Gas turbine starting process modeling method based on test run data

Abstract

The invention is suitable for the technical field of gas turbine modeling simulation, and provides a gas turbine starting process modeling method based on test run data, and the method comprises the steps: obtaining a measurement parameter of a target gas turbine starting process, and carrying out the segmented re-calibration processing of the measurement parameter according to a preset parameter of a preset gas turbine, and obtaining a calibration parameter; a Hammerstein model is established according to the calibration parameters; acquiring an initial time constant of the sensor model; based on the principle of aerodynamic thermodynamics, an iteration method is adopted to construct a part-level mechanism model of the target gas turbine slow, and the Hammerstein model and the part-level mechanism model are fused through a preset filter to obtain a target model; and optimizing the initial time constant of the sensor model and the preset time constant of the preset filter in the target model based on the actual test run data of the target gas turbine to obtain an optimization model.

Description

Technical Field

[0001] The present application belongs to the technical field of gas turbine modeling and simulation, and in particular to a gas turbine startup process modeling method based on test run data. Background Art

[0002] Gas turbine is a complex thermodynamic system with strong nonlinearity, time-varying and multivariable coupling, which consists of compressor, combustion chamber, gas turbine and power turbine. Finding the corresponding relationship between input and output during the startup of gas turbine, establishing a perfect model and performing numerical simulation on it is one of the important contents of design work. Starting numerical simulation can reduce the risk of physical test, shorten the development cycle, and effectively promote the design and development of new and efficient gas turbines in my country.

[0003] The research on high-precision simulation technology of gas turbine startup process at home and abroad is still in an insufficient stage. At present, researchers tend to use support vector machines to construct mathematical models of gas turbine startup process, but this method is highly dependent on a large amount of input and output data for training optimization. When facing the startup design of new gas turbines, due to the lack of targeted data resources, the common practice is to manually statically fine-tune the existing startup data based on traditional engineering experience to establish a suitable model. However, this method will lead to a certain degree of deviation between the numerical simulation results and the actual test data. Summary of the invention

[0004] The embodiment of the present application provides a gas turbine startup process modeling method based on test data, which addresses the problem that there is a deviation between numerical simulation results and actual test data in the prior art.

[0005] The present application embodiment provides a gas turbine startup process modeling optimization method based on test data, comprising the following steps:

[0006] Acquiring measurement parameters of the target gas turbine startup process, and performing segmented recalibration processing on the measurement parameters according to preset parameters of a preset gas turbine to obtain calibration parameters;

[0007] Establishing a Hammerstein model according to the calibration parameters, wherein the Hammerstein model is used to characterize the startup process of the target gas turbine, and the Hammerstein model includes a sensor model for monitoring operating parameters of the target gas turbine during the startup process;

[0008] Obtaining an initial time constant of the sensor model;

[0009] Based on the principle of aerodynamic thermodynamics, an iterative method is used to construct a component-level mechanism model of the target gas turbine idle engine, and the Hammerstein model and the component-level mechanism model are fused through a preset filter to obtain a target model, wherein a preset time constant of the preset filter is consistent with an initial time constant of the sensor model;

[0010] The initial time constant of the sensor model in the target model and the preset time constant of the preset filter are optimized based on the actual test data of the target gas turbine to obtain an optimized model.

[0011] Optionally, the step of acquiring the measurement parameters of the target gas turbine startup process, and performing segmented recalibration processing on the measurement parameters according to preset parameters of a preset gas turbine to obtain calibration parameters includes:

[0012] Acquire key nodes in the process of starting the target gas turbine, and segment the measurement parameters according to the key nodes to obtain measurement parameters before ignition and measurement parameters after ignition, wherein the key nodes include ignition time, ignition speed, idle fuel and idle speed;

[0013] According to the preset pre-ignition measurement parameter sequence in the preset parameters, the measurement parameters before ignition are recalibrated based on time data to obtain a first measurement parameter, and the formula is as follows:

[0014]

[0015] in is a measurement parameter sequence before ignition of the target gas turbine, wherein the measurement parameter sequence includes a gas turbine speed sequence, a power turbine speed sequence, a compressor outlet pressure sequence and a power turbine outlet temperature sequence; k 01 The proportionality coefficient of the measurement parameter sensor before the target gas turbine is ignited; a sequence of pre-set pre-ignition measurement parameters of the pre-set gas turbine;

[0016] According to the preset post-ignition measurement parameter sequence in the preset parameters, the measurement parameters after ignition are recalibrated based on the fuel data to obtain a second measurement parameter, and the formula is as follows:

[0017]

[0018] in k is a parameter sequence measured after the target gas turbine is ignited; 13 The proportional coefficient of the sensor for the measurement parameter after ignition; Measuring parameter values ​​at the ignition point of the preset gas turbine; Measuring parameter values ​​at the ignition point of the preset gas turbine; a sequence of pre-set post-ignition measurement parameters of the pre-set gas turbine;

[0019] A combination of the first measurement parameter and the second measurement parameter is used as the calibration parameter.

