Air extractor unit cooperative grid-connected control method based on target optimization and anti-interference strategy

Abstract

The invention provides an air extractor unit cooperative grid-connected control method based on target optimization and an anti-interference strategy, and belongs to the technical field of aviation power devices, and the method comprises the steps: dividing a system into an air extraction header pipe pressure control subsystem and an air extractor unit pressure ratio control subsystem; establishing a constraint optimization model according to the regulation and control target of the air exhaust unit, constructing an unconstrained target optimization function based on an internal penalty function and a flexible function, and solving the optimal value of the pressure of the air exhaust header pipe; constructing a cooperative anti-interference control law by taking the given pressure ratio of the air extractor unit as a target, and solving the control quantity of a plurality of inlet valves of the air extractor unit; a cooperative anti-interference control law is constructed based on the optimal pressure value of the exhaust manifold as a given value, and the control quantity of an exhaust manifold regulating valve is solved; the control method is realized by adjusting a pressure regulating valve of an air exhaust main pipe and inlet valves of the air exhaust units and controlling anti-surge valves of the multiple air exhaust units in a linkage manner according to a specified movement rate, and repeating the steps. According to the scheme, the anti-interference capability of the control system is improved, manual intervention is not needed, the safety is high, and the robustness is high.

Description

Technical Field

[0001] This application relates to the field of aviation power plant technology, and in particular to a method for coordinated grid connection control of extraction turbine units based on target optimization and anti-disturbance strategies. Background Technology

[0002] Large-scale extraction compressor units are indispensable and complex test equipment for simulating the in-flight environment of aero-engines, and are also the power source for simulating the flight altitude of aero-engines. The extraction system's compressor unit cluster establishes the test requirements for engine flight altitude and airflow through a grid-connected operation process. Currently, the grid-connection process of extraction compressor units mainly relies on a "multi-valve linkage" operation mode based on manual experience, adjusting multiple valves step by step. This excessive reliance on manual experience results in long process control transition times and high energy consumption. Furthermore, experience-based control inevitably introduces safety hazards; improper operation can easily induce unit surge, leading to loss of test functionality or even equipment damage.

[0003] While there is a wealth of research on intake and exhaust control systems for airborne environmental simulation tests, research on automatic grid connection of extraction systems is relatively lacking. Current research mainly involves simply converting traditional manual operation experience into a time-series automatic adjustment process. Each regulating valve is adjusted according to separate and independent control objectives, without considering the safety constraints of compressor unit operation and multi-objective collaborative control capabilities. Consequently, the system has low resource utilization, insufficient safety, and poor fault tolerance. Summary of the Invention

[0004] To address the technical shortcomings of existing technologies, such as low grid utilization rate of air extraction units, low safety, and lack of collaborative control, this application provides a collaborative grid-connected control method for air extraction units based on target optimization and anti-disturbance strategies. This method enables automatic collaborative grid-connected control of air extraction compressor units under safety constraints, eliminates the risks of manual operation, improves the degree of automation, and helps enhance the intelligent operation capability of experimental resource utilization.

[0005] This application provides a method for coordinated grid-connected control of extraction turbine units based on target optimization and disturbance rejection strategies. The method includes: Step 1: Divide the air extraction unit grid connection control system into an air extraction main pipe pressure control subsystem and an air extraction unit pressure ratio control subsystem. The air extraction main pipe pressure control subsystem and the air extraction unit pressure ratio control subsystem work together to achieve the automatic grid connection process of the air extraction unit by automatically adjusting the air extraction main pipe pressure regulating valve and the air extraction unit inlet valve and linking the anti-surge valve according to the specified movement speed. Step 2: Establish a constrained optimization model based on the control target of the extraction unit, construct an unconstrained objective optimization function based on the internal penalty function and the flexible function, and solve for the optimal value of the extraction manifold pressure based on the objective optimization function; Step 3: Based on the objective optimization function and the nonlinear ADRC disturbance rejection control strategy, construct the first cooperative disturbance rejection control law with the given pressure ratio of the extraction unit as the objective, solve the control quantity for controlling multiple inlet valves of the extraction unit, and realize the pressure ratio control of multiple extraction units. Step 4: Based on the objective optimization function and the nonlinear ADRC disturbance rejection control strategy, construct the second cooperative disturbance rejection control law with the optimal value of the extraction main pipe pressure as the given value, solve the control quantity for controlling the extraction main pipe regulating valve, and realize the control of the extraction main pipe pressure. Step 5: Based on the control quantity, control the anti-surge valves of multiple air extraction units at the specified motion rate and repeat steps 2 to 4 to achieve automatic coordinated grid-connected control of multiple air extraction compressor units based on target optimization and anti-disturbance strategies.

