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

Abstract

The application discloses a kind of closed loop identification method, device, terminal equipment and storage medium based on nuclear power steam generator model, by adopting autoregressive model structure, according to several historical operation data, the initial system model of nuclear power steam generator is structured, then least square method and auxiliary variable method are used, to determine the observation variable in initial system model, to be identified parameter, residual error and the parameter initial value of to be identified parameter, therefore, the present application can overcome the nonlinear problem when the nuclear power steam generator is modeled at present, and the problem of low quality of closed loop operation sampling data of nuclear power steam generator.Finally, by minimizing the way of prediction error square sum, the iteration model parameter is updated using prediction error method, effectively improve parameter identification precision, finally obtain the target value of to be identified parameter, to build the system coupling model that can be used to carry out real-time monitoring and control to the operating state of steam generator.

Description

TECHNICAL FIELD

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

[0002] The steam generator is a large and complex key equipment in a nuclear power plant, which converts the heat energy generated by the nuclear reactor into steam to drive the steam turbine to generate electricity, and forms an important protective barrier between the primary and secondary loops to prevent radioactive leakage. The steam generator water level is one of the key parameters for the safe and stable operation of the unit. An excessively high water level will result in excessive water content in the outlet steam, which will damage the service life of the unit, and an excessively low water level will expose the top of the U-shaped tube, causing an unexpected shutdown event, which greatly affects the safety and economy of nuclear power operation. According to the survey and research, about 30% of the unexpected shutdown events of the nuclear power plants in service abroad are related to the steam generator level. Therefore, it is necessary to construct a mathematical coupling model capable of describing the behavior of the nuclear power steam generator system according to the control parameters and the level of the nuclear power steam generator, for real-time monitoring and control of the operating state of the steam generator.

[0003] However, due to the large and complex control loop of the nuclear power steam generator, the establishment of the identification model faces the challenges of nonlinearity, coupling and low data signal-to-noise ratio. Specifically, the strict safety operation requirements of the nuclear power plant can only collect data in a closed-loop environment, and the low quality of the closed-loop operation sampling data can easily lead to low identification accuracy of the model parameters. Moreover, the basic problem of closed-loop identification is the correlation between the unmeasurable noise and the input signal, which leads to the fact that the open-loop identification method cannot be directly applied to the closed-loop data. Therefore, there are still great difficulties in modeling the nuclear power steam generator at present. SUMMARY

[0004] The embodiments of the present application provide a closed-loop identification method and device based on a nuclear power steam generator model, a terminal device and a storage medium, which can overcome the nonlinearity problem in the modeling of the nuclear power steam generator at present, and the problem of low quality of the closed-loop operation sampling data of the nuclear power steam generator, and effectively improve the parameter identification accuracy, so as to construct a system coupling model capable of real-time monitoring and control of the operating state of the steam generator.

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

[0006] Obtaining a plurality of historical operation data of the nuclear power steam generator; wherein the historical operation data comprises a plurality of actual levels of the nuclear power steam generator in a time period and a plurality of influence parameters affecting the level of the nuclear power steam generator in the time period;

[0007] An autoregressive model structure is adopted to construct an initial system model of the nuclear power steam generator according to a plurality of historical operation data;

[0008] A least square method is adopted to determine an observation variable, a parameter to be identified, and a residual error of the initial system model according to the initial system model, and to determine a start-up estimated parameter according to the observation variable, the parameter to be identified, and the residual error;

[0009] An auxiliary variable method is adopted to construct an auxiliary variable according to the start-up estimated parameter, and to generate a parameter initial value of the parameter to be identified according to the auxiliary variable, the observation variable, the parameter to be identified, and the residual error;

[0010] A plurality of predicted liquid levels in each time period are calculated according to the initial system model, the parameter initial value, and a plurality of influence parameters of each time period, and a prediction error method is adopted to calculate a prediction error of the initial system model under the parameter initial value according to a plurality of the predicted liquid levels and a plurality of corresponding actual liquid levels, and to update the parameter initial value iteratively according to the prediction error until a sum of squares of the prediction error is minimum, and the parameter initial value after the last update iteration is taken as a target value of the parameter 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] Further, the initial system model is:

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

[0014] A=diag(A1,A2,…A m );

[0015]

[0016]

[0017]

