Closed-loop identification method and device based on nuclear power steam generator model, terminal equipment and storage medium

Abstract

The invention discloses a closed-loop identification method and device based on a nuclear power steam generator model, terminal equipment and a storage medium, an initial system model of a nuclear power steam generator is constructed by adopting an autoregression model structure according to a plurality of historical operation data, and then the initial system model of the nuclear power steam generator is identified by adopting a least square method and an auxiliary variable method. According to the method, an observation variable, a to-be-identified parameter, a residual error and a parameter initial value of the to-be-identified parameter in an initial system model are determined, so that the problem of nonlinearity during modeling of the nuclear power steam generator at present can be solved, and the problem of low quality of sampling data in closed-loop operation of the nuclear power steam generator can be solved. And finally, a prediction error method is adopted, iteration model parameters are updated in a mode of minimizing the sum of squares of prediction errors, the parameter identification precision is effectively improved, a target numerical value of the to-be-identified parameters is obtained finally, and therefore a system coupling model capable of being used for monitoring and controlling the running state of the steam generator in real time is constructed.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a closed-loop identification method, device, terminal equipment and storage medium based on a nuclear power steam generator model. Background Art

[0002] Steam generators are key equipment in large and complex nuclear power plants. They convert the heat energy generated by nuclear reactors into steam, drive steam turbines to generate electricity, and form an important protective barrier between the primary and secondary circuits to prevent radioactive leakage. The water level of the steam generator is one of the key parameters for the safe and stable operation of the unit. Too high a water level will cause the water content of the outlet steam to exceed the standard and damage the life of the unit. Too low a water level will expose the top of the U-tube, causing unexpected shutdown events, which greatly affects the safety and economy of nuclear power operation. According to surveys and studies, about 30% of unexpected shutdown events in pressurized water reactor power plants in service abroad are related to the liquid level of the steam generator. Therefore, it is necessary to construct a mathematical coupling model that can describe the behavior of the steam generator system of the nuclear power plant based on the control parameters and liquid level of the steam generator of the nuclear power plant, so as to monitor and control the operating status of the steam generator in real time.

[0003] However, since nuclear power steam generators have large and complex control loops, the establishment of identification models faces challenges such as nonlinearity, coupling, and low data signal-to-noise ratio. Specifically, the strict safety operation requirements of nuclear power plants require that data can only be collected in a closed-loop environment, and the low quality of closed-loop operation sampling data can easily lead to low identification accuracy of model parameters. In addition, the basic problem of closed-loop identification is that there is a correlation between unmeasurable noise and input signals, which means that open-loop identification methods cannot be directly applied to closed-loop data. Therefore, there are still great difficulties in modeling nuclear power plant steam generators. Summary of the invention

[0004] The embodiments of the present invention provide a closed-loop identification method, apparatus, terminal equipment and storage medium based on a nuclear power steam generator model, which can overcome the nonlinearity problem in current nuclear power steam generator modeling and the problem of low quality of closed-loop operation sampling data of nuclear power steam generators, and effectively improve the parameter identification accuracy, thereby constructing a system coupling model that can be used to monitor and control the operating status of the steam generator in real time.

[0005] An embodiment of the present invention provides a closed-loop identification method based on a nuclear power steam generator model, comprising:

[0006] Acquire some historical operation data of the nuclear power steam generator; wherein the historical operation data includes: some actual liquid levels of the nuclear power steam generator in a period of time, and some influencing parameters of the nuclear power steam generator affecting the liquid level in the period of time;

[0007] Using an autoregressive model structure, constructing an initial system model of the nuclear power steam generator according to a number of the historical operation data;

[0008] Determine the observed variables, the parameters to be identified, and the residual errors of the initial system model according to the initial system model by using the least square method, and determine the startup estimation parameters according to the observed variables, the parameters to be identified, and the residual errors;

[0009] Adopting an auxiliary variable method, constructing an auxiliary variable according to the startup estimated parameter, and then generating an initial value of the parameter to be identified according to the auxiliary variable, the observed variable, the parameter to be identified, and the residual error;

[0010] According to the initial system model, the initial parameter values, and the several influencing parameters in each time period, several predicted liquid levels in each time period are calculated, and the prediction error method is adopted to calculate the prediction error of the initial system model under the initial parameter values ​​according to the several predicted liquid levels and the corresponding several actual liquid levels, and then according to the prediction error, the initial parameter values ​​are updated and iterated until the sum of squares of the prediction errors is minimized, and the last updated and iterated initial parameter values ​​are used as the target values ​​of the parameters to be identified;

[0011] A system coupling model of the nuclear power steam generator is generated according to the target value and the initial system model.

