Supercritical unit model prediction control method and device based on error self-adaption and extended state Kalman filtering

Abstract

The invention discloses a supercritical unit model prediction control method and device based on error self-adaption and extended state Kalman filtering, and belongs to the field of supercritical units. The method comprises the following steps: analyzing multivariable coupling characteristics of a supercritical unit, and determining multivariable control requirements; performing identification and discretization processing on a dynamic system of the supercritical unit based on a transfer function method, and determining a prediction model of a model prediction controller; designing an extended state Kalman filtering model to perform state estimation on the supercritical unit, and designing an error adaptive mechanism algorithm model; and integrating the error adaptive mechanism algorithm model and the extended state Kalman filtering model, and carrying out optimization control on the prediction model of the model prediction controller. According to the supercritical unit model prediction control method based on error self-adaption and extended state Kalman filtering, the dynamic response speed, the tracking precision and the anti-interference capability of a supercritical unit control system can be improved.

Description

Technical Field

[0001] The present application belongs to the field of supercritical units, and in particular relates to a supercritical unit model predictive control method and device based on error adaptation and extended state Kalman filtering. Background Art

[0002] Supercritical coal-fired power plants, due to their high thermal efficiency and low emissions, continue to play a vital role in modern energy systems. However, their boiler-turbine systems exhibit multivariable coupling, high inertia, and significant nonlinearity, posing challenges to control system design. Traditional control strategies, such as PID control, perform well in simple environments. However, when faced with complex operating conditions in supercritical units, they often suffer from poor performance due to time delays, parameter variations, and external disturbances, making it difficult to meet the flexibility and stability requirements.

[0003] Model predictive control (MPC) can effectively handle multivariable coupling and nonlinear problems by predicting the future behavior of the system and optimizing control actions, making it a hot topic in supercritical unit control research. However, MPC performance is highly dependent on model accuracy and real-time optimization capabilities. Traditional state-space modeling and filtering methods are difficult to fully adapt to the nonlinear dynamics of supercritical units, resulting in insufficient control accuracy and anti-interference capabilities. The extended state Kalman filter (ESKF) enhances its ability to handle nonlinearity and uncertainty by introducing disturbance state estimation, but its fixed parameter design still leaves room for improvement in dynamic error adjustment and multivariable coordination. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems existing in the related art. To this end, this application proposes a supercritical unit model predictive control method and device based on error adaptation and extended state Kalman filtering. By introducing a dynamic error adaptation mechanism and exponential smoothing technology, combined with the disturbance estimation capability of the extended state Kalman filter, the dynamic response speed, tracking accuracy, and anti-interference capability of the supercritical unit control system can be improved.

[0005] In a first aspect, the present application provides a supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering, the method comprising:

[0006] S1. Analyze the multivariable coupling characteristics of supercritical units and determine the multivariable control requirements;

[0007] S2. Identifying and discretizing the dynamic system of the supercritical unit based on a transfer function method to determine a prediction model of a model predictive controller;

[0008] S3. Designing an extended state Kalman filter model to perform state estimation on the supercritical unit and designing an error adaptive mechanism algorithm model;

[0009] S4. Integrate the error adaptive mechanism algorithm model with the extended state Kalman filter model to optimize the prediction model of the model predictive controller.

[0010] According to the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering of the present application, by analyzing the multivariable coupling characteristics of the supercritical unit, the multivariable control requirements are determined, the dynamic system of the supercritical unit is identified and discretized based on the transfer function method, the prediction model of the model predictive controller is determined, the extended state Kalman filter model is designed to estimate the state of the supercritical unit, and an error adaptive mechanism algorithm model is designed. The error adaptive mechanism algorithm model and the extended state Kalman filter model are integrated to optimize the control of the prediction model of the model predictive controller. This method can achieve the problem of multivariable coupling, large inertia and nonlinearity by utilizing dynamic error adjustment and disturbance estimation to improve the tracking accuracy and anti-interference ability of the main steam pressure, load and temperature, and coordinate the control of the steam inlet valve opening, coal feed rate and water feed rate to improve the robustness and stability under wide load operation. Multivariable coordinated control is achieved through state estimation, error adaptation and predictive optimization, effectively alleviating the multivariable coupling interference of the supercritical unit.

[0011] According to one embodiment of the present application, the control objects of the supercritical unit include: steam inlet valve opening, coal supply and water supply, as well as main steam pressure, unit load and water-cooled wall header outlet temperature, which are used to realize the prediction function of the prediction model of the model predictive controller.

[0012] According to one embodiment of the present application, the implementation of the prediction function of the prediction model of the model predictive controller includes:

[0013] A dynamic model of the supercritical unit is established, the dynamic system of the supercritical unit is identified using a transfer function method, and the identified dynamic model is discretized into a differential equation for use in adapting the model predictive controller.

[0014] According to one embodiment of the present application, the designing of an extended state Kalman filter model to perform state estimation on the supercritical unit includes:

[0015] Based on the discretized model, a state space form is constructed.

[0016] According to one embodiment of the present application, the discretization model dynamically adjusts the prediction error weight through real-time error analysis, and designs an error adaptive mechanism algorithm model in combination with exponential smoothing technology.

[0017] According to one embodiment of the present application, the step of integrating the error adaptive mechanism algorithm model with the extended state Kalman filter model to optimize the prediction model of the model predictive controller includes:

[0018] Predict future outputs based on difference equations.

