A GIS busbar electrical contact defect diagnosis method and related device integrating magnetic field and current characteristics

Abstract

The present invention provides a GIS busbar electrical contact defect diagnosis method and related devices that integrate magnetic field-current characteristics, including obtaining the GIS busbar conductor current and shell circulating current waveforms and the shell space magnetic field distribution under different electrical contact defects; using the reverse difference algorithm to extract the fault characteristic current of the conductor current and shell circulating current waveforms, and performing time embedding processing on the shell space magnetic field distribution through spectrum coding to obtain characteristic current signals and magnetic field time series data respectively; using the characteristic current signals and magnetic field time series data as a comprehensive feature set, and using a deep learning neural network to classify and train the comprehensive feature set, constructing a GIS busbar electrical contact defect diagnosis model and using the model to perform electrical contact defect diagnosis. By obtaining the GIS busbar related current and magnetic field data, the present invention can realize multi-dimensional detection, provide technical support for status maintenance, improve the level of intelligent operation and maintenance, and ensure the safe and stable operation of equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system equipment diagnosis, and specifically relates to a GIS (Gas Insulated Switchgear) busbar electrical contact defect diagnosis method and related devices integrating magnetic field-current characteristics. Background Art

[0002] Gas-insulated metal-enclosed switchgear (GIS) is a key device in the power system, and its busbar is responsible for the transmission of high voltage and high current. However, contact defects may occur in the GIS busbar during long-term operation, such as loose bolts and loose contact springs, which will increase the contact resistance, and then cause temperature rise, equipment overheating and even short circuit failure, seriously affecting the safe operation of the power system.

[0003] At present, the detection methods of GIS busbar electrical contact defects include loop resistance method, vibration method, partial discharge method and infrared detection method. Although these methods have certain applications, they have limitations, such as the inability to detect live, insensitivity to early faults, and strong environmental interference. In addition, the diagnosis method based on magnetic field distribution also has problems such as difficulty in feature extraction and susceptibility to background interference, which makes it difficult to meet the needs of high-precision detection.

[0004] When detecting GIS busbar electrical contact defects, existing technologies have problems such as large limitations of a single method, incomplete feature extraction, and susceptibility to environmental interference, making it difficult to achieve early and accurate fault diagnosis. Therefore, developing a diagnostic method that integrates multiple features and adapts to complex working conditions is of great significance for improving the operation and maintenance level of GIS busbars and ensuring the safe operation of power systems. Summary of the invention

[0005] In view of this, the present invention aims to provide a GIS busbar electrical contact defect diagnosis method and related devices integrating magnetic field-current characteristics, so as to overcome the deficiencies of the prior art and achieve accurate diagnosis of GIS busbar electrical contact defects.

[0006] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for diagnosing GIS busbar electrical contact defects by integrating magnetic field-current characteristics, comprising the following steps:

[0008] Obtain the GIS busbar conductor current and casing circulating current waveforms and the casing space magnetic field distribution under different electrical contact defects;

[0009] The inverse difference algorithm is used to extract fault characteristic current from the conductor current and shell circulating current waveforms, and the spatial magnetic field distribution of the shell is time-embedded through spectrum coding to obtain characteristic current signals and magnetic field time series data respectively.

[0010] The characteristic current signal and magnetic field time series data are taken as the comprehensive feature set, and the deep learning neural network is used to classify and train the comprehensive feature set. A GIS busbar electrical contact defect diagnosis model is constructed and used to diagnose GIS busbar electrical contact defects.

[0011] Furthermore, the GIS busbar conductor current and shell circulating current waveforms and the shell space magnetic field distribution under different electrical contact defects are obtained, including:

[0012] Install current sensors on GIS bus conductors to monitor conductor current waveforms in real time;

[0013] Arrange a Hall sensor array on the GIS busbar housing to measure the housing circulating current waveform and spatial magnetic field distribution;

[0014] The current waveform and magnetic field data under different electrical contact defect states are recorded to obtain the conductor current and shell circulating current waveforms as well as the shell space magnetic field distribution.

