Method and device for identifying dynamic parameters of synchronous generator

Abstract

The invention discloses a method and a device for identifying dynamic parameters of a synchronous generator, relates to the technical field of generators, and mainly aims to solve the problem of poor accuracy of dynamic parameter identification of an existing synchronous generator. Comprising the following steps: acquiring excitation voltage data, generator end voltage data, generator end current data, active power and reactive power of a synchronous generator, and solving the excitation voltage data, the generator end voltage data, the generator end current data, the active power and the reactive power based on a constructed objective function, obtaining a plurality of dynamic parameters; calculating a sensitivity value of each dynamic parameter based on a chain function, and determining a dominant identification parameter and a non-dominant identification parameter from the dynamic parameters based on the sensitivity values; and solving the non-dominant identification parameter based on the regularized objective function to obtain an identification result of the non-dominant identification parameter.

Description

Technical Field

[0001] The present application relates to the technical field of generators, and in particular to a method and device for identifying dynamic parameters of a synchronous generator. Background Art

[0002] Synchronous generators are the core components of power systems. In actual operation, generators will inevitably face aging and damage. Especially for synchronous generators, there will be eddy currents, saturation, hysteresis and other phenomena, which will cause the factory parameters to be inconsistent with the actual parameters, bringing adverse effects on the analysis and control of the power system. Therefore, the accurate identification of the dynamic parameters of the power system is an important issue that must be solved.

[0003] Currently, existing methods for identifying the dynamic parameters of synchronous generators rely on online grid operating data as the parameter identification data source to identify dynamic behavior. For example, these algorithms employ extended Kalman filtering or unscented Kalman filtering to identify the generator's state and parameters in real time. However, in practice, these algorithms suffer from poor convergence and poor accuracy and stability of the identification results, failing to meet the high adaptability requirements of synchronous generator parameter identification. Therefore, a method for identifying the dynamic parameters of synchronous generators is urgently needed to address this issue. Summary of the Invention

[0004] In view of this, the present application provides a method and device for identifying dynamic parameters of a synchronous generator, the main purpose of which is to solve the problem of poor accuracy in identifying dynamic parameters of existing synchronous generators.

[0005] According to one aspect of the present application, a method for identifying dynamic parameters of a synchronous generator is provided, comprising:

[0006] Acquiring excitation voltage data, machine-end voltage data, machine-end current data, active power, and reactive power of the synchronous generator, and solving the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power, and the reactive power based on the constructed objective function to obtain a plurality of dynamic parameters;

[0007] calculating a sensitivity value of each of the dynamic parameters based on a chain function, and determining a dominant identification parameter and a non-dominant identification parameter from the dynamic parameters based on the sensitivity value;

[0008] The non-dominant identification parameter is solved based on the regularized objective function to obtain an identification result of the non-dominant identification parameter.

[0009] Furthermore, before solving the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power, and the reactive power based on the constructed objective function to obtain a plurality of dynamic parameters, the method further includes:

[0010] constructing input variables based on the excitation voltage variable and the machine-end voltage variable, and constructing output variables based on the machine-end voltage variable, the machine-end current variable, the active power variable, and the reactive power variable;

[0011] Using multiple dynamic parameters as constraints, constructing an objective function that uses the input variables to solve the output variables in a weighted least squares manner;

[0012] Among them, the objective function is y represents the output variable of the synchronous generator, represents the measured value of the output variable, W represents the dynamic weight matrix of the dynamic parameter, t represents the time, and T f represents the length of the sampling time window, x represents the state variables of the synchronous generator, x0 represents the state variable set at the initial moment, u represents the measured value of the input variable, p represents the dynamic parameter set of the synchronous generator, f represents the differential equation group of the dynamic behavior of the synchronous motor, and h represents all the output algebraic equations of the dynamic system. Characterizes the upper limit of the value of the dynamic parameter, p Characterizes the lower limit of the value of the dynamic parameter.

[0013] Furthermore, before solving the non-dominant identification parameter based on the regularized objective function to obtain an identification result of the non-dominant identification parameter, the method further includes:

[0014] Constructing a penalty function based on the Sihonov regularization method, wherein the nameplate reference value of the non-dominant identification parameter and the nameplate reference weight are introduced into the penalty function;

[0015] Adding the penalty function to the objective function to obtain the regularized objective function;

[0016] Among them, the objective function after regularization is described represents the objective function, c represents the constraint condition set, v represents the variable after the algebraic-differential equation is discretized, and Ψ represents the penalty function obtained by regularization.

[0017] Furthermore, the calculating of the sensitivity value of each dynamic parameter based on the chain function includes:

[0018] Determine a sensitivity calculation formula corresponding to the output variable, the dynamic parameter, and the corresponding dynamic weight based on a chain function, and calculate a sensitivity value of each dynamic parameter based on the sensitivity calculation formula;

[0019] Wherein, the sensitivity calculation formula is: The S R is the time domain sensitivity, S j To obtain the frequency domain sensitivity after the average sum of squares, y is the output variable, p is the dynamic parameter, N is the number of moments after the sampling time window is discretized, and k is the moment.

[0020] Furthermore, the method further comprises:

[0021] The objective function is discretized according to an implicit trapezoidal integration method, so as to perform sensitivity calculation based on the discretized objective function.

[0022] Further, determining the dominant identification parameter and the non-dominant identification parameter from the dynamic parameters based on the sensitivity value includes:

[0023] When the sensitivity value is greater than a preset sensitivity threshold, determining the dynamic parameter corresponding to the sensitivity value as a dominant identification parameter, and determining an identification result of the dominant identification parameter;

[0024] If the sensitivity value is less than or equal to the preset sensitivity threshold, the dynamic parameter corresponding to the sensitivity value is determined to be a non-dominant identification parameter.

[0025] Furthermore, the method further comprises:

[0026] When the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power and the reactive power trigger the preset monitoring trigger conditions, dynamic monitoring is performed based on the identification results of the dominant identification parameters and the identification results of the non-dominant identification parameters to generate dynamic monitoring results.

