A transformer bushing fault identification method, device, equipment and medium

Abstract

The embodiment of the application discloses a transformer bushing fault identification method, device, equipment and medium, and relates to the technical field of transformers. The method comprises the following steps: constructing a target kernel function based on an original kernel function and a target symmetric positive semi-definite matrix; mapping a plurality of complete bushing data and missing bushing data to a reproducing kernel Hilbert space based on the target kernel function, and completing the missing values according to the similarity between the missing values and other data points to obtain complete data of a transformer bushing to be detected; and identifying the fault of the transformer bushing to be detected based on the complete data to obtain a fault identification result. The technical scheme can map the missing bushing data to the reproducing kernel Hilbert space through the matrix capable of reflecting the characteristics of the transformer bushing, and then complete the data in the reproducing kernel Hilbert space and identify the fault based on the complete data, so that the fault identification result of the transformer bushing to be detected can be accurately determined.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transformers, and in particular to a transformer bushing fault identification method, device, equipment and medium. BACKGROUND

[0002] At present, when identifying the fault of a transformer bushing, one method is to collect sound during the use of the bushing and identify the fault of the bushing according to the sound information. Another method is to identify the fault through an artificial intelligence model.

[0003] However, both the technical solution based on sound collection and the technical solution based on artificial intelligence rely on complete and large amounts of data, and cannot identify the fault of a transformer bushing when there is missing data. SUMMARY

[0004] The present application provides a transformer bushing fault identification method, device, equipment and medium, which can complete the bushing data and accurately identify the fault of a transformer bushing based on the completed data.

[0005] According to an aspect of the present application, a transformer bushing fault identification method is provided, which comprises:

[0006] constructing a target kernel function based on an original kernel function and a target symmetric positive semi-definite matrix; the target symmetric positive semi-definite matrix is a matrix that can reflect the characteristics of a transformer bushing

[0007] mapping a plurality of complete bushing data and missing bushing data to a reproducing kernel Hilbert space based on the target kernel function, and completing the missing values according to the similarity of the missing values and other data points to obtain completed data of a transformer bushing to be detected; the missing bushing data is the data of the transformer bushing to be detected, and the amount of data in the missing bushing data is less than the amount of data in the complete bushing data;

[0008] identifying the fault of the transformer bushing to be detected based on the completed data to obtain a fault identification result.

[0009] According to another aspect of the present application, a transformer bushing fault identification device is provided, which comprises:

[0010] a target kernel function construction module configured to construct a target kernel function based on an original kernel function and a target symmetric positive semi-definite matrix; the target symmetric positive semi-definite matrix is a matrix that can reflect the characteristics of a transformer bushing

[0011] The missing data determination module is configured to map the complete bushing data and the missing bushing data to a reproducing kernel Hilbert space based on a target kernel function, and determine the missing values based on the similarity of the missing values and other data points to obtain the completed data of the bushing to be detected of the transformer; the missing bushing data is the data of the bushing to be detected of the transformer, and the amount of data in the missing bushing data is less than the amount of data in the complete bushing data;

[0012] The fault identification module is configured to identify the fault of the bushing to be detected of the transformer based on the completed data to obtain a fault identification result.

[0013] According to another aspect of the present application, an electronic device is provided, which comprises:

[0014] at least one processor; and

[0015] a memory connected to the at least one processor in communication; wherein

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the fault identification method of the transformer bushing according to any one of the embodiments of the present application.

[0017] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the fault identification method of the transformer bushing according to any one of the embodiments of the present application when executed.

[0018] The technical scheme of the embodiments of the present application comprises: constructing a target kernel function based on an original kernel function and a target symmetric positive semi-definite matrix; mapping the complete bushing data and the missing bushing data to a reproducing kernel Hilbert space based on the target kernel function, and determining the missing values based on the similarity of the missing values and other data points to obtain the completed data of the bushing to be detected of the transformer; the missing bushing data is the data of the bushing to be detected of the transformer, and the amount of data in the missing bushing data is less than the amount of data in the complete bushing data; identifying the fault of the bushing to be detected of the transformer based on the completed data to obtain a fault identification result. The technical scheme can map the missing bushing data in the reproducing kernel Hilbert space through the matrix capable of reflecting the characteristics of the transformer bushing, and then determine the data points similar to the missing values in the reproducing kernel Hilbert space. After data completion, the fault identification is performed based on the completed data, and the fault identification result of the bushing to be detected of the transformer can be accurately determined.