[0012] Furthermore, the initial system model is:

[0013] A(q -1 )y(k)=B(q -1 )u(kn k )+ε(k);

[0014] A=diag(A1,A2,…A m );

[0015]

[0016]

[0017]

[0018] Among them, n k is the delay, y(k) is the m actual liquid levels of the nuclear power steam generator in the kth time period, u(kn k ) are the n influencing parameters of the nuclear power steam generator affecting the liquid level in the kth period, ε is the residual error, q -1 is the difference operator, A is the first model parameter, A iis the i-th first sub-parameter in the first model parameter corresponding to the i-th actual liquid level, A i No. The first component, B is the second model parameter, B ij is the second sub-parameter in the second model parameter representing the correlation between the j-th influencing parameter and the i-th actual liquid level, For B ij No. A second component.

[0019] Further, the least square method is used to determine the observed variables, the parameters to be identified, and the residual errors of the initial system model according to the initial system model, and the startup estimation parameters are determined according to the observed variables, the parameters to be identified, and the residual errors, including:

[0020] Converting the initial system model into a least squares form to generate a corresponding least squares model; wherein the least squares model is composed of observed variables, parameters to be identified, and residual errors;

[0021] Constructing a first objective function with the goal of minimizing the sum of squares of the residual errors;

[0022] The observed variables are iteratively optimized to minimize the first function value of the first objective function, and when the first function value is determined to be minimum, the startup estimation parameters are generated according to the observed variables optimized by the last iteration and several actual liquid levels in the several historical operation data.

[0023] Furthermore, the least squares model is:

[0024] y(k)=Φ(k)θ(k)+ε(k);

[0025] Φ(k)=diag(Φ1(k),Φ2(k),…,Φ m (k));

[0026]

[0027] θ(k)=[θ1(k)θ2(k)…θ m (k)] T ;

[0028]

[0029] Among them, Φ(k) is the observed variable, Φ i (k) is the third sub-parameter of the i-th observed variable; θ(k) is the parameter to be identified, θ i (k) is the i-th fourth sub-parameter in the parameters to be identified.

[0030] Furthermore, the auxiliary variable method is adopted to construct an auxiliary variable according to the startup estimated parameter, and then the initial value of the parameter to be identified is generated according to the auxiliary variable, the observed variable, the parameter to be identified, and the residual error, including:

[0031] The auxiliary variables constructed according to the startup estimation parameters are as follows:

[0032] ψ=diag(ψ1(k),ψ2(k),…,ψ m (k));

[0033]

[0034]

[0035] Among them, ψ is an auxiliary variable, ψ i (k) the ith fifth sub-parameter in the auxiliary variable, To start estimating parameters, is the i-th sixth sub-parameter in the startup estimation parameter.

[0036] According to the following formula, the initial value of the parameter to be identified is calculated:

[0037]

[0038] in, The initial value of the parameter.

[0039] Furthermore, the prediction error method is used to calculate the prediction error of the initial system model under the initial value of the parameter according to the predicted liquid levels and the corresponding actual liquid levels, including:

[0040] The following formula is used to calculate the prediction error of the initial system model under the initial values ​​of the parameters:

[0041]

[0042] in, is the prediction error, y(k) is the actual liquid level of the nuclear power steam generator in the kth time period, Predict the predicted liquid level of the nuclear power steam generator in the kth time period for the initial system model

[0043] Further, the initial parameter value is updated and iterated according to the forecast error until the sum of squares of the forecast error is minimized, and the initial parameter value of the last updated iteration is used as the target value of the parameter to be identified; and the system coupling model of the nuclear power steam generator is generated according to the target value and the initial system model, including:

[0044] Constructing a second objective function with the goal of minimizing the sum of squares of the forecast errors;

[0045] The initial parameter value is updated and iterated using the Newton-Raphson algorithm to minimize the second function value of the second objective function. When it is determined that the second function value is the minimum and the accuracy of the initial parameter value of the last iteration meets the preset requirements, the iteration is stopped, and the initial parameter value of the last updated iteration is used as the target value of the parameter to be identified;

[0046] Determine, according to the target value, a plurality of first components of each first sub-parameter in the first model parameter of the initial system model, and a plurality of second components of each second sub-parameter in the second model parameter;

[0047] A system coupling model of the nuclear power steam generator is constructed based on the first component and the second component.