[0027] According to another aspect of the present application, a device for identifying dynamic parameters of a synchronous generator is provided, comprising:

[0028] an acquisition module, configured to acquire excitation voltage data, generator-end voltage data, generator-end current data, active power, and reactive power of the synchronous generator, and solve the excitation voltage data, the generator-end voltage data, the generator-end current data, the active power, and the reactive power based on a constructed objective function to obtain a plurality of dynamic parameters;

[0029] a determination module, configured to calculate a sensitivity value of each of the dynamic parameters based on a chain function, and determine a dominant identification parameter and a non-dominant identification parameter from the dynamic parameters based on the sensitivity value;

[0030] The identification module is used to solve the non-dominant identification parameter based on the regularized objective function to obtain an identification result of the non-dominant identification parameter.

[0031] Furthermore, the device further comprises:

[0032] A construction module is used to construct input variables based on the excitation voltage variable and the terminal voltage variable, and to construct output variables based on the terminal voltage variable, the terminal current variable, the active power variable, and the reactive power variable; and to construct an objective function for solving the output variable using the input variables in a weighted least squares manner using multiple dynamic parameters as constraints;

[0033] Among them, the objective function is y represents the output variable of the synchronous generator, represents the measured value of the output variable, W represents the dynamic weight matrix of the dynamic parameter, t represents the time, and T f represents the length of the sampling time window, x represents the state variables of the synchronous generator, x0 represents the state variable set at the initial moment, u represents the measured value of the input variable, p represents the dynamic parameter set of the synchronous generator, f represents the differential equation group of the dynamic behavior of the synchronous motor, and h represents all the output algebraic equations of the dynamic system. Characterizes the upper limit of the value of the dynamic parameter, p Characterizes the lower limit of the value of the dynamic parameter.

[0034] Furthermore, the construction module is further configured to construct a penalty function based on the Sihonov regularization method, wherein the penalty function introduces the nameplate reference value of the non-dominant identification parameter and the nameplate reference weight; and add the penalty function to the objective function to obtain the regularized objective function.

[0035] Among them, the objective function after regularization is described represents the objective function, c represents the constraint condition set, v represents the variable after the algebraic-differential equation is discretized, and Ψ represents the penalty function obtained by regularization.

[0036] Furthermore, the determination module is specifically configured to determine a sensitivity calculation formula corresponding to the output variable, the dynamic parameter, and the corresponding dynamic weight based on a chain function, and calculate a sensitivity value of each dynamic parameter based on the sensitivity calculation formula;

[0037] Wherein, the sensitivity calculation formula is: The S R is the time domain sensitivity, S jTo obtain the frequency domain sensitivity after the average sum of squares, y is the output variable, p is the dynamic parameter, N is the number of moments after the sampling time window is discretized, and k is the moment.

[0038] Furthermore, the device further comprises:

[0039] The processing module is used to discretize the objective function according to an implicit trapezoidal integration method, so as to perform sensitivity calculation based on the discretized objective function.

[0040] Furthermore, the determination module is also used to determine that the dynamic parameter corresponding to the sensitivity value is the dominant identification parameter when the sensitivity value is greater than a preset sensitivity threshold, and determine the identification result of the dominant identification parameter; if the sensitivity value is less than or equal to the preset sensitivity threshold, determine that the dynamic parameter corresponding to the sensitivity value is a non-dominant identification parameter.

[0041] Furthermore, the device further comprises:

[0042] The monitoring module is used to perform dynamic monitoring based on the identification results of the dominant identification parameters and the non-dominant identification parameters to generate dynamic monitoring results when the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power and the reactive power trigger the preset monitoring trigger conditions.

[0043] According to another aspect of the present application, a storage medium is provided, wherein the storage medium stores at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for identifying dynamic parameters of a synchronous generator.

[0044] According to another aspect of the present application, there is provided a terminal, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0045] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for identifying dynamic parameters of a synchronous generator.

[0046] By means of the above technical solution, the technical solution provided by the embodiment of the present application has at least the following advantages:

[0047] The present application provides a method and device for identifying dynamic parameters of a synchronous generator. Compared with the prior art, the embodiment of the present application obtains the excitation voltage data, machine-end voltage data, machine-end current data, active power and reactive power of the synchronous generator, and solves the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power and the reactive power based on the constructed objective function to obtain multiple dynamic parameters; calculates the sensitivity value of each of the dynamic parameters based on a chain function, and determines the dominant identification parameter and the non-dominant identification parameter from the dynamic parameters based on the sensitivity value; solves the non-dominant identification parameter based on the regularized objective function to obtain the identification result of the non-dominant identification parameter, distinguishes the dominant identification parameter and the non-dominant identification parameter from the dynamic parameters through sensitivity, achieves the purpose of step-by-step identification, avoids the problem that the identification result is sensitive to the upper and lower limits of the dynamic parameter optimization range when all dynamic parameters are identified at the same time, and overcomes the deterioration effect of the dynamic parameters with poor identifiability on the identification result of the parameters with strong identifiability, thereby improving the identification accuracy and stability of the dynamic parameters.