[0006] According to a specific implementation of an embodiment of this application, the expression of the constrained optimization model is: , in, This refers to the pressure in the main extraction pipe. Let f be the compressor pressure ratio, and min f be the objective function of the constrained optimization model. For weight parameters, , The target pressure value; g q (p m ) represents the pressure ratio boundary range. When q=1, g1(p m () represents the lower boundary range of the pressure ratio. When q=2, g2(p) m ) represents the upper boundary range of the pressure ratio; st represents the constraint condition, and the feasible region S of the problem is... , This is the lower limit of the compressor pressure ratio constraint. This is the upper limit of the compressor pressure ratio constraint.

[0007] According to a specific implementation of an embodiment of this application, the step of constructing an unconstrained objective optimization function based on an internal penalty function and a flexible function includes: Using the inner penalty function, an augmented objective function is constructed and constraints are eliminated. The augmented objective function is: , , in, To augment the objective function, μ is the internal penalty factor. , Let it be the internal penalty function; Based on the augmented objective function, an unconstrained objective optimization function is designed by combining a flexible function. The expression of the objective optimization function is: , , Where σ is the flexibility factor, θ i θ is the adjustment coefficient of the flexible function. i >0; i=1,2; h1(p) is a flexible function. m ) is for p m The flexible element in the design, h2(p r ) is for p r The flexible element in the design is F, which is the objective optimization function.

[0008] According to a specific implementation of an embodiment of this application, h1(p m ) and h2(p r The expression for ) is: , in, This represents the pressure increment in the main extraction pipe. This represents the compressor pressure ratio increment. This represents the upper limit of the pressure increment in the main extraction pipe. This represents the upper bound of the compressor pressure ratio state increment. It is a norm.

[0009] According to a specific implementation of an embodiment of this application, the step of solving for the optimal value of the extraction manifold pressure based on the objective optimization function includes: Step S41: Set the parameters of the optimization problem and determine the initial state p. m and p r Determine the weight parameters Select the initial penalty factor μ1 and the initial flexibility factor σ1, and determine the adjustment coefficient of the flexibility function. and gradient learning rate ; Step S42: Construct the objective optimization function F based on the constraints and the flexible link, and calculate the gradient of the objective optimization function. , For gradient operators; Step S43: Solve the optimization problem using the gradient optimization algorithm, for the first... Step-by-step optimization problem solving: , Update state variables p m,k p is the pressure in the main extraction pipe at step k. m,k+1 p represents the pressure in the main extraction pipe at step k+1. r,k p is the compressor pressure ratio at step k. r,k+1 The compressor pressure ratio at step k+1; Step S44, if If the convergence criterion has been met, then stop running and obtain the solution to the unconstrained optimization problem. , Let ξ be the optimal value of the main extraction pipe pressure, and ξ be the convergence criterion coefficient. Otherwise, adjust the initial penalty factor and the initial flexibility factor. This is the adjustment coefficient; Step S45, let Return to step S42 and repeat the process.

[0010] According to a specific implementation of this application, the expression of the first cooperative disturbance rejection control law is: , in, , representing the One compressor inlet valve control circuit It is the first The control term is formed by the ADRC control strategy of each inlet valve. For the first Each inlet valve takes into account the pressure of the main extraction pipe. Collaborative optimization terms For the first The inlet valve controls the quantity. For gradient operators, For a given pressure ratio, For the first The filtered voltage ratio For the first The derivative of the filtered voltage ratio For the first Total disturbance estimate for each pressure ratio control loop These are the controller parameters. For the first Gain of the inlet valve control loop of the extraction unit. For a given pressure ratio, the first derivative is... The second derivative for a given pressure ratio; , Obtained from the first nonlinear discrete tracking differentiator, the expression is as follows: , For the first A real measured input pressure ratio signal For velocity factor, For integration time, For the filter factor, For fast control synthesis functions.

[0011] According to a specific implementation of an embodiment of this application, the first... Total disturbance estimate for each pressure ratio control loop Obtained by the first nonlinear extended state observer, the implementation of which is as follows: , in, This is the error scaling factor. For scaling error, , , These represent the gains of the nonlinear extended state observer. For the first Individual pressure ratio tracking error, For the nonlinear extended state observer, the first pressure ratio The estimated value, For the first The pressure ratio derivative value The estimated value, For the first Estimate of the total disturbance in the control loop of each inlet valve. for The first-order differential, for The first-order differential, for The first-order differential, , , These are the nonlinear state error feedback functions, , , The expression is: , in, For symbolic functions, , These are the index coefficients reflecting the degree of nonlinearity.

