Main steam temperature system parameter identification method based on enhanced human evolutionary algorithm

Abstract

The invention provides a main steam temperature system parameter identification method based on an enhanced human evolutionary algorithm, and relates to the technical field of main steam temperature system parameter identification. Constructing a mathematical model of a main steam temperature system by utilizing the intermediate point temperature, the spraying flow of the first-stage desuperheater and the spraying flow of the second-stage desuperheater, and determining parameters to be identified; constructing an error objective function containing identification parameters according to the root mean square error; and an enhanced human evolutionary algorithm is adopted, the error objective function is optimized and solved based on the main steam temperature system variables and historical data, and an identification result is output. The enhanced algorithm model is faster in speed, higher in precision, and excellent in performance in the aspect of global search. The identification method provided by the invention can effectively solve the parameter identification problem, and the final model correctly reflects the dynamic change of the main steam temperature.

Description

Technical Field

[0001] The present invention relates to the technical field of main steam temperature system parameter identification, and in particular to a main steam temperature system parameter identification method based on an enhanced human evolution algorithm. Background Art

[0002] Ultra-supercritical units are a type of high-temperature and high-pressure thermal power generating units with higher thermal efficiency and lower emissions. In order to meet the deep peak-shaving needs of the power grid, ultra-supercritical units require a wider range of operating conditions. A single linear model can no longer meet the needs of optimal control design. It is necessary to design an appropriate mathematical model to describe the global characteristics of the system for designing controllers and subsequent optimization problems.

[0003] In the operation of ultra-supercritical units, the main steam temperature is an important parameter to ensure the safe and stable operation of the unit. The main steam temperature system is responsible for heating the steam generated by the boiler to a specified temperature in response to load changes to meet the requirements of the steam turbine. However, in actual production, the control of the main steam temperature system is very difficult, with characteristics such as large time constant, long time delay, nonlinearity, time variation, and susceptibility to interference from various factors. Therefore, it is crucial to accurately and reliably describe the dynamic characteristics of the main steam temperature system.

[0004] The purpose of parameter identification is to estimate the unknown parameters of the system model through experimental data in order to establish an accurate mathematical model. Parameter identification of the main steam temperature system of the ultra-supercritical unit can better understand and control its dynamic behavior, improve control accuracy and system stability. Commonly used parameter identification methods include least squares method and algorithm optimization. In the early days, the least squares method could effectively identify the main steam temperature system in a simple way, but in response to the current power grid's requirements for deep peak regulation of ultra-supercritical units, the least squares method has great limitations. Algorithm optimization can cope with complex nonlinear systems by simulating scenarios, but the existing algorithms have slow search speeds and poor accuracy, and cannot be applied to the main steam temperature system scenario. Therefore, in order to ensure the safe and stable operation of the main steam temperature system, a more effective solution should be found. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a main steam temperature system parameter identification method based on an enhanced human evolution algorithm in view of the deficiencies of the above-mentioned prior art, so as to solve the problem of low accuracy of the existing main steam temperature system model.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for identifying parameters of a main steam temperature system based on an enhanced human evolution algorithm comprises the following steps:

[0008] Step 1: Construct a mathematical model of the main steam temperature system and determine the parameters to be identified;

[0009] Step 2: Construct an error objective function containing identification parameters;

[0010] Step 3: Use the enhanced human evolution algorithm to optimize the error objective function based on the main steam temperature system variables and historical data, and output the identification results.

[0011] Furthermore, the step 1 specifically includes:

[0012] Step 1.1: Construct a mathematical model of the main steam temperature system:

[0013] H o =G i (s)H i +G p (s)F p +G s (s)F s

[0014] Among them, H o is the main steam temperature, H i is the midpoint temperature, F p is the spray flow rate of the first-stage desuperheater, F s is the spray flow rate of the secondary desuperheater, G i (s) is the transfer function of the midpoint temperature, G p (s) is the transfer function of the spray flow rate of the first-stage desuperheater, G s (s) is the transfer function of the secondary desuperheater spray flow rate;

[0015] Step 1.2: Determine the parameters to be identified. The transfer functions of the midpoint temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate are as follows:

[0016]

[0017] Among them, τ i , τ p , τ s represents the time delay of the midpoint temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate, K i , K p , K s Represent the midpoint temperature, the first desuperheater spray flow rate and the second desuperheater spray flow rate respectively, T i 、T p 、T s Represent the time constants of the midpoint temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate respectively; where K i 、T i, τ i , K p 、T p , τ p , K s 、T s , τ s is the parameter to be identified.

