Parameter optimization method and device of thermal power generating unit simulation system and terminal equipment

Abstract

The invention relates to a parameter optimization method and device for a thermal power generating unit simulation system and terminal equipment, and the method comprises the steps: taking the boiler combustion efficiency, the steam turbine rotation speed and the generator load in the thermal power generating unit simulation system as to-be-optimized parameters, and constructing a coupling relation function, the coupling relation function is used for indicating a preset coupling relation among the boiler combustion efficiency, the turbine rotating speed and the generator load; based on the to-be-optimized parameters and the coupling relation function, an efficiency optimization model of the thermal power generating unit simulation system is constructed, and the efficiency optimization model comprises an efficiency output function; and solving the efficiency optimization model by adopting a particle swarm optimization algorithm, and obtaining target parameters of the thermal power generating unit simulation system by taking output value minimization of an efficiency output function as a target, the target parameters including target boiler combustion efficiency, target steam turbine rotating speed and target generator load. According to the invention, the problem of low simulation precision of a thermal power generating unit simulation system in the prior art is solved.

Description

Technical Field

[0001] The present application belongs to the technical field of thermal power generating units, and in particular, relates to a parameter optimization method, device and terminal equipment of a thermal power generating unit simulation system. Background Art

[0002] Thermal power generating units (also called thermal power units) are the main units that transmit electricity to the power grid. In actual operation, thermal power units will face various operating conditions and faults, such as load changes, short circuit faults, equipment aging, etc., which pose a potential threat to the safe operation of the power system. In order to ensure the stability, reliability and efficiency of the power system, it is necessary to deeply study the operating characteristics of thermal power units, analyze the impact of various faults and the effects of corresponding protection measures. Therefore, a simulation system (also called a simulation model) of thermal power units can be constructed, and the operating characteristics of thermal power units can be analyzed based on the simulation system of thermal power units.

[0003] However, in the existing simulation system of thermal power units, each subsystem of the thermal power unit (for example, boiler subsystem, turbine subsystem and generator subsystem, etc.) is often modeled separately, without considering the complex nonlinear coupling relationship between the subsystems in the actual operation of the thermal power unit. Therefore, the existing simulation system of thermal power units has limited response and adaptability when facing complex working conditions (for example, sudden load changes, changes in fuel quality, etc.), which will lead to reduced accuracy of the simulation system, and further affect the operating efficiency and anti-interference ability of the thermal power unit.

[0004] Currently, no effective solution has been proposed for the problem of low simulation accuracy of thermal power unit simulation systems in related technologies. Summary of the invention

[0005] The embodiments of the present application provide a method, an apparatus and a terminal device for optimizing parameters of a thermal power unit simulation system, so as to at least solve the problem of low simulation accuracy of the thermal power unit simulation system in the related art.

[0006] In a first aspect, an embodiment of the present application provides a parameter optimization method for a thermal power unit simulation system, comprising: taking the boiler combustion efficiency, turbine speed and generator load in the thermal power unit simulation system as parameters to be optimized, constructing a coupling relationship function, wherein the coupling relationship function is used to indicate a preset coupling relationship among the boiler combustion efficiency, the turbine speed and the generator load; constructing an efficiency optimization model of the thermal power unit simulation system based on the parameters to be optimized and the coupling relationship function, wherein the efficiency optimization model includes an efficiency output function; solving the efficiency optimization model using a particle swarm optimization algorithm, with the goal of minimizing the output value of the efficiency output function, to obtain target parameters of the thermal power unit simulation system, wherein the target parameters include a target boiler combustion efficiency, a target turbine speed and a target generator load.

[0007] In some embodiments, the mathematical expression of the efficiency output function is: Efficiency=-(ω1×B_efficiency+ω2×T_speed+ω3×G_load-C_effect); Among them, Efficiency is the total system efficiency of the thermal power unit simulation system, B_efficiency is the boiler combustion efficiency, T_speed is the turbine speed, G_load is the generator load, C_effect) is the output value of the coupling relationship function, ω1 is the preset first weight, ω2 is the preset second weight, and ω3 is the preset third weight.

[0008] In some embodiments, the mathematical expression of the coupling relationship function is: C_effect=sin(B_efficiency)×cos(T_speed)+exp(-G_load).

[0009] In some embodiments, the efficiency optimization model is solved by a particle swarm optimization algorithm, with the goal of minimizing the output value of the efficiency output function, and the target parameters of the thermal power unit simulation system are obtained, including: initializing a particle swarm; assigning the boiler combustion efficiency, the turbine speed and the generator load as the position of each particle in the particle swarm; calculating the fitness value of each particle based on the efficiency output function in the efficiency optimization model; based on the fitness value of each particle, finding the individual optimal position, individual optimal fitness value, group optimal position and group optimal fitness value of the particle; iteratively updating the speed and position of the particle based on the individual optimal position, individual optimal fitness value, group optimal position and group optimal fitness value of the particle; when the iterative update satisfies a preset end condition, outputting the optimal solution and the target position corresponding to the optimal solution, and using the target position corresponding to the optimal solution as the target parameter of the thermal power unit simulation system.

[0010] In some embodiments, when the iterative update satisfies a preset termination condition, outputting an optimal solution and a target position corresponding to the optimal solution includes: when the iterative update satisfies a preset termination condition, outputting the group optimal fitness value of the particles in the current iterative update as the optimal solution, and outputting the group optimal position corresponding to the group optimal fitness value as the target position.

