Surge margin estimation method of gas-steam combined cycle generator set

Abstract

The invention discloses a surge margin estimation method of a gas-steam combined cycle generator set, and belongs to the technical field of gas-steam combined cycle power generation, the method estimates the surge margin based on steady state identification-dynamic correction, and the identification process is divided into two ordered steps: 1, identifying a steady state process; 2, identifying a dynamic parameter, namely a state space matrix, by using a dynamic process; nonlinear model identification which is difficult to realize is converted into a linear least square problem of static parameter fitting and dynamic parameter correction, the number of undetermined coefficients is reduced through two-step identification of steady-state identification-dynamic correction and by analyzing constraint conditions among identification parameters, and the identification accuracy is improved. Therefore, engineering realization of complex identification problems becomes possible, surge margin estimation is realized, and safe and stable operation of a unit is ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of gas-steam combined cycle power generation, and in particular relates to a method for estimating a surge margin of a gas-steam combined cycle power generation unit. Background Art

[0002] Surge is an aerodynamically unstable working state in the compressor and an important source of obstacles to the improvement of the working efficiency and stable operation of the impeller machinery. With the improvement of the performance requirements of the gas-steam combined cycle generator set, it is required to give full play to the potential capacity of the gas turbine, the unit parameters are closer to the limit state, and the surge margin is more tense. Therefore, if the signs of unit surge can be measured, a real-time estimation model of the surge margin can be established, and the signal can be provided to the surge control system, the surge can be more effectively controlled and the anti-surge capability of the unit can be improved.

[0003] The traditional method starts from the aerodynamic and thermal process of the compressor. One method is to use the mature aerodynamic and thermal model of the turbine engine to obtain the characteristic interface output parameters, and then estimate the surge margin. The other method is to use a simplified one-dimensional model of the compression component to describe the surge dynamic process. According to the momentum conservation of the compressor channel, the mass conservation of the gas in the cavity, the angular momentum conservation of the main shaft and the thermodynamic equation of the gas, a dynamic model of the compression system surge is established to calculate the compressor characteristic parameters and then obtain the surge margin. The dynamic model obtained by this method can better simulate the surge dynamics of the impeller compression system and is also used in the design of surge control systems. However, since the aerodynamic and thermal model is composed of a series of complex differential equations, the calculation amount is large and it is difficult to achieve real-time calculation.

[0004] A modeling method for engine surge margin has been proposed in the prior art (see "Wang Jiankang, Zhang Haibo, Chen Ke, et al. Engine high stability control based on surge margin estimation model [J]. Journal of Aerospace Power, 2013, 28(09): 2145-2154. DOI: 10.13224 / j.cnki.jasp.2013.09.024."). The surge margin model is divided into two parts: a distortion-free model during conventional flight and a loss model during super maneuverable flight. The distortion-free model is based on the surge margin feature selection algorithm to select the optimal model input and is modeled using a nonlinear fitting method; the loss model is based on an online angle of attack prediction model to evaluate the engine inlet distortion in real time and then calculate it. The disadvantage of this method is that it first designs an online angle of attack prediction model based on the OSP-LSSVR algorithm, and then uses D c60 =D c60 (Ma,α) is the intake distortion index, and the intake distortion is converted into surge pressure ratio loss, and then the surge margin loss during distortion is obtained. This method has complicated steps and weak generalization ability.

[0005] Therefore, the estimation of surge margin for gas-steam combined cycle power generation units is a technical issue that needs to be addressed in particular. Summary of the invention

[0006] The purpose of the present invention is to solve the problem that the surge margin, an important parameter, cannot be measured during the operation of a gas-steam combined cycle generator set, and a surge margin estimation method for a gas-steam combined cycle generator set based on steady-state identification-dynamic correction is proposed to achieve surge margin estimation and ensure safe and stable operation of the unit.