[0048] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0050] Figure 1 A flow chart of a method for identifying dynamic parameters of a synchronous generator provided in an embodiment of the present application is shown;

[0051] Figure 2 A synchronous generator dynamic parameter identification framework diagram based on a step-by-step method provided in an embodiment of the present application is shown;

[0052] Figure 3 A schematic diagram of the overall identification results of all parameters under scenario 1 (short circuit fault) provided in an embodiment of the present application is shown;

[0053] Figure 4 A schematic diagram of the overall identification results of all parameters under scenario 2 (short circuit fault) provided in an embodiment of the present application is shown;

[0054] Figure 5A schematic diagram of time-domain relative trajectory sensitivity of some typical parameters provided in an embodiment of the present application is shown;

[0055] Figure 6 A schematic diagram of parameter step-by-step identification results under scenario 1 (step fault) provided in an embodiment of the present application is shown;

[0056] Figure 7 A schematic diagram of parameter step-by-step identification results under scenario 2 (step fault) provided in an embodiment of the present application is shown;

[0057] Figure 8 A schematic diagram of the output power verification results of step-by-step parameter identification in scenario 1 provided in an embodiment of the present application is shown;

[0058] Figure 9 A schematic diagram of the output power verification results of the step-by-step parameter identification of scenario 2 provided in an embodiment of the present application is shown;

[0059] Figure 10 A schematic diagram showing the relative trajectory sensitivity of various parameters in an actual identification scenario provided by an embodiment of the present application is shown;

[0060] Figure 11 A schematic diagram of the time domain relative trajectory sensitivity of some parameters of an actual recognition scenario provided by an embodiment of the present application is shown;

[0061] Figure 12 A schematic diagram of output power deviation in an actual unit calibration scenario provided by an embodiment of the present application is shown;

[0062] Figure 13 A block diagram of a device for identifying dynamic parameters of a synchronous generator provided in an embodiment of the present application is shown;

[0063] Figure 14 A schematic diagram of the structure of a terminal provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0064] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0065] In the description of the embodiments of the present application, for the terms defined below, unless a different definition is given elsewhere in the claims or this specification, these definitions should be applied. All numerical values, whether or not explicitly indicated, are defined herein as being modified by the term "about". The term "about" generally refers to a numerical range that a person of ordinary skill in the art would consider to be equivalent to the stated value to produce substantially the same properties, functions, results, etc. A numerical range indicated by a low value and a high value is defined as including all numerical values included in the numerical range and all subranges included in the numerical range.

[0066] It should be noted that the terms "first", "second", etc. in the description, claims, and drawings of the embodiments of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the objects used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present application can be implemented in an order other than the following diagrams or the following descriptions. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, or product comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, or products.

[0067] In one embodiment, an embodiment of the present application provides a method for displaying data query on a mobile terminal, and illustrates the method by taking the application of the method to a computer device such as a server as an example, wherein the server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms, such as financial transaction platforms, financial information management systems, etc.

[0068] The embodiment of the present application provides a method for identifying dynamic parameters of a synchronous generator, such as Figure 1 As shown, the method includes:

[0069] 101. Obtain excitation voltage data, machine-end voltage data, machine-end current data, active power, and reactive power of the synchronous generator, and solve the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power, and the reactive power based on the constructed objective function to obtain multiple dynamic parameters.

[0070] In the embodiment of the present application, the current execution end, as the identification subject of the dynamic parameters of the synchronous motor, can be a client device, a server device, or a controller embedded in the synchronous generator to obtain the real-time relevant parameters of the synchronous motor in real time, and the embodiment of the present application does not make specific restrictions. Among them, the excitation voltage data refers to the voltage value provided by the excitation system of the synchronous generator during operation, the machine-end voltage data and the machine-end current data are the voltage data and current data collected from the device end of the synchronous generator respectively, and the active power and reactive power can be calculated based on the aforementioned voltage and current, and the embodiment of the present application does not make specific restrictions. In addition, the objective function is a mathematical model constructed based on the aforementioned parameters for the operation of the synchronous generator. At this time, the mathematical model characterizes the motion situation based on multiple dynamic parameters. Therefore, the dynamic parameters can be determined by solving the mathematical model and the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power and the reactive power. Among them, the dynamic parameters include motor d-axis parameters, motor q-axis parameters, synchronous motor d-axis transient reactance, synchronous motor q-axis transient reactance, synchronous motor d-axis subtransient reactance, synchronous motor q-axis subtransient reactance, open-circuit transient time constant of synchronous motor d-axis winding, open-circuit transient time constant of synchronous motor d-axis winding, open-circuit subtransient time constant of synchronous motor d-axis winding, open-circuit subtransient time constant of synchronous motor d-axis winding, motor leakage reactance related to saturation, and generator inertia constant.

[0071] It should be noted that if Figure 2 As shown, in the embodiment of the present application, the machine-end measurement information (machine-end voltage, machine-end current) of the synchronous generator can be collected through the PMU measurement system. The current execution subject, as the online monitoring system of the power system, can collect algebraic variables connected to the generator in real time. In particular, when the power grid is subjected to various disturbances causing the generator-end signal to fluctuate, the online monitoring system collects various variables connected to the generator within the corresponding time window and saves them in the database for the identification of dynamic parameters in the embodiment of the present application. At this time, the collected physical quantities include the generator-end voltage, excitation voltage, machine-end current, active power, and reactive power.

[0072] 102. Calculate a sensitivity value of each of the dynamic parameters based on a chain function, and determine a dominant identification parameter and a non-dominant identification parameter from the dynamic parameters based on the sensitivity value.

[0073] In the embodiment of the present application, after solving for multiple dynamic parameters, the current execution end calculates the sensitivity corresponding to the dynamic parameters as a basis for classifying the dynamic parameters. Sensitivity refers to the magnitude of the change in the corresponding output variable caused by a small change in the parameter, which is an effective method for parameter identifiable analysis. Since there are many dynamic parameters in synchronous generators and the dimensions of the parameters are significantly different, the current execution end divides all dynamic parameters to be identified into dominant parameters and non-dominant parameters based on the relative trajectory sensitivity method, that is, the sensitivity value of each dynamic parameter is calculated by the chain function rule to determine the dominant identification parameter and the non-dominant identification parameter from all dynamic parameters based on the sensitivity.

