GIS partial discharge capacity detection method and system based on TEM wave time domain integration, medium and processor

Abstract

The invention provides a GIS partial discharge capacity detection method based on TEM wave time domain integration. The GIS partial discharge capacity detection method comprises the steps that signals generated when partial discharge happens to a GIS are collected; using an adaptive filter, and selecting a minimum mean square error algorithm to remove noise in the signal to obtain a first signal; filtering TE and TM waves in the first signal by adopting a low-pass filter at the lowest cut-off frequency to obtain a TEM wave component in the first signal of the electromagnetic wave; characterizing the discharge amount using a TEM wave-based time integral value STEM in order to calculate the discharge amount; parameters in a TEM wave time integral value STEM characterization discharge capacity formula are obtained, and the discharge capacity is calculated according to the TEM wave time integral value STEM. The method has the advantages of being small in attenuation along with the distance and not affected by the detection direction and the PD current waveform, quantitative calculation of the actual discharge capacity of GIS partial discharge can be achieved, and the accuracy of equipment operation state monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of GIS partial discharge detection technology, and in particular to a GIS partial discharge detection method, system, medium and processor based on TEM wave time domain integration. Background Art

[0002] In insulation systems, excessive electric field strength or defects in the insulation material can lead to localized insulation failure, causing minute discharges. This phenomenon is known as partial discharge (PD). If not detected and addressed promptly, it can cause insulation breakdown, leading to equipment failure or even accidents. Therefore, the detection and quantitative analysis of PD are crucial for the operation and maintenance of insulation systems.

[0003] When PD occurs in gas-insulated switchgear (GIS), charge displacement occurs in an extremely short time (nanoseconds), manifesting macroscopically as extremely steep current pulses. This excites broadband electromagnetic waves, including signals in the ultra-high frequency (UHF) range. These UHF signals include transverse electromagnetic waves (TEM), high-frequency transverse electric waves (TE), and transverse magnetic waves (TM). These signals typically range from 300 MHz to 3 GHz and propagate along the GIS gas chamber.

[0004] Disadvantages of existing technology:

[0005] (1) The operating frequency range of UHF signal detection is usually between 300MHz and 3GHz. In this frequency band, there may be a large number of interference sources in its working environment, such as inverters, switchgear, motor operation, etc. The electromagnetic wave signals generated by these interference sources may overlap with the partial discharge signal in the spectrum, resulting in signal confusion. In addition, the operation of the equipment itself (such as switching operation, arc discharge) may also generate high-frequency electromagnetic waves, further increasing the noise interference, which may cause the partial discharge signal to be submerged, making it difficult to detect the actual partial discharge activity.

[0006] (2) The magnitude of partial discharge has a significant impact on PD in GIS. The magnitude of partial discharge can be used to quickly identify the type of PD and avoid damage to the insulation materials in GIS. Accurate calculation of the magnitude of partial discharge directly affects the insulation performance, operational reliability, and maintenance strategy selection of GIS. Since partial discharge occurs inside the insulation device, the actual magnitude of the discharge cannot be directly measured. Current methods all measure / calculate the apparent discharge to indirectly reflect the magnitude of the actual discharge. However, the apparent discharge is the charge amount that the actual discharge shows in the external circuit of the equipment, which is much smaller than the actual discharge amount, making it impossible to achieve comprehensive detection of partial discharge.

[0007] In view of this, a GIS partial discharge detection method, system, medium and processor based on TEM wave time domain integration are needed. Summary of the Invention

[0008] To address the difficulty in achieving comprehensive detection of partial discharge in existing technologies, the present invention provides a GIS partial discharge detection method, system, medium, and processor based on TEM wave time-domain integration. These methods can eliminate noise signals and retain the required partial discharge signals to reduce system complexity and cost. They also provide a method for directly calculating the actual discharge amount, thereby reducing detection cost and complexity and achieving comprehensive detection of partial discharge. The specific technical solution is as follows:

[0009] A GIS partial discharge detection method based on TEM wave time domain integration includes:

[0010] S1: Several UHF sensors are placed on the inner wall of the GIS to collect the signals generated by partial discharge in the GIS, and the signal-to-noise ratio is improved through a low-noise amplifier;

[0011] S2: Use an adaptive filter and a minimum mean square error algorithm to remove noise from the signal to obtain a first signal;

[0012] S3: Using a low-pass filter with the lowest cutoff frequency to filter out the TE and TM waves in the first signal, thereby obtaining the TEM wave component in the first electromagnetic wave signal;

[0013] S4: Use the time integral value S based on the TEM wave TEM To characterize the discharge capacity in order to calculate the discharge capacity;

[0014] S5: Get the TEM wave time integral value S TEM Characterize the parameters in the discharge quantity formula and calculate the time integral value S of the TEM wave TEM Calculate the discharge amount.

