Identification method and system for static voltage analysis parameter model of electrolytic aluminum load

Abstract

Disclosed in the present invention are an identification method and system for a static voltage analysis parameter model of an electrolytic aluminum load. The method comprises: acquiring historical parameter data generated when an electrolytic aluminum load is connected to a power grid under the normal working condition, and selecting parameters to be identified; on the basis of said parameters, establishing a static voltage analysis model of the electrolytic aluminum load; on the basis of the static voltage analysis model of the electrolytic aluminum load in combination with a singular value decomposition method and a preset constraint condition, optimizing the static voltage analysis model of the electrolytic aluminum load, and acquiring a static voltage analysis model of the electrolytic aluminum load subjected to parameter identification; and using a BP neural network method to verify the static voltage analysis model of the electrolytic aluminum load subjected to parameter identification. A verification objective function is established on the basis of the difference value between data of two models and the actual measurement data, and a parameter model identification result of the electrolytic aluminum load is verified on the basis of the value of the verification objective function, so that the static voltage analysis model of the electrolytic aluminum load is accurately and efficiently established.

Description

A method and system for identifying parameter models of electrolytic aluminum load static voltage analysis Technical Field

[0001] The present invention relates to the technical field of parameter model identification of electrolytic aluminum loads, and in particular to a method and system for identifying a parameter model of static voltage analysis of an electrolytic aluminum load. Background Art

[0002] As electricity loads increase, power systems are operating closer and closer to their limits, increasing the risk of voltage instability. Numerous voltage instability incidents both domestically and internationally have caused significant economic losses and social impacts, drawing widespread attention to voltage stability issues among power system researchers. Research on the static voltage stability of power grids can provide dispatchers with information on vulnerable nodes and areas prone to voltage instability, as well as the grid's static voltage stability margin, enabling them to formulate control strategies to prevent voltage instability. Therefore, research on issues related to static voltage stability must be given high priority.

[0003] Currently, there is no unified standard for static voltage analysis models for aluminum electrolytic loads, and existing models are inaccurate. Consequently, a method for designing models tailored to different aluminum electrolytic loads is lacking. Furthermore, identification techniques can be used to modify theoretical models, making control algorithms more effective and avoiding poor simulation results. To develop a highly reliable static voltage analysis model for aluminum electrolytic loads, it is necessary to identify the various model parameters. However, there are currently no reports on model parameter identification using actual aluminum electrolytic load operating data, predictions from neural network prediction models, or field test data.

[0004] Summary of the Invention

[0005] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a method and system for identifying a static voltage analysis parameter model of an electrolytic aluminum load, which can solve the problems mentioned in the background technology.

[0008] To solve the above technical problems, the present invention provides the following technical solution: a method for identifying a static voltage analysis parameter model of an electrolytic aluminum load, comprising:

[0009] Obtain historical parameter data generated when the electrolytic aluminum load is connected to the power grid under normal operating conditions, and select the parameters to be identified;

[0010] Establishing a static voltage analysis model for electrolytic aluminum load based on the parameters to be identified;

[0011] According to the electrolytic aluminum load static voltage analysis model, combined with the singular value decomposition method and preset constraints, the electrolytic aluminum load static voltage analysis model is optimized to obtain the electrolytic aluminum load static voltage analysis model after parameter identification;

[0012] The BP neural network method is used to verify the static voltage analysis model of electrolytic aluminum load after parameter identification.

[0013] As a preferred solution of the electrolytic aluminum load static voltage analysis parameter model identification method of the present invention, wherein: the parameters to be identified are selected including:

[0014] The active power of the electrolytic aluminum load static voltage analysis model, the reactive power of the electrolytic aluminum load static voltage analysis model, the node voltage amplitude of the electrolytic aluminum load static voltage analysis model, the phase angle of the electrolytic aluminum load static voltage analysis model, the parameter matrix δ1 of the influence of the voltage phase angle on the active power of the electrolytic aluminum load static voltage analysis model, the parameter matrix δ2 of the influence of the voltage phase angle on the reactive power of the electrolytic aluminum load static voltage analysis model, the parameter matrix ε1 of the influence of amplitude change on the active power of the electrolytic aluminum load static voltage analysis model, and the parameter matrix ε2 of the influence of amplitude change on the reactive power of the electrolytic aluminum load static voltage analysis model.