[0074] It should be noted that, since there are many dynamic parameters in the power system, and each has a different actual physical meaning, there will be a large error when all dynamic parameters are identified at one time. Therefore, the embodiment of the present application adopts a step-by-step identification method, that is, based on the identifiability analysis of trajectory sensitivity, the sensitivity value is used as a measurement indicator, and some dynamic parameters with strong identifiability are used as variables for the first step of identification, so as to reduce the overfitting pathological degree of the inverse problem of dynamic parameter identification, and at the same time weaken the negative impact of low-sensitivity parameters on the identification and solution of sensitive parameters. Furthermore, the dynamic parameters are divided based on relative trajectory sensitivity to obtain non-dominant identification parameters that need to be further identified. At this time, the dominant identification parameters are dynamic parameters that can be directly used as identification results after being solved in step 101, and the non-dominant identification parameters are dynamic parameters that need to be identified and solved in step 103. The embodiment of the present application does not make specific limitations.

[0075] 103. Solve the non-dominant identification parameter based on the regularized objective function to obtain an identification result of the non-dominant identification parameter.

[0076] In an embodiment of the present application, after determining the non-dominant parameters, the current execution end performs regularization improvement on the objective function, thereby resolving the non-dominant parameters based on the regularized objective function to obtain the final identification result of the non-dominant parameters. Among them, the regularization method is to introduce additional prior knowledge on the basis of the original objective, and to alleviate the overfitting pathological characteristics of the parameter identification problem to a certain extent by balancing the original problem and the prior knowledge, that is, regularizing the objective function of the traditional identification by adding a penalty function. In step 103 of the step-by-step identification, based on the objective function of the original identification dynamic parameters, a penalty function is added as the regularized objective function to solve the dynamic parameters.

[0077] In another embodiment of the present application, for further definition and explanation, before the step of solving the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power, and the reactive power based on the constructed objective function to obtain multiple dynamic parameters, the method further includes:

[0078] constructing input variables based on the excitation voltage variable and the machine-end voltage variable, and constructing output variables based on the machine-end voltage variable, the machine-end current variable, the active power variable, and the reactive power variable;

[0079] A plurality of dynamic parameters are used as constraints, and an objective function for solving the output variable using the input variable is constructed in a weighted least squares manner.

[0080] In order to construct the objective function for solving the dynamic parameters based on the operation of the synchronous motor and to achieve the purpose of accurately identifying the dynamic parameters, the current execution end pre-sets the excitation voltage variable and the terminal voltage variable as input variables, and the terminal voltage variable, the terminal current variable, the active power variable and the reactive power variable as output variables to construct the objective function containing the dynamic parameters. Among them, multiple dynamic parameters are used as constraints of the objective function, and the objective function is constructed according to the weighted least squares method, that is, the objective function is expressed as y represents the output variable of the synchronous generator, represents the measured value of the output variable, W represents the dynamic weight matrix of the dynamic parameter, t represents the time, and T f represents the length of the sampling time window, x represents the state variables of the synchronous generator, x0 represents the state variable set at the initial moment, u represents the measured value of the input variable, p represents the dynamic parameter set of the synchronous generator, f represents the differential equation group of the dynamic behavior of the synchronous motor, and h represents all the output algebraic equations of the dynamic system. represents the upper limit of the value of the dynamic parameter, and p represents the lower limit of the value of the dynamic parameter.

[0081] It should be noted that before constructing the objective function, it is necessary to determine the dynamic parameters contained in the dynamic parameter set. At this time, the synchronous generator model is first constructed and expressed by differential method, where the rotor motion equation is expressed as:

[0082] δ is the generator power angle, ω is the motor speed, ω s is the synchronous speed of the power system, T j is the inertia constant of the generator, P e is the electromagnetic power of the generator set, P m is the mechanical power of the generator set. Furthermore, the differential equations of the synchronous generator winding flux are:

[0083] Among them, T' represents the open-circuit transient time constant of each winding of the synchronous generator, T" represents the open-circuit sub-transient time constant of each winding, E' is the transient electromotive force, E" is the sub-transient electromotive force, V f represents the synchronous generator terminal excitation voltage, I represents the synchronous generator stator current, X represents the synchronous generator reactance, X' and X" are the synchronous generator transient and sub-transient reactances respectively, X l " represents the motor leakage reactance associated with saturation, St represents the degree of flux saturation of the synchronous generator, and the subscripts d and q represent the physical variables corresponding to the generator's d-axis and q-axis, respectively. Furthermore, dynamic parameters are selected based on the mathematical relationship between the aforementioned rotor motion equations and the set of flux differential equations, and a dynamic parameter set is constructed, namely, the motor d-axis parameters, the motor q-axis parameters, the synchronous motor d-axis transient reactance, the synchronous motor q-axis transient reactance, the synchronous motor d-axis subtransient reactance, the synchronous motor q-axis subtransient reactance, the synchronous motor d-axis open-circuit transient time constant of the synchronous motor d-axis winding, the synchronous motor d-axis open-circuit transient time constant, the synchronous motor d-axis open-circuit subtransient time constant, the synchronous motor d-axis open-circuit subtransient time constant, the motor leakage reactance associated with saturation, and the generator's inertia constant.

[0084] In another embodiment of the present application, for further definition and explanation, before the step of solving the non-dominant identification parameter based on the regularized objective function to obtain the identification result of the non-dominant identification parameter, the method further includes:

[0085] Constructing a penalty function based on the Sihonov regularization method, wherein the nameplate reference value of the non-dominant identification parameter and the nameplate reference weight are introduced into the penalty function;

[0086] The penalty function is added to the objective function to obtain the regularized objective function.

[0087] In order to achieve step-by-step identification of dominant identification parameters and non-dominant identification parameters, and thus optimize the identification of non-dominant identification parameters, the current execution end needs to improve and optimize the objective function before solving the non-dominant identification parameters. Since the identification of dynamic parameters can be described by the optimization solution of the objective function, after constructing the objective function, the solution of non-dominant identification parameters belongs to the second step of identification, that is, the current execution end introduces prior knowledge based on actual engineering experience and manufacturer parameters, and combines the regularization method to identify dynamic parameters as insensitive parameters. Specifically, the objective function after adding the regularization term (penalty function) is expressed as:

[0088] described represents the objective function, c represents the constraint condition set, v represents the variable after the algebraic-differential equation is discretized, and Ψ represents the penalty function obtained by regularization.