[0018] wherein, n k is a delay, y(k) is m actual liquid levels of the nuclear power steam generator in the kth time period, u(k-n k ) is n influence parameters affecting the liquid level of the nuclear power steam generator in the kth time period, ε is a residual error, q -1 is a difference operator, A is a first model parameter, and A iAn i th first sub-parameter in the first model parameter corresponding to an i th actual liquid level, A is a first component of B, i An i th first component of B, A is a second model parameter, ij A second sub-parameter in the second model parameter representing a correlation between a j th influence parameter and an i th actual liquid level, A j th second component of B, ij

[0019] Further, the initial system model is converted into a least square form to generate a corresponding least square model; wherein the least square model is composed of observation variables, to-be-identified parameters, and residual errors;

[0020] Further, the initial system model is converted into a least square form to generate a corresponding least square model; wherein the least square model is composed of observation variables, to-be-identified parameters, and residual errors;

[0021] A first objective function is constructed to minimize a sum of squares of the residual errors;

[0022] The observation variables are iteratively optimized to minimize a first function value of the first objective function, and the start-up estimated parameters are generated according to the last iteratively optimized observation variables and a plurality of actual liquid levels in the plurality of historical operation data when a first function value is determined.

[0023] Further, the least square 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] wherein Φ(k) is an observation variable, Φ i (k) is an i th third sub-parameter in the observation variable; θ(k) is a to-be-identified parameter, θ i (k) is an i th fourth sub-parameter in the to-be-identified parameter.​​

[0030] Further, the auxiliary variable method is used to construct an auxiliary variable according to the start-up estimation parameter, and then the parameter initial value of the to-be-identified parameter is generated according to the auxiliary variable, the observation variable, the to-be-identified parameter and the residual error, and the parameter initial value of the to-be-identified parameter comprises:

[0031] The auxiliary variable constructed according to the start-up estimation parameter is as follows:

[0032] ψ = diag (ψ 1 (k), ψ 2 (k), …, ψ m (k) ) ;

[0033]

[0034]

[0035] Wherein, ψ is an auxiliary variable, ψ i (k) is the fifth sub-parameter in the i th auxiliary variable, is a start-up estimation parameter, is the sixth sub-parameter in the i th start-up estimation parameter.

[0036] The parameter initial value of the to-be-identified parameter is calculated according to the following formula:

[0037]

[0038] Wherein, is the parameter initial value.

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

[0040] The prediction error of the initial system model under the parameter initial value is calculated by using the following formula:

[0041]

[0042] Wherein, is the prediction error, y (k) is the actual liquid level of the nuclear steam generator in the k th period, is the predicted liquid level of the initial system model in the k th period

[0043] Further, the parameter initial value is updated iteratively according to the prediction error until the sum of squares of the prediction error is minimum, and the parameter initial value after the last update iteration is taken as a target value of the to-be-identified parameter; and a system coupling model of the nuclear power steam generator is generated according to the target value and the initial system model, comprising:

[0044] A second target function is constructed with the minimization of the sum of squares of the prediction error as a target;

[0045] The parameter initial value is updated iteratively by using a Newton-Raphson algorithm to minimize a second function value of the second target function, and when the second function value is determined to be minimum and the accuracy of the parameter initial value after the last update iteration meets a preset requirement, the iteration is stopped, and the parameter initial value after the last update iteration is taken as the target value of the to-be-identified parameter;

[0046] 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 are determined;

[0047] According to the first components and the second components, the system coupling model of the nuclear power steam generator is constructed.

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

[0049] A data acquisition module is configured to acquire a plurality of historical operation data of a nuclear power steam generator; wherein the historical operation data comprises a plurality of actual liquid levels of the nuclear power steam generator in a time period and a plurality of influence parameters affecting the liquid level of the nuclear power steam generator in the time period;

[0050] A model construction module is configured to construct an initial system model of the nuclear power steam generator according to a plurality of the historical operation data by using an autoregressive model structure;

[0051] A model structure determination module is configured to determine an observation variable, a to-be-identified parameter, and a residual error of the initial system model by using a least square method according to the initial system model, and determine a start estimation parameter according to the observation variable, the to-be-identified parameter, and the residual error;

[0052] An auxiliary variable module is configured to construct an auxiliary variable according to the start estimation parameter by using an auxiliary variable method, and then generate a parameter initial value of the to-be-identified parameter according to the auxiliary variable, the observation variable, the to-be-identified parameter, and the residual error;

[0053] The prediction error module is used to calculate several predicted liquid levels in each time period based on the initial system model, the initial values ​​of the parameters, and several influencing parameters for each time period. It then uses the prediction error method to calculate the prediction error of the initial system model under the initial values ​​of the parameters based on the several predicted liquid levels and the corresponding several actual liquid levels. Based on the prediction error, it updates and iterates the initial values ​​of the parameters until the sum of the squares of the prediction errors is minimized. The last updated initial values ​​of the parameters are then used as the target values ​​of the parameters to be identified.