[0019] According to one embodiment of the present application, the step of integrating the error adaptive mechanism algorithm model with the extended state Kalman filter model to optimize the prediction model of the model predictive controller includes:

[0020] The error adaptive mechanism algorithm model dynamically optimizes control priorities through real-time error analysis to ensure the system's response to load changes;

[0021] The model predictive controller based on the extended state Kalman filter model uses its predictive capability to adjust the main steam valve opening, coal feed rate and water feed rate;

[0022] Inputting a state estimation result of the extended state Kalman filter model into the model predictive controller;

[0023] The predictive control function of the model predictive controller identifies external disturbances and internal uncertainties in advance and optimizes the control strategy.

[0024] In a second aspect, the present application provides a supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering, the device comprising:

[0025] The first processing module is used to analyze the multivariable coupling characteristics of the supercritical unit and determine the multivariable control requirements;

[0026] A second processing module is used to identify and discretize the dynamic system of the supercritical unit based on a transfer function method to determine a prediction model of a model predictive controller;

[0027] The third processing module is used to design an extended state Kalman filter model to perform state estimation on the supercritical unit and design an error adaptive mechanism algorithm model;

[0028] The fourth processing module is used to integrate the error adaptive mechanism algorithm model with the extended state Kalman filter model to optimize the prediction model of the model predictive controller.

[0029] According to the supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering of the present application, by analyzing the multivariable coupling characteristics of the supercritical unit, the multivariable control requirements are determined, the dynamic system of the supercritical unit is identified and discretized based on the transfer function method, the prediction model of the model predictive controller is determined, the extended state Kalman filter model is designed to estimate the state of the supercritical unit, and an error adaptive mechanism algorithm model is designed. The error adaptive mechanism algorithm model and the extended state Kalman filter model are integrated to optimize the control of the prediction model of the model predictive controller. It can achieve the problem of multivariable coupling, large inertia and nonlinearity by using dynamic error adjustment and disturbance estimation to improve the tracking accuracy and anti-interference ability of the main steam pressure, load and temperature, and coordinate the control of the steam inlet valve opening, coal feed rate and water feed rate to improve the robustness and stability under wide load operation; and realize multivariable coordinated control through state estimation, error adaptation and predictive optimization, effectively alleviating the multivariable coupling interference of the supercritical unit.

[0030] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for model predictive control of a supercritical unit based on error adaptation and extended state Kalman filtering as described in the first aspect above is implemented.

[0031] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering as described in the first aspect above.

[0032] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0033] By analyzing the multivariable coupling characteristics of supercritical units, determining the multivariable control requirements, identifying and discretizing the dynamic system of the supercritical unit based on the transfer function method, determining the prediction model of the model predictive controller, designing an extended state Kalman filter model to estimate the state of the supercritical unit, designing an error adaptive mechanism algorithm model, integrating the error adaptive mechanism algorithm model with the extended state Kalman filter model, and optimizing the control of the prediction model of the model predictive controller, it is possible to achieve multivariable coupling, large inertia and nonlinear problems by using dynamic error adjustment and disturbance estimation to improve the tracking accuracy and anti-interference ability of the main steam pressure, load and temperature, and coordinate the control of the steam inlet valve opening, coal feed rate and water feed rate to improve the robustness and stability under wide load operation; and through state estimation, error adaptation and predictive optimization, multivariable coordinated control is achieved, effectively alleviating the multivariable coupling interference of the supercritical unit.

[0034] Furthermore, the error adaptive mechanism dynamically optimizes the control priority through real-time error analysis to ensure the system's rapid response to load changes. The model predictive controller based on the extended state Kalman filter can achieve precise adjustment of the main steam valve opening, coal feed rate and water feed rate by virtue of its predictive ability. This optimization synergy can improve the control accuracy and dynamic response capability of the system; and by inputting the state estimation results of the extended state Kalman filter into the model predictive controller, it can effectively suppress the interference caused by multivariable coupling in the supercritical unit, improve the coordination of main steam pressure, load and temperature control, and thus enhance the overall stability of the system; the state estimation results of the extended state Kalman filter model are input into the model predictive controller. At the same time, the error adaptive mechanism is combined with the exponential smoothing technology to quickly adjust the control action when interference occurs, which can enhance the system's anti-interference ability and operational robustness.

[0035] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0037] Figure 1 This is a diagram of a supercritical unit model predictive control structure based on error adaptation and extended state Kalman filtering provided in an embodiment of the present application;

[0038] Figure 2 1 is a flow chart of a supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in an embodiment of the present application;

[0039] Figure 3 This is one of the control effect comparison diagrams of the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application;

[0040] Figure 4 This is the second comparison diagram of the control effect of the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application;

[0041] Figure 5 This is one of the comparison diagrams of the disturbance condition control effect of the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application;

[0042] Figure 6This is the second comparison diagram of the disturbance condition control effect of the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in an embodiment of the present application;

[0043] Figure 7 This is the third comparison diagram of the disturbance condition control effect of the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in an embodiment of the present application;

[0044] Figure 8 This is one of the comparison diagrams between the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application and the actual control results of the power plant;

[0045] Figure 9 This is the second comparison diagram of the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application and the actual control results of the power plant;

[0046] Figure 10 This is the third comparison diagram of the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in an embodiment of the present application and the actual control results of the power plant;

[0047] Figure 11 Schematic diagram of the structure of a supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering provided in an embodiment of the present application;

[0048] Figure 12 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0050] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0051] In combination with the accompanying drawings, the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering, the supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering, the electronic device and the readable storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0052] Among them, the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering can be applied to the terminal, and can be specifically executed by hardware or software in the terminal.