[0019] It is to be understood that the details set forth in the description contained herein do not limit the scope of the embodiments of the application. Other embodiments of the application will be readily apparent to one of ordinary skill in the art from the description herein, including the working examples. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0021] Figure 1 is a flow chart of a transformer bushing fault identification method according to the transformer bushing fault identification method provided in the first embodiment of the present application;

[0022] Figure 2 is a flow chart of a transformer bushing fault identification method according to the transformer bushing fault identification method provided in the second embodiment of the present application;

[0023] Figure 3 is a structural schematic diagram of a transformer bushing fault identification device according to the transformer bushing fault identification device provided in the third embodiment of the present application;

[0024] Figure 4 is a structural schematic diagram of an electronic device for implementing a transformer bushing fault identification method according to the transformer bushing fault identification method provided in the third embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the technical personnel in the art better understand the present application scheme, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] Figure 1 This application provides a flowchart of a method for fault identification of transformer bushings according to Embodiment 1. This embodiment is applicable to situations involving fault identification of transformer bushings. The method can be executed by a fault identification device for the transformer bushings, which can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0029] S110, constructing the target kernel function based on the original kernel function and the target symmetric positive semi-definite matrix.

[0030] The original kernel function can be selected based on the data type of the transformer bushing. Original kernel functions include, but are not limited to, linear kernel functions, polynomial kernel functions, radial basis function kernel functions, and Laplace kernel functions. The target symmetric positive semi-definite matrix can be obtained by iterating over the initial symmetric positive semi-definite matrix. The target symmetric positive semi-definite matrix is ​​a matrix that reflects the characteristics of the transformer bushing.

[0031] Specifically, after determining the original kernel function and the target symmetric positive semi-definite matrix, the target kernel function is constructed based on the original kernel function and the target symmetric positive semi-definite matrix, so that the target kernel function is symmetric positive definite.

[0032] S120: Based on the target kernel function, multiple complete bushing data and missing bushing data are mapped to the regenerative kernel Hilbert space, and the missing values ​​are filled in according to the similarity between the missing values ​​and other data points to obtain the filled data of the bushings to be tested of the transformer.

[0033] The complete bushing data can include: factory manufacturer, voltage grade, dielectric loss factor, bushing thickness, dielectric constant, and no-load current. One transformer device corresponds to one set of complete bushing data, and multiple complete bushing data correspond to multiple sets of complete bushing data of multiple transformer devices.

[0034] The missing bushing data is the data of the bushing to be detected of the transformer, and the amount of data in the missing bushing data is less than the amount of data in the complete bushing data. For example, the complete bushing data includes: factory manufacturer, voltage grade, dielectric loss factor, bushing thickness, dielectric constant, and no-load current; the missing bushing data includes: factory manufacturer, voltage grade, dielectric loss factor, dielectric constant, and no-load current; in this case, the bushing thickness is a missing value.

[0035] Specifically, after the multiple complete bushing data and the missing bushing data are mapped to the reproducing kernel Hilbert space based on the target kernel function, the similarity between the transformer devices can be determined in the reproducing kernel Hilbert space. The similarity reflects the similarity of the missing value and other data points (i.e., data in the complete bushing data). The missing value can be completed according to the data similar to the missing value, and then the completed missing value and the missing bushing data are determined as the completed data of the bushing to be detected of the transformer.

[0036] S130, based on the completed data, identifying the fault of the bushing to be detected of the transformer to obtain a fault identification result.

[0037] Specifically, after obtaining the completed data, the completed data can be processed based on a pre-trained fault identification model to obtain a fault identification result. In one case, the fault identification result contains two cases: fault and no fault. In another case, if the bushing to be detected of the transformer has a fault, the fault type is also included in the fault identification result. It is obvious that the fault identification model can be selected according to the actual situation, and the embodiments of the present application do not limit this.

[0038] In the embodiments of the present application, based on the completed data, the fault of the bushing to be detected of the transformer is identified to obtain a fault identification result, which includes: inputting the completed data and normal transformer bushing data into a pre-trained double-tower model; if the output result of the double-tower model is greater than a similarity threshold, it is determined that the bushing to be detected of the transformer has no fault; otherwise, it is determined that the bushing to be detected of the transformer has a fault.

[0039] Specifically, the pre-trained double-tower model is used to identify the similarity between the completed data and the normal transformer bushing data. If the output result of the double-tower model is greater than a similarity threshold, the bushing to be detected is similar to the normal bushing, and it can be determined that the bushing to be detected of the transformer has no fault; otherwise, it is determined that the bushing to be detected of the transformer has a fault.