[0011] In some embodiments, the thermal power unit simulation system includes a boiler simulation subsystem, a turbine simulation subsystem and a generator simulation subsystem; after the efficiency optimization model is solved by a particle swarm optimization algorithm, with the output value of the efficiency output function as the goal to obtain the target parameters of the thermal power unit simulation system, the method also includes: based on the target boiler combustion efficiency in the target parameters, adjusting the first control parameter in the boiler simulation subsystem so that the boiler simulation subsystem outputs the target boiler combustion efficiency; based on the target turbine speed in the target parameters, adjusting the second control parameter in the turbine simulation subsystem so that the turbine simulation subsystem outputs the target turbine speed; based on the target generator load in the target parameters, adjusting the third control parameter in the generator simulation subsystem so that the generator simulation subsystem outputs the target generator load.

[0012] In some embodiments, before taking the boiler combustion efficiency, turbine speed and generator load in the thermal power unit simulation system as parameters to be optimized and constructing the coupling relationship function, the method also includes: obtaining historical operating data of the boiler subsystem, turbine subsystem and generator subsystem in the thermal power unit system; based on the historical operating data, simulating and modeling the thermal power unit system to obtain the thermal power unit simulation system.

[0013] In the second aspect, an embodiment of the present application provides a parameter optimization device for a thermal power unit simulation system, comprising: a first construction module, used to take the boiler combustion efficiency, turbine speed and generator load in the thermal power unit simulation system as parameters to be optimized, and construct a coupling relationship function, wherein the coupling relationship function is used to indicate a preset coupling relationship between the boiler combustion efficiency, the turbine speed and the generator load; a second construction module, used to construct an efficiency optimization model of the thermal power unit simulation system based on the parameters to be optimized and the coupling relationship function, wherein the efficiency optimization model includes an efficiency output function; a solution module, used to solve the efficiency optimization model using a particle swarm optimization algorithm, with the goal of minimizing the output value of the efficiency output function, to obtain target parameters of the thermal power unit simulation system, wherein the target parameters include target boiler combustion efficiency, target turbine speed and target generator load.

[0014] In a third aspect, an embodiment of the present application provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the parameter optimization method of a thermal power unit simulation system according to any one of the first aspects described above is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is run, the parameter optimization method of the thermal power unit simulation system described in any one of the first aspects above is executed.

[0016] Compared with the related art, the parameter optimization method, device and terminal equipment of the thermal power unit simulation system provided in the embodiment of the present application introduce the nonlinear coupling relationship between the boiler combustion efficiency, turbine speed and generator load in the thermal power unit simulation system into the thermal power unit simulation system by constructing a coupling relationship function, and construct an efficiency optimization model of the thermal power unit simulation system based on the coupling relationship function, and finally use the particle swarm optimization algorithm to solve the efficiency optimization model to obtain the target parameters of the thermal power unit simulation system; in this way, the nonlinear coupling relationship between the subsystems (for example, boiler subsystem, turbine subsystem and generator subsystem, etc.) in the thermal power unit in actual operation is considered through the coupling relationship function, which can effectively improve the similarity between the operating characteristics of the thermal power unit simulation system after the optimized parameters and the operating characteristics of the thermal power unit in actual operation, thereby improving the simulation accuracy of the thermal power unit simulation system; in addition, by solving the efficiency optimization model through the particle swarm optimization (PSO) algorithm, the overall optimal state of the thermal power unit simulation system can be ensured through global search while taking into account the above-mentioned nonlinear coupling relationship, which can further improve the simulation accuracy of the thermal power unit simulation system, thereby improving the operating efficiency of the thermal power unit. Through the present application, the problem of low simulation accuracy of the thermal power unit simulation system in the related art is solved, and the technical effect of improving the simulation accuracy of the thermal power unit simulation system is achieved.

[0017] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a structural schematic diagram of a thermal power unit simulation system according to an embodiment of the present application; Figure 2 is a flow chart of a parameter optimization method for a thermal power unit simulation system according to an embodiment of the present application; Figure 3 is a structural schematic diagram of a parameter optimization device for a thermal power unit simulation system according to an embodiment of the present application; Figure 4 It is a structural diagram of a terminal device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0021] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0022] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0024] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0025] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0026] Thermal power generating units (also called thermal power units) are the main units that transmit electricity to the power grid. In actual operation, thermal power units will face various operating conditions and faults, such as load changes, short circuit faults, equipment aging, etc., which pose a potential threat to the safe operation of the power system. In order to ensure the stability, reliability and efficiency of the power system, it is necessary to deeply study the operating characteristics of thermal power units, analyze the impact of various faults and the effects of corresponding protection measures. Therefore, a simulation system (also called a simulation model) of thermal power units can be constructed, and the operating characteristics of thermal power units can be analyzed based on the simulation system of thermal power units.

[0027] However, in the existing simulation system of thermal power units, each subsystem of the thermal power unit (for example, boiler subsystem, turbine subsystem and generator subsystem, etc.) is often modeled separately, without considering the complex nonlinear coupling relationship between the subsystems in the actual operation of the thermal power unit. Therefore, the existing simulation system of thermal power units has limited response and adaptability when facing complex working conditions (for example, sudden load changes, changes in fuel quality, etc.), which will lead to reduced accuracy of the simulation system, and further affect the operating efficiency and anti-interference ability of the thermal power unit.