[0015] As a preferred solution of the electrolytic aluminum load static voltage analysis parameter model identification method of the present invention, wherein: the electrolytic aluminum load static voltage analysis model includes:

[0016] If the static voltage is stable, then in the ideal state, the active power change rate and reactive power change rate in the static voltage analysis model of the electrolytic aluminum load are zero, that is, in the ideal state, there is: P (A m ,P h )=f Q (A m ,P h )=0

[0017] Under non-ideal conditions:

[0018] Among them, A m Indicates the voltage phase angle, P h Indicates the voltage amplitude, ΔA m Indicates the voltage phase angle change per unit time, ΔP hIndicates the voltage amplitude change per unit time, f P represents active power, f Q Indicates reactive power.

[0019] As a preferred solution of the electrolytic aluminum load static voltage analysis parameter model identification method of the present invention, wherein: the singular value decomposition method and the preset constraints include:

[0020] The singular value decomposition method includes:

[0021] Among them, S L is a singular combination matrix, A and B are any matrices that can form a singular combination matrix in this form, η i is the singular value corresponding to the matrix, a i is the left singular vector of the matrix, b i is the right singular vector of the matrix, n is the rank of the singular combination matrix when the power system is stable;

[0022] The preset constraints include: st:min||S L -S L0 ||2

[0023] Among them, S L0 is any singular combination matrix of rank k, k is the rank of the matrix, and k <n。

[0024] As a preferred solution of the electrolytic aluminum load static voltage analysis parameter model identification method of the present invention, wherein: the use of the BP neural network method to verify the electrolytic aluminum load static voltage analysis model after parameter identification includes:

[0025] According to the historical parameter data and parameters to be identified generated when the electrolytic aluminum load is connected to the power grid under normal working conditions, a data sample set is established.

[0026] Classifying the data sample set, selecting a training set and a test set, wherein the test set is at least 30% of the data sample set;

[0027] An electrolytic aluminum load parameter prediction model is trained, and the accuracy of the electrolytic aluminum load parameter prediction model is tested. When the accuracy reaches 95%, the training model is output.

[0028] As a preferred solution of the electrolytic aluminum load static voltage analysis parameter model identification method of the present invention, wherein: the verification of the electrolytic aluminum load static voltage analysis model after parameter identification using the BP neural network method also includes:

[0029] Design a verification objective function, the verification objective function is:

[0030] Among them, d is the parameter to be identified, α d is the weight when the parameter to be identified is d, C is the measured data, S is the simulation data of the electrolytic aluminum load static voltage analysis model after parameter identification, B p Predict data for the electrolytic aluminum load parameter prediction model.

[0031] As a preferred solution of the electrolytic aluminum load static voltage analysis parameter model identification method of the present invention, wherein: the verification of the electrolytic aluminum load static voltage analysis model after parameter identification using the BP neural network method also includes:

[0032] If the verification objective function value is less than 0.08, it is determined that the static voltage analysis model of the electrolytic aluminum load after parameter identification meets the design requirements;

[0033] If the verification objective function value is greater than 0.08 and less than 0.15, the electrolytic aluminum load parameter prediction model is retrained first, and the verification objective function value is recalculated using real-time data, and the size is re-judged until the verification objective function is greater than 0.15 or less than 0.08;

[0034] If the verification objective function value is greater than 0.15, it is determined that the static voltage analysis model of the electrolytic aluminum load does not meet the design requirements after parameter identification, and parameter identification and neural network training are performed again.

