Collaborative perception on-line monitoring system for partial discharge of ultrahigh-voltage power transmission cable

Abstract

The invention relates to a cooperative sensing online monitoring system for partial discharge of an ultrahigh-voltage power transmission cable, and relates to the technical field of power equipment monitoring. The system comprises a physical signal acquisition unit, an environmental parameter sensing unit, a multi-dimensional feature mapping unit, a dynamic field domain construction unit and an insulation defect diagnosis unit. The system collects broadband UHF signals and joint temperature and humidity parameters, calculates a signal distortion compensation radius by using an environment correction coefficient, and constructs a signal uncertainty sphere and a real-time insulation state domain; defect types are analyzed and recognized based on space projection coupling, and the positions of fault points are reversely solved through a transmission line equation. According to the invention, through deep fusion of the multi-dimensional feature space and the environment parameters, the diagnosis deviation caused by signal transmission dispersion is effectively solved, and the accuracy and the positioning precision of ultrahigh-voltage cable partial discharge monitoring are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring, and in particular to a collaborative sensing online monitoring system for partial discharge in ultra-high voltage transmission cables. Background Technology

[0002] As the lifeblood of urban power grids, ultra-high voltage transmission cables are directly related to the safety and stability of the entire power system. In the early stages of cable insulation deterioration, partial discharge is often present. Therefore, using ultra-high frequency sensors to capture discharge pulse signals has become a core method for assessing cable insulation condition and preventing insulation breakdown accidents. Existing monitoring technologies typically collect signal amplitude, phase, and frequency to identify insulation defect types and use the time difference between signal arrival times at both ends of the line to calculate fault location.

[0003] However, existing online cable monitoring technologies still have significant shortcomings in practical applications. First, traditional methods often treat cables as ideal transmission media in a constant environment, ignoring the impact of complex operating conditions on signals. In reality, large fluctuations in cable body temperature and changes in humidity at joints can significantly alter the dielectric constant of the insulation, causing partial discharge signals to drift in their transmission characteristics. Current technologies lack dynamic sensing and compensation mechanisms for these environmental parameters, making the system highly susceptible to misjudgments during seasonal changes or load variations.

[0004] Secondly, in defect diagnosis, existing feature analysis methods have weak anti-interference capabilities, making it difficult to describe the distribution patterns of discharge signals in multi-dimensional physical space and accurately extract weak defect signals from massive background noise, thus limiting diagnostic accuracy. Furthermore, in fault location, existing technologies typically ignore the group velocity dispersion effect generated during long-distance transmission of high-frequency signals. High-frequency pulses undergo waveform broadening and frequency center shift during transmission in cables, leading to significant physical deviations in time-difference location methods based on the constant wave velocity assumption, which fails to meet the urgent need for precise, targeted maintenance of ultra-high voltage cables. Therefore, developing a monitoring system that can deeply integrate environmental perception and correct for dispersion effects is a pressing technical problem that needs to be solved. Summary of the Invention

[0005] To address the shortcomings of existing technologies, embodiments of the present invention provide a collaborative sensing online monitoring system for partial discharge in ultra-high voltage transmission cables, comprising: a physical signal acquisition unit, an environmental parameter sensing unit, a multi-dimensional feature mapping unit, a dynamic field construction unit, and an insulation defect diagnosis unit;

[0006] Physical signal acquisition unit: used to synchronously acquire high-frequency partial discharge pulse signals through a wideband UHF sensor array distributed on the cable joint and the cable body;

[0007] Environmental parameter sensing unit: used to obtain cable temperature parameters through a temperature sensor attached to the outer surface of the cable joint protective shell, and to obtain ambient humidity parameters through a humidity sensor set in the near field area of ​​the cable joint;

[0008] Multidimensional feature mapping unit: connected to the physical signal acquisition unit, used to extract the pulse rise time, center frequency offset and apparent discharge quantity of the high-frequency partial discharge pulse signal, and map the three physical feature values ​​to a three-dimensional physical feature space to generate real-time signal feature points;

[0009] Dynamic field construction unit: It is used to calculate the signal distortion compensation radius of high-frequency partial discharge pulse signal based on cable temperature parameters, ambient humidity parameters and pre-stored cable dielectric parameters, and construct a signal uncertainty sphere in three-dimensional physical feature space with real-time signal feature points as the center and the signal distortion compensation radius as the radius. Then, based on multiple signal uncertainty spheres in the time series, a closed real-time insulation state domain is generated using a spatial envelope fitting algorithm.