[0028] Currently, no effective solution has been proposed for the problem of low simulation accuracy of thermal power unit simulation systems in related technologies.

[0029] In view of this, an embodiment of the present application provides a parameter optimization method for a thermal power unit simulation system, by constructing a coupling relationship function, the nonlinear coupling relationship between the boiler combustion efficiency, the turbine speed and the generator load in the thermal power unit simulation system is introduced into the thermal power unit simulation system, and an efficiency optimization model of the thermal power unit simulation system is constructed based on the coupling relationship function, and finally the efficiency optimization model is solved by a particle swarm optimization algorithm to obtain the target parameters of the thermal power unit simulation system; in this way, by considering the nonlinear coupling relationship between the subsystems (for example, the boiler subsystem, the turbine subsystem and the generator subsystem, etc.) in the thermal power unit in actual operation through the coupling relationship function, the similarity between the operating characteristics of the thermal power unit simulation system after the optimized parameters and the operating characteristics of the thermal power unit in actual operation can be effectively improved, thereby improving the simulation accuracy of the thermal power unit simulation system; in addition, by solving the efficiency optimization model through the PSO algorithm, the overall optimal state of the thermal power unit simulation system can be ensured through global search while taking into account the above-mentioned nonlinear coupling relationship, which can further improve the simulation accuracy of the thermal power unit simulation system, thereby improving the operating efficiency of the thermal power unit. Through the present application, the problem of low simulation accuracy of the thermal power unit simulation system in the related art is solved, and the technical effect of improving the simulation accuracy of the thermal power unit simulation system is achieved.

[0030] The following describes an exemplary structure of a thermal power plant simulation system provided in an embodiment of the present application. Figure 1 , Figure 1 is a structural diagram of a thermal power unit simulation system according to an embodiment of the present application, such as Figure 1 As shown, the thermal power unit simulation system includes a parameter optimization subsystem, a boiler simulation subsystem, a turbine simulation subsystem and a generator simulation subsystem.

[0031] Among them, the boiler simulation subsystem, the steam turbine simulation subsystem and the generator simulation subsystem can be simulated and modeled based on the historical operation data of the thermal power unit and the parameters of the boiler subsystem, the steam turbine subsystem and the generator subsystem of the thermal power unit. For example, a multi-physics simulation module can be integrated in Simulink (a visual simulation tool), and the thermodynamic characteristics of the boiler, the mechanical dynamic characteristics of the steam turbine and the electromagnetic characteristics of the generator can be jointly modeled through a joint simulation method, so as to more accurately describe the operating characteristics of the thermal power unit and improve the simulation accuracy of the thermal power unit simulation system.

[0032] In addition, the neural network toolbox in Simulink can be used to simulate and model the boiler subsystem, turbine subsystem and generator subsystem of the thermal power unit. The operating data of the boiler subsystem, turbine subsystem and generator subsystem can be collected in real time. Through the self-learning ability of the neural network, the control parameters in each simulation subsystem can be continuously optimized to adapt to different load fluctuations, fuel changes, environmental changes and other factors.

[0033] The parameter optimization subsystem is configured to obtain the target parameters of the thermal power unit simulation system based on the parameter optimization method of the thermal power unit simulation system provided in the embodiment of the present application, and based on the target parameters, modify the control parameters in the boiler simulation subsystem, the turbine simulation subsystem and the generator simulation subsystem.

[0034] Specifically, the target parameters include target boiler combustion efficiency, target turbine speed and target generator load; the parameter optimization subsystem is also configured to adjust the first control parameter in the boiler simulation subsystem based on the target boiler combustion efficiency in the target parameters, so that the boiler simulation subsystem outputs the target boiler combustion efficiency; based on the target turbine speed in the target parameters, adjust the second control parameter in the turbine simulation subsystem so that the turbine simulation subsystem outputs the target turbine speed; based on the target generator load in the target parameters, adjust the third control parameter in the generator simulation subsystem so that the generator simulation subsystem outputs the target generator load.

[0035] It should be noted that the first control parameter, the second control parameter and the third control parameter are set based on the application scenario of the thermal power unit simulation system and user needs. For example, the first control parameter may include parameters such as boiler temperature and boiler pressure of the boiler simulation subsystem; the second control parameter includes parameters such as power output, thermal efficiency and steam flow of the turbine simulation subsystem; and the third control parameter includes parameters such as speed and power of the generator simulation subsystem.

[0036] In some embodiments, the thermal power unit simulation system provided in the embodiments of the present application can be implemented in a software manner, for example, it can be installed on Figure 4 In the memory 41 of the terminal device 4 shown, the parameter optimization subsystem can be software in the form of programs and plug-ins, including: a first building module 30, a second building module 31 and a solution module 32. These modules are logical, and therefore can be arbitrarily combined or further split according to the functions implemented.

[0037] The functions of each module will be described below.

[0038] The parameter optimization method of the thermal power unit simulation system provided in the embodiment of the present application will be explained in combination with the exemplary structure of the thermal power unit simulation system provided in the embodiment of the present application.

[0039] The following will be combined Figure 2 The parameter optimization method of the thermal power unit simulation system provided by one embodiment of the present application is described in detail. Figure 2 , Figure 2 is a flow chart of a parameter optimization method for a thermal power unit simulation system according to an embodiment of the present application. Figure 2 As shown, the method includes: Step S201, taking the boiler combustion efficiency, turbine speed and generator load in the thermal power unit simulation system as parameters to be optimized, constructing a coupling relationship function, wherein the coupling relationship function is used to indicate a preset coupling relationship among the boiler combustion efficiency, turbine speed and generator load.

