Parameter optimization method and system for variable load boiler of secondary reheating unit and electronic equipment

Abstract

The invention discloses a secondary reheat unit variable load boiler parameter optimization method and system and electronic equipment, and relates to the technical field of temporary cogeneration heat supply units, and the method comprises the steps: S1, employing a particle swarm optimization algorithm to optimize the values of boiler feed water flow, boiler coal feed amount, boiler air supply amount and boiler flue baffle opening, the optimized values of the boiler feed water flow, the boiler coal feed amount, the boiler air feed amount and the boiler flue baffle opening degree are obtained; and S2, obtaining the maximum # imgabs0 # total amount of the primary reheat steam and the secondary reheat steam based on the optimized boiler feed water flow, boiler coal feed quantity, boiler air feed quantity and boiler flue baffle opening degree. According to the method, the variable load boiler parameters of the secondary reheating unit can be optimized under the actual operation condition of the unit so as to obtain the maximum # imgabs 1 # total amount.

Description

Technical Field

[0001] The present application relates to the technical field of cogeneration heating units, and in particular to a method and system for optimizing parameters of a variable load boiler of a secondary reheat unit. Background Art

[0002] China's industrial energy consumption is growing rapidly, among which thermal power generation is the industry with the largest industrial energy consumption and carbon emissions. At present, the boiler energy utilization rate of the secondary reheat unit under variable load is low, which leads to large energy consumption and increased carbon emissions during the combustion process. Variable operating conditions are the main operating conditions of the unit. How to optimize the variable load boiler parameters of the secondary reheat unit under the actual operating conditions of the unit to obtain the maximum The total amount is an issue that needs to be addressed urgently. Summary of the invention

[0003] The technical problem to be solved by this application is: how to optimize the variable load boiler parameters of the secondary reheat unit under the actual operating conditions of the unit to obtain the maximum Total amount.

[0004] The technical solution adopted by this application to solve its technical problem is:

[0005] In a first aspect, the present application provides a method for optimizing parameters of a variable load boiler of a secondary reheat unit, the method comprising the following steps:

[0006] S1, using particle swarm optimization algorithm to optimize the values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening, and obtain the optimized values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening;

[0007] S2, based on the optimized boiler feed water flow, boiler coal feed, boiler air flow, boiler flue damper opening, the maximum value of the primary reheat steam and the secondary reheat steam is obtained. Total amount.

[0008] Based on the first aspect, further, the specific method of optimizing the values ​​of boiler feed water flow, boiler coal feed, boiler air feed, and boiler flue damper opening by using the particle swarm optimization algorithm is as follows:

[0009] S11, the specific form of setting the position X of the particle group is as follows:

[0010] X = [M, B, A, O];

[0011] Among them, X is the position of the particle group, X is a vector; M is the first element of vector X, M is the boiler feed water flow; B is the second element of vector X, B is the boiler coal feed; A is the third element of vector X, A is the boiler air feed; O is the fourth element of vector X, O is the boiler flue damper opening;

[0012] Set the initial value of the individual optimal value dbest(j) and the initial value of the global optimal value gbest;

[0013] S12, calculating the values ​​of the primary reheat steam temperature T1 and the secondary reheat steam temperature T2 corresponding to the position of the current particle group, and the specific calculation formula is:

[0014]

[0015] Wherein, T1 is the primary reheat steam temperature, T2 is the secondary reheat steam temperature; M is the boiler feed water flow; B is the boiler coal supply; A is the boiler air supply; O is the boiler flue damper opening; a1, a2, a3, a4 are the polynomial coefficients of the primary reheat steam temperature relationship; n1 is the polynomial degree of the primary reheat steam temperature relationship; b1, b2, b3, b4 are the polynomial coefficients of the secondary reheat steam temperature relationship; n2 is the polynomial degree of the secondary reheat steam temperature relationship;

[0016] S13, obtain the values ​​of h0, h1, h2 and s0, s1, s2 corresponding to the primary reheat steam temperature T1 and the secondary reheat steam temperature T2 through the steam property table; obtain the primary reheat steam and secondary reheat steam corresponding to the position of the particle group from the following relationship: Total E:

[0017] E = E1 + E2;

[0018] Among them, the primary reheat steam E1 and secondary reheat steam E2 is obtained by the following formula:

[0019] E1=c1·[(h1-h0)-T0·(s1-s0)];

[0020] E2=c2·[(h2-h0)-T0·(s2-s0)];

