ARX-based water level control system setting value optimization method and storage medium

Abstract

The present invention discloses a method and storage medium for optimizing the set value of a water level control system based on ARX; the method comprises: collecting a plurality of segments of first identification data; constructing a plurality of water level control ARX models corresponding to each segment of the first identification data for simulating and outputting the actual water level; and constructing a plurality of PID simulation control models corresponding to each water level control ARX model for controlling the water level control ARX model; finally, optimizing the first setting parameters of each PID simulation control model based on the plurality of water level control ARX models to obtain the optimal setting parameters; the implementation of the present invention can effectively solve the problem of valve and feed water pump speed fluctuations of a steam generator during the water level control process in an offline state, not only can the optimal setting parameters of the water level control system be obtained, but also the complexity of the water level control system set value optimization work is reduced, the work risk is eliminated, and the method has the advantages of high optimization efficiency and high accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear power plant steam generators, and in particular to an ARX-based water level control system setting value optimization method and storage medium. Background Art

[0002] In a certain nuclear power plant, the steam generator water level control adopts a closed-loop control system. The original water level control system has the following problems: the control parameters fluctuate, resulting in oscillations in the main feedwater valve opening signal since operation, and oscillations in the feedwater pump speed; there is a lack of means to optimize the setting values ​​of the PID controller and PI controller. Currently, the only option is to use an online operation test scheme. This scheme not only has the risk of affecting the normal operation of the nuclear power plant, but also has defects such as complex testing work, low efficiency, and a limited number of tests. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and storage medium for optimizing the set value of a water level control system based on ARX in response to at least one defect in the prior art.

[0004] The technical solution adopted by the present invention to solve the technical problem is to construct an ARX-based water level control system setting value optimization method for offline obtaining the optimal setting parameters of the steam generator water level control system, including the following steps:

[0005] S10, collecting several segments of first identification data;

[0006] S20, constructing a plurality of water level control ARX models corresponding to each section of the first identification data for simulating and outputting actual water levels;

[0007] S30, constructing a plurality of PID simulation control models corresponding to each of the water level control ARX models for controlling the water level control ARX models;

[0008] S40. Optimize the first tuning parameters of each of the PID simulation control models based on the plurality of water level control ARX models, and obtain the optimal tuning parameters according to each of the first tuning parameters.

[0009] Preferably, in said S10, said first identification data includes a steam-water mismatch signal set value, a unit output power signal, a primary circuit temperature and said actual water level;

[0010] In the S20, the water level control ARX model is:

[0011] Y w (k) = Y w0 +a w1 Y w(k-1)+a w2 Y w (k-2)+...+a wny Y w (k-ny)+b w1 U w (kf a )+b w2 U w (kf a -1)+...+b wnu U w (kf a -nu+1)+c w1 D w (kg a )+c w2 D w (kg a -1)+...+c wnd D w (kg a -nd+1);

[0012] Among them, Y w is the actual water level; k is the time; Y w0 、a w1 ~a wny 、b w1 ~b wnu and c w1 ~c wnd is the first parameter set to be identified; Y w0 is the first offset value; ny, nu and nd are the model orders; U w f is the set value of the steam-water mismatch signal; a For U w Pure lag time; g a D is the pure lag time between the unit output power signal and the primary circuit temperature; w It is the combination of the unit output power signal and the primary circuit temperature.

[0013] Preferably, the S10 further includes: collecting a plurality of second identification data corresponding to each segment of the first identification data;

[0014] The ARX-based water level control system set value optimization method further includes:

[0015] S201, constructing a plurality of flow sub-ARX models corresponding to each segment of the second identification data for optimizing the steam-water mismatch signal setting value of the corresponding water level control ARX model, and constructing a plurality of PI simulation control models corresponding to each flow sub-ARX model for outputting a valve opening signal;

[0016] S202: Optimize the second tuning parameters of each of the PI simulation control models based on the plurality of flow sub-ARX models.

