A method and system for reconstructing monitoring data from an SF6 sensor

Abstract

The present invention relates to the field of online monitoring technology, and is a monitoring data reconstruction method and system for SF6 sensors, which specifically include: collecting mechanical vibrations of a preset amplitude generated by a metal casing of SF6 gas-insulated equipment; screening and analyzing required characteristic frequency bands based on the real-time density monitoring signal; determining a first resonance frequency of the metal casing; setting a stiffness detection mark in the characteristic frequency band based on the first resonance frequency; detecting the frequency shift relationship between stiffness change regions to determine a stiffness anomaly score of the metal casing; determining a health score of the SF6 gas-insulated equipment based on the stiffness anomaly score, the real-time density monitoring signal, and the equipment sealing monitoring signal; and reconstructing the monitoring signal based on the health score to generate corrected density data. The present invention solves the problem in the prior art of being unable to identify implicit distortion of monitoring data caused by progressive failure due to aging of the sealing ring or loose installation.

Description

Technical Field

[0001] This invention relates to the field of online monitoring technology, and is a method and system for reconstructing monitoring data from SF6 sensors. Background Art

[0002] In the field of high-voltage electrical equipment, sulfur hexafluoride (SF6) gas is widely used in gas-insulated switchgear (GIS) due to its excellent insulation and arc-quenching properties. SF6 gas density is a key parameter affecting the insulation performance of the equipment, and its monitoring accuracy directly relates to the operational safety of the equipment. Currently, SF6 gas density monitoring mainly relies on density sensors installed on the equipment. However, in actual operation, these sensors often experience gradual failures due to issues such as aging seals and loose installation, leading to subtle distortions in the monitoring data. These failures are slow and insidious, making them difficult to effectively identify using traditional threshold alarm mechanisms, easily resulting in delayed leak warnings or false alarms.

[0003] In existing technologies, some solutions achieve data verification by adding reference sensors. However, this method requires modification of the equipment structure and is costly. It cannot adapt to changes in the mechanical characteristics of the equipment caused by long-term operation (such as material fatigue and loosening of bolt preload). Secondly, the influence of environmental temperature and humidity fluctuations on vibration signals is not effectively isolated, leading to an increased false alarm rate. In addition, existing methods can only realize abnormal alarms of density sensor data and cannot perform online correction of distorted data. Maintenance personnel still need to conduct on-site verification, which cannot meet the real-time requirements of smart grids.

[0004] Therefore, there is an urgent need for a monitoring system that can accurately identify the hidden distortions caused by progressive failures under complex operating conditions and achieve self-correction of monitoring data to improve the reliability of equipment status perception. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the existing technology cannot identify the implicit distortion of monitoring data caused by the gradual failure of the sealing ring due to aging and loose installation. The present invention proposes a monitoring data reconstruction method and system for SF6 sensors.

[0006] To achieve the above objectives, the present invention provides a technical solution for a monitoring data reconstruction method for an SF6 sensor, comprising the following steps:

[0007] Step 1: Send an excitation signal to the SF6 gas insulation equipment to cause the metal shell of the SF6 gas insulation equipment to generate mechanical vibration of a preset amplitude, and activate the piezoelectric vibration sensor installed on the surface of the metal shell. The axial excitation direction of the sensor is consistent with the normal direction of the shell.

[0008] Step 2: Receive the real-time density monitoring signal collected by the SF6 sensor installed on the SF6 gas insulation equipment, and receive the time-domain vibration signal detected by the piezoelectric vibration sensor;

[0009] Step 3: Based on the real-time density monitoring signal, select and analyze the required characteristic frequency bands from multiple frequency bands of the time-domain vibration signal;

[0010] Determine the first resonant frequency of the metal casing in the characteristic frequency band;

[0011] Step 4: Based on the first resonant frequency, set stiffness detection markers in the characteristic frequency band, wherein the stiffness detection markers include multiple bandwidth windows centered on the first resonant frequency, the multiple bandwidth windows having the same center frequency but different bandwidths;

[0012] Step 5: Detect the frequency shift relationship between the bandwidth window and the stiffness variation region associated with gas density in the characteristic frequency band;

[0013] Step Six: Determine the stiffness anomaly score of the metal shell based on the frequency shift relationship;

[0014] The health score of the SF6 gas-insulated equipment is determined based on the stiffness anomaly score, the real-time density monitoring signal, and the equipment sealing monitoring signal.