[0012] According to a specific implementation of this application, the expression of the second cooperative disturbance rejection control law is as follows: , in, It is a control quantity acting on the regulating valve of the main exhaust pipe. These are control items formed by the ADRC control strategy. , For consideration A collaborative optimization term for the compressor pressure ratio. This represents the filtered pressure in the main extraction pipe. The differential of the pressure in the main extraction pipe, This is the estimated total disturbance in the control loop of the extraction main regulating valve. These are the controller parameters. The gain of the control loop for the main exhaust manifold regulating valve. for The first-order differential, for The second derivative; , Obtained by the second nonlinear discrete tracking differentiator, the expression is as follows: .

[0013] According to a specific implementation of an embodiment of this application, the total disturbance estimate of the control loop of the main extraction pipe regulating valve is... Obtained by a second nonlinear extended state observer, the implementation of which is as follows: , in, For the pressure tracking error of the main exhaust pipe, For nonlinear expansion state observers of the pumping manifold pressure The estimated value, The derivative of the pressure in the main extraction pipe The estimated value, This is an estimate of the total disturbance in the pressure control loop of the extraction main.

[0014] According to a specific implementation of an embodiment of this application, the expression of the fast control synthesis function is:

[0015] , Where x1 and x2 are the states of the second-order series system, and curve is the state sliding surface equation. For the sign function, t a t b These are the time calculation parameters related to the state sliding surface, and d and d0 are intermediate variables.

[0016] Beneficial effects: The cooperative grid-connected control method for extraction compressor units based on target optimization and anti-disturbance strategies in this application provides a control method and solution that integrates an optimized target model, unconstrained optimization problem solving, nonlinear active disturbance rejection, and cooperative strategies to address safety constraints, cooperative control, and anti-disturbance issues during the automatic grid connection process of extraction compressor units. Simulation verification of the designed method shows that it not only constrains the compressor pressure ratio within a safe range while ensuring the total pipe pressure reaches the desired value, but also effectively improves the anti-disturbance capability of the control system compared to existing methods. It offers advantages such as no need for manual intervention, high safety, and strong robustness. The application of this technology can significantly reduce the labor intensity and risk of operator error, facilitating the effective performance of extraction compressor unit clusters and the efficient operation of air environment simulation tests. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a multi-compressor unit operating in parallel in an air extraction system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the principle structure of a coordinated grid-connected control method for extraction turbine units based on target optimization and anti-disturbance strategies according to an embodiment of the present invention. Figure 3 This is a diagram illustrating the pressure control effect of the main pipe during the parallel operation of two compressor units in an air extraction system according to an embodiment of the present invention. Figure 4 This is a schematic diagram showing the opening of the inlet valve of a gas extraction system with two compressor units operating in parallel according to an embodiment of the present invention. Figure 5 This is a schematic diagram illustrating the compressor pressure ratio control effect of a gas extraction system with two compressor units operating in parallel according to an embodiment of the present invention. Figure 6 This is a schematic diagram of the opening of the main pressure regulating valve for a gas extraction system with two compressor units operating in parallel according to an embodiment of the present invention. Detailed Implementation

[0019] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0020] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0022] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The illustrations only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0023] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0024] This application provides a method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategies, which will be described in detail below with reference to the figures.

[0025] The applicant's research found that a high-performance automatic grid-connected control system for extraction systems can not only ensure that multiple compressor units always operate within a safe range, but also coordinate and schedule the smooth operation of each device and prevent large shocks to the system. A collaborative grid-connected control method for extraction units based on objective optimization and disturbance rejection strategies is an effective way to achieve the above objectives. This method first transforms the safe operation problem during the grid connection of extraction compressor units into a constrained optimization problem; then, by designing an objective optimization function, the constrained optimization problem is transformed into an unconstrained problem for solution, resulting in a collaborative control strategy for the operation of multiple devices, enabling each device to act collaboratively according to the optimization objective and constraints; finally, based on the collaborative optimization control, the robustness of the control system is further improved through the design of an active disturbance rejection controller. This method ensures that the compressor units always operate within a safe range and achieves the goal of a smooth control process without shock transitions. Therefore, designing a collaborative disturbance rejection control method for extraction system compressors based on objective optimization functions and active disturbance rejection strategies is an important prerequisite and foundation for ensuring the coordinated safe operation and efficient resource utilization of extraction compressor units, and it is necessary to conduct relevant technical research.

