Method and system for correcting active supporting capability evaluation model of synchronous generator, and computer equipment
By combining online remote experimental data with mechanistic modeling and system identification methods, the model parameters of the fuel-boiler system of thermal power units were optimized, solving the problem of large deviations between the evaluation results and actual values in existing technologies, and achieving higher model accuracy and precision.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
- Filing Date
- 2025-12-10
- Publication Date
- 2026-05-12
AI Technical Summary
Existing active power support capacity assessment models suffer from significant deviations between assessment results and actual values due to variations in model parameters with operating conditions, making it difficult to accurately depict the active power support capacity of power sources under actual operating conditions.
Using a method based on online remote experimental data, combined with mechanism modeling and system identification, the mathematical analytical model parameters of the fuel-boiler system of thermal power units are identified through nonlinear least squares algorithm and trust region reflection technology, and the model is optimized to improve accuracy.
By using online remote experimental data identification methods, the accuracy and precision of the model have been significantly improved, enabling a more accurate depiction of the active power support capability under actual power supply operating conditions.
Smart Images
Figure CN122020871A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system technology, and in particular to a method, system, and computer equipment for correcting a synchronous generator active power support capacity assessment model based on online remote experimental data. Background Technology
[0002] In modern power systems, the operating status of power sources exhibits significant time-varying characteristics. As the operating status and load conditions of power sources change, the model parameters in the active power support capacity assessment models often change accordingly. However, existing active power support capacity assessment models often select a set of typical values for simulation of model parameters, rarely considering the characteristics of model parameter changes with operating conditions. This leads to a large deviation between the assessment results and actual values, making it difficult to accurately characterize the active power support capacity of power sources under actual operating conditions.
[0003] Therefore, how to provide a system that can accurately characterize the active power support capability under the actual operating conditions of a power source is an urgent problem to be solved. Summary of the Invention
[0004] This invention provides a method, system, and computer device for correcting a synchronous generator active power support capacity assessment model, to address the problem in the prior art where the parameters of the active power support capacity assessment model change with operating conditions, leading to a large deviation between the assessment results and actual values. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.
[0005] According to a first aspect of the present invention, a method for correcting a synchronous generator active power support capability assessment model based on online remote experimental data is provided.
[0006] In one embodiment, a method for correcting a synchronous generator active power support capability assessment model includes the following steps:
[0007] Establish a fuel-boiler system model for thermal power units;
[0008] Online remote experiments were conducted based on the power grid control platform to obtain measured values of unit operation.
[0009] A mathematical analytical model of the fuel-boiler system of a thermal power unit is established. The parameters of the mathematical analytical model are identified so that the sum of squared residuals between the predicted and measured main steam pressure values is minimized, and the optimal parameters that meet the conditions are output.
[0010] Optionally, in the step of establishing the fuel-boiler system model of the thermal power unit, the fuel system includes a coal feeder and a coal mill, and the dynamic process of the coal feeder is represented as follows:
[0011] r B '=u B e -τs (1)
[0012] In the formula: u B r is the coal feed rate of the coal feeder. B ' represents the coal feed rate of the coal mill, and τ represents the delay time.
[0013] Optionally, the coal mill is represented as:
[0014] The dynamic process of coal grinding is represented as follows:
[0015]
[0016] In the formula: r B M is the coal output of the coal mill, and M is the coal storage capacity of the coal mill. Based on the characteristics of the mill and the coarse and fine powder separator, we can obtain:
[0017]
[0018] In the formula: k is the output coefficient of the coal mill, f w f H f R These are the coal moisture correction factor, grindability correction factor, and fineness correction factor, respectively, K. f Given the inertial time constant of the pulverizing process, combining equations (2) and (3), the dynamic equation of the inertial element can be obtained as follows:
[0019]
[0020] Optionally, in the step of establishing the fuel-boiler system model of the thermal power unit, the modeling of the boiler system includes:
[0021] The energy extracted and replenished can be expressed as:
[0022] Q w =K1r B (5)
[0023] Q g =K3p t u T (6)
[0024]
[0025] In the formula: Q w The effective heat absorbed by the boiler, K1 is the fuel gain coefficient, and Q is the effective heat absorbed by the boiler. gK3 is the effective heat output of the boiler, K3 is the turbine gain coefficient, p1 is the regulating stage pressure, and p t Main steam pressure, u T This refers to the valve opening.