[0015] Step 7: Reconstruct the real-time density monitoring signal based on the health score, generate corrected density data, and send it to the monitoring platform.

[0016] Specifically, in step three, based on the real-time density monitoring signal, the required characteristic frequency bands are selected and analyzed from multiple frequency bands of the time-domain vibration signal, including:

[0017] The molecular weight M of SF6 is calculated based on the real-time density monitoring signal. When the molecular weight of SF6 deviates from the theoretical molecular weight, it is determined that there is a gas leak or air mixing in the SF6 gas insulation equipment.

[0018] When the molecular weight of SF6 equals the theoretical molecular weight, the resonant frequency range of the metal shell is further determined. ;

[0019] By calculating the correlation coefficient, a frequency band of 0.5-5kHz was selected that is consistent with... Sub-bands with a correlation greater than 0.8 are designated as characteristic bands.

[0020] Specifically, in step five, detecting the frequency shift relationship between the bandwidth window and the stiffness variation region associated with gas density in the characteristic frequency band includes:

[0021] The reference frequency response curve is collected using the piezoelectric vibration sensor when no gas is introduced into the SF6 gas insulation equipment.

[0022] Based on the characteristic frequency band and the reference frequency response curve, a stiffness variation comparison spectrum is obtained;

[0023] In the stiffness variation comparison spectrum, the second resonant frequency of the plurality of bandwidth windows is determined;

[0024] Based on the amplitude gradient of the stiffness change comparison spectrum and the second resonance frequency, determine the frequency shift intersection point between the stiffness change region and the bandwidth window;

[0025] Based on the amplitude gradient of the stiffness change comparison spectrum and the frequency shift intersection, the portion of each bandwidth window that overlaps with the stiffness change region is determined.

[0026] Specifically, determining the frequency shift intersection point between the stiffness change region and the bandwidth window based on the amplitude gradient of the stiffness change reference spectrum and the second resonant frequency includes:

[0027] Determine the frequency shift score of the k-th frequency point within the j-th bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band. ;

[0028] In the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band, if the frequency shift score of the k-th frequency point within the j-th bandwidth window is greater than or equal to the first preset threshold, the frequency point is determined as the undetermined frequency shift intersection point between the stiffness change region and the bandwidth window.

[0029] If the number of the undetermined frequency shift points is even, the undetermined frequency shift points are determined as the frequency shift points of the stiffness variation region and the bandwidth window;

[0030] When the number of undetermined frequency shift intersection points is odd, the undetermined frequency shift intersection point with the lowest frequency shift score is removed, and the remaining undetermined frequency shift intersection points are determined as the frequency shift intersection points of the stiffness change region and the bandwidth window.

[0031] Specifically, based on the amplitude gradient of the stiffness change reference spectrum and the frequency shift intersection point, the overlapping portion of each bandwidth window with the stiffness change region is determined, specifically as follows:

[0032] Determining the frequency amplitude between two adjacent frequency shift intersection points in the stiffness variation reference spectrum corresponding to the i-th characteristic frequency band includes:

[0033] In the stiffness variation comparison spectrum corresponding to the i-th characteristic frequency band, determine the percentage of amplitude energy between the t-th and (t+1)-th frequency shift intersection points within the j-th bandwidth window. ;

[0034] When the amplitude energy ratio is greater than or equal to the second preset threshold, the portion between the t-th frequency shift intersection point and the (t+1)-th frequency shift intersection point on the j-th bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band is determined to be the portion where the bandwidth window overlaps with the stiffness change region.

[0035] Specifically, in step six, the stiffness anomaly score of the metal shell is determined based on the frequency shift relationship, including:

[0036] Based on the following strategy:

[0037] ;

[0038] Determine the stiffness anomaly score S of the metal casing;

[0039] in, Let be the resonant frequency shift of the i-th characteristic frequency band.

[0040] It is a temperature decay function; For frequency band weighting functions;

[0041] For cavity geometry factors, This is the reference resonant frequency.

