Coal-fired unit twinborn mechanism model boiler parameter self-correction method and device

Abstract

The invention discloses a coal-fired unit twinning mechanism model boiler parameter self-correction method and device, and the method comprises the steps: building an object unit twinning mechanism model and an offline correction mechanism model through employing a Modelica language according to design parameters; the data platform collects data from the DCS, and provides data services for off-line correction of heat exchange coefficients of all heat exchange surfaces of the boiler, on-line correction of heat exchange scaling factors of all the heat exchange surfaces of the boiler and twinning application; setting heat exchange scaling factors of each heat exchange surface of a unit twinning mechanism model and an off-line correction mechanism model as operating state intermediate values, and determining optimal heat exchange coefficients of each heat exchange surface of the boiler by the off-line correction mechanism model by adopting a differential evolution algorithm based on historical data; and based on the optimal heat exchange coefficient of each heat exchange surface of the boiler, a fuzzy control algorithm is adopted, and heat exchange scaling factors of each heat exchange surface of the unit twinning mechanism model boiler are circularly corrected in sequence on line. The invention further provides a corresponding device and equipment.

Description

Technical Field

[0001] The present invention belongs to the field of thermal power digital twin, and specifically relates to a method and device for self-correcting boiler parameters of a twin mechanism model of a coal-fired unit. Background Art

[0002] With the development of thermal power generation technology, thermal power plants have higher and higher requirements for the intelligent control of units. Digital twin technology, as an effective means to improve the level of intelligentization of units, has great application value. As a typical complex system, thermal power units have the characteristics of more equipment parameters, high nonlinearity, and strong coupling of production processes. At present, the modeling methods of thermal power units are mainly divided into two categories: mechanism modeling and data modeling. On the one hand, based on mechanism analysis, it is bound to be time-consuming and difficult to establish an accurate digital twin model of the unit. Therefore, in the traditional mechanism modeling process, it is inevitable to require a number of simplified assumptions to ignore some high-order dynamic characteristics of the unit in the modeling process (usually referred to as unmodeled dynamics). At the same time, it is inevitable that the unit equipment will have characteristic deviations or performance degradation in actual operation, resulting in deviations between the model and the equipment. On the other hand, with mathematical modeling, the accuracy of the data model is overly dependent on the data quality, and there is a lack of relationship between the parameters of the mechanism formula specification. Factors such as insufficient data volume, excessive data noise, and incomplete coverage will lead to a decrease in the generalization performance of the model and make it difficult to accurately describe the dynamic characteristics of the unit.

[0003] As a typical complex system, thermal power units have the characteristics of many equipment parameters, high nonlinearity, and strong coupling of production processes. In particular, the deposition of fly ash particles generated during fuel combustion on the boiler heat exchange surfaces (such as superheaters, reheaters, economizers, etc.) will reduce heat transfer efficiency, increase exhaust gas temperature, increase fan power consumption, and cause overheating of heat exchange surfaces. If the parameter self-correction problem is not solved, the parameters will have serious deviations when twinning online based on the precise object model. The longer the operation time, the more serious the parameter deviation will be.

[0004] Traditional tuning methods lack in-depth research on the physical process of boilers. Most studies are at the description of the coordinated operation layer, mostly relying on big data and operating conditions, and cannot provide effective correction methods from a mechanism perspective. Especially for the complex process of boiler heat exchange, there is a lack of feasible offline and online correction methods. The established twin model cannot objectively display the characteristics of the twin object boiler in real time. If the twin operation of the model and the unit is to be realized, it is necessary to analyze the composition of the boiler parameters, classify the parameters, and establish a parameter self-correction method to ensure the twin accuracy and reflect the actual operation status of the unit, which is a prerequisite for the development of twin applications. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and device for self-correction of boiler parameters of a twin mechanism model of a coal-fired unit. By establishing a twin mechanism model of the unit and an offline correction mechanism model, offline and online self-correction are adopted to realize self-correction of boiler parameters of the twin mechanism model of the coal-fired unit.

[0006] The present invention is achieved by adopting the following technical solutions: A method for self-correcting boiler parameters of a twin mechanism model of a coal-fired unit, comprising: According to the design parameters, the twin mechanism model and offline correction mechanism model of the target unit are established using Modelica language; The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat exchange surface of the boiler, online correction of the heat transfer scaling factor of each heat exchange surface of the boiler, and twin applications; The heat transfer scaling factors of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model of the unit are set to the middle value of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler. Based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, the fuzzy control algorithm is used to cyclically correct the heat transfer scaling factors of each heat transfer surface of the twin mechanism model boiler of the unit online in sequence.