[0018] Furthermore, the step 2 specifically includes:

[0019] Calculate the fitness of all optimal values ​​and construct the error objective function:

[0020]

[0021] Among them, J is the fitness value, N is the total number of samples, y(k) is the actual main steam temperature value, is the main steam temperature value after parameter identification, and k represents the parameter to be identified.

[0022] Furthermore, the step 3 specifically includes:

[0023] Step 3.1: The error objective function is optimized and solved based on the historical data of the operation of the main steam temperature system in the ultra-supercritical unit by using the enhanced human evolution algorithm. The optimization result is the parameter identification result of the mathematical model, and the identification result of the main steam temperature system parameters is obtained;

[0024] Step 3.2: Output the enhanced human evolution algorithm parameter identification results, and compare and analyze the actual main steam temperature change curve and the output curve of the identification model to determine the accuracy of the identified model.

[0025] Furthermore, the step 3.1 specifically includes the following steps:

[0026] Step 3.1.1: Initialize the location, size and activity range of the human group. ub and lb are the upper and lower bounds of the human group's activity space, respectively. Randomly generate human groups of different sizes within the activity range.

[0027] Step 3.1.2: The exploration phase of the human group is defined as 1 / 4 of the maximum number of iterations, and all individuals adopt a unified search strategy:

[0028]

[0029] in, represents the position of subsequent updates; β is an adaptive function, Max iter is the maximum number of iterations; t represents the current number of iterations, Indicates the current position; X best Corresponding to the current best position; Levy is the distribution function, and Levy distribution is Where μ and v are normally distributed random variables with mean 0 and variance D. μ represents the random displacement of individual search, μ~N(0,D), v represents the random perturbation in the search, v~N(0,D), and σ controls the extent of the distribution. γ is the exponential parameter of the distribution, which determines the shape of the distribution, Γ is the generalized factorial gamma function as the control coefficient; dim represents the number of variables involved, represents the average position in the current population, floor means round down, rand is a random number in [0,1], f jump is the jump function,

[0030] Step 3.1.3: In the development stage of the human group, the human group is divided into different roles, including leaders, explorers, followers and losers. Each role has a different search strategy and works together to find the best solution.

[0031] Furthermore, in step 3.1.3, the leader is defined as the first 40% of individuals located in the optimal region:

[0032]

[0033] Among them, Rn represents a random number that obeys the normal distribution, the function ones(1,dim) generates the row vector dim elements, R is a random number in [0,1], and ω is the knowledge acquisition difficulty coefficient; A=0.6;

[0034] Explorers are defined as the healthy 40% to 80% of individuals:

[0035]

[0036] in, Indicates the position of the t-th iteration of the individual with the worst fitness in the population; i indicates the i-th iteration value;

[0037] Followers are defined as 80% to 90% of individuals who follow the most adaptable leader:

[0038]

[0039] in, represents the position of the most adaptable individual in the population at the tth iteration, and Rd represents a number in the range [1, dim];

[0040] Individuals that are not adapted to the population are losers and will be eliminated, defining the population replenishment mechanism:

[0041]

[0042] Furthermore, in step 3.1, the most adaptable individual in the population adopts a search enhancement strategy, which is as follows:

[0043] Adopt local search enhancement strategy to update the optimal solution:

[0044]

[0045] Among them, Gauss(0,σ) is a Gaussian distribution with mean 0 and standard deviation σ;

[0046] According to the error objective function formula, the fitness values ​​of all optimal solutions are calculated, and the objective function fitness value J and the global optimal individual are updated. If the fitness value J reaches the minimum value, the corresponding optimal individual To identify the results.