[0017] Preferably, in said S10, said second identification data includes a steam-water mismatch signal, a steam flow signal, a unit output power signal and said valve opening signal;

[0018] In the S201, the flow sub-ARX model is:

[0019] Y f (k) = Y f0 +a f1 Y f (k-1)+a f2 Y f (k-2)+...+a fny Y f (k-ny)+b f1 U f (kf b )+b f2 U f (kf b -1)+...+b fnu U f (kf b -nu+1)+c f1 D f (kg b )+c f2 D f (kg b -1)+...+c fnd D f (kg b -nd+1);

[0020] Among them, Y f is the soda-water mismatch signal; Y f0 、a f1 ~a fny 、b f1 ~b fnu and c f1 ~c fnd is the second parameter set to be identified; Y f0 is the second offset value; ny, nu and nd are the model orders; U f is the valve opening signal; f b For U f Pure lag time; g b D is the pure lag time between the steam flow signal and the unit output power signal; f It is the combination of the steam flow signal and the unit output power signal.

[0021] Preferably, in said S201, it includes: constructing a simulated PI controller based on the PI controller in the actual flow subsystem, and performing zero-order hold processing and discretization processing on the output of the simulated PI controller in sequence to obtain the PI simulation control model;

[0022] The output expression of the PI simulation control model is:

[0023]

[0024] Among them, u f is the valve opening signal; e f is the error value function of the simulated PI controller; K 31 and T 33 The second tuning parameter constituting the PI simulation control model; T is the parameter sampling period.

[0025] Preferably, the ARX-based water level control system set value optimization method further includes:

[0026] S203: Based on the fmincon function and the first constraint condition, a coefficient optimization model for obtaining the best first parameter set to be identified and the best second parameter set to be identified is established:

[0027]

[0028]

[0029] Among them, θ * is the best first parameter set to be identified or the best second parameter set to be identified; is the long-term forward prediction output of the corresponding ARX model; Y is the actual output value of the corresponding ARX model, n is the maximum value of the model order; N is the data length; N P is the number of forward prediction steps; the first constraint condition includes the ARX model step response direction constraint, the ARX model stability constraint and the ARX model static amplification coefficient constraint.

[0030] Preferably, in said S30, it includes: constructing a simulation PID controller based on the PID controller in the actual water level control system, and performing zero-order hold processing and discretization processing on the output of the simulation PID controller in sequence to obtain the PID simulation control model;

[0031] The output expression of the PID simulation control model is obtained as follows:

[0032]

[0033] Among them, u w Set a value for the steam-water mismatch signal; ew is the error value function of the simulated PID controller; K 30 、T 31 and T 36 The first tuning parameter constituting the PID simulation control model; T is the parameter sampling period; and e is a natural constant.

[0034] Preferably, the S40 includes: establishing a tuning parameter optimization model for obtaining the optimal tuning parameter according to the fmincon function and the second constraint condition:

[0035]

[0036] st closed-loop system stability constraints;

[0037] Among them, β * =(K 30 , T 31 , T 36 ), is the optimal tuning parameter; M is the number of ARX models; N j The data length used for optimizing the j-th ARX model; is the predicted output based on the j-th ARX model; Y r (j) is the expected output corresponding to the j-th ARX model; R is the control weight coefficient; du (j) is the controller increment for the j-th ARX model; the second constraint includes a closed-loop system stability constraint.

[0038] Preferably, the tuning parameter optimization model is also used to obtain the optimal second tuning parameter of the PI simulation control model.

[0039] The present invention also constructs a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, the ARX-based water level control system set value optimization method provided by an embodiment of the present invention is implemented.