[0007] A further improvement of the present invention is that, according to the design parameters, the twin mechanism model and the offline correction mechanism model of the object unit are established using the Modelica language, including: According to the design parameters, the twin mechanism model of the target unit is established using the Modelica language. According to the layout of the temperature and pressure measurement points of the boiler of the target unit, the boiler body model is divided into regions, and the corresponding offline correction mechanism model is established. The twin mechanism model of the unit was established, including the boiler, steam turbine and electrical full process mechanism model; The offline correction mechanism model is divided into furnace area, horizontal flue area and tail flue area mechanism models. The offline correction mechanism model should be completely consistent with the corresponding area of ​​the unit's twin mechanism model.

[0008] A further improvement of the present invention is that the data platform collects data from the DCS to provide data services for offline correction of the heat transfer coefficient of each heat transfer surface of the boiler, online correction of the heat transfer scaling factor of each heat transfer surface of the boiler, and twin applications, including: The data platform collects data in real time from the OPC Server of the DCS system of the target unit through a single isolation network gateway. While forwarding the data in real time, it saves historical data. The DCS instructions in the real-time data are used as input instructions for the twin mechanism model of the unit. The historical data saved by the platform is used for self-correction of the heat transfer coefficients of each heat exchange surface of the boiler, and the correction results are synchronously updated to the corresponding heat exchange surfaces of the twin mechanism model of the unit. The outlet temperatures of each heat exchange surface of the boiler in the real-time data are used for on-line correction of the heat transfer scaling factors of each heat exchange surface of the boiler in the twin mechanism model of the unit. The calculation results of the twin mechanism model of the unit are output to the front-end display module for display.

[0009] A further improvement of the present invention is that the DCS instructions in the real-time data are used as input instructions of the twin mechanism model of the unit, including: Feed water flow, coal volume, air volume, total steam turbine valve control instruction, medium pressure valve control instruction, condensate pump frequency, circulating water temperature, circulating water control instruction, high and low water level adjustment instruction, deaerator water level adjustment instruction, each section steam extraction valve control instruction, etc.

[0010] A further improvement of the present invention is that the heat transfer scaling factor of each heat transfer surface of the boiler in the twin mechanism model of the unit and the offline correction mechanism model is set to the intermediate value of the operating state, and the offline correction mechanism model relies on historical data and adopts a differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler, including: Determine the stability and classify the historical data stored in the data platform; According to the design specifications and historical data, determine the heat transfer coefficient range of each heat transfer surface and the ratio between multiple groups of internal heat transfer coefficients, and constrain the heat transfer coefficient of each heat transfer surface of the boiler; The historical data is used as the boundary of the offline correction mechanism model. The difference between the outlet steam temperature of the offline correction mechanism model and the corresponding steam temperature in the historical data of the object unit is evaluated. The differential evolution algorithm is used to optimize the heat transfer coefficient, and the obtained optimal heat transfer coefficient is synchronously updated to the twin mechanism model of the unit.

[0011] A further improvement of the present invention is to judge and classify the historical data stored in the data platform, including: The load range covers 25%--100%. The stability judgment conditions are: (1) The load of the target unit changes negatively within a minute less than 2MW and the duration is greater than 10 minutes, (2) the change value of the total steam turbine control command per minute is less than 0.2, (3) Ambient temperature deviation Within 3℃.

[0012] A further improvement of the present invention is that, based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, a fuzzy control algorithm is used to cyclically correct the heat transfer scaling factor of each heat transfer surface of the twin mechanism model boiler of the unit online in sequence, including: After completing the offline calibration to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler, the outlet temperature of each heat transfer surface of the target unit boiler and the outlet temperature of the heat transfer surface corresponding to the twin mechanism model of the unit are collected in real time, and the fuzzy control algorithm is used to self-calibrate the heat transfer scaling factor of each heat transfer surface of the twin mechanism model boiler of the unit online; The heat exchange scaling factor correction of each heat exchange surface of the twin mechanism model boiler of the unit is based on the flue gas process, that is, the order of the vertical flue in the furnace - the horizontal flue - the tail flue. The heat exchange surface of the boiler is calibrated at preset intervals and calibrated one by one.

