A GIS metal particle partial discharge test method and system

Abstract

The present invention discloses a GIS metal particle partial discharge test method and system, which obtains partial discharge test information through an information acquisition unit, performs pre-processing on partial discharge parameters of metal particles and environmental parameters of GIS equipment, and then preliminarily analyzes the partial discharge test information through a preprocessing unit, and sequentially evaluates the risk impact degree of metal particles on GIS equipment and the impact degree of equipment environment changes on metal particles, and then constructs an impact model of metal particle partial discharge test through a core processing unit, evaluates the risk fluctuation change degree of GIS equipment, generates an early warning prompt signal and an equipment environment control plan, receives the early warning prompt signal through a signal control unit and performs corresponding control processing operations, thereby ensuring the accuracy and stability of GIS metal particle partial discharge test.

Description

Technical Field

[0001] The present invention relates to the technical field of metal particle testing, and in particular to a GIS metal particle partial discharge testing method and system. Background Art

[0002] Metal particles usually refer to tiny metal particles found in electrical equipment (such as substations, switchgear, etc.), which may be caused by equipment wear, poor contact or other problems. GIS metal particles refer to metal particles found in GIS equipment (Gas Insulated Switchgear). These particles may affect the performance and safe operation of the equipment. Therefore, to reduce these risks and maintain the stable performance of GIS equipment, it is usually necessary to control the production and maintenance process to avoid the accumulation of metal particles.

[0003] GIS metal particle partial discharge test is a test method to locate, monitor and analyze metal particles in GIS equipment. Through this test, potential equipment failure points can be identified, equipment problems can be discovered in advance, and the normal operation and safety of the equipment can be ensured;

[0004] However, the existing GIS metal particle partial discharge test is easily affected by changes in the equipment environment. Since it is difficult to establish a functional change relationship between metal particle risk and equipment environment fluctuations, the metal particle partial discharge test has poor accuracy and insufficient stability, which affects the performance and safe operation of GIS equipment. Summary of the invention

[0005] In view of the above technical defects, a solution is now proposed, specifically a GIS metal particle partial discharge testing method and system.

[0006] The purpose of the present invention is to solve the defects of the prior art that the partial discharge test of GIS metal particles is easily affected by the changes in the equipment environment, and it is difficult to establish a functional change relationship between the risk of metal particles and the fluctuation of the equipment environment, resulting in poor accuracy and insufficient stability of the partial discharge test of metal particles. By obtaining partial discharge test information, pre-processing is performed on the partial discharge parameters of the metal particles and the environmental parameters of the GIS equipment, and then the partial discharge test information is preliminarily analyzed to evaluate the degree of risk influence of metal particles on the GIS equipment and the degree of influence of the equipment environment changes on the metal particles in turn, and then an influence model of the partial discharge test of metal particles is constructed, the degree of risk fluctuation of GIS equipment is evaluated, an early warning prompt signal and an equipment environment control plan are generated, and corresponding control processing operations are performed, thereby ensuring the accuracy and stability of the partial discharge test of GIS metal particles.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A GIS metal particle partial discharge test system comprises an information acquisition unit, a preprocessing unit, a core processing unit and a signal control unit, wherein the information acquisition unit, the preprocessing unit, the core processing unit and the signal control unit are signal connected.

[0009] Furthermore, a GIS metal particle partial discharge test system, the information collection unit is used to obtain partial discharge test information, the preprocessing unit is used to preliminarily analyze the partial discharge test information, the core processing unit is used to construct an impact model of the metal particle partial discharge test, and the signal control unit is used to receive early warning prompt signals and perform corresponding control processing operations.

[0010] A GIS metal particle partial discharge test method comprises the following steps: step 1, obtaining partial discharge test information: setting an information collection period Tc to periodically collect partial discharge test information, wherein the partial discharge test information comprises partial discharge parameters of metal particles and environmental parameters of GIS equipment; step 2, preliminarily analyzing the partial discharge test information: firstly, by analyzing the partial discharge parameters of metal particles, obtaining a GIS equipment risk index, and evaluating the risk impact degree of the GIS equipment by the metal particles; and by analyzing the environmental parameters of the GIS equipment, obtaining an environmental comprehensive impact index, and evaluating the impact degree of the equipment environment change on the metal particles;

[0011] Step 3: Construct an impact model for metal particle partial discharge testing: First, fit and construct the change function of the GIS equipment risk index and the environmental comprehensive impact index, and then deeply analyze and generate early warning signals and equipment environment control plans;

[0012] Step 4: Perform control and processing operations: When receiving an early warning prompt signal, perform corresponding control and processing operations through the equipment environment control plan.