[0040] The present invention has the following beneficial effects: providing an ARX-based water level control system setting value optimization method; by collecting several segments of first identification data; constructing several water level control ARX models corresponding to each segment of first identification data for simulating and outputting actual water levels; and constructing several PID simulation control models corresponding to each water level control ARX model for controlling the water level control ARX model; finally, optimizing the first setting parameters of each PID simulation control model based on the several water level control ARX models to obtain optimal setting parameters; implementing the present invention can effectively solve the problem of valve and feed water pump speed fluctuations of the steam generator during the water level control process in an offline state, not only can the optimal setting parameters of the water level control system be obtained, but also the complexity of the water level control system setting value optimization work is reduced, the work risk is eliminated, and it also has the advantages of high optimization efficiency and high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0042] Figure 1 1 is a flow chart of a first embodiment of the method for optimizing the set value of a water level control system based on ARX provided by the present invention;

[0043] Figure 2 This is a schematic diagram of the structure of an actual water level control system in a nuclear power plant;

[0044] Figure 3 This is a flow chart of Example 2 of the ARX-based water level control system set value optimization method provided by the present invention;

[0045] Figure 4 It is a structural diagram of the connection between the PI simulation control model and the flow sub-ARX model provided by the present invention;

[0046] Figure 5 It is a structural diagram of the connection between the PID simulation control model provided by the present invention and the water level control ARX model. DETAILED DESCRIPTION

[0047] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0048] It should be noted that the flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all content and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0049] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0050] refer to Figure 1 The present invention provides an ARX-based water level control system setting value optimization method for offline obtaining the optimal setting parameters of the steam generator water level control system, including: step S10, step S20, step S30 and step S50.

[0051] Step S10 includes: collecting a plurality of first identification data.

[0052] Step S20 includes constructing several water level control ARX models corresponding to each segment of first identification data, each model being used to simulate and output the actual water level. Specifically, a water level control ARX model is constructed for each segment of first identification data. The primary function of the water level control ARX model is to simulate the actual water level control system of a nuclear power plant. As will be appreciated, constructing piecewise linearized water level control models is beneficial for addressing nonlinear variations in the actual water level, thereby increasing the reference value of the actual water level output by the water level control ARX model.

[0053] In some embodiments, the first identification data in step S10 includes the steam-water mismatch signal setpoint, the unit output power signal, the primary circuit temperature, and the actual water level. It should be noted that in a nuclear power plant, the water level setpoint (actual water level) output by the water level control system is not only affected by the steam-water mismatch signal setpoint, but also varies with changes in the unit output power and the primary circuit temperature. Alternatively, the first identification data can be obtained by monitoring the actual operation of the nuclear power plant units.

[0054] Furthermore, in step S20, a water level control ARX model with exogenous variables is constructed based on ARX:

[0055] Y w (k) = Y w0 +a w1 Y w (k-1)+a w2 Y w (k-2)+...+a wny Y w (k-ny)+b w1 U w (kf a )+b w2 U w (kf a -1)+...+b wnuU w (kf a -nu+1)+c w1 D w (kg a )+c w2 D w (kg a -1)+...+c wnd D w (kg a -nd+1);

[0056] Among them, Y w is the actual water level; k is the time, the unit is usually seconds, such as Y w (k-1) represents the actual water level output one second before time k; Y w0 、a w1 ~a wny 、b w1 ~b wnu and c w1 ~c wnd is the first parameter set to be identified; Y w0 is the first offset value; ny, nu and nd are the model orders; U w Set the value of the steam-water mismatch signal; f a For U w Pure lag time; g a is the pure lag time between the unit output power signal and the primary circuit temperature; D w is the combination of the unit output power signal and the primary circuit temperature, that is, D w ={D w1 , D w2}, D w1 is the unit output power signal, D w2 is the primary circuit temperature.

[0057] like Figure 2 As shown in Figure 1, the actual water level control system includes an actual flow subsystem for generating a steam-water mismatch signal based on the unit output power signal, the valve opening signal, and the set value of the steam-water mismatch signal. It should be noted that the actual flow subsystem's function is to reduce the impact of other operating factors (such as unit output power and valve opening) on ​​the set value of the steam-water mismatch signal, thereby improving actual water level control accuracy.

