Insulation test method for single-phase intelligent electric energy meter

Abstract

The invention relates to the technical field of electric meter testing, solves the technical problem of electric leakage misjudgment caused by metal micro chips on the surface of a wiring terminal in an insulation test, and particularly relates to an insulation test method of a single-phase intelligent electric energy meter. According to the invention, the structured stripe light is projected to the surface of the wiring terminal so as to capture the point cloud data; projecting a low-density grating to the surface of the wiring terminal to generate a basic curvature graph, and marking a high-risk area in the basic curvature graph; and dynamically adjusting the grating period based on the basic value, synchronously acquiring a multi-channel polarization image and calculating a Stokes vector. Through the curvature adaptive polarized light field technology, normal vector calculation error reduction is achieved in the area with the curvature radius smaller than the critical value, ultraviolet / near-infrared channel signal attenuation caused by overcompensation is eliminated, and the optical measurement precision on the extreme curved surface is guaranteed; and chromatography confocal scanning is combined with a deconvolution reconstruction algorithm, multilayer metal micro-chip stacking can be penetrated, and the problem of bottom-layer micro-chip leak detection caused by the shielding effect of a traditional optical system is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric meter testing, and in particular to an insulation testing method for a single-phase smart electric energy meter. BACKGROUND

[0002] Insulation testing of a single-phase smart electric energy meter is a procedure for evaluating whether the insulation performance between internal circuits of the electric energy meter and external contactable metal components (such as terminal connectors, terminal boxes, etc.) and between different live circuits (such as phase lines and zero lines, phase lines and ground lines, etc.) meets the standard requirements. Before testing the electric energy meter, the high-voltage or physically grounded contact parts need to be cleaned, and in particular, the terminal connectors will undergo irreversible electrochemical reactions in the environmental medium, resulting in the formation of an oxide layer on the surface of the terminal connectors. The oxide layer will create a false conductive path during insulation testing, causing deviations in the measured results from the actual insulation state. Therefore, before insulation testing, a precision eraser or glass fiber pen is used to wipe the surface of the terminal connectors to remove the oxide layer on the surface of the terminal connectors.

[0003] When wiping the surface of the terminal connectors, manual operation cannot accurately control the wiping force, which will cause the oxide layer on the surface of the terminal connectors to fall off fine metal chips under the action of micro-cutting. Since the oxide layer on the surface of the terminal connectors has mixed insulation and semiconductor properties, these metal chips scattered in the terminal gap will form a bridging channel, causing a leakage, resulting in an incorrect judgment of the insulation test, and misjudging a qualified product as unqualified. In addition, in the environment of applying high voltage during insulation testing, the high voltage will cause a high-strength electric field around the metal chips, causing the originally electrically neutral metal chips to accumulate electric charges through induction or contact, generate electrostatic adsorption force, and migrate under the action of the electrostatic adsorption force, thereby causing dynamic errors in the data correction strategy for the position distribution of the metal chips. SUMMARY

[0004] To overcome the deficiencies of the prior art, the present application provides an insulation testing method for a single-phase smart electric energy meter, which solves the technical problem of leakage misjudgment caused by metal chips on the surface of the terminal connectors in the insulation testing of a single-phase smart electric energy meter, and achieves the purpose of quantifying the number, size and position of the metal chips on the surface of the terminal connectors through a visual system, establishing an equivalent conductance model of the metal chips on the surface of the terminal connectors, correcting the measured resistance value in the insulation testing, and thereby avoiding the judgment of a qualified electric energy meter as unqualified.

[0005] To solve the above technical problems, the present application provides the following technical solution: an insulation testing method for a single-phase smart electric energy meter, the method comprising the following steps:

[0006] projecting a structured fringe light onto the surface of the terminal connector to capture point cloud data;

[0007] A low-density grating is projected onto the surface of the terminal to generate a basic curvature map, and a high-risk area is marked therein; a grating period is dynamically adjusted based on a basic value, multi-channel polarization images are synchronously collected and Stokes vectors are calculated, and optical distortion is corrected in combination with a surface normal vector compensation amount; tomographic confocal scanning is performed based on the basic curvature map, spectral wavelengths are switched according to curvature partitions, and a spectral data four-dimensional tensor is generated; the corrected polarization images and the spectral data four-dimensional tensor are fused, and metal microchip volume data is reconstructed through three-dimensional Fourier deconvolution;

[0008] The three-dimensional coordinates of the corrected metal microchip and the equivalent conductive area are calculated to obtain a risk index, and a metal microchip matrix of the surface of the terminal is constructed; a space admittance matrix is established based on the metal microchip matrix, and a Poisson equation is solved to simulate potential distribution and predict displacement of the metal microchip; the space admittance matrix is updated through the displacement of the metal microchip, and node current distribution is calculated in combination with a node potential vector.

[0009] A metal microchip branch current formed between two measured terminals is extracted from the node current vector, and a false leakage current component is obtained by summation; a real leakage current is calculated by subtracting the false leakage current component from a measured total leakage current to obtain a corrected measured insulation resistance value.