[0026] The pressure in the steam drum is obtained using the following formula:
[0027]
[0028] In the formula: C b p is the boiler heat storage coefficient. d The pressure of the steam drum;
[0029] Based on the unit's load-pressure curve, the superheater differential pressure equation is obtained through fitting.
[0030] p t =p d -K2(K1r B ) 1.3 (9)
[0031] In the formula: K2 is the superheater resistance coefficient.
[0032] Optionally, the mathematical analytical model of the thermal power unit's fuel-boiler system is expressed as follows:
[0033]
[0034] In the formula, τ is the delay time, and K f C is the inertial time constant of the powder making process. b K1 is the boiler heat storage coefficient, K2 is the fuel gain coefficient, K3 is the superheater resistance coefficient, and K4 is the turbine gain coefficient.
[0035] Optionally, the step of identifying the parameters of the mathematical analytical model includes:
[0036] Define the parameter vector θ to be identified:
[0037] θ * =[K f C b [K1,K2,K3] T (11)
[0038] The boundary conditions for each parameter are:
[0039]
[0040] The parameters of the mathematical analytical model are identified based on the nonlinear least squares algorithm, as follows:
[0041]
[0042] The residual vector r(θ) is defined as follows:
[0043]
[0044] Optionally, the step of identifying the parameters of the mathematical analytical model further includes the step of introducing a trust region:
[0045] In the k-th iteration, the mathematical expression for each step of the algorithm is:
[0046]
[0047] In the formula: r(θ) k ) represents the current residual vector; Δ is the Jacobian matrix of the residuals with respect to the parameters; p is the parameter search step size for this iteration; Δ k The radius of the current trust region is l; l and u are the upper and lower bounds set in the parameter boundary conditions.
[0048] Optionally, the step of introducing a trust region further includes:
[0049] The termination condition for the iteration is set as follows:
[0050]
[0051] According to a second aspect of the present invention, a synchronous generator active power support capability assessment model correction system is provided.
[0052] In one embodiment, the system includes:
[0053] Model of fuel-boiler system for thermal power unit and mathematical analytical model of fuel-boiler system for thermal power unit;
[0054] The measured value acquisition module is used to conduct online remote experiments based on the power grid control platform and acquire measured values of unit operation.
[0055] The identification module is used to identify the parameters of the mathematical analytical model, so as to minimize the sum of squared residuals between the predicted and measured main steam pressure values and output the optimal parameters that meet the conditions.
[0056] Optionally, in the thermal power unit fuel-boiler system model, the fuel system includes a coal feeder and a coal mill, and the dynamic process of the coal feeder is represented as follows:
[0057] r B '=u B e -τs (1)
[0058] In the formula: u B r is the coal feed rate of the coal feeder. B ' represents the coal feed rate of the coal mill, and τ represents the delay time.
[0059] Optionally, the coal mill is represented as:
[0060] The dynamic process of coal grinding is represented as follows:
[0061]
[0062] In the formula: r B M is the coal output of the coal mill, and M is the coal storage capacity of the coal mill. Based on the characteristics of the mill and the coarse and fine powder separator, we can obtain:
[0063]
[0064] In the formula: k is the output coefficient of the coal mill, f w f H f R These are the coal moisture correction factor, grindability correction factor, and fineness correction factor, respectively, K. f Given the inertial time constant of the pulverizing process, combining equations (2) and (3), the dynamic equation of the inertial element can be obtained as follows:
[0065]
[0066] Optionally, in the thermal power unit fuel-boiler system model, the modeling of the boiler system includes:
[0067] The energy extracted and replenished can be expressed as:
[0068] Q w =K1r B (5)
[0069] Q g =K3p t u T (6)
[0070]
[0071] In the formula: Q w The effective heat absorbed by the boiler, K1 is the fuel gain coefficient, and Q is the effective heat absorbed by the boiler. g K3 is the effective heat output of the boiler, K3 is the turbine gain coefficient, p1 is the regulating stage pressure, and p t Main steam pressure, u T This refers to the valve opening.