[0026] Therefore, to achieve automatic coordinated grid-connected control of the extraction compressor unit under safety constraints, ensuring the safe operation of the compressor unit while reducing the impact of various uncertain disturbances on the extraction main pressure and unit pressure ratio control, thereby improving the system's coordinated operation capability, resource utilization efficiency, and robustness, this application proposes a coordinated grid-connected control method for extraction compressor units based on objective optimization and disturbance rejection strategies. The method includes the following steps: Step 1: Divide the air extraction unit grid connection control system into an air extraction main pipe pressure control subsystem and an air extraction unit pressure ratio control subsystem. The air extraction main pipe pressure control subsystem and the air extraction unit pressure ratio control subsystem work together to achieve the automatic grid connection process of the air extraction unit by automatically adjusting the air extraction main pipe pressure regulating valve and the air extraction unit inlet valve and linking the anti-surge valve according to the specified movement speed. Step 2: To ensure the safe operation of the compressor and simultaneously control the pressure of the extraction main pipe, a constrained optimization model is established based on the control objectives of the extraction unit. An unconstrained objective optimization function is constructed based on the internal penalty function and the flexible function. The optimal value of the extraction main pipe pressure is then solved based on the objective optimization function. Step 3: Based on the objective optimization function and the nonlinear ADRC (Active Disturbance Rejection Control) disturbance rejection control strategy, construct the first cooperative disturbance rejection control law with the given pressure ratio of the extraction unit as the objective, solve the control quantity for controlling multiple inlet valves of the extraction unit, and realize the pressure ratio control of multiple extraction units. Step 4: Based on the objective optimization function and the nonlinear ADRC disturbance rejection control strategy, construct the second cooperative disturbance rejection control law with the optimal value of the extraction main pipe pressure as the given value, solve the control quantity for controlling the extraction main pipe regulating valve, and realize the control of the extraction main pipe pressure. Step 5: Based on the control quantity, control the anti-surge valves of multiple air extraction units at the specified motion rate and repeat steps 2 to 4 to achieve automatic coordinated grid-connected control of multiple air extraction compressor units based on target optimization and anti-disturbance strategies.

[0027] In practical implementation, a schematic diagram of the multi-compressor unit grid-connected operation of the air extraction system is shown below. Figure 1 As shown, the controlled objects mainly include sensors, the main extraction pipe, multiple compressor units and related pipeline cavities, a large-diameter main extraction pipe regulating valve, multiple compressor unit inlet valves, and anti-surge valves. Through effective control of the main extraction pipe regulating valve, multiple compressor unit inlet valves, and anti-surge valves, the goal of safe grid connection and establishing pressure in the main extraction pipe is achieved. The structural diagram of the cooperative grid connection control principle of the extraction unit based on target optimization and disturbance rejection strategies in this embodiment is shown below. Figure 2 As shown, this method first transforms the problem of safe grid-connected operation of the compressor into a constrained nonlinear optimization problem; then, it establishes an unconstrained objective optimization function by designing penalty functions and flexible functions and solves for the optimal value; finally, it designs a cooperative disturbance rejection control method by combining the optimal control strategy based on the objective optimization function with a nonlinear active disturbance rejection control strategy. This method can simultaneously ensure the safe range of the compressor pressure ratio and the disturbance rejection control performance of the extraction manifold pressure, improve experimental efficiency and safety, and effectively eliminate the risk of manual operation.

[0028] In one embodiment, the expression of the constrained optimization model is: , in, This refers to the pressure in the main extraction pipe. Let f be the compressor pressure ratio, and min f be the objective function of the constrained optimization model. For weight parameters, , The target pressure value; g q (p m ) represents the pressure ratio boundary range. When q=1, g1(p m () represents the lower boundary range of the pressure ratio. When q=2, g2(p) m ) represents the upper boundary range of the pressure ratio; st represents the constraint condition, and the feasible region S of the problem is... , This is the lower limit of the compressor pressure ratio constraint. This is the upper limit of the compressor pressure ratio constraint.

[0029] In practice, a constrained optimization model is established based on the control objectives of the extraction unit, which includes the following: The air extraction unit control system should meet the compressor pressure ratio requirements. Achieving the required main exhaust pressure within a safe margin. It stabilizes at the set value. This process can be transformed into a constrained optimization problem based on an objective optimization model.