[0042] Specifically, based on the stiffness anomaly score, the real-time density monitoring signal, and the equipment sealing monitoring signal, the health score of the SF6 gas-insulated equipment is determined, including:

[0043] Based on the following strategy:

[0044] ;

[0045] Determine the health score H of the SF6 gas-insulated equipment;

[0046] Wherein, S represents the stiffness anomaly score of the metal shell;

[0047] This represents the density value of the real-time density monitoring signal from the SF6 sensor. Indicates nominal density;

[0048] This indicates the indicator function, when the sealing parameter... Exceeding the threshold The function value is 1 if the function is active, and 0 otherwise; X represents the total number of sealing parameters. These are preset weighting coefficients.

[0049] Specifically, in step seven, reconstructing the real-time density monitoring signal based on the health score to generate corrected density data includes:

[0050] When the health score H exceeds the preset reconstruction threshold, the density compensation value is calculated based on the stiffness anomaly score S. ;

[0051] Based on density compensation value Generate corrected density data ;

[0052] When the health score H does not exceed the reconstruction threshold, there is no need to reconstruct the real-time density monitoring signal.

[0053] In addition, the monitoring data reconstruction system for SF6 sensors of the present invention includes the following modules:

[0054] The module includes an excitation signal module, a density acquisition module, a vibration receiving module, a frequency band selection module, a resonance positioning module, a bandwidth marking module, a frequency shift analysis module, a stiffness scoring module, a health assessment module, and a data reconstruction module.

[0055] The excitation signal module is used to send an excitation signal to the SF6 gas-insulated equipment, causing the metal casing of the equipment to generate mechanical vibration of a preset amplitude and activating the piezoelectric vibration sensor installed on the surface of the metal casing.

[0056] The density acquisition module is used to receive real-time density monitoring signals acquired by the SF6 density sensor installed on the SF6 gas insulation equipment;

[0057] The vibration receiving module is used to receive the time-domain vibration signal detected by the piezoelectric vibration sensor;

[0058] The frequency band filtering module is used to filter and analyze the required characteristic frequency bands from multiple frequency bands of the time-domain vibration signal based on the real-time density signal.

[0059] The resonance positioning module is used to determine the first resonant frequency of the metal casing in the characteristic frequency band;

[0060] The bandwidth marking module is used to set stiffness detection marks in the characteristic frequency band according to the first resonant frequency;

[0061] The frequency shift analysis module is used to detect the frequency shift relationship between the bandwidth window and the stiffness variation region associated with gas density in the characteristic frequency band;

[0062] The stiffness rating module is used to determine the stiffness anomaly rating of the metal shell based on the frequency shift relationship;

[0063] The health assessment module is used to determine the health score of SF6 gas-insulated equipment based on the stiffness anomaly score, the real-time density signal, and the sealing monitoring signal.

[0064] The data reconstruction module is used to reconstruct the real-time density signal based on the health score, generate corrected density data, and send it to the monitoring platform.

[0065] A storage medium storing instructions that, when read by a computer, cause the computer to execute the aforementioned monitoring data reconstruction method for an SF6 sensor.

[0066] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the above-described method for reconstructing monitoring data for an SF6 sensor.

[0067] Compared with the prior art, the technical effects of the present invention are as follows:

[0068] 1. This invention utilizes a piezoelectric vibration sensor to actively excite the mechanical resonance of a metal shell. By detecting the shift of the first resonant frequency within a characteristic frequency band, the attenuation of cavity stiffness caused by changes in gas density is quantified. This process establishes a physical correlation model of "gas density-cavity stiffness-resonant frequency," forming an independent verification channel with the direct density reading of the SF6 sensor, overcoming the monitoring blind spot caused by the gradual failure of a single sensor.

[0069] 2. This invention is based on amplitude gradient analysis of stiffness variation comparison spectrum, combined with a multi-bandwidth window frequency shift detection strategy. It uses the first derivative to identify amplitude abrupt change boundaries, the second derivative to suppress noise interference, and a temperature and humidity compensation function to eliminate environmental drift, accurately extracting the frequency shift intersection point between the stiffness variation region and the bandwidth window. This invention significantly improves the detection sensitivity of minute stiffness anomalies under complex operating conditions (such as temperature and humidity fluctuations, mechanical vibration noise).