[0053] The terminal includes but is not limited to portable communication devices such as mobile phones or tablet computers. It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer.

[0054] The embodiment of the present application provides a supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering. The execution subject of the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering can be an electronic device or a functional module or functional entity in the electronic device that can implement the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering. The electronic devices mentioned in the embodiment of the present application include but are not limited to mobile phones, tablet computers, computers, cameras and wearable devices, etc. The supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application is explained below using electronic devices as the execution subject as an example.

[0055] like Figure 2 As shown, the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering includes: step S1, step S2, step S3 and step S4.

[0056] Step S1, analyzing the multivariable coupling characteristics of the supercritical unit and determining the multivariable control requirements;

[0057] In this step, supercritical units are an efficient and environmentally friendly power generation technology, mainly used for thermal power generation, such as Figure 1 Coal-fired unit shown.

[0058] A supercritical unit may include major equipment such as boilers, steam turbines and generators.

[0059] The method is applicable to supercritical units of any power. For example, the method can be applicable to 350MW supercritical units, etc., without limitation herein.

[0060] The multivariable coupling characteristics of a supercritical unit may include the dynamic correlation characteristics between each control variable and the control target.

[0061] For example, rapid adjustment of the steam inlet valve opening directly affects the load and pressure, while the slow dynamics of the coal and water feed rates indirectly affect the temperature and pressure through combustion and heat transfer.

[0062] The multivariable control requirement may be determined based on the multivariable coupling characteristic, and corresponds to how the control object needs to be controlled when the control target is controlled to change to a desired value.

[0063] In the actual implementation process, the difference between fast and slow dynamics in the multivariable coupling characteristics leads to interference between loops. Therefore, it is necessary to clarify the multivariable control requirements and design a control strategy based on the multivariable control requirements that can balance fast tracking and slow coordination to meet both fast response and stability requirements.

[0064] In some embodiments, the control objects of the supercritical unit include: steam inlet valve opening, coal supply and water supply, main steam pressure, unit load and water wall header outlet temperature, etc., which are used to realize the prediction function of the prediction model of the model predictive controller (MPC).

[0065] In this embodiment, the control object may include a control variable and a control target, wherein Figure 1 As shown, the controlled variables may include the opening of the steam inlet valve, the coal supply rate, the water supply rate, etc., and the controlled targets may include the main steam pressure, the unit load, and the water wall header outlet temperature, etc.

[0066] Step S2: Identify and discretize the dynamic system of the supercritical unit based on the transfer function method to determine the prediction model of the model predictive controller;

[0067] In this step, the dynamic system of the supercritical unit can be understood as the multivariable coupling characteristics in step S1, or the multivariable control requirements determined based on the multivariable coupling characteristics.

[0068] The transfer function can be expressed as:

[0069]

[0070] Where a1, a2, ..., an-1, and an are the coefficients of the denominator polynomial; b1, b2, ..., bn-1, and bn are the coefficients of the numerator polynomial; n is the order of the denominator polynomial, and s represents the order of the system.

[0071] Discretization processing can convert the continuous-time model of the dynamic system of the supercritical unit obtained by transfer function identification into a discrete-time model suitable for digital control, thereby optimizing the control strategy.

[0072] In the actual implementation process, the discrete-time model after discretization processing can be determined as the prediction model of the model predictive controller, which will be specifically explained in the following embodiments and will not be described in detail here.

[0073] The prediction model of the model predictive controller is used to predict the future system output and future system input of the supercritical unit.

[0074] Step S3: Design an extended state Kalman filter model to perform state estimation on the supercritical unit and design an error adaptive mechanism algorithm model (EEA);

[0075] In this step, the extended state Kalman filter model (ESKF) is used to estimate the state of the supercritical unit.

[0076] State estimation may include estimating system states and disturbances of a supercritical plant.

[0077] Among them, the system state is a state vector, which records the historical state of the system, captures the large inertia characteristics, and facilitates the prediction of future behavior. It is usually composed of output variables and their delay terms; the disturbance is a disturbance vector, which represents the impact of unmodeled dynamics and external interference on the system, such as the nonlinearity of the combustion process and the complexity of heat transfer, changes in fuel quality, sudden load changes and environmental factors.

[0078] During actual execution, ESKF can estimate system states and disturbances, and be used to predict outputs (such as main steam pressure, load, and temperature) for multiple future time steps, thereby optimizing control inputs (such as valve opening and coal feed rate) and providing accurate initial state conditions for MPC. MPC can then use the initial state conditions as a starting point to predict the future system output and future system input of the supercritical unit.

[0079] In some embodiments, the error adaptation mechanism algorithm model can be used to adjust the amplitude of the residual correction during the state estimation process of the ESKF.

[0080] The residual can be the difference between the current system state and disturbance estimated by the ESKF and the actual current system state and disturbance.

[0081] In actual implementation, the adjustment residual correction is used to correct the current system state and disturbance estimated by the ESKF based on the actual current system state and disturbance.

[0082] In some embodiments, the error adaptive mechanism algorithm model can also be used to be integrated into an objective function, where the objective function is a function used to measure the deviation between the future system output of the supercritical unit predicted by the model predictive controller and the expected system output of the supercritical unit, as well as the cost of the control action.

[0083] In the actual implementation process, the objective function can be optimized and solved based on the optimization control algorithm to obtain the future system input of the supercritical unit that is consistent with the above-mentioned expected system output.

[0084] The future system output or expected system output may include the control target of the supercritical unit, and the future system input may include the control variable of the supercritical unit.

