Gas turbine IGV opening control method and device based on generalized predictive control

Abstract

The application provides a gas turbine IGV opening degree control method and device based on generalized predictive control, which comprises the following steps: establishing a transfer function model between the gas turbine IGV opening degree and the exhaust temperature, and identifying initial parameters based on historical operation data; using the initial parameters to establish a prediction model of the gas turbine IGV opening degree, and predicting the object output under different control quantities; introducing a step factor and performing rolling optimization on the control quantity to obtain a target control quantity as an IGV opening degree control instruction; and correcting the parameters of the prediction model online through a recursive least square method to improve the robustness of the control system. The gas turbine IGV opening degree control method provided in the embodiment of the application can adjust the IGV opening degree to control the exhaust temperature by using the generalized predictive control principle, can quickly control the exhaust temperature near the temperature control line, and still has good control effect when the object characteristics change.

Description

Technical Field

[0001] This invention relates to the field of gas turbine control technology, and specifically to a gas turbine IGV opening control method and device based on generalized predictive control. Background Technology

[0002] Combined cycle (CCC) power generation offers advantages such as high cycle efficiency, fast load response, and strong environmental adaptability, leading to its rapid development in the domestic market in recent years. A gas turbine mainly consists of three parts: a compressor, a combustion chamber, and a turbine. To improve power generation efficiency, the exhaust gas from the gas turbine is introduced into a waste heat boiler to generate steam, which drives a steam turbine to generate electricity, thus forming a combined cycle. To maximize combined cycle efficiency, the exhaust gas temperature of the gas turbine is typically kept as high as possible. However, excessively high exhaust temperatures can easily damage components and cause safety accidents, necessitating exhaust temperature control. Currently, when the gas turbine is under partial load, the exhaust temperature is primarily controlled by adjusting the compressor inlet guide vane (IGV) opening.

[0003] When the gas turbine is under partial load, to stabilize the exhaust temperature near the temperature control line, GE inputs the temperature control line reference and the actual exhaust temperature into the PID controller. The PID controller then calculates the IGV opening reference and controls the IGV opening. Mitsubishi's M701F4 gas turbine directly calculates the IGV opening reference using an open-loop function after correcting for ambient temperature and atmospheric pressure, and controls the IGV opening. When the gas turbine has been in service for a short time, both companies' IGV opening control methods can accurately control the exhaust temperature. However, as the gas turbine's operating conditions change and the unit's equipment ages, the control effect gradually declines. Gas turbine power plants need to pay high costs to have manufacturers optimize the IGV control parameters. Therefore, there is an urgent need for a robust gas turbine IGV opening control method that can quickly and accurately control the gas turbine exhaust temperature near the temperature control line, and still maintain good control over the gas turbine exhaust temperature when the gas turbine's operating conditions change or the unit's characteristics change due to aging. Summary of the Invention

[0004] In view of this, the present invention provides a gas turbine IGV opening control method and device based on generalized predictive control, which solves the problem of poor control effect on gas turbine exhaust temperature when the gas turbine operating conditions change or the characteristics of the unit change due to aging.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, embodiments of the present invention provide a gas turbine IGV opening control method based on generalized predictive control, the method comprising:

[0007] A transfer function model between the gas turbine IGV opening degree and the exhaust temperature was established, and the initial parameters were identified based on historical operating data.

[0008] A predictive model for the opening degree of the gas turbine IGV is established using the initial parameters, and the object output under different control variables is predicted.

[0009] A step factor is introduced and the control quantity is rolled to obtain the target control quantity, which is used as the IGV opening control command.

[0010] The parameters of the prediction model are corrected online by using the recursive least squares method.

[0011] The gas turbine IGV opening control method based on generalized predictive control provided in this invention reduces the computational load in the rolling optimization stage by introducing a step factor, improves the robustness of the control system by online identification using recursive least squares method, and controls the exhaust temperature by adjusting the IGV opening using the principle of generalized predictive control. It quickly and accurately controls the exhaust temperature of the gas turbine near the temperature control line, and the control effect remains good when the gas turbine operating conditions change or the characteristics of the unit change due to aging.