[0089] It should be noted that since the objective function is based on the weighted least squares form, in order to maintain consistency, the penalty function can be constructed using the Sihonov regularization method to solve the problem in this way, where the prior knowledge item is:

[0090] Ψ(p)=(pp ref ) T W p T W p (pp ref );

[0091] Among them, p ref is the reference value of the dynamic parameter to be identified (which can be the brand parameter of the synchronous generator equipment), W p Represents the weight of the prior parameters.

[0092] In another embodiment of the present application, for further definition and explanation, the step of calculating the sensitivity value of each of the dynamic parameters based on the chain function includes:

[0093] A sensitivity calculation formula corresponding to the output variable, the dynamic parameter and the corresponding dynamic weight is determined based on a chain function, and a sensitivity value of each dynamic parameter is calculated based on the sensitivity calculation formula.

[0094] In order to achieve the purpose of step-by-step identification of dynamic parameters, when the current execution end calculates the sensitivity value based on the chain function, specifically, firstly, the sensitivity calculation formula corresponding to the output variable, dynamic parameter and corresponding dynamic weight is obtained based on the chain function. At this time, since the synchronous generator has many dynamic parameters and the dimensional differences between the parameters are obvious, based on the relative trajectory sensitivity method, when all the dynamic parameters to be identified are divided into dominant parameters and non-dominant parameters, the trajectory sensitivity is solved by the analytical method of the chain function rule to overcome the numerical error brought by the perturbation method.

[0095] The sensitivity calculation formula is: The S R is the time domain sensitivity, S j To obtain the frequency domain sensitivity after the average sum of squares, y is the output variable, p is the dynamic parameter, N is the number of moments after the sampling time window is discretized, k is the moment, is the time domain sensitivity of the jth dynamic parameter in the dynamic parameter set.

[0096] In another embodiment of the present application, for further definition and explanation, the steps further include:

[0097] The objective function is discretized according to an implicit trapezoidal integration method, so as to perform sensitivity calculation based on the discretized objective function.

[0098] In order to optimize the stability of the solution for dynamic parameters and thus improve the accuracy of dynamic parameter identification, the embodiment of this application adopts the interior point method to solve the dynamic parameter identification optimization problem, and the state equation is discretized using the implicit trapezoidal integration method to ensure the stability of the objective function solution at the PMU system sampling frequency. The state variables in the objective function after discretization are expressed as:

[0099] Where Δt represents the time step and k represents the kth time step.

[0100] In another embodiment of the present application, for further definition and illustration, the step of determining the dominant identification parameter and the non-dominant identification parameter from the dynamic parameter based on the sensitivity value includes:

[0101] When the sensitivity value is greater than a preset sensitivity threshold, determining the dynamic parameter corresponding to the sensitivity value as a dominant identification parameter, and determining an identification result of the dominant identification parameter;

[0102] If the sensitivity value is less than or equal to the preset sensitivity threshold, the dynamic parameter corresponding to the sensitivity value is determined to be a non-dominant identification parameter.

[0103] In order to achieve the purpose of step-by-step dynamic parameter identification, thereby improving the accuracy of dynamic parameter identification, the current execution end obtains a pre-set sensitivity threshold when dividing the dynamic parameters based on the sensitivity value, which can be configured according to different identification scenarios, including but not limited to 0.01, 0.02, etc., which is not specifically limited in the embodiment of the present application. Furthermore, when the calculated sensitivity value of each dynamic parameter is greater than the preset sensitivity threshold, the dynamic parameter corresponding to the sensitivity value is determined to be the dominant identification parameter, and the identification result of this dominant identification parameter is determined. Correspondingly, if the calculated sensitivity value of each dynamic parameter is less than or equal to the preset sensitivity threshold, the dynamic parameter corresponding to the sensitivity value is determined to be a non-dominant identification parameter, which is not specifically limited in the embodiment of the present application.

[0104] In another embodiment of the present application, for further definition and explanation, the steps further include:

[0105] When the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power and the reactive power trigger the preset monitoring trigger conditions, dynamic monitoring is performed based on the identification results of the dominant identification parameters and the identification results of the non-dominant identification parameters to generate dynamic monitoring results.

[0106] In order to achieve the purpose of effectively monitoring the operating status of the synchronous generator using dynamic parameters, the current execution end can pre-configure monitoring trigger conditions corresponding to different excitation voltages, generator voltages, generator currents, and active power and reactive power, so that when the preset monitoring trigger conditions are triggered, dynamic monitoring judgment is performed based on the identification results of the dominant identification parameters and the non-dominant identification parameters to generate dynamic monitoring results. Among them, the preset monitoring trigger conditions can be time trigger conditions, for example, the time conditions for collecting any voltage or current are time trigger conditions such as 1 day, 1 week, etc., or they can be extreme value trigger conditions corresponding to voltage, current, etc., for example, extreme value trigger conditions such as extreme value a or extreme value b of voltage or current, which are not specifically limited in the embodiments of the present application. In addition, the preset monitoring trigger conditions can be configured as trigger conditions for any of the above-mentioned excitation voltage data, generator voltage data, generator current data, active power and reactive power, or they can be configured as trigger conditions for all of the above-mentioned excitation voltage data, generator voltage data, generator current data, active power and reactive power, so that dynamic monitoring is performed when the preset monitoring trigger conditions are triggered, which are not specifically limited in the embodiments of the present application. Among them, when dynamic monitoring is performed based on the recognition results of the dominant recognition parameters and the recognition results of the non-dominant recognition parameters, the two recognition results can be used for combined monitoring judgment, or the two recognition results can be judged separately to generate dynamic monitoring results. At this time, the dynamic monitoring method may include but is not limited to using the recognition results for threshold judgment, using the recognition results for evaluation model judgment, etc., and the embodiments of this application do not make specific limitations.