[0058] In order to make the construction and operation principle of the water level control ARX model closer to the actual water level control system and improve the reference value of the water level control ARX model, in some embodiments, first, to ensure the relevance of the data, step S10 further includes: collecting a plurality of second identification data corresponding to each segment of the first identification data; wherein the second identification data includes a steam-water mismatch signal, a steam flow signal, a unit output power signal, and a valve opening signal; then, constructing a plurality of flow sub-ARX models corresponding to each water level control ARX model based on the second identification data, such as Figure 3 As shown, the ARX-based water level control system setting value optimization method further includes: steps S201 and S202. The second identification data can also be obtained by monitoring the actual operation of the nuclear power plant unit. Preferably, the sampling period T of the first identification data and the second identification data is 1s.

[0059] Step S201 includes: constructing several flow sub-ARX models corresponding to each segment of second identification data for optimizing the steam-water mismatch signal setting value of the corresponding water level control ARX model, and constructing several PI simulation control models corresponding to each flow sub-ARX model for outputting valve opening signals.

[0060] It should be noted that each flow sub-ARX model is associated with a corresponding water level control ARX model based on the correlation between the first identification data and the second identification data. For example, if the first identification data includes A1 and A2, and the second identification data includes B1 collected during the same time period as A1, and B2 collected during the same time period as A2, then the first water level control ARX model constructed based on A1 is associated with the first flow sub-ARX model constructed based on B1. In other words, the first water level control ARX model will be constructed based on the steam-water mismatch signal output by the first flow sub-ARX model.

[0061] In some embodiments, the traffic sub-ARX model in step S201 is:

[0062] Y f (k) = Y f0 +a f1 Y f (k-1)+a f2 Y f (k-2)+...+a fny Y f (k-ny)+b f1 U f (kf b )+b f2 U f (kf b -1)+...+b fnu U f (kfb -nu+1)+c f1 D f (kg b )+c f2 D f (kg b -1)+...+c fn D f (kg b -nd+1);

[0063] Among them, Y f is the soda mismatch signal; k is the time; Y f0 、a f1 ~a fny 、b f1 ~b fnu and c f1 ~c fnd is the second parameter set to be identified; Y f0 is the second offset value; ny, nu and nd are the model orders; U f is the valve opening signal; f b For U f Pure lag time; g b is the pure lag time between the steam flow signal and the unit output power signal; D f is the combination of steam flow signal and unit output power signal, namely D f ={D f1 , D f2}, D f1 is the steam flow signal, D f2 Output power signal for the unit.

[0064] In some embodiments, in step S201, the process of constructing each PI simulation control model includes: constructing a simulated PI controller based on the PI controller in the actual flow subsystem, and performing zero-order hold processing and discretization processing on the output of the simulated PI controller in sequence to obtain the PI simulation control model.

[0065] Specifically, according to the PI controller in the actual flow subsystem, formula (1) can be obtained:

[0066] Among them, K 31 and T 33 The second tuning parameter that constitutes the PI simulation control model.

[0067] Adding a zero-order holder to formula (1) yields formula (2):

[0068] Where e is a natural constant.

[0069] After discretizing formula (2), we can get formula (3):

[0070]

[0071] Let r1 = K 31 , Formula (4) can be obtained:

[0072] Where T is the parameter sampling period.

[0073] The calculation formula (5) of the PI controller output signal under the reaction is as follows:

[0074]

[0075] Converting formula (5) from the z domain to the discrete time domain, the output expression of the PI simulation control model is:

[0076] u f (k)=u f (k-1)+r1e f (k)+(g1-r1)e f (k-1);

[0077] Among them, u f is the valve opening signal; e f is the error value function of the simulated PI controller. k is the time, such as u f (k-1) represents the signal value output at time k-1, e f (k-1) represents the error value output at time k.