[0020] Optionally, the step of establishing a Hammerstein model according to the calibration parameters comprises:

[0021] The starting process of the target gas turbine is divided into a first stage for representing the zero speed to the ignition speed, a second stage for representing the ignition speed to the starter motor disengagement, and a third stage for representing the starter motor disengagement to the idle speed;

[0022] The mathematical representation of the first stage is:

[0023]

[0024] The mathematical representation of the second stage is:

[0025]

[0026] The mathematical representation of the stage III is:

[0027]

[0028] Among them, M CT Torque provided to the starting motor, M T is the torque consumed by the gas turbine, M C is the torque consumed by the compressor, η m is the mechanical efficiency of friction and accessory consumption, ω is the angular velocity of the gas turbine, and J is the moment of inertia of the gas generator rotor;

[0029] A nonlinear function between the control input variable and the calibration parameter is obtained, and the mathematical representation of the nonlinear function is:

[0030] Δp d =g(u)

[0031] When the gas turbine reaches the idle speed, a linear dynamic system of the target gas turbine is established. The mathematical representation of the linear dynamic system is:

[0032] H(s)·Δp d =p 实际

[0033] The static nonlinear process and the dynamic linear process are integrated, and a Hammerstein model is established according to the calibration parameters and the linear dynamic system. The mathematical representation of the Hammerstein model is:

[0034] p 实际 (s) = H(s) g(u)

[0035] Where s is the complex frequency variable after Laplace transformation, and H(s) is the linear dynamic system.

[0036] Optionally, the step of establishing the Hammerstein model according to the calibration parameters further includes:

[0037] Modeling the sensor in the target gas turbine as a sensor linear dynamic system, and determining the time-varying initial time constant of the sensor by using a nonlinear system identification method and engineering experience;

[0038] A first cost function of the difference between the target gas turbine and the preset gas turbine is obtained, wherein the mathematical representation of the first cost function is:

[0039]

[0040] Among them, p d [n] is the calibration parameter at the nth sampling time, u[n] is the control input at the nth sampling time, and H(z,τ) is the transfer function in the discrete time domain;

[0041] Minimize the first cost function using an optimization algorithm to obtain a time-varying initial time constant of the target gas turbine startup process;

[0042] determining a target gas turbine speed corresponding to the initial time constant in preset corresponding data;

[0043] The optimal time constant matching the target gas turbine speed is obtained by a dynamic scheduling method, the initial time constant is updated according to the optimal time constant, and a sensor model for monitoring the operating parameters of the target gas turbine during startup is obtained.

[0044] Optionally, the step of constructing the component-level mechanism model of the target gas turbine engine at idle by an iterative method based on the principle of aerodynamic thermodynamics comprises:

[0045] Based on the principle of aerodynamic thermodynamics, an iterative method is used to construct a component-level mechanism model of the gas turbine idle engine, and the balance equations of the component-level model are established. The balance equations include a flow balance equation, a power balance equation, and a pressure balance equation:

[0046] The flow balance equation is:

[0047]

[0048] Among them, XW GTand XW PT are the actual inlet flows of the gas turbine and power turbine, respectively, and W GT and W PT are the gas turbine and power turbine inlet flows, respectively;

[0049] The power balance equation is:

[0050]

[0051] Among them, PW GT ,PW PT ,PW C and PW load are the power of gas turbine, power turbine, compressor and load shaft respectively, η GT and η PT are the efficiencies of the gas turbine and the power turbine, respectively;

[0052] The pressure balance equation is:

[0053]

[0054] Among them, P pamb and P N are the ambient pressure and the tail nozzle outlet pressure, ε i (i=1,2,...,5) is the deviation.

[0055] Optionally, before the step of fusing the Hammerstein model and the component-level mechanism model through a preset filter, the method further includes:

[0056] Determine the steady-state value of the slow point of the mechanism model based on the flow balance equation, the power balance equation and the pressure balance equation;

[0057] A slow point corresponding to the steady-state value is taken as a target point, and a filter corresponding to the sensor model is built at the target point, wherein a time constant of the filter is consistent with a time constant of the sensor.