[0072] The pressure in the steam drum is obtained using the following formula:
[0073]
[0074] In the formula: C b p is the boiler heat storage coefficient. d The pressure of the steam drum;
[0075] Based on the unit's load-pressure curve, the superheater differential pressure equation is obtained through fitting.
[0076] p t =p d -K2(K1r B ) 1.3 (9)
[0077] In the formula: K2 is the superheater resistance coefficient.
[0078] Optionally, the mathematical analytical model of the thermal power unit's fuel-boiler system is expressed as follows:
[0079]
[0080] In the formula, τ is the delay time, and K f C is the inertial time constant of the powder making process. b K1 is the boiler heat storage coefficient, K2 is the fuel gain coefficient, K3 is the superheater resistance coefficient, and K4 is the turbine gain coefficient.
[0081] Optionally, the step of the identification module identifying the parameters of the mathematical analytical model includes:
[0082] Define the parameter vector θ to be identified:
[0083] θ * =[K f C b [K1,K2,K3] T (11)
[0084] The boundary conditions for each parameter are:
[0085]
[0086] The parameters of the mathematical analytical model are identified based on the nonlinear least squares algorithm, as follows:
[0087]
[0088] The residual vector r(θ) is defined as follows:
[0089]
[0090] Optionally, the step of the identification module identifying the parameters of the mathematical analytical model further includes the step of introducing a trust region:
[0091] In the k-th iteration, the mathematical expression for each step of the algorithm is:
[0092]
[0093] In the formula: r(θ) k ) represents the current residual vector; Δ is the Jacobian matrix of the residuals with respect to the parameters; p is the parameter search step size for this iteration; Δ k The radius of the current trust region is l; l and u are the upper and lower bounds set in the parameter boundary conditions.
[0094] According to a third aspect of the present invention, a computer device is provided.
[0095] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.
[0096] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:
[0097] Based on the power grid control platform, unit operation data is obtained through online remote testing. An identification method combining mechanism modeling and system identification is adopted to analyze the physical structure and operating mechanism of the target and establish a mathematical analytical model. Then, using the nonlinear least squares algorithm based on trust region reflection, the main parameters of the established model are identified using the unit operation data, and the model is corrected to improve its accuracy.
[0098] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description
[0099] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.
[0100] Figure 1 This is a flowchart illustrating a method for correcting a synchronous generator active power support capacity assessment model according to an exemplary embodiment;
[0101] Figure 2 This is a simplified model schematic diagram of a thermal power unit fuel system according to an exemplary embodiment;
[0102] Figure 3 This is a simplified model schematic diagram of a boiler system for a thermal power unit, according to an exemplary embodiment.
[0103] Figure 4 This is a simplified model schematic diagram of a thermal power unit fuel-boiler system according to an exemplary embodiment;
[0104] Figure 5 This is a schematic diagram of simulation results of typical parameters for operating condition 1 according to an exemplary embodiment;
[0105] Figure 6 This is a schematic diagram of the simulation results after correction for operating condition 1, according to an exemplary embodiment.
[0106] Figure 7 This is a schematic diagram of simulation results for typical parameters of working condition 2 according to an exemplary embodiment;
[0107] Figure 8 This is a schematic diagram of the simulation results after correction for working condition 2, according to an exemplary embodiment.
[0108] Figure 9 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment. Detailed Implementation
[0109] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some embodiments may include or substitute parts and features of other embodiments. The scope of the embodiments herein encompasses the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.
[0110] The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" used in this document to indicate orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings. They are used solely for the convenience of describing the document and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description herein, unless otherwise specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two elements; they can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.
[0111] In this document, unless otherwise stated, the term "multiple" means two or more.
[0112] In this article, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.
[0113] In this article, the term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.
[0114] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the diagram may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.
[0115] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0116] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.
[0117] Existing synchronous generator active power support capacity assessment models suffer from significant deviations between assessment results and actual values due to the changing characteristics of parameters with operating conditions. This application proposes a method for correcting synchronous generator active power support capacity assessment models to improve model accuracy.