[0010] Insulation defect diagnosis unit: It is used to perform spatial projection coupling analysis between the real-time insulation state domain and multiple preset standard fault feature domains, calculate the overlap coupling degree between the two, select the insulation defect type corresponding to the standard fault feature domain with the largest overlap coupling degree as the diagnosis result, and calculate the fault point location based on the geometric centroid of the overlapping area.

[0011] According to a preferred embodiment, the insulation defect types include: insulation gap discharge, metal tip discharge, floating potential discharge, and insulation surface discharge.

[0012] According to a preferred embodiment, the dynamic field construction unit calculates the signal distortion compensation radius based on cable temperature parameters, ambient humidity parameters, and cable dielectric parameters, including:

[0013] Retrieve pre-stored cable dielectric parameters and signal attenuation factors; the cable dielectric parameters are relative permittivity.

[0014] The cable temperature parameters, ambient humidity parameters, and relative permittivity are input into the multi-parameter exponential correction model to calculate the environmental correction coefficient that reflects the impact of the current operating environment.

[0015] The dynamic relative permittivity is obtained by dynamically correcting the relative permittivity using an environmental correction factor. The dynamic relative permittivity is then substituted into the transmission line equation to construct a frequency response function. The frequency response function reflects the relationship between the signal group velocity and frequency.

[0016] The effective bandwidth and estimated transmission distance of the high-frequency partial discharge pulse signal are obtained. The upper and lower limits of the signal frequency are determined based on the effective bandwidth. The upper and lower limits of the frequency are substituted into the frequency response function to calculate the corresponding two group velocity values. The propagation time difference of the two group velocity values ​​on the transmission path is calculated using the estimated transmission distance, and the propagation time difference is defined as the group velocity dispersion value.

[0017] The group velocity dispersion value and the signal attenuation factor are normalized and weighted, and the calculation result is output as the signal distortion compensation radius.

[0018] According to a preferred embodiment, the dynamic field construction unit generates a closed real-time insulating state domain based on multiple signal uncertainty spheres over a time series using a spatial envelope fitting algorithm, including:

[0019] All signal uncertainty spheres within the current monitoring time window are obtained from the time series, and feature centripetal convergence operation is performed on each signal uncertainty sphere to obtain the intrinsic feature points corresponding to each signal uncertainty sphere.

[0020] A boundary centrifugal search is performed based on the coverage of all intrinsic feature points and all signal uncertainty spheres to identify several maximum distortion boundary points;

[0021] A real-time insulation state domain is generated by performing spatial closed envelope processing based on all maximum distortion boundary points.

[0022] According to a preferred embodiment, the dynamic field construction unit performs a feature centripetal convergence operation on each signal uncertainty sphere, including:

[0023] Calculate the geometric centroid of all real-time signal feature points within the current monitoring time window and define it as the centroid of the partial discharge cluster.

[0024] For each sphere with signal uncertainty, construct a convergent vector line segment pointing from its center to the centroid of the partial discharge cluster;

[0025] Calculate the intersection point of the convergent vector line segment and the surface of the corresponding signal uncertainty sphere, and define the intersection point as the intrinsic feature point corresponding to the signal uncertainty sphere; wherein, the intrinsic feature point is located on the line connecting the center of the signal uncertainty sphere and the centroid of the partial discharge cluster.

[0026] According to a preferred embodiment, the dynamic field construction unit performs a boundary centrifugal search to identify several maximum distortion boundary points, including:

[0027] Curve fitting is performed on all intrinsic feature points to generate insulation state evolution curves, and multiple local distance maxima points of the insulation state evolution curves relative to the centroid of the partial discharge cluster are identified. Then, the multiple local distance maxima points are defined as the signal fluctuation extreme point set.

[0028] For each extreme point of signal fluctuation in the set of extreme points of signal fluctuation, the direction from the centroid of the partial discharge cluster to the extreme point of signal fluctuation is defined as the centrifugal search direction. Starting from the extreme point of signal fluctuation, a step search is performed along the corresponding centrifugal search direction. At each search position, it is determined whether the effective coverage volume of all signal uncertainty spheres has been removed.

[0029] When the search position first deviates from the effective coverage volume of all signal uncertainty spheres, the search stops, and the corresponding search position is marked as the maximum distortion boundary point in that centrifugal search direction.