[0035] A static voltage analysis parameter model identification system for electrolytic aluminum loads, characterized by comprising an identification parameter acquisition module, a first model establishment module, a second model establishment module, and a verification module.

[0036] An identification parameter acquisition module is used to obtain historical parameter data generated when the electrolytic aluminum load is connected to the power grid under normal working conditions, and select parameters to be identified;

[0037] a first model building module, the first model building module being used to build an electrolytic aluminum load static voltage analysis model according to the parameters to be identified;

[0038] a second model building module, configured to optimize the electrolytic aluminum load static voltage analysis model based on the electrolytic aluminum load static voltage analysis model in combination with a singular value decomposition method and preset constraints, to obtain an electrolytic aluminum load static voltage analysis model after parameter identification;

[0039] A verification module is used to verify the static voltage analysis model of the electrolytic aluminum load after parameter identification using a BP neural network method.

[0040] A computer device includes a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the above method when executing the computer program.

[0041] A computer-readable storage medium stores a computer program thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0042] Beneficial effects of the present invention: The present invention proposes a method and system for identifying a static voltage analysis parameter model of an electrolytic aluminum load, which can use actual operation data of the electrolytic aluminum load, prediction data of a neural network prediction model and field test data to perform model parameter identification. By identifying the parameters of the static voltage analysis model of the electrolytic aluminum load, a static voltage analysis model of the electrolytic aluminum load under non-ideal conditions is obtained, and a training set and a test set are established based on historical data, an optimal prediction model is established, a verification objective function is established based on the difference between the two models and the actual measurement data, and the parameter model identification result of the electrolytic aluminum load is verified based on the value of the verification objective function, thereby accurately and efficiently realizing the establishment of the static voltage analysis model of the electrolytic aluminum load. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0044] FIG1 is a flow chart of a method and system method for identifying a static voltage analysis parameter model for an electrolytic aluminum load according to an embodiment of the present invention;

[0045] FIG2 is an internal structural diagram of a computer device of a method and system for identifying a static voltage analysis parameter model of an electrolytic aluminum load provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0049] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0050] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0051] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0052] Example 1

[0053] 1-2 , which is a first embodiment of the present invention, provides a method and system for identifying a static voltage analysis parameter model for an electrolytic aluminum load, including:

[0054] Obtain historical parameter data generated when the electrolytic aluminum load is connected to the power grid under normal operating conditions, and select the parameters to be identified;

[0055] Among them, the parameters to be identified include the active power of the electrolytic aluminum load static voltage analysis model, the reactive power of the electrolytic aluminum load static voltage analysis model, the node voltage amplitude of the electrolytic aluminum load static voltage analysis model, the phase angle of the electrolytic aluminum load static voltage analysis model, the parameter matrix δ1 of the influence of the voltage phase angle on the active power of the electrolytic aluminum load static voltage analysis model, the parameter matrix δ2 of the influence of the voltage phase angle on the reactive power of the electrolytic aluminum load static voltage analysis model, the parameter matrix ε1 of the influence of amplitude change on the active power of the electrolytic aluminum load static voltage analysis model, and the parameter matrix ε2 of the influence of amplitude change on the reactive power of the electrolytic aluminum load static voltage analysis model.

[0056] Furthermore, based on the parameters to be identified, a static voltage analysis model for electrolytic aluminum load is established;

[0057] In a preferred embodiment, the static voltage analysis model of the electrolytic aluminum load includes: if the static voltage is stable, then in an ideal state, the active power change rate and the reactive power change rate in the static voltage analysis model of the electrolytic aluminum load are zero, that is, in an ideal state, there is: f P (A m ,P h )=f Q (A m ,P h )=0

[0058] Furthermore, under non-ideal conditions:

[0059] Among them, A m Indicates the voltage phase angle, P h Indicates the voltage amplitude, ΔA m Indicates the voltage phase angle change per unit time, ΔP h Indicates the voltage amplitude change per unit time, f P represents active power, f Q Indicates reactive power.