[0058] Optionally, the step of optimizing the initial time constant of the sensor model in the target model and the preset time constant of the preset filter based on the actual test data of the target gas turbine comprises:

[0059] The fuel sequence in the actual test data and the variation rule of the measurement data in the actual test data are determined based on a preset differential equation, and the mathematical representation of the preset differential equation is:

[0060] p t [n] = (1-a)p t [n-1]+au[n]

[0061] Where a is a coefficient related to the time constant, and a is expressed by the transfer function Transformed to get t [n] is the result of the filter processing the input signal u[n];

[0062] According to the variation law, the time constant is identified by using the nonlinear least square method to construct a second cost function, and the mathematical representation of the second cost function is:

[0063]

[0064] Among them, ω[n] is the weight factor;

[0065] The second cost function is minimized by the Newton method, and the optimized time constant of the sensor model and the optimized time constant of the filter model in the Hammerstein model are obtained by using the coefficient a and the preset conversion function, and the mathematical representation of the preset conversion function is:

[0066]

[0067] Among them, T s is the sampling period, and τ is the optimization time constant.

[0068] Optionally, the method further includes:

[0069] Performing segmented identification on actual test data to obtain a time constant sequence and a gas turbine speed sequence corresponding to the time constant sequence;

[0070] Under the conditions of atmospheric temperature and atmospheric pressure that are the same as those of actual test data, a numerical simulation is performed on the start-up process of the target gas turbine.

[0071] Optionally, the step of numerically simulating the target gas turbine startup process comprises:

[0072] Acquiring data on the change in the gas turbine speed during the startup of the target gas turbine;

[0073] The optimal time constant corresponding to the changed gas turbine speed is dynamically selected according to the change data.

[0074] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by obtaining the measurement parameters of the target gas turbine startup process, and performing segmented recalibration processing on the measurement parameters according to the preset parameters of the preset gas turbine, the calibration parameters are obtained; a Hammerstein model is established according to the calibration parameters, the Hammerstein model is used to characterize the startup process of the target gas turbine, and the Hammerstein model includes a sensor model for monitoring the operating parameters of the target gas turbine during the startup process; the initial time constant of the sensor model is obtained; based on the principle of aerodynamic thermodynamics, an iterative method is used to construct a component-level mechanism model of the target gas turbine slow-running, and the Hammerstein model and the component-level mechanism model are fused through a preset filter to obtain a target model, and the preset time constant of the preset filter is consistent with the initial time constant of the sensor model; based on the actual test data of the target gas turbine, the initial time constant of the sensor model in the target model and the preset time constant of the preset filter are optimized to obtain an optimized model. High-precision dynamic numerical simulation of the gas turbine startup process is achieved, and the numerical simulation accuracy of the gas turbine startup process is improved. Taking the startup simulation of a certain type of gas turbine as an example, before the present invention was adopted, the maximum value of the relative error between the key measurement parameters in the startup phase and the actual test data was as high as 8.73%, which may cause emergency shutdown when certain restrictions are touched during the actual test. However, after the application of the present invention, the maximum value of the relative error of the key measurement parameters was successfully reduced to 3.88%, especially the relative error of the power turbine outlet temperature was greatly reduced by 4.92%, so that the simulation results meet the engineering accuracy requirements. This improvement not only ensures that the change trend of each measurement parameter in the simulation process is consistent with the actual situation, but also improves the accuracy of the numerical simulation of the gas turbine startup process. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0076] Figure 1 It is a flow chart of a gas turbine startup process modeling method based on test data provided by the present invention;

[0077] Figure 2 It is a flow chart of another gas turbine startup process modeling method based on test data provided by the present invention;

[0078] Figure 3The present invention provides a schematic diagram of data recalibration of the gas turbine speed based on time before ignition, taking the gas turbine speed as an example;

[0079] Figure 4 The present invention provides a schematic diagram of recalibrating data of a gas turbine speed based on fuel after ignition, taking the gas turbine speed as an example;

[0080] Figure 5 is a schematic diagram of the relationship between torque and gas turbine speed during the startup process provided by the present invention;

[0081] Figure 6 It is a schematic diagram of the Hammerstein model of the gas turbine startup process provided by the present invention;

[0082] Figure 7 It is a schematic diagram of the fusion of the data model and the mechanism model provided by the present invention. DETAILED DESCRIPTION

[0083] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0084] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0085] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0086] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]" depending on the context.

[0087] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0088] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0089] like Figure 1 As shown, the embodiment of the present application provides a gas turbine startup process modeling optimization method based on test data, comprising the following steps:

[0090] S101, obtaining measurement parameters of the target gas turbine startup process, and performing segmented recalibration processing on the measurement parameters according to preset parameters of the preset gas turbine to obtain calibration parameters;

[0091] S102, establishing a Hammerstein model according to the calibration parameters, wherein the Hammerstein model is used to characterize the startup process of the target gas turbine, and the Hammerstein model includes a sensor model for monitoring operating parameters of the target gas turbine during the startup process;

[0092] S103, obtaining an initial time constant of the sensor model;

[0093] S104, based on the principle of aerodynamic thermodynamics, an iterative method is used to construct a component-level mechanism model of the target gas turbine at idle, and the Hammerstein model and the component-level mechanism model are fused through a preset filter to obtain a target model, wherein a preset time constant of the preset filter is consistent with an initial time constant of the sensor model;

[0094] S105 , optimizing the initial time constant of the sensor model and the preset time constant of the preset filter in the target model based on the actual test data of the target gas turbine to obtain an optimized model.