[0015] Furthermore, in step S2, the adaptive filter is used to select the minimum mean square error algorithm to remove the noise in the signal to obtain the first signal, and the following calculation steps are included for each time point n:

[0016] S21: Calculate the filter output y(n):

[0017] y(n)=w T (n)x(n);

[0018] S22: Calculate the error signal e(n):

[0019] e(n)=d(n)-y(n);

[0020] S23: Update the filter coefficient vector w(n):

[0021] w(n+1)=w(n)+me(n)x(n);

[0022] S24: When the number of iterations reaches a maximum value, the algorithm terminates and the first partial discharge signal after noise removal is obtained;

[0023] Where: w(n) is the filter coefficient vector at the current time n, which represents the filter weight; the superscript T represents the transpose of the matrix; the step factor m is a parameter that controls the update speed of the filter coefficients; x(n) is the input signal vector at the current time n; d(n) is the expected signal at the current time n, which is the target output of the filter; e(n) is the error signal at the current time n, which represents the difference between the filter output and the expected signal.

[0024] Furthermore, in step S4, the time integral value S based on the TEM wave is used. TEM To characterize the discharge capacity in order to calculate the discharge capacity, the following steps are included:

[0025] S41: The frequency domain expressions for determining the electric field strength of each mode of electromagnetic wave at any point (r, Φ, z) in the GIS are as follows:

[0026]

[0027] S42: After performing inverse Fourier transform, the relationship between the PD current and the TEM wave electric field intensity at any point (r, Φ, z) in the time domain is obtained, and the expression is as follows:

[0028]

[0029] S43: Take the time integral of the formula in step S42 to obtain the PD discharge amount q and TEM wave component S TEM The relationship expression between them is as follows:

[0030]

[0031] In the above formula, r1 and r2 are radial straight lines from (r1,0,0) to (r2,0,0) for the partial discharge current path; a is the radius of the inner conductor; b is the radius of the outer conductor; I(ω) is the expression of the PD current in the frequency domain; Z0 is the vacuum plane wave impedance; c is the wave propagation velocity; ω is the angular frequency; j is used to describe the imaginary number characteristics of the phase; z is the height coordinate in the cylindrical coordinate system; q is the discharge amount; and K is the proportional coefficient related to the partial discharge path.

[0032] Furthermore, in step S5, the TEM wave time integral value S is obtained. TEM Characterize the parameters in the discharge quantity formula and calculate the time integral value S of the TEM wave TEM Calculating the discharge capacity includes the following steps:

[0033] S51: Obtain the value of the proportional coefficient K;

[0034] S52: Based on the TEM wave components collected by S3 and time integration, S is obtained. TEM , combined with the proportional coefficient K value and S4, the partial discharge amount of the actual operating GIS equipment unit on site is calculated in real time.

[0035] Furthermore, the value of the proportional coefficient K is obtained by comparing historical data, conducting simulation experiments on structural units of equipment of the same model, or constructing a finite element simulation model to obtain the value of the proportional coefficient K of the equipment.

[0036] Furthermore, the numerical value of the proportional coefficient K is obtained by combining three methods: comparing historical data, conducting simulation experiments on structural units of the same type of equipment, and building a finite element simulation model to improve the accuracy of the numerical value of the proportional coefficient K. Specifically, the following steps are included:

[0037] S511: Preliminarily determine the range and trend of the proportional coefficient K through statistical analysis of historical data;

[0038] S512: Based on the basic information provided by the historical data, a finite element simulation model is constructed to simulate the partial discharge process under different conditions. The simulated partial discharge amount is compared with the historical data, and the preliminarily determined K value is adjusted and optimized.

[0039] S513: After completing historical data comparison and finite element simulation, conduct simulation experiments on the structural units of the same model equipment, conduct a comprehensive comparative analysis of the experimental measurement results with the historical data and finite element simulation results, and calibrate the K value based on the experimental data;

[0040] S514: Continuously adjust the screening criteria for historical data, the parameters of the finite element simulation model, and the conditions of the simulation experiment, perform analysis and calculation again, and gradually improve the accuracy of the proportional coefficient K value.

[0041] Furthermore, step S4 further includes the following steps: the electromagnetic field in the coaxial waveguide satisfies the Maxwell equations and related boundary conditions as follows:

[0042]

[0043] Where ε is the dielectric constant, ε=ε0ε r , ε0 is the dielectric constant of vacuum, ε r is the relative dielectric constant of the medium; μ is the magnetic permeability, μ=μ0μ r , μ0 is the vacuum permeability, μ r is the relative magnetic permeability of the medium; E is the electric field intensity vector; H is the magnetic field intensity vector; t is time; Compute signs for partial derivatives; is the differential operator; B is the magnetic induction intensity vector; D is the electric displacement vector.