[0060] Furthermore, based on the static voltage analysis model of the electrolytic aluminum load, the singular value decomposition method and the preset constraints are combined to optimize the static voltage analysis model of the electrolytic aluminum load to obtain the static voltage analysis model of the electrolytic aluminum load after parameter identification;

[0061] In a preferred embodiment, the singular value decomposition method includes:

[0062] Among them, S L is a singular combination matrix, A and B are any matrices that can form a singular combination matrix in this form, η i is the singular value corresponding to the matrix, a i is the left singular vector of the matrix, bi is the right singular vector of the matrix, n is the rank of the singular combination matrix when the power system is stable;

[0063] Furthermore, the preset constraints include: st:min||S L -S L0 ||2

[0064] Among them, S L0 is any singular combination matrix of rank k, k is the rank of the matrix, and k <n。

[0065] Furthermore, the BP neural network method is used to verify the static voltage analysis model of electrolytic aluminum load after parameter identification.

[0066] Furthermore, a data sample set is established based on the historical parameter data and the parameters to be identified when the electrolytic aluminum load is connected to the power grid under normal working conditions.

[0067] Furthermore, the data sample set is classified and a training set and a test set are selected, and the test set is at least 30% of the data sample set;

[0068] Furthermore, the electrolytic aluminum load parameter prediction model is trained and the accuracy of the electrolytic aluminum load parameter prediction model is tested. When the accuracy value reaches 95%, the training model is output.

[0069] Furthermore, the BP neural network method is used to verify the static voltage analysis model of electrolytic aluminum load after parameter identification, and also includes:

[0070] In a preferred embodiment, a verification objective function is designed and verified as follows:

[0071] Among them, d is the parameter to be identified, α d is the weight when the parameter to be identified is d, C is the measured data, S is the simulation data of the electrolytic aluminum load static voltage analysis model after parameter identification, B p Predict data for the electrolytic aluminum load parameter prediction model.

[0072] It should be noted that if the verification objective function value is less than 0.08, it is determined that the static voltage analysis model of the electrolytic aluminum load meets the design requirements after parameter identification;

[0073] It should be noted that if the verification objective function value is greater than 0.08 and less than 0.15, the electrolytic aluminum load parameter prediction model should be retrained first, and the verification objective function value should be recalculated using real-time data, and the size should be re-judged until the verification objective function is greater than 0.15 or less than 0.08;

[0074] It should be noted that if the verification objective function value is greater than 0.15, it is determined that the static voltage analysis model of the electrolytic aluminum load does not meet the design requirements after parameter identification, and parameter identification and neural network training are performed again.

[0075] In a preferred embodiment, a static voltage analysis parameter model identification system for electrolytic aluminum load includes an identification parameter acquisition module, a first model establishment module, a second model establishment module, and a verification module.

[0076] Identification parameter acquisition module, which is used to obtain historical parameter data generated when the electrolytic aluminum load is connected to the power grid under normal working conditions and select parameters to be identified;

[0077] A first model building module, the first model building module is used to establish an electrolytic aluminum load static voltage analysis model according to the parameters to be identified;

[0078] The second model building module is used to optimize the electrolytic aluminum load static voltage analysis model based on the electrolytic aluminum load static voltage analysis model, combined with the singular value decomposition method and preset constraints, to obtain the electrolytic aluminum load static voltage analysis model after parameter identification;

[0079] Verification module, the verification module is used to verify the electrolytic aluminum load static voltage analysis model after parameter identification using the BP neural network method.

[0080] The above-mentioned unit modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0081] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure may be as shown in FIG2 . The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication. The wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for identifying parameter models for static voltage analysis of electrolytic aluminum loads. The display screen of the computer device may be a liquid crystal display or an electronic ink display. The input device of the computer device may be a touch screen covering the display screen, or may be buttons, a trackball, or a touchpad provided on the computer device housing, or may be an external keyboard, touchpad, or mouse.