[0085] In some embodiments, the optimization control algorithm may be a quadratic programming algorithm.

[0086] Step S4: Integrate the error adaptive mechanism algorithm model and the extended state Kalman filter model to optimize the prediction model of the model predictive controller.

[0087] In this step, the error adaptive mechanism algorithm model is integrated with the extended state Kalman filter model to optimize the accuracy of the future system input of the supercritical unit determined by the model predictive controller.

[0088] During the actual execution process, the error adaptive mechanism algorithm model dynamically adjusts the process noise covariance through real-time residual analysis, ESKF estimates the extended state (system state and disturbance of the supercritical unit), and MPC uses ESKF estimation and disturbance correction to predict future output, optimize control increments, balance output deviation and control cost, and improve control accuracy and robustness.

[0089] According to the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application, by analyzing the multivariable coupling characteristics of the supercritical unit, the multivariable control requirements are determined, the dynamic system of the supercritical unit is identified and discretized based on the transfer function method, the prediction model of the model predictive controller is determined, an extended state Kalman filter model is designed to estimate the state of the supercritical unit, an error adaptive mechanism algorithm model is designed, the error adaptive mechanism algorithm model and the extended state Kalman filter model are integrated, and the prediction model of the model predictive controller is optimized and controlled. It can achieve the problem of multivariable coupling, large inertia and nonlinearity by utilizing dynamic error adjustment and disturbance estimation to improve the tracking accuracy and anti-interference capability of main steam pressure, load and temperature, while coordinating the control of steam inlet valve opening, coal feed rate and water feed rate to improve robustness and stability under wide load operation; and realize multivariable coordinated control through state estimation, error adaptation and predictive optimization, effectively alleviating the multivariable coupling interference of the supercritical unit.

[0090] The supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering is described in detail below based on specific embodiments.

[0091] In some embodiments, implementing the prediction function of the prediction model of the model predictive controller may include:

[0092] A dynamic model of the supercritical unit is established, and the transfer function method is used to identify the dynamic system of the supercritical unit. The identified dynamic model is discretized into a difference equation for use in adapting the model predictive controller.

[0093] In this embodiment, based on the historical operating data of the unit, a back propagation algorithm can be used, with the control variable as input and the control target as output, to establish a BP neural network as a dynamic model of the controlled unit system for the simulation process.

[0094] The transfer function method is used to identify the dynamic system of the supercritical unit, and the dynamic model in the form of transfer function is discretized into a difference equation, which is used as the MPC prediction model to predict future system outputs and convert the continuous-time model into a discrete-time model suitable for digital control, thereby optimizing the control strategy.

[0095] The representation of the transfer function has been explained in the above steps and will not be repeated here.

[0096] In the actual implementation process, for a multi-input multi-output system, the discretized output is:

[0097]

[0098] Among them, k is the discrete time step, which represents the current sampling moment; y i (k) is the value of the i-th output at time k, for example, the control targets such as main steam pressure, unit load, and water wall header outlet temperature; y i (kp) is the value of the i-th output at discrete time step kp; u j (kq) is the value of the jth input variable at the discrete time step kq, for example, the control variables such as the steam inlet valve opening, coal feed rate and water feed rate; a i,p and b ij,q is the model coefficient, reflecting the dynamic characteristics of the system; n i represents the autoregressive order of the i-th output; nu represents the total number of input variables; m i,j represents the delay order of the jth input to the ith output; where n i 、m i,j , i, j, k, q and p are all integers greater than or equal to 0.

[0099] According to the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in an embodiment of the present application, a dynamic model of the supercritical unit is established, the dynamic system of the supercritical unit is identified using a transfer function method, and the identified dynamic model is discretized into a differential equation for use in adapting the model predictive controller, so that the model predictive controller predicts the future system input and output of the supercritical unit based on the differential equation.

[0100] In some embodiments, designing an extended state Kalman filter model to perform state estimation on a supercritical unit may include:

[0101] Based on the discretized model, a state space form is constructed.

[0102] In this embodiment, the ESKF is able to estimate the system state and disturbance through the state space form, i.e., the state space model, to predict the output (such as main steam pressure, load, and temperature) for multiple time steps in the future, thereby optimizing the control input (such as valve opening and coal feed rate) and providing accurate initial state conditions for MPC.

[0103] By introducing disturbance terms, the state-space model can capture unmodeled dynamics and external disturbances (such as load mutations and fuel quality changes), thereby improving the stability of the supercritical unit system under wide load operation and disturbance conditions.

[0104] In some embodiments, the state space form can be constructed by introducing a disturbance term based on the above discretization model as follows:

[0105] x e,k+1 =A e x e,k +B e u k +ξ k

[0106] y k =C e x e,k

[0107] Among them, k is the discrete time step, which represents the current sampling moment; x e,k is the state vector, which contains the system state, such as output and its delay term; u k is the input vector, including valve opening, coal supply, and water supply; ξ k is the disturbance, capturing unmodeled dynamics and external disturbances; y k is the output vector, including main steam pressure, load, and temperature; A e 、B e and C e is the state space matrix, which is converted from the discretized model.

[0108] In the supercritical unit scenario, the state space model describes the dynamic behavior of the supercritical unit system, for example, how control targets such as main steam pressure and load change with control inputs such as valve opening and coal feed rate.

[0109] Then, based on the state space model, an ESKF can be designed to estimate the system state and disturbance to improve the reliability of the system state and disturbance results estimated by the ESKF.