[0095] In a possible implementation, Figure 2As shown, the embodiment of the present application provides another gas turbine startup process modeling optimization method based on test data, the method comprising: step S1, obtaining the measurement parameters of the existing gas turbine startup process, and analyzing the gas turbine ignition point and slow point, wherein the measurement parameters are the gas turbine speed n g , power turbine speed n p , compressor outlet pressure P 31 , power turbine outlet temperature T6, fuel flow rate W f , ignition time t 1 and start time t 3 ; In combination with the design requirements of a new type of gas turbine (preset parameters of a preset gas turbine), the measured parameters of the start-up process of the existing gas turbine are recalibrated in different degrees (equivalent to obtaining the measured parameters of the start-up process of the target gas turbine, and performing segmented recalibration processing on the measured parameters according to the preset parameters of the preset gas turbine to obtain calibration parameters);

[0096] Step S2: Establish the gas turbine startup data model. The relationship between the torque and the gas turbine speed during the startup process is as follows: Figure 5 As shown. In the first stage of the startup process, the gas turbine is completely driven by the starter motor to accelerate the gas generator rotor until the combustion chamber is ignited. At this time, the residual work is the difference between the torque generated by the starter motor and the torque consumed in cold operation. It is called the ignition speed; in the second stage, the starting motor and the gas turbine work together to drive the engine shaft to accelerate. At this time, the residual work is the difference between the sum of the torque of the starting motor and the gas turbine and the power consumed by the compressor. It is called the disengagement speed; in the third stage, after the starter motor is disengaged, the gas turbine alone drives the engine rotor to accelerate until it reaches the slow speed. At this time, the residual work is the difference between the gas turbine work and the compressor work. Taking fuel as the control input variable as an example, combined with the gas turbine startup process W f [n] The measurement parameter p after recalibration with the segmented data d The nonlinear relationship between [n] and the dynamic characteristics when reaching the slow speed are established as follows: Figure 6 The Hammerstein data model shown (equivalent to establishing a Hammerstein model according to the calibration parameters, the Hammerstein model is used to characterize the startup process of the target gas turbine);

[0097] Step S3: construct gas turbine temperature sensor, pressure sensor and speed sensor models, each sensor corresponding to a different transfer function (equivalent to the Hammerstein model including a sensor model for monitoring the operating parameters of the target gas turbine during startup), such as Figure 6The dynamic linear link shown in . Analyze the influence of the change of gas turbine speed on the compressor compression ratio, the mixture concentration in the combustion chamber and the combustion rate; use the parameterized model identification method to obtain the time constants of the pressure sensor and the speed sensor of the gas turbine startup process, and obtain the time constant of the temperature sensor of the gas turbine startup process based on engineering experience; each sensor has a set of time constant sequences and the corresponding gas turbine speed sequence, and the optimal time constant matching the current speed is obtained by the dynamic scheduling method; due to the lack of targeted data resources, the temperature sensor is generally selected as 8 to 10 according to experience, that is, τ T6 =8~10; the time constant of the pressure sensor is consistent with that of the speed sensor, that is, τ ng =τ np =τ P31 (equivalent to obtaining the initial time constant of the sensor model);

[0098] Step S4, based on the aerodynamic thermodynamic principle of gas turbine operation, an iterative method is used to construct a mechanism model of the gas turbine component level above the slow start point. The fuel value at the slow start point is known, and when the deviations of the flow balance equation, the power balance equation, and the pressure balance equation are within the specified range, the steady-state value of the mechanism model at the slow start point is calculated. A filter is built at the switching point to fuse the data model with the mechanism model to avoid value jumps when the model is switched and touch the gas turbine startup restriction conditions. At this time, the time constant of the filter is consistent with the time constant of the sensor during the startup process, and the following is obtained: Figure 7 The fusion model shown (equivalent to constructing the component-level mechanism model of the target gas turbine slow engine by an iterative method based on the principle of aerodynamic thermodynamics, and fusing the Hammerstein model and the component-level mechanism model through a preset filter to obtain a target model, wherein the preset time constant of the preset filter is consistent with the initial time constant of the sensor model);

[0099] Step S5: Knowing the variation law Δp of the measured parameters in the actual test data t [n] is the input fuel sequence W through the sensor f [n] The processed result is to generate and optimize the time constant of each sensor using the nonlinear least square method and Newton method (equivalent to optimizing the initial time constant of the sensor model in the target model and the preset time constant of the preset filter based on the actual test data of the target gas turbine to obtain the optimized model). At this time, the time constants corresponding to the four sensors are inconsistent, that is, τ ng ≠τ np ≠τ P31 ≠τ T6The actual test data is segmented and identified to obtain a time constant sequence and a corresponding gas turbine speed sequence. During the startup process, when the gas turbine speed changes, the optimal time constant corresponding to the speed is dynamically selected to improve the simulation accuracy.