[0012] Optionally, the process of establishing a transfer function model between the gas turbine IGV opening degree and the exhaust temperature, and identifying initial parameters based on historical operating data, is as follows:

[0013] A transfer function model is established using the relationship between the gas turbine IGV opening degree and the exhaust temperature. Where K is the gain coefficient, T is the inertial time constant, s is the Laplace operator, and τ is the pure delay time;

[0014] Using ITAE as an indicator, the ITAE index values ​​for different values ​​of K, T, and τ are calculated according to the particle swarm optimization algorithm. The K, T, and τ values ​​at the minimum ITAE index value are the identification results, and K, T, and τ at this time are used as the initial parameters of the prediction model.

[0015] By establishing a transfer function model and using ITAE as an indicator, the initial parameters of the prediction model are identified offline, reflecting the characteristics of the object and facilitating the establishment of the prediction model.

[0016] Optionally, the initial parameters of the prediction model are the parameters of the difference equation after the transfer function model is transformed into a difference equation.

[0017] Optionally, the process of establishing a predictive model for the gas turbine IGV opening degree using the initial parameters and predicting the object output under different control variables is as follows:

[0018] A CARIMA model of the IGV temperature control object is established based on the principle of generalized predictive control.

[0019]

[0020] Where ε(k) is noise, Δ=1-z -1 ,A(z) -1 B(z) -1 ), C(z) -1 ) is z -1 polynomial, n a Let n be the system order. b Related to the object's latency characteristics, C(z) -1 It is usually taken as 1;

[0021] Introducing the Diophantine equation: 1 = E j (z -1 )A(z -1 )Δ+z -j F j (z -1 j = 1, 2, ...

[0022] E j (z -1 )=e0+e1z -1 +…+e j-1 z -j+1 , Solving the simultaneous equations of the CARIMA model and the Diophantine equation, and then transforming them into difference equation form, yields:

[0023]

[0024] in, The model predicts the output;

[0025] Let G j (z -1 ) = E j (z -1 B, where: G j (z -1 )=g0+g1z -1 +…+g j-1 z -j+1 Because of E j (z -1 )C(z -1 The ε(k+j) term contains future noise, which is ignored when calculating the estimate, so:

[0026]

[0027] Written in matrix form, we have: in,

[0028]

[0029] N is the size of the prediction time domain, N u To control the size of the time domain, f = HΔu(k) + Fy(k),

[0030]

[0031] Optionally, the process of introducing a step factor and performing rolling optimization on the control quantity to obtain the target control quantity is as follows:

[0032] Introducing performance metric J: Where λ is the adjustable coefficient, y is the output exhaust temperature, and w is the set value of the exhaust temperature;

[0033] Introduce the step factor η: Then for matrix have:

[0034]

[0035]

[0036] Performance index J is:

[0037] when At that time, the control law is as follows: The control quantity is: u(k) = u(k-1) + Δu(k).

[0038] Define the optimal performance index function to control the exhaust temperature to track the set value as quickly as possible, while minimizing the change in the control quantity. By introducing a step factor, matrix inversion is avoided when calculating the generalized predictive control quantity, greatly reducing the computational load.

[0039] Optionally, the step factor can be adjusted based on the relationship between the exhaust temperature and the set value.

[0040] When the difference between the exhaust temperature and the set value is greater than the preset difference, the size of the step factor is reduced;

[0041] When the difference between the exhaust temperature and the set value is less than the preset difference, the size of the step factor is increased.

[0042] The step factor is adjusted based on the difference between the exhaust temperature and the set value. A smaller step factor is used in the initial stage of adjustment to improve the response speed, and a larger step factor is used in the later stage of adjustment to improve the stability of the control system.