[0040] By means of the double-tower model, it can be quickly determined whether the to-be-detected bushing is similar to the normal transformer bushing, and whether the to-be-detected bushing has a fault.

[0041] In the embodiment of the application, the to-be-detected bushing of the transformer is subjected to fault identification based on the completed data, to obtain a fault identification result, including: inputting the completed data and the fault transformer bushing data into a pre-trained double-tower model; if the output result of the double-tower model is greater than a similarity threshold, determining that the fault type of the to-be-detected bushing of the transformer is a target fault type; the target fault type is the fault type reflected by the fault transformer bushing data; otherwise, determining that the to-be-detected bushing of the transformer does not have the target fault.

[0042] Specifically, the completed data and the fault transformer bushing data can be input into the double-tower model multiple times, and the fault type of the fault transformer bushing data input each time is different, so that the fault type of the to-be-detected bushing of the transformer can be confirmed successively according to the output result of the double-tower model.

[0043] For example, in the training process of the double-tower model, all transformer bushing feature information is sent into the double-tower model, and one feature information is input into the left tower and the right tower respectively. If both of the two transformer bushings are in a normal state, the output value after the cosine function should be close to 1. If the left tower inputs normal transformer bushing data and the right tower inputs abnormal transformer bushing data, the output value after the cosine function should be close to 0. If the left tower inputs heat dissipation abnormal data and the right tower inputs insulation abnormal data, the output value after the cosine function should be close to 0 because the abnormal situations are different. The double-tower model is trained according to the above situations, and finally a trained model is obtained, which is used for subsequent rapid detection of abnormal equipment.

[0044] The technical scheme of the embodiment of the application includes: constructing a target kernel function based on an original kernel function and a target symmetric positive semi-definite matrix; mapping multiple complete bushing data and missing bushing data to a reproducing kernel Hilbert space based on the target kernel function, and completing the missing values according to the similarity of the missing values and other data points, to obtain completed data of a to-be-detected bushing of a transformer; the missing bushing data is data of the to-be-detected bushing of the transformer, and the amount of data in the missing bushing data is less than the amount of data in the complete bushing data; and performing fault identification on the to-be-detected bushing of the transformer based on the completed data, to obtain a fault identification result. The technical scheme can map the missing bushing data in the reproducing kernel Hilbert space through the matrix that can reflect the characteristics of the transformer bushing, and then determine the data points similar to the missing values in the reproducing kernel Hilbert space. After data completion, fault identification is performed based on the completed data, so that the fault identification result of the to-be-detected bushing of the transformer can be accurately determined.

[0045] Embodiment Two

[0046] Figure 2 A flow chart of a transformer bushing fault identification method provided for Embodiment Two of the present application is based on the above-mentioned embodiments and is optimized.

[0047] As shown in Figure 2 , the method of the present embodiment specifically includes the following steps:

[0048] S210, constructing a target kernel function based on an original kernel function and a target symmetric semi-definite matrix.

[0049] The target symmetric semi-definite matrix is a matrix capable of reflecting the characteristics of the transformer bushing.

[0050] In the present embodiment, the target kernel function is constructed based on the original kernel function and the target symmetric semi-definite matrix, which includes determining the target kernel function according to the following formula:

[0051]

[0052] wherein, is the target kernel function, k b (x,y) is the original kernel function, η is a constant greater than 0, is the transpose matrix of , x is the source domain data set, β(,) is a binary function, x1 is the data in the source domain data set, M is the target symmetric semi-definite matrix, y is the target domain data set.

[0053] For example, M ∈ R H×H , i.e., all the values in the target symmetric semi-definite matrix are real numbers, H is the number of data in the data set, the source domain data set is the complete bushing data, and the target domain data set is the missing bushing data. β(,) can be a kernel function.

[0054] Further, the positive definiteness of is proved below. For any finite data z1,…,z N , there is

[0055]

[0056] Here

[0057]

[0058] Because k b is a kernel function, K b is a symmetric positive definite matrix, and because M is a symmetric semi-definite matrix, therefore ΦT MΦ∈R N×N is also a symmetric positive semi-definite matrix, then is a symmetric positive definite matrix.

[0059] Further, the mean difference between the source domain data and the target domain data can be calculated to determine a target function used by the target symmetric positive semi-definite matrix according to the mean difference:

[0060] Since is symmetric positive definite, then is a kernel function that can be used to generate a RKHS, and is the reproducing kernel of this RKHS (for a RKHS, the reproducing kernel is unique).