[0013] A further improvement of the present invention is that the outlet temperature of each heat exchange surface of the target unit boiler and the outlet temperature of the heat exchange surface corresponding to the twin mechanism model of the unit are collected in real time, and the fuzzy control algorithm is used to self-correct the heat exchange scaling factor of each heat exchange surface of the twin mechanism model boiler of the unit online, including: Error E is the outlet temperature of the boiler heat exchange surface of the target unit Corresponding model heat exchange surface outlet temperature The difference calculation, E= - , Error change EC target unit boiler heat exchange surface outlet temperature Changes and speed of change Classification is carried out, and the fuzzy control set of heat exchange scaling factor is established, and the boiler heat exchange scaling factor is dynamically corrected in real time.

[0014] A coal-fired unit twin mechanism model boiler parameter self-correction device, comprising: The model building module uses the Modelica language to build the twin mechanism model and offline correction mechanism model of the target unit according to the design parameters; Data acquisition module: The data platform collects data from DCS and provides data services for offline correction of heat transfer coefficients of various heat exchange surfaces of boilers, online correction of heat transfer scaling factors of various heat exchange surfaces of boilers, and twin applications; The calculation module sets the heat transfer scaling factor of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model of the unit as the intermediate value of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler. The correction module, based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, adopts a fuzzy control algorithm to perform online cyclic correction of the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit in sequence.

[0015] A further improvement of the present invention is that, in the model building module, the twin mechanism model and the offline correction mechanism model of the target unit are established in Modelica language according to the design parameters, including: According to the design parameters, the twin mechanism model of the target unit is established using the Modelica language. According to the layout of the temperature and pressure measurement points of the boiler of the target unit, the boiler body model is divided into regions, and the corresponding offline correction mechanism model is established. The twin mechanism model of the unit was established, including the boiler, steam turbine and electrical full process mechanism model; The offline correction mechanism model is divided into furnace area, horizontal flue area and tail flue area mechanism models. The offline correction mechanism model should be completely consistent with the corresponding area of ​​the unit's twin mechanism model.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: The twin modeling and application of the boiler system is the most complex core system in the entire coal-fired unit twin system. Realizing the self-correction of the boiler parameters of the twin mechanism model of the coal-fired unit is one of the key links and core issues in the twin application of the coal-fired unit. The method and device for self-correction of the boiler parameters of the twin mechanism model of the coal-fired unit provided by the present invention can realize the self-correction of the boiler parameters of the twin mechanism model of the coal-fired unit, and realize the real-time access to the DCS instructions of the unit under the load (25%--100%) working condition of the target unit, and run synchronously with the unit. The dynamic accuracy of the model can reach within 5%. Through the application of the twin mechanism model of the coal-fired unit, the ash pollution condition of the heat exchange surface of the boiler of the target unit can be monitored in real time, and soot blowing can be optimized; at the same time, it provides a solid foundation and strong support for improving the control performance of the unit, optimizing production strategies, predicting the health status of equipment, improving the energy efficiency of the unit, and reducing carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a method for self-correcting boiler parameters of a twin mechanism model of a coal-fired unit according to an embodiment of the present invention is schematically shown.

[0018] Figure 2 A structural diagram of a method for self-correcting boiler parameters of a twin mechanism model of a coal-fired unit according to an embodiment of the present invention is schematically shown.

[0019] Figure 3 The schematic diagram of the off-line optimization process of the heat exchange coefficients of each heat exchange surface of the twin mechanism model boiler of a coal-fired unit according to an embodiment of the present invention is schematically shown.

[0020] Figure 4 The structural block diagram of a boiler parameter self-correction device of a twin mechanism model of a coal-fired unit according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0021] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0022] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] Example 1 The present invention provides a method for self-correcting boiler parameters of a twin mechanism model of a coal-fired unit, comprising: According to the design parameters, the twin mechanism model and offline correction mechanism model of the target unit are established using Modelica language; The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat exchange surface of the boiler, online correction of the heat transfer scaling factor of each heat exchange surface of the boiler, and twin applications; The heat transfer scaling factors of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model of the unit are set to the middle value of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler. Based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, the fuzzy control algorithm is used to calibrate the heat transfer scaling factors of each heat transfer surface of the twin mechanism model boiler of the unit online in sequence.

[0024] Example 2 In the present invention, by analyzing the characteristics of twin modeling and twin application of coal-fired unit boiler, the twin mechanism model (FMU) and static optimization model (FMU) of the unit are established by using Modelica language, and the self-calibration of the boiler parameters of the twin mechanism model of the coal-fired unit is realized through offline and online self-calibration methods.