[0013] Furthermore, the specific process of obtaining partial discharge test information is as follows: the partial discharge parameters of metal particles include the density value, size and shape of the metal particles; the metal particles inside the GIS equipment are detected by ultrasonic technology to generate a metal particle distribution map, and the partial discharge parameters of the metal particles are obtained through the metal particle distribution map;

[0014] The environmental parameters of GIS equipment include the temperature value, vibration value and electric energy value of GIS equipment; the temperature value of GIS equipment is obtained by monitoring with infrared thermal imaging instruments, the vibration value of GIS equipment is obtained by monitoring with vibration sensors, and the electric energy value of GIS equipment is obtained by monitoring with electric energy meters. Furthermore, an image analysis model is constructed to extract parameters from the metal particle distribution map: the metal particle distribution map is input, the image is divided into N0 feature areas, any feature area is marked as i, and the metal particles in the feature area i are ultrasonically detected. It is assumed that there are n0 metal particles in the feature area i, and any metal particle is marked as J, and the density value of metal particle J is marked as ρj. The size and shape of metal particle J are simulated in three dimensions, and the volume value Vj of metal particle J is calculated, and then the metal particle morphology coefficient Xj of feature area i is obtained by combining the density value ρj of n0 metal particles with the volume value Vj.

[0015] Furthermore, the specific process of analyzing the partial discharge parameters of metal particles is as follows: by analyzing the partial discharge parameters of metal particles, the metal particle morphology coefficients Xj of N0 characteristic areas are combined to generate the GIS equipment risk index Zfx:

[0016] The mean value PJx of the metal particle morphology coefficient Xj of N0 characteristic areas is calculated, and then the standard deviation is calculated to obtain the fluctuation coefficient σx;

[0017] Then obtain the kurtosis coefficient FDx and skewness coefficient PTx of the metal particle morphology coefficient Xj in N0 characteristic areas:

[0018] Then, the GIS equipment risk index Zfx is obtained by combining the mean value PJx, fluctuation coefficient σx, kurtosis coefficient FDx and skewness coefficient PTx of the metal particle morphology coefficient Xj in N0 characteristic areas;

[0019] Set the evaluation range of the GIS equipment risk index Zfx, and evaluate the risk impact of metal particles on GIS equipment through interval comparison.

[0020] Furthermore, the specific process of analyzing the environmental parameters of GIS equipment is as follows: By analyzing the environmental parameters of GIS equipment, the comprehensive environmental impact index Zhy is obtained:

[0021] Obtain the environmental parameters of the GIS equipment corresponding to N1 information collection cycles Tc. The environmental parameters of the GIS equipment include the temperature value, vibration value and electric energy value of the GIS equipment, and construct a parameter matrix V;

[0022] The eigenvalues ​​of the eigenvectors in the matrix are obtained by calculating the standard deviation of the column vectors, and the eigenvalues ​​corresponding to the temperature value, vibration value, and electric energy value of the GIS equipment are marked as γ1, γ2, and γ3 respectively;

[0023] Then, by calculating the proportion of any eigenvalue in the sum of the three eigenvalues, the weight factors corresponding to the temperature value, vibration value, and electrical energy value of the GIS device are obtained respectively, and are marked as υ1, υ2, and υ3;

[0024] Then, by combining the eigenvalues corresponding to the temperature value, vibration value, and electrical energy value of the GIS device with the weight factors, the comprehensive environmental impact index Zhy is obtained;

[0025] Set the evaluation interval of the comprehensive environmental impact index Zhy, and evaluate the impact degree of the equipment environmental change on metal particles through interval comparison.