[0030] compressor pressure ratio As an important indicator for measuring compressor safety and needing to be constrained within a certain range, it is represented by the ratio of the pressure after the compressor to the pressure before the compressor: (1) in, These are the compressor outlet and inlet pressures, respectively. Due to the pressure in the main exhaust pipe The actual changes will affect the compressor pressure ratio. Then a mapping exists. Defined as: (2) Considering that the compressor pressure ratio constraint is within a certain range, the compressor safety constraint condition can be defined as: (3) in, This is the lower limit of the compressor pressure ratio constraint. g1(pm) represents the upper limit of the compressor pressure ratio constraint, g2(pm) represents the lower boundary range of the pressure ratio, and g2(pm) represents the upper boundary range of the pressure ratio. Based on the above, the constrained optimization model is established as follows: (4) In the formula, when At times, more attention Approaching the target pressure value; when At that time, more emphasis was placed on meeting the safety constraints of the compressor pressure ratio.

[0031] In one embodiment, constructing the unconstrained objective optimization function based on the internal penalty function and the flexibility function includes: Using the inner penalty function, an augmented objective function is constructed and constraints are eliminated. The augmented objective function is: (5)

[0032] in, To augment the objective function, μ is the internal penalty factor. , The inner penalty function is used to transform the constrained optimization problem (Equation (4)) into an unconstrained optimization problem (Equation (5)). Based on the augmented objective function, an unconstrained objective optimization function is designed by combining a flexible function. To prevent the designed penalty factor from being too large and causing the system to be affected by shock jumps and abrupt disturbances, a flexible cooperative algorithm is further designed to ensure smooth state transitions and reduce the damage caused by valve jitter. The expression of the objective optimization function combined with the flexible function is: (6) (7) Where σ is the flexibility factor, θ i θ is the adjustment coefficient of the flexible function. i >0; i=1,2; h1(p) is a flexible function. m ) is for p m The flexible element in the design, h2(p r ) is for p r The flexible element in the design is F, which is the objective optimization function.

[0033] Furthermore, the h1(p m ) and h2(p r The expression for ) is: (8) in, This represents the pressure increment in the main extraction pipe. This represents the compressor pressure ratio increment. and Updated by the second state estimate from the extended state observer in the controller. This represents the upper limit of the pressure increment in the main extraction pipe. This represents the upper bound of the compressor pressure ratio state increment. It is a norm.

[0034] Therefore, the constraint problem has been transformed into: (9) The purpose of the above design is to solve the unconstrained objective function when the system reaches its optimum. optimal value This ensures that the pressure in the main extraction pipe reaches the desired value while keeping the compressor pressure ratio within safe limits.

[0035] In one embodiment, solving for the optimal value of the extraction manifold pressure according to the objective optimization function includes: Step S41: Set the parameters of the optimization problem and determine the initial state p. m and p rDetermine the weight parameters Select the initial penalty factor μ1 and the initial flexibility factor σ1, and determine the adjustment coefficient of the flexibility function. and gradient learning rate ; Step S42: Construct the objective optimization function F based on the constraints and the flexible link, and calculate the gradient of the objective optimization function. , For gradient operators; Step S43: Solve the optimization problem using the gradient optimization algorithm, for the first... Step-by-step optimization problem solving: , Update state variables p m,k p is the pressure in the main extraction pipe at step k. m,k+1 p represents the pressure in the main extraction pipe at step k+1. r,k p is the compressor pressure ratio at step k. r,k+1 The compressor pressure ratio at step k+1; Step S44, if If the convergence criterion has been met, then stop running and obtain the solution to the unconstrained optimization problem. , Let ξ be the optimal value of the main extraction pipe pressure, and ξ be the convergence criterion coefficient. Otherwise, adjust the initial penalty factor and the initial flexibility factor. This is the adjustment coefficient; Step S45, let Return to step S42 and repeat the process.

[0036] In one embodiment, the expression for the first cooperative disturbance rejection control law is: (10) in, , representing the One compressor inlet valve control circuit It is the first The control term is formed by the ADRC control strategy of each inlet valve. For the first Each inlet valve takes into account the pressure of the main extraction pipe. Collaborative optimization terms For the first The inlet valve controls the quantity. For gradient operators, For a given pressure ratio, For the first The filtered voltage ratio For the first The derivative of the filtered voltage ratio For the first Total disturbance estimate for each pressure ratio control loop These are the controller parameters. For the first Gain of the inlet valve control loop of the extraction unit. For a given pressure ratio, the first derivative is... The second derivative for a given pressure ratio; , Obtained by the first nonlinear discrete tracking differentiator (TD1), the expression is as follows: (11) For the first A real measured input pressure ratio signal For velocity factor, For integration time, For the filter factor, For fast control synthesis functions.