[0040] In the actual operation of thermal power units, there is a multi-physics coupling between the thermodynamic characteristics of the boiler, the mechanical dynamic characteristics of the steam turbine, and the electromagnetic characteristics of the generator. For example, the combustion process inside the boiler generates heat, and the water is heated by the boiler to form steam, and the steam temperature, steam pressure, and steam flow are the key parameters that affect the performance of the steam turbine; the steam turbine drives the rotor of the generator through mechanical power, and then generates electricity through the principle of electromagnetic induction. Therefore, the speed and power output of the steam turbine directly affect the output voltage, frequency, and power of the generator.

[0041] In related technologies, simulation modeling of thermal power units often adopts the form of separate modeling, in which each subsystem (for example, boiler subsystem, turbine subsystem, and generator subsystem, etc.) is modeled separately, ignoring the mutual influence between the subsystems and not considering the nonlinear coupling relationship between the subsystems, which will reduce the simulation accuracy of the thermal power unit simulation system.

[0042] In this embodiment, a nonlinear coupling relationship among boiler combustion efficiency, steam turbine speed and generator load can be introduced into the thermal power unit simulation system by constructing a coupling relationship function. The nonlinear coupling relationship can be organized based on historical operating data of the boiler subsystem, steam turbine subsystem and generator subsystem in the thermal power unit.

[0043] In one embodiment, the mathematical expression of the coupling relationship function may be: C_effect=sin(B_efficiency)×cos(T_speed)+exp(-G_load); Among them, B_efficiency is the boiler combustion efficiency, T_speed is the turbine speed, G_load is the generator load, and C_effect) is the output value of the coupling relationship function.

[0044] In this embodiment, the coupling relationship function can simulate the complex interaction between boiler combustion efficiency, turbine speed and generator load. Since there are certain periodic or nonlinear fluctuations between boiler combustion efficiency and thermal load, and the turbine speed may affect the operation of the entire thermal power unit in a periodic manner, the change of boiler combustion efficiency will affect the steam quality and steam quality, and the change of turbine speed will affect the use efficiency of steam. Therefore, the nonlinear dynamic relationship between the boiler and the turbine can be simulated by the sine function sin(B_efficiency) and the cosine function cos(T_speed). In addition, the generator load will affect the power output of the thermal power unit. Excessive load may reduce the total system efficiency of the thermal power unit and increase the burden on the generator and boiler. The increase in load will increase the load of the boiler, thereby affecting steam generation and the power output of the turbine. Therefore, the natural exponential function exp(-G_load) can be used to represent the inhibitory effect of the generator load (that is, a higher generator load will reduce the total system efficiency of the thermal power unit).

[0045] In one embodiment, before taking the boiler combustion efficiency, turbine speed and generator load in the thermal power unit simulation system as parameters to be optimized and constructing the coupling relationship function in the above step S201, the method further includes the following steps: Step 1: Obtain historical operating data of the boiler subsystem, the steam turbine subsystem, and the generator subsystem in the thermal power unit system.

[0046] Step 2: Based on the historical operation data, the thermal power unit system is simulated and modeled to obtain a thermal power unit simulation system.

[0047] In this embodiment, the thermal power unit simulation system may include a boiler simulation subsystem, a steam turbine simulation subsystem, and a generator simulation subsystem. Simulation modeling can be performed based on the historical operation data of the thermal power unit and various parameters of the boiler subsystem, steam turbine subsystem, and generator subsystem of the thermal power unit to obtain the boiler simulation subsystem, steam turbine simulation subsystem, and generator simulation subsystem, thereby obtaining the thermal power unit simulation system. For example, a multi-physics field simulation module can be integrated in Simulink, and the thermodynamic characteristics of the boiler, the mechanical dynamic characteristics of the steam turbine, and the electromagnetic characteristics of the generator can be jointly modeled through a joint simulation method, so as to more accurately describe the operating characteristics of the thermal power unit and improve the simulation accuracy of the thermal power unit simulation system.

[0048] In addition, the neural network toolbox in Simulink can be used to simulate and model the boiler subsystem, turbine subsystem and generator subsystem of the thermal power unit. The operating data of the boiler subsystem, turbine subsystem and generator subsystem can be collected in real time. Through the self-learning ability of the neural network, the control parameters in each simulation subsystem can be continuously optimized to adapt to different load fluctuations, fuel changes, environmental changes and other factors.

[0049] Step S202: constructing an efficiency optimization model of the thermal power unit simulation system based on the parameters to be optimized and the coupling relationship function, wherein the efficiency optimization model includes an efficiency output function.

[0050] In this embodiment, the efficiency optimization model can also be called a nonlinear coupling model of a thermal power unit, which includes a coupling relationship function and an efficiency output function. The parameters of the efficiency optimization model include boiler combustion efficiency, turbine speed, and generator load, the coupling relationship function is used to simulate the nonlinear coupling relationship between them, and the efficiency output function can output a negative value, the total system efficiency of the thermal power unit simulation system.

[0051] In one embodiment, the mathematical expression of the efficiency output function is: Efficiency=-(ω1×B_efficiency+ω2×T_speed+ω3×G_load-C_effect); Wherein, Efficiency is the total system efficiency of the thermal power unit simulation system, ω1 is the preset first weight, ω2 is the preset second weight, and ω3 is the preset third weight.

