Monitoring data reconstruction method and system for SF6 sensor

Abstract

The invention relates to the technical field of online monitoring, in particular to a monitoring data reconstruction method and system for an SF6 sensor, and the method specifically comprises the steps: collecting the mechanical vibration of a preset amplitude generated by a metal housing of SF6 gas insulation equipment; according to the real-time density monitoring signal, a required characteristic frequency band is screened and analyzed; determining a first resonant frequency of the metal shell; setting a rigidity detection mark in the characteristic frequency band according to the first resonant frequency; detecting a frequency shift relation between the rigidity change areas, and determining a rigidity abnormity score of the metal shell; determining a health score of the SF6 gas insulation equipment according to the stiffness anomaly score, the real-time density monitoring signal and the equipment sealing performance monitoring signal; reconstructing the monitoring signal according to the health score to generate correction density data; the problem that in the prior art, monitoring data hidden distortion caused by progressive failure due to sealing ring aging and installation loosening cannot be recognized is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of online monitoring, and is a monitoring data reconstruction method and system for an SF6 sensor. 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-extinguishing properties. SF6 gas density is a key parameter affecting the insulation performance of the equipment, and its monitoring accuracy is directly related to the safe operation of the equipment. Currently, SF6 gas density monitoring mainly relies on density sensors installed on the equipment. However, in actual operation, these sensors often suffer from progressive failures such as aging of the sealing ring and loose installation, resulting in hidden distortion of monitoring data. Such failures are slow and hidden, making them difficult to effectively identify with traditional threshold alarm mechanisms, which can easily lead to untimely leak warnings or false alarms.

[0003] Some existing solutions implement data verification by adding reference sensors. However, this method requires costly modifications to the equipment structure and cannot adapt to changes in mechanical properties caused by long-term operation (such as material fatigue and loosening of bolt preload). Furthermore, the impact of ambient temperature and humidity fluctuations on vibration signals is not effectively isolated, resulting in increased misjudgment rates. Furthermore, existing methods only provide abnormal alarms for density sensor data and cannot perform online correction of distorted data. Operations and maintenance personnel still need to conduct on-site verification, which fails to meet the real-time requirements of smart grids. Therefore, there is an urgent need for a monitoring system that can accurately identify the implicit distortion caused by progressive failures under complex working conditions and realize self-correction of monitoring data to improve the reliability of equipment status perception. Summary of the Invention

[0004] The technical problem to be solved by the present invention is that the existing technology cannot identify the hidden distortion of monitoring data caused by progressive failure due to aging of the sealing ring and loose installation, and proposes a monitoring data reconstruction method and system for SF6 sensors.

[0005] In order to achieve the above object, the present invention provides a technical solution for a monitoring data reconstruction method for an SF6 sensor, comprising the following steps: Step 1: Send an excitation signal to the SF6 gas-insulated equipment, causing the metal shell of the SF6 gas-insulated equipment to generate mechanical vibrations of a preset amplitude and activate a piezoelectric vibration sensor mounted on the surface of the metal shell, with the axial excitation direction of the sensor being consistent with the normal direction of the shell; Step 2: receiving a real-time density monitoring signal collected by an SF6 sensor installed on the SF6 gas-insulated equipment, and receiving a time-domain vibration signal detected by the piezoelectric vibration sensor; Step 3: screening and analyzing the characteristic frequency band required in the multiple frequency bands of the time domain vibration signal according to the real-time density monitoring signal; determining a first resonant frequency of the metal shell in the characteristic frequency band; Step 4: setting a stiffness detection mark in the characteristic frequency band according to the first resonant frequency, wherein the stiffness detection mark includes a plurality of bandwidth windows centered on the first resonant frequency, the plurality of bandwidth windows having the same center frequency and different bandwidths; Step 5: detecting a frequency shift relationship between the bandwidth window and a stiffness change region associated with gas density in the characteristic frequency band; Step 6: Determine the stiffness abnormality score of the metal casing according to the frequency shift relationship; 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; Step 7: Reconstruct the real-time density monitoring signal according to the health score, generate corrected density data, and send it to the monitoring platform.

