Tower type photo-thermal power station heat absorber outlet temperature prediction anti-interference control method and system

Abstract

The invention belongs to the technical field of tower type photo-thermal power station heat absorber temperature control. The invention provides a tower type photo-thermal power station heat absorber outlet temperature prediction anti-interference control method and system, and the method comprises the steps: constructing a linear discretization control model, building an expansion form of the linear discretization control model, and generating an expansion state observer according to the expansion form; performing lumped interference estimation according to the extended state observer to obtain an observed value of the lumped interference; establishing a disturbance predictor based on a least square support vector machine according to the observed lumped disturbance sequence at the current moment and the past moment, and obtaining the future dynamic state of the lumped disturbance; the estimated lumped disturbance is dynamically fused into a steady-state target sequence in the future, a bimodal model prediction anti-interference control law with disturbance estimation feedforward compensation is solved, and tower type photo-thermal power station heat absorber outlet temperature prediction anti-interference control is carried out; effective control over the outlet temperature of the solar heat absorber is achieved under the condition that disturbance exists, and safe and economical operation of the tower type photo-thermal power station heat absorber is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control of a tower-type solar thermal power station absorber, and in particular to a tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method and system. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] The effective control of the molten salt temperature at the outlet of the tower-type CSP power station receiver (hereinafter referred to as "outlet temperature") involves the safety and economy of the operation of the entire CSP power generation unit. If the outlet temperature is too low, it will affect the unit's power generation efficiency and cause molten salt solidification problems. On the contrary, if the outlet temperature is too high, it will cause decomposition and corrosion of the molten salt in the tube. Therefore, the outlet temperature of the receiver is one of the process parameters that need to be monitored during the operation of the unit. The receiver control goal is to keep the outlet temperature of the receiver near the rated value by controlling the flow of molten salt flowing into the receiver. If the outlet temperature is low, it means that the flow is larger than the amount of solar energy absorbed. At this time, the molten salt flow needs to be reduced, otherwise it needs to be increased.

[0004] However, there are the following difficulties in controlling the outlet temperature of the heat absorber: first, due to the flow characteristics of molten salt, the system has large delay and large inertia characteristics; second, the system has multi-source disturbances, including external disturbances (such as solar radiation and inlet molten salt temperature changes) and internal disturbances (such as parameter perturbations), and most of these disturbances are unmeasurable or difficult to measure accurately. The characteristics of the heat absorber itself and the existence of disturbances greatly increase the difficulty of control. At present, a control scheme combining feedback control based on PID control, adaptive control, robust control, and feedforward control based on Kalman filter is usually adopted: Kalman filter is used to observe the estimated value of external disturbance, and then disturbance compensation is performed in the feedback controller, but Kalman filter cannot take into account the estimation of other disturbances except solar radiation, and the aforementioned feedback controller cannot effectively handle delay and inertia, so the control effect on the outlet temperature of the heat absorber is limited. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method and system, which realizes effective control of the solar absorber outlet temperature in the presence of disturbances and ensures safe and economical operation of the tower-type solar thermal power station absorber.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides an anti-interference control method for predicting the outlet temperature of a tower-type solar thermal power station absorber.

[0008] A tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method includes the following processes:

[0009] Constructing a linear discretization control model, establishing an expanded form of the linear discretization control model, and generating an expanded state observer according to the expanded form;

[0010] According to the extended state observer, the aggregate interference is estimated to obtain an observed value of the aggregate interference;

[0011] According to the observed aggregate disturbance sequence at the current and past moments, a disturbance predictor based on the least squares support vector machine is established to obtain the future dynamics of the aggregate disturbance.

[0012] The estimated future dynamics of the lumped disturbance are integrated into the steady-state target sequence, and the dual-mode model predictive anti-disturbance control law with disturbance prediction feedforward compensation is solved to perform predictive anti-disturbance control of the outlet temperature of the tower-type solar thermal power station receiver.

[0013] In a second aspect, the present invention provides an anti-interference control system for predicting the outlet temperature of a tower-type solar thermal power station absorber.