[0015] Furthermore, the steps of extracting fault characteristic current from the conductor current and the casing circulating current waveform using an inverse differential algorithm include:

[0016] Filter the collected conductor current and shell circulating current waveforms;

[0017] According to the determined differential step length, an inverse differential calculation is performed on the preprocessed signal;

[0018] Compare the result obtained by differential calculation with the set threshold. If the absolute value of the differential result is greater than the threshold, it is determined that the signal change at this point may be related to an electrical contact defect, and the point and its related information are recorded. When the differential result is less than the threshold, it is considered that the signal change at this point is normal and is not included in the scope of fault feature analysis.

[0019] The recorded signal points that may be related to the fault are screened, the screened signals are integrated, and the characteristic current signals that can accurately reflect the electrical contact defects are extracted.

[0020] Furthermore, the spatial magnetic field distribution of the shell is subjected to time embedding processing through atlas coding, including:

[0021] According to the spatial magnetic field distribution data of the shell measured by the Hall sensor array, a spatial magnetic field map is constructed with the spatial position as the coordinate and the magnetic field strength and direction as the attributes;

[0022] Convert the spatial magnetic field spectrum into a time series code according to a preset coding rule;

[0023] The generated time series code is verified, and if it meets the characteristics of spatial magnetic field distribution and time variation information, the magnetic field time series data is obtained.

[0024] In a second aspect, the present invention provides a GIS busbar electrical contact defect diagnosis device integrating magnetic field-current characteristics, comprising:

[0025] Data acquisition module, data processing module and model building and diagnosis module;

[0026] The data acquisition module is used to obtain the GIS busbar conductor current and shell circulating current waveforms and the shell space magnetic field distribution under different electrical contact defects;

[0027] The data processing module is used to extract fault characteristic current from the conductor current and shell circulating current waveforms using an inverse differential algorithm, and to perform time embedding processing on the shell spatial magnetic field distribution through spectrum coding to obtain characteristic current signals and magnetic field time series data respectively;

[0028] The characteristic current signal and magnetic field time series data are taken as the comprehensive feature set, and the deep learning neural network is used to classify and train the comprehensive feature set. A GIS busbar electrical contact defect diagnosis model is constructed and used to diagnose GIS busbar electrical contact defects.

[0029] Furthermore, the data acquisition module includes a current sensor and a Hall sensor array. The data acquisition module acquires the GIS busbar conductor current and shell circulating current waveforms and the shell space magnetic field distribution under different electrical contact defects, including:

[0030] Install current sensors on GIS bus conductors to monitor conductor current waveforms in real time;

[0031] Arrange a Hall sensor array on the GIS busbar housing to measure the housing circulating current waveform and spatial magnetic field distribution;

[0032] The current waveform and magnetic field data under different electrical contact defect states are recorded to obtain the conductor current and shell circulating current waveforms as well as the shell space magnetic field distribution.

[0033] Furthermore, the data processing module includes a reverse differential algorithm module, and the data processing module extracts fault characteristic current from the conductor current and the shell circulating current waveform through the reverse differential algorithm module, including:

[0034] Filter the collected conductor current and shell circulating current waveforms;

[0035] According to the determined differential step length, an inverse differential calculation is performed on the preprocessed signal;

[0036] Compare the result obtained by differential calculation with the set threshold. If the absolute value of the differential result is greater than the threshold, it is determined that the signal change at this point may be related to an electrical contact defect, and the point and its related information are recorded. When the differential result is less than the threshold, it is considered that the signal change at this point is normal and is not included in the scope of fault feature analysis.

[0037] The recorded signal points that may be related to the fault are screened, the screened signals are integrated, and the characteristic current signals that can accurately reflect the electrical contact defects are extracted.

[0038] Furthermore, the data processing module also includes a spectrum encoding module, and the data processing module performs time embedding processing on the magnetic field distribution of the shell space through the spectrum encoding module, including:

[0039] According to the spatial magnetic field distribution data of the shell measured by the Hall sensor array, a spatial magnetic field map is constructed with the spatial position as the coordinate and the magnetic field strength and direction as the attributes;

[0040] Convert the spatial magnetic field spectrum into a time series code according to a preset coding rule;

[0041] The generated time series code is verified, and if it meets the characteristics of spatial magnetic field distribution and time variation information, the magnetic field time series data is obtained.