[0021] Among them, c1 is the first reheat steam flow rate; c2 is the second reheat steam flow rate; h0 is the low pressure cylinder exhaust steam specific enthalpy; h1 is the first reheat steam specific enthalpy; h2 is the second reheat steam specific enthalpy; T0 is the low pressure cylinder exhaust steam temperature; s0 is the low pressure cylinder exhaust steam specific entropy; s1 is the first reheat steam specific entropy; s2 is the second reheat steam specific entropy;

[0022] S14, update the velocity and position of each particle using the following formula:

[0023]

[0024] Where j is the particle number, j = 1, ..., R, R is the total number of particles in the particle group; v j ′ is the particle velocity of the particle numbered j after updating; ω is the inertia weight; v j is the particle velocity before the update of particle number j; e1 is the individual learning factor; f1 is the social learning factor; r1 and r2 are random numbers between [0,1]; dbest(j) is the individual optimal value of particle number j; gbest is the global optimal value of the particle group; x j Update the previous particle position for the particle numbered j; x j ′ is the particle position of the particle numbered j after updating;

[0025] S15, execute step S12 and step S13 to obtain the first reheat steam and the second reheat steam corresponding to the position of the current particle group. Total amount;

[0026] S16, determine the current primary reheat steam and secondary reheat steam Is the total amount greater than the current individual optimal value? If the current primary reheat steam and secondary reheat steam If the total amount is greater than the current individual optimal value, the individual optimal value is updated to the current primary reheat steam and secondary reheat steam. total amount, otherwise the individual optimal value will not be updated; determine the current primary reheat steam and secondary reheat steam Is the total amount greater than the current global optimal value? If the current primary reheat steam and secondary reheat steam If the total amount is greater than the current global optimal value, the global optimal value is updated to the current primary reheat steam and secondary reheat steam. The total amount, otherwise the global optimal value is not updated; execute step S14;

[0027] S17, after a preset number of iterations, if the global optimal value obtained in the last iteration is the same as the global optimal value obtained in the previous iteration, then the parameters corresponding to the global optimal value obtained in the last iteration are taken as the optimal parameters, and the primary reheat steam and secondary reheat steam corresponding to the global optimal value obtained in the last iteration are taken as the optimal parameters. The total amount is the maximum of the primary reheat steam and the secondary reheat steam. Total amount.

[0028] Based on the first aspect, further, the above step S11 also includes: setting a maximum value of the particle velocity.

[0029] Based on the first aspect, further, the total number of particles R of the particle group in the above step S14 is in the range of [10, 100].

[0030] Based on the first aspect, further, the value range of the above-mentioned individual learning factor e1 is [1,2].

[0031] Based on the first aspect, further, the value range of the above social learning factor f1 is [1,2].

[0032] Based on the first aspect, further, the value range of the inertia weight ω is [0.4, 0.9].

[0033] Based on the first aspect, further, the maximum value of the particle velocity is set in the range of [0.1, 0.5].

[0034] In the second aspect, the present application provides a parameter optimization system for a variable load boiler of a secondary reheat unit, including a parameter optimization module and Total amount acquisition module, including:

[0035] The parameter optimization module is used to optimize the values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening by using a particle swarm optimization algorithm to obtain the optimized values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening;

[0036] The total amount acquisition module is used to obtain the maximum value of the primary reheat steam and the secondary reheat steam based on the optimized boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening. Total amount.

[0037] In a third aspect, the present application also provides an electronic device, comprising a memory and a processor; the memory is used to store one or more programs; when one or more programs are executed by the processor, a method as described in any one of the first aspects above is implemented.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method according to any one of the above-mentioned first aspects is implemented.

[0039] The beneficial effects of this application are:

[0040] This application can optimize the variable load boiler parameters of the secondary reheat unit under the actual operating conditions of the unit to obtain the maximum total amount, improving the reliability and economy of unit operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flow chart of a method for optimizing parameters of a variable load boiler of a secondary reheat unit provided in an embodiment of the present application;

[0042] Figure 2 A principle block diagram of a variable load boiler parameter optimization system for a secondary reheat unit provided in an embodiment of the present application;

[0043] Figure 3 A structural block diagram of an electronic device provided in an embodiment of the present application.

[0044] The reference numerals are: 100, parameter optimization module; 200, Total quantity acquisition module; 101, memory; 102, processor; 103, communication interface. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0046] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0047] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0048] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0049] In the description of the embodiments of the present application, "plurality" means at least 2.