[0014] A tower-type solar thermal power station absorber outlet temperature prediction anti-interference control system, comprising:

[0015] The extended state observer generation unit is configured to: construct a linear discretization control model, establish an extended form of the linear discretization control model, and generate an extended state observer according to the extended form;

[0016] The observed value estimation unit of the aggregate interference is configured to: perform the aggregate interference estimation according to the extended state observer to obtain the observed value of the aggregate interference;

[0017] The aggregate disturbance future dynamics prediction unit is configured to: establish a disturbance predictor based on the least squares support vector machine according to the aggregate disturbance sequence observed at the current and past moments, and obtain the future dynamics of the aggregate disturbance;

[0018] The temperature prediction and anti-interference control unit is configured to: dynamically integrate the estimated lumped disturbance into the steady-state target sequence in the future, solve the dual-mode model prediction and anti-interference control law with disturbance prediction feedforward compensation, and perform the tower-type solar thermal power station receiver outlet temperature prediction and anti-interference control.

[0019] In a third aspect, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium;

[0020] a processor adapted to execute a computer program;

[0021] A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method as described in the first aspect of the present invention is implemented.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method as described in the first aspect of the present invention.

[0023] In a fifth aspect, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method as described in the first aspect of the present invention.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention innovatively proposes a dual-modal predictive anti-interference control method for the outlet temperature of the absorber of a tower-type solar thermal power station. By designing an extended state observer and a disturbance predictor based on least squares and regression ideas, the lumped disturbance prediction dynamics can be obtained online. The disturbance prediction dynamics are organically integrated into the model predictive control framework by introducing a steady-state target sequence to ensure the optimality of the final solution. Finally, the dual-modal control strategy is combined to solve the optimal control quantity at the current moment. The organic introduction of the future dynamics of the lumped disturbance increases the system feedforward information, which can make the control action more timely, thereby improving the anti-interference ability of the controller.

[0026] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0028] Figure 1 A schematic diagram of a flow chart of dual-mode predictive anti-interference control of the outlet temperature of a tower-type solar thermal power station absorber provided in Example 1 of the present invention;

[0029] Figure 2 This is a diagram showing the control effect of the outlet temperature of the tower-type solar thermal power station absorber provided in Example 1 of the present invention;

[0030] Figure 3A schematic diagram of a tower-type solar thermal power station absorber outlet temperature prediction anti-interference control system provided in Example 2 of the present invention;

[0031] Figure 4 A schematic diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0034] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0035] Embodiment 1:

[0036] This implementation proposes a tower-type CSP power station absorber outlet temperature prediction and anti-interference control method. Figure 1 As shown, the following process is included:

[0037] S1: Establish a linear discretization control model;

[0038] S2: Design an extended state observer;

[0039] S3: Establish a disturbance predictor based on least squares support vector machine;

[0040] S4: Solve the dual-mode model with disturbance prediction feedforward compensation and predict the disturbance rejection control law.

[0041] Specifically, in step S1, a linear discretization control model is established, including the following process:

[0042] S1.1: Under a certain steady-state condition, the outlet temperature model of the tower CSP power station receiver is linearized as (1):

[0043]

[0044] In the formula, d represents the unknown dynamic characteristics of the system, x (state variable) = y (controlled variable)

[0045] =T f,out -T f,out,r , T f,out and T f,out,r are the outlet temperature of the heat absorber and its set value, respectively, and the control variable u=q f -q f,r ,q fand q f,r are the molten salt flow rate and its set value, A m =-a·h f ·A f,o -a·c f ·q f,r , B m =-a·c f ·[T f,out -T f,in,r ], C m =1, B d,m =1, a = 1 / (ρ f c f A f,i Δl), ρ f ,c f and h f are the density, specific heat capacity and convection heat transfer coefficient of the molten salt, A f,i and A f,o are the outer / inner surface areas of the absorber respectively, and Δl is the length of each tube section.

[0046] S1.2: Discretization of linear models.