[0110] Among them, the system state is a state vector, which records the historical state of the system, captures the large inertia characteristics, and facilitates the prediction of future behavior. It is usually composed of the output variable and its delay term; the disturbance is a disturbance vector, which represents the impact of unmodeled dynamics and external interference on the system, such as the nonlinearity of the combustion process and the complexity of heat transfer, changes in fuel quality, sudden load changes and environmental factors.

[0111] In actual execution, ESKF not only estimates the system state, but also estimates the disturbance. The prediction steps of ESKF are as follows:

[0112]

[0113] P k|k-1 =AP k-1|k-1 A T +Q k

[0114] Among them, k is the discrete time step, which represents the current sampling moment, represents the prediction of the extended state, i.e. in, is the prediction of the system state, is the prediction of the disturbance; A is the expanded state transfer matrix, Among them, A x is the original state transfer matrix, describing the dynamics of the control target changing with the historical value, I x and I w is the identity matrix, with dimensions matching the state and disturbance respectively, assuming the disturbance varies slowly; B x is the original input matrix, which defines the influence of the control command on the control target. 0 means no direct influence on the disturbance. k|k-1 is the extended forecast covariance matrix, reflecting the uncertainty of state and disturbance forecasts; Among them, Q x is the system state noise covariance, Q w is the disturbance noise covariance, which reflects the randomness of fluctuations.

[0115] During the actual execution process, ESKF predicts the system state and disturbance at the current sampling moment. After collecting the actual system state and disturbance at the current sampling moment, ESKF also updates the predicted system state and disturbance at the current sampling moment based on the actual system state and disturbance at the current sampling moment.

[0116] In some embodiments, the ESKF is updated as follows:

[0117]

[0118]

[0119]

[0120] P k|k =(IK k C)P k|k-1

[0121] Among them, k is the discrete time step, which represents the current sampling moment, e k is the measurement residual, which represents the difference between the actual measurement value and the expected output based on the predicted state; C = [C x 0] is the extended output matrix, where C x Mapping the system state to the output is defined as how the control target historical value is mapped to the measurement value, where 0 means that the disturbance does not directly affect the measurement; S k is the innovation covariance, which measures the statistical uncertainty of the measurement residuals and is equal to the covariance of the predicted output plus the covariance of the measurement noise; P k|k-1 is the extended prediction covariance matrix, which represents the uncertainty of state and disturbance prediction; R is the measurement noise covariance matrix, which represents the statistical characteristics of sensor error; K k is the Kalman gain matrix, which determines the weight of the measurement residual on the state update according to the sensor accuracy and prediction error; β is the adaptive weight coefficient, which adjusts the amplitude of the residual correction and can be determined based on the error adaptive mechanism; fal(e k ,α,δ) is an adaptive correction function that depends on the residual e k , adaptive factor α and threshold δ, used to enhance robustness; I is the identity matrix, the dimension matches the extended state, ensuring the correct matrix operation in the covariance update; Forecast of the updated extended state; represents the prediction of the expansion state; P k|k is the expanded updated covariance matrix, indicating The uncertainty of the revised control target is reduced.

[0122] According to the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application, a state space form is constructed based on a discretized model, which can capture unmodeled dynamics and external interference, and improve the stability of the supercritical unit system under wide load operation and disturbance conditions; and on the basis of the state space model, an ESKF can be designed to estimate the system state and disturbance, thereby improving the accuracy of the ESKF in predicting the system state and disturbance at the current sampling moment.

[0123] In some embodiments, the discretized model dynamically adjusts the prediction error weight through real-time error analysis, and combines exponential smoothing technology to design an error adaptive mechanism algorithm model.

[0124] In this embodiment, the core of the error adaptive mechanism lies in the real-time adjustment of dynamic weights. The adjustment rules are as follows:

[0125] Q k (k) = S q (1+α1|e(k)|)

[0126]

[0127] Among them, k is the discrete time step, which represents the current sampling moment, Q k (k) is the unsmoothed dynamic covariance; S q is the base value covariance; e(k) is the real-time error, i.e., the measurement residual in the above ESKF; α1 and β1 are the amplification factor and smoothing factor respectively; is the smoothed dynamic covariance.

[0128] It should be noted that the amplification factor α1 is greater than 1, and the smoothing factor β1 is greater than 0 and less than 1. Due to the above adjustment rules, when the amplification factor α1 and / or the smoothing factor β1 are set to 1, it is considered that the amplification factor α1 and / or the smoothing factor β1 are not added to the error adaptive mechanism algorithm model, so the amplification factor α1 and the smoothing factor β1 are not set to 1.

[0129] During the actual execution process, α1 and β1 can be set based on the experimental test results. For example, when the experimental test results show that α1 and β1 are 1.7 and 0.8 respectively, if the adjustment effect is better, α1 and β1 can be set to 1.7 and 0.8 respectively. Of course, in other embodiments, α1 and β1 can also be set to other values, which are not limited here.

[0130] The prediction error weight refers to the dynamically adjusted part of the prediction error covariance, which can affect the Kalman gain by adjusting the process noise covariance, so that it trusts the measurement value more when the prediction error is large, making the prediction more suitable for disturbances.

[0131] Exponential smoothing is used to improve stability and reduce the effects of noise while preserving the trend of disturbances.

[0132] According to the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application, the discretized model dynamically adjusts the prediction error weight through real-time error analysis, which can make the ESKF prediction result more suitable for disturbances. The error adaptive mechanism algorithm model is designed in combination with the exponential smoothing technology. While retaining the disturbance trend, it can reduce the sharp fluctuations in the prediction results caused by measurement noise and sensor instantaneous errors, reduce the occurrence of changes in the prediction direction due to single errors, improve stability and reduce the impact of noise.