[0037] Furthermore, the fast control synthesis function The expression is:

[0038] (12) Where x1 and x2 are the states of the second-order series system, and curve is the state sliding surface equation. For the sign function, t a t b These are the time calculation parameters related to the state sliding surface, and d and d0 are intermediate variables.

[0039] Furthermore, the first in equation (10) Total disturbance estimate for each pressure ratio control loop Obtained by the first nonlinear extended state observer (NESO1), the implementation of which is as follows: (13) in, This is the error scaling factor. For scaling error, , , These represent the gains of the nonlinear extended state observer. For the first Individual pressure ratio tracking error, For the nonlinear extended state observer, the first pressure ratio The estimated value, For the first The pressure ratio derivative value The estimated value, For the first Estimate of the total disturbance in the control loop of each inlet valve. for The first-order differential, for The first-order differential, for The first-order differential, , , These are the nonlinear state error feedback functions, , , The expression is: (14) in, For symbolic functions, , These are the index coefficients reflecting the degree of nonlinearity.

[0040] In one embodiment, the expression for the second cooperative disturbance rejection control law is: (15) in, It is a control quantity acting on the regulating valve of the main exhaust pipe. These are control items formed by the ADRC control strategy. , For consideration A collaborative optimization term for the compressor pressure ratio. This represents the filtered pressure in the main extraction pipe. The differential of the pressure in the main extraction pipe, This is the estimated total disturbance in the control loop of the extraction main regulating valve. These are the controller parameters. The gain of the control loop for the main exhaust manifold regulating valve. for The first-order differential, for The second derivative; , Obtained by the second nonlinear discrete tracking differentiator (TD2), the expression is as follows: (16) To quickly control the synthesis function, its expression is as follows:

[0041] (17) Furthermore, the estimated total disturbance value of the control loop of the extraction main regulating valve in equation (17) Obtained by the second nonlinear extended state observer (NESO2), the implementation of which is as follows: (18) in, For the pressure tracking error of the main exhaust pipe, For nonlinear expansion state observers of the pumping manifold pressure The estimated value, The derivative of the pressure in the main extraction pipe The estimated value, This is an estimate of the total disturbance in the pressure control loop of the extraction main.

[0042] In equation (18), , , The nonlinear state error feedback function is designed according to the following formula: (19) In equation (19), For symbolic functions, , The index coefficient reflects the degree of nonlinearity.

[0043] In one specific embodiment, to verify the control effect of the cooperative grid-connected control method for extraction compressor units based on target optimization and anti-disturbance strategy proposed in this invention, the process control simulation of automatic grid connection of two extraction compressor units is used for illustration.

[0044] 1) Setting initial simulation conditions Considering the scenario of two extraction compressors automatically connecting to the grid, in the initial state, the compressor unit's backflow start-up process has already ended. All compressor unit inlet valves are closed, the anti-surge valve is fully open, and the main pipe pressure regulating valve is fully closed.

[0045] 2) Control target requirements During the automatic grid connection process of the entire compressor unit, the pressure ratio of each compressor should be kept within the safe pressure ratio constraint range of 4.0 to 6.0 as much as possible. Simultaneously, the pressure in the main extraction pipe should gradually and smoothly transition from the initial atmospheric pressure of approximately 96.0 kPa to the final target value of 20.0 kPa. The entire control process should be smooth and oscillating. After the extraction unit is connected to the grid, all compressor inlet valves should be fully open, anti-surge valves should be fully closed, and the main pipe pressure regulating valve should be opened to a certain angle to establish a stable operating state of 20.0 kPa.

[0046] 3) Simulation Result Analysis Simulations were conducted using a coordinated grid-connected control method for the extraction turbine unit based on objective optimization and disturbance rejection strategies, and a separate "individual control" method, respectively, to verify the achievement of the control objectives. The simulation results for the two control methods are as follows: Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown.