[0048] Another embodiment of the present invention provides a closed-loop identification device based on a nuclear power steam generator model, comprising:

[0049] A data acquisition module is used to acquire some historical operation data of the nuclear power steam generator; wherein the historical operation data includes: some actual liquid levels of the nuclear power steam generator within a period of time, and some influencing parameters of the nuclear power steam generator affecting the liquid level within the period of time;

[0050] A model building module, used to construct an initial system model of the nuclear power steam generator according to a number of the historical operation data by using an autoregressive model structure;

[0051] A model structure determination module, used to determine the observed variables, the parameters to be identified, and the residual errors of the initial system model according to the initial system model by using the least square method, and determine the startup estimation parameters according to the observed variables, the parameters to be identified, and the residual errors;

[0052] An auxiliary variable module, used to construct auxiliary variables according to the startup estimated parameters by using an auxiliary variable method, and then generate an initial value of the parameter to be identified according to the auxiliary variables, the observed variables, the parameter to be identified, and the residual error;

[0053] A prediction error module is used to calculate a number of predicted liquid levels in each time period according to the initial system model, the initial parameter values, and a number of the influencing parameters in each time period, and to calculate the prediction error of the initial system model under the initial parameter values ​​according to the predicted liquid levels and the corresponding actual liquid levels using a prediction error method, and then to update and iterate the initial parameter values ​​according to the prediction errors until the sum of squares of the prediction errors is minimized, and to use the last updated and iterated initial parameter values ​​as the target values ​​of the parameters to be identified;

[0054] A model building module is used to generate a system coupling model of the nuclear power steam generator according to the target value and the initial system model.

[0055] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a closed-loop identification method based on a nuclear power steam generator model as described in any one of the embodiments.

[0056] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a closed-loop identification method based on a nuclear power steam generator model as described in any one of the above embodiments.

[0057] The following beneficial effects are achieved by implementing the present invention:

[0058] The present invention discloses a closed-loop identification method, device, terminal equipment and storage medium based on a nuclear power steam generator model. The method adopts an autoregressive model structure and constructs an initial system model of the nuclear power steam generator according to a number of historical operation data, and then adopts the least square method and the auxiliary variable method to determine the observed variables, the parameters to be identified, the residual errors, and the initial values ​​of the parameters to be identified in the initial system model. It can be understood that by introducing auxiliary variables, complex nonlinear problems and noise problems are converted into linear and easier to handle problems. Therefore, the present invention can overcome the nonlinearity problem in the current nuclear power steam generator modeling and the problem of low quality of closed-loop operation sampling data of nuclear power steam generators. Finally, the prediction error method is adopted to update the iterative model parameters by minimizing the sum of squares of the prediction errors, so as to convert the closed-loop identification problem into an open-loop identification problem, effectively improve the parameter identification accuracy, and finally obtain the target value of the parameter to be identified, thereby constructing a system coupling model that can be used for real-time monitoring and control of the operating state of the steam generator. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1It is a flow chart of a closed-loop identification method based on a nuclear power steam generator model provided by one embodiment of the present invention.