[0043] Optionally, when using the recursive least squares method for online identification, historical data can be filtered by setting a forgetting factor, wherein the forgetting factor ranges from 0.95 to 1.0.

[0044] Secondly, embodiments of the present invention provide a gas turbine IGV opening control device based on generalized predictive control, the device comprising:

[0045] The identification module is used to establish a transfer function model between the gas turbine IGV opening degree and the exhaust temperature, and to identify the initial parameters based on historical operating data;

[0046] The prediction module is used to establish a prediction model of the gas turbine IGV opening degree using the initial parameters, and to predict the object output under different control variables;

[0047] The rolling optimization module is used to introduce a step factor and perform rolling optimization on the control quantity to obtain the target control quantity, which serves as the IGV opening control command.

[0048] The feedback correction module is used to identify and correct the parameters of the prediction model online using the recursive least squares method.

[0049] The gas turbine IGV opening control device based on generalized predictive control provided in this embodiment of the invention uses the principle of generalized predictive control to adjust the IGV opening to control the exhaust temperature. It can quickly control the exhaust temperature near the temperature control line, and the control effect is still good when the gas turbine operating conditions change or the characteristics of the object change due to unit aging.

[0050] Thirdly, embodiments of the present invention provide a computer device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method described in the first aspect, or any optional embodiment of the first aspect.

[0051] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions for causing the computer to perform the method described in the first aspect, or any optional embodiment of the first aspect. Attached Figure Description

[0052] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0053] Figure 1 A flowchart of a gas turbine IGV opening control method based on generalized predictive control is provided for an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of a specific embodiment of a gas turbine IGV opening control method based on generalized predictive control provided by an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram showing the comparison results of PID control and the gas turbine IGV opening control method based on generalized predictive control provided in the embodiments of the present invention in a specific embodiment.

[0056] Figure 4 This is a schematic diagram showing the comparison results of PID control and the gas turbine IGV opening control method based on generalized predictive control provided in this embodiment of the invention when the characteristics of the object change, respectively, in another specific embodiment.

[0057] Figure 5 This is a schematic diagram of a gas turbine IGV opening control device based on generalized predictive control, provided in an embodiment of the present invention.

[0058] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0061] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0062] The technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0063] Example 1

[0064] This invention provides a gas turbine IGV opening control method based on generalized predictive control, such as... Figure 1 As shown, the method includes:

[0065] Step S1: Establish a transfer function model between the gas turbine IGV opening degree and the exhaust temperature, and identify the initial parameters based on historical operating data.

[0066] Specifically, in one embodiment, the process of establishing a transfer function model between the gas turbine IGV opening degree and the exhaust temperature, and identifying the initial parameters based on historical operating data, is as follows:

[0067] Step S11: Establish a transfer function model using the relationship between the gas turbine IGV opening and the exhaust temperature. Where K is the gain coefficient, T is the inertial time constant, s is the Laplace operator, and τ is the pure delay time;

[0068] Step S12: Using the integral of time and absolute error (ITAE) as an indicator, calculate the ITAE values ​​for different values ​​of K, T, and τ using the particle swarm optimization algorithm. The K, T, and τ values ​​at the minimum ITAE value are the identification results, and these values ​​are used as the initial parameters of the prediction model. For example, in the field of system identification, given the known input and output data of the object, input data is input into the model, and the integral of the absolute value of the deviation between the model output and the actual object output over time (i.e., the ITAE index) is analyzed. The model parameters are continuously adjusted through an optimization algorithm until the ITAE index is minimized; the model parameters at this minimum are the identification results. Based on the initial parameters obtained through identification, according to the subsequent difference method, we have... Among them, T s For sampling time, z -1 It is a shift operator, obtained by combining the transfer function model:

[0069] Let y = T0 be the output of the object (i.e., the exhaust temperature), and u = U IGV As the control variable, and also the input to the object (i.e., IGV opening), the difference equation form of the above expression is obtained: Where k represents a discrete time point.