[0007] To achieve the above object, the present invention adopts the following technical solution: a method for estimating surge margin of a gas-steam combined cycle power generation unit, the method estimates the surge margin based on steady-state identification-dynamic correction, and divides the identification process into two orderly steps: the first step is to identify the steady-state process; the second step is to identify the dynamic parameters, i.e., the state space matrix, by using the dynamic process;

[0008] The method specifically comprises:

[0009] Step 1: Determine the simplest structure of the expansion model of the steady-state model of the gas-steam combined cycle power generation unit, so as to determine the parameters to be identified;

[0010] The simplest structure of the steady-state model expansion model is:

[0011]

[0012] Among them, a characteristic variable α representing the steady-state model is selected, the steady-state model is expressed as a function of α, the gas turbine rotor speed n is selected as the characteristic variable α, and n is used as the state variable of the steady-state model; q mf represents the fuel flow rate, which is used as the input variable of the steady-state model. f1 represents q mf arrive The mapping relationship, represents the fuel flow parameterized by the characteristic variable α; SM represents the compressor surge margin, which is the output variable of the steady-state model. e (α) represents the compressor surge margin parameterized by the characteristic variable α;

[0013] Step 2: Determine the identification signal and establish identification data

[0014] The state variable of the steady-state model is the gas turbine rotor speed n, and the input variable is the fuel flow rate q mf ; The output variable is the surge margin SM, and the step signal is selected as the input signal for identification;

[0015] Step 3: Find the parameterized representation n of the steady-state model e (α), and SM e (α), n e (α), and SM e (α) represent the state variables, input variables and output variables of the steady-state model parameterized by the characteristic variable α;

[0016] Step 4: Calculate the deviation Δn=nn in the dynamic process e (α), Δq mf =q mf -q mfe (α) and ΔSM=SM-SM e (α);

[0017] Step 5: Obtain a parameterized representation of the state space matrix

[0018] Substitute the dynamic process deviation in step 4 into the steady-state model expansion model to determine the parameterized Jacobian matrix coefficients A1(α) and C(α) of the steady-state model expansion model; obtain the dynamic parameters A1(α) and C(α) of the parameterized point of the characteristic variable α through numerical calculation;

[0019] The specific method for obtaining A1(α) and C(α) is:

[0020] The rotor speed and the changes of each output quantity are obtained by extracting the power on the rotor shaft of the unit and changing the steady-state balance calculation of the unit input variables in sequence, and the partial derivatives of each item are calculated according to the changes of each parameter obtained;

[0021] or;

[0022] The data is obtained by using the test data or the output data of the nonlinear component-level model simulation through the data identification method;

[0023] At this point, the surge margin estimation method for the gas-steam combined cycle generator set based on steady-state identification-dynamic correction is completed.

[0024] In the surge margin estimation method of the gas-steam combined cycle power generation unit, the compressor surge margin SM is defined by using the compressor pressure ratio and the air flow rate:

[0025]

[0026] where π w is the pressure ratio of the current operating point; π s is the pressure ratio of the surge boundary point at the same reduced speed; m aw is the air flow rate at the current working point; m as It is the air flow rate at the surge boundary point corresponding to the same reduced speed.

[0027] The surge margin estimation method of the gas-steam combined cycle generator set, in step 3, uses the polyfit function in MATLAB to perform polynomial fitting on the characteristic variable α of each steady-state point to obtain the state variables, input variables and output variables of the steady-state model, that is, the function of the characteristic variable α is used to fit other variables.

[0028] Through the above design scheme, the present invention can bring the following beneficial effects: the gas-steam combined cycle generator set surge margin estimation method based on steady-state identification-dynamic correction provided by the present invention converts a difficult-to-implement nonlinear model identification into a linear least squares problem of static parameter fitting and dynamic parameter correction, and through the two-step identification of steady-state identification-dynamic correction, and by analyzing the constraints between the identification parameters, the number of undetermined coefficients is reduced, thereby making the engineering implementation of complex identification problems possible. The fuel command is selected as the input signal, and the signal is maintained for 100s after each step, which is enough for the system to reach a better steady state to meet the identification needs of the steady-state model. The command signal drives the fuel actuator, so that the unit speed increases by 2% to 6% each time, and the final system dynamic process can cover the entire operating area. The gas-steam combined cycle generator set surge margin estimation method based on steady-state identification-dynamic correction realizes surge margin estimation and ensures safe and stable operation of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is the general characteristic diagram of the compressor;

[0030] Figure 2 It is the principle diagram of the estimation method based on steady-state identification-dynamic correction;

[0031] Figure 3 Expand the model schematic for the steady-state model;

[0032] Figure 4 This is the result diagram of surge margin estimation;

[0033] Figure 5 This is the speed estimation result diagram;

[0034] Figure 6 The present invention is a flow chart of a surge margin estimation method for a gas-steam combined cycle power generating unit. DETAILED DESCRIPTION

[0035] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the following is a detailed description of the present invention in combination with the embodiments of the present invention. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6The technical solution in the present invention is described clearly and completely. Obviously, the present invention is not limited by the following embodiments, and the specific implementation method can be determined according to the technical solution of the present invention and the actual situation. In order to avoid confusing the essence of the present invention, the known methods, processes and procedures are not described in detail.