[0025] like Figure 1 A method and a flow chart of a device for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to an embodiment of the present invention are schematically shown. The method comprises: S11. According to the design parameters, the twin mechanism model and the offline correction mechanism model of the target unit are established using the Modelica language; S12. The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat transfer surface of the boiler, online correction of the heat transfer scaling factor of each heat transfer surface of the boiler, and twin applications; S13. Set the heat transfer scaling factor of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model of the unit to the intermediate value of the operating state. The offline correction mechanism model relies on historical data and adopts a differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler; S14. Based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, the fuzzy control algorithm is used to cyclically correct the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit online.

[0026] Through the above implementation methods, a twin mechanism model of the unit and an offline correction mechanism model are established, and offline and online self-correction is adopted to realize self-correction of boiler parameters of the twin mechanism model of the coal-fired unit.

[0027] In the implementation mode provided by the present invention, a twin mechanism model of the whole unit is established, including boiler, steam turbine, and electrical full process mechanism models. The offline correction mechanism model is divided into furnace area, horizontal flue area, and tail flue area mechanism models by region, and the offline correction mechanism model should be completely consistent with the corresponding area of ​​the twin mechanism model of the unit.

[0028] As an embodiment of the present invention, according to the different heat exchange modes of each heat exchange surface of the coal-fired boiler, the heat exchange modes can be divided into three categories, namely, radiation heat exchange, convection heat exchange and radiation convection heat exchange. By deeply analyzing the calculation formulas when the mechanism model adopts different heat exchange modes, the parameters that need to be optimized are derived. The formula adopted in an embodiment of the present invention is as follows: 1) When the model uses radiation heat transfer: Heat flux output from the model's radiation heat transfer The calculation formula is as follows:

[0029]

[0030]

[0031] ---Formula 1 in, The Stefan-Boltzmann constant is 5.67E-8 W / (m 2 ·K 4 ); is the radiation view factor (calculated by formula 2); is the heat exchange fouling factor; The radiation coefficient of the upper space; Corrected heat exchange area.

[0032]

[0033]

[0034] Radiation View Factor

[0035]

[0036] ---Formula 2 Gas temperature weighting factor ---Formula 3 Wall temperature weighting factor ---Formula 4 Smoke load ---Formula 5 Coal Particle Loading ---Formula 6 Fly ash loading ---Formula 7 in, is the fly ash content in flue gas; is the density of smoke; is the fuel content in the flue gas; Coke particle absorbance ---Formula 8 Absorption of fly ash particles ---Formula 9 Suspended particle radiation coefficient

[0037] ---Formula 10 When the model adopts radiation heat transfer, according to the above, it can be concluded that when the model parameters are self-calibrated, it is only necessary to determine the heat transfer fouling factor and the radiation coefficient of the upper space value.

[0038] 2) When the model uses convection heat transfer: Model convection heat transfer is selected based on temperature difference calculation ,available , the heat transfer formula is: ---Formula 11 in ， It represents the heat exchange surface of all tube bundles in the furnace; is the heat exchange temperature difference; is the heat transfer coefficient; is the heat exchange fouling factor; By the state of smoke gas ( , , ) to obtain the physical properties of the flue gas, such as density ( ), dynamic viscosity of gas molecules ( ), the Prandtl constant of gas molecules ( ), thermal conductivity of gas molecules ( ) ---Formula 12 Forward cross-sectional area Orthogonal alignment ratio Horizontal alignment ratio Tube outer diameter Number of tube rows, Flue gas velocity ---Formula 13 Corrected Reynolds number ---Formula 14 Among them, the characteristic length

[0039] Laminar Nusselt number

[0040] Turbulent Nusselt number ---Formula 15 Nusselt number ---Formula 16 Tube bundle arrangement correction factor

[0041] ---Formula 17 Constrained Nusselt number

[0042] ---Formula 18 Heat transfer coefficient ---Formula 19 When the model adopts convection heat transfer, according to the above formula, it can be concluded that when the model parameters are self-calibrated, it is only necessary to determine the heat transfer fouling factor and heat transfer coefficient .

[0043] 3) When the model uses radiation convection heat transfer: Calculate and select based on temperature difference ,available , for specific formulas and details, please refer to the heat exchange module.

[0044] For convective heat transfer, For radiant heat transfer ---Formula 20 The heat flux of radiation heat transfer is

[0045] ---Formula 21 Among them, the emissivity of the tube is Radiation coefficient of the upper space of radiation heat transfer formula 1 ; is the total heat exchange area of ​​the tube bundle; The Stefan-Boltzmann constant is 5.67E-8 W / (m 2 ·K 4 ); Emissivity of furnace gases and particulate matter and absorbance Reference.