[0030] According to a preferred embodiment, the dynamic field construction unit generates a real-time insulating state domain by performing spatial closed envelope processing based on all maximum distortion boundary points, including:

[0031] A minimum closed surface capable of enclosing all maximum distortion boundary points is constructed, and the solid space enclosed by the minimum closed surface is defined as the real-time insulation state domain; the real-time insulation state domain characterizes the maximum physical distribution boundary of the partial discharge signal characteristics after dispersion compensation in the three-dimensional physical feature space under the current cable temperature and ambient humidity conditions.

[0032] According to a preferred embodiment, the process of constructing a standard fault feature domain includes:

[0033] Construct solid models of typical defects corresponding to insulation gap discharge, metal tip discharge, floating potential discharge, and insulation surface discharge, respectively;

[0034] For each typical defect entity model, multiple rounds of high-voltage partial discharge excitation experiments were performed within the temperature and humidity variation range covering the entire range. The pulse rise time, center frequency offset and apparent discharge quantity of the sample partial discharge pulse signal were extracted and mapped to the three-dimensional physical feature space to form the original sample feature point set.

[0035] Calculate the spatial probability density field of the original sample feature point set in the three-dimensional physical feature space, and based on the preset probability density threshold gradient, divide the spatial probability density field from the inside out into nested high-density core region, medium-density transition region and low-density diffusion region.

[0036] The high-density core region, medium-density transition region, and low-density diffusion region are encapsulated to generate the standard fault feature domain corresponding to this insulation defect type.

[0037] According to a preferred embodiment, the insulation defect diagnosis unit performs spatial projection coupling analysis on the real-time insulation state domain and multiple standard fault feature domains, and calculates the overlap coupling degree between the two, including:

[0038] Multiple standard fault feature domains are retrieved, and coupling weight coefficients with successively decreasing values ​​are assigned to the high-density core region, medium-density transition region, and low-density diffusion region within each standard fault feature domain, thereby transforming each standard fault feature domain into a standard fault energy field with non-uniform density distribution characteristics.

[0039] The real-time insulation state domain is projected onto the standard fault energy field, and the intersection volume of the real-time insulation state domain with the high-dense core region, the medium-dense transition region and the low-dense diffusion region is calculated respectively.

[0040] The total intersection volume is obtained by weighting and summing the intersection space volumes of each level using coupling weight coefficients, and the ratio of the total intersection volume to the total volume of the real-time insulation state domain is defined as the overlap coupling degree.

[0041] According to a preferred embodiment, the insulation defect diagnosis unit calculates the fault location based on the geometric centroid of the overlapping region, including:

[0042] In the standard fault feature domain with the highest degree of overlap and coupling, the solid part that overlaps with the high-density core region of the real-time insulation state domain is extracted, the geometric centroid of the solid part is calculated and defined as the fault feature anchor point.

[0043] The coordinate values ​​of the fault feature anchor points are analyzed to obtain the first feature component corresponding to the pulse rise time and the second feature component corresponding to the center frequency offset.

[0044] Based on the current cable temperature and ambient humidity parameters, the first and second characteristic components are mapped and converted into single-end equivalent propagation distances using a multi-parameter exponential correction model and the inverse solution logic of the transmission line equation.

[0045] The single-end equivalent propagation distances at both ends of the cable line are obtained separately. A weighted average algorithm is then used to fuse and correct the single-end equivalent propagation distances at both ends, thereby determining the specific physical location of the fault point.

[0046] The present invention has the following beneficial effects:

[0047] 1. This system effectively eliminates signal characteristic drift caused by environmental changes by dynamically calculating the signal distortion compensation radius and constructing an uncertainty sphere through real-time acquisition of cable temperature and ambient humidity, restoring the true discharge characteristics, and significantly improving the anti-interference capability and environmental adaptability of online monitoring.

[0048] 2. This system performs volume projection and coupling degree calculation between the real-time insulation state domain and the standard fault characteristic domain. By quantitatively analyzing the energy distribution in the overlapping area, the system can accurately identify weak defect types such as air gaps and tips from discrete pulse signals, improving the robustness and accuracy of insulation fault diagnosis.

[0049] 3. By combining real-time temperature and humidity to dynamically correct cable dielectric parameters, the pulse waveform distortion characteristics are accurately inverted into transmission distance. Combined with weighted fusion correction of dual-end data, the ranging deviation caused by the dispersion effect of high-frequency signals is overcome, achieving precise physical location of latent fault points in ultra-high voltage cables. Attached Figure Description

[0050] Figure 1 The diagram below illustrates a structural block diagram of a collaborative sensing online monitoring system for partial discharge of ultra-high voltage transmission cables, provided as an exemplary embodiment. Detailed Implementation

[0051] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0052] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0053] It should be understood that although the terms first, second, third, etc., may be used in this invention to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of this invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0054] See Figure 1 The present invention discloses a collaborative sensing online monitoring system for partial discharge of ultra-high voltage transmission cables, comprising: a physical signal acquisition unit, an environmental parameter sensing unit, a multi-dimensional feature mapping unit, a dynamic field construction unit, and an insulation defect diagnosis unit.