[0052] In this embodiment, the contribution of the boiler subsystem, the steam turbine subsystem and the generator subsystem in the thermal power unit to the total system efficiency can be determined based on the historical operation data of the thermal power unit, and the contribution can be quantified using ω1, ω2 and ω3. For example, ω1 can be set to 0.6, ω2 can be set to 0.3, and ω3 can be set to 0.1, which means that the contribution of the boiler combustion efficiency of the boiler subsystem to the total system efficiency is much greater than that of the steam turbine speed and the generator load.

[0053] In addition, the negative output value of the efficiency output function can transform the maximum value problem into a minimum value problem, thereby facilitating the subsequent solution of the PSO algorithm (the default goal of the PSO algorithm is to find the minimum value).

[0054] In this embodiment, the nonlinear coupling relationship between the subsystems (for example, boiler subsystem, turbine subsystem and generator subsystem, etc.) in the actual operation of the thermal power unit can be considered through the coupling relationship function in the efficiency optimization model, which can effectively improve the similarity between the operating characteristics of the thermal power unit simulation system after the optimization parameters and the operating characteristics of the thermal power unit in actual operation; by constructing the efficiency output function in the efficiency optimization model, the optimal solution can be obtained when the efficiency output function is subsequently solved, and the overall optimal state of the thermal power unit simulation system (the highest total system efficiency) is ensured while taking into account the above-mentioned nonlinear coupling relationship, which can further improve the simulation accuracy of the thermal power unit simulation system, thereby improving the operating efficiency of the thermal power unit.

[0055] Step S203, the efficiency optimization model is solved by using a particle swarm optimization algorithm, with the goal of minimizing the output value of the efficiency output function, to obtain the target parameters of the thermal power unit simulation system, wherein the target parameters include the target boiler combustion efficiency, the target turbine speed, and the target generator load.

[0056] In this embodiment, the efficiency optimization model in step S203 is solved by using a particle swarm optimization algorithm, with the goal of minimizing the output value of the efficiency output function, and obtaining the target parameters of the thermal power unit simulation system includes the following steps: Step 1: Initialize the particle swarm.

[0057] In this embodiment, the size, dimension, individual cognitive factor, group collaboration factor, particle position and speed, number of iterations, etc. of the particle swarm can be initialized. For example, the value range of the particle position can be set to 0 to 1, and the particle speed can be set to a smaller value (within 0.1), indicating the initial movement trend of the particle.

[0058] Among them, the size of the particle swarm can be set to 30, that is, the number of particles contained in the particle swarm is 30; the number of iterations can be set to 100; the parameter space of the particle swarm is three-dimensional, corresponding to the boiler combustion efficiency, turbine speed and generator load respectively.

[0059] In addition, the upper and lower bounds of the particle position can be defined to limit the particle search space. When the particle position is subsequently iteratively updated, the particle position is prevented from exceeding the upper and lower bounds to ensure that the particle moves within a reasonable range, thereby ensuring that the boiler combustion efficiency, turbine speed, and generator load represented by the particle position are also within a reasonable range.

[0060] Step 2: Assign the boiler combustion efficiency, turbine speed and generator load as the position of each particle in the particle swarm.

[0061] In this embodiment, the parameter space of the particle swarm is three-dimensional, so the position of the particle also has three dimensions, that is, the position of the particle in the parameter space can represent the three variables to be optimized, namely, boiler combustion efficiency, turbine speed and generator load.

[0062] Step 3, based on the efficiency output function in the efficiency optimization model, the fitness value of each particle is calculated; based on the fitness value of each particle, the individual optimal position, individual optimal fitness value, group optimal position and group optimal fitness value of the particle are found.

[0063] In this embodiment, the boiler combustion efficiency, turbine speed and generator load input efficiency represented by the particle position can be used as the efficiency output function in the optimization model, and the total system efficiency output by the efficiency output function can be used as the fitness value of the particle. The goal of the particle swarm optimization algorithm is to minimize the output value of the efficiency output function (because the total system efficiency output by the efficiency output function is a negative value, the smaller the output value, the greater the total system efficiency).

[0064] Specifically, the individual optimal position and individual optimal fitness value of each particle in the particle group can be recorded, and the individual optimal fitness value corresponding to the particle with the smallest fitness value among all particles can be recorded, and the individual optimal fitness value corresponding to the particle with the smallest fitness value can be used as the group optimal fitness value, and the position of the particle corresponding to the group optimal fitness value can be used as the group optimal position.

[0065] Step 4, based on the individual optimal position of the particle, the individual optimal fitness value, the group optimal position and the group optimal fitness value, the particle speed and position are iteratively updated.

[0066] In this embodiment, the velocity of the particle can be iteratively updated based on the following mathematical expression: inertia=0.5; cognitive=2.0×rand(1,dim).×(personalBestPositions(i,:)-particles(i,:)); social=2.0×rand(1,dim).×(globalBestPosition-particles(i,:)); V'(i,:)=inertia×V(i,:)+cognitive+social; Among them, inertia is the inertia weight, which is used to control the historical movement trend of the particle and can be set to 0.5; cognitive is the individual cognitive factor, rand() is the random function, dim is the dimension of the parameter space, which is set to 3, personalBestPositions(i,:) is the individual optimal position of the i-th particle, particles(i,:) is the current position of the i-th particle; social is the group collaboration factor, globalBestPosition is the group optimal position; V'(i,:) is the updated velocity of the i-th particle; V(i,:) is the current velocity of the i-th particle.