[0047] The beneficial effects of the above technical solution are: the main steam temperature system parameter identification method based on the enhanced human evolution algorithm provided by the present invention proposes a mathematical model containing the intermediate point temperature, the first-stage desuperheater spray flow rate and the second-stage desuperheater spray flow rate; constructs an error objective function; optimizes the fitness value through the enhanced human evolution algorithm, divides the human group into different roles, and determines the optimal individual as the parameter to be identified when the fitness value approaches the minimum value. The enhanced parameter identification speed is faster and more accurate, and it performs well in global search. The final model correctly reflects the dynamic changes of the main steam temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic flow chart of a main steam temperature system parameter identification method based on an enhanced human evolution algorithm provided in an embodiment of the present invention;

[0049] Figure 2 It is a schematic diagram of the structure of a three-input single-output steam heater in a main steam temperature system parameter identification method based on an enhanced human evolution algorithm provided in an embodiment of the present invention;

[0050] Figure 3 is a comparative evolution curve provided by an embodiment of the present invention comparing the enhanced human evolution algorithm proposed by the present invention and the simple human evolution algorithm;

[0051] Figure 4 It is a schematic diagram of the enhanced human evolution algorithm flow in the main steam temperature system parameter identification method based on the enhanced human evolution algorithm provided by an embodiment of the present invention;

[0052] Figure 5 The parameter identification method provided by the embodiment of the present invention correctly reflects the dynamic change curve of the main steam temperature of a 1000MW unit;

[0053] Figure 6 The parameter identification method provided by the embodiment of the present invention correctly reflects the dynamic change curve of the main steam temperature of a 660MW unit;

[0054] Figure 7 The parameter identification method provided by the embodiment of the present invention correctly reflects the dynamic change curve of the main steam temperature of a 300MW unit. DETAILED DESCRIPTION

[0055] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0056] like Figure 1 As shown, the method of this embodiment is described as follows.

[0057] Step 1: Construct a mathematical model of the main steam temperature system, such as Figure 2 As shown, the parameters to be identified are determined.

[0058] Step 1.1: Construct a mathematical model of the main steam temperature system:

[0059] H o =G i (s)H i +G p (s)F p +G s (s)F s

[0060] Among them, H o is the main steam temperature, H i is the midpoint temperature, F p is the spray flow rate of the first-stage desuperheater, F s is the spray flow rate of the secondary desuperheater, G i (s) is the transfer function of the midpoint temperature, G p (s) is the transfer function of the spray flow rate of the first-stage desuperheater, G s (s) is the transfer function of the secondary desuperheater spray flow rate.

[0061] Step 1.2: Determine the parameters to be identified. The transfer functions of the midpoint temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate are as follows:

[0062]

[0063] Among them, τ i , τ p , τ s represents the time delay of the midpoint temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate, K i, K p , K s Represent the midpoint temperature, the first desuperheater spray flow rate and the second desuperheater spray flow rate respectively, T i 、T p 、T s Represent the time constants of the midpoint temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate respectively; where K i 、T i , τ i , K p 、T p , τ p , K s 、T s , τ s is the parameter to be identified.

[0064] Step 2: Construct the error objective function containing the identification parameters.

[0065] Calculate the fitness of all optimal values ​​and construct the error objective function:

[0066]

[0067] Among them, J is the fitness value, N is the total number of samples, y(k) is the actual main steam temperature value, is the main steam temperature value after parameter identification, and k represents the parameter to be identified.

[0068] Step 3: If Figure 3 , Figure 4 As shown in the figure, the enhanced human evolution algorithm is used to optimize the error objective function based on the main steam temperature system variables and historical data, and the identification results are output, including:

[0069] Step 3.1: The enhanced human evolution algorithm is used to optimize the error objective function based on the historical data of the main steam temperature system operation in a certain ultra-supercritical unit. The optimization result is the parameter identification result of the mathematical model, and the identification result of the main steam temperature system parameters is obtained.

[0070] Step 3.1.1: Initialize the position, size and activity range of the human group. ub and lb are the upper and lower bounds of the human group's activity space, respectively. Randomly generate human groups of different sizes within the activity range.