[0055] The physical signal acquisition unit is used to synchronously acquire high-frequency partial discharge pulse signals through a wideband UHF sensor array distributed on the cable joint and the main body.

[0056] It employs a built-in or surface-mount ultra-high frequency (UHF) sensor array, with an operating frequency band covering 300MHz to 1.5GHz. The sensor synchronously acquires high-frequency partial discharge pulse signals, with a sampling rate set at no less than 2GS / s to ensure the integrity of waveform details.

[0057] The environmental parameter sensing unit is used to obtain cable temperature parameters through a temperature sensor attached to the outer surface of the cable joint protective shell, and to obtain ambient humidity parameters through a humidity sensor set in the near field area of ​​the cable joint.

[0058] Optionally, a PT100 platinum resistance thermometer is used as the temperature sensor and is attached to the outer surface of the cable joint protective shell and the cable body shielding layer to obtain the cable temperature parameters in real time.

[0059] Optionally, a capacitive humidity sensor is selected and placed in the near-field area of ​​the cable joint (such as the inner wall of the joint well) to obtain the ambient humidity parameters.

[0060] The multidimensional feature mapping unit is connected to the physical signal acquisition unit and is used to extract the pulse rise time, center frequency offset and apparent discharge quantity of the high-frequency partial discharge pulse signal, and map the three physical feature values ​​to the three-dimensional physical feature space to generate real-time signal feature points.

[0061] The pulse rise time refers to the time required for a signal to rise from 10% to 90% of its peak value. This characteristic reflects the attenuation of the high-frequency components of the signal. The steeper the rise time (the shorter the time), the closer the monitoring point is to the fault point; the gentler the rise time (the longer the time), the farther the monitoring point is from the fault point.

[0062] The center frequency offset refers to the difference between the frequency of the center of gravity of the spectral energy and the reference frequency. This characteristic reflects the offset of the frequency position of the center of gravity of the signal's spectral energy relative to the original reference frequency.

[0063] Different defects produce different initial spectra. For example, "floating potential discharge" typically has a very wide spectrum and high energy, while "internal air gap discharge" has a relatively narrow spectrum. By analyzing the frequency characteristics, the type of fault can be distinguished.

[0064] Apparent discharge is the amount of charge obtained by integrating the pulse waveform. It characterizes the severity of the fault. If the apparent discharge increases exponentially, it indicates that the insulation defect is rapidly deteriorating and breakdown is imminent. Power must be cut off and repairs must be carried out immediately.

[0065] The dynamic field construction unit is used to calculate the signal distortion compensation radius of the high-frequency partial discharge pulse signal based on the cable temperature parameters, ambient humidity parameters, and pre-stored cable dielectric parameters. It constructs a signal uncertainty sphere in a three-dimensional physical feature space with the real-time signal feature point as the center and the signal distortion compensation radius as the radius. Then, based on multiple signal uncertainty spheres in the time series, a closed real-time insulation state domain is generated using a spatial envelope fitting algorithm.

[0066] Preferably, the dynamic field construction unit calculates the signal distortion compensation radius based on cable temperature parameters, ambient humidity parameters, and cable dielectric parameters, including:

[0067] Retrieve pre-stored cable dielectric parameters and signal attenuation factors; the cable dielectric parameters are relative permittivity.

[0068] The cable temperature parameters, ambient humidity parameters, and relative permittivity are input into the multi-parameter exponential correction model to calculate the environmental correction coefficient that reflects the impact of the current operating environment.

[0069] The dynamic relative permittivity is obtained by dynamically correcting the relative permittivity using an environmental correction factor. The dynamic relative permittivity is then substituted into the transmission line equation to construct a frequency response function. The frequency response function reflects the relationship between the signal group velocity and frequency.

[0070] The effective bandwidth and estimated transmission distance of the high-frequency partial discharge pulse signal are obtained. The upper and lower limits of the signal frequency are determined based on the effective bandwidth. The upper and lower limits of the frequency are substituted into the frequency response function to calculate the corresponding two group velocity values. The propagation time difference of the two group velocity values ​​on the transmission path is calculated using the estimated transmission distance, and the propagation time difference is defined as the group velocity dispersion value.