[0118] Figure 1 An embodiment of the synchronous generator active power support capacity assessment model correction method of the present invention is shown.
[0119] In this optional embodiment, the method includes:
[0120] Step S1: Establish a fuel-boiler system model for thermal power units;
[0121] Step S2: Conduct online remote experiments based on the power grid control platform to obtain measured values of unit operation;
[0122] Step S3: Establish a mathematical analytical model of the fuel-boiler system of the thermal power unit, identify the parameters of the mathematical analytical model, minimize the sum of squared residuals between the predicted and measured main steam pressure values, and output the optimal parameters that meet the conditions.
[0123] Optionally, in the step of establishing the fuel-boiler system model of the thermal power unit, the fuel system includes a coal feeder and a coal mill. The main control method of the system is that the coal feeder ensures that the coal feed rate follows the coal feed command, transporting raw coal to the coal mill, which grinds the raw coal into pulverized coal. The pulverized coal inside the coal mill is blown up by primary air, and after passing through the coarse powder separator at the top of the coal mill, qualified pulverized coal is sent into the boiler furnace for combustion with the primary air, while unqualified pulverized coal falls back into the coal mill for further grinding. Therefore, the dynamic process of the coal feeder is represented as follows:
[0124] r B '=u B e -τs (1)
[0125] In the formula: u B r is the coal feed rate of the coal feeder. B ' represents the coal feed rate of the coal mill, and τ represents the delay time.
[0126] The dynamic process of coal pulverizing can be represented by an inertial element, and the dynamic equilibrium equation for the mass of pulverized coal inside the coal mill is as follows:
[0127]
[0128] In the formula: r B M is the coal output of the coal mill, and M is the coal storage capacity of the coal mill. Based on the characteristics of the mill and the coarse and fine powder separator, we can obtain:
[0129]
[0130] In the formula: k is the output coefficient of the coal mill, f w f H f R These are the coal moisture correction coefficient, grindability correction coefficient, and fineness correction coefficient, respectively, all of which can be approximated as constants, K. f Given the inertial time constant of the pulverizing process, combining equations (2) and (3), the dynamic equation of the inertial element can be obtained as follows:
[0131]
[0132] Therefore, a simplified model of the fuel system of a thermal power unit is as follows: Figure 2 As shown.
[0133] Optionally, in the step of establishing the fuel-boiler system model of the thermal power unit, during boiler operation, the boiler outputs hot steam to the turbine, causing the boiler's heat storage to be continuously extracted, while fuel combustion and feedwater continuously replenish the boiler's heat storage. The energy extracted and replenished by the boiler system can be expressed as:
[0134] Q w =K1r B (5)
[0135] Q g =K3p t u T (6)
[0136]
[0137] In the formula: Q w The effective heat absorbed by the boiler, K1 is the fuel gain coefficient, and Q is the effective heat absorbed by the boiler. g K3 is the effective heat output of the boiler, K3 is the turbine gain coefficient, p1 is the regulating stage pressure, and p t Main steam pressure, u T This refers to the opening degree of the damper.
[0138] Because turbine control valves exhibit significant dead zones and hysteresis nonlinearities, directly fitting the valve opening command results in substantial errors. Therefore, p1 / p, which can represent the valve opening signal, is used instead. t To replace it. When extraction and replenishment are balanced, the boiler pressure remains constant. When the output heat load is unbalanced with the input fuel, the boiler heat storage will be extracted to cover the power deficit. The drum pressure is obtained according to the following formula:
[0139]
[0140] In the formula: C b p is the boiler heat storage coefficient. d The pressure of the steam drum;
[0141] The presence of the superheater increases the pressure p in the steam drum. d With main steam pressure p t There is a pressure difference between the boiler and the boiler. Experiments have shown that the pressure difference is related to the effective heat Q absorbed by the boiler. w There is a nonlinear relationship between them. Based on the unit's load-pressure curve, the superheater differential pressure equation is obtained by fitting:
[0142] p t =p d -K2(K1r B ) 1.3 (9)
[0143] In the formula: K2 is the superheater resistance coefficient.
[0144] A simplified model of a thermal power unit boiler system is as follows: Figure 3 As shown.