[0133] In some embodiments, integrating an error adaptive mechanism algorithm model with an extended state Kalman filter model to optimize the prediction model of a model predictive controller may include:

[0134] Predict future outputs based on difference equations.

[0135] In this embodiment, the model predictive controller (MPC) realizes future prediction output based on the difference equation. The prediction output process is shown in the following equation:

[0136]

[0137]

[0138] ΔU=[Δu k|k ,…,Δu k+Nc-1|k ] T

[0139] Among them, k is the discrete time step, which represents the current sampling moment, y k+1|k represents the output at time k+i predicted at time k; u k+i-q|k represents the input instruction at time k+iq predicted at time k; a p and b q are the coefficients of the difference equation, describing the system dynamics; n and m are the orders of the difference equation; is the state vector, which contains historical inputs and outputs; ΔU is the control increment vector, Np is the prediction window, Nc is the control window, F and Φ are prediction matrices, representing free response and forced response respectively; Y is the predicted output vector, whose length is the prediction window Np.

[0140] According to the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application, future output is predicted based on the differential equation, which enables the ESKF-based MPC to utilize the differential equation, initialize through historical state, predict future output in a single step, iteratively calculate multi-step predicted output, and correct it in combination with the ESKF disturbance estimation to obtain a predicted output vector, adapt to external disturbances, and improve prediction accuracy.

[0141] In some embodiments, integrating an error adaptive mechanism algorithm model with an extended state Kalman filter model to optimize the prediction model of a model predictive controller may include:

[0142] The error adaptive mechanism algorithm model dynamically optimizes control priorities through real-time error analysis to ensure the system's response to load changes;

[0143] The model predictive controller based on the extended state Kalman filter model can adjust the main steam valve opening, coal feed rate and water feed rate by virtue of its predictive ability.

[0144] Inputting the state estimation result of the extended state Kalman filter model into the model predictive controller;

[0145] The predictive control function of the model predictive controller identifies external disturbances and internal uncertainties in advance and optimizes the control strategy.

[0146] In this embodiment, the error adaptation mechanism dynamically adjusts the process noise covariance through real-time residual analysis, the ESKF estimates the expanded state, and the MPC uses the ESKF estimate and disturbance correction to predict future outputs, optimize the control increment, balance the output deviation and control cost, and improve the control accuracy and robustness.

[0147] In the actual execution process, after MPC realizes the future predicted output based on the difference equation, it can solve the objective function in the form of quadratic programming based on the future predicted output to obtain the future system input. Among them, the objective function is constructed by the output deviation term and the control cost term to measure the deviation between the predicted output and the target value and the cost of the control action.

[0148] The quadratic programming form is used to optimize and solve the objective function.

[0149] In some embodiments, the objective function and quadratic programming form are shown as follows:

[0150] J=(YY r ) T S q (YY r )+ΔU T S r ΔU

[0151]

[0152] ω=Φ T S q Φ+r w S r

[0153]

[0154] Among them, k is the discrete time step, which represents the current sampling moment; is the state vector; J is the objective function, which measures the prediction output deviation and control cost; Y is the prediction output vector, the length of which is the prediction window Np; Y r is the reference output vector, i.e. the set target value; ΔU is the control increment vector, the length of which is the control window Nc; ω is the quadratic term coefficient matrix; FF is the linear term coefficient matrix; r w is a scalar weight that adjusts the relative importance of the control increment; where ω and FF are calculated from the prediction matrix, state, and weight; F and Φ are the prediction matrices; S q is the output error weight matrix, S r is the control increment weight matrix.

[0155] In the supercritical unit scenario, the ESKF estimates the state initialization, predicts the output and combines it with disturbance correction, solves the optimal control increment vector by quadratic programming, and adjusts the control input to achieve load tracking, pressure and temperature stabilization.

[0156] In some embodiments, to meet the safe operation requirements of the supercritical unit, control variable constraints may be set:

[0157] Δu min ≤Δu(k)≤Δu max

[0158]

[0159] Where k is the discrete time step, representing the current sampling moment; the control variable refers to the control input u(k) and its increment Δu(k); in supercritical units, the control inputs typically include valve opening (%), coal feed (kg / s), and water feed (kg / s), which are used to adjust load, pressure, and temperature; the control increment Δu (Δu(k) = u(k) - u(k-1)) represents the change in the control input. The constraints ensure that the control action is smooth and within a safe range.

[0160] The constraint values ​​set therein can be determined based on the physical limits of the equipment and the requirements for operational stability.

[0161] The above constraints can be embedded in MPC through quadratic programming optimization to ensure that the control increment ΔU and the control input u(k) are within a safe range, reducing equipment overload or excessive action. At the same time, the disturbance estimation and error adaptation mechanism of ESKF can be combined to improve control smoothness and robustness.

[0162] In the actual implementation process, after integrating the error adaptive mechanism, the objective function becomes:

[0163]

[0164] Where J is the objective function, which measures the prediction output deviation and control cost; k is the discrete time step, which represents the current sampling moment; y is the prediction output vector, the length of which is the prediction window Np; y r is the reference output vector, i.e. the set target value; ΔU is the control increment vector, the length of which is the control window Nc; S r is the control increment weight matrix, Q K (i) is the dynamic covariance at the i-th moment.