[0044] A GIS partial discharge detection system based on TEM wave time domain integration is applied to the above-mentioned GIS partial discharge detection method based on TEM wave time domain integration, comprising:

[0045] The acquisition module is used to arrange several UHF sensors on the inner wall of the GIS to collect the signals generated by partial discharge in the GIS and improve the signal-to-noise ratio through a low-noise amplifier;

[0046] A first filtering module is configured to use an adaptive filter and a minimum mean square error algorithm to remove noise from the signal to obtain a first signal;

[0047] a second filtering module, configured to filter out TE and TM waves in the first signal using a low-pass filter at a minimum cutoff frequency to obtain a TEM wave component in the first electromagnetic wave signal;

[0048] A conversion module for using the time integral value S based on the TEM wave TEM To characterize the discharge capacity in order to calculate the discharge capacity;

[0049] Acquisition module, which is used to obtain the TEM wave time integral value S TEM Characterize the parameters in the discharge quantity formula and calculate the time integral value S of the TEM wave TEM Calculate the discharge amount.

[0050] A computer-readable storage medium includes a stored program, wherein when the program is executed, the device containing the computer-readable storage medium is controlled to execute the above-mentioned GIS partial discharge detection method based on TEM wave time domain integration.

[0051] A processor is used to run a program, wherein when the program is run, the above-mentioned GIS partial discharge detection method based on TEM wave time domain integration is executed.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The invention uses the time integral value of TEM wave as the characteristic parameter. This parameter maintains a linear relationship with the amount of partial discharge during the propagation of TEM wave, has the characteristics of small attenuation with distance, and is not affected by the detection direction and PD current waveform. Based on this, a method based on TEM wave time integral (S TEM The GIS partial discharge calculation method based on the GIS PD method can quantitatively calculate the actual GIS partial discharge amount and improve the accuracy of equipment operating status monitoring. The purpose is to accurately and comprehensively calculate the actual PD discharge amount to reduce the probability of GIS equipment failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0055] Figure 1 The figure is a flow chart of a GIS partial discharge detection method based on TEM wave time domain integration;

[0056] Figure 2 This is a schematic diagram of the sensor installation location;

[0057] Figure 3 Schematic diagram of the coaxial waveguide system model structure;

[0058] Figure 4 Schematic diagram of partial discharge path in GIS;

[0059] Figure 5 Schematic diagram of the electromagnetic field structure of the coaxial waveguide main mode TEM mode;

[0060] Figure 6 This is a structural diagram of a GIS partial discharge detection system based on TEM wave time domain integration. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0062] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0063] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0064] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0065] Example 1

[0066] Gas-insulated switchgear (GIS) is a medium- and high-voltage power equipment that uses low-pressure SF6, N2, or a mixed gas as the insulating medium and combines vacuum or SF6 arc extinguishing technology. Its core advantages lie in its compact size, high safety, and resistance to harsh environments. It is widely used in urban power grids, rail transit, industrial and mining enterprises, and other applications.

[0067] Figure 1 The figure shows a flow chart of a GIS partial discharge detection method based on TEM wave time domain integration, which specifically includes the following steps:

[0068] S1: Several UHF (ultra-high frequency) sensors are arranged on the inner wall of GIS (gas insulated switchgear) to collect the signals generated when partial discharge occurs in GIS, and the signal-to-noise ratio of the signals is improved through low-noise amplifiers.

[0069] UHF stands for Ultra High Frequency (UHF), and UHF sensors operate in the 300MHz-3GHz frequency band. Signals in this frequency band have shorter wavelengths, allowing sensors to more sensitively detect specific signal changes. For example, in wireless communications, the UHF band enables high data transmission rates. In partial discharge monitoring, it can effectively detect weak partial discharge signals. For example, when partial discharge occurs in power equipment, a current pulse with a rising edge in the nanosecond range is generated. This pulse can excite electromagnetic waves with frequencies as high as several gigahertz within the equipment. UHF partial discharge detection technology exploits this characteristic, detecting these electromagnetic wave signals. After receiving the electromagnetic wave signal, the sensor converts it into an electrical signal, which is then processed and analyzed to determine the partial discharge condition.

[0070] Specific examples Figure 2 As shown in the figure, five UHF sensors are placed on the inner wall of the GIS to collect UHF signals generated when PD (partial discharge) occurs in the GIS. The UHF sensors determine the frequency band of the PD signal (300MHz to 3GHz), and then use a low-noise amplifier to improve the signal-to-noise ratio.

[0071] When PD occurs in GIS equipment, the charge displacement occurs within an extremely short time (nanoseconds), which manifests as a very steep current pulse on a macroscopic scale. This generates broadband electromagnetic waves, including signals in the UHF range. These signals typically range from 300MHz to 3GHz and propagate along the GIS air chamber. Some frequencies can radiate outside the equipment through the insulator casting holes. Sensors installed at specific locations can detect the UHF signals.