[0070] 3. This invention quantifies the degree of shell structure deterioration and uses a logarithmic density compensation function to nonlinearly reconstruct the original density data. The correction amount is adaptively adjusted according to the degree of stiffness deterioration, compensating for the falsely high readings of the real-time density monitoring signal caused by sealing failure. This achieves early warning and data self-correction for progressive sealing failure, and significantly reduces the long-term drift error of SF6 density monitoring. Attached Figure Description

[0071] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0072] Figure 1 This is a flowchart illustrating a monitoring data reconstruction method for an SF6 sensor according to the present invention.

[0073] Figure 2 This is a schematic diagram of a monitoring data reconstruction system for an SF6 sensor according to the present invention. Detailed Implementation

[0074] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0075] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0076] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0077] Example 1:

[0078] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for reconstructing monitoring data from an SF6 sensor, such as... Figure 1 As shown, the specific steps include the following:

[0079] Step 1: Send an excitation signal to the SF6 gas insulation equipment to cause the metal shell of the SF6 gas insulation equipment to generate mechanical vibration of a preset amplitude, and activate the piezoelectric vibration sensor installed on the surface of the metal shell. The axial excitation direction of the sensor is consistent with the normal direction of the shell.

[0080] Step 2: Receive the real-time density monitoring signal collected by the SF6 sensor installed on the SF6 gas insulation equipment, and receive the time-domain vibration signal detected by the piezoelectric vibration sensor;

[0081] Step 3: Based on the real-time density monitoring signal, select and analyze the required characteristic frequency bands from multiple frequency bands of the time-domain vibration signal;

[0082] In step three, based on the real-time density monitoring signal, the required characteristic frequency bands are selected and analyzed from multiple frequency bands of the time-domain vibration signal, including:

[0083] The molecular weight M of SF6 is calculated based on the real-time density monitoring signal. When the molecular weight of SF6 deviates from the theoretical molecular weight, it is determined that there is a gas leak or air mixing in the SF6 gas insulation equipment.

[0084] For example, in this embodiment, a strategy for calculating the molecular weight of SF6 is provided, specifically as follows: ; The density value represents the real-time density monitoring signal from the SF6 sensor; R is the SF6 gas constant. Where is the absolute temperature of the gas; P is the absolute pressure of the gas. It should be noted that in this embodiment, the calculation strategy for the molecular weight of SF6 is derived from an extension of the ideal gas law.

[0085] When the molecular weight of SF6 equals the theoretical molecular weight, the resonant frequency range of the metal shell is further determined. ;

[0086] In this embodiment, Where k is the stiffness coefficient and E is the Young's modulus of the metal shell; Where is the density of the outer shell material; L is the outer shell dimension, taking the height for a cylindrical shell and the diameter for a spherical container.

[0087] By calculating the correlation coefficient, a frequency band of 0.5-5kHz was selected that is consistent with... Sub-bands with a correlation greater than 0.8 are designated as characteristic bands.

[0088] Determine the first resonant frequency of the metal casing in the characteristic frequency band;

[0089] Step 4: Based on the first resonant frequency, set stiffness detection markers in the characteristic frequency band, wherein the stiffness detection markers include multiple bandwidth windows centered on the first resonant frequency, the multiple bandwidth windows having the same center frequency but different bandwidths;

[0090] Step 5: Detect the frequency shift relationship between the bandwidth window and the stiffness variation region associated with gas density in the characteristic frequency band;

[0091] Step five involves detecting the frequency shift relationship between the bandwidth window and the stiffness variation region associated with gas density in the characteristic frequency band, including:

[0092] The reference frequency response curve is collected using the piezoelectric vibration sensor when no gas is introduced into the SF6 gas insulation equipment.

[0093] Based on the characteristic frequency band and the reference frequency response curve, a stiffness variation comparison spectrum is obtained;

[0094] In the stiffness variation comparison spectrum, the second resonant frequency of the plurality of bandwidth windows is determined;

[0095] Based on the amplitude gradient of the stiffness change comparison spectrum and the second resonance frequency, determine the frequency shift intersection point between the stiffness change region and the bandwidth window;

[0096] include:

[0097] Based on the following strategy:

[0098] ;

[0099] Determine the frequency shift score of the k-th frequency point within the j-th bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band. ;

[0100] in, This represents the frequency vector at the k-th frequency point of the j-th bandwidth window in the stiffness variation comparison spectrum corresponding to the i-th characteristic frequency band.