[0042] In a third aspect, the present invention provides a computer device, the device comprising a processor and a memory:

[0043] The memory is used to store the computer program and send the instructions of the computer program to the processor;

[0044] The processor executes a GIS busbar electrical contact defect diagnosis method integrating magnetic field-current characteristics as described in the first aspect according to the instructions of the computer program.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a GIS busbar electrical contact defect diagnosis method that integrates magnetic field-current characteristics as in the first aspect.

[0046] In summary, the present invention provides a GIS busbar electrical contact defect diagnosis method integrating magnetic field-current characteristics, including obtaining the GIS busbar conductor current and shell circulating current waveforms and the shell spatial magnetic field distribution under different electrical contact defects; using the inverse difference algorithm to extract the fault characteristic current of the conductor current and shell circulating current waveforms, and performing time embedding processing on the shell spatial magnetic field distribution through spectrum coding to obtain characteristic current signals and magnetic field time series data respectively; using the characteristic current signals and magnetic field time series data as a comprehensive feature set, and using a deep learning neural network to classify and train the comprehensive feature set, constructing a GIS busbar electrical contact defect diagnosis model and using the GIS busbar electrical contact defect diagnosis model to perform GIS busbar electrical contact defect diagnosis. By obtaining the GIS busbar related current and magnetic field data, the present invention can realize multi-dimensional detection of GIS operating status, provide technical support for status maintenance, improve the level of intelligent operation and maintenance, and ensure the safe and stable operation of equipment.

[0047] The present invention also provides a GIS busbar electrical contact defect diagnosis device, computer equipment and computer-readable storage medium that integrate magnetic field-current characteristics. When implemented, they have similar effects to the above method and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A flow chart of a GIS busbar electrical contact defect diagnosis method integrating magnetic field-current characteristics provided by an embodiment of the present invention;

[0050] Figure 2 A wiring schematic diagram of a GIS busbar electrical contact defect diagnosis device integrating magnetic field-current characteristics provided by an embodiment of the present invention;

[0051] Figure 3 A block diagram of a computer device provided in an embodiment of the present invention.

[0052] In the attached figure: 1-Hall sensor array, 2-current sensor, 3-reverse differential algorithm module, 4-spectrum encoding module, 5-data fusion module, 6-neural network training module, 7-real-time analysis module, 8-GIS module to be tested. DETAILED DESCRIPTION

[0053] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 The embodiment of the present invention provides a GIS busbar electrical contact defect diagnosis method integrating magnetic field-current characteristics, comprising the following steps:

[0055] S1: Obtain the GIS busbar conductor current and casing circulating current waveforms and the casing space magnetic field distribution under different electrical contact defects.

[0056] It should be noted that conductor current refers to the current flowing in the GIS bus conductor, which can reflect the power transmission of the bus. The shell circulation is the current formed in the GIS bus shell, and its size and distribution are related to the operating status of the bus. The shell space magnetic field distribution refers to the intensity and direction distribution of the magnetic field in the space around the GIS bus, which is closely related to the current distribution.

[0057] This step obtains the above basic data. Different electrical contact defects will cause specific changes in the conductor current, the shell circulating current waveform and the shell space magnetic field distribution. By obtaining these data, information related to the defects can be captured.

[0058] S2: The inverse difference algorithm is used to extract the fault characteristic current from the conductor current and the shell circulating current waveform, and the spatial magnetic field distribution of the shell is time-embedded through spectrum coding to obtain the characteristic current signal and magnetic field time series data respectively.

[0059] It should be noted that the inversion differential algorithm is a signal processing algorithm that highlights the changing characteristics of the signal by performing inversion and differential operations on the signal, and can be used for fault feature extraction. The fault characteristic current is a current characteristic signal related to electrical contact defects extracted from the conductor current and casing circulating current waveforms. Spectral coding is a method of encoding and processing spatial magnetic field distribution data, converting spatial information into data in the form of time series. Time embedding processing converts the original spatial information (such as magnetic field distribution) into time series data for subsequent time series analysis.