[0054] The model building module is used to generate a system coupling model of the nuclear power steam generator based on 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. 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 of the embodiments.

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

[0057] The following benefits can be obtained by implementing the present invention:

[0058] This invention discloses a closed-loop identification method, apparatus, terminal device, and storage medium based on a nuclear power steam generator model. The method employs an autoregressive model structure to construct an initial system model of the nuclear power steam generator based on historical operating data. Then, it uses the least squares method and auxiliary variable method to determine the observed variables, parameters to be identified, residual errors, and initial values ​​of the parameters to be identified in the initial system model. It is understood that by introducing auxiliary variables, complex nonlinear and noise problems are transformed into linear and easier-to-handle problems. Therefore, this invention can overcome the nonlinearity problems in current nuclear power steam generator modeling and the low quality of closed-loop operation sampling data for nuclear power steam generators. Finally, a prediction error method is used to update the model parameters by minimizing the sum of squared prediction errors, thereby transforming the closed-loop identification problem into an open-loop identification problem, effectively improving the parameter identification accuracy, and ultimately obtaining the target values ​​of the parameters to be identified. This constructs a system coupled model capable of real-time monitoring and control of the steam generator's operating status. Attached Figure Description

[0059] Figure 1is a flow diagram of a closed-loop identification method based on a nuclear power steam generator model according to an embodiment of the present application.

[0060] Figure 2 is a structural diagram of a closed-loop identification device based on a nuclear power steam generator model according to an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "comprise" and "have" and any variations thereof in the specification and claims of the present application and the above description of drawings are intended to cover non-exclusive inclusion.

[0063] In the description of the embodiments of the present application, the technical terms "first", "second", and the like 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 "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0064] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily independent or alternative embodiments to other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can 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, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the associated objects.

[0066] In the description of the embodiments of the present application, the term "a plurality of" refers to two or more (including two), and similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0067] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connection", "connection", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled 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] Referring to Figure 1 , a flowchart of a closed-loop identification method based on a nuclear power steam generator model provided by an embodiment of the present application, comprising:

[0069] S1, obtaining a plurality of historical operation data of the nuclear power steam generator; wherein the historical operation data comprises a plurality of actual liquid levels of the nuclear power steam generator in a time period, and a plurality of influence parameters of the nuclear power steam generator affecting the liquid level in the time period;

[0070] In a preferred embodiment of the present application, most of the current nuclear power plant steam generator modeling is for simulation purposes rather than control. Such modeling method usually requires a large amount of computing resources and time, and has limitations in real-time monitoring and control. The steam generator modeling research for control purposes is mostly based on the NSG simplified mathematical model proposed by E. Irving, and most of the current research is based on simplified models or multiple-input single-output models, which has limitations in practical application. Therefore, the present embodiment proposes a method for establishing a multiple-input multiple-output model for the nuclear power plant steam generator under closed-loop operation for practical application.

[0071] Specifically, the present embodiment takes a plurality of actual liquid levels y(k) of the nuclear power steam generator in a time period, and a plurality of influence parameters u(k-n k ) of the nuclear power steam generator affecting the liquid level in the time period as a set of historical operation data. It should be noted that in the present embodiment, the influence parameters are: feedwater flow, steam flow, steam generator load, and feedwater pipe pressure, which can also be other related parameters as influence parameters, which are not limited by the present embodiment. The present embodiment constructs a multiple-input multiple-output model by obtaining a plurality of historical operation data, wherein:

[0072]

[0073]

[0074] S2. Using an autoregressive model structure, an initial system model of the nuclear power steam generator is constructed based on several historical operating 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] Where, n k Let y(k) be the delay, and y(k) be the m actual liquid levels of the nuclear power steam generator during the k-th time period, and u(kn) be the liquid level. k Let ) represent the n influencing parameters affecting the liquid level of the nuclear power steam generator during the k-th time period, ε be the residual error, and q be the residual error. -1 Here, A is the difference operator, and A is the first model parameter. i This refers to the i-th sub-parameter in the first model parameters that corresponds to the i-th actual liquid level. For A i The The first component is B, and the second model parameter is B. ij This refers to the second sub-parameter in the second model parameters, which represents the correlation between the j-th influencing parameter and the ith actual liquid level. For B ij The The second component.