[0072] In practical engineering applications, UHF sensors can be installed inside or outside the GIS. Built-in sensors often require pre-designed installation spaces before the equipment leaves the factory. These sensors are typically disc-shaped. Because they are installed internally, they offer excellent interference immunity and sensitivity. External sensors can be installed later, typically in the casting holes of pot-type insulators. Compared to built-in sensors, external sensors do not alter the internal electric field distribution of the equipment and are easier to install. However, they may be susceptible to external electromagnetic interference, resulting in lower interference immunity and sensitivity.

[0073] S2: Use an adaptive filter and select a minimum mean square error algorithm to remove noise from the signal to obtain a first signal.

[0074] Adaptive filter is used to remove noise from the collected signal, and the noise-free signal in the laboratory environment is taken as the desired signal and output as the target.

[0075] Furthermore, adaptive filtering is a signal processing technique that dynamically adjusts filter parameters based on the statistical characteristics of the input signal. Its core concept is to continuously optimize the filter coefficients to make the filter output signal as close as possible to the desired signal. Adaptive filtering has the following advantages: noise cancellation, removing background noise from speech and audio signals; system identification, estimating the transfer function of an unknown system; channel equalization, removing distortion in the communication channel; and signal prediction, predicting the future values of time series signals.

[0076] Furthermore, the minimum mean square error (LMS) algorithm is a classic adaptive filtering algorithm that dynamically adjusts the filter coefficients through the gradient descent method to minimize the mean square error (MSE).

[0077] Furthermore, the adaptive filter usually includes the following parts:

[0078] Input signal: The signal to be processed, which may contain noise and interference.

[0079] Desired signal: The target output of a filter, usually a pure signal or a reference signal.

[0080] Error signal: The difference between the filter output and the desired signal.

[0081] Coefficient update: Dynamically adjust the filter coefficient vector based on the error signal to minimize the error.

[0082] Furthermore, the method of using an adaptive filter and selecting a minimum mean square error algorithm to remove noise from the signal to obtain the first signal includes the following calculation steps for each time point n:

[0083] a: Calculate the filter output y(n):

[0084] y(n)=w T (n)x(n); (1)

[0085] b: Calculate the error signal:

[0086] e(n)=d(n)-y(n); (2)

[0087] c: Update filter coefficient vector:

[0088] w(n+1)=w(n)+me(n)x(n); (3)

[0089] When the number of iterations reaches the maximum value, the algorithm terminates and the first partial discharge signal after noise removal is obtained.

[0090] Where: w(n) is the filter coefficient vector at the current time n, which represents the filter weight; the superscript T represents the transpose of the matrix; the step factor m is a parameter that controls the update speed of the filter coefficients; x(n) is the input signal vector at the current time n; d(n) is the expected signal at the current time n, which is the target output of the filter; e(n) is the error signal at the current time n, which represents the difference between the filter output and the expected signal.

[0091] S3: The lowest cutoff frequency of TE and TM waves is obtained through simulation. The TE and TM waves in the first signal are filtered out using a low-pass filter at the lowest cutoff frequency to obtain the TEM wave component in the first electromagnetic wave signal.

[0092] The lowest cutoff frequency of each mode of transverse electric wave TE and transverse magnetic wave TM is 0.39 GHz. As the main propagation mode in GIS, the frequency range of TEM does not exceed 300 MHz. Therefore, the electromagnetic waves processed in the above step S2 with a frequency above 0.39 GHz are filtered out by a low-pass filter to obtain the TEM component in the electromagnetic wave.

[0093] TEM wave is a non-dispersive electromagnetic wave with a main mode. The electric field and magnetic field are perpendicular to the axis of the waveguide. There is no cutoff frequency. It can propagate at any frequency and does not change with the axial direction. Its mode field lines are as follows: Figure 5 As shown in the figure, the solid lines represent electric lines, the dotted lines represent magnetic lines, and the electric field lines are distributed radially, that is, the radial component is the strongest in the coaxial waveguide.

[0094] S4: Use the time integral value S based on the TEM wave TEM To characterize the discharge capacity in order to calculate the discharge capacity.

[0095] like Figure 3 As shown in the figure, based on its structural characteristics, the GIS can be considered a coaxial waveguide (a coaxial waveguide is a double-layer transmission line consisting of an inner and outer conductor, with an insulating dielectric filling the middle, used to guide electromagnetic waves in high-frequency transmission). The inner conductor (high-voltage busbar) and the outer conductor (GIS cavity) are generally made of aluminum. The inner conductor is a, the outer conductor has a radius of b, and the space between the inner and outer conductors is filled with SF6 insulating gas.