[0047] Assume sampling time T s ,According to the zero-order hold theorem, the linear model (1) can be further discretized as: In the formula, x k ,u k and k Represent the discretized values ​​of x, u and y respectively. Specifically, in step S2, the extended state observer is designed, including the following process:

[0048] Establish the expanded form of the linear discrete model (i.e., formula (2)):

[0049] According to the expansion form (i.e., formula (3)), the corresponding extended state observer (ESO) is designed:

[0050] Where z1 and z2 are the estimated values ​​of y and d respectively, L = [β 01 ,β 02 ] T It is the benefit of ESO.

[0051] Using the designed ESO (i.e., formula (4)) to calculate the aggregate interference d k Make an estimate and obtain the observation value at the current k moment

[0052] Specifically, in step S3, a disturbance predictor based on a least squares support vector machine is established, including the following process:

[0053] According to the observed lumped disturbance sequence at the current and past moments, the training sample D = (d in ,d out ):

[0054]

[0055] At the current k moment, update the support vector (d in,up , d out,up ):

[0056] Update training samples: Delete the first row of D and update the support vector (d in,up ,d out,up ) is added to the last line of D, that is:

[0057] Fit the least squares parameters b and α based on the latest sample:

[0058] Where E = [1, 1, ..., 1] T , H is the kernel function matrix. To predict the future sequence of lumped disturbances, As the test sample input, the predicted value of the lumped disturbance is:

[0059] get After that, update According to formula (9), we can predict And so on, it is predicted that Until now, the future dynamics of the lumped disturbance is finally obtained n a is the prediction length, that is, in the prediction time domain

[0060] Specifically, in step S4, solving the dual-mode model with disturbance prediction feedforward compensation and predicting the anti-disturbance control law includes the following processes:

[0061] The future dynamics of the predicted disturbance Organically integrated into the steady-state target sequence: The corrected steady-state target sequence is: In the formula, [M x ,M t ;M u ,M c ]=[AI,B;C,0] -1 . Construct performance indicators in the infinite-step prediction / control domain with steady-state target sequences:

[0062] In the formula, Q and R are weight matrices, and the performance index J shown in formula (12) k The organic fusion of the lumped disturbance prediction sequence can be achieved. In addition, by minimizing J k The feasible solution obtained will not destroy the optimality of the solution. According to the two modes of transient adjustment area and gradual convergence area in the dual-mode control strategy, J in (12) k Divided into J 1k and J 2k :

[0063] In the formula, J 1k Represents the performance index of transient regulation area, J 2k Represents the performance index of the asymptotic convergence region, n c is the step size of the transient regulation region.

[0064] Derivation J 1k Expression: In the transient regulation area, the control law u k+i Using free response format, that is, u k+i -u ss|k+i-1 =dof,i=0,...,n c -1(14); Therefore, combining equations (2), (10), (11) and (13), we can get J 1k :

[0065] In the formula,

[0066] Derivation J 2k Expression: In the asymptotic convergence region, the control law u k+i Using pure state feedback form, that is:

[0067] u k+i -u ss|k+i-1 =-K(x k+i -x ss|k+i ),i=n c ,...,∞(16); where K is the feedback gain to be designed. In addition, (x ss|k+i ,u ss|k+i ) remains constant and does not change, so the following relationship holds: In the formula, Φ = A-BK. Combining equations (13) and (17), we get J 2k for:

[0068] Where P t It can be calculated according to the Lyapunov equation. The expression is:

[0069] In the formula, H cn , H xn and H un They are H c , H x and H u The nth c Row block elements. Combining equations (18)-(19), we get J 2k The expression is:

[0070]

[0071] Solve infinite-horizon optimization problems:

[0072] Combine equations (15) and (20) and let have to:

[0073] Where:

[0074] Take the first element and get the control law at the current k moment:

[0075]

[0076] In the formula,

[0077] This implementation provides the following specific example: reconstruct the lumped disturbance of the tower-type CSP power station absorber, and linearize the tower-type CSP power station absorber model into formula (1) at the operating point where the total molten salt flow rate is 600 kg / s. In this embodiment, A m =-0.031, B m =-0.018, B dm =1, C m =1;

[0078] Discretization of linear model: According to the zero-order hold theorem, the linear model (Formula (1)) is further discretized as shown in Formula (2). In this embodiment, the sampling time T s =1s.