[0026] Furthermore, the specific construction process of the influence model for metal particle partial discharge testing is as follows:

[0027] Through the GIS device risk index Zfx and the comprehensive environmental impact index Zhy, fit and construct the change function F1(x) of the two;

[0028] By deeply analyzing the change function F1(x), the risk fluctuation coefficient Xσ is obtained; by setting the risk interval of the risk fluctuation coefficient Xσ and conducting interval comparison, a warning prompt signal and the corresponding equipment environment regulation plan are generated. Furthermore, the specific process of obtaining the risk fluctuation coefficient Xσ is as follows:

[0029] Through the GIS device risk index Zfx and the comprehensive environmental impact index Zhy, fit and construct the change function F1(x): Zfx = F1(Zhy), substitute the comprehensive environmental impact index Zhy into the change function F1(x), so as to obtain the GIS device risk index Zfx;

[0030] By deeply analyzing the change function F1(x), the risk fluctuation coefficient Xσ is obtained: Xσ = Zhy * Δzfx; where, ΔZfx refers to the absolute value of the difference between the GIS device risk indices obtained in adjacent information collection periods Tc. Mark the GIS device risk index in any information collection period Tc as Zhy1, and mark the GIS device risk index in the adjacent information collection period Tc as Zhy2, then ΔZhy = |Zhy1 - Zhy2|. Furthermore, the specific process of generating the equipment environment regulation plan is as follows:

[0031] Set the risk interval of the risk fluctuation coefficient Xσ and conduct interval comparison to generate a warning prompt signal and the corresponding equipment environment regulation plan:

[0032] Presuppose there are R risk intervals, mark any one risk interval as Qr, where r is the serial number of the risk interval, 0 < r ≤ R. When the risk fluctuation coefficient Xσ is within the risk interval Qr, an r-level warning prompt signal is generated, output and visually presented.

[0033] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows: the present invention obtains partial discharge test information through the information acquisition unit, and obtains partial discharge parameters and

[0034] The environmental parameters of the GIS equipment are pre-processed, and then the partial discharge test information is preliminarily analyzed by the preprocessing unit. The risk impact degree of metal particles on the GIS equipment and the impact degree of equipment environment changes on the metal particles are evaluated in turn. Then, the impact model of the metal particle partial discharge test is constructed through the core processing unit, and the risk fluctuation degree of GIS equipment is evaluated. Early warning prompt signals and equipment environment control plans are generated. The early warning prompt signals are received by the signal control unit and corresponding control processing operations are performed, thereby ensuring the accuracy and stability of the GIS metal particle partial discharge test. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Shows a schematic diagram of the modules of the present invention;

[0036] Figure 2 A schematic diagram of the process of the present invention is shown. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Embodiment 1:

[0039] like Figure 1-2 As shown, a GIS metal particle partial discharge test system includes an information acquisition unit, a preprocessing unit, a core processing unit and a signal control unit, wherein the information acquisition unit, the preprocessing unit, the core processing unit and the signal control unit are signal connected;

[0040] The working steps are as follows: Step 1: The information collection unit obtains partial discharge test information: the information collection period Tc is set to collect partial discharge test information at regular intervals. The partial discharge test information includes partial discharge parameters of metal particles and environmental parameters of GIS equipment. The specific process of obtaining partial discharge test information is as follows:

[0041] S1-1, the partial discharge parameters of metal particles include the density value, size and shape of metal particles; the metal particles inside the GIS equipment are detected by ultrasonic technology to generate a metal particle distribution map, and the partial discharge parameters of the metal particles are obtained through the metal particle distribution map; an image analysis model is constructed to perform parameter extraction and analysis on the metal particle distribution map: the metal particle distribution map is input, the image is divided into N0 feature areas, any feature area is marked as i, and the metal particles in the feature area i are ultrasonically detected. It is preset that there are n0 metal particles in the feature area i, any metal particle is marked as J, and the density value of the metal particle J is marked as ρj. The size and shape of the metal particle J are simulated in three dimensions, and the volume value Vj of the metal particle J is obtained by calculation, and then the metal particle morphology coefficient Xj of the feature area i is obtained by combining the density value ρj of n0 metal particles with the volume value Vj:

[0042]

[0043] Wherein, μ is the conversion coefficient and μ is greater than 0. The conversion coefficient μ refers to the constant value of the morphology coefficient Xj converted by combining the value ρj of the metal particle with the volume value Vj. The conversion coefficient μ is preset and obtained through a large number of experimental measurements. When the value ρj and the volume value Vj of the metal particle are higher, the metal particle morphology coefficient Xj of the characteristic region i is higher, indicating that the number n0 of the metal particles is denser, and the higher the density and volume of the metal particles, the higher the metal particle morphology coefficient Xj of the characteristic region i, which may make the overall risk level of the metal particle distribution map higher.