[0006] Specifically, in step 3, based on the real-time density monitoring signal, the characteristic frequency band required for analysis is screened from multiple frequency bands of the time domain vibration signal, including: Calculate the SF6 molecular weight M based on the real-time density monitoring signal. When the SF6 molecular weight deviates from the theoretical molecular weight, it is determined that there is gas leakage or air mixing in the SF6 gas insulation equipment; When the molecular weight of SF6 is equal to the theoretical molecular weight, the main frequency range of the metal shell resonance is further determined. ; By calculating the correlation coefficient, the 0.5-5kHz frequency band is selected. The sub-bands with correlation greater than 0.8 are regarded as feature bands.

[0007] Specifically, in step 5, detecting the frequency shift relationship between the bandwidth window and the stiffness change region associated with the gas density in the characteristic frequency band includes: Using the piezoelectric vibration sensor, when no gas is introduced into the SF6 gas-insulated equipment, a reference frequency response curve is collected; Obtaining a stiffness change comparison spectrum according to the characteristic frequency band and the reference frequency response curve; determining second resonance frequencies of the plurality of bandwidth windows in the stiffness change comparison spectrum; determining a frequency shift intersection point between the stiffness change region and the bandwidth window according to the amplitude gradient of the stiffness change comparison spectrum and the second resonance frequency; The overlapping portion of each bandwidth window and the stiffness change region is determined according to the amplitude gradient of the stiffness change comparison spectrum and the frequency shift intersection point.

[0008] Specifically, determining a frequency shift intersection point between the stiffness change region and the bandwidth window according to the amplitude gradient of the stiffness change comparison spectrum and the second resonance frequency includes: Determine the frequency shift score of the kth frequency point in the jth 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 in the j-th bandwidth window is greater than or equal to a first preset threshold, determine the frequency point as the frequency shift intersection point to be determined between the stiffness change region and the bandwidth window; When the number of the to-be-determined frequency handover points is an even number, determining the to-be-determined frequency handover point to be a frequency handover point between the stiffness change region and the bandwidth window; When the number of the pending frequency handover points is an odd number, the pending frequency handover point with the lowest frequency shift score is removed, and the remaining pending frequency handover points are determined as the frequency handover points between the stiffness change region and the bandwidth window.

[0009] Specifically, the overlapping portion of each bandwidth window and the stiffness change region is determined according to the amplitude gradient of the stiffness change comparison spectrum and the frequency shift intersection point, specifically: Determining the frequency point amplitude between two adjacent frequency shift intersection points on the jth bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band includes: Determine the amplitude energy ratio between the tth frequency shift intersection point and the t+1th frequency shift intersection point on the jth bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band ; When the amplitude energy ratio is greater than or equal to the second preset threshold, it is determined that in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band, the part between the t-th frequency shift intersection point and the t+1-th frequency shift intersection point on the j-th bandwidth window is the part where the bandwidth window overlaps with the stiffness change area.

[0010] Specifically, in step 6, determining the stiffness abnormality score of the metal casing according to the frequency shift relationship includes: According to the following strategy: ; Determining a stiffness anomaly score S of the metal housing; in, is the resonant frequency shift of the i-th characteristic frequency band, is the temperature decay function; is the frequency band weight function; is the cavity geometry factor, is the base resonant frequency.

[0011] Specifically, determining the health score of the SF6 gas-insulated equipment according to the stiffness abnormality score, the real-time density monitoring signal, and the equipment sealing monitoring signal includes: According to the following strategy: ; Determining a health score H of the SF6 gas-insulated equipment; Wherein, S represents the stiffness abnormality score of the metal shell; Indicates the density value of the real-time density monitoring signal of the SF6 sensor. represents the nominal density; Represents the indicator function, when the sealing parameter Exceeding the threshold The function value is 1 when , otherwise it is 0; X represents the total number of sealing parameters; is the preset weight coefficient.

[0012] Specifically, in step seven, reconstructing the real-time density monitoring signal according to the health score to generate corrected density data includes: When the health score H exceeds the preset reconstruction threshold, the density compensation value is calculated according to the stiffness abnormality score S. ; Density compensation value Generating 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.