[0070] By establishing a transfer function model and using ITAE as an indicator, the initial parameters of the prediction model can be identified offline, reflecting the characteristics of the object and facilitating the establishment of the prediction model.

[0071] Step S2: Establish a predictive model for the gas turbine IGV opening degree using the initial parameters, and predict the object output under different control variables. A schematic diagram of the gas turbine IGV opening degree control method based on generalized predictive control provided in this embodiment of the invention is shown below. Figure 2 As shown, the inputs are the temperature control reference and the actual exhaust temperature, and the output is the control quantity (i.e., IGV opening degree).

[0072] Specifically, in one embodiment, the process of establishing a predictive model for the IGV temperature control object is as follows:

[0073] Step S21: Establish the CARIMA model of the IGV temperature control object based on the principle of generalized predictive control:

[0074]

[0075] Where ε(k) is noise, Δ=1-z -1 ,A(z) -1 B(z) -1 ), C(z) -1 ) is z -1 polynomial, n a Let n be the system order. b Related to the object's latency characteristics, C(z) -1 It is usually taken as 1;

[0076] Step S22: Introduce the Diophantine equation: 1 = E j (z -1 )A(z -1 )Δ+z -j F j (z -1 j = 1, 2, ..., E j (z -1 )=e0+e1z -1 +…+e j-1 z -j+1 , Solving the simultaneous equations of the CARIMA model and the Diophantine equation, and then transforming them into difference equation form, yields:

[0077]

[0078] in, The model predicts the output;

[0079] Step S23: Let G j (z -1 ) = E j (z -1 B, where: G j (z -1 )=g0+g1z -1 +…+g j-1 z -j+1 Because of E j (z -1 )C(z -1 The ε(k+j) term contains future noise, which is ignored when calculating the estimate, so:

[0080]

[0081] Written in matrix form, we have: in,

[0082]

[0083] N is the size of the prediction time domain, N u To control the size of the time domain, f = HΔu(k) + Fy(k),

[0084]

[0085] Step S3: Introduce a step factor and perform rolling optimization on the control quantity to obtain the target control quantity, which serves as the IGV opening control command.

[0086] Specifically, in one embodiment, during the control process, it is desirable for the target output y (i.e., the gas turbine exhaust temperature) to track the set value w as quickly as possible, while minimizing the change in the control quantity and reducing the action of the IGV. The specific process of step S3 is as follows:

[0087] Step S31: Introduce performance metric J: Where λ is the adjustable coefficient, y is the output exhaust temperature, and w is the set value of the exhaust temperature;

[0088] For example, a larger λ indicates a greater focus on IGV actions, while a smaller λ indicates a greater focus on the effect of the object's output y tracking the setpoint w. A common optimization method is to set a softening factor α on the setpoint w to prevent abrupt changes in the control quantity, resulting in:

[0089] w(k+j)=α j y(k)+(1-αj R, where 0 ≤ α < 1, is the temperature control reference. As time increases, the setpoint w gradually transitions from the object output y to the temperature control reference R.

[0090] Let W T = [w(k+1)w(k+2)…w(k+N)], and replacing the output of the future object with the model's predicted output, we have: When the partial derivatives of the objective function and the change in the control quantity are 0, that is... When the change in control quantity Δu can minimize the performance index J, that is, the optimal control law is obtained at this time, i.e.:

[0091] in For N u When solving for control quantities using optimal control laws, the order identity matrix requires finding its inverse matrix, which involves a large amount of computation. Furthermore, there may be cases where singular matrices do not have an inverse matrix. Therefore, this embodiment of the invention introduces a step factor, which eliminates the need to find the inverse matrix and reduces the amount of computation.

[0092] Step S32:

[0093] Introduce the step factor η: Then for matrix have:

[0094]

[0095]

[0096] Performance index J is:

[0097] Step S33: When the partial derivatives of the objective function and the change in the control quantity are 0, i.e. At that time, the control law is as follows: The control quantity is: u(k) = u(k-1) + Δu(k).