[0096] According to the coaxial geometry, a cylindrical coordinate system is used to describe the components of electromagnetic waves in all directions. The cylindrical coordinate system is a three-dimensional orthogonal coordinate system that is applicable to physical problems with cylindrical symmetry. Its core is to describe the position of a spatial point through three parameters: radial distance, azimuth, and height. The coordinates of any point in the coordinate system are (r, Φ, z), where r represents the radial distance from the point to the z-axis; Φ represents the azimuth, which is the angle formed by the projection of the spatial point on the xOy plane and the line connecting the origin, starting from the positive direction of the x-axis and rotating counterclockwise around the z-axis. Its value range is usually 0≤Φ<2π (in radians); z represents the height of the point, that is, the coordinate value of the spatial point on the z-axis. The value range of z is (-∞,+∞), which describes the position of the spatial point in the direction parallel to the z-axis. In actual engineering, GIS built-in sensors are mostly installed near the inner wall of the GIS, and the radial component E of the main mode TEM electric field is r The strongest, and E Φ and E z It is close to 0 according to the coaxial waveguide boundary conditions.

[0097] Furthermore, the electric and magnetic field distributions of the TEM wave components at any point in the coaxial structure are:

[0098]

[0099] Where U0 is the signal amplitude; r is the vertical distance between the point and the axis; ω is the angular frequency; k is the phase constant; z is the axial direction of the coaxial structure, that is, the propagation direction of the TEM wave; η TEM is the wave impedance of TEM wave.

[0100] like Figure 4 As shown in Figure 2, the partial discharge current path is a radial straight line from (r1,0,0) to (r2,0,0). The frequency domain expression of the electromagnetic wave electric field intensity of the TEM wave component at any point (r,Φ,z) in the GIS is:

[0101]

[0102] In the above formula, a is the radius of the inner conductor, b is the radius of the outer conductor; I(ω) is the expression of the PD current in the frequency domain; Z0 is the vacuum plane wave impedance, which is 60Ω; c is the wave propagation speed; ω is the angular frequency; j is used to describe the imaginary number characteristics of the phase; and z is the height coordinate in the cylindrical coordinate system.

[0103] After performing inverse Fourier transform on equation (4), we get equation (5), which is the relationship between the PD current and the TEM wave electric field intensity at any point (r, Φ, z) in the time domain. Then, taking the definite integral of equation (5) over time, we get equation (6), which is the relationship between the PD discharge amount q and the TEM electromagnetic wave.

[0104]

[0105] In formula (6), S TEM is the time integral value of the TEM wave, and K is the proportional coefficient related to the partial discharge path.

[0106] Further, such as Figure 5 As shown in the figure, TEM wave is a non-dispersive electromagnetic wave with the electric field and magnetic field perpendicular to the axis of the waveguide. It has no cutoff frequency and can propagate at any frequency without changing with the axial direction. Its mode field lines are as follows: Figure 5 As shown in the figure, the solid lines represent electric lines, the dotted lines represent magnetic lines, and the electric field lines are distributed radially, that is, the radial component is the strongest in the coaxial waveguide. The Maxwell equations and related boundary conditions satisfied by the electromagnetic field in the coaxial waveguide are:

[0107]

[0108] Where ε is the dielectric constant, ε=ε0ε r , ε0 is the dielectric constant of vacuum, ε r is the relative dielectric constant of the medium; μ is the magnetic permeability, μ=μ0μ r , μ0 is the vacuum permeability, μ r is the relative magnetic permeability of the medium; E is the electric field intensity vector; H is the magnetic field intensity vector; j is used to describe the imaginary characteristics of the phase; t is time; Compute signs for partial derivatives; is the differential operator; ω is the angular frequency; B is the magnetic induction intensity vector; D is the electric displacement vector.

[0109] S5: Get the TEM wave time integral value S TEM Characterize the parameters in the discharge quantity formula and calculate the time integral value S of the TEM wave TEM Calculate the discharge amount.

[0110] The value of the equipment unit scale factor K is obtained by comparing historical data, conducting simulation experiments on the same type of equipment structure unit, or building a finite element simulation model. The TEM wave component collected by S3 is used and time-integrated to obtain S TEM , combined with the proportional coefficient K value and S4, the partial discharge amount of the actual operating GIS equipment unit on site is calculated in real time.

[0111] By extracting the TEM wave through the above steps, the time domain waveform of the TEM wave after low-pass filtering of the electromagnetic wave can be obtained. The time domain integral value S of the waveform is calculated by using the integral term of the PID controller. TEM For the GIS equipment actually in operation on site, the value of the unit scale coefficient K of the equipment is obtained by comparing historical data, conducting simulation experiments on the structural units of the same type of equipment, or building a finite element simulation model.

[0112] In the process of obtaining the proportional coefficient K value, if the experimental or simulation method is used, the injection of 3 or more different set discharge values q should be simulated, and the S corresponding to each q value should be collected using steps S1 to S3. TEM Combine the formula (6) in step 4 with the correlation coefficient method to obtain the proportional coefficient K value of the detected device.