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

[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] where n k is the delay, y is the m level of the steam generator, u is the n relevant input variables affecting the steam generator level, q -1 is the difference operator, k is the time period, and ε is the residual error, as shown in the following equation:

[0086]

[0087] The first model parameter A is a diagonal matrix, A = diag(A1, A2, … A m ), and the i-th parameter A i is expressed as shown in the following equation:

[0088]

[0089] where, is the i-th component of A i .

[0090] The second model parameter B is shown in the following equation:

[0091]

[0092]

[0093] where B ij represents the parameter of the j-th input to the i-th output, is the i-th component of the parameter B ij .

[0094] S3, using the least squares method, determining the observation variable, the to-be-identified parameter, and the residual error of the initial system model according to the initial system model, and determining the starting estimation parameter according to the observation variable, the to-be-identified parameter, and the residual error;

[0095] Preferably, the least squares method is used to determine the observation variable, the to-be-identified parameter, and the residual error of the initial system model according to the initial system model, and to determine the starting estimation parameter according to the observation variable, the to-be-identified parameter, and the residual error, 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 an observation variable, a to-be-identified parameter, and a residual error;

[0097] Preferably, the least squares model is as follows:

[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] wherein Φ(k) is an observation variable, Φ i (k) is an i-th third sub-parameter in the observation variable; θ(k) is a to-be-identified parameter, θ i (k) is an i-th fourth sub-parameter in the to-be-identified parameter.

[0104] S32, constructing a first objective function aiming at minimizing a square sum of the residual error;

[0105] S33, iteratively optimizing the observation variable to minimize a first function value of the first objective function, and generating the start-up estimation parameter according to the last iteratively optimized observation variable and a plurality of actual liquid levels in the plurality of historical running data when a first function value is determined.

[0106] In a preferred embodiment of the present application, a least square method is used as a parameter start-up algorithm to construct an auxiliary variable of an auxiliary variable method. Firstly, an initial system model is converted into a least square form:

[0107] y(k) = Φ(k)θ(k) + ε(k) ;

[0108] wherein Φ(k) is an observation variable, Φ(k) = diag(Φ1(k), Φ2(k),..., Φ m (k)), Φ i (k) is shown as follows.

[0109]

[0110] θ(k) is a to-be-identified parameter, θ(k) = [θ1(k) θ2(k)... θ m (k)] T , θ i (k) is shown as follows.

[0111]

[0112] The least square method minimizes the residual error as a criterion, which is shown as follows:

[0113]

[0114] By solving solve θ E{Φ(k) T ε(k)}=0 and The starting estimation parameter is obtained as follows:

[0115]

[0116] It should be noted that, in addition to using the least square method as the parameter starting algorithm, the generalized least square method, the correlation least square method, the maximum likelihood method, etc. can also be used, thereby providing a new solution idea for the poor closed-loop identification effect in the actual field.

[0117] S4, using an auxiliary variable method, constructing an auxiliary variable according to the starting estimation parameter, and then generating a parameter initial value of the parameter to be identified according to the auxiliary variable, the observation variable, the parameter to be identified, and the residual error;

[0118] Preferably, the auxiliary variable method is used to construct an auxiliary variable according to the starting estimation parameter, and then generate a parameter initial value of the parameter to be identified according to the auxiliary variable, the observation variable, the parameter to be identified, and the residual error, comprising:

[0119] S41, the auxiliary variable constructed according to the starting estimation parameter is as follows:

[0120] ψ=diag(ψ1(k),ψ2(k),…,ψ m (k));

[0121]

[0122]

[0123] Wherein, ψ is an auxiliary variable, ψ i (k) is the fifth sub-parameter in the auxiliary variable, is the starting estimation parameter, is the sixth sub-parameter in the starting estimation parameter.

[0124] S42, the parameter initial value of the parameter to be identified is calculated according to the following formula:

[0125]

[0126] Wherein, is the parameter initial value.