[0078] In addition, the structure diagram of each PI simulation control model and its associated flow sub-ARX model can be referred to Figure 4 .

[0079] Step S202 includes: optimizing the second tuning parameters of each PI simulation control model based on the plurality of flow sub-ARX models. Step S202 is performed to obtain the most recent second tuning parameters.

[0080] In order to enable the flow sub-ARX model and the water level control ARX model to describe the dynamic characteristics of the actual object, the long-term prediction error of the model is used as the objective function, and the ARX model parameters are optimized by minimizing the objective function. At the same time, constraints on the dynamic mode of the model are imposed during the parameter optimization process, so that the ARX model has a dynamic response mode consistent with the actual system. Therefore, in some embodiments, such as Figure 3 As shown, the ARX-based water level control system set value optimization method further includes: step S203.

[0081] Step S203 includes: establishing a coefficient optimization model for obtaining the best first parameter set to be identified and the best second parameter set to be identified according to the fmincon function and the first constraint condition:

[0082]

[0083]

[0084] Among them, θ * is the best first parameter set to be identified or the best second parameter set to be identified; is the long-term forward prediction output of the corresponding ARX model; Y is the actual output value of the corresponding ARX model, n is the maximum value of the model order; N is the data length; N P is the number of forward prediction steps; the first constraint condition includes the ARX model step response direction constraint, the ARX model stability constraint and the ARX model static amplification coefficient constraint.

[0085] In some embodiments, the step response direction of the flow sub-ARX model is constrained to increase the output of the simulated PI controller and decrease the output of the steam-water mismatch signal; the stability constraint of the flow sub-ARX model is that all poles of the object ARX model are within the unit circle; and the static amplification factor of the flow sub-ARX model is constrained to be between -80 and -5. In addition, when the coefficient optimization model obtains the best first parameter set to be identified, θ * =(Y f0 , a f1 ,…,a fny , b f1 ,…,b fnu , c f1 ,…,c fnd )

[0086] In some embodiments, the step response direction constraint of the water level control ARX model is that the steam-water mismatch signal set value increases (the output of the simulated PID controller increases), and the steam generator water level rises; the stability constraint of the water level control ARX model is that all poles of the object ARX model are within the unit circle; and the static gain coefficient constraint of the water level control ARX model is 0.01 to 0.08. In addition, when the coefficient optimization model obtains the best first parameter set to be identified, θ * =(Y w0 , a w1 ,…,a wny , b w1 ,…,b wnu , c w1 ,...,c wnd ).

[0087] Step S30 includes: constructing a plurality of PID simulation control models corresponding to each water level control ARX model and used to control the water level control ARX model.

[0088] In some embodiments, in step S30, the process of constructing each PID simulation control model includes: constructing a simulation PID controller based on the PID controller in the actual water level control system, and performing zero-order hold processing and discretization processing on the output of the simulation PID controller in turn to obtain a PID simulation control model.

[0089] Specifically, according to the PID controller in the actual water level control system, formula (6) can be obtained:

[0090]

[0091] Among them, K 30 、T 31 and T 36 The first tuning parameter that constitutes the PID simulation control model.

[0092] Adding a zero-order holder to formula (6) yields formula (7):

[0093]

[0094] After discretizing formula (7), we can get formula (8):

[0095]

[0096] Where T is the parameter sampling period.

[0097] make Formula (9) can be obtained:

[0098]

[0099] The calculation formula (10) for the output signal of the PID controller under positive action is as follows:

[0100]

[0101] Converting formula (10) from the z domain to the discrete time domain, the output expression of the PID simulation control model is:

[0102] The output expression of the PID simulation control model is:

[0103] u w (k)=(1+d2)u w (k-1)-d2u w (k-2)-g2e w (k)-[r2(1-d2)-2g2+c2]ew (k-1)-[g2-r2(1-d2)-c2d2]e w (k-2);

[0104] Among them, u w Set the value for the soda-water mismatch signal; e w is the error value function of the simulated PID controller; T is the parameter sampling period. 0 <g c ≤1,g c The gain coefficient converted from the feed water temperature through the function is a proportional coefficient that depends on the feed water temperature; y wm (k) is the actual water level output by the PID simulation control model at time w, y wr (k) is the water level set value input to the PID simulation control model at time w.