[0079] Establish the expanded form (Formula (3)) of the linear discrete model (Formula (2));

[0080] According to the expansion form (Equation (3)), the corresponding expanded state observer (Equation (4)) is established. In this embodiment, according to the pole placement method, L = [80, 1600] T .

[0081] According to the current and past observed aggregate disturbances, the training sample D = (d in ,dout );

[0082] At the current k moment, the support vector (d in,up ,d out,up );

[0083] According to formula (7), the training sample D is updated and the updated support vector (d in,up ,d out,up ) is added to D;

[0084] Combine equation (8) with the latest sample to fit the least squares parameters b and α;

[0085] According to formula (9), the future dynamics of the lumped disturbance is recursively predicted, and finally the future dynamics of the lumped disturbance is obtained In this embodiment, the prediction length n a =3.

[0086] According to formula (10), the predicted future dynamics of disturbance Fusion to the steady-state target sequence (x ss|k+i ,u ss|k+i )middle;

[0087] Construct the performance index of infinite step prediction / control time domain with steady-state target sequence (12). According to the two modes of transient adjustment area and gradual convergence area in the dual-mode control strategy, J k It is divided into J as shown in formula (13) 1k and J 2k Two parts;

[0088] Derivation J 1k Expression: In the transient regulation area, the control law u k+i Using the free response form shown in formula (14), substituting it into formula (13) yields J 1k The expression is shown in (15). In this embodiment, the step length n of the transient adjustment area is c =4.

[0089] Derivation J 2k Expression: In the asymptotic convergence region, the control law u k+i The pure state feedback form shown in equation (16) is adopted. In this embodiment, K = -0.03. In addition, (x ss|k+i ,u ss|k+i ) remains constant and does not change. Calculate P according to the Lyapunov equation t , combining equations (18)-(19), we get J 2k The expression of is shown in formula (20).

[0090] Solve the infinite time domain optimization problem: Combine equations (15) and (20) and let And take the first element, take the first element, and get the control law (22) at the current time k.

[0091] Under this condition, the external interference added is: the radiation power I drops from 455.54MW to 300MW at a certain moment, and then recovers to 455.54M after a period of time, simulating the situation where the solar radiation temporarily drops due to cloud cover. The results are shown in the attached figure. Figure 2 As shown. Figure 2 It can be seen that after considering the feedforward compensation of the future dynamics of the lumped disturbance, the outlet temperature of the heat sink T f,out The fact that it can still quickly stabilize at the set value after a relatively small fluctuation indicates that the present invention can effectively control the outlet temperature of the heat absorber.

[0092] Embodiment 2:

[0093] like Figure 3 As shown, this implementation provides a tower-type CSP power station absorber outlet temperature prediction and anti-interference control system, including:

[0094] The extended state observer generation unit is configured to: construct a linear discretization control model, establish an extended form of the linear discretization control model, and generate an extended state observer according to the extended form;

[0095] The observed value estimation unit of the aggregate interference is configured to: perform the aggregate interference estimation according to the extended state observer to obtain the observed value of the aggregate interference;

[0096] The aggregate disturbance future dynamics prediction unit is configured to: establish a disturbance predictor based on the least squares support vector machine according to the aggregate disturbance sequence observed at the current and past moments, and obtain the future dynamics of the aggregate disturbance;

[0097] The temperature prediction and anti-interference control unit is configured to: dynamically integrate the estimated lumped disturbance into the steady-state target sequence in the future, solve the dual-mode model prediction and anti-interference control law with disturbance prediction feedforward compensation, and perform the tower-type solar thermal power station receiver outlet temperature prediction and anti-interference control.