[0082] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0083] Obtain historical parameter data generated when the electrolytic aluminum load is connected to the power grid under normal operating conditions, and select the parameters to be identified;

[0084] According to the parameters to be identified, a static voltage analysis model for electrolytic aluminum load is established;

[0085] According to the electrolytic aluminum load static voltage analysis model, combined with the singular value decomposition method and preset constraints, the electrolytic aluminum load static voltage analysis model is optimized to obtain the electrolytic aluminum load static voltage analysis model after parameter identification;

[0086] The BP neural network method is used to verify the static voltage analysis model of electrolytic aluminum load after parameter identification.

[0087] Example 2

[0088] 1-2 , which is an embodiment of the present invention, provides a method and system for identifying a parameter model for static voltage analysis of an electrolytic aluminum load. In order to verify the beneficial effects of the present invention, a comparative experiment is conducted to conduct scientific demonstration.

[0089] Table 1 Accuracy verification of the static voltage analysis parameter model identification method using electrolytic aluminum load

[0090] As shown in Table 1, except for two cases where the neural network prediction model was inaccurate and the neural network needed to be retrained to improve the accuracy requirements of the neural network, the remaining six tests can well demonstrate the effectiveness of the parameter model identification method for static voltage analysis of electrolytic aluminum load.

[0091] The present invention proposes a method and system for identifying a static voltage analysis parameter model of an electrolytic aluminum load, which can use actual operation data of the electrolytic aluminum load, prediction data of a neural network prediction model and field test data to perform model parameter identification. By identifying the parameters of the static voltage analysis model of the electrolytic aluminum load, a static voltage analysis model of the electrolytic aluminum load under a non-ideal state is obtained, and a training set and a test set are established based on historical data to establish an optimal prediction model. A verification objective function is established based on the difference between the two models and the actual measurement data. The parameter model identification result of the electrolytic aluminum load is verified based on the value of the verification objective function, thereby accurately and efficiently realizing the establishment of the static voltage analysis model of the electrolytic aluminum load.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0093] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0094] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0095] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0097] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0098] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for identifying a static voltage analysis parameter model for an electrolytic aluminum load, characterized in that: include, Obtain historical parameter data generated when the electrolytic aluminum load is connected to the power grid under normal working conditions, and select the parameters to be identified; According to the parameters to be identified, a static voltage analysis model of electrolytic aluminum load is established; According to the electrolytic aluminum load static voltage analysis model, in combination with the singular value decomposition method and preset constraints, the electrolytic aluminum load static voltage analysis model is optimized to obtain the electrolytic aluminum load static voltage analysis model after parameter identification; The BP neural network method is used to verify the static voltage analysis model of electrolytic aluminum load after parameter identification.

2. The electrolytic aluminum load static voltage analysis parameter model identification method according to claim 1, characterized in that: The selection of parameters to be identified includes: The active power of the electrolytic aluminum load static voltage analysis model, the reactive power of the electrolytic aluminum load static voltage analysis model, the node voltage amplitude of the electrolytic aluminum load static voltage analysis model, the phase angle of the electrolytic aluminum load static voltage analysis model, the parameter matrix δ1 of the influence of the voltage phase angle on the active power of the electrolytic aluminum load static voltage analysis model, the parameter matrix δ2 of the influence of the voltage phase angle on the reactive power of the electrolytic aluminum load static voltage analysis model, the parameter matrix ε1 of the influence of amplitude change on the active power of the electrolytic aluminum load static voltage analysis model, and the parameter matrix ε2 of the influence of amplitude change on the reactive power of the electrolytic aluminum load static voltage analysis model.