[0105] In addition, the structure diagram of each PID simulation control model and its associated water level control ARX model can be referred to Figure 5 .

[0106] Step S40 includes: optimizing the first tuning parameters of each PID simulation control model based on a plurality of water level control ARX models, and obtaining the optimal tuning parameters according to each first tuning parameter.

[0107] In order to obtain optimal tuning parameters that are controllable for all water level control ARX models and have good control performance, in some embodiments, step S40 includes: establishing a tuning parameter optimization model for obtaining the optimal tuning parameters based on the fmincon function and the second constraint condition:

[0108]

[0109] st closed-loop system stability constraints;

[0110] Among them, β * =(K 30 , T 31 , T 36 ), is the optimal tuning parameter; M is the number of ARX models; N j The data length used for optimizing the j-th ARX model; is the predicted output based on the j-th ARX model; Y r (j) is the expected output corresponding to the j-th ARX model; R is the control weight coefficient; du (j)is the controller increment for the jth ARX model; the second constraint includes the closed-loop system stability constraint. Specifically, the closed-loop system stability constraint is intended to ensure that the water level control ARX model is in a stable state during optimization, thereby improving the accuracy of the optimal tuning parameters. Furthermore, the control weighting coefficient R can be adjusted to suppress excessive changes in the output of the simulated PID controller. During the optimization process, different control weighting coefficients R can be used to observe the impact of different R values ​​on the control effect. Generally, a larger R value results in a weaker controller effect or a slower change in the controlled variable.

[0111] In order to make the flow sub-ARX model perform optimization work in a stable state, in some embodiments, the tuning parameter optimization model is also used to obtain the best second tuning parameter of the PI simulation control model. It can be understood that when the tuning parameter optimization model obtains the best second tuning parameter, β * =(K 31 , T 33 ).

[0112] The present invention also provides a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, the ARX-based water level control system set value optimization method provided in an embodiment of the present invention is implemented.

[0113] It can be understood that the implementation of the present invention can effectively solve the problem of valve and feed water pump speed fluctuations in the water level control process of the steam generator in an offline state. It can not only obtain the optimal setting parameters of the water level control system, but also reduce the complexity of the water level control system setting value optimization work, eliminate work risks, and also has the advantages of high optimization efficiency and high accuracy.

[0114] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0115] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0116] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0117] It is understandable that the above embodiments only express the preferred implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the patent scope of the present invention. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present invention, the above technical features can be freely combined, and several deformations and improvements can be made, all of which fall within the scope of protection of the present invention. Therefore, all equivalent changes and modifications made to the scope of the claims of the present invention should fall within the scope of coverage of the claims of the present invention.

Claims

1. A water level control system setting value optimization method based on ARX, used for offline obtaining the optimal setting parameters of the steam generator water level control system, characterized in that: The following steps are involved: S10, collecting several segments of first identification data; S20, constructing a plurality of water level control ARX models corresponding to each section of the first identification data for simulating and outputting actual water levels; S30, constructing a plurality of PID simulation control models corresponding to each of the water level control ARX models for controlling the water level control ARX models; S40, optimizing the first tuning parameters of each of the PID simulation control models based on the plurality of the water level control ARX models, and obtaining the optimal tuning parameters according to each of the first tuning parameters; In said S10, said first identification data includes a steam-water mismatch signal set value, a unit output power signal, a primary circuit temperature and said actual water level; In the S20, the water level control ARX model is: in, is the actual water level; k is the time; ~ 、 ~ and ~ is the first parameter set to be identified; is the first offset value; is the model order; setting a value for the steam-water mismatch signal; for Pure lag time; is the pure lag time between the unit output power signal and the primary circuit temperature; It is the combination of the unit output power signal and the primary circuit temperature.