[0047] The simulation results lead to the following conclusions: Figure 5 The results show that the coordinated grid-connected control method for extraction turbine units based on target optimization and disturbance rejection strategies can ensure that the compressor pressure ratio remains within the safe pressure ratio range of 4.0 to 6.0 throughout the entire grid connection process. In contrast, the separate "individual control" method using coordinated optimization strategies cannot guarantee the target safe pressure ratio range throughout the entire grid connection process. Figure 5 The readings show that the pressure ratio exceeded the safe range in the range of 132~305s, reaching a maximum of 7.5. Additionally, from... Figure 3 It can be seen that, compared with the separate "individual control" method, the coordinated grid-connected control method of the extraction unit based on target optimization and disturbance rejection strategy achieves a faster adjustment speed of the extraction manifold pressure, shortening the time to reach the final target pressure steady state by approximately 50 seconds, and eliminating control overshoot. Simulation results show that the coordinated grid-connected control method of the extraction unit based on target optimization and disturbance rejection strategy can effectively coordinate the actuators to adjust according to the optimized target, while ensuring the safe range of the compressor pressure ratio and the control performance of the extraction manifold pressure, thus meeting the control requirements.

[0048] In summary, this invention not only ensures that the compressor pressure ratio is within a safe range and the main pipe pressure reaches the desired value during the grid connection of the extraction unit, but also effectively improves the anti-interference capability and regulation efficiency of the control system compared to existing methods. It offers advantages such as requiring no manual intervention, high safety, and strong robustness. The application of this technology can significantly reduce the labor intensity and risk of operator error, and also facilitate the effective performance of the extraction unit cluster and the efficient conduct of experiments.

[0049] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for coordinated grid-connected control of extraction turbine units based on target optimization and disturbance rejection strategies, characterized in that, The method includes: Step 1: Divide the air extraction unit grid connection control system into an air extraction main pipe pressure control subsystem and an air extraction unit pressure ratio control subsystem. The air extraction main pipe pressure control subsystem and the air extraction unit pressure ratio control subsystem work together to achieve the automatic grid connection process of the air extraction unit by automatically adjusting the air extraction main pipe pressure regulating valve and the air extraction unit inlet valve and linking the anti-surge valve according to the specified movement speed. Step 2: Establish a constrained optimization model based on the control target of the extraction unit, construct an unconstrained objective optimization function based on the internal penalty function and the flexible function, and solve for the optimal value of the extraction manifold pressure based on the objective optimization function; Step 3: Based on the objective optimization function and the nonlinear ADRC disturbance rejection control strategy, construct the first cooperative disturbance rejection control law with the given pressure ratio of the extraction unit as the objective, solve the control quantity for controlling multiple inlet valves of the extraction unit, and realize the pressure ratio control of multiple extraction units. Step 4: Based on the objective optimization function and the nonlinear ADRC disturbance rejection control strategy, construct the second cooperative disturbance rejection control law with the optimal value of the extraction main pipe pressure as the given value, solve the control quantity for controlling the regulating valve of the extraction main pipe, and realize the control of the extraction main pipe pressure. Step 5: Based on the control quantity, control the anti-surge valves of multiple air extraction units at the specified motion rate and repeat steps 2 to 4 to achieve automatic coordinated grid-connected control of multiple air extraction compressor units based on target optimization and anti-disturbance strategies.

2. The method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategy according to claim 1, characterized in that, The expression for the constrained optimization model is: ， in, This refers to the pressure in the main extraction pipe. Let f be the compressor pressure ratio, and min f be the objective function of the constrained optimization model. For weight parameters, , The target pressure value; g q (p m ) represents the pressure ratio boundary range. When q=1, g1(p m () represents the lower boundary range of the pressure ratio. When q=2, g2(p) m ) represents the upper boundary range of the pressure ratio; st represents the constraint condition, and the feasible region S of the problem is... , This is the lower limit of the compressor pressure ratio constraint. This represents the upper limit of the compressor pressure ratio constraint.

3. The method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategies according to claim 2, characterized in that, The construction of the unconstrained objective optimization function based on the internal penalty function and the flexibility function includes: Using the inner penalty function, an augmented objective function is constructed and constraints are eliminated. The augmented objective function is: ， ， in, To augment the objective function, μ is the internal penalty factor. , Let it be the internal penalty function; Based on the augmented objective function, an unconstrained objective optimization function is designed by combining a flexible function. The expression of the objective optimization function is: ， ， Where σ is the flexibility factor, θ i θ is the adjustment coefficient of the flexible function. i >0; i=1,2; h1(p) is a flexible function. m ) is for p m The flexible element in the design, h2(p r ) is for p r The flexible element in the design is F, which is the objective optimization function.

4. The method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategy according to claim 3, characterized in that, The h1(p) m ) and h2(p r The expression for ) is: ， in, This represents the pressure increment in the main extraction pipe. This represents the compressor pressure ratio state increment. This represents the upper limit of the pressure increment in the main extraction pipe. This represents the upper bound of the compressor pressure ratio state increment. It is a norm.