[0010] Further, the manner of correcting optical distortion in combination with the surface normal vector compensation amount is:

[0011] A surface gradient is calculated based on point cloud data of the surface of the terminal, and a compensation vector is generated, that is:

[0012]

[0013] In the formula, is the normal vector compensation amount, and δ is a modifier, is a basic normal vector; α is a curvature and error conversion factor; unit(...) is a vector normalization operator; is a radial unit vector; is a curvature change rate along the radial direction; r is a radial coordinate; κ is a curvature value; and z is a depth direction;

[0014] A final real normal vector is generated based on the normal vector compensation amount and an optical normal vector original amount, that is:

[0015]

[0016] In the formula, is the real normal vector; k is a parameter index of a Stokes component; S k is a Stokes component; is a polarization basis vector.

[0017] Further, the manner of switching spectral wavelengths according to curvature partitions is:

[0018] Activate single wavelength in low curvature region;

[0019] Enable complementary dual wavelength combination in medium curvature region;

[0020] Trigger triple wavelength synergy in high curvature region.

[0021] Further, the way of reconstructing the metal microdebris volume data through three-dimensional Fourier deconvolution is:

[0022] Based on the terminal surface curvature value, calculate the enhanced data set of high-risk area;

[0023] Based on the true normal vector, calculate the polarization compensation angle of each coordinate point of the terminal surface, based on the polarization compensation angle, construct the inverse compensation matrix, and perform depolarization processing on the original polarization image, and perform convolution operation on the polarization image and the inverse compensation matrix, that is:

[0024]

[0025] In the formula, I corr (x,y) is the corrected image; G UV is the ultraviolet gain factor; Conv(...) is the matrix convolution operation; I raw is the original polarization image; is the inverse compensation matrix;

[0026] Fuse the corrected image and the four-dimensional tensor of spectral data in the depth direction to form an image sequence with depth information, and then perform tomographic deconvolution processing on the image sequence, that is:

[0027]

[0028] Wherein,

[0029] In the formula, V vol is the reconstructed metal microdebris volume data; F{...} represents a three-dimensional Fourier transform operator; F -1 [...] represents the inverse Fourier transform; H * (w) is a curvature-adaptive hybrid transfer function; w is a spatial frequency vector; OTF(w) is an optical transfer function; CTF(w) is a confocal transfer function; D(x,y,z) is an image sequence with depth information; κ th is a risk threshold.

[0030] Further, the way of identifying the corrected three-dimensional coordinates of the metal microdebris is:

[0031] The reflection ratios of ultraviolet light and near-infrared light of each coordinate point on the terminal surface are calculated, the material of the terminal surface is classified according to the reflection ratios, including metal debris, mixed debris and non-metal residues; the metal weight, non-metal weight and mixed weight are calculated based on the reflection ratios, that is, the multispectral information is fused into a final fusion image based on the three weights;

[0032] The partition mapping function is established based on the base curvature map of the terminal surface, that is

[0033] Ω i ={(x,y)|κ th,i ≤κ(x,y)<κ th,i +Δκ max}

[0034] In the formula, Ω i is the i-th curvature partition, κ th,i is the reference curvature threshold of the i-th curvature partition, Δκ max is the maximum curvature difference threshold; κ(x,y) is the base curvature map;

[0035] The watershed segmentation is independently performed on each curvature partition, and the morphological filtering is used for denoising processing to generate a binary label matrix of the metal debris; the geometric center of each connected domain is calculated as a coordinate point based on three-dimensional weighted average to generate accurate three-dimensional coordinates r obs of each metal debris on the terminal surface; the accurate three-dimensional coordinates are corrected through the curvature gradient, that is

[0036]

[0037] In the formula, r real is the three-dimensional coordinates of the metal debris after correction; τ is a correction coefficient for adjusting the compensation strength of the curvature gradient; is the depth gradient vector at the accurate three-dimensional coordinates of the metal debris.

[0038] Further, the method for identifying the equivalent conductive area is:

[0039] The light intensity distribution along the depth direction z is extracted from the reconstructed metal debris volume data, the vertical projection component of the metal debris is calculated, and then the normal vector inclination compensation is introduced to generate the equivalent conductive area of the metal debris, that is

[0040]

[0041] In the formula, A eff is the equivalent conductive area of the metal debris; V(z) is the light intensity distribution along the depth direction z; max(V(z)) is the light intensity peak value of the terminal surface, z maxis the depth of the peak light intensity position; τ′ is the tilt magnification factor; cos -1 (...) is the arccosine function; is the unit vector in the direction of the absolute optical axis.

[0042] Furthermore, the calculation formula of the three-dimensional risk index is:

[0043]

[0044] Where R risk,i is the risk index of the i-th metal chip; A eff,i is the equivalent conductive area of ​​the i-th metal chip; d i is the distance from the i-th metal chip to the nearest conductor; where:

[0045]

[0046] Where, f(R u / ir ) is the mapping function from micro-chip material to weight.