[0071] Step 3.1.2: The exploration phase of the human group is defined as 1 / 4 of the maximum number of iterations, and all individuals adopt a unified search strategy:

[0072]

[0073] in, represents the position of subsequent updates, β is the adaptive function, Max iter is the maximum number of iterations, t represents the current number of iterations, Indicates the current position, X best Corresponding to the current best position, Levy is the allocation function, dim represents the number of variables involved, represents the average position in the current population, floor means rounding down, rand is a random number in [0,1], and f jump is a jump function.

[0074] The relevant parameters are further explained as follows:

[0075] Average position the average position of the current population;

[0076] Adaptive function It means that it is becoming increasingly difficult for humans to explore knowledge, and it has the characteristic of converging in one direction;

[0077] Levy distribution is Where μ and v are normally distributed random variables with mean 0 and variance D. μ represents the random displacement of individual search, μ~N(0,D), v represents the random perturbation in the search, v~N(0,D), and σ controls the extent of the distribution. γ is the exponential parameter of the distribution, which determines the shape of the distribution. In the specific implementation, the value of γ is 1.5; Γ is the generalized factorial gamma function as the control coefficient;

[0078] Jumping strategy Increase the dispersion of search locations.

[0079] Step 3.1.3: The development stage of the human group, divide the human group into different roles, including leaders, explorers, followers and losers. Each role has a different search strategy and works together to find the best solution.

[0080] Defining leaders: The top 40% of individuals in the optimal region:

[0081]

[0082] Among them, Rn represents a random number that obeys the normal distribution, the function ones(1,dim) generates the row vector dim elements, R is a random number in [0,1], and ω is the knowledge acquisition difficulty coefficient; A=0.6.

[0083] Defining Explorers: Healthy 40%-80% individuals:

[0084]

[0085] in, It represents the position of the worst-fit individual in the population at the tth iteration, and i represents the i-th iteration value.

[0086] Definition of followers: 80%-90% of individuals follow the most adaptable leader:

[0087]

[0088] in, It represents the position of the most adaptable individual in the population at the tth iteration, and Rd represents a number in the range [1, dim].

[0089] Individuals that are not adapted to the population are losers and will be eliminated, defining the population replenishment mechanism:

[0090]

[0091] Adopt local search enhancement strategy to update the optimal solution:

[0092]

[0093] Where Gauss(0,σ) is a Gaussian distribution with a mean of 0 and a standard deviation of σ.

[0094] Step 3.1.4: Calculate the fitness values ​​of all optimal solutions according to the error objective function formula, update the objective function fitness value J and the global optimal individual If the fitness value J reaches the minimum value, the corresponding optimal individual To identify the results.

[0095] Step 3.2: Output the enhanced human evolution algorithm parameter identification results, and compare and analyze the actual main steam temperature change curve and the output curve of the identification model to determine the accuracy of the identified model.

[0096] Through the above-mentioned manner, the main steam temperature system parameter identification method of the present invention can quickly and accurately identify the model, facilitate the design of subsequent optimization control, and be beneficial to the safe and stable operation of the unit. First, a mathematical model of the main steam temperature system is constructed to determine a three-input single-output system. Secondly, an error objective function containing identification parameters is constructed based on the root mean square error; finally, the enhanced human evolution algorithm is used to divide human evolution into an exploration stage and a development stage. The error objective function is optimized and solved based on the main steam temperature system variables and historical data. The present invention can effectively solve the main steam temperature system parameter identification problem and correctly reflect the dynamic changes of the main steam temperature.

[0097] Ultra-supercritical units under different operating conditions have different system parameters. In order to verify that the present invention can cope with the main steam temperature systems in ultra-supercritical units with different installed capacities, historical data of the main steam temperature systems of ultra-supercritical units at three different locations are selected for verification and analysis.

[0098] Example 1

[0099] The historical data of the system under the main steam temperature of a 1000MW ultra-supercritical unit in a certain province were selected. These data included key indicators such as the intermediate point temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate, and parameter identification based on the enhanced human evolution algorithm was performed. After the identification was completed, some data were selected for verification. In order to ensure the continuity and integrity of the data, the sampling period was set to 5 seconds, and the total sampling time was 3600 seconds. Combined with the proposed enhanced human evolution algorithm, the results of algorithm optimization and output parameter identification of the error objective function were verified, as shown in Table 1, which is the result of output parameter identification.