[0100] The measured parameters of the target gas turbine startup process are obtained, and the measured parameters are recalibrated in sections according to the preset parameters of the preset gas turbine to obtain calibration parameters; a Hammerstein model is established according to the calibration parameters, and the Hammerstein model is used to characterize the startup process of the target gas turbine, and the Hammerstein model includes a sensor model for monitoring the operating parameters of the target gas turbine during the startup process; the initial time constant of the sensor model is obtained; based on the principle of aerodynamic thermodynamics, a component-level mechanism model of the target gas turbine slow-running is constructed by an iterative method, and the Hammerstein model and the component-level mechanism model are fused through a preset filter to obtain a target model, and the preset time constant of the preset filter is consistent with the initial time constant of the sensor model; based on the actual test data of the target gas turbine, the initial time constant of the sensor model in the target model and the preset time constant of the preset filter are optimized to obtain an optimized model. The high-precision dynamic numerical simulation of the gas turbine startup process is realized, and the numerical simulation accuracy of the gas turbine startup process is improved. Taking the startup simulation of a certain type of gas turbine as an example, before the present invention was adopted, the maximum value of the relative error between the key measurement parameters in the startup phase and the actual test data was as high as 8.73%, which may cause emergency shutdown when certain restrictions are touched during the actual test. However, after the application of the present invention, the maximum value of the relative error of the key measurement parameters was successfully reduced to 3.88%, especially the relative error of the power turbine outlet temperature was greatly reduced by 4.92%, so that the simulation results meet the engineering accuracy requirements. This improvement not only ensures that the change trend of each measurement parameter in the simulation process is consistent with the actual situation, but also improves the accuracy of the numerical simulation of the gas turbine startup process.

[0101] In a possible implementation manner, the step of obtaining the measurement parameters of the target gas turbine startup process, and performing segmented recalibration processing on the measurement parameters according to preset parameters of a preset gas turbine to obtain calibration parameters includes:

[0102] Acquire key nodes in the process of starting the target gas turbine, and segment the measurement parameters according to the key nodes to obtain measurement parameters before ignition and measurement parameters after ignition, wherein the key nodes include ignition time, ignition speed, idle fuel and idle speed;

[0103] like Figure 3As shown, the measurement parameters before ignition are recalibrated based on time data according to the preset pre-ignition measurement parameter sequence in the preset parameters to obtain the first measurement parameter, and the formula is as follows:

[0104]

[0105] in is a measurement parameter sequence before ignition of the target gas turbine, wherein the measurement parameter sequence includes a gas turbine speed sequence, a power turbine speed sequence, a compressor outlet pressure sequence and a power turbine outlet temperature sequence; k 01 The proportionality coefficient of the measurement parameter sensor before the target gas turbine is ignited; a sequence of pre-set pre-ignition measurement parameters of the pre-set gas turbine;

[0106] like Figure 4 As shown, the measurement parameters after ignition are recalibrated based on the fuel data according to the preset post-ignition measurement parameter sequence in the preset parameters to obtain the second measurement parameter, and the formula is as follows:

[0107]

[0108] in k is a parameter sequence measured after the target gas turbine is ignited; 13 The proportional coefficient of the sensor for the measurement parameter after ignition; Measuring parameter values ​​at the ignition point of the preset gas turbine; Measuring parameter values ​​at the ignition point of the preset gas turbine; a sequence of pre-set post-ignition measurement parameters of the pre-set gas turbine;

[0109] A combination of the first measurement parameter and the second measurement parameter is used as the calibration parameter.

[0110] In a possible implementation, the step of establishing the Hammerstein model according to the calibration parameters includes:

[0111] The starting process of the target gas turbine is divided into a first stage for representing the zero speed to the ignition speed, a second stage for representing the ignition speed to the starter motor disengagement, and a third stage for representing the starter motor disengagement to the idle speed;

[0112] The mathematical representation of the first stage is:

[0113]

[0114] The mathematical representation of the second stage is:

[0115]

[0116] The mathematical representation of the stage III is:

[0117]

[0118] Among them, M CT Torque provided to the starting motor, M T is the torque consumed by the gas turbine, M C is the torque consumed by the compressor, η m is the mechanical efficiency of friction and accessory consumption, ω is the angular velocity of the gas turbine, and J is the moment of inertia of the gas generator rotor;

[0119] A nonlinear function between the control input variable and the calibration parameter is obtained, and the mathematical representation of the nonlinear function is:

[0120] Δp d =g(u)

[0121] When the gas turbine reaches the idle speed, a linear dynamic system of the target gas turbine is established. The mathematical representation of the linear dynamic system is:

[0122] H(s)·Δp d =p 实际

[0123] The static nonlinear process and the dynamic linear process are integrated, and a Hammerstein model is established according to the calibration parameters and the linear dynamic system. The mathematical representation of the Hammerstein model is:

[0124] p 实际 (s) = H(s) g(u)

[0125] Where s is the complex frequency variable after Laplace transformation, and H(s) is the linear dynamic system.