[0013] In addition, the monitoring data reconstruction system for SF6 sensors of the present invention includes the following modules: Excitation signal module, density acquisition module, vibration receiving module, frequency band screening module, resonance positioning module, bandwidth marking module, frequency shift analysis module, stiffness scoring module, health assessment module and data reconstruction module; The excitation signal module is used to send an excitation signal to the SF6 gas-insulated equipment, so that the metal shell of the equipment generates mechanical vibrations of a preset amplitude and activates the piezoelectric vibration sensor installed on the surface of the metal shell; The density acquisition module is used to receive real-time density monitoring signals collected by the SF6 density sensor installed on the SF6 gas-insulated equipment; The vibration receiving module is used to receive the time domain vibration signal detected by the piezoelectric vibration sensor; The frequency band screening module is used to screen the characteristic frequency band required for analysis from the multiple frequency bands of the time domain vibration signal according to the real-time density signal; The resonance positioning module is used to determine the first resonance frequency of the metal shell in the characteristic frequency band; The bandwidth marking module is used to set a stiffness detection mark 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 change region associated with the gas density in the characteristic frequency band; The stiffness scoring module is used to determine the stiffness abnormality score of the metal shell according to the frequency shift relationship; The health assessment module is used to determine the health score of the SF6 gas-insulated equipment according to the stiffness abnormality score, the real-time density signal and the sealing monitoring signal; The data reconstruction module is used to reconstruct the real-time density signal according to the health score, generate corrected density data, and send it to the monitoring platform.

[0014] A storage medium stores instructions, and when a computer reads the instructions, the computer is caused to execute the monitoring data reconstruction method for an SF6 sensor.

[0015] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for reconstructing monitoring data of an SF6 sensor is implemented.

[0016] Compared with the prior art, the technical effects of the present invention are as follows: 1. This invention utilizes a piezoelectric vibration sensor to actively excite the mechanical resonance of the metal housing. By detecting the offset of the first resonant frequency within a characteristic frequency band, it quantifies the attenuation of cavity stiffness caused by changes in gas density. This process establishes a physical correlation model of "gas density-cavity stiffness-resonance frequency," forming an independent verification channel with the direct density reading of the SF6 sensor, overcoming the monitoring blind spots caused by the gradual failure of a single sensor.

[0017] 2. This invention, based on amplitude gradient analysis of the stiffness variation comparison spectrum and combining it with a multi-bandwidth window frequency shift detection strategy, uses first-order derivatives to identify amplitude mutation boundaries, second-order derivatives to suppress noise interference, and a temperature and humidity compensation function to eliminate environmental drift. This allows for precise extraction of the frequency shift intersection between the stiffness variation region and the bandwidth window. This significantly improves the detection sensitivity of subtle stiffness anomalies under complex operating conditions (such as temperature and humidity fluctuations, and mechanical vibration and noise).

[0018] 3. The present invention quantifies the degree of degradation of the shell structure and adopts a logarithmic density compensation function to perform nonlinear reconstruction on the original density data. The correction amount is adaptively adjusted with the degree of stiffness degradation to compensate for the false high reading of the real-time density monitoring signal caused by sealing failure, thereby realizing early warning of progressive sealing failure and data self-correction, and significantly reducing the long-term drift error of SF6 density monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them: Figure 1 Schematic diagram of a flow chart of a monitoring data reconstruction method for an SF6 sensor according to the present invention; Figure 2 The figure is a structural diagram of a monitoring data reconstruction system for SF6 sensors according to the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0023] Example 1: like Figure 1 As shown, a monitoring data reconstruction method for SF6 sensor according to an embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows: Step 1: Send an excitation signal to the SF6 gas-insulated equipment, causing the metal shell of the SF6 gas-insulated equipment to generate mechanical vibrations of a preset amplitude and activate a piezoelectric vibration sensor mounted on the surface of the metal shell, with the axial excitation direction of the sensor being consistent with the normal direction of the shell; Step 2: receiving a real-time density monitoring signal collected by an SF6 sensor installed on the SF6 gas-insulated equipment, and receiving a time-domain vibration signal detected by the piezoelectric vibration sensor; Step 3: screening and analyzing the characteristic frequency band required in the multiple frequency bands of the time domain vibration signal according to the real-time density monitoring signal; In step three, based on the real-time density monitoring signal, the characteristic frequency band required for analysis is screened from the multiple frequency bands of the time domain vibration signal, including: Calculate the SF6 molecular weight M based on the real-time density monitoring signal. When the SF6 molecular weight deviates from the theoretical molecular weight, it is determined that there is gas leakage or air mixing in the SF6 gas insulation equipment; For example, in this embodiment, a calculation strategy for the molecular weight of SF6 is provided, specifically: ; Indicates the density value of the real-time density monitoring signal of the SF6 sensor; R is the SF6 gas constant; 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 of the SF6 molecular weight is obtained by expanding the ideal gas state equation; When the molecular weight of SF6 is equal to the theoretical molecular weight, the main frequency range of the metal shell resonance is further determined. ; In this embodiment, , where k is the stiffness coefficient and E is the Young's modulus of the metal shell; is the shell material density; L is the shell size, which is the height for cylindrical shells and the diameter for spherical containers; By calculating the correlation coefficient, the 0.5-5kHz frequency band is selected. The sub-bands with correlation greater than 0.8 are regarded as feature bands.