[0044] It should be noted that the morphology, size and density of metal particles may have a serious impact on the performance of GIS (gas-insulated metal-enclosed switchgear), such as dielectric breakdown risk, changes in thermal conductivity, partial discharge risk, and reduced insulation performance. The specific manifestations are:

[0045] The presence of metal particles may increase the risk of electrical breakdown and affect the insulation performance of GIS; larger metal particles may cause changes in thermal conductivity and may cause overheating; under high voltage, metal particles may cause partial discharge, which in turn affects equipment performance and reliability; the presence of metal particles may cause the strength of insulating materials to decrease, accelerate insulation aging, and reduce the life of equipment; therefore, to reduce these risks and maintain stable performance of GIS equipment, it is usually necessary to control the accumulation of metal particles during production and maintenance, and strictly control their morphology, size and density;

[0046] S1-2, the environmental parameters of the GIS equipment include the temperature value, vibration value and electric energy value of the GIS equipment; the temperature value of the GIS equipment is obtained by monitoring with an infrared thermal imaging instrument, the vibration value of the GIS equipment is obtained by monitoring with a vibration sensor, and the electric energy value of the GIS equipment is obtained by monitoring with an electric energy meter; Step 2, the pre-processing unit preliminarily analyzes the partial discharge test information: first, by analyzing the partial discharge parameters of the metal particles, the GIS equipment risk index is obtained, and the risk impact degree of the GIS equipment being affected by the metal particles is evaluated; and by analyzing the environmental parameters of the GIS equipment, the comprehensive environmental impact index is obtained to evaluate the impact degree of the equipment environment change on the metal particles;

[0047] S2-1, the specific process of analyzing the partial discharge parameters of metal particles is as follows: by analyzing the partial discharge parameters of metal particles, the metal particle morphology coefficients Xj of N0 characteristic areas are combined to generate the GIS equipment risk index Zfx:

[0048] The mean value PJx of the metal particle morphology coefficient Xj of N0 characteristic areas is calculated, and then the standard deviation is calculated to obtain the fluctuation coefficient σx;

[0049] Then obtain the kurtosis coefficient FDx and skewness coefficient PTx of the metal particle morphology coefficient Xj in N0 characteristic areas:

[0050] Kurtosis coefficient FDx:

[0051]

[0052] When the kurtosis coefficient FDx is higher, it means that the distribution difference of the metal particle morphology coefficient Xj in the N0 characteristic regions is greater; when the kurtosis coefficient FDx is lower, it means that the distribution of the metal particle morphology coefficient Xj in the N0 characteristic regions is smoother and tends to be consistent;

[0053] Skewness coefficient PTx:

[0054] When the skewness coefficient PTx is greater than 0, it means that the average PJx of the metal particle morphology coefficient Xj of N0 characteristic regions is higher than the mode of the metal particle morphology coefficient Xj, indicating that the distribution of the metal particle morphology coefficient Xj of N0 characteristic regions is right-skewed, and the higher the value of the skewness coefficient PTx, the higher the degree of the right-skewness trend; when the skewness coefficient PTx is less than 0, it means that the average PJx of the metal particle morphology coefficient Xj of N0 characteristic regions is lower than the mode of the metal particle morphology coefficient Xj, indicating that the distribution of the metal particle morphology coefficient Xj of N0 characteristic regions is left-skewed, and the lower the value of the skewness coefficient PTx, the higher the degree of the left-skewness trend; when the skewness coefficient PTx is closer to 0, the average PJx of the metal particle morphology coefficient Xj of N0 characteristic regions is closer to the mode of the metal particle morphology coefficient Xj, which further indicates that the distribution of the metal particle morphology coefficient Xj of N0 characteristic regions is more uniform;

[0055] Then, the GIS equipment risk index Zfx is obtained by combining the mean PJx, fluctuation coefficient σx, kurtosis coefficient FDx and skewness coefficient PTx of the metal particle morphology coefficient Xj in N0 characteristic areas:

[0056] Zfx=λ1*PJx σx +λ2*FDx+λ3*PTx 2

[0057] Among them, λ1, λ2 and λ3 are weight factor coefficients of mean PJx, kurtosis coefficient FDx and skewness coefficient PTx respectively, and λ1, λ2 and λ3 are all greater than 0; when the mean PJx, fluctuation coefficient σx and kurtosis coefficient FDx of metal particle morphology coefficient Xj are higher, and the skewness coefficient PTx deviates more from 0, the GIS equipment risk index Zfx is higher, indicating that the aggregation degree of metal particles is higher, and further indicating that the GIS equipment is more affected by the risk of metal particles;