[0126] In a possible implementation manner, the step of establishing the Hammerstein model according to the calibration parameters further includes:

[0127] Modeling the sensor in the target gas turbine as a sensor linear dynamic system, and determining the time-varying initial time constant of the sensor by using a nonlinear system identification method and engineering experience;

[0128] A first cost function of the difference between the target gas turbine and the preset gas turbine is obtained, wherein the mathematical representation of the first cost function is:

[0129]

[0130] Among them, pd [n] is the calibration parameter at the nth sampling time, u[n] is the control input at the nth sampling time, and H(z,τ) is the transfer function in the discrete time domain;

[0131] Minimize the first cost function using an optimization algorithm to obtain a time-varying initial time constant of the target gas turbine startup process;

[0132] determining a target gas turbine speed corresponding to the initial time constant in preset corresponding data;

[0133] The optimal time constant matching the target gas turbine speed is obtained by a dynamic scheduling method, the initial time constant is updated according to the optimal time constant, and a sensor model for monitoring the operating parameters of the target gas turbine during startup is obtained.

[0134] In a possible implementation, the step of constructing the component-level mechanism model of the target gas turbine engine at idle by an iterative method based on the principle of aerodynamic thermodynamics includes:

[0135] Based on the principle of aerodynamic thermodynamics, an iterative method is used to construct a component-level mechanism model of the gas turbine idle engine, and the balance equations of the component-level model are established. The balance equations include a flow balance equation, a power balance equation, and a pressure balance equation:

[0136] The flow balance equation is:

[0137]

[0138] Among them, XW GT and XW PT are the actual inlet flows of the gas turbine and power turbine, respectively, and W GT and W PT are the gas turbine and power turbine inlet flows, respectively;

[0139] The power balance equation is:

[0140]

[0141] Among them, PW GT ,PW PT ,PW C and PW load are the power of gas turbine, power turbine, compressor and load shaft respectively, η GT and η PT are the efficiencies of the gas turbine and the power turbine, respectively;

[0142] The pressure balance equation is:

[0143]

[0144] Among them, P pamb and P N are the ambient pressure and the tail nozzle outlet pressure, ε i (i=1,2,...,5) is the deviation.

[0145] In a possible implementation manner, before the step of fusing the Hammerstein model and the component-level mechanism model through a preset filter, the method further includes:

[0146] Determine the steady-state value of the slow point of the mechanism model based on the flow balance equation, the power balance equation and the pressure balance equation;

[0147] A slow point corresponding to the steady-state value is taken as a target point, and a filter corresponding to the sensor model is built at the target point, wherein a time constant of the filter is consistent with a time constant of the sensor.

[0148] In a possible implementation manner, the step of optimizing the initial time constant of the sensor model in the target model and the preset time constant of the preset filter based on the actual test data of the target gas turbine includes:

[0149] The fuel sequence in the actual test data and the variation rule of the measurement data in the actual test data are determined based on a preset differential equation, and the mathematical representation of the preset differential equation is:

[0150] p t [n] = (1-a)p t [n-1]+au[n]

[0151] Where a is a coefficient related to the time constant, and a is expressed by the transfer function Transformed to get t [n] is the result of the filter processing the input signal u[n];

[0152] According to the variation law, the time constant is identified by using the nonlinear least square method to construct a second cost function, and the mathematical representation of the second cost function is:

[0153]

[0154] Among them, ω[n] is the weight factor;

[0155] The second cost function is minimized by the Newton method, and the optimized time constant of the sensor model and the optimized time constant of the filter model in the Hammerstein model are obtained by using the coefficient a and the preset conversion function, and the mathematical representation of the preset conversion function is:

[0156]

[0157] Among them, T s is the sampling period, and τ is the optimization time constant.

[0158] In a possible implementation, the method further includes:

[0159] Performing segmented identification on actual test data to obtain a time constant sequence and a gas turbine speed sequence corresponding to the time constant sequence;

[0160] Under the conditions of atmospheric temperature and atmospheric pressure that are the same as those of actual test data, a numerical simulation is performed on the start-up process of the target gas turbine.

[0161] In a possible implementation manner, the step of numerically simulating the target gas turbine startup process includes:

[0162] Acquiring data on the change in the gas turbine speed during the startup of the target gas turbine;

[0163] The optimal time constant corresponding to the changed gas turbine speed is dynamically selected according to the change data.