[0071] The group velocity dispersion value and the signal attenuation factor are normalized and weighted, and the calculation result is output as the signal distortion compensation radius.

[0072] Optionally, considering that the polarization characteristics of the insulating medium change exponentially with temperature and ambient humidity, this embodiment constructs the following formula for calculating the environmental correction factor.

[0073]

[0074] in, , To collect cable temperature and ambient humidity in real time, , For standard temperature and standard humidity, The thermal sensitivity index factor of the material. Environmental humidity sensitivity index factor, This is the environmental correction factor.

[0075] Optionally, the dynamic relative permittivity is obtained by dynamically correcting the relative permittivity using an environmental correction factor, and the following formula is used:

[0076]

[0077] in, The dynamic relative permittivity, The relative permittivity, This is the environmental correction factor.

[0078] The system constructs a frequency response function based on transmission line theory and extracts its phase-frequency characteristics. Since group velocity is defined as the derivative of angular frequency with respect to the phase constant, the system derives a group velocity function accordingly. Subsequently, by substituting the upper and lower frequency limits of the signal bandwidth into this group velocity function, the corresponding two group velocity values ​​can be calculated.

[0079] The frequency response function is constructed using transmission line theory, a common approach in electrical engineering. Those skilled in the art understand that the transmission characteristics of high-frequency signals in cables depend on the distributed parameters per unit length of the cable (i.e., distributed resistance, distributed inductance, distributed conductance, and distributed capacitance). In this embodiment, the system substitutes the dynamic relative permittivity calculated in the previous step into the calculation logic for the distributed capacitance (i.e., the distributed capacitance is proportional to the permittivity). Subsequently, based on the aforementioned distributed parameters, a propagation coefficient including an attenuation constant and a phase constant is constructed using telegraph equations, thereby generating a frequency response function that reflects the amplitude attenuation and phase delay characteristics of the signal along the transmission path. Further, based on the relationship between the phase constant and frequency, the system obtains the group velocity function through differentiation. Since the aforementioned dynamic relative permittivity already includes real-time temperature and humidity correction information, the frequency response function and group velocity value calculated based on this process can accurately reflect the actual transmission characteristics of the cable under current environmental conditions.

[0080] Preferably, the dynamic field construction unit, based on multiple signal uncertainty spheres in a time series, generates a closed real-time insulation state domain using a spatial envelope fitting algorithm, including:

[0081] All signal uncertainty spheres within the current monitoring time window are obtained from the time series, and feature centripetal convergence operation is performed on each signal uncertainty sphere to obtain the intrinsic feature points corresponding to each signal uncertainty sphere.

[0082] A boundary centrifugal search is performed based on the coverage of all intrinsic feature points and all signal uncertainty spheres to identify several maximum distortion boundary points;

[0083] A real-time insulation state domain is generated by performing spatial closed envelope processing based on all maximum distortion boundary points.

[0084] In a preferred embodiment, the dynamic field construction unit performs a feature centripetal convergence operation on each signal uncertainty sphere, including:

[0085] Calculate the geometric centroid of all real-time signal feature points within the current monitoring time window and define it as the centroid of the partial discharge cluster.

[0086] For each sphere with signal uncertainty, construct a convergent vector line segment pointing from its center to the centroid of the partial discharge cluster;

[0087] Calculate the intersection point of the convergent vector line segment and the surface of the corresponding signal uncertainty sphere, and define the intersection point as the intrinsic feature point corresponding to the signal uncertainty sphere; wherein, the intrinsic feature point is located on the line connecting the center of the signal uncertainty sphere and the centroid of the partial discharge cluster.

[0088] Optionally, intrinsic feature points represent the feature points that best represent the essential properties of the current partial discharge after removing random discrete noise interference.

[0089] In a preferred embodiment, the dynamic field construction unit performs a boundary centrifugal search to identify several maximum distortion boundary points, including:

[0090] Curve fitting is performed on all intrinsic feature points to generate insulation state evolution curves, and multiple local distance maxima points of the insulation state evolution curves relative to the centroid of the partial discharge cluster are identified. Then, the multiple local distance maxima points are defined as the signal fluctuation extreme point set.

[0091] For each extreme point of signal fluctuation in the set of extreme points of signal fluctuation, the direction from the centroid of the partial discharge cluster to the extreme point of signal fluctuation is defined as the centrifugal search direction. Starting from the extreme point of signal fluctuation, a step search is performed along the corresponding centrifugal search direction. At each search position, it is determined whether the effective coverage volume of all signal uncertainty spheres has been removed.