[0058] Set the evaluation interval of the GIS equipment risk index Zfx, and evaluate the risk impact of metal particles on GIS equipment by comparing the intervals:

[0059] There are U evaluation intervals preset, and any evaluation interval is marked as Qu, where u is the serial number of the evaluation interval, 0<u≤U. When the GIS equipment risk index Zfx is in the evaluation interval Qu, the risk impact degree of the GIS equipment by metal particles is evaluated to be u level, and it is output and visualized; when the GIS equipment risk index Zfx is lower, the risk impact degree of the GIS equipment by metal particles is evaluated to be lower;

[0060] S2-2, the specific process of analyzing the environmental parameters of GIS equipment is:

[0061] By analyzing the environmental parameters of GIS equipment, the comprehensive environmental impact index Zhy is obtained:

[0062] Obtain the environmental parameters of the GIS equipment corresponding to N1 information collection cycles Tc. The environmental parameters of the GIS equipment include the temperature value, vibration value and electric energy value of the GIS equipment, and construct the parameter matrix V:

[0063]

[0064] The eigenvalues ​​of the eigenvectors in the matrix are obtained by calculating the standard deviation of the column vectors, and the eigenvalues ​​corresponding to the temperature value, vibration value, and electric energy value of the GIS equipment are marked as γ1, γ2, and γ3 respectively;

[0065] Then, by calculating the proportion of any eigenvalue in the sum of the three eigenvalues, the weight factors corresponding to the temperature value, vibration value and electric energy value of the GIS equipment are obtained respectively, and marked as υ1, υ2 and υ3 respectively;

[0066] Then, by combining the characteristic values ​​corresponding to the temperature value, vibration value and electric energy value of the GIS equipment with the weight factor, the comprehensive environmental impact index Zhy is obtained:

[0067] When the characteristic values ​​and weight factors corresponding to the temperature, vibration and electric energy values ​​of GIS equipment are higher, the comprehensive environmental impact index Zhy is higher, which means that the fluctuation difference of the temperature, vibration and electric energy values ​​of GIS equipment is greater, and the influence of equipment environment changes on metal particles is higher;

[0068] Set the evaluation interval of the comprehensive environmental impact index Zhy, and evaluate the impact of equipment environment changes on metal particles by comparing the intervals:

[0069] There are W evaluation intervals preset, and any evaluation interval is marked as Qw, where w is the serial number of the evaluation interval, 0<w≤W. When the comprehensive environmental impact index Zhy is in the evaluation interval Qw, the impact of the equipment environment change on the metal particles is evaluated to be w level, and it is output and visualized; the lower the comprehensive environmental impact index Zhy is, the lower the impact of the equipment environment change on the metal particles is; Step three, the core processing unit constructs an impact model for the partial discharge test of metal particles: first, through the GIS equipment risk index and the comprehensive environmental impact index, the change function of the two is fitted and constructed, and then the early warning prompt signal and the equipment environment control plan are deeply analyzed and generated;

[0070] S3-1, the specific construction process of the impact model of metal particle partial discharge test is as follows:

[0071] Through the GIS equipment risk index Zfx and the comprehensive environmental impact index Zhy, the change function F1(x) of the two is constructed by fitting, Zfx=F1(Zhy), and the comprehensive environmental impact index Zhy is substituted into the change function F1(x) to obtain the GIS equipment risk index Zfx;

[0072] By deeply analyzing the variation function F(x), the risk fluctuation coefficient Xσ is obtained: Xσ=Zhy*ΔZfx; wherein ΔZfx refers to the absolute value of the difference in the GIS equipment risk index obtained in the adjacent information collection period Tc. The GIS equipment risk index of any information collection period Tc is marked as Zhy1, and the GIS equipment risk index of the adjacent information collection period Tc is marked as Zhy2, then ΔZhy=|Zhy1-Zhy2|;

[0073] When the comprehensive environmental impact index Zhy is higher and the change in the GIS equipment risk index is greater, the risk fluctuation coefficient Xσ is higher, indicating that the degree of risk fluctuation of GIS equipment is higher; by setting the risk interval of the risk fluctuation coefficient Xσ and comparing the intervals, an early warning signal and a corresponding equipment environment control plan are generated. The specific process is as follows:

[0074] There are R risk intervals preset, and any risk interval is marked as Qr, where r is the serial number of the risk interval, 0<r≤R. When the risk volatility coefficient Xσ is in the risk interval Qr, an r-level warning signal is generated, output and visualized; the lower the risk volatility coefficient Xσ, the lower the level of the warning signal;

[0075] Step 4: The signal control unit performs control processing operations: when receiving the early warning prompt signal, the corresponding control processing operations are performed through the equipment environment control scheme;

[0076] The equipment environment control scheme reduces the comprehensive environmental impact index Zhy by regulating the environmental parameters of GIS equipment and keeping them stable, thereby reducing the risk fluctuation coefficient Xσ to reduce the degree of risk fluctuation of GIS equipment, thereby ensuring the accuracy and stability of GIS metal particle partial discharge testing.

[0077] In summary, the present invention obtains partial discharge test information through an information acquisition unit, performs pre-processing on the partial discharge parameters of metal particles and the environmental parameters of GIS equipment, and then preliminarily analyzes the partial discharge test information through a preprocessing unit, and sequentially evaluates the risk impact degree of metal particles on GIS equipment and the impact degree of equipment environment changes on metal particles. Then, an impact model of metal particle partial discharge test is constructed through a core processing unit, the risk fluctuation degree of GIS equipment is evaluated, an early warning prompt signal and an equipment environment control plan are generated, and the early warning prompt signal is received through a signal control unit and corresponding control processing operations are performed, thereby ensuring the accuracy and stability of GIS metal particle partial discharge test.

[0078] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0079] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0080] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A GIS metal particle partial discharge test method, characterized by: The following steps are involved: Step 1: Obtaining partial discharge test information: Setting the information collection period Tc to periodically collect partial discharge test information, the partial discharge test information includes partial discharge parameters of metal particles and environmental parameters of GIS equipment; Step 2: Preliminary analysis of partial discharge test information: First, by analyzing the partial discharge parameters of metal particles, the GIS equipment risk index is obtained to evaluate the risk impact of metal particles on GIS equipment; by analyzing the environmental parameters of GIS equipment, the comprehensive environmental impact index is obtained to evaluate the impact of equipment environment changes on metal particles; Step 3: Construct an impact model for metal particle partial discharge testing: First, fit and construct the change function of the GIS equipment risk index and the environmental comprehensive impact index, and then deeply analyze and generate early warning signals and equipment environment control plans; Step 4: Receive the signal and perform control and processing operations: When the warning prompt signal is received, the corresponding control and processing operations are performed through the equipment environment control plan; The specific process of obtaining partial discharge test information is as follows: The partial discharge parameters of metal particles include the density, size and shape of metal particles. The metal particles inside the GIS equipment are detected by ultrasonic technology to generate a metal particle distribution map. The metal particle distribution map is analyzed and extracted by an image analysis model to obtain the partial discharge parameters of the metal particles. The environmental parameters of GIS equipment include the temperature value, vibration value and electric energy value of GIS equipment. The temperature value of GIS equipment is obtained by infrared thermal imaging instrument, the vibration value of GIS equipment is obtained by vibration sensor, and the electric energy value of GIS equipment is obtained by electric energy meter. The specific process of the image analysis model for parameter extraction and analysis of the metal particle distribution map is as follows: input the metal particle distribution map, divide the image into N0 feature areas, mark any feature area as i, perform ultrasonic detection on the metal particles in the feature area i, preset the feature area i to have n0 metal particles, mark any metal particle as J, mark the density value of the metal particle J as ρj, perform three-dimensional simulation on the size and shape of the metal particle J, calculate and obtain the volume value Vj of the metal particle J, and then obtain the metal particle morphology coefficient Xj of the feature area i by combining the density value ρj of n0 metal particles with the volume value Vj; The specific process of analyzing the partial discharge parameters of metal particles is as follows: By analyzing the partial discharge parameters of metal particles, the metal particle morphology coefficients Xj of N0 characteristic areas are combined to generate the GIS equipment risk index Zfx: The mean value PJx of the metal particle morphology coefficient Xj of N0 characteristic areas is calculated, and then the standard deviation is calculated to obtain the fluctuation coefficient σx; Obtain the kurtosis coefficient FDx and skewness coefficient PTx of the metal particle morphology coefficient Xj in N0 characteristic areas; The GIS equipment risk index Zfx is obtained by combining the mean PJx, fluctuation coefficient σx, kurtosis coefficient FDx and skewness coefficient PTx of the metal particle morphology coefficient Xj in N0 characteristic areas; Set the evaluation interval of GIS equipment risk index Zfx, and evaluate the risk impact of metal particles on GIS equipment through interval comparison; The specific process of analyzing the environmental parameters of GIS equipment is: By analyzing the environmental parameters of GIS equipment, the comprehensive environmental impact index Zhy is obtained; Obtain the environmental parameters of the GIS equipment corresponding to N1 information collection cycles Tc, which include the temperature value, vibration value and electric energy value of the GIS equipment, and construct a parameter matrix V; obtain the eigenvalues ​​of the eigenvectors in the matrix by calculating the standard deviation of the column vector, and mark the eigenvalues ​​corresponding to the temperature value, vibration value and electric energy value of the GIS equipment as γ1, γ2 and γ3 respectively; then, by calculating the proportion of any eigenvalue in the sum of the three eigenvalues, obtain the weight factors corresponding to the temperature value, vibration value and electric energy value of the GIS equipment, and mark them as υ1, υ2 and υ3 respectively; Then, the comprehensive environmental impact index Zhy is obtained by combining the characteristic values ​​corresponding to the temperature value, vibration value and electric energy value of the GIS equipment with the weight factor; Set the evaluation range of the comprehensive environmental impact index Zhy, and evaluate the impact of equipment environment changes on metal particles through interval comparison.