[0098] This invention defines an optimal performance index function to control the exhaust temperature to track the set value as quickly as possible, while minimizing the change in the control quantity. By introducing a step factor, matrix inversion is avoided when calculating the generalized predictive control quantity, greatly reducing the computational load.

[0099] Specifically, in one embodiment, the smaller the step factor η, the smaller the control quantity at future times, the more the system depends on the control quantity at the current time, and the faster the system response. Therefore, the size of the step factor η is adjusted according to the difference between the system output value y (i.e., exhaust temperature) and the setpoint w, where y(k) represents the current object output:

[0100]

[0101] With K y The evaluation system output value y is compared with the set value w, where K y >0, η1≤η2. When the difference between the exhaust temperature and the set value is greater than K. y When the difference between the exhaust temperature and the set value is less than K, decrease the step factor; y When this happens, increase the size of the step factor. Where K y It is set manually based on the characteristic parameters of the gas turbine system. The size of the step factor is adjusted according to the relationship between the exhaust temperature and the set value. In the initial stage of adjustment, a smaller step factor is used to improve the response speed, and in the later stage of adjustment, a larger step factor is used to improve the stability of the control system.

[0102] Step S4: Use the recursive least squares method to identify and correct the parameters of the prediction model online.

[0103] For example, since the characteristics of an object are constantly changing, generalized predictive control typically improves the robustness of the control system by continuously modifying model parameters through online identification methods. The most commonly used online identification method is recursive least squares identification. Recursive least squares involves adjusting the model parameters at the next time step based on the previous estimate after acquiring new observation data. The control quantity for the next time step is then calculated using the predicted model element and the rolling optimization element based on the adjusted model parameters, and the feedback correction and adjustment of model parameters continues, repeating this process. The information vector h and parameter vector θ are defined as follows:

[0104]

[0105] The difference equation of the prediction model can then be expressed as:

[0106] During online identification, object characteristics change dynamically, necessitating the setting of a forgetting factor μ to appropriately "forget" historical data, ensuring the online identification results better reflect the current object characteristics. Generally, the forgetting factor μ is between 0.95 and 1. The online iterative formula for the recursive least squares method is:

[0107] Γ(k)=P(k-1)h(k)[h T (k)P(k-1)h(k)+μ] -1

[0108]

[0109]

[0110] Where the initial value of P Let v be a sufficiently large number. For n a +nb Unit matrix of order, For the estimated value of θ(k), the initial... To be identified, that is:

[0111] The gas turbine IGV opening control method based on generalized predictive control provided in this invention reduces the computational load in the rolling optimization stage by introducing a step factor, improves the robustness of the control system by online identification using recursive least squares method, and controls the exhaust temperature by adjusting the IGV opening using the principle of generalized predictive control. It quickly and accurately controls the exhaust temperature of the gas turbine near the temperature control line, and the control effect remains good when the gas turbine operating conditions change or the characteristics of the unit change due to aging.

[0112] In one specific embodiment, the IGV opening degree U of a certain unit IGV The transfer function G(s) between the gas turbine exhaust temperature T0 and the gas turbine exhaust temperature T0 is: Sampling time T s If we take 0.1s, the prediction model output will be:

[0113] y(k)=1.931×y(k-1)-0.9322×y(k-2)-0.0026u(k-10)

[0114] Further results were obtained:

[0115] Assuming the initial exhaust temperature T0 is stable at 580℃, the reference exhaust temperature R jumps to 590℃ at 10s, and an internal disturbance occurs at 50s, PID control and the gas turbine IGV opening control method based on generalized predictive control provided in this embodiment of the invention are used for control. The PID controller parameters are obtained by Matlab self-tuning, and K... y If we set η to 2, η1 to 0.6, and η2 to 0.9, the final result is as follows: Figure 3 As shown.