[0165] like Figure 3 and Figure 4 As shown, the present invention compares and analyzes the command disturbance response performance of EEA-ESKF-MPC, EA-ESKF-MPC, ESKF-MPC and traditional MPC on main steam pressure, unit load and header temperature; among them, EEA-ESKF-MPC is the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering of the present application; EA-ESKF-MPC is the case where the amplification factor and smoothing factor in the error adaptive mechanism algorithm model are both set to 1 compared with the control method of the present application, that is, the amplification factor and smoothing factor are not added to the error adaptive mechanism algorithm model; ESKF-MPC is a supercritical unit model predictive control method based only on extended state Kalman filtering.

[0166] Traditional MPC responds slowly and has large deviations; ESKF-MPC improves stability through disturbance estimation; EA-ESKF-MPC is sensitive to adjustment and has significant fluctuations because it does not add amplification factors and smoothing factors; in contrast, EEA-ESKF-MPC combines the disturbance estimation advantages of ESKF and the adaptive adjustment advantages of EA-ESKF-MPC. By optimizing the amplification factors and smoothing factors, its response curve may recover to the set value more quickly with less fluctuation, showing higher anti-interference accuracy and stability.

[0167] like Figure 5 、 Figure 6 and Figure 7 As shown, in order to evaluate the performance of different control strategies during load ramping, the present invention conducted a load variation test in the operating range of 70MW to 320MW.

[0168] exist Figure 5 In the results, the pressure response of EEA-ESKF-MPC reduces the significant overshoot phenomenon compared with other methods, and its curve shows a smooth transition characteristic, demonstrating its excellent ability to maintain system stability during dynamic changes.

[0169] Figure 6 This further highlights the advantages of EEA-ESKF-MPC in load tracking. Its response curve closely follows the set value with minimal deviation. It can maintain precise control even when the load climbs rapidly, reflecting its high adaptability to changes in the set value.

[0170] same, Figure 7 The superiority of EEA-ESKF-MPC in temperature control is demonstrated, with its response reducing fluctuations, maintaining tighter stability, and ensuring reliable regulation of temperature parameters during the ramp-up process.

[0171] This comparison shows that EEA-ESKF-MPC achieved excellent control accuracy, fast response, and stability in ramp-up tests through comprehensive optimization of dynamic error adjustment, amplification factor, and smoothing technology. It can effectively cope with the complex dynamic requirements of multi-variable coupling and large inertia of supercritical units, and provides an efficient and reliable control solution for variable load operation.

[0172] like Figure 8 、 Figure 9 and Figure 10 As shown, the present invention compares and analyzes the tracking performance of EEA-ESKF-MPC and the existing controller of the power plant on main steam pressure, unit load and header temperature based on the operating data of a 350MW supercritical unit.

[0173] Depend on Figure 8 、 Figure 9 and Figure 10 It can be seen that the existing PID controller of the power plant may show large fluctuations and tracking lag under real operating conditions; while the response curve of EEA-ESKF-MPC is closer to the set value and has smaller fluctuations, reflecting its potential to improve coordination and stability in practical applications.

[0174] According to the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application, the control priority is dynamically optimized through real-time error analysis based on the error adaptive mechanism to ensure the system's rapid response to load changes, and the model predictive controller based on the extended state Kalman filter can achieve precise adjustment of the main steam valve opening, coal feed rate and water feed rate by virtue of its prediction ability. This optimization synergy can improve the control accuracy and dynamic response capability of the system; and by inputting the state estimation result of the extended state Kalman filter into the model predictive controller, the interference caused by multivariable coupling in the supercritical unit can be effectively suppressed, and the coordination of the main steam pressure, load and temperature control can be improved, thereby enhancing the overall stability of the system; the state estimation result of the extended state Kalman filter model is input into the model predictive controller. At the same time, the error adaptive mechanism is combined with the exponential smoothing technology to quickly adjust the control action when interference occurs, which can enhance the system's anti-interference ability and operational robustness.

[0175] The supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application can be executed by a supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering. In the embodiment of the present application, the supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering is used as an example to execute the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering to illustrate the supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application.

[0176] The embodiment of the present application also provides a supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering.

[0177] like Figure 11 As shown, the supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering includes: a first processing module 1110, a second processing module 1120, a third processing module 1130 and a fourth processing module 1140.

[0178] The first processing module 1110 is used to analyze the multivariable coupling characteristics of the supercritical unit and determine the multivariable control requirements;

[0179] The second processing module 1120 is used to identify and discretize the dynamic system of the supercritical unit based on the transfer function method to determine the prediction model of the model predictive controller;

[0180] The third processing module 1130 is used to design an extended state Kalman filter model to perform state estimation on the supercritical unit and design an error adaptive mechanism algorithm model;

[0181] The fourth processing module 1140 is used to integrate the error adaptive mechanism algorithm model and the extended state Kalman filter model to optimize the prediction model of the model predictive controller.

[0182] According to the supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering provided by the embodiment of the present application, by analyzing the multivariable coupling characteristics of the supercritical unit, the multivariable control requirements are determined, the dynamic system of the supercritical unit is identified and discretized based on the transfer function method, the prediction model of the model predictive controller is determined, the extended state Kalman filter model is designed to estimate the state of the supercritical unit, and an error adaptive mechanism algorithm model is designed. The error adaptive mechanism algorithm model and the extended state Kalman filter model are integrated to optimize the prediction model of the model predictive controller. It can achieve the problem of multivariable coupling, large inertia and nonlinearity by using dynamic error adjustment and disturbance estimation to improve the tracking accuracy and anti-interference capability of main steam pressure, load and temperature, while coordinating the control of steam inlet valve opening, coal feed rate and water feed rate to improve robustness and stability under wide load operation. Multivariable coordinated control is achieved through state estimation, error adaptation and predictive optimization, effectively alleviating the multivariable coupling interference of the supercritical unit.