[0098] It is understandable that the above-mentioned various units can be separately or all combined into one or several other units to constitute, or some (some) units therein can also be split into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the function of a unit can also be realized by multiple units, or the function of multiple units can be realized by one unit. In other embodiments of the present application, the system can also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0099] According to another embodiment of the present application, the system described in this embodiment can be constructed, and the method of Example 1 of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general computing device such as a computer including processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0100] Embodiment 3:

[0101] like Figure 4 As shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0102] Among them, the communication interface 1002 is used to receive and send data, the computer-readable storage medium 1003 can be stored in the memory of the electronic device, the computer-readable storage medium 1003 is used to store a computer program, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0103] Processor 1001 (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0104] The processor 1001 is configured to execute the following process:

[0105] Constructing a linear discretization control model, establishing an expanded form of the linear discretization control model, and generating an expanded state observer according to the expanded form;

[0106] According to the extended state observer, the aggregate interference is estimated to obtain an observed value of the aggregate interference;

[0107] According to the observed aggregate disturbance sequence at the current and past moments, a disturbance predictor based on the least squares support vector machine is established to obtain the future dynamics of the aggregate disturbance.

[0108] The estimated future dynamics of the lumped disturbance are integrated into the steady-state target sequence, and the dual-mode model predictive anti-disturbance control law with disturbance prediction feedforward compensation is solved to perform predictive anti-disturbance control of the outlet temperature of the tower-type solar thermal power station receiver.

[0109] Embodiment 4:

[0110] This implementation provides a computer-readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides a storage space that stores the processing system of the electronic device.

[0111] In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory; optionally, it may also be at least one computer-readable storage medium located away from the aforementioned processor.

[0112] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process:

[0113] Constructing a linear discretization control model, establishing an expanded form of the linear discretization control model, and generating an expanded state observer according to the expanded form;

[0114] According to the extended state observer, the aggregate interference is estimated to obtain an observed value of the aggregate interference;

[0115] According to the observed aggregate disturbance sequence at the current and past moments, a disturbance predictor based on the least squares support vector machine is established to obtain the future dynamics of the aggregate disturbance.

[0116] The estimated future dynamics of the lumped disturbance are integrated into the steady-state target sequence, and the dual-mode model predictive anti-disturbance control law with disturbance prediction feedforward compensation is solved to perform predictive anti-disturbance control of the outlet temperature of the tower-type solar thermal power station receiver.

[0117] Embodiment 5:

[0118] The present implementation provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs the following process:

[0119] Constructing a linear discretization control model, establishing an expanded form of the linear discretization control model, and generating an expanded state observer according to the expanded form;

[0120] According to the extended state observer, the aggregate interference is estimated to obtain an observed value of the aggregate interference;

[0121] According to the observed aggregate disturbance sequence at the current and past moments, a disturbance predictor based on the least squares support vector machine is established to obtain the future dynamics of the aggregate disturbance.

[0122] The estimated future dynamics of the lumped disturbance are integrated into the steady-state target sequence, and the dual-mode model predictive anti-disturbance control law with disturbance prediction feedforward compensation is solved to perform predictive anti-disturbance control of the outlet temperature of the tower-type solar thermal power station receiver.

[0123] A person skilled in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0124] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instruction can be stored in a computer-readable storage medium or transmitted by a computer-readable storage medium. The computer instruction can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (for example, coaxial cable, optical fiber, digital line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server, a data center, etc. that contains one or more available media integrated. Available media can be magnetic media (for example, floppy disk, hard disk, tape), optical media (for example, DVD), or semiconductor media (for example, solid-state drive (Solid State Disk, SSD)) and the like.

[0125] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method, characterized in that: The process includes: Constructing a linear discretization control model, establishing an expanded form of the linear discretization control model, and generating an expanded state observer according to the expanded form; According to the extended state observer, the aggregate interference is estimated to obtain an observed value of the aggregate interference; According to the observed aggregate disturbance sequence at the current and past moments, a disturbance predictor based on the least squares support vector machine is established to obtain the future dynamics of the aggregate disturbance. The estimated future dynamics of the lumped disturbance are integrated into the steady-state target sequence, and the dual-mode model predictive anti-disturbance control law with disturbance prediction feedforward compensation is solved to perform predictive anti-disturbance control of the outlet temperature of the tower-type solar thermal power station receiver.