[0145] Optionally, the mathematical analytical model of the fuel-boiler system of the thermal power unit can be expressed by a system of differential equations as follows:
[0146]
[0147] In the formula, τ is the delay time, and K f C is the inertial time constant of the powder making process. b K1 is the boiler heat storage coefficient, K2 is the fuel gain coefficient, K3 is the superheater resistance coefficient, and K4 is the turbine gain coefficient. It can be seen that the system is nonlinear, with a total of 6 parameters to be identified.
[0148] Optionally, the step of identifying the parameters of the mathematical analytical model includes:
[0149] Define the parameter vector θ to be identified:
[0150] θ * =[K f C b [K1,K2,K3] T (11)
[0151] To ensure that the parameters have clear physical meaning, in addition to using differential equations in the mathematical analytical model to impose physical constraints on the parameters, it is also necessary to constrain the range of values for each parameter. The boundary conditions for each parameter are as follows:
[0152]
[0153] The parameters of the mathematical analytical model are identified based on the nonlinear least squares algorithm, as follows:
[0154]
[0155] The residual vector r(θ) is defined as follows:
[0156]
[0157] Optionally, to solve the aforementioned constrained nonlinear least squares problem, a trust region is introduced. A quadratic approximation model of the objective function is constructed at each step, and the subproblems are solved within the trust region. Therefore, the step of identifying the parameters of the mathematical analytical model further includes the step of introducing a trust region.
[0158] In the k-th iteration, the mathematical expression for each step of the algorithm is:
[0159]
[0160] In the formula: r(θ) k ) represents the current residual vector; Δ is the Jacobian matrix of the residuals with respect to the parameters; p is the parameter search step size for this iteration; Δ k The radius of the current trust region is l; l and u are the upper and lower bounds set in the parameter boundary conditions.
[0161] Optionally, the step of introducing a trust region further includes:
[0162] The termination condition for the iteration is set as follows:
[0163]
[0164] A specific embodiment of the method of this application is given below.
[0165] The 300MW unit of Weifang Power Plant was selected as the research object. This unit is an N300-16.7 / 537 / 537 subcritical intermediate reheat twin-cylinder double-exhaust condensing steam turbine. The identification data came from the unit's operating data from 18:13:23 to 18:16:23 on January 8, 2025, with a sampling interval of 1 second. During this period, the unit's load condition was increased from 61% to 66%. A simulation model of the fuel-boiler system was established in MATLAB / Simulink, such as... Figure 4 As shown in Table 1, simulations were performed using typical parameters and modified parameters respectively.
[0166] Table 1 Parameter values for operating condition 1
[0167]
[0168] The simulated data of the main steam pressure under the two parameters were compared with the actual data, such as... Figure 5 and Figure 6 As shown, the modification method of this application has obvious feasibility and effectiveness.
[0169] To further verify the effectiveness of this method in correcting the model when operating conditions change, the 300MW unit of Weifang Power Plant was still selected as the research object. However, the identification data came from the unit's operating data from 16:28:04 to 16:31:24 on December 18, 2024, with a sampling interval of 1 second. During this period, the unit's load condition was reduced from 54% to 49%. The initial parameters of the model were set to the corrected values of operating condition 1, and the simulation results of the model parameters under this operating condition using the initial and corrected values were observed and compared.
[0170] Table 2 Parameter values for operating condition 2
[0171]
[0172] like Figure 7 and Figure 8 As shown in the simulation comparison, it can be seen that if only typical values or parameters under a certain working condition are used for simulation without considering the variation characteristics of model parameters with operating conditions, the simulation results deviate greatly from reality and cannot be used for subsequent research and analysis. However, by using the method proposed in this application, after correcting the model based on online remote experimental data, the simulation accuracy is significantly improved, proving the feasibility and effectiveness of the method proposed in this application.
[0173] In other embodiments, this application also proposes a synchronous generator active power support capability assessment model correction system, including: a thermal power unit fuel-boiler system model and a mathematical analytical model of the thermal power unit fuel-boiler system; a measured value acquisition module, used to conduct online remote experiments based on the power grid control platform to acquire measured values of unit operation; and an identification module, used to identify the parameters of the mathematical analytical model so that the sum of squared residuals between the output predicted value of main steam pressure and the measured value is minimized, and the optimal parameters that meet the conditions are output.