[0107] In a specific scenario of an embodiment of the present application, the dynamic parameters of a unit of a provincial power grid in East China are identified, and various disturbance data are generated by the power system simulation software BPA for analysis, including two measured disturbance scenarios. Scenario 1 is a fault scenario measured by the power system simulation software BPA input step signal, and scenario 2 is a fault scenario measured by the power system simulation software BPA machine end three-phase short circuit fault, in order to distinguish different scenarios of different fault types, wherein the pre-configured data is added with random noise with a measured mean of 0 and a variance of 0.2%, and the IPOPT solver is used to solve the nonlinear constrained optimization problem. Specifically, after solving the dynamic parameters based on the objective function, the obtained dynamic parameters include the motor d-axis parameter X d =2.4830pu, motor q-axis parameter X q =2.3580pu, synchronous motor d-axis transient reactance X d '=0.2640pu、Q-axis transient reactance of synchronous motor X q '=0.7100pu, synchronous motor subtransient reactance X"=0.2350, synchronous motor d-axis winding open circuit transient time constant T d0'=8.860s, open circuit transient time constant T of synchronous motor d-axis winding q0 '=2.500s, open circuit subtransient time constant T of synchronous motor d-axis winding d0 = 0.0360s, the open circuit subtransient time constant T of the synchronous motor q-axis winding q0 = 0.200s, the inertia constant of the generator is T j =10s, motor leakage reactance X related to saturation l =0.15, and then determine the dominant identification parameters and non-dominant identification parameters based on the sensitivity value from the above dynamic parameters. Specifically, the relative trajectory sensitivity analysis calculation is performed based on the sensitivity calculation formula, and 0.01 is used as the preset sensitivity threshold to determine the X in scene 1. d 、X q , X d ', X q ', X", T d0 ', X l The trajectory sensitivity of the parameter is higher than the preset threshold, indicating better identifiability. It is determined as the dominant parameter in this scenario 1 for identification. The remaining dynamic parameters with poor identifiability are placed in the second step for identification and correction. Similarly, in scenario 2, X d 、X q 、X d ', X q ', X", T j 、X l The parameter is the dominant identification parameter. Figure 3 The overall identification results of all parameters in scenario 1 are shown as follows: Figure 4 The overall identification results of all parameters in scenario 2 are shown as follows: Figure 5 The time domain relative trajectory sensitivity of the representative parameters in scenario 1 and scenario 2 is shown. It can be concluded that under step fault, the power change amplitude is relatively small, the relative sensitivity of Tj in scenario 1 is low, and X d 'It has high sensitivity and strong identifiability. In scenario 2, due to the three-phase short circuit fault at the machine end, the power has significant fluctuations, T j The time domain sensitivity curve of Scenario 1 is stronger than that of Scenario 1, which further illustrates the correctness and reliability of the step-by-step identification based on the trajectory sensitivity method in the embodiment of the present application.

[0108] In addition, if Figure 6 as well as Figure 7As shown in the figure, in the process of step-by-step identification of dynamic parameters, whether it is scenario 1 of step fault measurement or scenario 2 of short circuit fault measurement, accurate results of dynamic parameters with high relative trajectory sensitivity and strong identifiability can be obtained in the first step (step 101), solving the problem of inaccurate parameter identification and changes with the changes in the upper and lower limits of the constraints. More importantly, the accurate and effective method of selecting the dominant identification parameters can significantly reduce the identification error of the dominant identification parameters. For example, in the two scenarios, compared with identifying the dynamic parameters in one step, the step-by-step identification makes X q , X q The error of parameters such as ' has been significantly reduced, and the dynamic parameter recognition results of the above running scenarios 1 and 2 do not change with the change of the upper limit of parameter optimization, and stable and accurate recognition results are obtained. Figure 8 ,as well as Figure 9 The operation of scenarios 1 and 2 shown further illustrates that the identification of dynamic parameters can be well adapted to the output power measurement.

[0109] In another specific scenario of an embodiment of the present application, a synchronous generator set of a power plant in a certain city in a certain province of Jiangsu is tested on the spot. The nameplate parameters of the test generator are: d =1.870pu、X q =1.820pu、X d '=0.2360pu、X q '=0.3890pu, X”=0.1760, T d0 '=8.600s、T d0 '=0.044s、T d0 ”=0.9560s、T q0 ”=0.0740s、T j =9.200s, X l =0.15. At this time, the actual operating unit of the power plant uses the disturbance scenario data extracted by the provincial dispatching system signal acquisition device, with the terminal voltage and excitation voltage as input, and the power and terminal current as output, to perform dynamic parameter identification and solution. At this time, the time domain relative trajectory sensitivity sum of each dynamic parameter in this scenario can also be calculated, such as Figure 10 The relative trajectory sensitivity of each parameter in the actual identification scene is shown as follows: Figure 11 The sensitivity of some parameters of the actual recognition scene in the time domain relative trajectory is shown, with 0.01 as the sensitivity threshold to determine X d , X q , X d ',X",T d0 ' is the dominant identification parameter for the actual operation disturbance scenario in the embodiment of the present application.

[0110] By comparing the upper and lower limits of different parameter optimization ranges of the measured unit based on the step-by-step dynamic parameter identification results of the embodiment of this application with the existing one-time overall identification method, it can be seen that, as shown in Table 1, the dynamic parameter identification results of the embodiment of this application can obtain more stable dynamic parameter identification results, and the values of the dynamic parameters are in line with engineering applications. In addition, the step-by-step dynamic parameter identification can also be applied to the verification scenario of the generator dynamic parameter identification, and further comparative verification is carried out through transient simulation, such as Figure 12 The measured active-reactive power curve in the non-identification scenario shown, and the deviation of the nameplate / identification parameter simulated active-reactive power compared with the measured data can be intuitively compared to conclude that the step-by-step dynamic parameter identification method in the embodiment of the present application determines that the average deviation between the transient simulation and measured power of the dynamic parameters is smaller.