3. The electrolytic aluminum load static voltage analysis parameter model identification method according to claim 2, characterized in that: The electrolytic aluminum load static voltage analysis model includes: If the static voltage is stable, then in the ideal state, the active power change rate and reactive power change rate in the static voltage analysis model of the electrolytic aluminum load are zero, that is, in the ideal state: f P (A m ,P h )=f Q (A m ,P h )=0 Under non-ideal conditions: Among them, A m Represents the voltage phase angle, P h Indicates the voltage amplitude, ΔA m Indicates the voltage phase angle change per unit time, ΔP h Indicates the change in voltage amplitude per unit time, f P represents active power, f Q Indicates reactive power.

4. The electrolytic aluminum load static voltage analysis parameter model identification method according to claim 3, characterized in that: The singular value decomposition method and the preset constraints include: The singular value decomposition method includes: Among them, S L is a singular combination matrix, A and B are any matrices that can form a singular combination matrix in this form, η i is the singular value corresponding to the matrix, a i is the left singular vector of the matrix, b i is the right singular vector of the matrix, n is the rank of the singular combination matrix when the power system is stable; The preset constraints include: s.t.:min||S L -S L0 ||2 Among them, S L0 is any singular combination matrix of rank k, k is the rank of the matrix, and k <n。 5. The electrolytic aluminum load static voltage analysis parameter model identification method according to claim 4, characterized in that: The verification of the static voltage analysis model of electrolytic aluminum load after parameter identification using the BP neural network method includes: According to the historical parameter data and the parameters to be identified generated when the electrolytic aluminum load is connected to the power grid under normal working conditions, a data sample set is established. Classifying the data sample set, selecting a training set and a test set, wherein the test set is at least 30% of the data sample set; The electrolytic aluminum load parameter prediction model is trained, and the accuracy of the electrolytic aluminum load parameter prediction model is tested. When the accuracy value reaches 95%, the training model is output.

6. The electrolytic aluminum load static voltage analysis parameter model identification method according to claim 5, characterized in that: The verification of the static voltage analysis model of electrolytic aluminum load after parameter identification by using the BP neural network method also includes: Design a verification objective function, the verification objective function is: Among them, d is the parameter to be identified, α d is the weight when the parameter to be identified is d, C is the measured data, S is the simulation data of the static voltage analysis model of electrolytic aluminum load after parameter identification, B p Predict data for the electrolytic aluminum load parameter prediction model.

7. The electrolytic aluminum load static voltage analysis parameter model identification method according to claim 6, characterized in that: The verification of the static voltage analysis model of electrolytic aluminum load after parameter identification by using the BP neural network method also includes: If the verification objective function value is less than 0.08, it is determined that the static voltage analysis model of the electrolytic aluminum load meets the design requirements after parameter identification; If the verification objective function value is greater than 0.08 and less than 0.15, the electrolytic aluminum load parameter prediction model is retrained first, and the verification objective function value is recalculated using real-time data, and the size is re-judged until the verification objective function is greater than 0.15 or less than 0.08; If the verification objective function value is greater than 0.15, it is determined that the static voltage analysis model of the electrolytic aluminum load after parameter identification does not meet the design requirements, and parameter identification and neural network training are performed again.

8. An electrolytic aluminum load static voltage analysis parameter model identification system, characterized by: It includes an identification parameter acquisition module, a first model building module, a second model building module and a verification module, An identification parameter acquisition module, which is used to acquire historical parameter data generated when the electrolytic aluminum load is connected to the power grid under normal working conditions, and select parameters to be identified; A first model building module, the first model building module is used to build an electrolytic aluminum load static voltage analysis model according to the parameters to be identified; A second model building module, the second model building module is used to optimize the electrolytic aluminum load static voltage analysis model according to the electrolytic aluminum load static voltage analysis model, combined with a singular value decomposition method and preset constraints, to obtain the electrolytic aluminum load static voltage analysis model after parameter identification; A verification module is used to verify the static voltage analysis model of the electrolytic aluminum load after parameter identification using a BP neural network method.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images