[0060] Figure 2 It is a structural schematic diagram of a closed-loop identification device based on a nuclear power steam generator model provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0063] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0064] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0065] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0066] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0067] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0068] See also Figure 1 , is a flow chart of a closed-loop identification method based on a nuclear power steam generator model provided by an embodiment of the present invention, comprising:

[0069] S1. Acquire some historical operation data of the nuclear power steam generator; wherein the historical operation data includes: some actual liquid levels of the nuclear power steam generator in a period of time, and some influencing parameters of the nuclear power steam generator affecting the liquid level in the period of time;

[0070] In a preferred embodiment of the present invention, most of the current modeling of nuclear power plant steam generators is for simulation purposes rather than control. Such modeling methods usually require a large amount of computing resources and time, and have limited applications in real-time monitoring and control. Most of the research on steam generator modeling for the purpose of control is based on the NSG simplified mathematical model proposed by Elving, and most of the current theoretical research is based on simplified models or multi-input single-output models, which has limitations in practical applications. Therefore, this embodiment proposes a method for establishing a multi-input multi-output model for a nuclear power plant steam generator in closed-loop operation for practical applications.

[0071] Specifically, in this embodiment, several actual liquid levels y(k) of the nuclear power steam generator in a period of time, and several influencing parameters u(kn) of the nuclear power steam generator affecting the liquid level in the period of time are calculated. k ), as a set of historical operation data, it should be noted that, in this embodiment, the influencing parameters are: feed water flow, steam flow, steam generator load, and feed water pipe pressure, and other related parameters can also be used as influencing parameters, which are not limited in this embodiment. This embodiment constructs a multi-input and multi-output model by acquiring some historical operation data, wherein:

[0072]

[0073]

[0074] S2, using an autoregressive model structure to construct an initial system model of the nuclear power steam generator according to a number of the historical operation data;

[0075] Preferably, the initial system model is:

[0076] A(q -1 )y(k)=B(q -1 )u(kn k )+ε(k);

[0077] A=diag(A1,A2,…A m );

[0078]

[0079]

[0080]

[0081] Among them, n k is the delay, y(k) is the m actual liquid levels of the nuclear power steam generator in the kth time period, u(kn k ) are the n influencing parameters of the nuclear power steam generator affecting the liquid level in the kth period, ε is the residual error, q -1 is the difference operator, A is the first model parameter, A i is the i-th first sub-parameter in the first model parameter corresponding to the i-th actual liquid level, A i No. The first component, B is the second model parameter, B ij is the second sub-parameter in the second model parameter representing the correlation between the j-th influencing parameter and the i-th actual liquid level, For B ij No. A second component.

[0082] In a preferred embodiment of the present invention, the adopted autoregressive model structure is a high-order auto-regressive model (Auto-Regressive with Extra Inputs, ARX).

[0083] Specifically, the ARX model structure is shown in the following formula.

[0084] A(q -1 )y(k)=B(q -1 )u(kn k )+ε(k);

[0085] Among them, n k is the delay, y is the m liquid level of the steam generator, u is the n related input variables affecting the liquid level of the steam generator, q -1 is the difference operator, k is the time period, and ε is the residual error, as shown in the following formula:

[0086]

[0087] The first model parameter A is a diagonal matrix, A = diag(A1, A2, ... A m ), the i-th parameter A i The expression is as follows:

[0088]

[0089] in, A i No. A quantity.

[0090] The second model parameter B is as follows:

[0091]

[0092]

[0093] Among them, B ij represents the parameter of the j-th input to the i-th output, is parameter B ij No. A quantity.

[0094] S3, using the least square method, according to the initial system model, determine the observed variables, the parameters to be identified, and the residual errors of the initial system model, and determine the startup estimation parameters according to the observed variables, the parameters to be identified, and the residual errors;

[0095] Preferably, the least square method is used to determine the observed variables, the parameters to be identified, and the residual errors of the initial system model according to the initial system model, and the startup estimation parameters are determined according to the observed variables, the parameters to be identified, and the residual errors, including:

[0096] S31, converting the initial system model into a least squares form to generate a corresponding least squares model; wherein the least squares model is composed of observed variables, parameters to be identified, and residual errors;

[0097] Preferably, the least squares model is:

[0098] y(k)=Φ(k)θ(k)+ε(k);

[0099] Φ(k)=diag(Φ1(k),Φ2(k),…,Φ m (k));

[0100]

[0101] θ(k)=[θ1(k)θ2(k)…θ m (k)] T ;

[0102]

[0103] Among them, Φ(k) is the observed variable, Φ i (k) is the third sub-parameter of the i-th observed variable; θ(k) is the parameter to be identified, θ i (k) is the i-th fourth sub-parameter in the parameters to be identified.