[0036] Principle of the method:

[0037] The surge boundary of the compressor is generally determined through experimental methods. The present invention adopts a curve fitting method based on the limit values ​​of component characteristic data to give the surge boundary of the compressor. First, according to the compressor characteristic data points, the corresponding equal-conversion speed characteristic lines of each section are made on the general characteristic diagram of the compressor, and the extreme points on each equal-conversion speed characteristic line close to the compressor surge boundary are selected. Assume that the obtained extreme points are the intersection points of the corresponding equal-conversion speed characteristic line and the compressor surge boundary. In order to increase the fitting data points and improve the accuracy of the surge boundary fitting, it can be known from the working principle of the compressor that the compressor must have a theoretical starting working point: the pressure ratio is 1 and the air flow is 0. It means that when the air flow through the compressor is 0, the pressure ratio of the compressor must be 1, that is, there is no pressurization effect. The surge boundary of the compressor can be made through the data points obtained above. Figure 1 As shown; Figure 1 As shown by the solid line, Figure 1 In the middle, the horizontal axis m a,cor Indicates the converted flow rate, vertical axis Indicates the pressure ratio.

[0038] In order to facilitate the use in actual calculations, the explicit expression of the compressor surge boundary is given by the curve fitting method. Under the premise of ensuring the accuracy of the fitting, the third order is selected for fitting. The results are as follows: Figure 1 After determining the surge boundary line, the present invention uses the compressor pressure ratio and air flow rate to define the surge margin:

[0039]

[0040] Where: SM is the degree to which the compressor operating point deviates from the surge boundary at a certain reduced speed, that is, the surge margin; π w is the pressure ratio of the current operating point; π s is the pressure ratio of the surge boundary point at the same reduced speed; m aw is the air flow rate at the current working point; m as It is the air flow rate at the surge boundary point corresponding to the same reduced speed.

[0041] The surge margin estimation method based on steady-state identification and dynamic correction divides the identification process into two orderly steps: the first step is to identify the steady-state process; the second step is to identify the dynamic parameters, that is, the state space matrix. Although the identification process is completed in only two steps, the detailed modeling process still involves many problems.

[0042] The model structure of a gas-steam combined cycle generator set is generally:

[0043]

[0044] Where X is the unit state variable; U is the unit input variable, i.e., the control variable; Y is the unit output variable, including all parameters of the unit. f and g represent the corresponding mapping relationship. The mapping relationship is abstracted as f and g. The present invention is to solve the specific mapping relationship between f and g.

[0045] The steady-state model of the gas-steam combined cycle generator set is:

[0046] {(X e (α),U e (α),Y e (α))|f(X e (α),U e (α))=0,Y e (α) = g(X e (α),U e (α))}(3)

[0047] Select the characteristic variable α that represents the steady-state model, express the steady-state model as a function of α, and write the steady-state model in parameterized form: e (α), U e (α) and Y e (α) respectively represent the state variables, input variables and output variables of the steady-state model parameterized by the characteristic variable α. The polyfit function in MATLAB is used to perform polynomial fitting on the characteristic variable α at each steady-state point to obtain the state variables, input variables and output variables of the steady-state model, that is, to fit other variables with the function of the characteristic variable α. The expanded structure of the steady-state model of the gas-steam combined cycle generator set is:

[0048]

[0049] Among them: each partial derivative represents the corresponding parameter of the Jacobian matrix at the steady-state point of α parameterization, that is, It is expressed as the first-order derivative of the mapping relationship f to the state variable X, It is expressed as the first-order derivative of the mapping relationship f to the input variable U, It is expressed as the first-order derivative of the mapping relationship g to the state variable X, It is expressed as the first-order derivative of the mapping relationship g to the input variable U, and the deviations are: ΔX=XX e (α) and ΔU=UU e (α).

[0050] For the fuel flow rate q mf As the control variable of the gas-steam combined cycle power generation unit, the state variable is the gas turbine rotor speed n; the input variable is the fuel flow rate q mf ; The output variables are characteristic parameters of any cross section: such as compressor outlet pressure, turbine inlet temperature, turbine outlet temperature, compressor surge margin SM. The steady-state model of the system satisfies the following set:

[0051]

[0052] Write the steady-state model in parameterized form: e (α), and Y e (α) represent the state variables, input variables and output variables of the steady-state model parameterized by the characteristic variable α.