[0113] The TEM wave components collected in step 3 are used to obtain S after time integration. TEM , combined with the proportional coefficient K value and formula (6) in step 4, the partial discharge amount of the actual operating GIS equipment unit on site is calculated in real time.

[0114] In a specific implementation, the numerical value of the proportional coefficient K is obtained by combining three methods: comparing historical data, conducting simulation experiments on structural units of the same type of equipment, and building a finite element simulation model to improve the accuracy of the numerical value of the proportional coefficient K. Specifically, the following steps are included:

[0115] Based on historical data comparison: First, a detailed analysis of a large amount of historical data was conducted. This data covers various conditions of actual equipment operation, such as partial discharge data under different environmental conditions, load levels, and operating age. Through statistical analysis of this historical data, the approximate range and trend of the proportionality coefficient K were preliminarily determined. For example, this analysis revealed that within a specific range of operating conditions, the K value fluctuated within a relatively stable range. Historical data can reflect actual operating conditions, providing a foundational reference for subsequent methods and reducing blindness.

[0116] Refine and verify using finite element simulation models: Based on the basic information provided by historical data, a finite element simulation model is constructed. The structural parameters, material properties, and various operating conditions of the GIS equipment are precisely set in the model to simulate the partial discharge process under different conditions. Through finite element simulation, the distribution of physical fields such as electric fields and magnetic fields in the partial discharge area, as well as the relationship between these physical fields and the partial discharge path, can be deeply analyzed. The simulated partial discharge amount is compared with the historical data, and the preliminarily determined K value is adjusted and optimized. For example, if there is a deviation between the simulation results and the historical data, it can be analyzed whether it is a problem with the model parameter settings or other factors, and then the model and K value can be corrected. Finite element simulation can simulate complex physical processes and make up for the problem of incomplete information that may exist in historical data.

[0117] Calibrate and supplement with the help of simulation experiments on the structural units of the same model of equipment: After completing the historical data comparison and finite element simulation, carry out simulation experiments on the structural units of the same model of equipment. In the experiment, simulate the actual operating environment and partial discharge conditions of the GIS equipment as realistically as possible, and measure the corresponding partial discharge amount by changing the experimental conditions, such as discharge position, discharge type, operating voltage, etc. The experimental measurement results are comprehensively compared and analyzed with the historical data and finite element simulation results. If there is a difference between the experimental results and the previous two, the reasons can be further explored to determine whether it is an experimental error or limitations of the model and historical data. The K value is calibrated with experimental data to make it closer to the actual situation. Simulation experiments can provide real experimental data to verify the accuracy of the model, while supplementing information that may be missing in historical data and simulations.

[0118] Iterative Optimization: The data and analysis results obtained from the three methods above are integrated into an iterative process. Based on the comprehensive analysis results, the historical data screening criteria, finite element simulation model parameters, and experimental conditions are continuously adjusted. Further analysis and calculations are then performed to gradually improve the accuracy of the proportional coefficient K. For example, through multiple iterations, the error range of the K value is continuously narrowed, making it more accurate and reliable.

[0119] Beneficial effects:

[0120] 1. Compared with the traditional method, this method uses TEM wave S TEM The parameter has the characteristic of being linearly related to the amount of partial discharge. It can be used as a monitoring indicator to achieve comprehensive monitoring and effective management of the GIS insulation system, ensuring the safe operation of the equipment and extending its service life.

[0121] 2. Compared with traditional methods, this method can accurately reflect the size of partial discharge by accurately calculating the actual discharge amount, avoiding misjudgment of partial discharge type, GIS equipment failure, etc. due to errors in the calculation of partial discharge amount. Combined with the actual needs of the project, this method can quickly determine the type of GIS failure by accurately calculating the discharge amount, avoiding large losses and ensuring the real-time and effectiveness of monitoring.

[0122] 3. Compared with the first shortcoming of the background technology, this solution method can accurately extract TEM waves, optimize signal processing efficiency, simplify the detection process, reduce computing resources and time consumption, and ensure the real-time and effectiveness of partial discharge detection.

[0123] 4. Compared with the second disadvantage of the background technology, this method is based on the characteristic parameter S TEMThere is always a linear relationship between the discharge amount and the PD discharge amount. The proportional coefficient K obtained through simulation can be used to accurately calculate the discharge amount, which can realize comprehensive monitoring and effective management of the GIS insulation system, ensuring the safe operation of the equipment and extending its service life.