[0092] When the search position first deviates from the effective coverage volume of all signal uncertainty spheres, the search stops, and the corresponding search position is marked as the maximum distortion boundary point in that centrifugal search direction.

[0093] Specifically, the process of identifying local distance maxima includes: calculating the Euclidean distance from each point on the insulation state evolution curve to the centroid of the partial discharge cluster, generating a distance variation curve; identifying all local maxima on this distance variation curve, i.e., the distance value at that point is greater than the distance values ​​of all points in its neighborhood; and marking the spatial location points corresponding to these local maxima as extreme points of signal fluctuation. These points geometrically represent the prominent distortion locations of the partial discharge feature cluster in various discrete directions.

[0094] In a preferred embodiment, the dynamic field construction unit generates a real-time insulating state domain by performing spatial closed envelope processing based on all maximum distortion boundary points, including:

[0095] A minimum closed surface capable of enclosing all maximum distortion boundary points is constructed, and the solid space enclosed by the minimum closed surface is defined as the real-time insulation state domain; the real-time insulation state domain characterizes the maximum physical distribution boundary of the partial discharge signal characteristics after dispersion compensation in the three-dimensional physical feature space under the current cable temperature and ambient humidity conditions.

[0096] The insulation defect diagnosis unit is used to perform spatial projection coupling analysis between the real-time insulation state domain and multiple preset standard fault feature domains, calculate the overlap coupling degree between the two, select the insulation defect type corresponding to the standard fault feature domain with the largest overlap coupling degree as the diagnosis result, and calculate the fault point location based on the geometric centroid of the overlapping area.

[0097] Optionally, the insulation defect types include: insulation gap discharge, metal tip discharge, floating potential discharge, and insulation surface discharge.

[0098] Preferably, the process of constructing the standard fault feature domain includes:

[0099] Construct solid models of typical defects corresponding to insulation gap discharge, metal tip discharge, floating potential discharge, and insulation surface discharge, respectively;

[0100] For each typical defect entity model, multiple rounds of high-voltage partial discharge excitation experiments were performed within the temperature and humidity variation range covering the entire range. The pulse rise time, center frequency offset and apparent discharge quantity of the sample partial discharge pulse signal were extracted and mapped to the three-dimensional physical feature space to form the original sample feature point set.

[0101] Calculate the spatial probability density field of the original sample feature point set in the three-dimensional physical feature space, and based on the preset probability density threshold gradient, divide the spatial probability density field from the inside out into nested high-density core region, medium-density transition region and low-density diffusion region.

[0102] The high-density core region, medium-density transition region, and low-density diffusion region are encapsulated to generate the standard fault feature domain corresponding to this insulation defect type.

[0103] Optionally, the probability density threshold gradient includes two pre-defined probability density thresholds, denoted as the first threshold and the second threshold (1 > first threshold > second threshold > 0). The first threshold (core limit) is defined as follows: [Equation omitted for brevity]. The second threshold (transition limit) is defined as [Equation omitted for brevity]. The cutoff threshold (effective boundary, used to remove background noise) is defined as [Equation omitted for brevity].

[0104] Specifically, this embodiment uses a Gaussian kernel function to estimate the kernel density of the feature point set and normalizes the density values. A first threshold of 0.8 and a second threshold of 0.3 are set. Using isosurface extraction techniques, regions with density values ​​greater than 0.8 are defined as high-density core regions; regions with density values ​​between 0.3 and 0.8 are defined as medium-density transition regions; and regions with density values ​​between 0.05 and 0.3 are defined as low-density diffusion regions.

[0105] Preferably, the insulation defect diagnosis unit performs spatial projection coupling analysis on the real-time insulation state domain and multiple standard fault feature domains, and calculates the overlap coupling degree between the two, including:

[0106] Multiple standard fault feature domains are retrieved, and coupling weight coefficients with successively decreasing values ​​are assigned to the high-density core region, medium-density transition region, and low-density diffusion region within each standard fault feature domain, thereby transforming each standard fault feature domain into a standard fault energy field with non-uniform density distribution characteristics.

[0107] The real-time insulation state domain is projected onto the standard fault energy field, and the intersection volume of the real-time insulation state domain with the high-dense core region, the medium-dense transition region and the low-dense diffusion region is calculated respectively.