[0053] The structure of the steady-state model expansion of the gas-steam combined cycle unit is:

[0054]

[0055] The partial derivatives represent the corresponding parameters of the Jacobian matrix at the steady-state point parameterized by the characteristic variable α, that is, It is expressed as the first-order derivative of the mapping relationship f1 to the gas turbine rotor speed n, Expressed as the mapping relationship f1 to fuel flow q mf The first derivative of It is expressed as the first-order derivative of the mapping relationship g to the gas turbine rotor speed n, Expressed as a mapping relationship g to fuel flow q mf The first-order derivative of , the deviations are:

[0056]

[0057] In order to make the model structure simpler, the gas turbine rotor speed n is selected as the characteristic variable α, then n e (α)=n, substituting into formula (7), we get Δn=0.

[0058] Substituting Δn=0 into equation (6), the structure of the steady-state model of the gas-steam combined cycle generator set can be simplified to:

[0059]

[0060] For the convenience of expression, remember:

[0061]

[0062] Substituting equations (7) and (9) into equation (8), the steady-state model structure of the constructed gas-steam combined cycle generator set and its output parameters can be obtained as follows:

[0063]

[0064] The steady-state model expansion model can be obtained from the complex nonlinear aerodynamic and thermal model. The steady-state model of the gas-steam combined cycle generator set expressed in equation (5) is e (α),q mfe (α) and Y e (α) Use the polyfit function in MATLAB to perform polynomial fitting on the characteristic variable α of each steady-state point to obtain the state variables, input variables and output variables of the steady-state model, that is, use the function of the characteristic variable α to fit other variables. The dynamic parameters A1(α) and C(α) parameterized by the characteristic variable α on the steady-state model can be obtained by numerical calculation. The specific method of obtaining the parameters can be obtained by extracting the power on the unit rotor shaft and calculating the steady-state balance of the unit input variables in turn to obtain the rotor speed and the changes in the output variables, and calculating the partial derivatives based on the changes in the parameters obtained. It can also be obtained by using test data or nonlinear component-level model simulation output data through data identification methods.

[0065] The surge margin estimation method based on steady-state identification-dynamic correction proposed in the present invention transforms a difficult-to-implement nonlinear model identification into a linear least squares problem of static parameter fitting and dynamic parameter correction. Through the two-step identification of steady-state identification-dynamic correction and by analyzing the constraints between the identification parameters, the number of undetermined coefficients is reduced, thereby making the engineering implementation of complex identification problems possible.

[0066] Figure 2 The schematic diagram of the estimation method based on steady-state identification-dynamic correction is shown. The surge margin estimation method based on steady-state identification-dynamic correction divides the identification process into two orderly steps: the first step is to identify the steady-state process; the second step is to use the dynamic process to identify the dynamic parameters, namely the state space matrix. Figure 3 Expand the model diagram for the steady-state model.

[0067] The implementation of this method is:

[0068] See also Figure 6 The specific process of the surge margin estimation method for a gas-steam combined cycle generator set based on steady-state identification-dynamic correction proposed in the present invention is as follows:

[0069] Step 1: Determine the simplest structure of the unit. Determine the simplest structure of the expansion model of the steady-state model to be identified of the gas-steam combined cycle generator unit, so as to determine the parameters to be identified.

[0070] Step 2: Determine the identification signal and establish the identification data. The two-step identification method of steady-state identification and dynamic correction requires that the data used for identification contain both the steady-state characteristics and dynamic characteristics of the system. Therefore, a step signal is generally used as the input signal for identification.

[0071] Step 3: Find the parameterized representation n of the steady-state model e (α),q mfe (α) and Y e (α). Establish the relationship between steady-state parameters, that is, fit other variables with the function of characteristic variables.

[0072] Step 4: Calculate the deviation in the dynamic process. For a gas-steam combined cycle generator set, it is to calculate Δn=nn e (α), Δq mf =q mf -q mfe (α) and ΔSM=SM-SM e (α).

[0073] Step 5: Obtain the parameterized representation of the state space matrix.