[0047] Furthermore, the elements of the spatial admittance matrix satisfy:

[0048]

[0049] Where y i,j is the matrix element; d ij is the distance between the i-th metal chip and the j-th metal chip; R ele Electric field radius, A eff,i is the equivalent conductive area of ​​the i-th metal chip, A eff,j is the equivalent conductive area of ​​the jth metal chip.

[0050] Furthermore, the method for predicting the displacement of metal chips is:

[0051] The charge density is weighted based on the risk index of metal chips, namely:

[0052]

[0053] Where, ρ( x,y,z ) is the charge density, is the Dirac function;

[0054] The potential distribution Φ(x,y,z) on the terminal surface is solved based on the Poisson equation. The calculation formula is:

[0055]

[0056] Where, is the Laplace operator of electric potential; ε is the dielectric constant of the medium;

[0057] Then use the potential gradient to obtain the electric field strength vector And calculate the displacement of metal chips, the calculation formula is:

[0058]

[0059] Where, is the position change vector of the metal chips; ψ is the mobility coefficient of the metal chips; Δt is the time step of the electric field; Represents the electric field intensity vector at the i-th metal chip.

[0060] Furthermore, the method of calculating the node current distribution is:

[0061] Calculate the potential value of each metal chip location and arrange the potential values ​​of all metal chip locations in order as a node potential vector The calculation formula is:

[0062]

[0063] is the node current vector, and Y′ is the new spatial admittance matrix.

[0064] By means of the above technical solution, the present invention provides an insulation testing method for a single-phase smart energy meter, which has at least the following beneficial effects:

[0065] 1. The present invention uses curvature-adaptive polarized light field technology to reduce the normal vector calculation error in areas where the curvature radius is less than a critical value (such as arc-shaped crimped structures), eliminate the ultraviolet / near-infrared channel signal attenuation caused by over-compensation, and ensure the optical measurement accuracy on extreme curved surfaces; and the tomographic confocal scanning combined with the deconvolution reconstruction algorithm can penetrate multiple layers of metal chip stacks, solving the problem of missed detection of bottom-layer chips caused by the shielding effect of traditional optical systems.

[0066] 2. The present invention accurately calculates the equivalent conductive area by integrating the data of chip volume, spatial distribution and surface morphology (normal vector inclination compensation); constructs a risk index model based on three-dimensional coordinates and conductive area, significantly reduces the misjudgment rate of insulation testing to below the industrial threshold, and realizes the accurate quantification of metal chip electrical risks.

[0067] 3. The present invention simulates the displacement of metal chips under high-voltage electric field by establishing a spatial admittance matrix and solving the Poisson equation, dynamically updates the position of the chips and calculates the false leakage current component, thereby achieving accurate correction of the measured total leakage current. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0069] Figure 1 A schematic diagram of the method flow of the present application. DETAILED DESCRIPTION

[0070] In order to make the above objectives, features and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Thus, the realization process of how to apply technical means to solve technical problems and achieve technical effects of the present application can be fully understood and implemented.

[0071] The embodiment provides an insulation test method of a single-phase intelligent electric energy meter. The number, size and position of metal micro-particles on a wiring terminal surface are quantified through a visual system. An equivalent conductance model of the metal micro-particles on the wiring terminal surface is established. The measured resistance value in the insulation test is corrected, so that a qualified electric energy meter is not judged as unqualified. As shown in the figure, the method comprises the following steps: Figure 1

[0072] After the surface of the wiring terminal is polished, the particles loosely attached to the surface of the wiring terminal are cleaned through mechanical vibration. The surface image of the wiring terminal is collected by using a high-resolution industrial camera. The metal micro-particle features are highlighted in combination with a multi-waveband ring-shaped light source, such as white light (surface topography) and ultraviolet light (metal reflection enhancement). Specifically, the method for correcting the measured resistance value is as follows:

[0073] Firstly, structured stripe light is projected onto the surface of the wiring terminal. The surface image of the wiring terminal is captured from different angles by the industrial camera (for example, an elevation angle of 15° can obtain the groove shadow feature generated by polishing on the surface of the wiring terminal, and a depression angle of 60° can capture the protruding profile of the metal micro-particles on the surface of the wiring terminal). The point cloud data of the surface of the wiring terminal is generated in combination. All concave-convex points on the point cloud data are marked, including scratches, metal micro-particles and grooves.

[0074] ​The multispectral light source separation feature is simultaneously projected onto the terminal surface to generate a multispectral image of the terminal surface. The multispectral light source includes white light, ultraviolet light, and near-infrared light. White light can suppress specular reflections on the terminal surface; ultraviolet light activates the fluorescence of metal debris on the terminal surface, enhancing the metal reflection signal; and near-infrared light penetrates the electrolytic film and abrasive residue on the terminal surface. The surface normal vector is calculated in real time using the terminal point cloud data, and the multispectral light source incident angle and polarizer orientation are dynamically adjusted to eliminate surface reflection interference, ensure clear imaging of metal debris on the surface, and generate a multispectral image of the terminal surface. A global coordinate system is established on the terminal surface, and the multispectral image and point cloud data are fused and aligned to the global coordinate system based on the coordinate mapping relationship to generate a fused feature map.