2. A GIS metal particle partial discharge testing method according to claim 1, characterized in that: The specific process of constructing the impact model of metal particle partial discharge testing is as follows: Through the GIS equipment risk index Zfx and the comprehensive environmental impact index Zhy, the change function F1(x) of the two is constructed by fitting; By deeply analyzing the change function F1(x), the risk fluctuation coefficient Xσ is obtained; by setting the risk interval of the risk fluctuation coefficient Xσ and performing interval comparison, an early warning signal and a corresponding equipment environment control plan are generated.

3. A GIS metal particle partial discharge testing method according to claim 2, characterized in that: The specific process of obtaining the risk volatility coefficient Xσ is: Through the GIS equipment risk index Zfx and the comprehensive environmental impact index Zhy, the variation function F1(x) of the two is constructed by fitting: Zfx=F1(Zhy), and the comprehensive environmental impact index Zhy is substituted into the variation function F1(x) to obtain the GIS equipment risk index Zfx; By deeply analyzing the change function F1(x), we can obtain the risk volatility coefficient Xσ: Xσ=Zhy*ΔZfx; Among them, ΔZfx refers to the absolute value of the difference in GIS equipment risk index obtained in adjacent information collection cycles Tc. The GIS equipment risk index of any information collection cycle Tc is marked as Zhy1, and the GIS equipment risk index of the adjacent information collection cycle Tc is marked as Zhy2, then ΔZhy=[Zhy1-Zhy2|.

4. A GIS metal particle partial discharge testing method according to claim 3, characterized in that: The specific process of generating the equipment environment control plan is as follows: Set the risk interval of the risk fluctuation coefficient Xσ, compare the intervals, generate early warning signals and corresponding equipment environment control plans: There are R risk intervals preset, and any risk interval is marked as Qr, where r is the serial number of the risk interval, 0<r≤R. When the risk volatility coefficient Xσ is in the risk interval Qr, an r-level early warning prompt signal is generated, output and visualized.

5. A GIS metal particle partial discharge test system, characterized by: The system applies a GIS metal particle partial discharge testing method as described in any one of claims 1 to 4 above, comprising an information acquisition unit, a preprocessing unit, a core processing unit and a signal control unit, wherein the information acquisition unit, the preprocessing unit, the core processing unit and the signal control unit are signal connected.

6. A GIS metal particle partial discharge testing system according to claim 5, characterized in that: The information acquisition unit is used to obtain partial discharge test information, the preprocessing unit is used to preliminarily analyze the partial discharge test information, the core processing unit is used to build an impact model of metal particle partial discharge test, and the signal control unit is used to receive early warning prompt signals and perform corresponding control and processing operations.

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