2. The ARX-based water level control system setting value optimization method according to claim 1 is characterized in that: The S10 further includes: collecting a plurality of second identification data corresponding to each segment of the first identification data; The ARX-based water level control system set value optimization method further includes: S201, constructing a plurality of flow sub-ARX models corresponding to each segment of the second identification data for optimizing the steam-water mismatch signal setting value of the corresponding water level control ARX model, and constructing a plurality of PI simulation control models corresponding to each flow sub-ARX model for outputting a valve opening signal; S202: Optimize the second tuning parameters of each of the PI simulation control models based on the plurality of flow sub-ARX models.

3. The ARX-based water level control system setting value optimization method according to claim 2, characterized in that: In said S10, said second identification data includes a steam-water mismatch signal, a steam flow signal, a unit output power signal and said valve opening signal; In the S201, the flow sub-ARX model is: in, is the soda-water mismatch signal; ~ 、 ~ and ~ is the second parameter set to be identified; is the second offset value; is the model order; is the valve opening signal; for Pure lag time; is the pure lag time between the steam flow signal and the unit output power signal; It is the combination of the steam flow signal and the unit output power signal.

4. The ARX-based water level control system setting value optimization method according to claim 3 is characterized in that: In the S201, it includes: constructing a simulated PI controller based on the PI controller in the actual flow subsystem, and performing zero-order hold processing and discretization processing on the output of the simulated PI controller in sequence to obtain the PI simulation control model; The output expression of the PI simulation control model is: ； in, is the valve opening signal; is the error value function of the simulated PI controller; and A second tuning parameter constituting the PI simulation control model; is the parameter sampling period.

5. The ARX-based water level control system setting value optimization method according to claim 3, characterized in that: Also includes: S203: Based on the fmincon function and the first constraint condition, a coefficient optimization model for obtaining the best first parameter set to be identified and the best second parameter set to be identified is established: s.t. ; in, is the best first parameter set to be identified or the best second parameter set to be identified; is the long-term forward forecast output of the corresponding ARX model; is the actual output value of the corresponding ARX model, is the maximum value of the model order; is the data length; is the number of forward prediction steps; the first constraint condition includes the ARX model step response direction constraint, the ARX model stability constraint and the ARX model static amplification coefficient constraint.

6. The ARX-based water level control system setting value optimization method according to claim 5, characterized in that: In the S30, it includes: constructing a simulation PID controller based on the PID controller in the actual water level control system, and performing zero-order hold processing and discretization processing on the output of the simulation PID controller in sequence to obtain the PID simulation control model; The output expression of the PID simulation control model is obtained as follows: ； in, setting a value for the steam-water mismatch signal; is the error value function of the simulated PID controller; 、 and A first tuning parameter constituting the PID simulation control model; is the parameter sampling period; is a natural constant.

7. The ARX-based water level control system setting value optimization method according to claim 6, characterized in that: The step S40 includes: establishing a tuning parameter optimization model for obtaining the optimal tuning parameters according to the fmincon function and the second constraint condition: st closed-loop system stability constraints; in, is the optimal tuning parameter, equal 、 and Any parameter in ; is the number of ARX models; For the The data length used for ARX model optimization; Based on the The predicted output of the ARX model; For the The expected output corresponding to the ARX model; R is the control weight coefficient; For the first The controller increment of an ARX model; the second constraint includes a closed-loop system stability constraint.

8. The ARX-based water level control system setting value optimization method according to claim 6, characterized in that: The tuning parameter optimization model is also used to obtain the optimal second tuning parameter of the PI simulation control model.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the ARX-based water level control system setting value optimization method according to any one of claims 1 to 8 is implemented.

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