[0024] determining a first resonant frequency of the metal shell in the characteristic frequency band; Step 4: setting a stiffness detection mark in the characteristic frequency band according to the first resonant frequency, wherein the stiffness detection mark includes a plurality of bandwidth windows centered on the first resonant frequency, the plurality of bandwidth windows having the same center frequency and different bandwidths; Step 5: detecting a frequency shift relationship between the bandwidth window and a stiffness change region associated with gas density in the characteristic frequency band; In step five, detecting the frequency shift relationship between the bandwidth window and the stiffness change region associated with the gas density in the characteristic frequency band includes: Using the piezoelectric vibration sensor, when no gas is introduced into the SF6 gas-insulated equipment, a reference frequency response curve is collected; Obtaining a stiffness change comparison spectrum according to the characteristic frequency band and the reference frequency response curve; determining second resonance frequencies of the plurality of bandwidth windows in the stiffness change comparison spectrum; determining a frequency shift intersection point between the stiffness change region and the bandwidth window according to the amplitude gradient of the stiffness change comparison spectrum and the second resonance frequency; include: According to the following strategy: ; Determine the frequency shift score of the kth frequency point in the jth bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band ; in, Indicates the frequency vector of the j-th bandwidth window at the k-th frequency point in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band, represents the frequency coordinate, represents the amplitude, is the temperature and humidity compensation factor; is the reference frequency; illustratively, in this embodiment, the reference frequency is 1 Hz; It should be noted that is the first-order derivative, which is used to detect the amplitude mutation point, that is, to locate the boundary of the stiffness change area; is the second-order derivative, which 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, namely the thermal expansion and moisture expansion effect of the sealing material; 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 in the j-th bandwidth window is greater than or equal to a first preset threshold, determine the frequency point as the frequency shift intersection point to be determined between the stiffness change region and the bandwidth window; When the number of the to-be-determined frequency handover points is an even number, determining the to-be-determined frequency handover point to be a frequency handover point between the stiffness change region and the bandwidth window; When the number of the pending frequency handover points is an odd number, the pending frequency handover point with the lowest frequency shift score is removed, and the remaining pending frequency handover points are determined as the frequency handover points between the stiffness change region and the bandwidth window.

[0025] determining, according to the amplitude gradient of the stiffness change comparison spectrum and the frequency shift intersection point, a portion where each of the bandwidth windows overlaps with the stiffness change region; Specifically: Determining the frequency point amplitude between two adjacent frequency shift intersection points on the jth bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band includes: The specific strategies are as follows: ; Determine the amplitude energy ratio between the tth frequency shift intersection point and the t+1th frequency shift intersection point on the jth bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band ; in, Indicates the frequency corresponding to the tth frequency shift intersection point on the jth bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band, Indicates the frequency corresponding to the t+1th frequency shift intersection point on the jth bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band; is the current vibration amplitude spectrum; is the reference amplitude spectrum; It should be noted that the above strategy is used to confirm whether the frequency band between the two frequency shift intersection points is indeed in the stiffness change region to avoid noise interference; When the amplitude energy ratio is greater than or equal to the second preset threshold, it is determined that in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band, the part between the t-th frequency shift intersection point and the t+1-th frequency shift intersection point on the j-th bandwidth window is the part where the bandwidth window overlaps with the stiffness change area.