[0100] Then the main steam temperature H o It can be expressed as the following formula:

[0101]

[0102] Among them, H i is the midpoint temperature, F p is the spray flow rate of the first-stage desuperheater, F s is the spray flow of the secondary desuperheater.

[0103] Table 1 Results of output parameter identification of Example 1

[0104]

[0105] In order to meet the needs of rapid wide load peak regulation of the power grid, there is a situation where rapid load changes are required, and the historical data of the unit under rapid load increase changes are selected to verify whether the present invention can still quickly and accurately identify parameters when the unit responds to rapid load increase, and correctly reflect the change trend of the main steam temperature.

[0106] Figure 5 The figure shows the comparison between the actual change curve of the main steam temperature of a 1000MW unit in a certain province under the condition of rapid load increase and the identification response curve of the present invention, the ordinate is the main steam temperature (℃), and the abscissa is time (s). From the figure, we can observe the change trend of the main steam temperature when the load increases rapidly, as well as the response characteristics of the system under the load increase condition.

[0107] The actual load increases and tends to stabilize at about 1500s, and the main steam temperature also increases and gradually stabilizes. During the rapid load increase process, the main steam temperature fluctuates by 15°C, ranging from 592.2°C to 606.2°C, and finally stabilizes at about 602°C. By comparing and analyzing the actual change curve in the figure with the response curve of the identification result of the present invention, it is verified that even under extreme rapid load increase conditions, the present invention still has a high degree of accuracy and rapidity, and can quickly and accurately identify the change law of the main steam temperature, which proves the effectiveness and robustness of the present invention in dealing with complex operating conditions of the main steam temperature system of ultra-supercritical units.

[0108] Example 2

[0109] The historical data of the system under the main steam temperature of a 660MW ultra-supercritical unit in a certain province were selected. These data included key indicators such as the intermediate point temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate, and parameter identification based on the enhanced human evolution algorithm was performed. After the identification was completed, some data were selected for verification. In order to ensure the continuity and integrity of the data, the sampling period was selected as 5 seconds, and the total sampling time was 3600 seconds. Combined with the proposed enhanced human evolution algorithm, the results of algorithm optimization and output parameter identification of the error objective function were verified, as shown in Table 2, which is the result of output parameter identification.

[0110] Then the main steam temperature H o It can be expressed as the following formula:

[0111]

[0112] Among them, H i is the midpoint temperature, F p is the spray flow rate of the first-stage desuperheater, F s is the spray flow of the secondary desuperheater.

[0113] Table 2 Results of output parameter identification of Example 2

[0114]

[0115] In order to select the fast wide load peak regulation of the corresponding power grid, there is a situation where the load needs to change rapidly, and the historical data of the unit under the condition of rapid load reduction is selected; verify whether the present invention can still quickly and accurately identify parameters when the unit copes with rapid load reduction, and correctly reflect the changing trend of the main steam temperature.

[0116] Figure 6This is a comparison between the actual change curve of the main steam temperature under the condition of rapid load reduction of a 660MW unit in a certain province and the identification response curve of the present invention. The ordinate is the main steam temperature (℃), and the abscissa is time (s). From the figure, we can observe the change trend of the main steam temperature when the load drops rapidly, as well as the response characteristics of the system under load reduction conditions.

[0117] The actual load decreased and stabilized at about 1500s, and the main steam temperature also decreased and gradually stabilized. During the rapid load reduction process, the main steam temperature fluctuated by 13°C, ranging from 564.6 to 577.3°C, and finally stabilized at around 568°C. By comparing and analyzing the actual change curve in the figure with the response curve of the identification result of the present invention, it is verified that even under extreme rapid load reduction conditions, the present invention still has a high degree of accuracy and rapidity, and can quickly and accurately identify the change law of the main steam temperature, which proves the effectiveness and robustness of the present invention in dealing with complex operating conditions of the main steam temperature system of ultra-supercritical units.