[0075] 3D morphology analysis is then used to remove scratches on the terminal surface, ultraviolet-infrared reflectance spectra are used to distinguish materials, and the dimensions are calibrated in combination with the surface inclination to achieve precise identification of metal chips and accurate measurement of geometric parameters, providing reliable data input for correcting the measured resistance value.

[0076] It should be noted that during the dynamic adjustment of the incident angle of the multispectral light source and the orientation of the polarizer, the terminal blocks of high-voltage, high-current electricity meters typically utilize an arc-shaped crimp structure to withstand high-current electrodynamic forces. This results in abrupt curvature changes on the terminal block surface during the real-time calculation of the surface normal vector based on the terminal block's 3D point cloud data. This leads to significant deviations in the standard normal vector formula at the point of sudden curvature changes due to phase resolution errors, which in turn leads to inaccurate polarization angle compensation, abnormal signals in the ultraviolet and near-infrared channels (a sudden drop in ultraviolet reflectivity and Fresnel transmission artifacts in the near-infrared), and failure to identify metal chip fragments. In particular, when polishing the oxide layer on the surface of a terminal block with an arc-shaped crimp structure, the bending stress in the arc-shaped area of ​​the terminal block surface causes the chip to generate with greater internal stress, resulting in more irregularly shaped metal chips (such as curls and hooks). These large and irregularly shaped metal chips can cause large metal chips (especially curls) to accumulate on the concave surface of the arc, forming a stacking area that obscures the underlying metal chips and makes them unrecognizable by the optical detection system. This results in significant errors in the number, size, and location of metal chips. To address this issue, we utilize curvature-adaptive polarization control and tomographic confocal scanning to overcome the optical distortion and metal chip stacking limitations of high-curvature terminal surfaces. This allows for panoramic, high-precision identification of metal chips on terminal surfaces with extreme geometric structures, providing a reliable data foundation for insulation performance evaluation. More specific implementation methods include:

[0077] A low-density grating (e.g., with a period of 200 μm) is projected onto the surface of the terminal, and a basic curvature map κ(x, y) of the terminal surface is obtained by phase-shift interferometry, where κ(x, y) represents the curvature value κ at the coordinate (x, y) of the terminal surface.

[0078] Based on the basic curvature map of the terminal surface, the projected grating period is dynamically calculated and adjusted. The grating period in low-curvature regions (κ < 1) is adjusted to 20 μm; in medium-curvature regions (1 < κ < 2) to 20-8(κ - 1) μm; and in high-curvature regions (κ > 1) to 4 μm. By segmenting the curvature regions of the terminal surface, the high-curvature regions, where the surface geometry changes dramatically, require a smaller grating period to capture the details. By dynamically adjusting the grating period, the fringe deformation accurately reflects the terminal's surface topography.

[0079] Four polarization camera channels (0°, 45°, 90°, and 135°) are used to simultaneously expose the polarization-modulated structured light reflected from the terminal surface. The exposure time is inversely proportional to the curvature of the terminal surface, as higher curvature areas require shorter exposure times to avoid saturation. The Stokes component is then calculated using the polarization image collected by each polarization camera channel. The expression is:

[0080]

[0081] Where S0 is the sum of the non-polarized components; S1 is the linear polarization degree of the 0° polarization camera channel; S2 is the linear polarization degree of the 45° polarization camera channel; I0 is the light intensity collected by the 0° polarization camera channel, which is the reflection of the metal body; I 45 The light intensity collected by the 45° polarization camera channel reflects the characteristics of the oxide layer; I 90 The light intensity collected by the 90° polarization camera channel reflects the surface geometric characteristics; I 135 The light intensity collected by the 135° polarization camera channel represents microstructure scattering. Applying a Mueller matrix transformation to the Stokes vector yields a corrected Stokes vector, eliminating polarization measurement deviations caused by surface reflectivity.

[0082] The surface gradient is calculated based on the point cloud data of the terminal surface, and a compensation vector is generated, namely:

[0083]

[0084] Where, is the normal vector compensation, which represents the polarization measurement error correction value; δ is a modifier, is the basic normal vector of the terminal surface calculated based on the point cloud data; α is the curvature and error conversion factor, which is a dimensionless coefficient obtained based on experimental data calibration; unit(...) is the vector normalization operator used to normalize the vector; r is the radial coordinate, is the radial unit vector, obtained based on the surface gradient; is the radial second-order derivative, which represents the rate of change of curvature along the radial direction; z is the depth direction.

[0085] Then, the final true normal vector is generated based on the normal vector compensation and the original optical normal vector, namely:

[0086]

[0087] Where, is the true normal vector. By integrating confocal geometry measurement and polarization optical data, the interference caused by the curved surface of the terminal block is eliminated, and the problem of identifying metal chips on strongly reflective surfaces is solved. k is the parameter index of the Stokes component, [0, 1, 2] corresponds to S0, S1, and S2 respectively. S k is the Stokes component; is the polarization basis vector.