[0026] Step 6: Determine the stiffness abnormality score of the metal casing according to the frequency shift relationship; In step six, determining the abnormal stiffness score of the metal housing according to the frequency shift relationship includes: According to the following strategy: ; Determining a stiffness anomaly score S of the metal housing; in, is the resonant frequency shift of the i-th characteristic frequency band, and N is the total number of characteristic frequency bands; is the temperature decay function. In this embodiment, , T is the current ambient temperature; Calibrate the temperature of the device; is the thermal expansion coefficient of the material; is a frequency band weight function. In this embodiment, a specific acquisition strategy of a frequency band weight function is provided. ; is the center frequency of the i-th characteristic frequency band; is the cavity geometry factor, is the base resonant frequency.

[0027] Regarding the above strategy, it should be noted that the change in stiffness leads to a shift in the resonant frequency. The larger the shift, the more serious the stiffness attenuation. ) Eliminate the influence of device size. At the same time, amplify significant anomalies through square operations, compensate for material thermal expansion effects through temperature functions, and use frequency band weight functions to focus on sensitive frequency bands.

[0028] 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; include: According to the following strategy: ; Determining a health score H of the SF6 gas-insulated equipment; Wherein, S represents the stiffness abnormality score of the metal shell; Indicates the density value of the real-time density monitoring signal of the SF6 sensor. represents the nominal density; Represents the indicator function, when the sealing parameter Exceeding the threshold The function value is 1 when , otherwise it is 0; X represents the total number of sealing parameters (such as micro-water content and pressure decay rate); is the preset weight coefficient.

[0029] Step 7: Reconstruct the real-time density monitoring signal according to the health score, generate corrected density data, and send it to the monitoring platform.

[0030] Reconstructing the real-time density monitoring signal according to the health score to generate corrected density data includes: When the health score H exceeds the preset reconstruction threshold, the density compensation value is calculated according to the stiffness abnormality score S. ; For example, in this embodiment, the density compensation value is calculated according to the stiffness abnormality score S , specifically ; in, is the density reconstruction coefficient, is the abnormal stiffness benchmark value; Density compensation value Generating Corrected Density Data ; In this embodiment, specifically: ;in, is the health score threshold; When the health score H does not exceed the reconstruction threshold, there is no need to reconstruct the real-time density monitoring signal.

[0031] It should be noted that there is a nonlinear relationship between the stiffness abnormality score S and the density drop caused by gas leakage (i.e., S does not change significantly in the early stage of leakage, but increases significantly in the later stage), which is described by a logarithmic function. For the generation of corrected density data, a smooth transition is achieved through the tanh function. When H is much smaller than When the compensation amount is small, H is much larger than When the compensation amount is close to .

[0032] Example 2: like Figure 2 As shown, a monitoring data reconstruction system for SF6 sensors according to an embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules: Excitation signal module, density acquisition module, vibration receiving module, frequency band screening module, resonance positioning module, bandwidth marking module, frequency shift analysis module, stiffness scoring module, health assessment module and data reconstruction module; The excitation signal module is used to send an excitation signal to the SF6 gas-insulated equipment, so that the metal shell of the equipment generates mechanical vibrations of a preset amplitude and activates the piezoelectric vibration sensor installed on the surface of the metal shell; The density acquisition module is used to receive real-time density monitoring signals collected by the SF6 density sensor installed on the SF6 gas-insulated equipment; The vibration receiving module is used to receive the time domain vibration signal detected by the piezoelectric vibration sensor; The frequency band screening module is used to screen the characteristic frequency band required for analysis from the multiple frequency bands of the time domain vibration signal according to the real-time density signal; The resonance positioning module is used to determine the first resonance frequency of the metal shell in the characteristic frequency band; The bandwidth marking module is used to set a stiffness detection mark 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 change region associated with the gas density in the characteristic frequency band; The stiffness scoring module is used to determine the stiffness abnormality score of the metal shell according to the frequency shift relationship; The health assessment module is used to determine the health score of the SF6 gas-insulated equipment according to the stiffness abnormality score, the real-time density signal and the sealing monitoring signal; The data reconstruction module is used to reconstruct the real-time density signal according to the health score, generate corrected density data, and send it to the monitoring platform.