[0101] Represents frequency coordinates. Indicates amplitude. Temperature and humidity compensation factor; The reference frequency is 1Hz; for example, in this embodiment, the reference frequency is 1Hz.

[0102] It should be noted that, It is the first derivative, used to detect abrupt changes in amplitude, i.e., to locate the boundary of the stiffness variation region; The second derivative is used to enhance the recognition of curvature features and suppress noise interference; Used to compensate for baseline drift caused by ambient temperature and humidity, i.e., the thermal expansion and moisture expansion effects of the sealing material;

[0103] In the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band, if the frequency shift score of the k-th frequency point within the j-th bandwidth window is greater than or equal to the first preset threshold, the frequency point is determined as the undetermined frequency shift intersection point between the stiffness change region and the bandwidth window.

[0104] If the number of the undetermined frequency shift points is even, the undetermined frequency shift points are determined as the frequency shift points of the stiffness variation region and the bandwidth window;

[0105] When the number of undetermined frequency shift intersection points is odd, the undetermined frequency shift intersection point with the lowest frequency shift score is removed, and the remaining undetermined frequency shift intersection points are determined as the frequency shift intersection points of the stiffness change region and the bandwidth window.

[0106] Based on the amplitude gradient of the stiffness change comparison spectrum and the frequency shift intersection, determine the portion of each bandwidth window that overlaps with the stiffness change region;

[0107] Specifically:

[0108] Determining the frequency amplitude between two adjacent frequency shift intersection points in the stiffness variation reference spectrum corresponding to the i-th characteristic frequency band includes:

[0109] Specifically, the strategy is as follows:

[0110] ;

[0111] In the stiffness variation comparison spectrum corresponding to the i-th characteristic frequency band, determine the percentage of amplitude energy between the t-th and (t+1)-th frequency shift intersection points within the j-th bandwidth window. ;

[0112] in, This represents the frequency corresponding to the t-th frequency shift intersection point within the j-th bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band. This represents the frequency corresponding to the (t+1)th frequency shift intersection point on the j-th bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band. This is the current vibration amplitude spectrum; As the reference amplitude spectrum;

[0113] It should be noted that the above strategy is used to confirm whether the frequency band between the two frequency shift intersections is indeed a stiffness variation region, in order to avoid noise interference;

[0114] When the amplitude energy ratio is greater than or equal to the second preset threshold, the portion between the t-th frequency shift intersection point and the (t+1)-th frequency shift intersection point on the j-th bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band is determined to be the portion where the bandwidth window overlaps with the stiffness change region.

[0115] Step Six: Determine the stiffness anomaly score of the metal shell based on the frequency shift relationship;

[0116] In step six, based on the frequency shift relationship, the stiffness anomaly score of the metal shell is determined, including:

[0117] Based on the following strategy:

[0118] ;

[0119] Determine the stiffness anomaly score S of the metal casing;

[0120] in, Let N be the resonant frequency shift of the i-th characteristic frequency band, and N be the total number of characteristic frequency bands.

[0121] It is a temperature decay function; in this embodiment, T represents the current ambient temperature; To calibrate the temperature of the equipment;

[0122] The coefficient of thermal expansion of the material; In this embodiment, a specific strategy for obtaining the frequency band weighting function is provided. ; The center frequency of the i-th characteristic frequency band;

[0123] For cavity geometry factors, This is the reference resonant frequency.

[0124] Regarding the above strategy, it should be noted that changes in stiffness cause a shift in the resonant frequency; the larger the shift, the more severe the stiffness attenuation. A relative frequency shift (i.e.,...) is employed. It eliminates the influence of equipment size, amplifies significant anomalies through squaring operations, compensates for the thermal expansion effect of materials through temperature functions, and focuses sensitive frequency bands using frequency band weighting functions.

[0125] The health score of the SF6 gas-insulated equipment is determined based on the stiffness anomaly score, the real-time density monitoring signal, and the equipment sealing monitoring signal.