[0104] S32, constructing a first objective function with the goal of minimizing the sum of squares of the residual errors;

[0105] S33, iteratively optimizing the observed variables to minimize the first function value of the first objective function, and when determining that the first function value is minimum, generating the startup estimation parameters according to the observed variables optimized by the last iteration and several actual liquid levels in the several historical operation data.

[0106] In a preferred embodiment of the present invention, the least square method is used as a parameter to start the algorithm and construct the auxiliary variables of the auxiliary variable method. First, the initial system model is converted into the least square form:

[0107] y(k)=Φ(k)θ(k)+ε(k);

[0108] Among them, Φ(k) is the observed variable, Φ(k)=diag(Φ1(k),Φ2(k),…,Φ m (k)), Φ i (k) is shown in the following formula.

[0109]

[0110] θ(k) is the parameter to be identified, θ(k)=[θ1(k)θ2(k)…θ m (k)] T ,θ i (k) is shown in the following formula.

[0111]

[0112] Minimizing the residual error as the criterion of the least squares method is shown in the following equation:

[0113]

[0114] By solving θ E{Φ(k) T ε(k)}=0 and Get the startup estimated parameters As shown below:

[0115]

[0116] It should be noted that in addition to using the least squares method as the parameter startup algorithm, this embodiment can also use the generalized least squares method, the correlated least squares method, the maximum likelihood method, etc., to provide a new solution to the problem of poor closed-loop identification effect in actual field.

[0117] S4, using an auxiliary variable method to construct an auxiliary variable according to the startup estimated parameter, and then generating an initial value of the parameter to be identified according to the auxiliary variable, the observed variable, the parameter to be identified, and the residual error;

[0118] Preferably, the auxiliary variable method is adopted to construct auxiliary variables according to the startup estimated parameters, and then the initial value of the parameter to be identified is generated according to the auxiliary variables, the observed variables, the parameter to be identified, and the residual error, including:

[0119] S41. The auxiliary variables constructed according to the startup estimation parameters are as follows:

[0120] ψ=diag(ψ1(k),ψ2(k),…,ψ m (k));

[0121]

[0122]

[0123] Among them, ψ is an auxiliary variable, ψ i (k) the ith fifth sub-parameter in the auxiliary variable, To start estimating parameters, is the i-th sixth sub-parameter in the startup estimation parameter.

[0124] S42. Calculate the initial value of the parameter to be identified according to the following formula:

[0125]

[0126] in, is the initial value of the parameter.

[0127] In a preferred embodiment of the present invention, auxiliary variables are constructed based on the startup estimation parameters to enter the initial value estimation stage.

[0128] According to the first stage parameters Construct auxiliary variables ψ, ψ=diag(ψ1(k),ψ2(k),…,ψ m (k)), ψ i (k) is shown as follows:

[0129]

[0130] By solving θ E{ψ(k) T ε(k)}=0 and Get the initial value of the parameter The initial value of the parameter is shown as follows:

[0131]

[0132] S5. According to the initial system model, the initial parameter values, and the influencing parameters of each time period, several predicted liquid levels in each time period are calculated, and the prediction error method is used to calculate the prediction error of the initial system model under the initial parameter values ​​according to the predicted liquid levels and the corresponding actual liquid levels, and then the initial parameter values ​​are updated and iterated according to the prediction errors until the sum of squares of the prediction errors is minimized, and the last updated and iterated initial parameter values ​​are used as the target values ​​of the parameters to be identified;

[0133] Preferably, the prediction error method is used to calculate the prediction error of the initial system model under the initial value of the parameter according to the predicted liquid levels and the corresponding actual liquid levels, including:

[0134] S51, using the following formula to calculate the prediction error of the initial system model under the initial value of the parameter:

[0135]

[0136] in, is the prediction error, y(k) is the actual liquid level of the nuclear power steam generator in the kth time period, The predicted liquid level of the nuclear power steam generator in the kth time period is predicted for the initial system model.

[0137] S6. Generate a system coupling model of the nuclear power steam generator according to the target value and the initial system model.