[0060] This step uses the inverse difference algorithm to perform an inverse operation on the conductor current and the shell circulating current waveform, and then performs a differential operation, that is, calculating the difference between the signals at adjacent moments. This can highlight the sudden change in the signal, and electrical contact defects often cause sudden changes in the current signal, thereby extracting the fault characteristic current. Through the spectrum coding, the magnetic field strength and direction information at different positions in the shell space are encoded according to certain rules, and then converted into a time series form, so that the magnetic field distribution information can be analyzed in the time dimension.

[0061] The current changes caused by electrical contact defects are often relatively small and hidden in the normal current signal, making them difficult to detect directly. Inverse differential processing can amplify these tiny changes, highlighting the features related to the defect from the complex background signal, enhancing the recognition of the fault features and facilitating subsequent analysis and processing.

[0062] In the process of spectral encoding and time embedding of the magnetic field distribution, the spatial characteristic information of the magnetic field distribution can be retained in the time series data, so that the model can not only utilize the dynamic characteristics of the time series, but also consider the spatial characteristics of the magnetic field distribution during analysis, and more comprehensively mine the information related to electrical contact defects in the magnetic field data, thereby improving the accuracy of diagnosis.

[0063] S3: The characteristic current signal and magnetic field time series data are used as a comprehensive feature set, and a deep learning neural network is used to classify and train the comprehensive feature set. A GIS busbar electrical contact defect diagnosis model is constructed and used to diagnose GIS busbar electrical contact defects.

[0064] It should be noted that this step combines the characteristic current signal and magnetic field time series data into a comprehensive feature set and inputs it into the deep learning neural network. The neural network learns the mapping relationship between the comprehensive feature set and the type of electrical contact defects by continuously adjusting the internal weights and biases. During the training process, a large amount of sample data of known defect types is used, the error between the predicted results and the actual labels is calculated, and the parameters of the neural network are updated using the back propagation algorithm, so that the diagnostic accuracy of the model is continuously improved.

[0065] The diagnostic method provided in this embodiment is based on the physical phenomenon that the GIS busbar electrical contact defect will cause changes in the conductor current, the shell circulation waveform, and the shell space magnetic field distribution. By acquiring these changing data, the data is processed using technologies such as the inverse differential algorithm and the spectrum coding, and the feature information related to the defect is extracted. Then, these feature information is used as input, and the deep learning neural network is used for training and classification, so as to realize the diagnosis of electrical contact defects. This method integrates a variety of data such as conductor current, shell circulation waveform, and shell space magnetic field distribution, and comprehensively utilizes the information related to electrical contact defects contained in different types of data. Compared with the diagnostic method of a single data source, it can more comprehensively reflect the defect situation and improve the accuracy and reliability of the diagnosis. At the same time, unique data processing methods such as the inverse differential algorithm and the spectrum coding are used to process and extract features of the original data in a targeted manner, highlighting the features related to the defect, so that the subsequent deep learning neural network can better learn and classify.

[0066] In one embodiment, obtaining the GIS busbar conductor current and shell circulating current waveforms and the shell space magnetic field distribution under different electrical contact defects includes:

[0067] S11: Install current sensors on the GIS bus conductors and monitor the conductor current waveform in real time through the current sensors.

[0068] It should be noted that the current sensor is based on the principle of electromagnetic induction. When current passes through a conductor, a magnetic field is generated around it. The current sensor detects changes in the magnetic field and converts it into a corresponding electrical signal, thereby measuring the conductor current.

[0069] S12: Arrange a Hall sensor array on the GIS busbar casing to measure the casing circulating current waveform and spatial magnetic field distribution.

[0070] It should be noted that the Hall sensor uses the Hall effect. When there is a magnetic field around the GIS busbar housing, the Hall sensor will generate a voltage signal proportional to the magnetic field strength. By arranging multiple Hall sensors to form an array, the magnetic field distribution at different locations can be measured. At the same time, the housing circulation will also generate a magnetic field, and the waveform of the housing circulation can be indirectly measured through the Hall sensor array.

[0071] S13: Record the current waveform and magnetic field data under different electrical contact defect states, so as to obtain the conductor current and shell circulating current waveforms and the shell space magnetic field distribution.