[0108] The total intersection volume is obtained by weighting and summing the intersection space volumes of each level using coupling weight coefficients, and the ratio of the total intersection volume to the total volume of the real-time insulation state domain is defined as the overlap coupling degree.

[0109] Optionally, the overlap coupling degree characterizes the similarity matching probability between the currently monitored partial discharge signal characteristics and a certain known standard fault type in terms of "morphological distribution" and "energy density", representing the degree of holographic matching between the current insulation state and the typical fault model.

[0110] Optionally, the coupling weight coefficient characterizes the "importance" of different regions in the standard fault feature domain, with the highest coupling weight coefficient in the high-density core region and the lowest coupling weight coefficient in the low-density diffusion region.

[0111] Preferably, the insulation defect diagnosis unit calculates the fault location based on the geometric centroid of the overlapping region, including:

[0112] In the standard fault feature domain with the highest degree of overlap and coupling, the solid part that overlaps with the high-density core region of the real-time insulation state domain is extracted, the geometric centroid of the solid part is calculated and defined as the fault feature anchor point.

[0113] The coordinate values ​​of the fault feature anchor points are analyzed to obtain the first feature component corresponding to the pulse rise time and the second feature component corresponding to the center frequency offset.

[0114] Based on the current cable temperature and ambient humidity parameters, the first and second characteristic components are mapped and converted into single-end equivalent propagation distances using a multi-parameter exponential correction model and the inverse solution logic of the transmission line equation.

[0115] The single-end equivalent propagation distances at both ends of the cable line are obtained separately. A weighted average algorithm is then used to fuse and correct the single-end equivalent propagation distances at both ends, thereby determining the specific physical location of the fault point.

[0116] Specifically, after the insulation defect diagnosis unit determines the defect type, the system performs precise fault location. The core of this step lies in using the physical model established in the previous steps, which includes environmental correction information, to determine the physical location of the fault through computational inversion techniques.

[0117] Fault feature anchor point extraction: Within the standard fault feature domain determined by diagnosis, the system extracts the geometric centroid of the overlapping portion between the real-time insulation state domain and the core area of ​​the standard domain, defining it as the fault feature anchor point. The coordinates of this anchor point are analyzed to obtain two key observation values ​​of the signal to be located: the rise time of the observed pulse and the frequency offset of the observed center.

[0118] Constructing the reverse solution logic: This embodiment employs a numerical inversion strategy based on transmission line theory. The system directly calls the frequency response function generated in the "Dynamic Field Construction" step, which already includes correction coefficients for the current temperature and humidity. Based on the frequency response function, the system can establish a monotonic mapping relationship between transmission distance and signal distortion characteristics.

[0119] Attenuation mapping logic: Based on the real part (attenuation constant) of the frequency response function, the theoretical center frequency offset caused by the attenuation of high-frequency components after the signal has traveled a certain distance can be calculated.

[0120] Dispersion mapping logic: Based on the group velocity function derived from the imaginary part of the frequency response function, the theoretical pulse rise time caused by the velocity difference of different frequency components after the signal has traveled a certain distance can be calculated.

[0121] Since there is a strict monotonically increasing relationship between the degree of signal distortion (slower rise time, increased frequency offset) and the transmission distance, it is possible to infer the unknown transmission distance from the known characteristic values.

[0122] Numerical Solution Process: To deduce the distance from the observed values, the system employs a numerical iterative approximation method (or a pre-set multidimensional lookup table method). The system first sets an initial test distance, substitutes this distance into the aforementioned forward physical model, calculates the corresponding "theoretical characteristic value," and calculates the deviation between the "theoretical characteristic value" and the "observed characteristic value" at the anchor point. Based on the magnitude and direction of the deviation, the test distance is dynamically adjusted (e.g., using Newton's iteration method or a binary search method) until the deviation between the theoretical and observed values ​​is less than a preset convergence threshold. The test distance at this point is the environmentally corrected single-end equivalent propagation distance.

[0123] Dual-end fusion correction: The system performs the aforementioned reverse solution process on the data collected from both ends of the cable line, obtaining two independent single-end ranging results. Finally, the system uses the signal-to-noise ratio or waveform integrity as confidence weights to perform weighted fusion calculations on the ranging results from both ends. This step effectively eliminates random errors and the influence of near-end blind zones in single-end measurements, ultimately determining the specific physical location of the fault point.

[0124] This system effectively eliminates signal characteristic drift caused by environmental changes by dynamically calculating the signal distortion compensation radius and constructing an uncertainty sphere through real-time acquisition of cable temperature and ambient humidity, restoring the true discharge characteristics and significantly improving the anti-interference capability and environmental adaptability of online monitoring.