[0033] Example 3: This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned monitoring data reconstruction method for the SF6 sensor by calling the computer program stored in the memory.

[0034] This electronic device can vary significantly depending on its configuration or performance. It can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memories store 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 method embodiment. The electronic device can also include other components for implementing the device's functions. For example, the electronic device can also include components such as a wired or wireless network interface and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0035] Example 4: This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program is executed on a computer device, the computer device is caused to execute the above-mentioned monitoring data reconstruction method for an SF6 sensor.

[0036] 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 device.

[0037] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0038] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0039] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention 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. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0040] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0041] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, 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.

[0042] 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.

[0043] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0044] 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.

[0045] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A monitoring data reconstruction method for SF6 sensor, characterized in that: The method comprises: Step 1: Send an excitation signal to the SF6 gas-insulated equipment, causing the metal shell of the SF6 gas-insulated equipment to generate mechanical vibrations of a preset amplitude and activate a piezoelectric vibration sensor mounted on the surface of the metal shell, with the axial excitation direction of the sensor being consistent with the normal direction of the shell; Step 2: receiving a real-time density monitoring signal collected by an SF6 sensor installed on the SF6 gas-insulated equipment, and receiving a time-domain vibration signal detected by the piezoelectric vibration sensor; Step 3: screening and analyzing the characteristic frequency band required in the multiple frequency bands of the time domain vibration signal according to the real-time density monitoring signal; determining a first resonant frequency of the metal shell in the characteristic frequency band; Step 4: setting a stiffness detection mark in the characteristic frequency band according to the first resonant frequency, wherein the stiffness detection mark includes a plurality of bandwidth windows centered on the first resonant frequency, the plurality of bandwidth windows having the same center frequency and different bandwidths; Step 5: detecting a frequency shift relationship between the bandwidth window and a stiffness change region associated with gas density in the characteristic frequency band; Step 6: Determine the stiffness abnormality score of the metal casing according to the frequency shift relationship; 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; Step 7: Reconstruct the real-time density monitoring signal according to the health score, generate corrected density data, and send it to the monitoring platform.

2. A monitoring data reconstruction method for SF6 sensor according to claim 1, characterized in that: In step five, detecting the frequency shift relationship between the bandwidth window and the stiffness change region associated with the gas density in the characteristic frequency band includes: Using the piezoelectric vibration sensor, when no gas is introduced into the SF6 gas-insulated equipment, a reference frequency response curve is collected; Obtaining a stiffness change comparison spectrum according to the characteristic frequency band and the reference frequency response curve; determining second resonance frequencies of the plurality of bandwidth windows in the stiffness change comparison spectrum; determining a frequency shift intersection point between the stiffness change region and the bandwidth window according to the amplitude gradient of the stiffness change comparison spectrum and the second resonance frequency; The overlapping portion of each bandwidth window and the stiffness change region is determined according to the amplitude gradient of the stiffness change comparison spectrum and the frequency shift intersection point.

3. The monitoring data reconstruction method for SF6 sensor according to claim 2, characterized in that: Determining a frequency shift intersection point between the stiffness change region and the bandwidth window according to the amplitude gradient of the stiffness change comparison spectrum and the second resonant frequency, comprising: Determine the frequency shift score of the kth frequency point in the jth 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 in the j-th bandwidth window is greater than or equal to a first preset threshold, determine the frequency point as the frequency shift intersection point to be determined between the stiffness change region and the bandwidth window; When the number of the to-be-determined frequency handover points is an even number, determining the to-be-determined frequency handover point to be a frequency handover point between the stiffness change region and the bandwidth window; When the number of the pending frequency handover points is an odd number, the pending frequency handover point with the lowest frequency shift score is removed, and the remaining pending frequency handover points are determined as the frequency handover points between the stiffness change region and the bandwidth window.