[0067] Additionally, the particle position can be iteratively updated based on the following mathematical expression: Particles'(i,:)=particles(i,:)+V'(i,:); Among them, Particles'(i,:) is the updated position of the i-th particle.

[0068] In this embodiment, each iterative update can find the individual optimal position of the particle, the individual optimal fitness value, the group optimal position and the group optimal fitness value until the preset end condition is reached.

[0069] Step 5: When the iterative update satisfies the preset end condition, the optimal solution and the target position corresponding to the optimal solution are output, and the target position corresponding to the optimal solution is used as the target parameter of the thermal power unit simulation system.

[0070] In this embodiment, in the above step 5, when the iterative update satisfies the preset termination condition, outputting the optimal solution and the target position corresponding to the optimal solution includes: when the iterative update satisfies the preset termination condition, outputting the group optimal fitness value of the particles in the current iterative update as the optimal solution, and outputting the group optimal position corresponding to the group optimal fitness value as the target position.

[0071] In this embodiment, the group optimal position includes the target boiler combustion efficiency, the target turbine speed and the target generator load. The group optimal position is the optimal solution found after multiple iterative updates; the group optimal fitness value is the optimal system efficiency (absolute value) of the thermal power unit simulation system, which can represent the final performance of the thermal power unit simulation system.

[0072] In addition, the preset termination condition may include: the number of iterative updates has reached the number of iterations (eg, 100), or the optimal fitness value of the group has converged.

[0073] Through the above step S203, the efficiency optimization model is solved by the PSO algorithm. While taking into account the nonlinear coupling relationship between the boiler subsystem, the turbine subsystem and the generator subsystem of the thermal power unit, the overall optimal state of the thermal power unit simulation system (the highest total system efficiency) can be ensured through global search, so as to ensure that each subsystem can be coordinated and optimized under dynamically changing working conditions, which can further improve the simulation accuracy of the thermal power unit simulation system, thereby improving the operating efficiency of the thermal power unit. For example, during the boiler combustion process, the PSO algorithm can adjust the boiler combustion efficiency in real time according to the steam demand of the turbine; at the same time, according to the status of the turbine and the generator, the output and fuel utilization efficiency of the boiler are optimized. The PSO algorithm can avoid the local optimal problem that may occur in the traditional optimization method, and ensure the overall optimal state of the thermal power unit simulation system through global search.

[0074] In one embodiment, each iterative update can output the optimal fitness value of the swarm in the current iterative update to help the user track the optimization progress of the particle swarm optimization algorithm.

[0075] In one embodiment, the thermal power unit simulation system includes a boiler simulation subsystem, a turbine simulation subsystem and a generator simulation subsystem; after the efficiency optimization model is solved by a particle swarm optimization algorithm, and the output value of the efficiency output function is minimized to obtain the target parameters of the thermal power unit simulation system, the method further includes the following steps: Step 1: Based on the target boiler combustion efficiency in the target parameters, adjust the first control parameter in the boiler simulation subsystem so that the boiler simulation subsystem outputs the target boiler combustion efficiency.

[0076] Step 2: Based on the target turbine speed in the target parameter, adjust the second control parameter in the turbine simulation subsystem so that the turbine simulation subsystem outputs the target turbine speed.

[0077] Step 3: Based on the target generator load in the target parameter, adjust the third control parameter in the generator simulation subsystem so that the generator simulation subsystem outputs the target generator load.

[0078] In this embodiment, after the optimal solution (i.e., target parameter) of the efficiency optimization model is obtained by the PSO algorithm, the various control parameters of the thermal power unit simulation system can be adjusted based on the target parameter. While taking into account the nonlinear coupling relationship between the boiler subsystem, turbine subsystem and generator subsystem of the thermal power unit, the overall optimal state of the thermal power unit simulation system (the highest total system efficiency) is ensured through global search.

[0079] Through the above steps S201 to S203, by constructing an efficiency optimization model, a parameter optimization process based on a nonlinear coupling relationship (for example, the mutual influence between the combustion efficiency of the boiler and the steam demand of the turbine, and between the turbine and the generator are all nonlinear relationships, and these relationships will change with factors such as load fluctuations, fuel properties, and equipment aging) is introduced into the simulation system of the thermal power unit. While taking into account the nonlinear coupling relationship between the subsystems of the thermal power unit, the overall optimal state of the simulation system of the thermal power unit can be ensured through global search to ensure that each subsystem can be coordinated and optimized under dynamically changing working conditions. For example, when the load fluctuates, complex nonlinear coupling will occur between multiple parameters such as the boiler combustion efficiency, the turbine speed, and the generator load. The above parameter optimization process based on nonlinear coupling relationships jointly adjusts these parameters to be optimized through the PSO algorithm to ensure that each subsystem can operate in a coordinated manner, which enables the simulation system of the thermal power unit to respond to complex and time-varying load fluctuations in a timely manner, ensuring the high accuracy and reliability of the simulation results, thereby improving the simulation accuracy of the simulation system of the thermal power unit, and further improving the operating efficiency of the thermal power unit.