[0072] It should be noted that the GIS busbar is controlled to be in different electrical contact defect states, and the current sensor and the Hall sensor array are used to record the current waveform and magnetic field data in the corresponding state in real time.

[0073] This embodiment can simultaneously obtain multi-dimensional data such as conductor current waveform, shell circulating current waveform and shell space magnetic field distribution by installing current sensors on the GIS bus conductor and arranging Hall sensor arrays on the GIS bus shell. Compared with a single data source, multi-dimensional data can more comprehensively reflect the operating status of the GIS bus, providing a rich information basis for subsequent accurate diagnosis of electrical contact defects.

[0074] In one embodiment, the steps of extracting fault characteristic current from conductor current and casing circulating current waveforms using an inverse differential algorithm include:

[0075] S21: Filter the collected conductor current and casing circulating current waveforms.

[0076] It should be noted that according to the characteristics of the collected conductor current and shell circulating current waveforms, a suitable filter type is selected, such as a low-pass filter, which can remove high-frequency noise and make the signal smoother. The filter performs frequency analysis on the signal and filters out unnecessary frequency components.

[0077] S22: performing inverse differential calculation on the preprocessed signal according to the determined differential step size.

[0078] It should be noted that, according to the determined differential step size, the preprocessed conductor current and casing circulating current waveform signals are inverted, that is, the amplitude of the signal is inverted, and then the difference between adjacent sampling points is calculated to obtain the inverted differential signal.

[0079] S23: Compare the result obtained by differential calculation with the set threshold. If the absolute value of the differential result is greater than the threshold, it is determined that the signal change at this point may be related to an electrical contact defect, and the point and its related information are recorded. When the differential result is less than the threshold, it is considered that the signal change at this point is normal and is not included in the fault feature analysis range.

[0080] It should be noted that the result obtained by the inverse differential calculation is compared with the set threshold. When the absolute value of the differential result is greater than the threshold, it means that the signal change at this point is large, which may be caused by an electrical contact defect. Therefore, the position, amplitude and other related information of this point are recorded; when the differential result is less than the threshold, it means that the signal change at this point is within the normal range and is not considered to be a fault feature.

[0081] S24: Screening recorded signal points that may be related to the fault, integrating the screened signals, and extracting characteristic current signals that can accurately reflect the electrical contact defect.

[0082] It should be noted that the recorded signal points that may be related to the fault are further screened to remove possible misjudgment points, and then the screened signals are integrated, such as through statistical analysis, signal reconstruction and other methods, to extract characteristic current signals that can accurately reflect electrical contact defects.

[0083] This embodiment uses the inverse difference algorithm to process the preprocessed signal, which can highlight the change characteristics in the signal, especially the characteristics related to electrical contact defects. Through differential calculation, the mutation information related to the fault in the signal can be amplified, making the fault characteristics more obvious, which is convenient for subsequent analysis and judgment.

[0084] In one embodiment, time embedding processing is performed on the shell space magnetic field distribution by using atlas coding, including:

[0085] S25: Based on the spatial magnetic field distribution data of the shell measured by the Hall sensor array, a spatial magnetic field map is constructed with the spatial position as coordinates and the magnetic field strength and direction as attributes.

[0086] It should be noted that, according to the magnetic field strength and direction data at different positions measured by the Hall sensor array, a two-dimensional or three-dimensional spatial magnetic field map is constructed by taking the spatial position as the coordinate and the magnetic field strength and direction as the attributes.

[0087] S26: Convert the spatial magnetic field spectrum into time series coding according to a preset coding rule.

[0088] It should be noted that, according to the preset coding rules, each spatial position in the spatial magnetic field map is encoded, and its magnetic field strength and direction information is converted into time series coding, that is, spatial information is converted into information in the time dimension.

[0089] S27: Verify the generated time series code. If it meets the characteristics of spatial magnetic field distribution and time variation information, the magnetic field time series data is obtained.