[0111] Table 1 Step-by-step identification results of actual operating units

[0112]

[0113]

[0114] An embodiment of the present application provides a method for identifying the dynamic parameters of a synchronous generator. The embodiment of the present application distinguishes the dominant identification parameters and the non-dominant identification parameters through sensitivity of the dynamic parameters, thereby achieving the purpose of step-by-step identification, avoiding the problem that the identification results are sensitive to the upper and lower limits of the dynamic parameter optimization range when all dynamic parameters are identified at the same time, and overcoming the deteriorating effect of dynamic parameters with poor identifiability on the identification results of parameters with strong identifiability, thereby improving the identification accuracy and stability of the dynamic parameters.

[0115] Furthermore, as a response to the above Figure 1 The embodiment of the present application provides a device for identifying the dynamic parameters of a synchronous generator, such as Figure 13 As shown, the device includes:

[0116] an acquisition module 21 for acquiring excitation voltage data, generator-end voltage data, generator-end current data, active power, and reactive power of the synchronous generator, and solving the excitation voltage data, the generator-end voltage data, the generator-end current data, the active power, and the reactive power based on a constructed objective function to obtain a plurality of dynamic parameters;

[0117] a determination module 22 for calculating a sensitivity value of each of the dynamic parameters based on a chain function, and determining a dominant identification parameter and a non-dominant identification parameter from the dynamic parameters based on the sensitivity value;

[0118] The identification module 23 is configured to solve the non-dominant identification parameter based on the regularized objective function to obtain an identification result of the non-dominant identification parameter.

[0119] Furthermore, the device further comprises:

[0120] A construction module is used to construct input variables based on the excitation voltage variable and the terminal voltage variable, and to construct output variables based on the terminal voltage variable, the terminal current variable, the active power variable, and the reactive power variable; and to construct an objective function for solving the output variable using the input variables in a weighted least squares manner using multiple dynamic parameters as constraints;

[0121] Among them, the objective function is y represents the output variable of the synchronous generator, represents the measured value of the output variable, W represents the dynamic weight matrix of the dynamic parameter, t represents the time, and T f represents the length of the sampling time window, x represents the state variables of the synchronous generator, x0 represents the state variable set at the initial moment, u represents the measured value of the input variable, p represents the dynamic parameter set of the synchronous generator, f represents the differential equation group of the dynamic behavior of the synchronous motor, and h represents all the output algebraic equations of the dynamic system. Characterizes the upper limit of the value of the dynamic parameter, p Characterizes the lower limit of the value of the dynamic parameter.

[0122] Furthermore, the construction module is further configured to construct a penalty function based on the Sihonov regularization method, wherein the penalty function introduces the nameplate reference value of the non-dominant identification parameter and the nameplate reference weight; and add the penalty function to the objective function to obtain the regularized objective function.

[0123] Among them, the objective function after regularization is described represents the objective function, c represents the constraint condition set, v represents the variable after the algebraic-differential equation is discretized, and Ψ represents the penalty function obtained by regularization.

[0124] Furthermore, the determination module is specifically configured to determine a sensitivity calculation formula corresponding to the output variable, the dynamic parameter, and the corresponding dynamic weight based on a chain function, and calculate a sensitivity value of each dynamic parameter based on the sensitivity calculation formula;

[0125] Wherein, the sensitivity calculation formula is: The S R is the time domain sensitivity, S jTo obtain the frequency domain sensitivity after the average sum of squares, y is the output variable, p is the dynamic parameter, N is the number of moments after the sampling time window is discretized, and k is the moment.

[0126] Furthermore, the device further comprises:

[0127] The processing module is used to discretize the objective function according to an implicit trapezoidal integration method, so as to perform sensitivity calculation based on the discretized objective function.

[0128] Furthermore, the determination module is also used to determine that the dynamic parameter corresponding to the sensitivity value is the dominant identification parameter when the sensitivity value is greater than a preset sensitivity threshold, and determine the identification result of the dominant identification parameter; if the sensitivity value is less than or equal to the preset sensitivity threshold, determine that the dynamic parameter corresponding to the sensitivity value is a non-dominant identification parameter.

[0129] Furthermore, the device further comprises:

[0130] The monitoring module is used to perform dynamic monitoring based on the identification results of the dominant identification parameters and the non-dominant identification parameters to generate dynamic monitoring results when the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power and the reactive power trigger the preset monitoring trigger conditions.

[0131] An embodiment of the present application provides an identification device for dynamic parameters of a synchronous generator. Compared with the prior art, the embodiment of the present application obtains excitation voltage data, machine-end voltage data, machine-end current data, active power and reactive power of the synchronous generator, and solves the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power and the reactive power based on a constructed objective function to obtain multiple dynamic parameters; calculates the sensitivity value of each of the dynamic parameters based on a chain function, and determines the dominant identification parameter and the non-dominant identification parameter from the dynamic parameters based on the sensitivity value; solves the non-dominant identification parameter based on the regularized objective function to obtain an identification result of the non-dominant identification parameter, distinguishes the dominant identification parameter from the non-dominant identification parameter through sensitivity, achieves the purpose of step-by-step identification, avoids the problem that the identification result is sensitive to the upper and lower limits of the dynamic parameter optimization range when all dynamic parameters are identified at the same time, and overcomes the deterioration effect of dynamic parameters with poor identifiability on the identification result of parameters with strong identifiability, thereby improving the identification accuracy and stability of the dynamic parameters.

[0132] According to one embodiment of the present application, a storage medium is provided, wherein the storage medium stores at least one executable instruction. The computer-executable instruction can execute the method for identifying dynamic parameters of a synchronous generator in any of the above method embodiments.

[0133] Figure 14 A schematic diagram of the structure of a terminal provided according to an embodiment of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the terminal.