[0174] The working principle of the above system is the same as that of the above method embodiments, and will not be repeated here.
[0175] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.
[0176] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0177] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.
[0178] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the method embodiments described above.
[0179] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0180] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.
Claims
1. A method for correcting a synchronous generator active power support capability assessment model, characterized in that, Includes the following steps: Establish a fuel-boiler system model for thermal power units; Online remote experiments were conducted based on the power grid control platform to obtain measured values of unit operation. A mathematical analytical model of the fuel-boiler system of a thermal power unit is established. The parameters of the mathematical analytical model are identified so that the sum of squared residuals between the predicted and measured main steam pressure values is minimized, and the optimal parameters that meet the conditions are output.
2. The method for correcting the active power support capacity assessment model of a synchronous generator as described in claim 1, characterized in that, In the step of establishing the fuel-boiler system model of the thermal power unit, the fuel system includes a coal feeder and a coal mill, and the dynamic process of the coal feeder is represented as follows: r B '=u B e -τs (1) In the formula: u B r is the coal feed rate of the coal feeder. B ' represents the coal feed rate of the coal mill, and τ represents the delay time.
3. The method for correcting the active power support capacity assessment model of a synchronous generator as described in claim 2, characterized in that, The coal mill is referred to as: The dynamic process of coal grinding is represented as follows: In the formula: r B M is the coal output of the coal mill, and M is the coal storage capacity of the coal mill. Based on the characteristics of the mill and the coarse and fine powder separator, we can obtain: In the formula: k is the output coefficient of the coal mill, f w f H f R These are the coal moisture correction factor, grindability correction factor, and fineness correction factor, respectively, K. f Given the inertial time constant of the pulverizing process, combining equations (2) and (3), the dynamic equation of the inertial element can be obtained as follows:
4. The method for correcting the active power support capacity assessment model of a synchronous generator as described in claim 3, characterized in that, In the step of establishing the fuel-boiler system model of the thermal power unit, the boiler system modeling includes: The energy extracted and replenished can be expressed as: Q w =K1r B (5) Q g =K3p t u T (6) In the formula: Q w The effective heat absorbed by the boiler, K1 is the fuel gain coefficient, and Q is the effective heat absorbed by the boiler. g K3 is the effective heat output of the boiler, K3 is the turbine gain coefficient, p1 is the regulating stage pressure, and p t Main steam pressure, u T This refers to the valve opening. The pressure in the steam drum is obtained using the following formula: In the formula: C b p is the boiler heat storage coefficient. d For steam drum pressure; Based on the unit's load-pressure curve, the superheater differential pressure equation is obtained through fitting. p t =p d -K2(K1r B ) 1.3 (9) In the formula: K2 is the superheater resistance coefficient.
5. The method for correcting the active power support capacity assessment model of a synchronous generator as described in claim 4, characterized in that, The mathematical analytical model of the thermal power unit's fuel-boiler system is expressed as follows: In the formula, τ is the delay time, and K f C is the inertial time constant of the powder making process. b K1 is the boiler heat storage coefficient, K2 is the fuel gain coefficient, K3 is the superheater resistance coefficient, and K4 is the turbine gain coefficient.
6. The method for correcting the active power support capacity assessment model of a synchronous generator as described in claim 5, characterized in that, The step of identifying the parameters of the mathematical analytical model includes: Define the parameter vector θ to be identified: θ * =[K f ,C b ,K1,K2,K3] T (11) The boundary conditions for each parameter are: The parameters of the mathematical analytical model are identified based on the nonlinear least squares algorithm, as follows: The residual vector r(θ) is defined as follows:
7. The method for correcting the active power support capacity assessment model of a synchronous generator as described in claim 6, characterized in that, The step of identifying the parameters of the mathematical analytical model also includes the step of introducing a trust region: In the k-th iteration, the mathematical expression for each step of the algorithm is: In the formula: r(θ) k ) represents the current residual vector; Δ is the Jacobian matrix of the residuals with respect to the parameters; p is the parameter search step size for this iteration; Δ k The radius of the current trust region is l; l and u are the upper and lower bounds set in the parameter boundary conditions.