[0126] include:

[0127] Based on the following strategy:

[0128] ;

[0129] Determine the health score H of the SF6 gas-insulated equipment;

[0130] Wherein, S represents the stiffness anomaly score of the metal shell;

[0131] This represents the density value of the real-time density monitoring signal from the SF6 sensor. Indicates nominal density;

[0132] This indicates the indicator function, when the sealing parameter... Exceeding the threshold The function value is 1 when the time is right, and 0 otherwise; X represents the total number of sealing parameters (such as moisture content and pressure decay rate); These are preset weighting coefficients.

[0133] Step 7: Reconstruct the real-time density monitoring signal based on the health score, generate corrected density data, and send it to the monitoring platform.

[0134] Based on the health score, the real-time density monitoring signal is reconstructed to generate corrected density data, including:

[0135] When the health score H exceeds the preset reconstruction threshold, the density compensation value is calculated based on the stiffness anomaly score S. ;

[0136] For example, in this embodiment, the density compensation value is calculated based on the stiffness anomaly score S. Specifically

[0137] ;

[0138] in, For density reconstruction coefficients, This serves as the baseline value for stiffness anomalies.

[0139] Based on density compensation value Generate corrected density data ;

[0140] In this embodiment, specifically:

[0141] ;in, The threshold for health scores;

[0142] When the health score H does not exceed the reconstruction threshold, there is no need to reconstruct the real-time density monitoring signal.

[0143] It should be noted that the stiffness anomaly score S has a non-linear relationship with the density decrease caused by gas leakage (i.e., S changes little in the initial stage of leakage but increases significantly in the later stage), described by a logarithmic function. For the generation of corrected density data, a smooth transition is achieved using the tanh function, when H is much smaller than... The compensation amount is small, and H is much larger than 1. Time compensation amount close to .

[0144] Example 2:

[0145] like Figure 2 As shown in the figure, an embodiment of the present invention provides a monitoring data reconstruction system for an SF6 sensor, such as... Figure 2 As shown, it includes the following modules:

[0146] The module includes an excitation signal module, a density acquisition module, a vibration receiving module, a frequency band selection module, a resonance positioning module, a bandwidth marking module, a frequency shift analysis module, a stiffness scoring module, a health assessment module, and a data reconstruction module.

[0147] The excitation signal module is used to send an excitation signal to the SF6 gas-insulated equipment, causing the metal casing of the equipment to generate mechanical vibration of a preset amplitude and activating the piezoelectric vibration sensor installed on the surface of the metal casing.

[0148] The density acquisition module is used to receive real-time density monitoring signals acquired by the SF6 density sensor installed on the SF6 gas insulation equipment;

[0149] The vibration receiving module is used to receive the time-domain vibration signal detected by the piezoelectric vibration sensor;

[0150] The frequency band filtering module is used to filter and analyze the required characteristic frequency bands from multiple frequency bands of the time-domain vibration signal based on the real-time density signal.

[0151] The resonance positioning module is used to determine the first resonant frequency of the metal casing in the characteristic frequency band;

[0152] The bandwidth marking module is used to set stiffness detection marks in the characteristic frequency band according to the first resonant frequency;

[0153] The frequency shift analysis module is used to detect the frequency shift relationship between the bandwidth window and the stiffness variation region associated with gas density in the characteristic frequency band;

[0154] The stiffness rating module is used to determine the stiffness anomaly rating of the metal shell based on the frequency shift relationship;

[0155] The health assessment module is used to determine the health score of SF6 gas-insulated equipment based on the stiffness anomaly score, the real-time density signal, and the sealing monitoring signal.

[0156] The data reconstruction module is used to reconstruct the real-time density signal based on the health score, generate corrected density data, and send it to the monitoring platform.

[0157] Example 3:

[0158] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0159] The processor executes the aforementioned method for reconstructing monitoring data for SF6 sensors by calling a computer program stored in memory.

[0160] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the monitoring data reconstruction method for an SF6 sensor provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Further details are omitted here.

[0161] Example 4:

[0162] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0163] When a computer program runs on a computer device, it causes the computer device to execute the aforementioned method for reconstructing monitoring data for an SF6 sensor.