[0165] For example, for a certain type of gas turbine (target gas turbine), under the initial conditions of the same atmospheric temperature and atmospheric pressure as the actual test data, the model is simulated until the gas turbine speed reaches the slow point. When the actual test data is not used for optimization, the maximum value of the relative error between the key measurement parameters and the actual test data is as high as 8.73%, and the power turbine outlet temperature changes greatly at the model fusion point, which may cause emergency shutdown when certain restrictions are touched during the actual test. However, after optimization using the actual test data, the maximum value of the relative error of the key measurement parameters is successfully reduced to 3.88%, especially the relative error of the power turbine outlet temperature is greatly reduced by 4.92%, and the simulation results meet the engineering accuracy requirements; before ignition, the combustion chamber has not yet been supplied with oil for combustion, the power turbine speed remains at zero, and the power turbine outlet temperature is consistent with the atmospheric temperature during the test; at ignition, the gas turbine speed increases, the power turbine speed increases from zero, and the power turbine outlet temperature increases significantly; when approaching the slow fuel, the rate of change of the gas turbine speed and the power turbine outlet temperature slows down, which is in line with the mechanism characteristics of the actual start-up process of the gas turbine.

[0166] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0167] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0168] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A gas turbine startup process modeling optimization method based on test data, characterized in that: The following steps are involved: Acquiring measurement parameters of the target gas turbine startup process, and performing segmented recalibration processing on the measurement parameters according to preset parameters of a preset gas turbine to obtain calibration parameters; Establishing a Hammerstein model according to the calibration parameters, wherein the Hammerstein model is used to characterize the startup process of the target gas turbine, and the Hammerstein model includes a sensor model for monitoring operating parameters of the target gas turbine during the startup process; Obtaining an initial time constant of the sensor model; Based on the principle of aerodynamic thermodynamics, an iterative method is used to construct a component-level mechanism model of the target gas turbine idle engine, and the Hammerstein model and the component-level mechanism model are fused through a preset filter to obtain a target model, wherein a preset time constant of the preset filter is consistent with an initial time constant of the sensor model; The initial time constant of the sensor model in the target model and the preset time constant of the preset filter are optimized based on the actual test data of the target gas turbine to obtain an optimized model.

2. A gas turbine startup process modeling and optimization method based on test run data according to claim 1, characterized in that: The step of obtaining the measurement parameters of the target gas turbine startup process, and performing segmented recalibration processing on the measurement parameters according to the preset parameters of the preset gas turbine to obtain calibration parameters includes: Acquire key nodes in the process of starting the target gas turbine, and segment the measurement parameters according to the key nodes to obtain measurement parameters before ignition and measurement parameters after ignition, wherein the key nodes include ignition time, ignition speed, idle fuel and idle speed; According to the preset pre-ignition measurement parameter sequence in the preset parameters, the measurement parameters before ignition are recalibrated based on time data to obtain a first measurement parameter, and the formula is as follows: in is a measurement parameter sequence before ignition of the target gas turbine, wherein the measurement parameter sequence includes a gas turbine speed sequence, a power turbine speed sequence, a compressor outlet pressure sequence and a power turbine outlet temperature sequence; k 01 The proportionality coefficient of the measurement parameter sensor before the target gas turbine is ignited; a sequence of pre-set pre-ignition measurement parameters of the pre-set gas turbine; According to the preset post-ignition measurement parameter sequence in the preset parameters, the measurement parameters after ignition are recalibrated based on the fuel data to obtain a second measurement parameter, and the formula is as follows: in k is a parameter sequence measured after the target gas turbine is ignited; 13 The proportional coefficient of the sensor for the measurement parameter after ignition; Measuring parameter values ​​at the ignition point of the preset gas turbine; Measuring parameter values ​​at the ignition point of the preset gas turbine; a sequence of pre-set post-ignition measurement parameters of the pre-set gas turbine; A combination of the first measurement parameter and the second measurement parameter is used as the calibration parameter.

3. A gas turbine startup process modeling method based on test data according to claim 1, characterized in that: The step of establishing the Hammerstein model according to the calibration parameters comprises: The starting process of the target gas turbine is divided into a first stage for representing the zero speed to the ignition speed, a second stage for representing the ignition speed to the starter motor disengagement, and a third stage for representing the starter motor disengagement to the idle speed; The mathematical representation of the first stage is: The mathematical representation of the second stage is: The mathematical representation of the stage III is: Among them, M CT Torque provided to the starting motor, M T is the torque consumed by the gas turbine, M C is the torque consumed by the compressor, η m is the mechanical efficiency of friction and accessory consumption, ω is the angular velocity of the gas turbine, and J is the moment of inertia of the gas generator rotor; A nonlinear function between the control input variable and the calibration parameter is obtained, and the mathematical representation of the nonlinear function is: Δp d =g(u) When the gas turbine reaches the idle speed, a linear dynamic system of the target gas turbine is established. The mathematical representation of the linear dynamic system is: H(s)·Δp d =p 实际 The static nonlinear process and the dynamic linear process are integrated, and a Hammerstein model is established according to the calibration parameters and the linear dynamic system. The mathematical representation of the Hammerstein model is: p 实际 (s)=H(s)·g(u) Where s is the complex frequency variable after Laplace transformation, and H(s) is the linear dynamic system.