[0124] Example 2

[0125] like Figure 6 As shown, a GIS partial discharge detection system based on TEM wave time domain integration is applied to the above-mentioned GIS partial discharge detection method based on TEM wave time domain integration, comprising:

[0126] The acquisition module is used to arrange several UHF sensors on the inner wall of the GIS to collect the signals generated by partial discharge in the GIS and improve the signal-to-noise ratio through a low-noise amplifier;

[0127] A first filtering module is configured to use an adaptive filter and a minimum mean square error algorithm to remove noise from the signal to obtain a first signal;

[0128] a second filtering module, configured to filter out TE and TM waves in the first signal using a low-pass filter at a minimum cutoff frequency to obtain a TEM wave component in the first electromagnetic wave signal;

[0129] A conversion module for using the time integral value S based on the TEM wave TEM To characterize the discharge capacity in order to calculate the discharge capacity;

[0130] Acquisition module, which is used to obtain the TEM wave time integral value S TEM Characterize the parameters in the discharge quantity formula and calculate the time integral value S of the TEM wave TEM Calculate the discharge amount.

[0131] Example 3

[0132] A computer-readable storage medium includes a stored program, wherein when the program is executed, the device containing the computer-readable storage medium is controlled to execute the above-mentioned GIS partial discharge detection method based on TEM wave time domain integration.

[0133] Example 4

[0134] A processor is used to run a program, wherein when the program is run, the above-mentioned GIS partial discharge detection method based on TEM wave time domain integration is executed.

[0135] The present application provides a GIS partial discharge detection method based on TEM wave time domain integration, comprising: S1: arranging a plurality of UHF sensors on the inner wall of the GIS to collect signals generated when partial discharge occurs in the GIS, and improving the signal-to-noise ratio of the signal through a low-noise amplifier; S2: using an adaptive filter and selecting a minimum mean square error algorithm to remove noise in the signal to obtain a first signal; S3: using a low-pass filter with the lowest cutoff frequency to filter out TE and TM waves in the first signal to obtain the TEM wave component in the electromagnetic wave first signal; S4: using the time integral value S based on the TEM wave to obtain the TEM wave component in the electromagnetic wave first signal. TEM To characterize the discharge amount in order to calculate the discharge amount; S5: obtain the TEM wave time integral value S TEM Characterize the parameters in the discharge quantity formula and calculate the time integral value S of the TEM wave TEM Calculating the discharge amount. This application uses the time integral of the TEM wave as a characteristic parameter. This parameter maintains a linear relationship with the amount of partial discharge during TEM wave propagation, exhibits minimal attenuation with distance, and is unaffected by the detection direction or PD current waveform. Based on this, a GIS partial discharge calculation method based on TEM wave time integral (STEM) is proposed. This method can quantitatively calculate the actual amount of GIS partial discharge and improve the accuracy of equipment operating status monitoring. The goal is to accurately and comprehensively calculate the actual PD discharge amount to reduce the probability of GIS equipment failure.

[0136] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0137] In the embodiments provided by the present invention, it should be understood that the division of units is merely a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0138] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0139] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A GIS partial discharge detection method based on TEM wave time domain integration, characterized in that: include: S1: Several UHF sensors are placed on the inner wall of the GIS to collect the signals generated by partial discharge in the GIS, and the signal-to-noise ratio is improved through a low-noise amplifier; S2: Use an adaptive filter and a minimum mean square error algorithm to remove noise from the signal to obtain a first signal; S3: Using a low-pass filter with the lowest cutoff frequency to filter out the TE and TM waves in the first signal, thereby obtaining the TEM wave component in the first electromagnetic wave signal; S4: Use the time integral value S based on the TEM wave TEM To characterize the discharge capacity in order to calculate the discharge capacity; S5: Get the TEM wave time integral value S TEM Characterize the parameters in the discharge quantity formula and calculate the time integral value S of the TEM wave TEM Calculate the discharge amount.

2. The GIS partial discharge detection method based on TEM wave time domain integration according to claim 1 is characterized in that: In step S2, the adaptive filter is used to select the minimum mean square error algorithm to remove the noise in the signal to obtain the first signal, and the following calculation steps are included for each time point n: S21: Calculate the filter output y(n): y(n)=w T (n)x(n)? S22: Calculate the error signal e(n): e(n)=d(n)-y(n); S23: Update the filter coefficient vector w(n): w(n+1)=w(n)+me(n)x(n); S24: When the number of iterations reaches a maximum value, the algorithm terminates and the first partial discharge signal after noise removal is obtained; Where: w(n) is the filter coefficient vector at the current time n, which represents the filter weight; the superscript T represents the transpose of the matrix; the step factor m is a parameter that controls the update speed of the filter coefficients; x(n) is the input signal vector at the current time n; d(n) is the expected signal at the current time n, which is the target output of the filter; e(n) is the error signal at the current time n, which represents the difference between the filter output and the expected signal.