[0138] Preferably, the updating and iterating of the initial parameter value according to the forecast error until the sum of squares of the forecast error is minimized, and taking the initial parameter value of the last updated iteration as the target value of the parameter to be identified, and then generating the system coupling model of the nuclear power steam generator according to the target value and the initial system model, including:

[0139] S61, constructing a second objective function with the goal of minimizing the sum of squares of the forecast errors;

[0140] S62, using the Newton-Raphson algorithm to iterate the initial parameter value so as to minimize the second function value of the second objective function, and when it is determined that the second function value is the minimum and the accuracy of the initial parameter value of the last iteration meets the preset requirement, stopping the iteration and taking the initial parameter value of the last updated iteration as the target value of the parameter to be identified;

[0141] S63, determining, according to the target value, a number of first components of each first sub-parameter in the first model parameter of the initial system model, and a number of second components of each second sub-parameter in the second model parameter;

[0142] S64. Construct a system coupling model of the nuclear power steam generator based on the first component and the second component.

[0143] In a preferred embodiment of the present invention, after obtaining the initial parameter value, the parameter optimization stage is entered, and the identified parameters are further optimized by the prediction error method to improve the identification accuracy. As the initial value of the forecast error method identification parameter, the forecast initial value is obtained through the forecast model based on historical data As shown below:

[0144]

[0145] Given an observation y(k), with parameters The forecast error under As shown below:

[0146]

[0147] The sum of squares of the forecast errors is minimized to estimate the parameters, and the criterion function of the forecast error method is as follows.

[0148]

[0149] By minimizing the objective function, the Newton-Raphson algorithm is used to iteratively update the model parameters, as shown in the following formula:

[0150]

[0151] Among them, α is the optimization step length, and the parameters are continuously updated through iteration. The iteration stops until the estimated accuracy meets the requirements or the specified number of iterations is reached.

[0152] This embodiment provides a closed-loop identification method based on a nuclear power steam generator model. By adopting an autoregressive model structure, the initial system model of the nuclear power steam generator is constructed according to a number of historical operation data, and then the least square method and the auxiliary variable method are used to determine the observed variables, the parameters to be identified, the residual errors, and the initial values ​​of the parameters to be identified in the initial system model. It can be understood that by introducing auxiliary variables, complex nonlinear problems and noise problems are converted into linear and easier to handle problems. Therefore, the present invention can overcome the nonlinearity problem in the current nuclear power steam generator modeling and the problem of low quality of closed-loop operation sampling data of nuclear power steam generators. Finally, the prediction error method is adopted to update the iterative model parameters by minimizing the sum of squares of the prediction errors, so as to convert the closed-loop identification problem into an open-loop identification problem, effectively improve the parameter identification accuracy, and finally obtain the target value of the parameter to be identified, thereby constructing a system coupling model that can be used to monitor and control the operating status of the steam generator in real time.

[0153] See also Figure 2 , is a schematic structural diagram of a closed-loop identification device based on a nuclear power steam generator model provided by an embodiment of the present invention, comprising:

[0154] A data acquisition module is used to acquire some historical operation data of the nuclear power steam generator; wherein the historical operation data includes: some actual liquid levels of the nuclear power steam generator within a period of time, and some influencing parameters of the nuclear power steam generator affecting the liquid level within the period of time;

[0155] A model structure determination module, used to construct an initial system model of the nuclear power steam generator according to a number of the historical operation data by using an autoregressive model structure;

[0156] A least squares module, used to determine the observed variables, the parameters to be identified, and the residual errors of the initial system model according to the initial system model by using the least squares method, and to determine the startup estimation parameters according to the observed variables, the parameters to be identified, and the residual errors;

[0157] An auxiliary variable module, used to construct auxiliary variables according to the startup estimated parameters by using an auxiliary variable method, and then generate an initial value of the parameter to be identified according to the auxiliary variables, the observed variables, the parameter to be identified, and the residual error;

[0158] A prediction error module is used to calculate a number of predicted liquid levels in each time period according to the initial system model, the initial parameter values, and a number of the influencing parameters in each time period, and to calculate the prediction error of the initial system model under the initial parameter values ​​according to the predicted liquid levels and the corresponding actual liquid levels using a prediction error method, and then to update and iterate the initial parameter values ​​according to the prediction errors until the sum of squares of the prediction errors is minimized, and to use the last updated and iterated initial parameter values ​​as the target values ​​of the parameters to be identified;

[0159] A model building module is used to generate a system coupling model of the nuclear power steam generator according to the target value and the initial system model.