[0134] like Figure 14 As shown, the terminal may include: a processor (processor) 302 , a communication interface (Communications Interface) 304 , a memory (memory) 306 , and a communication bus 308 .

[0135] The processor 302 , the communication interface 304 , and the memory 306 communicate with each other via a communication bus 308 .

[0136] The communication interface 304 is used to communicate with other devices such as clients or other servers.

[0137] The processor 302 is configured to execute the program 310 , and specifically to execute the relevant steps in the embodiment of the method for identifying dynamic parameters of a synchronous generator.

[0138] Specifically, the program 310 may include program codes, which include computer operation instructions.

[0139] Processor 302 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the terminal may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0140] The memory 306 is used to store the program 310. The memory 306 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0141] The program 310 may be specifically configured to cause the processor 302 to perform the following operations:

[0142] Acquiring excitation voltage data, machine-end voltage data, machine-end current data, active power, and reactive power of the synchronous generator, and solving the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power, and the reactive power based on the constructed objective function to obtain a plurality of dynamic parameters;

[0143] calculating a sensitivity value of each of the dynamic parameters based on a chain function, and determining a dominant identification parameter and a non-dominant identification parameter from the dynamic parameters based on the sensitivity value;

[0144] The non-dominant identification parameter is solved based on the regularized objective function to obtain an identification result of the non-dominant identification parameter.

[0145] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices. Alternatively, they can be implemented using program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than herein, or they can be made into separate integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0146] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Persons skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for identifying dynamic parameters of a synchronous generator, characterized in that: include: Acquiring excitation voltage data, machine-end voltage data, machine-end current data, active power, and reactive power of the synchronous generator, and solving the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power, and the reactive power based on the constructed objective function to obtain a plurality of dynamic parameters; calculating a sensitivity value of each of the dynamic parameters based on a chain function, and determining a dominant identification parameter and a non-dominant identification parameter from the dynamic parameters based on the sensitivity value; The non-dominant identification parameter is solved based on the regularized objective function to obtain an identification result of the non-dominant identification parameter.

2. The method according to claim 1, characterized in that Before solving the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power, and the reactive power based on the constructed objective function to obtain a plurality of dynamic parameters, the method further includes: constructing input variables based on the excitation voltage variable and the machine-end voltage variable, and constructing output variables based on the machine-end voltage variable, the machine-end current variable, the active power variable, and the reactive power variable; Using multiple dynamic parameters as constraints, constructing an objective function that uses the input variables to solve the output variables in a weighted least squares manner; Among them, the objective function is y represents the output variable of the synchronous generator, represents the measured value of the output variable, W represents the dynamic weight matrix of the dynamic parameter, t represents the time, and T f represents the length of the sampling time window, x represents the state variables of the synchronous generator, x0 represents the state variable set at the initial moment, u represents the measured value of the input variable, p represents the dynamic parameter set of the synchronous generator, f represents the differential equation group of the dynamic behavior of the synchronous motor, and h represents all the output algebraic equations of the dynamic system. represents the upper limit of the value of the dynamic parameter, and p represents the lower limit of the value of the dynamic parameter.

3. The method according to claim 1, characterized in that Before solving the non-dominant identification parameter based on the regularized objective function to obtain an identification result of the non-dominant identification parameter, the method further includes: Constructing a penalty function based on the Sihonov regularization method, wherein the nameplate reference value of the non-dominant identification parameter and the nameplate reference weight are introduced into the penalty function; Adding the penalty function to the objective function to obtain the regularized objective function; Among them, the objective function after regularization is described represents the objective function, c represents the constraint condition set, v represents the variable after the algebraic-differential equation is discretized, and Ψ represents the penalty function obtained by regularization.

4. The method according to claim 2, characterized in that Calculating the sensitivity value of each dynamic parameter based on the chain function includes: Determine a sensitivity calculation formula corresponding to the output variable, the dynamic parameter, and the corresponding dynamic weight based on a chain function, and calculate a sensitivity value of each dynamic parameter based on the sensitivity calculation formula; Wherein, the sensitivity calculation formula is: The S R is the time domain sensitivity, S j To obtain the frequency domain sensitivity after the average sum of squares, y is the output variable, p is the dynamic parameter, N is the number of moments after the sampling time window is discretized, and k is the moment.

5. The method according to claim 2, characterized in that The method further comprises: The objective function is discretized according to an implicit trapezoidal integration method, so as to perform sensitivity calculation based on the discretized objective function.

6. The method according to claim 1, characterized in that Determining the dominant identification parameter and the non-dominant identification parameter from the dynamic parameter based on the sensitivity value includes: When the sensitivity value is greater than a preset sensitivity threshold, determining the dynamic parameter corresponding to the sensitivity value as a dominant identification parameter, and determining an identification result of the dominant identification parameter; If the sensitivity value is less than or equal to the preset sensitivity threshold, the dynamic parameter corresponding to the sensitivity value is determined to be a non-dominant identification parameter.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: When the excitation voltage data, the machine-end voltage data, the machine-end current data, the active power and the reactive power trigger the preset monitoring trigger conditions, dynamic monitoring is performed based on the identification results of the dominant identification parameters and the identification results of the non-dominant identification parameters to generate dynamic monitoring results.

8. A device for identifying dynamic parameters of a synchronous generator, characterized in that: include: an acquisition module, configured to acquire excitation voltage data, generator-end voltage data, generator-end current data, active power, and reactive power of the synchronous generator, and solve the excitation voltage data, the generator-end voltage data, the generator-end current data, the active power, and the reactive power based on a constructed objective function to obtain a plurality of dynamic parameters; a determination module, configured to calculate a sensitivity value of each of the dynamic parameters based on a chain function, and determine a dominant identification parameter and a non-dominant identification parameter from the dynamic parameters based on the sensitivity value; The identification module is used to solve the non-dominant identification parameter based on the regularized objective function to obtain an identification result of the non-dominant identification parameter.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to claim 1.

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