[0061] The data without missing values of the same problem is regarded as the source domain data, and the data with missing values of the same problem is regarded as the target domain data.

[0062] The source domain data X s and the target domain data X t are transformed into the RKHS, and become and The maximum mean difference between them is:

[0063]

[0064] The mean difference can be expressed as:

[0065]

[0066] Here

[0067]

[0068]

[0069]

[0070]

[0071] Further, we use the sample-dependent and learnable kernel function proposed by us, then

[0072]

[0073]

[0074] Finally, we have

[0075]

[0076]

[0077] Here

[0078]

[0079] Thus, the maximum mean difference is converted to:

[0080]

[0081] In the embodiments of the present application, optionally, the determination process of the target symmetric positive semi-definite matrix comprises: iteratively optimizing an initial symmetric positive semi-definite matrix based on the Riemann flow, wherein in the iteration process, the objective function is the mean difference between the source domain data and the target domain data, and the iteration target is that the objective function is as small as possible; and if it is determined that the iteration has ended, the finally obtained matrix is determined as the target symmetric positive semi-definite matrix.

[0082] In the embodiments of the present application, optionally, the objective function is:

[0083]

[0084] wherein, Ns is the number of source domain data points, Nt is the number of target domain data points, Φ is the mapping value of the source domain data or the target domain data in the reproducing kernel Hilbert space, M is a symmetric positive semi-definite matrix, and μ is a constant.

[0085] For example, the objective function is: Γ T Φ T MΦΓ+μM 2 , and the iteration target is that the objective function is as small as possible. The constraint is: M T =M and M is greater than or equal to 0.

[0086] In this way, the target kernel function can reflect the characteristic information of the transformer bushing, so that the similarity can be determined more accurately in the reproducing kernel Hilbert space.

[0087] In S220, the plurality of complete bushing data and the missing bushing data are mapped to the reproducing kernel Hilbert space based on the target kernel function, and a first number of data points satisfying the similarity condition with the missing value are determined in the complete bushing data.

[0088] For example, the first number can be 10. Specifically, in the similarity of each data point and the missing value, the data points located in the top 10 in the descending order of the similarity are selected, and the missing value in the completed data is determined according to the average value of the top 10 data points.

[0089] In S230, the average value of the first number of data points is determined as the missing value in the completed data.

[0090] S240, performing fault identification on the to-be-detected bushing of the transformer based on the completed data, to obtain a fault identification result.

[0091] The technical scheme of the embodiment of the application determines the objective function through the maximum mean difference, and then iteratively optimizes the initial symmetric positive semi-definite matrix based on the Riemann flow, the iteration target being that the objective function is as small as possible, so that the objective symmetric positive semi-definite matrix is obtained, and the objective kernel function is determined. When calculating the similarity in the reproducing kernel Hilbert space, the objective kernel function reflects the characteristics of the transformer bushing, so that the similarity calculation is more accurate, and the subsequent data completion process can be completed based on better data points according to the similarity, and finally a more accurate fault identification result is obtained.

[0092] Embodiment Three

[0093] Figure 3 A structural schematic diagram of a transformer bushing fault identification device provided by Embodiment Three of the application is provided, which can execute the transformer bushing fault identification method provided by any embodiment of the application, and has the corresponding function modules and beneficial effects of the execution method. As shown in the figure, the device comprises: Figure 3

[0094] The target kernel function construction module 310 is configured to construct a target kernel function based on an original kernel function and a target symmetric positive semi-definite matrix; the target symmetric positive semi-definite matrix is a matrix capable of reflecting the characteristics of the transformer bushing

[0095] The completed data determination module 320 is configured to map a plurality of complete bushing data and missing bushing data to a reproducing kernel Hilbert space based on the target kernel function, and complete the missing values according to the similarity of the missing values and other data points, to obtain completed data of the to-be-detected bushing of the transformer; the missing bushing data is the data of the to-be-detected bushing of the transformer, and the amount of data in the missing bushing data is less than the amount of data in the complete bushing data.

[0096] The fault identification module 330 is configured to perform fault identification on the to-be-detected bushing of the transformer based on the completed data, to obtain a fault identification result.

