monitoring unit with respect to the operation restriction condition, and a margin value evaluation unit. A plurality of files that define the schedule modification amount of the startup schedule corresponding to the size of the obtained margin value. A knowledge base that stores fuzzy rules, a schedule correction amount calculation unit that calculates the schedule correction amount by fuzzy inference using the fuzzy rules stored in the knowledge base, and a past start schedule and operating conditions of the thermal power plant. When given as an input value to the neural network, adaptive knowledge acquisition means for storing the input / output correspondence relationship so that the schedule correction amount obtained by the schedule correction amount calculation means as an output value is obtained, and the adaptive knowledge acquisition means. Learning management means for learning the input / output correspondence by determining the internal connection state of the neural network in the learning means, and a first activation created based on the schedule correction amount obtained by the schedule correction amount calculation means. The schedule and the adaptive knowledge acquisition means A second startup schedule created based on the obtained schedule modification amount is used to create an actual startup schedule, and the first startup schedule is used when creating the actual startup schedule.
And an autonomy management means for increasing the adoption ratio of the second startup schedule by increasing the autonomous coefficient λ that defines the adoption ratio of the second startup schedule as the startup is repeated, and created by the autonomy management means. Schedule execution control means for starting and controlling the thermal power plant according to the activated start schedule, the autonomous adaptive control system for the thermal power plant. 5. The autonomous adaptive control system for a thermal power plant according to claim 1, wherein the predetermined operation limiting factor is a stress in a steam superheater outlet steam header of the boiler, a stress in the steam turbine, An autonomous adaptive control system for a thermal power plant, which is at least one of NOx emitted from the boiler. 6. The thermal power plant autonomous adaptive control system according to claim 3 or 4, wherein the predetermined operation limiting factors are stress in a steam header at a superheater outlet of the exhaust heat recovery boiler, and the steam turbine. And at least one of the NOx discharged from the exhaust heat recovery boiler and the autonomous adaptive control system for a thermal power plant. 7. The thermal power plant autonomous adaptive control system according to claim 3, wherein the start-up schedule comprises a start-up schedule of the gas turbine and a start-up schedule of the steam turbine, and the gas. The start-up schedule of the turbine is configured by a predetermined parameter that defines the start-up schedule, and the predetermined parameter is one of a speed increase rate, a rated speed holding time, an initial load, an initial load holding time, a load increasing rate, and a load holding time. There is at least one or more, and the start-up schedule of the steam turbine is configured by predetermined parameters that define the start-up schedule, and the predetermined parameters are the high-pressure turbine bypass valve operating speed, the intermediate-pressure turbine bypass valve operating speed, and the low-pressure. Turbine bypass valve operating speed, high pressure adjusting valve operating speed, medium pressure Reduced valve operating speed, thermal power plant autonomous adaptive control system characterized in that at least one or more of the low-pressure control valve operation speed. 8. The thermal power plant autonomous adaptive control system according to claim 3 or 4, wherein the predetermined operation limiting factors are the superheater outlet steam header stress of the exhaust heat recovery boiler, and the steam turbine The stress is NOx discharged from the exhaust heat recovery boiler, and the allowance value evaluation means performs the starting process of the thermal power plant for the superheater outlet steam header stress of the exhaust heat recovery boiler and the steam turbine stress. The minimum margin value for the operation limit value is calculated for each of the sections divided into a plurality of sections, and for NOx discharged from the exhaust heat recovery boiler, an instantaneous value and a moving average value are calculated to start the thermal power plant. A thermal power plant autonomous adaptive control system, characterized in that a minimum margin value for the operation limit value is obtained for each section obtained by dividing the process into a plurality of sections. 9. The thermal power plant autonomous adaptive control system according to any one of claims 3 and 4, wherein the schedule correction amount calculation means is an exhaust heat recovery boiler superheater outlet steam header obtained from the margin value evaluation means. Fuzzy inference for steam turbine master plan (STPS), fuzzy inference for gas turbine wide area adjustment (GTGT), and minimum margin of exhaust heat recovery boiler exhaust NOx based on minimum margin of stress and minimum margin of steam turbine Inference and Steam Turbine Local Adjustment for Gas Turbine Master Plan (GTPS) Based on
(STLT) fuzzy inference is performed, the STPS compares a wider range of schedule parameters with the STLT, the GTPS performs more precise correction with respect to the GTGT, and the GTGT performs the correction. G
An autonomous adaptive control system for a thermal power plant, which is characterized by performing a more comprehensive correction as compared with TPS. 10. The thermal power plant autonomous adaptive control system according to claim 3, wherein the knowledge base is for STPS, STLT, GTPS, G
An autonomous adaptive control system for a thermal power plant, which is configured to support fuzzy inference for TGT. 11. The thermal power plant autonomous adaptive control system according to claim 3, wherein said schedule correction amount calculation means is for STPS, STLT.