[0090] It should be noted that the generated time series code is verified to check whether it meets the characteristics of spatial magnetic field distribution (such as the magnitude of magnetic field intensity, changes in direction, etc.) and time change information (such as the trend of magnetic field changes over time). If the requirements are met, the time series code is considered valid and the magnetic field time series data is obtained; if the requirements are not met, the coding rules need to be readjusted or data processing needs to be performed.

[0091] This embodiment converts the spatial magnetic field map into time series coding according to the preset coding rules, realizing the conversion from spatial information to time series information. Time series coding is more convenient for computer processing and analysis, and can use the time series analysis method to mine potential information in magnetic field data, such as the trend of magnetic field changes over time, etc., providing a more suitable data form for subsequent deep learning models.

[0092] Based on the same inventive concept, the embodiment of the present application also provides a GIS busbar electrical contact defect diagnosis device that integrates magnetic field and current characteristics for implementing the above-mentioned GIS busbar electrical contact defect diagnosis method that integrates magnetic field and current characteristics. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiment of the GIS busbar electrical contact defect diagnosis device that integrates magnetic field and current characteristics provided below can be referred to the limitations of the GIS busbar electrical contact defect diagnosis method that integrates magnetic field and current characteristics above, and will not be repeated here.

[0093] The embodiment of the present invention further provides a GIS busbar electrical contact defect diagnosis device integrating magnetic field-current characteristics, comprising:

[0094] Data acquisition module, data processing module and model building and diagnosis module;

[0095] The data acquisition module is used to obtain the GIS busbar conductor current and shell circulating current waveforms and the shell space magnetic field distribution under different electrical contact defects;

[0096] The data processing module is used to extract fault characteristic current from the conductor current and shell circulating current waveforms using an inverse differential algorithm, and to perform time embedding processing on the shell spatial magnetic field distribution through spectrum coding to obtain characteristic current signals and magnetic field time series data respectively;

[0097] The characteristic current signal and magnetic field time series data are taken as the comprehensive feature set, and the deep learning neural network is used to classify and train the comprehensive feature set. A GIS busbar electrical contact defect diagnosis model is constructed and used to diagnose GIS busbar electrical contact defects.

[0098] See also Figure 2, wherein the Hall sensor array 1 is used to measure the magnetic field distribution of the GIS busbar shell, and is connected to the GIS module 8 to be tested and the spectrum encoding module 4: the current sensor 2 is used to monitor the current waveform in the GIS busbar conductor in real time, and is connected to the GIS module 8 to be tested and the reverse differential algorithm module 3; the reverse differential algorithm 3 processes the current signal collected by the current sensor 2, and is connected to the data fusion module 5 and the current sensor 2; the spectrum encoding module 4 is used to convert the spatial magnetic field distribution data measured by the Hall sensor array 1 into time series data, and is connected to the data fusion module 5 and the Hall sensor array 1; the data fusion module 5 fuses the characteristic current signal extracted by the reverse differential algorithm module 3 with the GIS busbar spatial magnetic field spectrum encoded by the spectrum encoding module 4, and is connected to the reverse differential algorithm module 3 and the spectrum encoding module 4; the neural network training module 6 is used to construct a GIS busbar electrical contact defect diagnosis model, and is connected to the data fusion module 5 and the real-time analysis module 7; the real-time analysis module 7 identifies the electrical contact state, and is connected to the neural network training module 6.

[0099] Furthermore, the data acquisition module includes a current sensor and a Hall sensor array. The data acquisition module acquires the GIS busbar conductor current and shell circulating current waveforms and the shell space magnetic field distribution under different electrical contact defects, including:

[0100] Install current sensors on GIS bus conductors to monitor conductor current waveforms in real time;

[0101] Arrange a Hall sensor array on the GIS busbar housing to measure the housing circulating current waveform and spatial magnetic field distribution;

[0102] The current waveform and magnetic field data under different electrical contact defect states are recorded to obtain the conductor current and shell circulating current waveforms as well as the shell space magnetic field distribution.