3. The GIS partial discharge detection method based on TEM wave time domain integration according to claim 1 is characterized in that: In step S4, the time integral value S based on the TEM wave is used. TEM To characterize the discharge capacity in order to calculate the discharge capacity, the following steps are included: S41: The frequency domain expressions for determining the electric field strength of each mode of electromagnetic wave at any point (r, Φ, z) in the GIS are as follows: S42: After performing inverse Fourier transform, the relationship between the PD current and the TEM wave electric field intensity at any point (r, Φ, z) in the time domain is obtained, and the expression is as follows: S43: Take the time integral of the formula in step S42 to obtain the PD discharge amount q and TEM wave component S TEM The relationship expression between them is as follows: In the above formula, r1 and r2 are radial straight lines from (r1,0,0) to (r2,0,0) for the partial discharge current path; a is the radius of the inner conductor; b is the radius of the outer conductor; I(ω) is the expression of the PD current in the frequency domain; Z0 is the vacuum plane wave impedance; c is the wave propagation velocity; ω is the angular frequency; j is used to describe the imaginary number characteristics of the phase; z is the height coordinate in the cylindrical coordinate system; q is the discharge amount; and K is the proportional coefficient related to the partial discharge path.

4. The GIS partial discharge detection method based on TEM wave time domain integration according to claim 3 is characterized in that: In step S5, the TEM wave time integral value S is obtained. TEM Characterize the parameters in the discharge quantity formula and calculate the time integral value S of the TEM wave TEM Calculating the discharge capacity includes the following steps: S51: Obtain the value of the proportional coefficient K; S52: Based on the TEM wave components collected by S3 and time integration, S is obtained. TEM , combined with the proportional coefficient K value and S4, the partial discharge amount of the actual operating GIS equipment unit on site is calculated in real time.

5. The GIS partial discharge detection method based on TEM wave time domain integration according to claim 4 is characterized in that: The numerical value of the proportional coefficient K is obtained by comparing historical data, conducting simulation experiments on structural units of equipment of the same model, or constructing a finite element simulation model to obtain the proportional coefficient K value of the equipment.

6. The GIS partial discharge detection method based on TEM wave time domain integration according to claim 4 is characterized in that: The numerical value of the proportional coefficient K is obtained by combining three methods: comparing historical data, conducting simulation experiments on structural units of the same type of equipment, and building a finite element simulation model to improve the accuracy of the numerical value of the proportional coefficient K. Specifically, the following steps are included: S511: Preliminarily determine the range and trend of the proportional coefficient K through statistical analysis of historical data; S512: Based on the basic information provided by the historical data, a finite element simulation model is constructed to simulate the partial discharge process under different conditions. The simulated partial discharge amount is compared with the historical data, and the preliminarily determined K value is adjusted and optimized. S513: After completing historical data comparison and finite element simulation, conduct simulation experiments on the structural units of the same model equipment, conduct a comprehensive comparative analysis of the experimental measurement results with the historical data and finite element simulation results, and calibrate the K value based on the experimental data; S514: Continuously adjust the screening criteria for historical data, the parameters of the finite element simulation model, and the conditions of the simulation experiment, perform analysis and calculation again, and gradually improve the accuracy of the proportional coefficient K value.

7. The GIS partial discharge detection method based on TEM wave time domain integration according to claim 3 is characterized in that: Step S4 further includes the following steps: the electromagnetic field in the coaxial waveguide satisfies the Maxwell equations and related boundary conditions as follows: Where ε is the dielectric constant, ε=ε0ε r , ε0 is the dielectric constant of vacuum, ε r is the relative dielectric constant of the medium; μ is the magnetic permeability, μ=μ0μ r , μ0 is the vacuum permeability, μ r is the relative magnetic permeability of the medium; E is the electric field intensity vector; H is the magnetic field intensity vector; t is time; Compute signs for partial derivatives; is the differential operator; B is the magnetic induction intensity vector; D is the electric displacement vector.

8. A GIS partial discharge detection system based on TEM wave time domain integration, characterized in that: The GIS partial discharge detection method based on TEM wave time domain integration as described in any one of claims 1 to 7 comprises: The acquisition module is used to arrange several UHF sensors on the inner wall of the GIS to collect the signals generated by partial discharge in the GIS and improve the signal-to-noise ratio through a low-noise amplifier; A first filtering module is configured to use an adaptive filter and a minimum mean square error algorithm to remove noise from the signal to obtain a first signal; a second filtering module, configured to filter out TE and TM waves in the first signal using a low-pass filter at a minimum cutoff frequency to obtain a TEM wave component in the first electromagnetic wave signal; A conversion module for using the time integral value S based on the TEM wave TEM To characterize the discharge capacity in order to calculate the discharge capacity; Acquisition module, which is used to obtain the TEM wave time integral value S TEM Characterize the parameters in the discharge quantity formula and calculate the time integral value S of the TEM wave TEM Calculate the discharge amount.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the GIS partial discharge detection method based on TEM wave time domain integration according to any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the GIS partial discharge detection method based on TEM wave time domain integration according to any one of claims 1 to 7.

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