8. The method for correcting the active power support capacity assessment model of a synchronous generator as described in claim 7, characterized in that, The step of introducing a trust region also includes: The termination condition for the iteration is set as follows:
9. A correction system for a synchronous generator active power support capability assessment model, characterized in that, include: Model of fuel-boiler system for thermal power unit and mathematical analytical model of fuel-boiler system for thermal power unit; The measured value acquisition module is used to conduct online remote experiments based on the power grid control platform and acquire measured values of unit operation. The identification module is used to identify the parameters of the mathematical analytical model, so as to minimize the sum of squared residuals between the predicted and measured main steam pressure values and output the optimal parameters that meet the conditions.
10. The synchronous generator active power support capacity assessment model correction system as described in claim 9, characterized in that, In the aforementioned thermal power unit fuel-boiler system model, the fuel system includes a coal feeder and a coal mill. The dynamic process of the coal feeder is represented as follows: r B '=u B e -τs (1) In the formula: u B r is the coal feed rate of the coal feeder. B ' represents the coal feed rate of the coal mill, and τ represents the delay time.
11. The synchronous generator active power support capacity assessment model correction system as described in claim 10, characterized in that, The coal mill is referred to as: The dynamic process of coal grinding is represented as follows: In the formula: r B M is the coal output of the coal mill, and M is the coal storage capacity of the coal mill. Based on the characteristics of the mill and the coarse and fine powder separator, we can obtain: In the formula: k is the output coefficient of the coal mill, f w f H f R These are the coal moisture correction factor, grindability correction factor, and fineness correction factor, respectively, K. f Given the inertial time constant of the pulverizing process, combining equations (2) and (3), the dynamic equation of the inertial element can be obtained as follows:
12. The synchronous generator active power support capacity assessment model correction system as described in claim 11, characterized in that, In the aforementioned fuel-boiler system model for thermal power units, the modeling of the boiler system includes: The energy extracted and replenished can be expressed as: Q w =K1r B (5) Q g =K3p t u T (6) In the formula: Q w The effective heat absorbed by the boiler, K1 is the fuel gain coefficient, and Q is the effective heat absorbed by the boiler. g K3 is the effective heat output of the boiler, K3 is the turbine gain coefficient, p1 is the regulating stage pressure, and p t Main steam pressure, u T This refers to the valve opening. The pressure in the steam drum is obtained using the following formula: In the formula: C b p is the boiler heat storage coefficient. d For steam drum pressure; Based on the unit's load-pressure curve, the superheater differential pressure equation is obtained through fitting. p t =p d -K2(K1r B ) 1.3 (9) In the formula: K2 is the superheater resistance coefficient.
13. The synchronous generator active power support capacity assessment model correction system as described in claim 12, characterized in that, The mathematical analytical model of the thermal power unit's fuel-boiler system is expressed as follows: In the formula, τ is the delay time, and K f C is the inertial time constant of the powder making process. b K1 is the boiler heat storage coefficient, K2 is the fuel gain coefficient, K3 is the superheater resistance coefficient, and K4 is the turbine gain coefficient.
14. The synchronous generator active power support capacity assessment model correction system as described in claim 13, characterized in that, The identification module identifies the parameters of the mathematical analytical model, including the following steps: Define the parameter vector θ to be identified: θ * =[K f ,C b ,K1,K2,K3] T (11) The boundary conditions for each parameter are: The parameters of the mathematical analytical model are identified based on the nonlinear least squares algorithm, as follows: The residual vector r(θ) is defined as follows:
15. The synchronous generator active power support capacity assessment model correction system as described in claim 14, characterized in that, The identification module's step of identifying the parameters of the mathematical analytical model also includes the step of introducing a trust region: In the k-th iteration, the mathematical expression for each step of the algorithm is: In the formula: r(θ) k ) represents the current residual vector; Δ is the Jacobian matrix of the residuals with respect to the parameters; p is the parameter search step size for this iteration; Δ k The radius of the current trust region is l; l and u are the upper and lower bounds set in the parameter boundary conditions.
16. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.