[0183] In some embodiments, the third processing module 1130 may also be used to:

[0184] Based on the discretized model, a state space form is constructed.

[0185] In some embodiments, the fourth processing module 1140 may also be configured to:

[0186] Predict future outputs based on difference equations.

[0187] In some embodiments, the fourth processing module 1140 may also be configured to:

[0188] The error adaptive mechanism algorithm model dynamically optimizes control priorities through real-time error analysis to ensure the system's response to load changes;

[0189] The model predictive controller based on the extended state Kalman filter model can adjust the main steam valve opening, coal feed rate and water feed rate by virtue of its predictive ability.

[0190] Inputting the state estimation result of the extended state Kalman filter model into the model predictive controller;

[0191] The predictive control function of the model predictive controller identifies external disturbances and internal uncertainties in advance and optimizes the control strategy.

[0192] In some embodiments, the apparatus may further include a fifth processing module configured to:

[0193] A dynamic model of the supercritical unit is established, and the dynamic system of the supercritical unit is identified using the transfer function method. The identified dynamic model is discretized into a differential equation to adapt the model predictive controller and realize the prediction function of the prediction model of the model predictive controller.

[0194] The supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering in the embodiment of the present application can be an electronic device or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices other than a terminal. For example, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc. It can also be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine or a self-service machine, etc., and the embodiment of the present application does not specifically limit it.

[0195] The supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering in the embodiments of the present application can be a device having an operating system. The operating system can be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiments of the present application.

[0196] The supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering provided in the embodiment of the present application can achieve Figure 2 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0197] In some embodiments, as Figure 12As shown, an embodiment of the present application also provides an electronic device 1200, including a processor 1201, a memory 1202, and a computer program stored in the memory 1202 and executable on the processor 1201. When the program is executed by the processor 1201, each process of the embodiment of the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0198] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0199] An embodiment of the present application also provides a non-transitory computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the various processes of the above-mentioned supercritical unit model predictive control method embodiment based on error adaptation and extended state Kalman filtering, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0200] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0201] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering.

[0202] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0203] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned supercritical unit model predictive control method embodiment based on error adaptation and extended state Kalman filtering, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0204] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0205] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0206] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0207] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0208] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0209] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering, characterized in that: include: S1. Analyze the multivariable coupling characteristics of supercritical units and determine the multivariable control requirements; S2. Identifying and discretizing the dynamic system of the supercritical unit based on a transfer function method to determine a prediction model of a model predictive controller; S3. Designing an extended state Kalman filter model to perform state estimation on the supercritical unit and designing an error adaptive mechanism algorithm model; S4. Integrate the error adaptive mechanism algorithm model with the extended state Kalman filter model to optimize the prediction model of the model predictive controller.

2. The supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering according to claim 1 is characterized in that: The control objects of the supercritical unit include: steam inlet valve opening, coal feed rate and water feed rate, as well as main steam pressure, unit load and water wall header outlet temperature, which are used to realize the prediction function of the prediction model of the model predictive controller.

3. The supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering according to claim 2 is characterized in that: The prediction function of the prediction model of the model predictive controller is realized, including: A dynamic model of the supercritical unit is established, the dynamic system of the supercritical unit is identified using a transfer function method, and the identified dynamic model is discretized into a differential equation for use in adapting the model predictive controller.

4. The supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering according to claim 1 is characterized in that: The designing of an extended state Kalman filter model to perform state estimation on the supercritical unit includes: Based on the discretized model, a state space form is constructed.

5. The supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering according to claim 4 is characterized in that: The discretization model dynamically adjusts the prediction error weight through real-time error analysis, and designs an error adaptive mechanism algorithm model in combination with exponential smoothing technology.

6. The supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering according to any one of claims 1 to 5, characterized in that: The step of integrating the error adaptive mechanism algorithm model with the extended state Kalman filter model to optimize the prediction model of the model predictive controller includes: Predict future outputs based on difference equations.

7. The supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering according to any one of claims 1 to 5, characterized in that: The step of integrating the error adaptive mechanism algorithm model with the extended state Kalman filter model to optimize the prediction model of the model predictive controller includes: The error adaptive mechanism algorithm model dynamically optimizes control priorities through real-time error analysis to ensure the system's response to load changes; The model predictive controller based on the extended state Kalman filter model uses its predictive capability to adjust the main steam valve opening, coal feed rate and water feed rate; Inputting a state estimation result of the extended state Kalman filter model into the model predictive controller; The predictive control function of the model predictive controller identifies external disturbances and internal uncertainties in advance and optimizes the control strategy.

8. A supercritical unit model predictive control device based on error adaptation and extended state Kalman filtering, characterized in that: include: The first processing module is used to analyze the multivariable coupling characteristics of the supercritical unit and determine the multivariable control requirements; A second processing module is used to identify and discretize the dynamic system of the supercritical unit based on a transfer function method to determine a prediction model of a model predictive controller; The third processing module is used to design an extended state Kalman filter model to perform state estimation on the supercritical unit and design an error adaptive mechanism algorithm model; The fourth processing module is used to integrate the error adaptive mechanism algorithm model with the extended state Kalman filter model to optimize the prediction model of the model predictive controller.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the supercritical unit model predictive control method based on error adaptation and extended state Kalman filtering as described in any one of claims 1 to 7 is implemented.

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