[0088] Based on the basic curvature map of the terminal surface, the tomographic confocal scanning range and variable step size are calculated to match the thickness of the metal chip stack on the terminal surface, maximizing penetration depth while ensuring accuracy. Wavelengths are switched based on the curvature region of the terminal surface to enhance surface detail and improve deep penetration of the metal chip stack. Different spectral wavelengths for tomographic confocal scanning are implemented based on the curvature region of the terminal surface. For low-curvature regions, a single wavelength (visible spectrum region) is activated to prioritize fast scanning efficiency. For medium-curvature regions, a complementary dual-wavelength combination (visible light + near-infrared) is enabled to construct a spectral contrast channel. For high-curvature regions, a three-wavelength synergy (ultraviolet + visible light + infrared) is triggered to form a wide-spectrum penetration solution. A wavelength-depth feature mapping is established, and spectral differentiation is used to eliminate masking artifacts. A pulsed laser is emitted onto the terminal surface and the photon arrival time is recorded to generate a depth-to-photon curve. The optical path difference is used to accurately separate the metal chip signals at different levels. Finally, a four-dimensional tensor T(x, y, z, λ) of spectral data containing the depth attenuation curve is generated. UV ), where z is the depth direction, λ UV is the spectral channel.

[0089] The curvature value of the terminal surface is greater than the preset risk threshold κ thThe area is defined as a high-risk area; based on the curvature value of the high-risk area, the scanning density coefficient β = min (3, 1 + 2κ) is increased to enhance the scanning density. The optimal incident angle of the multi-spectral light source, the additional compensation angle of the polarizer direction, and the UV gain factor are calculated based on the curvature value of the terminal surface, and an enhanced dataset is constructed for the high-risk area; that is:

[0090]

[0091] Where θ opt is the optimal incident angle of the multispectral light source; arcsin(...) is the inverse sine function; NA is the numerical aperture, which represents the light-gathering ability of the optical lens; α′ is the material compensation coefficient, which is determined by fitting experimental data; γ is the curvature correction term; is the compensation angle increment in the direction of the polarizer, which is used to solve the polarization distortion problem during curved surface reflection; 25° is the maximum polarization deviation of the copper terminal under extreme curvature; 0.35 is the attenuation coefficient, which is a constant of the material and wavelength coupling characteristics; G UV is the UV gain factor; η is the basic compensation coefficient; α″ is the curvature sensitivity factor.

[0092] The polarization compensation angle of each coordinate point on the terminal surface is calculated based on the true normal vector, that is:

[0093]

[0094] Where, is the polarization compensation angle, which represents the angle between the incident light and the surface normal and determines the change in polarization state; is the incident light direction vector.

[0095] Then, an inverse compensation matrix is ​​constructed based on the polarization compensation angle, which contains the polarization state change information of the polarized light after it is reflected on the terminal surface. The matrix expression is:

[0096]

[0097] Where, is the inverse compensation matrix, which is used to eliminate the polarization distortion caused by curvature; is the secant function, Used to compensate for the optical path difference caused by normal tilt; is the tangent function, is the cosecant function, which is used to compensate the intensity of the cross-polarization component; Polarization rotation used to correct for reflections from curved surfaces.

[0098] Depolarize the original polarization image and perform convolution operation on the polarization image and the inverse compensation matrix, that is:

[0099]

[0100] Where, I corr (x, y) is the corrected image, which is the output result after eliminating polarization distortion; Conv(...) is the matrix convolution operation, which is used to apply the inverse compensation matrix to each coordinate point; I raw is the original polarization image.

[0101] The corrected image and the spectral data four-dimensional tensor are fused in the depth direction to form an image sequence with depth information, namely:

[0102]

[0103] Where D(x,y,z) is an image sequence with depth information; is the depthwise convolution.

[0104] Then the image sequence is subjected to tomographic deconvolution, namely:

[0105]

[0106] in,

[0107] Where V vol The reconstructed metal chip volume data is the real structure after eliminating optical distortion; F{...} represents the three-dimensional Fourier transform operator, which converts the masked data in the spatial domain into the frequency domain representation by performing a three-dimensional Fourier transform on the image sequence; F -1 [...] denotes the inverse Fourier transform; H * (w) is the curvature-adaptive hybrid transfer function, which is used to solve the optical distortion problem in surface detection; w is the spatial frequency vector; OTF(w) is the optical transfer function, which is used to correct lateral blur and diffraction effects; CTF(w) is the confocal transfer function, which is used to enhance the depth resolution.

[0108] The filtering parameters of each coordinate position are calculated based on the basic curvature map of the terminal surface, and a three-dimensional Gaussian kernel function is generated in real time. The three-dimensional Gaussian kernel function is used to perform a three-dimensional convolution operation on the reconstructed metal micro-chip volume data, and the optimized metal micro-chip volume data is output.