[0097] ​The technical solution of this application includes: a target kernel function construction module 310, used to construct a target kernel function based on the original kernel function and a target symmetric positive semi-definite matrix; the target symmetric positive semi-definite matrix is ​​a matrix that can reflect the characteristics of the transformer bushing; a data completion determination module 320, used to map multiple complete bushing data and missing bushing data to a regenerative kernel Hilbert space based on the target kernel function, and to complete the missing values ​​according to the similarity between the missing values ​​and other data points, to obtain the completed data of the bushing to be tested of the transformer; the missing bushing data is the data of the bushing to be tested of the transformer, and the amount of data in the missing bushing data is less than the amount of data in the complete bushing data; a fault identification module 330, used to perform fault identification on the bushing to be tested of the transformer based on the completed data, to obtain the fault identification result. This technical solution maps the missing bushing data to a regenerative kernel Hilbert space using a matrix that can reflect the characteristics of the transformer bushing, and then determines data points similar to the missing values ​​in the regenerative kernel Hilbert space. After data completion, fault identification is performed based on the completed data, which can accurately determine the fault identification result of the bushing to be tested of the transformer.

[0098] Optionally, the target kernel function construction module 310 includes:

[0099] The target kernel function determination unit is used to determine the target kernel function according to the following formula:

[0100]

[0101] in, Let k be the target kernel function. b (x,y) is the original kernel function, and η is a constant greater than 0. for The transpose of the matrix, x is the source domain dataset, β(,) is a bivariate function, x1 is the data in the source domain dataset, and M is the target symmetric positive semi-definite matrix. y represents the target domain dataset.

[0102] Optionally, the apparatus further includes: a target symmetric positive semi-definite matrix determination module, comprising:

[0103] The objective function determination unit is used to iteratively optimize the initial symmetric positive semidefinite matrix based on the Riemannian manifold. During the iteration process, the objective function is the difference between the mean values ​​of the source domain data and the target domain data, and the iteration objective is to minimize the objective function.

[0104] The target symmetric positive semi-definite matrix determination unit is used to determine the final matrix as the target symmetric positive semi-definite matrix if the determination iteration has ended.

[0105] Optionally, the objective function is:

[0106]

[0107] wherein, Ns is the number of source domain data points, Nt is the number of target domain data points, Φ is the mapping value of the source domain data or the target domain data in the reproducing kernel Hilbert space, M is a symmetric semi-definite matrix, and μ is a constant.

[0108] Optionally, the complement data determination module 320 comprises:

[0109] a first number of data point determination unit, configured to determine, in the complete bushing data, a first number of data points satisfying a similarity condition with the missing value;

[0110] a complement data determination unit, configured to determine the average value of the first number of data points as the missing value in the complement data.

[0111] Optionally, the fault identification module 330 is specifically configured to:

[0112] input the complement data and the normal transformer bushing data into a pre-trained double-tower model;

[0113] if the output result of the double-tower model is greater than a similarity threshold value, it is determined that the to-be-detected bushing of the transformer has no fault;

[0114] otherwise, it is determined that the to-be-detected bushing of the transformer has a fault.

[0115] Optionally, the fault identification module 330 is specifically configured to:

[0116] input the complement data and the fault transformer bushing data into a pre-trained double-tower model;

[0117] if the output result of the double-tower model is greater than a similarity threshold value, it is determined that the fault type of the to-be-detected bushing of the transformer is a target fault type; the target fault type is the fault type reflected by the fault transformer bushing data;

[0118] otherwise, it is determined that the to-be-detected bushing of the transformer has no target fault.

[0119] The fault identification device for transformer bushing provided in the embodiments of the present application can execute the fault identification method for transformer bushing provided in any embodiment of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0120] Embodiment four

[0121] Figure 4A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0122] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0123] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0124] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the fault identification method for transformer bushings.

[0125] In some embodiments, the transformer bushing fault identification method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the above-described transformer bushing fault identification method can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the transformer bushing fault identification method by other any suitable means, e.g., by way of firmware.