[0046] The heat flux of convective heat transfer is ---Formula 22 Among them, the heat transfer coefficient Refer to the heat transfer coefficient of convective heat transfer formula 11 .

[0047] The overall heat transfer of the pipe is ---Formula 23 When the model adopts radiation convection heat transfer, according to the above formula, it can be obtained that when the model parameters are self-calibrated, it is only necessary to determine the heat transfer fouling factor and emissivity and heat transfer coefficient 2.

[0048] In this embodiment, when the radiation heat exchange method is adopted, the heat transfer coefficient is the radiation coefficient of the upper space When convection heat transfer is used, the heat transfer coefficient is When radiation convection heat transfer is used, the heat transfer coefficient is the radiation rate and heat transfer coefficient 2.

[0049] like Figure 2 The structure diagram of a method and device for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to an embodiment of the present invention is schematically shown, and the device comprises: ① OPC_Server of the unit DCS system: provides real-time data communication services of the actual unit; ②Data platform: data platform and equipment responsible for data storage and forwarding management; ③Unit twin mechanism model: a full system twin model established based on the twin object unit; ④ Offline correction module: a module used to call historical data offline and use differential evolution algorithm to optimize the heat transfer coefficient of each heating surface of the boiler, including offline correction mechanism model, offline optimization algorithm and program; ⑤Online correction module: an algorithm module that uses fuzzy control algorithm to synchronously correct the heat exchange scaling factor of the boiler heat exchange surface of the twin mechanism model of the unit according to the online real-time data of the unit; ⑥Front-end display module: front-end display interface, equipment and programs for user management and viewing.

[0050] like Figure 2 As shown, the structural relationship of each module of the present invention includes: the data platform collects data in real time from the OPC Server of the DCS system of the target unit through a single-way isolation network switch, and saves historical data while forwarding the data in real time; the DCS instructions in the real-time data are used as input instructions of the twin mechanism model of the unit; the offline correction module calls the historical data saved on the data platform, self-corrects the heat transfer coefficient of each heat exchange surface of the boiler through the differential evolution algorithm and synchronously updates it to the twin mechanism model of the unit; the outlet temperature of each heat exchange surface of the boiler in the real-time data is sent to the online correction module of the twin mechanism model of the unit in real time, and the heat exchange scaling factor of each heat exchange surface of the boiler is corrected online through the fuzzy control algorithm; the above calculation results are output to the front-end display module for display.

[0051] The DCS instructions in the real-time data are used as input instructions for the twin mechanism model of the unit. The control instructions include: feed water flow, coal volume, air volume, total steam turbine valve instruction, medium pressure valve instruction, condensate pump frequency, circulating water temperature, circulating water control instruction, high and low water level adjustment instruction, deaerator water level adjustment instruction, each section steam extraction valve control instruction, etc. By calling a large amount of historical operating condition data saved in the data platform, the historical operating condition data is judged to be stable and classified, and the heat transfer scaling factor of each heat exchange surface of the unit twin mechanism model and the offline correction mechanism model boiler is set to the middle value of the operating state. Then, the differential evolution algorithm is used to complete the optimization of the boiler heat transfer coefficient and synchronously update it to the unit twin mechanism model. Based on the optimal heat transfer coefficient of each heat exchange surface of the boiler, the data of the DCS system of the target unit is collected online, and the difference change between the outlet temperature of each heat exchange surface of the target unit boiler and the outlet temperature of each heat exchange surface of the corresponding model is synchronously sensed, as well as the change and change speed of each heat exchange surface outlet temperature of the target unit boiler, and the heat transfer scaling factor of each heat exchange surface of the twin mechanism model boiler of the unit is corrected online in real time.

[0052] like Figure 3 A schematic diagram of an off-line optimization process for heat exchange coefficients of heat exchange surfaces of a twin mechanism model boiler of a coal-fired unit according to an embodiment of the present invention is shown schematically, and the method comprises: Set the heat transfer and scaling factors of each heat exchange surface of the boiler in the twin-machine mechanism model and the off-line calibration mechanism model to the intermediate value of the operating state. Use the differential evolution algorithm to optimize the heat transfer coefficients of each heat exchange surface of the boiler, and determine the optimal heat transfer coefficients of each section of the boiler's heat exchange surface, including: S21. Judge the stability and classify the historical data stored in the data platform; Judge the stability and classify the historical data, including: the load range of the target unit covers 25% - 100%, and the stability judgment conditions are: (1) the negative change range of the unit load per minute is less than 2MW and the duration is greater than 10 minutes, (2) the total command change value of the steam turbine control valve per minute is less than 0.2, (3) the ambient temperature deviation is within 3°C, and obtain the steady-state operating conditions at different times under each load in the historical data. The above three conditions are in a parallel relationship.