[0164] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0165] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0166] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0167] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0168] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0169] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0170] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0171] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0172] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0173] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for reconstructing monitoring data from an SF6 sensor, characterized in that, The method includes: Step 1: Send an excitation signal to the SF6 gas insulation equipment to cause the metal shell of the SF6 gas insulation equipment to generate mechanical vibration of a preset amplitude, and activate the piezoelectric vibration sensor installed on the surface of the metal shell. The axial excitation direction of the sensor is consistent with the normal direction of the shell. Step 2: Receive the real-time density monitoring signal collected by the SF6 sensor installed on the SF6 gas insulation equipment, and receive the time-domain vibration signal detected by the piezoelectric vibration sensor; Step 3: Based on the real-time density monitoring signal, select and analyze the required characteristic frequency bands from multiple frequency bands of the time-domain vibration signal; Determine the first resonant frequency of the metal casing in the characteristic frequency band; Step 4: Based on the first resonant frequency, set stiffness detection markers in the characteristic frequency band, wherein the stiffness detection markers include multiple bandwidth windows centered on the first resonant frequency, the multiple bandwidth windows having the same center frequency but different bandwidths; Step 5: Detect the frequency shift relationship between the bandwidth window and the stiffness variation region associated with gas density in the characteristic frequency band; Step Six: Determine the stiffness anomaly score of the metal shell based on the frequency shift relationship; The health score of the SF6 gas-insulated equipment is determined based on the stiffness anomaly score, the real-time density monitoring signal, and the equipment sealing monitoring signal. Step 7: Reconstruct the real-time density monitoring signal based on the health score, generate corrected density data, and send it to the monitoring platform.

2. The method for reconstructing monitoring data for an SF6 sensor according to claim 1, characterized in that, Step five involves detecting the frequency shift relationship between the bandwidth window and the stiffness variation region associated with gas density in the characteristic frequency band, including: The reference frequency response curve is collected using the piezoelectric vibration sensor when no gas is introduced into the SF6 gas insulation equipment. Based on the characteristic frequency band and the reference frequency response curve, a stiffness variation comparison spectrum is obtained; In the stiffness variation comparison spectrum, the second resonant frequency of the plurality of bandwidth windows is determined; Based on the amplitude gradient of the stiffness change comparison spectrum and the second resonance frequency, determine the frequency shift intersection point between the stiffness change region and the bandwidth window; Based on the amplitude gradient of the stiffness change comparison spectrum and the frequency shift intersection, the portion of each bandwidth window that overlaps with the stiffness change region is determined.

3. The method for reconstructing monitoring data for an SF6 sensor according to claim 2, characterized in that, Based on the amplitude gradient of the stiffness change reference spectrum and the second resonant frequency, the frequency shift intersection point between the stiffness change region and the bandwidth window is determined, including: Determine the frequency shift score of the k-th frequency point within the j-th bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band. ; In the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band, if the frequency shift score of the k-th frequency point within the j-th bandwidth window is greater than or equal to the first preset threshold, the frequency point is determined as the undetermined frequency shift intersection point between the stiffness change region and the bandwidth window. If the number of the undetermined frequency shift points is even, the undetermined frequency shift points are determined as the frequency shift points of the stiffness variation region and the bandwidth window; When the number of undetermined frequency shift intersection points is odd, the undetermined frequency shift intersection point with the lowest frequency shift score is removed, and the remaining undetermined frequency shift intersection points are determined as the frequency shift intersection points of the stiffness change region and the bandwidth window.

4. The method for reconstructing monitoring data for an SF6 sensor according to claim 3, characterized in that, Based on the amplitude gradient of the stiffness change reference spectrum and the frequency shift intersection point, the overlapping portion of each bandwidth window with the stiffness change region is determined, specifically as follows: Determining the frequency amplitude between two adjacent frequency shift intersection points in the stiffness variation reference spectrum corresponding to the i-th characteristic frequency band includes: In the stiffness variation comparison spectrum corresponding to the i-th characteristic frequency band, determine the percentage of amplitude energy between the t-th and (t+1)-th frequency shift intersection points within the j-th bandwidth window. ; When the amplitude energy ratio is greater than or equal to the second preset threshold, the portion between the t-th frequency shift intersection point and the (t+1)-th frequency shift intersection point on the j-th bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band is determined to be the portion where the bandwidth window overlaps with the stiffness change region.