[0109] Utilize the multi-spectral information of the terminal surface, including: the reflection intensity I in the ultraviolet band u , the reflection intensity I in the near infrared band ir and the reflection intensity I in the visible light band v Then calculate the reflectance ratio R of ultraviolet light to near infrared light at each coordinate point on the terminal surface. u / ir ,Right now: The material of the terminal surface is classified according to the reflectance, including metal chips, mixed chips, and non-metal residues. The metal weight, non-metal weight, and mixed weight are dynamically calculated based on the reflectance, namely:

[0110]

[0111] ω d =1-ω m

[0112]

[0113] Where, ω m is the metal weight, ω d is the non-metal weight, ω c is the mixing weight; b is the slope coefficient, μ is the decision point, used to distinguish metal and non-metal; R ref is the reference point, reflecting the reflectance under ideal mixing conditions, based on the statistical mean of typical mixed materials; S is the normalization factor.

[0114] Then, the multispectral information is fused into the final fused image F based on three weights, namely:

[0115] F(x,y,z)=ω m I v +ω c I u +ω d I ir

[0116] A partition mapping function is established based on the basic curvature map of the terminal surface, namely

[0117] Ω i ={(x,y)|κ th,i ≤κ(x,y)<κ th,i +Δκ max}

[0118] Where, Ω i is the i-th curvature partition, κ th,i is the base curvature threshold of the i-th curvature partition, Δκ max is the maximum curvature difference threshold.

[0119] Each curvature partition is then independently segmented using watershed segmentation and denoised using morphological filtering to generate a binary label matrix for metal chips. The originally connected area is segmented into independent parts, where each label represents a different metal chip individual. Continuous pixel regions with the same label value in the binary label matrix are identified, that is, a connected domain is an independent metal chip; the geometric center of each connected domain is calculated as a coordinate point based on a three-dimensional weighted average to generate the precise three-dimensional coordinates r of each metal chip on the terminal surface. obs The precise three-dimensional coordinates are corrected by the curvature gradient to solve the projection error caused by three-dimensional distortion and metal chip tilt in the curved area, and the equivalent conductive area of ​​the metal chip in the actual working state is accurately quantified.

[0120]

[0121] Where r real is the corrected three-dimensional coordinate of the metal chip; κ is the curvature value at the precise three-dimensional coordinate of the metal chip; τ is the correction coefficient used to adjust the compensation strength of the curvature gradient, which is determined by experiment; is the depth gradient vector at the precise three-dimensional coordinates of the metal chips.

[0122] Then, the light intensity distribution along the depth direction z is extracted from the reconstructed metal chip volume data, and the vertical projection component of the metal chip is calculated. Then, the normal vector inclination compensation is introduced to generate the equivalent conductive area of ​​the metal chip, that is:

[0123]

[0124] Where A eff is the equivalent conductive area of ​​metal chips; V(z) is the light intensity distribution along the depth direction z; max(V(z)) is the peak light intensity on the surface of the terminal, z max is the depth of the peak position of light intensity. By comparing the two, it reflects the maximum projection density of metal chips in the vertical direction; τ′ is the inclination magnification factor; cos -1 (...) is the arccosine function; is the unit vector in the direction of the absolute optical axis.

[0125] Finally, the three-dimensional risk index of metal chips is calculated based on the equivalent conductive area of ​​the metal chips on the terminal surface, the corrected three-dimensional coordinates, and the reflectance ratio of ultraviolet light and near-infrared light. This quantifies the electrical short circuit risk that may be caused by metal chips on the curved surface of the terminal. The calculation formula for the three-dimensional risk index is:

[0126]

[0127] Where R risk,iA is the risk index of the i-th metal chip; eff,i is the equivalent conductive area of ​​the i-th metal chip; d i is the distance from the ith metal chip to the nearest conductor, calculated based on the point cloud data of the terminal surface; f(R u / ir ) is the mapping function from micro-chip material to weight, and the expression is:

[0128]

[0129] Finally, the metal chip matrix on the terminal surface is output, namely:

[0130]

[0131] Where M is the metal chip matrix and m is the number of metal chips.

[0132] The way to correct the measured resistance value is:

[0133] The equivalent conductance of each metal chip is calculated based on the metal chip matrix. The calculation formula is:

[0134]

[0135] Where g i is the equivalent conductivity of the i-th metal chip; σ is the electrical conductivity; f(R risk,i ) is the mapping function from the risk index of the i-th metal chip to the conductivity weight, which is used to nonlinearly convert the risk index of the metal chip into the conductivity weight. The expression is:

[0136]

[0137] Where exp(...) is the exponential function, R risk,min is the minimum risk index in the metal chip matrix, R risk,max It is the maximum risk index in the metal chip matrix.

[0138] Establish the spatial admittance matrix Y of all metal chips on the terminal surface, where the matrix element y i,j Express the mutual conductance between the i-th metal chip and the j-th metal chip, that is:

[0139]

[0140] Where, d ij is the distance between the i-th metal chip and the j-th metal chip, R ele Electric field action radius: When the distance between two metal chips is smaller than the electric field action radius, a conductive circuit will be formed.