[0103] Furthermore, the data processing module includes a reverse differential algorithm module, and the data processing module extracts fault characteristic current from the conductor current and the shell circulating current waveform through the reverse differential algorithm module, including:

[0104] Filter the collected conductor current and shell circulating current waveforms;

[0105] According to the determined differential step length, an inverse differential calculation is performed on the preprocessed signal;

[0106] Compare the result obtained by differential calculation with the set threshold. If the absolute value of the differential result is greater than the threshold, it is determined that the signal change at this point may be related to an electrical contact defect, and the point and its related information are recorded. When the differential result is less than the threshold, it is considered that the signal change at this point is normal and is not included in the scope of fault feature analysis.

[0107] The recorded signal points that may be related to the fault are screened, the screened signals are integrated, and the characteristic current signals that can accurately reflect the electrical contact defects are extracted.

[0108] Furthermore, the data processing module also includes a spectrum encoding module, and the data processing module performs time embedding processing on the magnetic field distribution of the shell space through the spectrum encoding module, including:

[0109] According to the spatial magnetic field distribution data of the shell measured by the Hall sensor array, a spatial magnetic field map is constructed with the spatial position as the coordinate and the magnetic field strength and direction as the attributes;

[0110] Convert the spatial magnetic field spectrum into a time series code according to a preset coding rule;

[0111] The generated time series code is verified, and if it meets the characteristics of spatial magnetic field distribution and time variation information, the magnetic field time series data is obtained.

[0112] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0113] Reference Figure 3 An embodiment of the present invention further provides a computer device, comprising: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, a GIS busbar electrical contact defect diagnosis method integrating magnetic field-current characteristics as described in any one of the above methods is implemented.

[0114] The computer device may be a desktop computer, a notebook, a PDA, a cloud server or other computing device. The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that Figure 3It is only an example of a computer device and does not constitute a limitation of the computer device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0115] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0116] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Further, the memory may include both an internal storage unit and an external storage device of the computer device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is to be output.

[0117] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a GIS busbar electrical contact defect diagnosis method integrating magnetic field-current characteristics as described in any one of the above methods is implemented.

[0118] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0119] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0120] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0121] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0122] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A GIS busbar electrical contact defect diagnosis method integrating magnetic field and current characteristics, characterized in that: The steps include: Obtain the GIS busbar conductor current and casing circulating current waveforms and the casing space magnetic field distribution under different electrical contact defects; The fault characteristic current is extracted from the conductor current and the shell circulating current waveform by using an inverse difference algorithm, and the spatial magnetic field distribution of the shell is time-embedded by using a spectrum coding to obtain characteristic current signals and magnetic field time series data respectively; The characteristic current signal and the magnetic field time series data are used as a comprehensive feature set, and a deep learning neural network is used to classify and train the comprehensive feature set, a GIS busbar electrical contact defect diagnosis model is constructed, and the GIS busbar electrical contact defect diagnosis model is used to perform GIS busbar electrical contact defect diagnosis.

2. The GIS busbar electrical contact defect diagnosis method integrating magnetic field and current characteristics according to claim 1 is characterized in that: Obtain the GIS busbar conductor current and shell circulating current waveforms and shell space magnetic field distribution under different electrical contact defects, including: A current sensor is installed on the GIS bus conductor to monitor the conductor current waveform in real time through the current sensor; Arrange a Hall sensor array on the GIS busbar housing to measure the housing circulating current waveform and spatial magnetic field distribution; The current waveform and magnetic field data under different electrical contact defect states are recorded to obtain the conductor current and shell circulating current waveforms and the shell space magnetic field distribution.

3. The GIS busbar electrical contact defect diagnosis method integrating magnetic field and current characteristics according to claim 1 is characterized in that: The steps of extracting fault characteristic current from the conductor current and the casing circulating current waveform using an inverse differential algorithm include: Filter the collected conductor current and shell circulating current waveforms; According to the determined differential step length, an inverse differential calculation is performed on the preprocessed signal; Compare the result obtained by differential calculation with the set threshold. If the absolute value of the differential result is greater than the threshold, it is determined that the signal change at the signal point may be related to an electrical contact defect, and the point and its related information are recorded. When the differential result is less than the threshold, it is considered that the signal change at the point is normal and is not included in the fault feature analysis range. The recorded signal points that may be related to the fault are screened, the screened signals are integrated, and the characteristic current signals that can accurately reflect the electrical contact defects are extracted.