[0118] Example 3

[0119] The historical data of the system under the main steam temperature of a 300MW ultra-supercritical unit in a certain province were selected. These data included key indicators such as the intermediate point temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate, and parameter identification based on the enhanced human evolution algorithm was performed. After the identification was completed, some data were selected for verification. In order to ensure the continuity and integrity of the data, the sampling period was selected as 5 seconds, and the total sampling time was 3600 seconds. Combined with the proposed enhanced human evolution algorithm, the results of algorithm optimization and output parameter identification of the error objective function were verified, as shown in Table 3, which is the result of output parameter identification.

[0120] Then the main steam temperature H o It can be expressed as the following formula:

[0121]

[0122] Among them, H i is the midpoint temperature, F p is the spray flow rate of the first-stage desuperheater, F s is the spray flow of the secondary desuperheater.

[0123] Table 3 Results of output parameter identification of Example 3

[0124]

[0125] In order to respond to the rapid wide load peak regulation selection of the corresponding power grid, there is a situation where the load needs to change rapidly, and the historical data of the unit under the condition of continuous and rapid load changes are selected; verify whether the present invention can still quickly and accurately identify parameters when the unit copes with rapid load changes, and correctly reflect the changing trend of the main steam temperature.

[0126] Figure 7 This is a comparison between the actual change curve of the main steam temperature of a 300MW unit in a certain province under rapid load changes and the identification response curve of the present invention. The ordinate is the main steam temperature (℃), and the abscissa is time (s). From the figure, we can observe the change trend of the main steam temperature when the load changes rapidly and continuously, as well as the response characteristics of the system under complex load conditions.

[0127] The actual load increased and decreased several times and tended to stabilize at about 1500s, and the main steam temperature also changed accordingly and gradually stabilized. During the rapid load change process, the main steam temperature fluctuated by 13°C, ranging between 532.4°C and 547.1°C, and finally stabilized at around 542°C. By comparing and analyzing the actual change curve in the figure with the response curve of the identification result of the present invention, it is verified that even under extreme rapid load change conditions, the present invention still has a high degree of accuracy and rapidity, and can quickly and accurately identify the change law of the main steam temperature, which proves the effectiveness and robustness of the present invention in dealing with the complex operating conditions of the main steam temperature system of the ultra-supercritical unit.

[0128] Ultra-supercritical units currently play the role of rapid peak load regulation in the power grid and need to face sudden load changes. Such changes require each system in the ultra-supercritical unit to quickly adjust from one stable state to another. Among them, the main steam temperature system, as an important part of the ultra-supercritical unit, needs to respond quickly. Figure 5-Figure 7 As shown, the main steam temperature system parameter identification method based on the enhanced human evolution algorithm proposed in the present invention can correctly reflect the change curve of the main steam temperature. The enhanced algorithm has fast speed and high accuracy. The model proposed in the present invention is conducive to the subsequent control design and optimization of the main steam temperature system.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A method for main steam temperature system parameter identification based on enhanced human evolution algorithm, characterized in that: The method comprises the following steps: Step 1: Construct a mathematical model of the main steam temperature system and determine the parameters to be identified; Step 2: Construct an error objective function containing identification parameters; Step 3: Use the enhanced human evolution algorithm to optimize the error objective function based on the main steam temperature system variables and historical data, and output the identification results.

2. The main steam temperature system parameter identification method based on enhanced human evolution algorithm according to claim 1 is characterized by: The step 1 specifically includes: Step 1.1: Construct a mathematical model of the main steam temperature system: H o =G i (s)H i +G p (s)F p +G s (s)F s Among them, H o is the main steam temperature, H i is the midpoint temperature, F p is the spray flow rate of the first-stage desuperheater, F s is the spray flow rate of the secondary desuperheater, G i (s) is the transfer function of the midpoint temperature, G p (s) is the transfer function of the spray flow rate of the first-stage desuperheater, G s (s) is the transfer function of the secondary desuperheater spray flow rate; Step 1.2: Determine the parameters to be identified. The transfer functions of the midpoint temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate are as follows: Among them, τ i , τ p , τ s represents the time delay of the midpoint temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate, K i , K p , K s Represent the midpoint temperature, the first desuperheater spray flow rate and the second desuperheater spray flow rate respectively, T i 、T p 、T s Represent the time constants of the midpoint temperature, the first-stage desuperheater spray flow rate, and the second-stage desuperheater spray flow rate respectively; where K i 、T i , τ i , K p 、T p , τ p , K s 、T s , τ s is the parameter to be identified.