[0127] In a preferred embodiment of the present application, the auxiliary variable is constructed based on the starting estimation parameter, and the initial value estimation stage is entered.

[0128] According to the first stage parameter The auxiliary variable ψ is constructed, ψ = diag(ψ1(k), ψ2(k), …, ψ m (k)), ψ i (k) is shown in the following formula:

[0129]

[0130] The parameter initial value is obtained by solving solve θ E{ψ(k) T ε(k)} = 0 and The parameter initial value is obtained by solving solve The parameter initial value is shown in the following formula:

[0131]

[0132] S5, according to the initial system model, the parameter initial value, and a plurality of influence parameters of each period, a plurality of predicted liquid levels in each period are calculated, and a prediction error method is used to calculate the prediction error of the initial system model under the parameter initial value according to a plurality of predicted liquid levels and corresponding actual liquid levels, and then the parameter initial value is updated and iterated according to the prediction error, until the sum of squares of the prediction error is minimum, and the last updated and iterated parameter initial value is taken as the target value of the to-be-identified parameter;

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

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

[0135]

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

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

[0138] Preferably, the step of updating and iterating the initial parameter values ​​according to the prediction error until the sum of squares of the prediction errors is minimized, and using the last updated initial parameter value as the target value of the parameter to be identified, and then generating a system coupling model of the nuclear power steam generator according to the target value and the initial system model, includes:

[0139] S61. Construct a second objective function with the goal of minimizing the sum of squares of the prediction errors;

[0140] S62. The initial value of the parameter is updated and iterated using the Newton-Raphson algorithm to minimize the second function value of the second objective function. When the second function value is determined to be the minimum and the accuracy of the initial value of the parameter in the last iteration meets the preset requirements, the iteration is stopped and the initial value of the parameter in the last update is taken as the target value of the parameter to be identified.

[0141] S63. Based on the target value, determine several first components of each first sub-parameter in the first model parameters of the initial system model, and several second components of each second sub-parameter in the second model parameters.

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

[0143] In a preferred embodiment of the present invention, after obtaining the initial parameter values, a parameter optimization stage is entered. The identified parameters are further optimized using a prediction error method to improve identification accuracy. Firstly, the parameters are... As initial values ​​for parameters identified by the forecast error method, initial forecast values ​​are obtained from historical data through the forecast model. As shown in the following formula:

[0144]

[0145] Given the observation y(k), in the parameters The forecast error below As shown in the following formula:

[0146]

[0147] To minimize the sum of squared forecast errors in order to estimate the parameters, the criterion function of the forecast error method is shown in the following equation.

[0148]

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

[0150]

[0151] where a is the optimization step size, which is updated iteratively until the estimation accuracy meets the requirement or the specified number of iterations is reached, the iteration is stopped.

[0152] The embodiment provides a closed-loop identification method based on a nuclear power steam generator model. By adopting an autoregressive model structure, an initial system model of the nuclear power steam generator is constructed according to a plurality of historical operation data. Then, a least square method and an auxiliary variable method are adopted to determine an observation variable, a parameter to be identified, a residual error and a parameter initial value of the parameter to be identified in the initial system model. It can be understood that by introducing an auxiliary variable, a complex nonlinear problem and a noise problem are converted into a linear and easier-to-handle problem. Therefore, the nonlinear problem in the modeling of the nuclear power steam generator and the problem of low quality of the closed-loop operation sampling data of the nuclear power steam generator can be overcome. Finally, a prediction error method is adopted to update the iterative model parameters by minimizing the sum of squares of 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 a target value of the parameter to be identified, thereby constructing a system coupling model which can be used for real-time monitoring and control of the operation state of the steam generator.

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

[0154] A data acquisition module is configured to acquire a plurality of historical operation data of the nuclear power steam generator. The historical operation data comprises a plurality of actual liquid levels of the nuclear power steam generator in a time period and a plurality of influence parameters affecting the liquid level of the nuclear power steam generator in the time period.

[0155] A model structure determination module is configured to adopt an autoregressive model structure to construct an initial system model of the nuclear power steam generator according to a plurality of historical operation data.

[0156] A least square module is configured to adopt a least square method to determine an observation variable, a parameter to be identified and a residual error of the initial system model according to the initial system model, and to determine a start estimation parameter according to the observation variable, the parameter to be identified and the residual error.