[0074] The input signal selected by the present invention, namely the fuel command signal, is maintained for 100 seconds after each step, which is sufficient for the system to reach a good steady state to meet the identification needs of the steady-state model. The command signal drives the fuel actuator, so that the unit speed increases by 2% to 6% each time, and the final system dynamic process can cover the entire operating area.

[0075] Next, the dynamic identification effect of the steady-state model expansion model is verified. In this process, the IGV position is assumed to remain unchanged to simulate the rapid change of fuel flow during acceleration and deceleration of the actual gas turbine. When the IGV is fully opened and the fuel flow signal is continuously input in steps, the simulation results of the system state parameters calculated by the component-level nonlinear model (NCL) and the system state parameters obtained based on steady-state identification-dynamic correction are as follows: Figure 4 and Figure 5 It can be seen that the estimation result of the surge margin of the present invention can well follow the component-level nonlinear model (NCL), that is, accurately give the dynamic law of the gas-steam combined cycle generator set in the operating area.

Claims

1. A method for estimating surge margin of a gas-steam combined cycle generator set, characterized in that: This method estimates the surge margin based on steady-state identification-dynamic correction, and divides the identification process into two orderly steps: the first step is to identify the steady-state process; The second step is to use the dynamic process to identify the dynamic parameters, i.e., the state space matrix; The method specifically comprises: Step 1: Determine the simplest structure of the expansion model of the steady-state model of the gas-steam combined cycle power generation unit, so as to determine the parameters to be identified; The simplest structure of the steady-state model expansion model is: Among them, a characteristic variable α representing the steady-state model is selected, the steady-state model is expressed as a function of α, the gas turbine rotor speed n is selected as the characteristic variable α, and n is used as the state variable of the steady-state model; q mf represents the fuel flow rate, which is used as the input variable of the steady-state model. f1 represents q mf arrive The mapping relationship, q mfe (α) represents the fuel flow rate parameterized by the characteristic variable α; SM represents the compressor surge margin, which is the output variable of the steady-state model, SM e (α) represents the compressor surge margin parameterized by the characteristic variable α; Step 2: Determine the identification signal and establish identification data The state variable of the steady-state model is the gas turbine rotor speed n, and the input variable is the fuel flow rate q mf ; The output variable is the surge margin SM, and the step signal is selected as the input signal for identification; Step 3: Find the parameterized representation n of the steady-state model e (α), and SM e (α), n e (α), and SM e (α) represent the state variables, input variables and output variables of the steady-state model parameterized by the characteristic variable α; Step 4: Calculate the deviation Δn=nn in the dynamic process e (α), Δq mf =q mf -q mfe (α) and ΔSM=SM-SM e (α); Step 5: Obtain a parameterized representation of the state space matrix Substitute the dynamic process deviation in step 4 into the steady-state model expansion model to determine the parameterized Jacobian matrix coefficients A1(α) and C(α) of the steady-state model expansion model; obtain the dynamic parameters A1(α) and C(α) of the parameterized point of the characteristic variable α through numerical calculation; At this point, the surge margin estimation method for the gas-steam combined cycle generator set based on steady-state identification-dynamic correction is completed.

2. The combined cycle unit surge margin estimation method based on steady-state identification and dynamic correction according to claim 1, characterized in that: The compressor surge margin SM is defined using the compressor pressure ratio and air flow rate: where π w is the pressure ratio of the current operating point; π s is the pressure ratio of the surge boundary point at the same reduced speed; m aw is the air flow rate at the current operating point; m as It is the air flow rate at the surge boundary point corresponding to the same reduced speed.

3. The method for estimating surge margin of a gas-steam combined cycle power generation unit according to claim 1, characterized in that: In step 3, the polyfit function in MATLAB is used to perform polynomial fitting on the characteristic variable α of each steady-state point to obtain the state variables, input variables and output variables of the steady-state model, that is, to fit other variables with the function of the characteristic variable α.

4. The method for estimating surge margin of a gas-steam combined cycle power generation unit according to claim 1, characterized in that: The specific method for obtaining A1(α) and C(α) is: The rotor speed and the changes of each output quantity are obtained by extracting the power on the unit's rotor shaft and changing the steady-state balance calculation of the unit's input variables in turn, and the partial derivatives are calculated based on the changes of each parameter obtained.

5. The method for estimating surge margin of a gas-steam combined cycle power generation unit according to claim 1, characterized in that: The specific method for obtaining A1(α) and C(α) is to obtain them by using test data or nonlinear component-level model simulation output data through a data identification method.

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