[0053] S22. Determine the heat transfer coefficient range of each heat exchange surface and the ratio between multiple groups of internal heat transfer coefficients according to the design specification and historical data, and constrain the heat transfer coefficients of each heat exchange surface of the boiler; Use the historical data as the boundary of the off-line calibration mechanism model, evaluate with the difference between the outlet steam temperature of the off-line calibration mechanism model and the corresponding steam temperature in the historical data of the target unit, use the differential evolution algorithm to optimize the heat transfer coefficient, and synchronously update the obtained optimal heat transfer coefficient to the twin-machine mechanism model of the unit.

[0054] In an embodiment of the present invention, a method for online self-calibration of the heat transfer and scaling factors of each heat exchange surface of the boiler in the twin-machine mechanism model of the unit is provided according to the outlet temperature of each heat exchange surface of the boiler of the target unit collected in real time and the corresponding outlet temperature of the heat exchange surface of the twin-machine mechanism model, including: The error E is calculated by the difference between the outlet temperature of the heat exchange surface of the unit boiler and the corresponding outlet temperature of the heat exchange surface of the model, E = - . The error change EC is classified by the change and the change speed of the outlet temperature

[0055] of the heat exchange surface of the unit boiler, establish a fuzzy control set for the heat transfer and scaling factors, and dynamically correct the heat transfer and scaling factors of each heat exchange surface of the boiler in real time. ① If E <= -20°C, E is negative large (NB); ② If -20 < E <= -10°C, E is negative medium (NM); ③ If -10 < E <= -5°C, E is negative small (NS); ④ If when -5 < E <= 5°C, E is zero (0); ⑤ If when 5 < E <= 10°C, E is positive small (PS); ⑥ If when 10 < E <= 20°C, E is positive medium (PM); ⑦ If when E > 20°C, E is positive big (PB); Judgment conditions for the change and change rate of the outlet temperature of the heat exchange surface of the unit: ① When > 0, the outlet temperature of the heat exchange surface of the unit rises; ② When = 0, the outlet temperature of the heat exchange surface of the unit remains unchanged; ③ When < 0, the outlet temperature of the heat exchange surface of the unit drops; ④ When > 0, the outlet temperature of the heat exchange surface of the unit changes rapidly; ⑤ When = 0, the outlet temperature of the heat exchange surface of the unit changes at a constant speed; ⑥ When < 0, the outlet temperature of the heat exchange surface of the unit changes slowly; The classification judgment method for the error change (EC) is as follows: ① If "the outlet temperature of the heat exchange surface of the unit rises" & "the outlet temperature of the heat exchange surface of the unit changes rapidly" = true, EC is negative big (NB); ② If "the outlet temperature of the heat exchange surface of the unit rises" & "the outlet temperature of the heat exchange surface of the unit changes at a constant speed" = true, EC is negative medium (NM); ③ If "the outlet temperature of the heat exchange surface of the unit rises" & "the outlet temperature of the heat exchange surface of the unit changes slowly" = true, EC is negative small (NS); ④ If "the outlet temperature of the heat exchange surface of the unit remains unchanged" = true, EC is zero (0); ⑤ If "the outlet temperature of the heat exchange surface of the unit drops" & "the outlet temperature of the heat exchange surface of the unit changes slowly" = true, EC is positive small (PS); ⑥ If "the outlet temperature of the heat exchange surface of the unit drops" & "the outlet temperature of the heat exchange surface of the unit changes at a constant speed" = true, EC is positive medium (PM); ⑦ If "the outlet temperature of the heat exchange surface of the unit drops" & "the outlet temperature of the heat exchange surface of the unit changes rapidly" = true, EC is positive big (PB).

[0056] According to the above conditions, the control signal U, i.e., the heat exchange fouling factor, can be established for the real-time correction fuzzy control rule table.