2. The tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method according to claim 1, characterized in that: The extended state observer is: Among them, z1 and z2 are the estimated values ​​of y and d respectively, L = [β 01 , β 02 ] T is the gain of the extended state observer, A m =-a·h f ·A f,o -a·c f ·q f,r , B m =-a·c f ·[T f,out -T f,in,r ], y is the controlled variable, u is the control variable, d is the unknown dynamic characteristic, T f,out and T f,out,r are the outlet temperature of the heat absorber and its set value, q f and q f,r are the molten salt flow rate and its set value, respectively, a=1 / (ρ f c f A f,i Δl), ρ f 、c f and h f are the density, specific heat capacity and convection heat transfer coefficient of the molten salt, A f,i and A f,o are the outer and inner surface areas of the absorber respectively, and Δl is the length of each tube section.

3. The tower-type CSP power station absorber outlet temperature prediction and anti-interference control method according to claim 2, characterized in that: According to the observed lumped disturbance sequence at the current and past moments, a disturbance predictor based on the least squares support vector machine is established to obtain the future dynamics of the lumped disturbance, including: Construct training samples D based on the observed aggregate disturbance sequence at the current and past moments; At the current k moment, update the support vector; Update the training sample: delete the first row of D, add the updated support vector to the last row of the training sample D, and obtain a new training sample; Fit the least squares parameters based on the latest sample; Predict the future sequence of lumped disturbances, As the test sample input, the predicted value of the lumped disturbance is: K is the feedback gain, where α i and b are the least squares parameters, N is the number of disturbances in the lumped disturbance sequence; according to renew Reforecast And so on, it is predicted that Until now, the future dynamics of the lumped disturbance is finally obtained n a is the predicted length.

4. The tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method according to claim 3, characterized in that: The future dynamics of the predicted disturbance Fusion into steady-state target sequence: Get the corrected steady-state target sequence: Among them, [M x , M t , M u , M c ] = [AI, B, C, 0] - 1, Ts is the sampling time, I is the unit matrix, r k+1 =Cx ss|k+i .

5. The tower-type CSP power station absorber outlet temperature prediction and anti-interference control method according to claim 4, characterized in that: The control law at the current time k is: in, P t It can be calculated according to the Lyapunov equation, H un H u The nth c Line-block elements, x k is the discretized value of the state variable x.

6. The tower-type CSP power station absorber outlet temperature prediction and anti-interference control method according to claim 5, characterized in that: Constructing performance indicators with steady-state target sequences J k , J k Divided into J 1k and J 2k , J 1k Represents the performance index of transient regulation area, J 2k Represents the performance index of the asymptotic convergence region, let but: Take the first element and get the control law u at the current time k k .

7. A tower-type solar thermal power station absorber outlet temperature prediction anti-interference control system, characterized in that: include: The extended state observer generation unit is configured to: construct a linear discretization control model, establish an extended form of the linear discretization control model, and generate an extended state observer according to the extended form; The observed value estimation unit of the aggregate interference is configured to: perform the aggregate interference estimation according to the extended state observer to obtain the observed value of the aggregate interference; The aggregate disturbance future dynamics prediction unit is configured to: establish a disturbance predictor based on the least squares support vector machine according to the aggregate disturbance sequence observed at the current and past moments, and obtain the future dynamics of the aggregate disturbance; The temperature prediction and anti-interference control unit is configured to: dynamically integrate the estimated lumped disturbance into the steady-state target sequence in the future, solve the dual-mode model prediction and anti-interference control law with disturbance prediction feedforward compensation, and perform the tower-type solar thermal power station receiver outlet temperature prediction and anti-interference control.

8. A computer device, characterized in that: include: A processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method is implemented according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the tower-type solar thermal power station absorber outlet temperature prediction and anti-interference control method according to any one of claims 1 to 6.

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