[0116] from Figure 3 It can be seen that the gas turbine IGV opening control method based on generalized predictive control provided in this embodiment of the invention can not only stabilize the exhaust temperature at the set value more quickly, but also has a significantly stronger anti-interference ability than PID control. After the internal disturbance occurs, the exhaust temperature under generalized predictive control remains basically unchanged.

[0117] In another specific embodiment, when the gas turbine operating conditions change or the service time increases, causing changes in the unit characteristics, assuming the IGV opening degree U... IGV The transfer function between the gas turbine exhaust temperature T0 and the gas turbine exhaust temperature T0 becomes G′(s):

[0118]

[0119] Assuming the initial exhaust temperature T0 is stable at 580℃, the exhaust temperature reference changes to 590℃ at 10s, and an internal disturbance occurs at 50s, while the controller parameters remain unchanged, the results are as follows. Figure 4 As shown.

[0120] from Figure 4 It can be seen that when the characteristics of the object change, the performance of both the IGV opening control method provided in this embodiment and the traditional PID control decreases. However, the control performance of the method provided in this embodiment remains good, while the oscillation amplitude of the traditional PID control increases significantly. This is because the recursive least squares method is used to adjust the model parameters online in the feedback correction stage to adapt to the changes in the object characteristics. Even if the object characteristics change, the method provided in this embodiment still has a good control effect on the exhaust temperature of the gas turbine.

[0121] Example 2

[0122] This invention provides a gas turbine IGV opening control device based on generalized predictive control, such as... Figure 5 As shown, the device includes:

[0123] Identification module 1 is used to establish a transfer function model between the gas turbine IGV opening degree and the exhaust temperature, and to identify initial parameters based on historical operating data. For details, please refer to the relevant description of step S1 in the above method embodiments, which will not be repeated here.

[0124] Prediction module 2 is used to establish a prediction model for the gas turbine IGV opening degree using the initial parameters, and to predict the object output under different control variables. For details, please refer to the relevant description of step S2 in the above method embodiment, which will not be repeated here.

[0125] Rolling optimization module 3 is used to introduce a step factor and perform rolling optimization on the control quantity to obtain the target control quantity, which serves as the IGV opening control command. For details, please refer to the relevant description of step S3 in the above method embodiment, which will not be repeated here.

[0126] Feedback correction module 4 is used to correct the parameters of the prediction model online using the recursive least squares method. For details, please refer to the relevant description of step S4 in the above method embodiment, which will not be repeated here.

[0127] The gas turbine IGV opening control device based on generalized predictive control provided in this invention reduces the computational load in the rolling optimization stage by introducing a step factor, improves the robustness of the control system by online identification using recursive least squares method, and controls the exhaust temperature by adjusting the IGV opening using the generalized predictive control principle. It quickly and accurately controls the exhaust temperature of the gas turbine near the temperature control line, and the control effect remains good when the gas turbine operating conditions change or the characteristics of the object change due to unit aging.

[0128] Example 3

[0129] Figure 6 A schematic diagram of a computer device according to an embodiment of the present invention is shown, including: a processor 901 and a memory 902, wherein the processor 901 and the memory 902 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0130] Processor 901 can be a Central Processing Unit (CPU). Processor 901 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0131] The memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the above method embodiments. The processor 901 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 902, thereby implementing the methods in the above method embodiments.

[0132] The memory 902 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 901, etc. Furthermore, the memory 902 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 902 may optionally include memory remotely located relative to the processor 901, and these remote memories may be connected to the processor 901 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0133] One or more modules are stored in memory 902, and when executed by processor 901, they perform the methods described in the above method embodiments.

[0134] The specific details of the aforementioned computer equipment can be understood by referring to the relevant descriptions and effects in the above method embodiments, and will not be repeated here.