[0080] In addition, the parameter optimization method of the thermal power unit simulation system provided in the embodiment of the present application can not only optimize the various parameters to be optimized according to the current working conditions, but also use the PSO algorithm to predict possible changes in the future and make adjustments in advance. During the operation of the thermal power unit simulation system, the PSO algorithm can continuously adjust the control strategy according to the historical operation data of the thermal power unit to optimize the coordination between the subsystems. For example, when the possibility of load fluctuation or equipment failure is predicted, the PSO algorithm can adjust the boiler combustion efficiency and the turbine speed in advance to ensure that the thermal power unit simulation system can respond quickly when the above-mentioned load fluctuation or equipment failure occurs, thereby maintaining a higher overall system efficiency. Based on such a thermal power unit simulation system, if load fluctuations or equipment failures occur in the actual operation of the thermal power unit, the real parameters of the thermal power unit can be adjusted in combination with the response of the thermal power unit simulation system to these situations, so as to respond in time, reduce the downtime of the thermal power unit, and ensure that the power generation efficiency of the thermal power unit is not affected. This response speed is significantly higher than the traditional method, thereby improving the stability and safety of the thermal power unit.

[0081] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0082] Corresponding to the parameter optimization method of the thermal power unit simulation system described in the above embodiment, Figure 3 A structural schematic diagram of a parameter optimization device for a thermal power unit simulation system according to an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0083] See also Figure 3 The parameter optimization device 3 of the thermal power unit simulation system includes: a first construction module 30, which is used to take the boiler combustion efficiency, turbine speed and generator load in the thermal power unit simulation system as parameters to be optimized, and construct a coupling relationship function, wherein the coupling relationship function is used to indicate the preset coupling relationship between the boiler combustion efficiency, turbine speed and generator load; a second construction module 31, which is used to construct an efficiency optimization model of the thermal power unit simulation system based on the parameters to be optimized and the coupling relationship function, wherein the efficiency optimization model includes an efficiency output function; a solution module 32, which is used to solve the efficiency optimization model using a particle swarm optimization algorithm, with the goal of minimizing the output value of the efficiency output function, to obtain the target parameters of the thermal power unit simulation system, wherein the target parameters include the target boiler combustion efficiency, the target turbine speed and the target generator load.

[0084] In one embodiment, the mathematical expression of the efficiency output function is: Efficiency=-(ω1×B_efficiency+ω2×T_speed+ω3×G_load-C_effect); Among them, Efficiency is the total system efficiency of the thermal power unit simulation system, B_efficiency is the boiler combustion efficiency, T_speed is the turbine speed, G_load is the generator load, C_effect) is the output value of the coupling relationship function, ω1 is the preset first weight, ω2 is the preset second weight, and ω3 is the preset third weight.

[0085] In one embodiment, the mathematical expression of the coupling relationship function is: C_effect=sin(B_efficiency)*cos(T_speed)+exp(-G_load).

[0086] In one embodiment, the solution module 32 is also used to initialize the particle swarm; assign the boiler combustion efficiency, turbine speed and generator load as the position of each particle in the particle swarm; calculate the fitness value of each particle based on the efficiency output function in the efficiency optimization model; based on the fitness value of each particle, find the individual optimal position, individual optimal fitness value, group optimal position and group optimal fitness value of the particle; based on the individual optimal position, individual optimal fitness value, group optimal position and group optimal fitness value of the particle, iteratively update the speed and position of the particle; when the iterative update meets the preset end condition, output the optimal solution and the target position corresponding to the optimal solution, and use the target position corresponding to the optimal solution as the target parameter of the thermal power unit simulation system.

[0087] In one embodiment, the solution module 32 is further configured to output the optimal fitness value of the group of particles in the current iterative update as the optimal solution when the iterative update satisfies a preset termination condition, and output the optimal position of the group corresponding to the optimal fitness value of the group as the target position.

[0088] In one embodiment, the thermal power unit simulation system includes a boiler simulation subsystem, a turbine simulation subsystem and a generator simulation subsystem; the parameter optimization device 3 of the thermal power unit simulation system also includes an adjustment module, which is used to adjust the first control parameter in the boiler simulation subsystem based on the target boiler combustion efficiency in the target parameter so that the boiler simulation subsystem outputs the target boiler combustion efficiency; based on the target turbine speed in the target parameter, the second control parameter in the turbine simulation subsystem is adjusted so that the turbine simulation subsystem outputs the target turbine speed; based on the target generator load in the target parameter, the third control parameter in the generator simulation subsystem is adjusted so that the generator simulation subsystem outputs the target generator load.

[0089] In one embodiment, the parameter optimization device 3 of the thermal power unit simulation system also includes a modeling module for obtaining historical operating data of the boiler subsystem, the turbine subsystem and the generator subsystem in the thermal power unit system; based on the historical operating data, the thermal power unit system is simulated and modeled to obtain a thermal power unit simulation system.

[0090] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0091] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0092] Figure 4 is a schematic diagram of the structure of a terminal device according to an embodiment of the present application. Figure 4 As shown, the terminal device 4 includes: at least one processor 40 ( Figure 4 Only one is shown in the figure) a processor, a memory 41, and a computer program 42 stored in the memory 41 and executable on at least one processor 40. When the processor 40 executes the computer program 42, the steps in any of the above-mentioned embodiments of the parameter optimization method for the thermal power unit simulation system are implemented.

[0093] The terminal device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 4 may include but is not limited to a processor 40 and a memory 41. It is understood by those skilled in the art that Figure 4 It is only an example of the terminal device 4 and does not constitute a limitation on the terminal device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0094] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0095] In some embodiments, the memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or memory of the terminal device 4. In other embodiments, the memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 4. In other embodiments, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of a computer program 42. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0096] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the steps in the above-mentioned parameter optimization method embodiments of each thermal power unit simulation system.