[0057] Table: Real-time correction fuzzy control rule table for heat exchange fouling factor

[0058] As an embodiment of the present invention, the real-time online application of the twin mechanism model of the unit described in the present invention receives the real-time instructions of the unit DCS system as the control instructions of the model, and the control instructions include: feed water flow, coal volume, air volume, total steam turbine valve instruction, medium pressure valve instruction, condensate pump frequency, circulating water temperature, circulating water control instruction, high and low water level adjustment instruction, deaerator water level adjustment instruction, each section steam extraction valve control instruction, etc. After the system is put into operation, at 25%-100% of the rated power of the unit, the dynamic accuracy of the model reaches within 5%. The important parameters for comparison include, but are not limited to the following: (1) Generator power; (2) Feedwater pump outlet main pipe pressure; (3) Economizer inlet feed water temperature; (4) Economizer inlet feed water pressure; (5) Boiler feed water flow rate; (6) Water wall outlet header temperature; (7) Temperature of the inner wall of the steam-water separator; (8) Lower than the inlet steam temperature; (9) Lower than outlet steam temperature; (10) Steam temperature at the outlet of superheater A; (11) Steam temperature at the outlet of superheater B; (12) Steam temperature at the inlet of superheater secondary desuperheater A; (13) Steam temperature at the inlet of the superheater secondary desuperheater B; (14) Steam temperature at the outlet of superheated secondary desuperheater A; (15) Steam temperature at the outlet of superheated secondary desuperheater B; (16) Higher than the outlet steam temperature; (17)Higher than outlet steam pressure; (18) Main steam pressure (engine side); (19) Main steam temperature; (20) Reheat cold section steam temperature; (21) Reheat cold section steam pressure; (22) Low re-export steam temperature A; (23) Low re-export steam temperature B; (24) High re-inlet steam temperature A; (25) High re-inlet steam temperature B; (26) High re-export steam pressure; (27) High re-export steam temperature; (28) Reheating hot section steam temperature; (29) Reheating hot section steam pressure; (30) Extraction steam pressure of each section; (31) Extraction steam temperature of each section; (32) High and low inlet and outlet temperatures; (33) High and low inlet and outlet pressures; (34) Deaerator pressure; (35) Deaerator temperature; (36) Condensate temperature; (37) Condenser vacuum; (38) Circulating water temperature.

[0059] Example 3 like Figure 4 As shown, the present invention provides a coal-fired unit twin mechanism model boiler parameter self-correction device, comprising: The model building module uses the Modelica language to build the twin mechanism model and offline correction mechanism model of the target unit according to the design parameters; Data acquisition module: The data platform collects data from DCS and provides data services for offline correction of heat transfer coefficients of various heat exchange surfaces of boilers, online correction of heat transfer scaling factors of various heat exchange surfaces of boilers, and twin applications; The calculation module sets the heat transfer scaling factor of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model of the unit as the intermediate value of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler. The correction module, based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, adopts a fuzzy control algorithm to perform online cyclic correction of the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit in sequence.

[0060] Example 4 This embodiment provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method for self-correction of boiler parameters of a twin mechanism model of a coal-fired unit.

[0061] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0063] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0065] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached 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 are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0066] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit, characterized in that: include: According to the design parameters, the twin mechanism model and offline correction mechanism model of the target unit are established using Modelica language; The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat exchange surface of the boiler, online correction of the heat transfer scaling factor of each heat exchange surface of the boiler, and twin applications; The heat transfer scaling factors of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model of the unit are set to the middle value of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler. Based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, the fuzzy control algorithm is used to cyclically correct the heat transfer scaling factors of each heat transfer surface of the twin mechanism model boiler of the unit online in sequence.

2. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 1 is characterized in that: According to the design parameters, the twin mechanism model and offline correction mechanism model of the target unit are established using Modelica language, including: According to the design parameters, the twin mechanism model of the target unit is established using the Modelica language. According to the layout of the temperature and pressure measurement points of the boiler of the target unit, the boiler body model is divided into regions, and the corresponding offline correction mechanism model is established. The twin mechanism model of the unit was established, including the boiler, steam turbine and electrical full process mechanism model; The offline correction mechanism model is divided into furnace area, horizontal flue area and tail flue area mechanism models. The offline correction mechanism model should be completely consistent with the corresponding area of ​​the unit's twin mechanism model.

3. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 1 is characterized in that: The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat exchange surface of the boiler, online correction of the heat transfer scaling factor of each heat exchange surface of the boiler, and twin applications, including: The data platform collects data in real time from the OPC Server of the DCS system of the target unit through a single isolation network gateway. While forwarding the data in real time, it saves historical data. The DCS instructions in the real-time data are used as input instructions for the twin mechanism model of the unit. The historical data saved by the platform is used for self-correction of the heat transfer coefficients of each heat exchange surface of the boiler, and the correction results are synchronously updated to the corresponding heat exchange surfaces of the twin mechanism model of the unit. The outlet temperatures of each heat exchange surface of the boiler in the real-time data are used for on-line correction of the heat transfer scaling factors of each heat exchange surface of the boiler in the twin mechanism model of the unit. The calculation results of the twin mechanism model of the unit are output to the front-end display module for display.

4. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 3 is characterized in that: The DCS instructions in the real-time data serve as input instructions for the twin mechanism model of the unit, including: Feed water flow, coal volume, air volume, total steam turbine valve control instruction, medium pressure valve control instruction, condensate pump frequency, circulating water temperature, circulating water control instruction, high and low water level adjustment instruction, deaerator water level adjustment instruction, each section steam extraction valve control instruction, etc.

5. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 1, characterized in that: The heat transfer scaling factor of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model of the unit is set to the middle value of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler, including: Determine the stability and classify the historical data stored in the data platform; According to the design specifications and historical data, determine the heat transfer coefficient range of each heat transfer surface and the ratio between multiple groups of internal heat transfer coefficients, and constrain the heat transfer coefficient of each heat transfer surface of the boiler; The historical data is used as the boundary of the offline correction mechanism model. The difference between the outlet steam temperature of the offline correction mechanism model and the corresponding steam temperature in the historical data of the object unit is evaluated. The differential evolution algorithm is used to optimize the heat transfer coefficient, and the obtained optimal heat transfer coefficient is synchronously updated to the twin mechanism model of the unit.

6. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 5, characterized in that: Determine the stability and classify the historical data stored in the data platform, including: The load range covers 25%--100%. The stability judgment conditions are: (1) The load of the target unit changes negatively within a minute less than 2MW and the duration is greater than 10 minutes, (2) the change value of the total steam turbine control command per minute is less than 0.2, (3) Ambient temperature deviation Within 3℃.

7. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 1, characterized in that: Based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, the fuzzy control algorithm is used to cyclically correct the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit online, including: After completing the offline calibration to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler, the outlet temperature of each heat transfer surface of the target unit boiler and the outlet temperature of the heat transfer surface corresponding to the twin mechanism model of the unit are collected in real time, and the fuzzy control algorithm is used to self-calibrate the heat transfer scaling factor of each heat transfer surface of the twin mechanism model boiler of the unit online; The heat exchange scaling factor correction of each heat exchange surface of the twin mechanism model boiler of the unit is based on the flue gas process, that is, the order of the vertical flue in the furnace - the horizontal flue - the tail flue. The heat exchange surface of the boiler is calibrated at preset intervals and calibrated one by one.

8. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 7, characterized in that: The outlet temperature of each heat exchange surface of the target unit boiler and the outlet temperature of the heat exchange surface corresponding to the twin mechanism model of the unit are collected in real time. The fuzzy control algorithm is used to self-correct the heat exchange scaling factor of each heat exchange surface of the twin mechanism model boiler of the unit online, including: Error E is the outlet temperature of the boiler heat exchange surface of the target unit Corresponding model heat exchange surface outlet temperature The difference calculation, E = - , Error change EC target unit boiler heat exchange surface outlet temperature Changes and speed of change Classification is carried out, and the fuzzy control set of heat exchange scaling factor is established, and the boiler heat exchange scaling factor is dynamically corrected in real time.

9. A coal-fired unit twin mechanism model boiler parameter self-correction device, characterized in that: include: The model building module uses the Modelica language to build the twin mechanism model and offline correction mechanism model of the target unit according to the design parameters; Data acquisition module: The data platform collects data from DCS and provides data services for offline correction of heat transfer coefficients of various heat exchange surfaces of boilers, online correction of heat transfer scaling factors of various heat exchange surfaces of boilers, and twin applications; The calculation module sets the heat transfer scaling factor of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model of the unit as the intermediate value of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler. The correction module, based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, adopts a fuzzy control algorithm to perform online cyclic correction of the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit in sequence.

10. The boiler parameter self-correction device of the twin mechanism model of a coal-fired unit according to claim 9, characterized in that: In the model building module, the twin mechanism model and offline correction mechanism model of the target unit are established using the Modelica language according to the design parameters, including: According to the design parameters, the twin mechanism model of the target unit is established using the Modelica language. According to the layout of the temperature and pressure measurement points of the boiler of the target unit, the boiler body model is divided into regions, and the corresponding offline correction mechanism model is established. The twin mechanism model of the unit was established, including the boiler, steam turbine and electrical full process mechanism model; The offline correction mechanism model is divided into furnace area, horizontal flue area and tail flue area mechanism models. The offline correction mechanism model should be completely consistent with the corresponding area of ​​the unit's twin mechanism model.

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