[0135] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The implemented program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0136] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A gas turbine IGV opening control method based on generalized predictive control, characterized in that, include: A transfer function model between the gas turbine IGV opening degree and the exhaust temperature was established, and the initial parameters were identified based on historical operating data. A predictive model for the opening degree of the gas turbine IGV is established using the initial parameters, and the object output under different control variables is predicted. A step factor is introduced and the control quantity is rolled to obtain the target control quantity, which is used as the IGV opening control command. The parameters of the prediction model are corrected online by recursive least squares method. The process of establishing a transfer function model between the gas turbine IGV opening degree and the exhaust temperature, and identifying initial parameters based on historical operating data, includes: establishing a transfer function model using the correspondence between the gas turbine IGV opening degree and the exhaust temperature. ,in, K This is the gain coefficient. T Let be the inertial time constant, and s be the Laplace operator. τ The pure delay time; calculated using the ITAE (Intense Time-Effective Acute) algorithm. K , T , τ The ITAE index values ​​for different values, with the ITAE index value being the lowest. K , T , τ That is, the identification result, K , T , τ These serve as the initial parameters for the prediction model.

2. The gas turbine IGV opening control method based on generalized predictive control according to claim 1, characterized in that, The initial parameters of the prediction model are the parameters of the difference equation after the transfer function model is transformed into the difference equation.

3. The gas turbine IGV opening control method based on generalized predictive control according to claim 1, characterized in that, When using the recursive least squares method for online identification, historical data is filtered by setting a forgetting factor, the value of which ranges from 0.95 to 1.

0.

4. The gas turbine IGV opening control method based on generalized predictive control according to claim 1, characterized in that, The process of establishing a predictive model for the gas turbine IGV opening using the initial parameters and predicting the object output under different control variables is as follows: A CARIMA model of the IGV temperature control object is established based on the principle of generalized predictive control. Where, ε( k () represents noise. , A (z -1 ), B (z -1 ), C (z -1 ) is z -1 polynomial, , Let be the system order. Related to the object's latency characteristics, C (z -1 Take 1; Introducing the Diophantine equation: , By simultaneously solving the CARIMA model and the Diophantine equation and then transforming it into a difference equation form, we obtain: in, The model predicts the output; make ,in: ,because The term contains future noise, which is ignored when calculating the estimated value, so: Written in matrix form, we have: ,in, N To predict the size of the time domain, N u To control the size of the time domain, , 。 5. The gas turbine IGV opening control method based on generalized predictive control according to claim 4, characterized in that, The process of introducing a step factor and performing rolling optimization on the control quantity to obtain the target control quantity is as follows: Introducing performance metrics J : ,in, The coefficient to be adjusted. y For the output exhaust temperature, w This is the set value for the exhaust temperature; Introduce the step factor η: For the matrix have: Performance indicators J for: ; when At that time, the control law is as follows: Then the control quantity is: .

6. The gas turbine IGV opening control method based on generalized predictive control according to claim 5, characterized in that, The step factor is adjusted based on the relationship between the exhaust temperature and the set value: When the difference between the exhaust temperature and the set value is greater than the preset difference, the size of the step factor is reduced; When the difference between the exhaust temperature and the set value is less than the preset difference, the size of the step factor is increased.

7. A gas turbine IGV opening control device based on generalized predictive control, characterized in that, The gas turbine IGV opening control device is used to execute the gas turbine IGV opening control method according to any one of claims 1-6, and the device includes: The identification module is used to establish a transfer function model between the gas turbine IGV opening degree and the exhaust temperature, and to identify the initial parameters based on historical operating data; The prediction module is used to establish a prediction model of the gas turbine IGV opening degree using the initial parameters, and to predict the object output under different control variables; The rolling optimization module is used to introduce a step factor and perform rolling optimization on the control quantity to obtain the target control quantity, which serves as the IGV opening control command. The feedback correction module is used to identify and correct the parameters of the prediction model online using the recursive least squares method.

8. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method as described in any one of claims 1-6.

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