4. A gas turbine startup process modeling method based on test data according to claim 1, characterized in that: The step of establishing the Hammerstein model according to the calibration parameters further includes: Modeling the sensor in the target gas turbine as a sensor linear dynamic system, and determining the time-varying initial time constant of the sensor by using a nonlinear system identification method and engineering experience; A first cost function of the difference between the target gas turbine and the preset gas turbine is obtained, wherein the mathematical representation of the first cost function is: Among them, p d [n] is the calibration parameter at the nth sampling time, u[n] is the control input at the nth sampling time, and H(z,τ) is the transfer function in the discrete time domain; Minimize the first cost function using an optimization algorithm to obtain a time-varying initial time constant of the target gas turbine startup process; determining a target gas turbine speed corresponding to the initial time constant in preset corresponding data; The optimal time constant matching the target gas turbine speed is obtained by a dynamic scheduling method, the initial time constant is updated according to the optimal time constant, and a sensor model for monitoring the operating parameters of the target gas turbine during startup is obtained.

5. A gas turbine startup process modeling and optimization method based on test run data as claimed in claim 1, characterized in that: The step of constructing the component-level mechanism model of the target gas turbine engine at idle speed by an iterative method based on the principle of aerodynamic thermodynamics comprises: Based on the principle of aerodynamic thermodynamics, an iterative method is used to construct a component-level mechanism model of the gas turbine idle engine, and the balance equations of the component-level model are established. The balance equations include a flow balance equation, a power balance equation, and a pressure balance equation: The flow balance equation is: Among them, XW GT and XW PT are the actual inlet flows of the gas turbine and power turbine, respectively, and W GT and W PT are the gas turbine and power turbine inlet flows, respectively; The power balance equation is: Among them, PW GT ,PW PT ,PW C and PW load are the power of gas turbine, power turbine, compressor and load shaft respectively, η GT and η PT are the efficiencies of the gas turbine and the power turbine, respectively; The pressure balance equation is: Among them, P pamb and P N are the ambient pressure and the tail nozzle outlet pressure, ε i (i=1,2,...,5) is the deviation.

6. A gas turbine startup process modeling optimization method based on test data as claimed in claim 5, characterized in that: Before the step of fusing the Hammerstein model and the component-level mechanism model through a preset filter, the method further includes: Determine the steady-state value of the slow point of the mechanism model based on the flow balance equation, the power balance equation and the pressure balance equation; A slow point corresponding to the steady-state value is taken as a target point, and a filter corresponding to the sensor model is built at the target point, wherein a time constant of the filter is consistent with a time constant of the sensor.

7. A gas turbine startup process modeling and optimization method based on test run data as claimed in claim 1, characterized in that: The step of optimizing the initial time constant of the sensor model in the target model and the preset time constant of the preset filter based on the actual test data of the target gas turbine comprises: The fuel sequence in the actual test data and the variation rule of the measurement data in the actual test data are determined based on the preset differential equation, and the mathematical representation of the preset differential equation is: p t [n]=(1-a)p t [n-1]+au[n] Where a is a coefficient related to the time constant, and a is expressed by the transfer function Transformed to get t [n] is the result of the filter processing the input signal u[n]; According to the variation law, the time constant is identified by using the nonlinear least square method to construct a second cost function, and the mathematical representation of the second cost function is: Among them, ω[n] is the weight factor; The second cost function is minimized by the Newton method, and the optimized time constant of the sensor model and the optimized time constant of the filter model in the Hammerstein model are obtained by using the coefficient a and the preset conversion function, and the mathematical representation of the preset conversion function is: Among them, T s is the sampling period, and τ is the optimization time constant.

8. A gas turbine startup process modeling method based on test data according to claim 1, characterized in that: The method further comprises: Performing segmented identification on actual test data to obtain a time constant sequence and a gas turbine speed sequence corresponding to the time constant sequence; Under the conditions of atmospheric temperature and atmospheric pressure that are the same as those of actual test data, a numerical simulation is performed on the start-up process of the target gas turbine.

9. A gas turbine startup process modeling method based on test run data as claimed in claim 8, characterized in that: The step of numerically simulating the target gas turbine startup process comprises: Acquiring data on the change in the gas turbine speed during the startup of the target gas turbine; The optimal time constant corresponding to the changed gas turbine speed is dynamically selected according to the change data.

Images