[0141] Each metal chip in the metal chip matrix is ​​regarded as a point charge source, and the charge density is weighted based on the risk index of the metal chip, that is:

[0142]

[0143] Where, ρ (x,y,z) is the charge density, is a Dirac function used to mathematically describe the positioning of discrete metal chips in three-dimensional space; if and only if the position vector of any point on the terminal surface and the position vector of the i-th metal chip When overlapped, In all other locations,

[0144] When applying the high voltage for the insulation test of the energy meter, the potential distribution Φ(x, y, z) on the terminal surface is solved based on the Poisson equation. The calculation formula is:

[0145]

[0146] Where, is the Laplace operator of electric potential, which represents the difference between the electric potential at a certain point in space and the surrounding electric potential, and is used to describe the non-uniformity of electric field distribution; ε is the dielectric constant of the dielectric, which reflects the response characteristics of the insulating material to the electric field. For each metal chip, the potential value at its location is taken Arrange the potential values ​​of all metal chip positions in order into a column vector, namely the node potential vector It reflects the specific electric potential of each metal chip under the action of the test voltage.

[0147] Then use the potential gradient to obtain the electric field strength vector The electric field intensity vector is used to calculate the displacement of metal chips caused by the Coulomb force under high pressure. The calculation formula is:

[0148]

[0149] Where, is the position change vector of the metal chips, reflecting the displacement vector of the metal chips within Δt under the action of the electric field; ψ is the mobility coefficient of the metal chips, reflecting the movement rate of the metal chips under unit electric field strength, determined based on experimental data; Δt is the time step of the electric field action, used to consider the time increment of the dynamic migration of metal chips; Represents the electric field intensity vector at the i-th metal chip.

[0150] The spatial admittance matrix is ​​recalculated using the position change vector of the metal chip, and the conductive path change caused by the displacement of the metal chip is corrected to obtain the new spatial admittance matrix Y′. The node current distribution is calculated using the new spatial admittance matrix and the node potential vector. The calculation formula is:

[0151]

[0152] Where, is the node current vector, which represents the total current flowing into the node, that is, the algebraic sum of the currents passing through the mutual conduction channels between the metal chip and all adjacent metal chips.

[0153] The metal chip branch currents that form a path between the two terminals under test (e.g., the high-voltage terminal and the reference ground) are then extracted from the node current vector and summed to obtain the total chip current, also known as the false leakage current component. The true leakage current is obtained by subtracting the false leakage current component from the measured total leakage current, and the corrected insulation resistance is then calculated.

[0154] Those skilled in the art will appreciate that all or part of the steps in the above-mentioned embodiment methods can be accomplished by instructing the relevant hardware through a program. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to in detail. For the above embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For relevant parts, please refer to the partial description of the method embodiments.

[0156] The above embodiments provide a detailed introduction to the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for insulation testing of a single-phase smart electric energy meter, characterized in that: The method comprises the following steps: Projecting structured stripe light onto the surface of the terminal block to capture point cloud data; A low-density grating is projected onto the surface of the terminal block to generate a base curvature map, marking high-risk areas. The grating period is dynamically adjusted based on the base value, and multi-channel polarization images are simultaneously acquired and Stokes vectors are calculated. Optical distortion is corrected by combining surface normal compensation. A tomographic confocal scan is performed based on the base curvature map, and the spectral wavelength is switched according to the curvature partition to generate a four-dimensional tensor of spectral data. The corrected polarization image and the four-dimensional tensor of spectral data are fused, and the metal chip volume data is reconstructed through three-dimensional Fourier deconvolution. The corrected three-dimensional coordinates of metal chips and the equivalent conductive area are identified, and the risk index is calculated. A metal chip matrix of the terminal surface is constructed. A spatial admittance matrix is ​​then established based on the metal chip matrix, and the Poisson equation is solved to simulate the potential distribution and predict the displacement of the metal chips. The spatial admittance matrix is ​​updated based on the displacement of the metal chips, and the node current distribution is calculated in combination with the node potential vector. The metal chip branch current that forms a path between the two measured terminals is then extracted from the node current vector and summed to obtain the false leakage current component. The true leakage current that corrects the measured insulation resistance value is calculated by subtracting the false leakage current component from the measured total leakage current.

2. The insulation testing method according to claim 1, wherein: The method of correcting optical distortion by combining the surface normal vector compensation amount is: The surface gradient is calculated based on the point cloud data of the terminal surface, and a compensation vector is generated, namely: Where, is the normal vector compensation, δ is the modifier, is the basic normal vector; α is the curvature and error conversion factor; unit(...) is the vector normalization operator; is the radial unit vector; is the curvature change rate along the radial direction; r is the radial coordinate; κ is the curvature value; z is the depth direction; The final true normal vector is generated based on the normal vector compensation and the original optical normal vector, namely: Where, is the true normal vector; k is the parameter index of the Stokes component; S k is the Stokes component; is the polarization basis vector.

3. The insulation testing method according to claim 1, wherein: The method of switching the spectral wavelength according to the curvature partition is: Activate a single wavelength in a region of low curvature; Enable complementary dual-wavelength combinations in the mid-curvature region; Triggering three-wavelength synergy in high curvature regions.