3. The main steam temperature system parameter identification method based on enhanced human evolution algorithm according to claim 2 is characterized by: The step 2 specifically includes: Calculate the fitness of all optimal values ​​and construct the error objective function: Among them, J is the fitness value, N is the total number of samples, y(k) is the actual main steam temperature value, is the main steam temperature value after parameter identification, and k represents the parameter to be identified.

4. The main steam temperature system parameter identification method based on enhanced human evolution algorithm according to claim 3 is characterized by: The step 3 specifically includes: Step 3.1: The error objective function is optimized and solved based on the historical data of the operation of the main steam temperature system in the ultra-supercritical unit by using the enhanced human evolution algorithm. The optimization result is the parameter identification result of the mathematical model, and the identification result of the main steam temperature system parameters is obtained; Step 3.2: Output the enhanced human evolution algorithm parameter identification results, and compare and analyze the actual main steam temperature change curve and the output curve of the identification model to determine the accuracy of the identified model.

5. The main steam temperature system parameter identification method based on enhanced human evolution algorithm according to claim 4 is characterized in that: The step 3.1 specifically includes the following steps: Step 3.1.1: Initialize the location, size and activity range of the human group. ub and lb are the upper and lower bounds of the human group's activity space, respectively. Randomly generate human groups of different sizes within the activity range. Step 3.1.2: The exploration phase of the human group is defined as 1 / 4 of the maximum number of iterations, and all individuals adopt a unified search strategy: in, represents the position of subsequent updates; β is an adaptive function, Max iter is the maximum number of iterations; t represents the current number of iterations, Indicates the current position; X best Corresponding to the current best position; Levy is the distribution function, and Levy distribution is Where μ and v are normally distributed random variables with mean 0 and variance D. μ represents the random displacement of individual search, μ~N(0,D), v represents the random perturbation in the search, v~N(0,D), and σ controls the extent of the distribution. γ is the exponential parameter of the distribution, which determines the shape of the distribution, Γ is the generalized factorial gamma function as the control coefficient; dim represents the number of variables involved, represents the average position in the current population, floor means round down, rand is a random number in [0,1], f jump is the jump function, δ∈[100,2000]; Step 3.1.3: In the development stage of the human group, the human group is divided into different roles, including leaders, explorers, followers, and losers. Each role has a different search strategy and works together to find the best solution.

6. The main steam temperature system parameter identification method based on enhanced human evolution algorithm according to claim 5 is characterized by: In step 3.1.3, the leader is defined as the top 40% of individuals located in the optimal region: Among them, Rn represents a random number that obeys the normal distribution, the function ones(1,dim) generates the row vector dim elements, R is a random number in [0,1], and ω is the knowledge acquisition difficulty coefficient; A = 0.6; Explorers are defined as the healthy 40% to 80% of individuals: in, Indicates the position of the t-th iteration of the individual with the worst fitness in the population; i indicates the i-th iteration value; Followers are defined as 80% to 90% of individuals who follow the most adaptable leader: in, represents the position of the most adaptable individual in the population at the tth iteration, and Rd represents a number in the range [1, dim]; Individuals that are not adapted to the population are losers and will be eliminated, defining the population replenishment mechanism:

7. The main steam temperature system parameter identification method based on enhanced human evolution algorithm according to claim 6 is characterized by: In step 3.1, the most adaptable individual in the population adopts a search enhancement strategy, which is as follows: Adopt local search enhancement strategy to update the optimal solution: Among them, Gauss(0,σ) is a Gaussian distribution with mean 0 and standard deviation σ; According to the error objective function formula, the fitness values ​​of all optimal solutions are calculated, and the objective function fitness value J and the global optimal individual are updated. If the fitness value J reaches the minimum value, the corresponding optimal individual To identify the results.

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