[0160] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0161] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0162] Another preferred embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a closed-loop identification method based on a nuclear power steam generator model as described in any one of the above embodiments.

[0163] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0164] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0165] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med i aCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0166] Another preferred embodiment of the present invention provides a storage medium, the storage medium is a computer-readable storage medium, the computer program is stored in the computer-readable storage medium, and the computer program, when executed by the processor, can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0167] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A closed-loop identification method based on a nuclear power steam generator model, characterized in that: include: Acquire some historical operation data of the nuclear power steam generator; wherein the historical operation data includes: some actual liquid levels of the nuclear power steam generator in a period of time, and some influencing parameters of the nuclear power steam generator affecting the liquid level in the period of time; Using an autoregressive model structure, constructing an initial system model of the nuclear power steam generator according to a number of the historical operation data; Determine the observed variables, the parameters to be identified, and the residual errors of the initial system model according to the initial system model by using the least square method, and determine the startup estimation parameters according to the observed variables, the parameters to be identified, and the residual errors; Adopting an auxiliary variable method, constructing an auxiliary variable according to the startup estimated parameter, and then generating an initial value of the parameter to be identified according to the auxiliary variable, the observed variable, the parameter to be identified, and the residual error; According to the initial system model, the initial parameter values, and the several influencing parameters in each time period, several predicted liquid levels in each time period are calculated, and the prediction error method is adopted to calculate the prediction error of the initial system model under the initial parameter values ​​according to the several predicted liquid levels and the corresponding several actual liquid levels, and then according to the prediction error, the initial parameter values ​​are updated and iterated until the sum of squares of the prediction errors is minimized, and the last updated and iterated initial parameter values ​​are used as the target values ​​of the parameters to be identified; A system coupling model of the nuclear power steam generator is generated according to the target value and the initial system model.

2. A closed-loop identification method based on a nuclear power steam generator model as claimed in claim 1, characterized in that: The initial system model is: A(q -1 )y(k)=B(q -1 )u(k-n k )+ε(k); <h2 style=";text-align:left;direction:ltr">A=diag(A1,A2,…A<h2 style=";text-align:left;direction:ltr"> m <h2 style=";text-align:left;direction:ltr"> ); Among them, n k is the delay, y(k) is the m actual liquid levels of the nuclear power steam generator in the kth time period, u(kn k ) are the n influencing parameters of the nuclear power steam generator affecting the liquid level in the kth period, ε is the residual error, q -1 is the difference operator, A is the first model parameter, A i is the i-th first sub-parameter in the first model parameter corresponding to the i-th actual liquid level, A i No. The first component, B is the second model parameter, B ij is the second sub-parameter in the second model parameter representing the correlation between the j-th influencing parameter and the i-th actual liquid level, For B ij No. A second component.

3. A closed-loop identification method based on a nuclear power steam generator model as claimed in claim 2, characterized in that: The method of using the least square method to determine the observed variables, the parameters to be identified, and the residual errors of the initial system model according to the initial system model, and determining the startup estimation parameters according to the observed variables, the parameters to be identified, and the residual errors, includes: Converting the initial system model into a least squares form to generate a corresponding least squares model; wherein the least squares model is composed of observed variables, parameters to be identified, and residual errors; Constructing a first objective function with the goal of minimizing the sum of squares of the residual errors; The observed variables are iteratively optimized to minimize the first function value of the first objective function, and when the first function value is determined to be minimum, the startup estimation parameters are generated according to the observed variables optimized by the last iteration and several actual liquid levels in the several historical operation data.

4. A closed-loop identification method based on a nuclear power steam generator model as claimed in claim 3, characterized in that: The least squares model is: y(k)=Φ(k)θ(k)+ε(k); Φ(k)=diag(Φ1(k),Φ2(k),…,Φ m (k)); θ(k)=[θ1(k)θ2(k)…θ m (k)] T ; Among them, Φ(k) is the observed variable, Φ i (k) is the third sub-parameter of the i-th observed variable; θ(k) is the parameter to be identified, θ i (k) is the i-th fourth sub-parameter in the parameters to be identified.