[0050] First, as Figure 1 As shown, an embodiment of the present application provides a method for optimizing parameters of a variable load boiler of a secondary reheat unit, the method comprising the following steps:

[0051] S1, using particle swarm optimization algorithm to optimize the values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening, and obtain the optimized values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening;

[0052] S2, based on the optimized boiler feed water flow, boiler coal feed, boiler air flow, boiler flue damper opening, the maximum value of the primary reheat steam and the secondary reheat steam is obtained. Total amount.

[0053] In one embodiment of the present application, the specific method of optimizing the values ​​of boiler feed water flow, boiler coal feed, boiler air feed, and boiler flue damper opening by using the particle swarm optimization algorithm is as follows:

[0054] S11, the specific form of setting the position X of the particle group is as follows:

[0055] X = [M, B, A, O];

[0056] Among them, X is the position of the particle group, X is a vector; M is the first element of vector X, M is the boiler feed water flow; B is the second element of vector X, B is the boiler coal feed; A is the third element of vector X, A is the boiler air feed; O is the fourth element of vector X, O is the boiler flue damper opening;

[0057] Set the initial value of the individual optimal value dbest(j) and the initial value of the global optimal value gbest;

[0058] S12, calculating the values ​​of the primary reheat steam temperature T1 and the secondary reheat steam temperature T2 corresponding to the position of the current particle group, and the specific calculation formula is:

[0059]

[0060] Wherein, T1 is the primary reheat steam temperature, T2 is the secondary reheat steam temperature; M is the boiler feed water flow; B is the boiler coal supply; A is the boiler air supply; O is the boiler flue damper opening; a1, a2, a3, a4 are the polynomial coefficients of the primary reheat steam temperature relationship; n1 is the polynomial degree of the primary reheat steam temperature relationship; b1, b2, b3, b4 are the polynomial coefficients of the secondary reheat steam temperature relationship; n2 is the polynomial degree of the secondary reheat steam temperature relationship;

[0061] S13, obtain the values ​​of h0, h1, h2 and s0, s1, s2 corresponding to the primary reheat steam temperature T1 and the secondary reheat steam temperature T2 through the steam property table; obtain the primary reheat steam and secondary reheat steam corresponding to the position of the particle group from the following relationship: Total E:

[0062] E = E1 + E2;

[0063] Among them, the primary reheat steam E1 and secondary reheat steam E2 is obtained by the following formula:

[0064] E1=c1·[(h1-h0)-T0·(s1-s0)];

[0065] E2=c2·[(h2-h0)-T0·(s2-s0)];

[0066] Among them, c1 is the first reheat steam flow rate; c2 is the second reheat steam flow rate c2; h0 is the low pressure cylinder exhaust steam specific enthalpy; h1 is the first reheat steam specific enthalpy; h2 is the second reheat steam specific enthalpy; T0 is the low pressure cylinder exhaust steam temperature; s0 is the low pressure cylinder exhaust steam specific entropy; s1 is the first reheat steam specific entropy; s2 is the second reheat steam specific entropy;

[0067] S14, update the velocity and position of each particle using the following formula:

[0068] vj′=ωvj+e1r1(dbest(j)-xj)+f1r2(gbest-xj);

[0069] x j ′=x j +v j ′;

[0070] Where j is the particle number, j = 1, ..., R, R is the total number of particles in the particle group; v j ′ is the particle velocity of the particle numbered j after updating; ω is the inertia weight; v jis the particle velocity before the update of particle number j; e1 is the individual learning factor; f1 is the social learning factor; r1 and r2 are random numbers between [0,1]; dbest(j) is the individual optimal value of particle number j; gbest is the global optimal value of the particle group; x j Update the previous particle position for the particle numbered j; x j ′ is the particle position of the particle numbered j after updating;

[0071] S15, execute step S12 and step S13 to obtain the first reheat steam and the second reheat steam corresponding to the position of the current particle group. Total amount;

[0072] S16, determine the current primary reheat steam and secondary reheat steam Is the total amount greater than the current individual optimal value? If the current primary reheat steam and secondary reheat steam If the total amount is greater than the current individual optimal value, the individual optimal value is updated to the current primary reheat steam and secondary reheat steam. total amount, otherwise the individual optimal value will not be updated; determine the current primary reheat steam and secondary reheat steam Is the total amount greater than the current global optimal value? If the current primary reheat steam and secondary reheat steam If the total amount is greater than the current global optimal value, the global optimal value is updated to the current primary reheat steam and secondary reheat steam. The total amount, otherwise the global optimal value is not updated; execute step S14;

[0073] S17, after a preset number of iterations, if the global optimal value obtained in the last iteration is the same as the global optimal value obtained in the previous iteration, then the parameters corresponding to the global optimal value obtained in the last iteration are taken as the optimal parameters, and the primary reheat steam and secondary reheat steam corresponding to the global optimal value obtained in the last iteration are taken as the optimal parameters. The total amount is the maximum of the primary reheat steam and the secondary reheat steam. Total amount.