4. The insulation testing method according to claim 1, wherein: The method of reconstructing the metal chip volume data by three-dimensional Fourier deconvolution is as follows: An enhanced dataset for calculating high-risk areas based on the curvature values ​​of the terminal surface; The polarization compensation angle of each coordinate point on the terminal surface is calculated based on the true normal vector. The inverse compensation matrix is ​​constructed based on the polarization compensation angle. The original polarization image is depolarized and the polarization image is convolved with the inverse compensation matrix, namely: Where, I corr (x,y) is the corrected image; G UV is the UV gain factor; Conv(...) is the matrix convolution operation; I raw is the original polarization image; is the inverse compensation matrix; The corrected image and the spectral data four-dimensional tensor are fused in the depth direction to form an image sequence with depth information. The image sequence is then subjected to tomographic deconvolution, namely: in, Where V vol is the reconstructed metal chip volume data; F{...} represents the three-dimensional Fourier transform operator; F -1 [...] denotes the inverse Fourier transform; H * (w) is the curvature-adaptive hybrid transfer function; w is the spatial frequency vector; OTF(w) is the optical transfer function; CTF(w) is the confocal transfer function; D(x, y, z) is the image sequence with depth information; κ th is the risk threshold.

5. The insulation testing method according to claim 1, wherein: The method for identifying the corrected three-dimensional coordinates of metal chips is: The reflectance ratio of ultraviolet light to near-infrared light at each coordinate point on the terminal surface is calculated. The material of the terminal surface is classified according to the reflectance ratio, including metal debris, mixed debris, and non-metal residue. The metal weight, non-metal weight, and mixed weight are calculated based on the reflectance ratio. In other words, the multispectral information is then fused into the final fused image based on these three weights. A partition mapping function is established based on the basic curvature map of the terminal surface, namely Oh i ={(x,y)|κ th,i ≤κ(x,y)<κ th,i +Dk max } Where, Ω i is the i-th curvature partition, κ th,i is the base curvature threshold of the i-th curvature partition, Δκ max is the maximum curvature difference threshold; κ(x,y) is the basic curvature map; Then, each curvature partition is independently segmented by watershed, and morphological filtering is used for denoising to generate a binary labeling matrix of metal chips. Based on the three-dimensional weighted average, the geometric center of each connected domain is calculated as the coordinate point to generate the precise three-dimensional coordinates r of each metal chip on the terminal surface. obs ; Correct the precise three-dimensional coordinates by curvature gradient, that is: r real =r obs +t·k·▽z Where r real is the corrected three-dimensional coordinate of the metal chip; τ is the correction coefficient used to adjust the compensation strength of the curvature gradient; ▽z is the depth gradient vector at the precise three-dimensional coordinate of the metal chip.

6. The insulation testing method according to claim 1, characterized in that: The method for identifying the equivalent conductive area is: The light intensity distribution along the depth direction z is extracted from the reconstructed metal chip volume data, and the vertical projection component of the metal chip is calculated. Then, the normal vector inclination compensation is introduced to generate the equivalent conductive area of ​​the metal chip, that is: Where A eff is the equivalent conductive area of ​​metal chips; V(z) is the light intensity distribution along the depth direction z; max(V(z)) is the peak light intensity on the surface of the terminal, z max is the depth of the peak light intensity position; τ′ is the tilt magnification factor; cos -1 (...) is the arccosine function; is the unit vector in the direction of the absolute optical axis.

7. The insulation testing method according to claim 1, wherein: The calculation formula of the three-dimensional risk index is: Where R risk,i A is the risk index of the i-th metal chip; eff,i is the equivalent conductive area of ​​the i-th metal chip; d i is the distance from the i-th metal chip to the nearest conductor; where: Where, f(R u / ir ) is the mapping function from micro-chip material to weight.

8. The insulation testing method according to claim 1, wherein: The elements of the spatial admittance matrix satisfy: Where y i,j is the matrix element; d ij is the distance between the i-th metal chip and the j-th metal chip; R ele Electric field radius, A eff,i is the equivalent conductive area of ​​the i-th metal chip, A eff,j is the equivalent conductive area of ​​the jth metal chip.

9. The insulation testing method according to claim 1, wherein: The method for predicting the displacement of metal chips is: The charge density is weighted based on the risk index of metal chips, namely: Where, ρ( x,y,z ) is the charge density, is the Dirac function; The potential distribution Φ(x,y,z) on the terminal surface is solved based on the Poisson equation. The calculation formula is: Where, 2 Φ is the Laplace operator of electric potential; ε is the dielectric constant of the medium; Then use the potential gradient to obtain the electric field strength vector And calculate the displacement of metal chips, the calculation formula is: Where, is the position change vector of the metal chips; ψ is the mobility coefficient of the metal chips; Δt is the time step of the electric field; represents the electric field intensity vector at the i-th metal chip.

10. The insulation testing method according to claim 1, wherein: The method for calculating the node current distribution is: Calculate the potential value of each metal chip location and arrange the potential values ​​of all metal chip locations in order as a node potential vector The calculation formula is: is the node current vector, and Y′ is the new spatial admittance matrix.

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