[0157] An auxiliary variable module is configured to adopt an auxiliary variable method to construct an auxiliary variable according to the start estimation parameter, and to generate a parameter initial value of the parameter to be identified according to the auxiliary variable, the observation variable, the parameter to be identified and the residual error.

[0158] a prediction error module, configured to calculate a plurality of predicted liquid levels in each time period according to the initial system model, the parameter initial value and the plurality of influence parameters of each time period, and calculate a prediction error of the initial system model under the parameter initial value according to the plurality of predicted liquid levels and the corresponding plurality of actual liquid levels by using a prediction error method, and then update the parameter initial value iteratively according to the prediction error, until a sum of squares of the prediction error is minimum, and take the parameter initial value updated finally as a target value of the to-be-identified parameter;

[0159] a model construction module, configured to generate a system coupling model of the nuclear power steam generator according to the target value and the initial system model.

[0160] It is to be noted that the apparatus embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment. In addition, the connection between the modules in the apparatus embodiments provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0161] Those skilled in the art can clearly understand that, for the convenience and brevity, the specific working process of the apparatus described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

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

[0163] The terminal device can be a desktop computer, a notebook computer, a palm computer and a cloud server, etc. The terminal device can include, but is not limited to, a processor and a memory.

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

[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 data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by 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 media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0166] Another preferred embodiment of the present application provides a storage medium, which is a computer readable storage medium, and a computer program is stored in the computer readable storage medium. The computer program, when executed by a processor, can implement the steps of each method embodiment described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can 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), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0167] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements are also considered to be within the scope of the present application.

Claims

1. A closed loop identification method based on a nuclear power steam generator model, characterized in that, The method comprises the following steps: acquiring a plurality of historical operation data of a nuclear power steam generator; wherein, the historical operation data comprises a plurality of actual liquid levels of the nuclear power steam generator in a time period and a plurality of influence parameters affecting the liquid level of the nuclear power steam generator in the time period; constructing an initial system model of the nuclear power steam generator according to a plurality of the historical operation data by using an autoregressive model structure; determining an observation variable, a parameter to be identified and a residual error of the initial system model according to the initial system model by using a least square method, and determining a starting estimation parameter according to the observation variable, the parameter to be identified and the residual error; constructing an auxiliary variable according to the starting estimation parameter by using an auxiliary variable method, and then generating a parameter initial value of the parameter to be identified according to the auxiliary variable, the observation variable, the parameter to be identified and the residual error; calculating a plurality of predicted liquid levels in each time period according to the initial system model, the parameter initial value and a plurality of the influence parameters of each time period, and calculating a prediction error of the initial system model under the parameter initial value according to a plurality of the predicted liquid levels and a plurality of the actual liquid levels corresponding to the predicted liquid levels by using a prediction error method, and then updating the parameter initial value iteratively according to the prediction error until a sum of squares of the prediction error is minimum, and taking the parameter initial value updated last as a 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.

2. The closed loop identification method based on nuclear power steam generator model of claim 1, wherein, The initial system model is as follows: ； ； ； ； ； wherein, is a delay, is an mth actual level of the nuclear power steam generator in a kth period, is an nth influencing parameter of the nuclear power steam generator affecting the level in the kth period, is a residual error, is a difference operator, is a first model parameter, is an ith first sub-parameter of the first model parameter corresponding to an ith actual level, is an ith first component of is an ith first component of is a second model parameter, is an ith second sub-parameter of the second model parameter representing a correlation between an ith influencing parameter and an ith actual level, is an ith second component of is an ith second component of is an ith second component of​​​ 3. The closed loop identification method based on nuclear power steam generator model of claim 2, wherein, The step of determining the observation variable, the parameter to be identified and the residual error of the initial system model according to the initial system model by using the least square method, and determining the starting estimation parameter according to the observation variable, the parameter to be identified and the residual error comprises the following steps: converting the initial system model into a least square form to generate a corresponding least square model; wherein, the least square model is composed of the observation variable, the parameter to be identified and the residual error; constructing a first target function with a target of minimizing a sum of squares of the residual error; iteratively optimizing the observation variable to minimize a first function value of the first target function, and generating the starting estimation parameter according to the observation variable optimized last and a plurality of the actual liquid levels in a plurality of the historical operation data when the first function value is minimum.