[0074] In one embodiment of the present application, step S11 further includes: setting a maximum value of the particle velocity.

[0075] In one embodiment of the present application, the total number of particles R of the particle group in step S14 ranges from [10, 100].

[0076] In one embodiment of the present application, the value range of the individual learning factor e1 is [1, 2].

[0077] In one embodiment of the present application, the value range of the social learning factor f1 is [1,2].

[0078] In one embodiment of the present application, the value range of the inertia weight ω is [0.4, 0.9].

[0079] In one embodiment of the present application, the maximum value of the particle velocity is set to a range of [0.1, 0.5].

[0080] Second, as Figure 2 As shown, the embodiment of the present application also provides a parameter optimization system for a variable load boiler of a secondary reheat unit, the system comprising a parameter optimization module 100 and Total amount acquisition module 200, wherein:

[0081] The parameter optimization module 100 is used to optimize the values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening by using a particle swarm optimization algorithm to obtain optimized values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening;

[0082] The total amount acquisition module 200 is used to obtain the maximum value of the primary reheat steam and the secondary reheat steam based on the optimized boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening. Total amount.

[0083] Thirdly, Figure 3 As shown, an embodiment of the present application also provides an electronic device, which includes a memory 101 and a processor 102; the memory 101 is used to store one or more programs; when one or more programs are executed by the processor 102, a method as described in any one of the first aspects above is implemented.

[0084] The electronic device may further include a communication interface 103, and the memory 101, the processor 102 and the communication interface 103 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these elements may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules, and the processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 101. The communication interface 103 may be used to communicate signaling or data with other node devices.

[0085] Among them, the memory 101 can be but is not limited to: Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electric Erasable Programmable Read-Only Memory (EEPROM), etc.

[0086] The processor 102 may be an integrated circuit chip with signal processing capability. The processor 102 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0087] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor 102, a method as described in any one of the first aspects above is implemented. If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or part of the technical solution that contributes to the prior art or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk.

[0088] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0089] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for optimizing parameters of a variable load boiler of a secondary reheat unit, characterized in that: The following steps are involved: S1, using particle swarm optimization algorithm to optimize the values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening, and obtain the optimized values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening; S2, based on the optimized boiler feed water flow, boiler coal feed, boiler air flow, boiler flue damper opening, the maximum value of the primary reheat steam and the secondary reheat steam is obtained. Total amount.