4. The GIS busbar electrical contact defect diagnosis method integrating magnetic field and current characteristics according to claim 2 is characterized in that: The time embedding process of the outer shell space magnetic field distribution is performed by atlas coding, including: According to the shell space magnetic field distribution data measured by the Hall sensor array, a spatial magnetic field map is constructed with spatial position as coordinates and magnetic field strength and direction as attributes; Converting the spatial magnetic field spectrum into a time series code according to a preset coding rule; The generated time series code is verified, and if it satisfies the characteristics of the spatial magnetic field distribution and the time variation information, the magnetic field time series data is obtained.

5. A GIS busbar electrical contact defect diagnosis device integrating magnetic field and current characteristics, characterized in that: include: Data acquisition module, data processing module and model building and diagnosis module; The data acquisition module is used to obtain the GIS busbar conductor current and shell circulating current waveforms and the shell space magnetic field distribution under different electrical contact defects; The data processing module is used to extract fault characteristic current from the conductor current and shell circulating current waveforms using an inverse difference algorithm, and to perform time embedding processing on the shell spatial magnetic field distribution through spectrum coding to obtain characteristic current signals and magnetic field time series data respectively; The characteristic current signal and the magnetic field time series data are used as a comprehensive feature set, and a deep learning neural network is used to classify and train the comprehensive feature set, a GIS busbar electrical contact defect diagnosis model is constructed, and the GIS busbar electrical contact defect diagnosis model is used to perform GIS busbar electrical contact defect diagnosis.

6. The GIS busbar electrical contact defect diagnosis device integrating magnetic field and current characteristics according to claim 5 is characterized in that: The data acquisition module includes a current sensor and a Hall sensor array. The data acquisition module acquires the GIS busbar conductor current and shell circulating current waveforms and the shell space magnetic field distribution under different electrical contact defects, including: The current sensor is installed on the GIS bus conductor, and the conductor current waveform is monitored in real time by the current sensor; Arranging the Hall sensor array on the GIS busbar housing to measure the housing circulating current waveform and spatial magnetic field distribution; The current waveform and magnetic field data under different electrical contact defect states are recorded to obtain the conductor current and shell circulating current waveforms and the shell space magnetic field distribution.

7. The GIS busbar electrical contact defect diagnosis device integrating magnetic field and current characteristics according to claim 5 is characterized in that: The data processing module includes a reverse differential algorithm module, and the data processing module extracts fault characteristic current from the conductor current and the shell circulating current waveform through the reverse differential algorithm module, including: Filter the collected conductor current and shell circulating current waveforms; According to the determined differential step length, an inverse differential calculation is performed on the preprocessed signal; Compare the result obtained by differential calculation with the set threshold. If the absolute value of the differential result is greater than the threshold, it is determined that the signal change at the signal point may be related to an electrical contact defect, and the point and its related information are recorded. When the differential result is less than the threshold, it is considered that the signal change at the point is normal and is not included in the fault feature analysis range. The recorded signal points that may be related to the fault are screened, the screened signals are integrated, and the characteristic current signals that can accurately reflect the electrical contact defects are extracted.

8. The GIS busbar electrical contact defect diagnosis device integrating magnetic field and current characteristics according to claim 6 is characterized in that: The data processing module further includes a spectrum encoding module, and the data processing module performs time embedding processing on the magnetic field distribution of the shell space through the spectrum encoding module, including: According to the shell space magnetic field distribution data measured by the Hall sensor array, a spatial magnetic field map is constructed with spatial position as coordinates and magnetic field strength and direction as attributes; Converting the spatial magnetic field spectrum into a time series code according to a preset coding rule; The generated time series code is verified, and if it satisfies the characteristics of the spatial magnetic field distribution and the time variation information, the magnetic field time series data is obtained.

9. A computer device, characterized in that: The device comprises a processor and a memory: The memory is used to store a computer program and send instructions of the computer program to the processor; The processor executes a GIS busbar electrical contact defect diagnosis method integrating magnetic field-current characteristics as described in any one of claims 1-4 according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements a GIS busbar electrical contact defect diagnosis method integrating magnetic field-current characteristics as described in any one of claims 1-4.

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