5. The method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategy according to claim 3, characterized in that, The process of solving for the optimal value of the extraction manifold pressure based on the objective optimization function includes: Step S41: Set the parameters of the optimization problem and determine the initial state p. m and p r Determine the weight parameters Select the initial penalty factor μ1 and the initial flexibility factor σ1, and determine the adjustment coefficient of the flexibility function. and gradient learning rate ; Step S42: Construct the objective optimization function F based on the constraints and the flexible link, and calculate the gradient of the objective optimization function. , For gradient operators; Step S43: Solve the optimization problem using the gradient optimization algorithm, for the first... Step-by-step optimization problem solving: ， Update state variables p m,k p is the pressure in the main extraction pipe at step k. m,k+1 p represents the pressure in the main extraction pipe at step k+1. r,k p is the compressor pressure ratio at step k. r,k+1 The compressor pressure ratio at step k+1; Step S44, if If the convergence criterion has been met, then stop running and obtain the solution to the unconstrained optimization problem. , Let ξ be the optimal value of the main extraction pipe pressure, and ξ be the convergence criterion coefficient. Otherwise, adjust the initial penalty factor and the initial flexibility factor. This is the adjustment coefficient; Step S45, let Return to step S42 and repeat the process.

6. The method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategies according to claim 5, characterized in that, The expression for the first cooperative disturbance rejection control law is: ， in, , representing the One compressor inlet valve control circuit It is the first The control term is formed by the ADRC control strategy of each inlet valve. For the first Each inlet valve takes into account the pressure of the main extraction pipe. Collaborative optimization terms For the first The inlet valve controls the quantity. For gradient operators, For a given pressure ratio, For the first The filtered voltage ratio For the first The derivative of the filtered voltage ratio For the first Total disturbance estimate for each pressure ratio control loop These are the controller parameters. For the first Gain of the inlet valve control loop of the extraction unit. For a given pressure ratio, the first derivative is... The second derivative for a given pressure ratio; , Obtained from the first nonlinear discrete tracking differentiator, the expression is as follows: ， For the first A real measured input pressure ratio signal For velocity factor, For integration time, For the filter factor, For fast control synthesis functions.

7. The method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategies according to claim 6, characterized in that, The first Total disturbance estimate for each pressure ratio control loop Obtained by the first nonlinear extended state observer, the implementation of which is as follows: ， in, This is the error scaling factor. For scaling error, , , These represent the gains of the nonlinear extended state observer. For the first Individual pressure ratio tracking error, For the nonlinear extended state observer, the first pressure ratio The estimated value, For the first The pressure ratio derivative value The estimated value, For the first Estimate of the total disturbance in the control loop of each inlet valve. for The first-order differential, for The first-order differential, for The first-order differential, , , These are the nonlinear state error feedback functions, , , The expression is: ， in, For symbolic functions, , These are the index coefficients reflecting the degree of nonlinearity.

8. The method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategy according to claim 7, characterized in that, The expression for the second cooperative disturbance rejection control law is: ， in, It is a control quantity acting on the regulating valve of the main exhaust pipe. These are control items formed by the ADRC control strategy. , For consideration A collaborative optimization term for the compressor pressure ratio. This represents the filtered pressure in the main extraction pipe. The differential of the pressure in the main extraction pipe, This is the estimated total disturbance in the control loop of the extraction main regulating valve. These are the controller parameters. The gain of the control loop for the main exhaust manifold regulating valve. for The first-order differential, for The second derivative; , Obtained by the second nonlinear discrete tracking differentiator, the expression is as follows: 。 9. The method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategy according to claim 8, characterized in that, The estimated total disturbance value of the control loop of the main extraction pipe regulating valve Obtained by a second nonlinear extended state observer, the implementation of which is as follows: ， in, For the pressure tracking error of the main exhaust pipe, For nonlinear expansion state observers of the pumping manifold pressure The estimated value, The derivative of the pressure in the main extraction pipe The estimated value, This is an estimate of the total disturbance in the pressure control loop of the extraction main.

10. The method for coordinated grid-connected control of extraction turbine units based on target optimization and anti-disturbance strategies according to any one of claims 6-9, characterized in that, The expression for the fast control synthesis function is: ， Where x1 and x2 are the states of the second-order series system, and curve is the state sliding surface equation. For the sign function, t a t b These are the time calculation parameters related to the state sliding surface, and d and d0 are intermediate variables.

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