[0126] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0127] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as part of a standalone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0128] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0129] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0130] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0131] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0132] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0133] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of fault identification of a transformer bushing, characterized in that, The method comprises the following steps: constructing a target kernel function based on an original kernel function and a target symmetric positive semi-definite matrix; the target symmetric positive semi-definite matrix is a matrix capable of reflecting the characteristics of the transformer bushing; mapping a plurality of complete bushing data and missing bushing data to a reproducing kernel Hilbert space based on the target kernel function, and completing the missing values according to the similarity of the missing values and other data points to obtain complete data of the transformer bushing to be detected; the missing bushing data is the data of the transformer bushing to be detected, and the amount of data in the missing bushing data is less than that in the complete bushing data; based on the complete data, identifying the fault of the transformer bushing to be detected to obtain a fault identification result; wherein, constructing a target kernel function based on an original kernel function and a target symmetric positive semi-definite matrix comprises: the target kernel function is determined according to the following formula: ； wherein, is a target kernel function, is a raw kernel function, is a constant greater than 0, is a transpose matrix of , , is a source domain dataset, is a binary function, is data in the source domain dataset, is a target symmetric positive semi-definite matrix, , y is a target domain dataset, and H is a number of data in the dataset. wherein, the determination process of the target symmetric positive semi-definite matrix comprises: iteratively optimizing the initial symmetric positive semi-definite matrix based on the Riemann flow, and in the iteration process, the objective function is the mean difference between the source domain data and the target domain data, and the iteration target is to make the objective function as small as possible; if it is determined that the iteration has ended, the finally obtained matrix is determined as the target symmetric positive semi-definite matrix; wherein, the objective function is: ； wherein, , , , N s is the number of source domain data points, N t is the number of target domain data points, is the mapped value of the source domain data or target domain data in the reproducing kernel Hilbert space, is a symmetric positive semi-definite matrix, is a constant.

2. The method of claim 1, wherein, completing the missing values according to the similarity of the missing values and other data points to obtain complete data of the transformer bushing to be detected comprises: determining a first number of data points in the complete bushing data that satisfy the similarity condition with the missing values; determining the average value of the first number of data points as the missing value in the complete data.

3. The method of claim 1, wherein, based on the complete data, identifying the fault of the transformer bushing to be detected to obtain a fault identification result, comprising: inputting the complete data and the normal transformer bushing data into a pre-trained double-tower model; if the output result of the double-tower model is greater than a similarity threshold, it is determined that the transformer bushing to be detected is fault-free; otherwise, it is determined that the transformer bushing to be detected has a fault.

4. The method of claim 1, wherein, based on the complete data, identifying the fault of the transformer bushing to be detected to obtain a fault identification result, comprising: inputting the complete data and the fault transformer bushing data into a pre-trained double-tower model; if the output result of the double-tower model is greater than a similarity threshold, it is determined that the fault type of the transformer bushing to be detected is a target fault type; the target fault type is the fault type reflected by the fault transformer bushing data; otherwise, it is determined that the transformer bushing to be detected does not have the target fault.

5. A fault recognition device for a transformer bushing, characterized by The method comprises the following steps: a target kernel function construction module for constructing a target kernel function based on an original kernel function and a target symmetric positive semi-definite matrix; the target symmetric positive semi-definite matrix is a matrix capable of reflecting the characteristics of the transformer bushing; a complete data determination module for mapping a plurality of complete bushing data and missing bushing data to a reproducing kernel Hilbert space based on the target kernel function, and completing the missing values according to the similarity of the missing values and other data points to obtain complete data of the transformer bushing to be detected; the missing bushing data is the data of the transformer bushing to be detected, and the amount of data in the missing bushing data is less than that in the complete bushing data; The fault identification module is configured to perform fault identification on the to-be-detected bushing of the transformer based on the completion data, and obtain a fault identification result. The target kernel function construction module comprises: The target kernel function determination unit is configured to determine the target kernel function according to the following formula: ； wherein, is a target kernel function, is a raw kernel function, is a constant greater than 0, is a transpose matrix of , is a source domain dataset, is a binary function, is data in the source domain dataset, is a target symmetric positive semi-definite matrix, , y is a target domain dataset, and H is a number of data in the dataset.​ The device further comprises a target symmetric positive semi-definite matrix determination module comprising: The target function determination unit is configured to perform iterative optimization on the initial symmetric positive semi-definite matrix based on Riemann flow, wherein in the iterative process, the target function is the mean difference between the source domain data and the target domain data, and the iterative target is to make the target function as small as possible. The target symmetric positive semi-definite matrix determination unit is configured to determine the final obtained matrix as the target symmetric positive semi-definite matrix if it is determined that the iteration has ended. The target function is as follows: ； wherein, , , , N s is the number of source domain data points, N t is the number of target domain data points, is the mapped value of the source domain data or the target domain data in the reproducing kernel Hilbert space, is a symmetric positive semi-definite matrix, is a constant.

6. An electronic device, comprising: The electronic device comprises: at least one processor; and The memory is in communication connection with the at least one processor; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the fault identification method of the transformer bushing according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to execute the fault identification method of the transformer bushing according to any one of claims 1-4 when executed.

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