2. The method for optimizing parameters of a variable load boiler of a secondary reheat unit according to claim 1, characterized in that: The specific method of optimizing the values ​​of boiler feed water flow, boiler coal feed, boiler air feed, and boiler flue damper opening by using the particle swarm optimization algorithm is as follows: S11, the specific form of setting the position X of the particle group is as follows: X = [M, B, A, O]; Among them, X is the position of the particle group, X is a vector; M is the first element of vector X, M is the boiler feed water flow; B is the second element of vector X, B is the boiler coal feed; A is the third element of vector X, A is the boiler air feed; O is the fourth element of vector X, O is the boiler flue damper opening; Set the initial value of the individual optimal value dbest(j) and the initial value of the global optimal value gbest; S12, calculating the values ​​of the primary reheat steam temperature T1 and the secondary reheat steam temperature T2 corresponding to the position of the current particle group, and the specific calculation formula is: Wherein, T1 is the primary reheat steam temperature, T2 is the secondary reheat steam temperature; M is the boiler feed water flow; B is the boiler coal supply; A is the boiler air supply; O is the boiler flue damper opening; a1, a2, a3, a4 are the polynomial coefficients of the primary reheat steam temperature relationship; n1 is the polynomial degree of the primary reheat steam temperature relationship; b1, b2, b3, b4 are the polynomial coefficients of the secondary reheat steam temperature relationship; n2 is the polynomial degree of the secondary reheat steam temperature relationship; S13, obtain the values ​​of h0, h1, h2 and s0, s1, s2 corresponding to the primary reheat steam temperature T1 and the secondary reheat steam temperature T2 through the steam property table; obtain the primary reheat steam and secondary reheat steam corresponding to the position of the particle group from the following relationship: Total E: E = E1 + E2; Among them, the primary reheat steam E1 and secondary reheat steam E2 is obtained by the following formula: E1=c1·[(h1-h0)-T0·(s1-s0)]; E2=c2·[(h2-h0)-T0·(s2-s0)]; Among them, c1 is the first reheat steam flow rate; c2 is the second reheat steam flow rate; h0 is the low pressure cylinder exhaust steam specific enthalpy; h1 is the first reheat steam specific enthalpy; h2 is the second reheat steam specific enthalpy; T0 is the low pressure cylinder exhaust steam temperature; s0 is the low pressure cylinder exhaust steam specific entropy; s1 is the first reheat steam specific entropy; s2 is the second reheat steam specific entropy; S14, update the velocity and position of each particle using the following formula: vj′=ωvj+e1r1(dbest(j)-xj)+f1r2(gbest-xj); x j ′=x j +v j ′; Where j is the particle number, j = 1, ..., R, R is the total number of particles in the particle group; v j ′ is the particle velocity of the particle numbered j after updating; ω is the inertia weight; v j is the particle velocity before the update of particle number j; e1 is the individual learning factor; f1 is the social learning factor; r1 and r2 are random numbers between [0,1]; dbest(j) is the individual optimal value of particle number j; gbest is the global optimal value of the particle group; x j Update the previous particle position for the particle numbered j; x j ′ is the particle position of the particle numbered j after updating; S15, execute step S12 and step S13 to obtain the first reheat steam and the second reheat steam corresponding to the position of the current particle group. Total amount; S16, determine the current primary reheat steam and secondary reheat steam Is the total amount greater than the current individual optimal value? If the current primary reheat steam and secondary reheat steam If the total amount is greater than the current individual optimal value, the individual optimal value is updated to the current primary reheat steam and secondary reheat steam. total amount, otherwise the individual optimal value will not be updated; determine the current primary reheat steam and secondary reheat steam Is the total amount greater than the current global optimal value? If the current primary reheat steam and secondary reheat steam If the total amount is greater than the current global optimal value, the global optimal value is updated to the current primary reheat steam and secondary reheat steam. The total amount, otherwise the global optimal value is not updated; execute step S14; S17, after a preset number of iterations, if the global optimal value obtained in the last iteration is the same as the global optimal value obtained in the previous iteration, then the parameters corresponding to the global optimal value obtained in the last iteration are taken as the optimal parameters, and the primary reheat steam and secondary reheat steam corresponding to the global optimal value obtained in the last iteration are taken as the optimal parameters. The total amount is the maximum of the primary reheat steam and the secondary reheat steam. Total amount.

3. The method for optimizing parameters of a variable load boiler of a secondary reheat unit according to claim 2, characterized in that: The step S11 further includes: setting a maximum value of the particle velocity.

4. The method for optimizing parameters of a variable load boiler of a secondary reheat unit according to claim 2, characterized in that: The total number of particles R of the particle group in step S14 ranges from [10, 100].

5. The method for optimizing parameters of a variable load boiler of a secondary reheat unit according to claim 2, characterized in that: The value range of the individual learning factor e1 is [1,2].

6. The method for optimizing parameters of a variable load boiler of a secondary reheat unit according to claim 2, characterized in that: The value range of the social learning factor f1 is [1,2].

7. The method for optimizing parameters of a variable load boiler of a secondary reheat unit according to claim 2, characterized in that: The value range of the inertia weight ω is [0.4, 0.9].

8. The method for optimizing parameters of a variable load boiler of a secondary reheat unit according to claim 3, characterized in that: The maximum value of the particle speed is in the range of [0.1, 0.5].

9. A variable load boiler parameter optimization system for a secondary reheat unit, characterized in that: Includes parameter optimization module and Total amount acquisition module, including: The parameter optimization module is used to optimize the values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening by using a particle swarm optimization algorithm to obtain the optimized values ​​of boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening; The total amount acquisition module is used to obtain the maximum value of the primary reheat steam and the secondary reheat steam based on the optimized boiler feed water flow, boiler coal feed, boiler air flow, and boiler flue damper opening. Total amount.

10. An electronic device, characterized in that: The electronic device comprises a memory and a processor; the memory is used to store one or more programs; when the one or more programs are executed by the processor, the method according to any one of claims 1 to 8 is implemented.

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