4. The closed loop identification method based on nuclear power steam generator model of claim 3, wherein, The least square model is as follows: ； ； ； ； ； in, For observed variables, The i-th third sub-parameter in the observed variables; For the parameters to be identified, The fourth sub-parameter in the parameters to be identified is the i-th parameter. For nuclear power steam generators in the first The i-th actual liquid level within a time period. This is the nth parameter affecting the liquid level in the k-th time period of the nuclear power plant's steam generator. For delay, The first sub-parameter The number of coefficients, For the second sub-parameter The number of coefficients.

5. The closed loop identification method based on nuclear power steam generator model of claim 4, wherein, The step of constructing the auxiliary variable according to the starting estimation parameter by using the auxiliary variable method, and then generating the parameter initial value of the parameter to be identified according to the auxiliary variable, the observation variable, the parameter to be identified and the residual error comprises the following steps: the auxiliary variable constructed according to the starting estimation parameter is as follows: ； ； ； wherein, is an auxiliary variable, is an i-th fifth sub-parameter in the auxiliary variable, is a start estimation parameter, is an i-th sixth sub-parameter in the start estimation parameter; the parameter initial value of the parameter to be identified is calculated according to the following formula: ； wherein is the parameter initial value.

6. The closed loop identification method based on nuclear power steam generator model of claim 5, wherein, The step of calculating the prediction error of the initial system model under the parameter initial value according to a plurality of the predicted liquid levels and a plurality of the actual liquid levels corresponding to the predicted liquid levels by using the prediction error method comprises the following steps: the prediction error of the initial system model under the parameter initial value is calculated according to the following formula: ； wherein, is the prediction error, is the actual level of the nuclear steam generator in the kth time period, is the predicted level of the nuclear steam generator in the kth time period predicted by the initial system model.

7. The closed loop identification method based on nuclear power steam generator model of claim 6, wherein, updating and iterating the parameter initial value according to the prediction error until a sum of squares of the prediction error is minimum, and taking the parameter initial value of the last updating iteration as a 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, comprising: constructing a second objective function aiming at minimizing a sum of squares of the prediction error; updating and iterating the parameter initial value by using a Newton-Raphson algorithm to minimize a second function value of the second objective function, stopping iteration when the second function value is determined to be minimum and an accuracy of the parameter initial value of the last iteration meets a preset requirement, and taking the parameter initial value of the last updating iteration as the target value of the parameter to be identified; determining a plurality of first components of each first sub-parameter in the first model parameter and a plurality of second components of each second sub-parameter in the second model parameter of the initial system model according to the target value; constructing the system coupling model of the nuclear power steam generator according to the first components and the second components.

8. A closed loop identification device based on a nuclear power steam generator model, characterized by, comprise: a data acquisition module configured to acquire a plurality of historical operation data of a nuclear power steam generator; wherein the historical operation data comprises a plurality of actual liquid levels of the nuclear power steam generator within a time period and a plurality of influence parameters affecting the liquid levels of the nuclear power steam generator within the time period; a model structure determination module configured to construct an initial system model of the nuclear power steam generator by using an autoregressive model structure according to the plurality of historical operation data; a least square module configured to determine an observation variable, a parameter to be identified, and a residual error of the initial system model by using a least square method according to the initial system model, and determine a start estimation parameter according to the observation variable, the parameter to be identified, and the residual error; an auxiliary variable module configured to construct an auxiliary variable according to the start estimation parameter by using an auxiliary variable method, and then generate a parameter initial value of the parameter to be identified according to the auxiliary variable, the observation variable, the parameter to be identified, and the residual error; a prediction error module configured to calculate a plurality of predicted liquid levels within each time period according to the initial system model, the parameter initial value, and the plurality of influence parameters of each time period, and calculate a prediction error of the initial system model under the parameter initial value according to a plurality of the predicted liquid levels and a plurality of the actual liquid levels corresponding to the predicted liquid levels by using a prediction error method, and then update and iterate the parameter initial value according to the prediction error until a sum of squares of the prediction error is minimum, and take the parameter initial value of the last updating iteration as a target value of the parameter to be identified; a model construction module configured 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, comprising: comprise a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements a closed-loop identification method based on a nuclear power steam generator model as claimed in any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized by The storage medium comprises a stored computer program, wherein the computer program controls a device where the storage medium is located to perform the closed-loop identification method based on the nuclear power steam generator model as claimed in any one of claims 1 to 7 when the computer program is running.

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