, GTPS and GTGT fuzzy inference, the acceleration rate, load change rate,
A thermal power plant autonomous adaptive control system characterized by selecting low values for parameters related to valve operation speed and selecting high values for parameters related to speed holding time and load holding time. 12. A thermal power plant autonomous adaptive control system according to any one of claims 1 to 4, wherein the neural network used in the adaptive knowledge acquisition means is:
A plurality of units having a signal conversion function are collected to form a multilayer structure including at least an input layer and an output layer, and the internal connection strength provided at the connection between the output of the previous layer unit and the input of the next layer unit is The number of units corresponding to the startup schedule parameter and the operating condition is set in the input layer, the number of units corresponding to the startup schedule parameter is arranged in the output layer, and the signal input to the input layer is set. Is set in advance by the learning management means, converted according to the internal connection strength, and output from the output layer. 13. A thermal power plant autonomous adaptive control system according to claim 2, wherein the learning management means is obtained from the schedule correction amount calculation means each time the thermal power plant is repeatedly started. A cumulative correction amount calculation means for cumulatively calculating a new correction amount, a cumulative correction amount storage means for storing the cumulative correction amount obtained by the cumulative correction amount calculation means, and an actual start in the past. A schedule storage means for storing a start schedule, an operation condition storage means for storing an operation condition at the time of actual start in the past, and a past start schedule from the schedule storage means and the operation condition storage means, respectively. The operating condition is taken out, and is input to the input part of the neural network in the adaptive knowledge acquisition means. A learning unit for determining the internal connection strength of the neural network so that the output value obtained from the output unit when the input is applied matches the cumulative correction amount stored in the cumulative correction amount storage unit. An autonomous adaptive control system for a thermal power plant, which is characterized in that 14. The thermal power plant autonomous adaptive control system according to claim 13, wherein the operating conditions stored in said operating condition storage means are: the thermal power plant stop period, plant equipment temperature, pressure, and condenser. An autonomous adaptive control system for a thermal power plant, wherein at least one of cooling water temperature, atmospheric temperature, humidity, and plant operation restriction condition is satisfied. 15. The thermal power plant autonomous adaptive control system according to any one of claims 1 to 4, wherein the autonomy managing means sets a startup schedule X ₁ determined by a correction amount obtained from the schedule correction amount calculating means. First
As the startup schedule for the first startup schedule X ₀
And the value λX ₀ obtained by multiplying the
The activation schedule X ₁ to the said autonomous coefficient complement (1-λ) value obtained by multiplying the (1-λ) obtained by adding the X ₁ starts scheduled X _IN together with the operating conditions to the neural network When the startup schedule X ₂ obtained by adding the correction amount ΔX _OUT obtained from the output unit when input and the first startup schedule X ₀ is set as the second startup schedule, the first startup schedule X ₁ is set. The value obtained by multiplying the complement of the autonomous coefficient (1-λ) (1-
λ) X ₁ and the second activation schedule X ₂ and the value λX ₂ obtained by multiplying the autonomous coefficient λ are added, and the activation schedule X _R obtained is adopted as the actual activation schedule, and the schedule execution is performed. An autonomous adaptive control system for a thermal power plant characterized by transferring to a control means. 16. The thermal power plant autonomous adaptive control system according to any one of claims 1 to 4, wherein the autonomy managing means has an autonomous coefficient generating means for varying the autonomous coefficient λ, At the time of initial activation, λ = 0, and λ is increased within the range of 0 ≦ λ ≦ 1 as the number of activations is increased, corresponding to the number of activations i or the schedule modification amount obtained by the schedule modification amount calculation means. An autonomous adaptive control system for a thermal power plant, which is characterized in that 17. The autonomous adaptive control system for a thermal power plant according to any one of claims 3 and 4, wherein the operation limiting factors are at least the difference in expansion between the rotor of the steam turbine and the casing, and the drum of the exhaust heat recovery boiler. An autonomous adaptive control system for a thermal power plant, wherein the stress is generated in the etc. and SO or CO discharged from the exhaust heat recovery boiler. 18. Use of the autonomous adaptive control system for a thermal power plant according to any one of claims 3 and 4 as a start control system for a gasification combined cycle power plant, an atmospheric pressure and a pressurized fluidized bed boiler power plant. Characteristic thermal power plant autonomous autonomous control system. 19. A guidance for creating or modifying a startup schedule in a simulator for plant operation training, wherein the series of means for creating a startup schedule in the thermal power plant autonomous adaptive control system according to any one of claims 1 to 4 is presented. A thermal power plant autonomous adaptive control system characterized by being used as means. 20. A thermal power plant autonomous adaptive control system comprising: the thermal power plant autonomous adaptive control system according to claim 1, further comprising means for displaying the actual start-up schedule.