5. The method for reconstructing monitoring data for an SF6 sensor according to claim 4, characterized in that, In step six, based on the frequency shift relationship, the stiffness anomaly score of the metal shell is determined, including: Based on the following strategy: ； Determine the stiffness anomaly score S of the metal casing; in, Let N be the resonant frequency shift of the i-th characteristic frequency band, and N be the total number of characteristic frequency bands. It is a temperature decay function; For frequency band weighting functions; For cavity geometry factors, This is the reference resonant frequency.

6. The method for reconstructing monitoring data for an SF6 sensor according to claim 5, characterized in that, In step six, the health score of the SF6 gas-insulated equipment is determined based on the stiffness anomaly score, the real-time density monitoring signal, and the equipment sealing monitoring signal, including: Based on the following strategy: ； Determine the health score H of the SF6 gas-insulated equipment; Wherein, S represents the stiffness anomaly score of the metal shell; This represents the density value of the real-time density monitoring signal from the SF6 sensor. Indicates nominal density; This indicates the indicator function, when the sealing parameter... Exceeding the threshold The function value is 1 if the function is active, and 0 otherwise; X represents the total number of sealing parameters. These are preset weighting coefficients.

7. The method for reconstructing monitoring data for an SF6 sensor according to claim 6, characterized in that, In step seven, the real-time density monitoring signal is reconstructed based on the health score to generate corrected density data, including: When the health score H exceeds the preset reconstruction threshold, the density compensation value is calculated based on the stiffness anomaly score S. ; Based on density compensation value Generate corrected density data ; When the health score H does not exceed the reconstruction threshold, there is no need to reconstruct the real-time density monitoring signal.

8. The method for reconstructing monitoring data for an SF6 sensor according to claim 1, characterized in that, In step three, based on the real-time density monitoring signal, the required characteristic frequency bands are selected and analyzed from multiple frequency bands of the time-domain vibration signal, including: The molecular weight M of SF6 is calculated based on the real-time density monitoring signal. When the molecular weight of SF6 deviates from the theoretical molecular weight, it is determined that there is a gas leak or air mixing in the SF6 gas insulation equipment. When the molecular weight of SF6 equals the theoretical molecular weight, the resonant frequency range of the metal shell is further determined. ; By calculating the correlation coefficient, a frequency band of 0.5-5kHz was selected that is consistent with... Sub-bands with a correlation greater than 0.8 are designated as characteristic bands.

9. A monitoring data reconstruction system for an SF6 sensor, used to implement the monitoring data reconstruction method for an SF6 sensor as described in any one of claims 1-8, characterized in that, The system includes the following modules: The module includes an excitation signal module, a density acquisition module, a vibration receiving module, a frequency band selection module, a resonance positioning module, a bandwidth marking module, a frequency shift analysis module, a stiffness scoring module, a health assessment module, and a data reconstruction module. The excitation signal module is used to send an excitation signal to the SF6 gas-insulated equipment, causing the metal casing of the equipment to generate mechanical vibration of a preset amplitude and activating the piezoelectric vibration sensor installed on the surface of the metal casing. The density acquisition module is used to receive real-time density monitoring signals acquired by the SF6 density sensor installed on the SF6 gas insulation equipment; The vibration receiving module is used to receive the time-domain vibration signal detected by the piezoelectric vibration sensor; The frequency band filtering module is used to filter and analyze the required characteristic frequency bands from multiple frequency bands of the time-domain vibration signal based on the real-time density signal. The resonance positioning module is used to determine the first resonant frequency of the metal casing in the characteristic frequency band; The bandwidth marking module is used to set stiffness detection marks in the characteristic frequency band according to the first resonant frequency; The frequency shift analysis module is used to detect the frequency shift relationship between the bandwidth window and the stiffness variation region associated with gas density in the characteristic frequency band; The stiffness rating module is used to determine the stiffness anomaly rating of the metal shell based on the frequency shift relationship; The health assessment module is used to determine the health score of SF6 gas-insulated equipment based on the stiffness anomaly score, the real-time density signal, and the sealing monitoring signal. The data reconstruction module is used to reconstruct the real-time density signal based on the health score, generate corrected density data, and send it to the monitoring platform.

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