[0097] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal implements the steps in the above-mentioned parameter optimization method embodiments for the thermal power unit simulation system.

[0098] The present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the terminal device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0099] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0100] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A parameter optimization method for a thermal power unit simulation system, characterized in that: include: The boiler combustion efficiency, the steam turbine speed and the generator load in the thermal power unit simulation system are taken as parameters to be optimized, and a coupling relationship function is constructed, wherein the coupling relationship function is used to indicate a preset coupling relationship between the boiler combustion efficiency, the steam turbine speed and the generator load; Based on the parameters to be optimized and the coupling relationship function, constructing an efficiency optimization model of the thermal power unit simulation system, wherein the efficiency optimization model includes an efficiency output function; The efficiency optimization model is solved by a particle swarm optimization algorithm, and the target parameters of the thermal power unit simulation system are obtained by minimizing the output value of the efficiency output function, wherein the target parameters include the target boiler combustion efficiency, the target turbine speed and the target generator load.

2. The method according to claim 1, characterized in that The mathematical expression of the efficiency output function is: Efficiency=-(ω1×B_efficiency+ω2×T_speed+ω3×G_load-C_effect); Among them, Efficiency is the total system efficiency of the thermal power unit simulation system, B_efficiency is the boiler combustion efficiency, T_speed is the turbine speed, G_load is the generator load, C_effect) is the output value of the coupling relationship function, ω1 is the preset first weight, ω2 is the preset second weight, and ω3 is the preset third weight.

3. The method according to claim 2, characterized in that The mathematical expression of the coupling relationship function is: C_effect=sin(B_efficiency)×cos(T_speed)+exp(-G_load).

4. The method according to any one of claims 1 to 3, characterized in that The efficiency optimization model is solved by using a particle swarm optimization algorithm, with the goal of minimizing the output value of the efficiency output function, and the target parameters of the thermal power unit simulation system are obtained, including: Initialize particle swarm; Assigning the boiler combustion efficiency, the turbine speed and the generator load to the position of each particle in the particle group; Calculating the fitness value of each particle based on the efficiency output function in the efficiency optimization model; Based on the fitness value of each particle, find the individual optimal position, individual optimal fitness value, group optimal position and group optimal fitness value of the particle; Iteratively updating the speed and position of the particle based on the individual optimal position of the particle, the individual optimal fitness value, the group optimal position and the group optimal fitness value; When the iterative update satisfies a preset termination condition, an optimal solution and a target position corresponding to the optimal solution are output, and the target position corresponding to the optimal solution is used as the target parameter of the thermal power unit simulation system.

5. The method according to claim 4, characterized in that When the iterative update satisfies a preset termination condition, outputting an optimal solution and a target position corresponding to the optimal solution includes: When the iterative update satisfies a preset termination condition, the group optimal fitness value of the particles in the current iterative update is output as the optimal solution, and the group optimal position corresponding to the group optimal fitness value is output as the target position.

6. The method according to any one of claims 1 to 3, characterized in that The thermal power unit simulation system includes a boiler simulation subsystem, a turbine simulation subsystem and a generator simulation subsystem; after the efficiency optimization model is solved by a particle swarm optimization algorithm, with the output value of the efficiency output function minimized as the goal, and the target parameters of the thermal power unit simulation system are obtained, the method further includes: Based on the target boiler combustion efficiency in the target parameter, adjusting a first control parameter in the boiler simulation subsystem so that the boiler simulation subsystem outputs the target boiler combustion efficiency; Based on the target steam turbine speed in the target parameter, adjusting a second control parameter in the steam turbine simulation subsystem so that the steam turbine simulation subsystem outputs the target steam turbine speed; Based on the target generator load in the target parameters, a third control parameter in the generator simulation subsystem is adjusted so that the generator simulation subsystem outputs the target generator load.

7. The method according to any one of claims 1 to 3, characterized in that Before taking the boiler combustion efficiency, turbine speed and generator load in the thermal power unit simulation system as parameters to be optimized and constructing the coupling relationship function, the method further includes: Obtain historical operating data of the boiler subsystem, steam turbine subsystem and generator subsystem in the thermal power unit system; Based on the historical operation data, simulation modeling is performed on the thermal power unit system to obtain the thermal power unit simulation system.

8. A parameter optimization device for a thermal power unit simulation system, characterized in that: include: The first construction module is used to take the boiler combustion efficiency, the turbine speed and the generator load in the thermal power unit simulation system as parameters to be optimized, and to construct a coupling relationship function, wherein the coupling relationship function is used to indicate a preset coupling relationship between the boiler combustion efficiency, the turbine speed and the generator load; A second construction module is used to construct an efficiency optimization model of the thermal power unit simulation system based on the parameters to be optimized and the coupling relationship function, wherein the efficiency optimization model includes an efficiency output function; A solution module is used to solve the efficiency optimization model using a particle swarm optimization algorithm, with the goal of minimizing the output value of the efficiency output function, to obtain target parameters of the thermal power unit simulation system, wherein the target parameters include a target boiler combustion efficiency, a target turbine speed, and a target generator load.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the parameter optimization method of the thermal power unit simulation system as described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that It comprises a computer program, and when the computer program is run, the parameter optimization method of the thermal power unit simulation system as claimed in any one of claims 1 to 7 is executed.

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