4. A monitoring data reconstruction method for SF6 sensor according to claim 3, characterized in that: The overlapping portion of each bandwidth window and the stiffness change region is determined according to the amplitude gradient of the stiffness change comparison spectrum and the frequency shift intersection point, specifically: Determining the frequency point amplitude between two adjacent frequency shift intersection points on the jth bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band includes: Determine the amplitude energy ratio between the tth frequency shift intersection point and the t+1th frequency shift intersection point on the jth bandwidth window in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band ; When the amplitude energy ratio is greater than or equal to the second preset threshold, it is determined that in the stiffness change comparison spectrum corresponding to the i-th characteristic frequency band, the part between the t-th frequency shift intersection point and the t+1-th frequency shift intersection point on the j-th bandwidth window is the part where the bandwidth window overlaps with the stiffness change area.

5. The monitoring data reconstruction method for SF6 sensor according to claim 4, characterized in that: In step six, determining the abnormal stiffness score of the metal housing according to the frequency shift relationship includes: According to the following strategy: ； Determining a stiffness anomaly score S of the metal housing; in, is the resonant frequency shift of the i-th characteristic frequency band, and N is the total number of characteristic frequency bands; is the temperature decay function; is the frequency band weight function; is the cavity geometry factor, is the base resonant frequency.

6. A monitoring data reconstruction method for SF6 sensor according to claim 5, characterized in that: In step 6, the health score of the SF6 gas-insulated equipment is determined based on the stiffness abnormality score, the real-time density monitoring signal, and the equipment sealing monitoring signal, including: According to the following strategy: ； Determining a health score H of the SF6 gas-insulated equipment; Wherein, S represents the stiffness abnormality score of the metal shell; Indicates the density value of the real-time density monitoring signal of the SF6 sensor. represents the nominal density; Represents the indicator function, when the sealing parameter Exceeding the threshold The function value is 1 when , otherwise it is 0; X represents the total number of sealing parameters; is the preset weight coefficient.

7. A monitoring data reconstruction method for SF6 sensor according to claim 6, characterized in that: In step seven, the real-time density monitoring signal is reconstructed according to 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 according to the stiffness abnormality score S. ; Compensate for density Generating 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 monitoring data reconstruction method for SF6 sensor according to claim 1, characterized in that: In step three, based on the real-time density monitoring signal, the characteristic frequency band required for analysis is screened from the multiple frequency bands of the time domain vibration signal, including: Calculate the SF6 molecular weight M based on the real-time density monitoring signal. When the SF6 molecular weight deviates from the theoretical molecular weight, it is determined that there is gas leakage or air mixing in the SF6 gas insulation equipment; When the molecular weight of SF6 is equal to the theoretical molecular weight, the main frequency range of the metal shell resonance is further determined. ; By calculating the correlation coefficient, the 0.5-5kHz frequency band is selected. The sub-bands with correlation greater than 0.8 are regarded as feature bands.

9. A monitoring data reconstruction system for an SF6 sensor, configured to implement a monitoring data reconstruction method for an SF6 sensor according to any one of claims 1 to 8, characterized in that: The system includes the following modules: Excitation signal module, density acquisition module, vibration receiving module, frequency band screening module, resonance positioning module, bandwidth marking module, frequency shift analysis module, stiffness scoring module, health assessment module and data reconstruction module; The excitation signal module is used to send an excitation signal to the SF6 gas-insulated equipment, so that the metal shell of the equipment generates mechanical vibrations of a preset amplitude and activates the piezoelectric vibration sensor installed on the surface of the metal shell; The density acquisition module is used to receive real-time density monitoring signals collected by the SF6 density sensor installed on the SF6 gas-insulated equipment; The vibration receiving module is used to receive the time domain vibration signal detected by the piezoelectric vibration sensor; The frequency band screening module is used to screen the characteristic frequency band required for analysis from the multiple frequency bands of the time domain vibration signal according to the real-time density signal; The resonance positioning module is used to determine the first resonance frequency of the metal shell in the characteristic frequency band; The bandwidth marking module is used to set a stiffness detection mark 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 change region associated with the gas density in the characteristic frequency band; The stiffness scoring module is used to determine the stiffness abnormality score of the metal shell according to the frequency shift relationship; The health assessment module is used to determine the health score of the SF6 gas-insulated equipment according to the stiffness abnormality score, the real-time density signal and the sealing monitoring signal; The data reconstruction module is used to reconstruct the real-time density signal according to the health score, generate corrected density data, and send it to the monitoring platform.

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