5. A closed-loop identification method based on a nuclear power steam generator model as claimed in claim 4, characterized in that: The auxiliary variable method is adopted to construct an auxiliary variable according to the startup estimated parameter, and then the initial value of the parameter to be identified is generated according to the auxiliary variable, the observed variable, the parameter to be identified, and the residual error, including: The auxiliary variables constructed according to the startup estimation parameters are as follows: ψ=diag(ψ1(k),ψ2(k),…,ψ m (k)); Among them, ψ is an auxiliary variable, ψ i (k) the ith fifth sub-parameter in the auxiliary variable, To start estimating parameters, is the i-th sixth sub-parameter in the startup estimation parameter; According to the following formula, the initial value of the parameter to be identified is calculated: in, is the initial value of the parameter.

6. A closed-loop identification method based on a nuclear power steam generator model as claimed in claim 5, characterized in that: The prediction error method is used to calculate the prediction error of the initial system model under the initial value of the parameter according to the predicted liquid levels and the corresponding actual liquid levels, including: The following formula is used to calculate the prediction error of the initial system model under the initial values ​​of the parameters: in, is the prediction error, y(k) is the actual liquid level of the nuclear power steam generator in the kth time period, The predicted liquid level of the nuclear power steam generator in the kth time period is predicted for the initial system model.

7. A closed-loop identification method based on a nuclear power steam generator model as claimed in claim 6, characterized in that: The initial parameter value is updated and iterated according to the prediction error until the sum of squares of the prediction error is minimized, and the initial parameter value of the last updated iteration is used as the target value of the parameter to be identified; Generating a system coupling model of the nuclear power steam generator according to the target value and the initial system model, including: Constructing a second objective function with the goal of minimizing the sum of squares of the forecast errors; The Newton-Raphson algorithm is used to iterate the initial parameter value so as to minimize the second function value of the second objective function. When it is determined that the second function value is the minimum and the accuracy of the initial parameter value of the last iteration meets the preset requirements, the iteration is stopped, and the initial parameter value of the last updated iteration is used as the target value of the parameter to be identified; Determine, according to the target value, a plurality of first components of each first sub-parameter in the first model parameter of the initial system model, and a plurality of second components of each second sub-parameter in the second model parameter; A system coupling model of the nuclear power steam generator is constructed based on the first component and the second component.

8. A closed-loop identification device based on a nuclear power steam generator model, characterized in that: include: A data acquisition module is used to acquire some historical operation data of the nuclear power steam generator; wherein the historical operation data includes: some actual liquid levels of the nuclear power steam generator within a period of time, and some influencing parameters of the nuclear power steam generator affecting the liquid level within the period of time; A model structure determination module, used to construct an initial system model of the nuclear power steam generator according to a number of the historical operation data by using an autoregressive model structure; A least squares module, used to determine the observed variables, the parameters to be identified, and the residual errors of the initial system model according to the initial system model by using the least squares method, and to determine the startup estimation parameters according to the observed variables, the parameters to be identified, and the residual errors; An auxiliary variable module, used to construct auxiliary variables according to the startup estimated parameters by using an auxiliary variable method, and then generate an initial value of the parameter to be identified according to the auxiliary variables, the observed variables, the parameter to be identified, and the residual error; A prediction error module is used to calculate a number of predicted liquid levels in each time period according to the initial system model, the initial parameter values, and a number of the influencing parameters in each time period, and to calculate the prediction error of the initial system model under the initial parameter values ​​according to the predicted liquid levels and the corresponding actual liquid levels using a prediction error method, and then to update and iterate the initial parameter values ​​according to the prediction errors until the sum of squares of the prediction errors is minimized, and to use the last updated and iterated initial parameter values ​​as the target values ​​of the parameters to be identified; A model building module is used to generate a system coupling model of the nuclear power steam generator according to the target value and the initial system model.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a closed-loop identification method based on a nuclear power steam generator model as described in any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a closed-loop identification method based on a nuclear power steam generator model as described in any one of claims 1 to 7.

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