[0125] This system performs volume projection and coupling degree calculation between the real-time insulation state domain and the standard fault characteristic domain. By quantitatively analyzing the energy distribution in the overlapping region, the system can accurately identify weak defect types such as air gaps and tips from discrete pulse signals, improving the robustness and accuracy of insulation fault diagnosis.

[0126] By combining real-time temperature and humidity to dynamically correct cable dielectric parameters, the pulse waveform distortion characteristics are accurately inverted into transmission distance. Combined with weighted fusion correction of dual-end data, the ranging deviation caused by the dispersion effect of high-frequency signals is overcome, achieving precise physical location of latent fault points in ultra-high voltage cables.

[0127] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0128] The present invention discloses a non-transitory computer-readable storage medium storing computer instructions, which, when executed by a processor, cause the processor to perform the above-described method.

[0129] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware (e.g., processor, FPGA, ASIC, etc.), and the program can be stored in a readable storage medium, such as a read-only memory, a disk, or an optical disk. All or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module in the above embodiments can be implemented in hardware, such as by using integrated circuits to implement its corresponding function, or it can be implemented as a software functional module, such as by a processor executing a program / instruction stored in memory to implement its corresponding function. The embodiments of the present invention are not limited to any particular combination of hardware and software.

[0130] Furthermore, the functional units in the various embodiments of this document can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0131] If the integrated unit is implemented as 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 this article, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this article. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0132] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A collaborative sensing online monitoring system for partial discharge in ultra-high voltage transmission cables, characterized in that, include: The unit comprises a physical signal acquisition unit, an environmental parameter sensing unit, a multi-dimensional feature mapping unit, a dynamic field construction unit, and an insulation defect diagnosis unit. Physical signal acquisition unit: used to synchronously acquire high-frequency partial discharge pulse signals through a wideband UHF sensor array distributed on the cable joint and the cable body; Environmental parameter sensing unit: used to obtain cable temperature parameters through a temperature sensor attached to the outer surface of the cable joint protective shell, and to obtain ambient humidity parameters through a humidity sensor set in the near field area of ​​the cable joint; Multidimensional feature mapping unit: connected to the physical signal acquisition unit, used to extract the pulse rise time, center frequency offset and apparent discharge quantity of the high-frequency partial discharge pulse signal, and map the three physical feature values ​​to a three-dimensional physical feature space to generate real-time signal feature points; Dynamic field construction unit: It is used to calculate the signal distortion compensation radius of high-frequency partial discharge pulse signal based on cable temperature parameters, ambient humidity parameters and pre-stored cable dielectric parameters, and construct a signal uncertainty sphere in three-dimensional physical feature space with real-time signal feature points as the center and the signal distortion compensation radius as the radius. Then, based on multiple signal uncertainty spheres in the time series, a closed real-time insulation state domain is generated using a spatial envelope fitting algorithm. Insulation defect diagnosis unit: It is used to perform spatial projection coupling analysis between the real-time insulation state domain and multiple preset standard fault feature domains, calculate the overlap coupling degree between the two, select the insulation defect type corresponding to the standard fault feature domain with the largest overlap coupling degree as the diagnosis result, and calculate the fault point location based on the geometric centroid of the overlapping area.

2. The system according to claim 1, characterized in that, The types of insulation defects include: insulation gap discharge, metal tip discharge, floating potential discharge, and insulation surface discharge.

3. The system according to claim 2, characterized in that, The dynamic field construction unit calculates the signal distortion compensation radius based on cable temperature parameters, ambient humidity parameters, and cable dielectric parameters, including: Retrieve pre-stored cable dielectric parameters and signal attenuation factors; the cable dielectric parameters are relative permittivity. The cable temperature parameters, ambient humidity parameters, and relative permittivity are input into the multi-parameter exponential correction model to calculate the environmental correction coefficient that reflects the impact of the current operating environment. The dynamic relative permittivity is obtained by dynamically correcting the relative permittivity using an environmental correction factor. The dynamic relative permittivity is then substituted into the transmission line equation to construct a frequency response function. The frequency response function reflects the relationship between the signal group velocity and frequency. The effective bandwidth and estimated transmission distance of the high-frequency partial discharge pulse signal are obtained. The upper and lower limits of the signal frequency are determined based on the effective bandwidth. The upper and lower limits of the frequency are substituted into the frequency response function to calculate the corresponding two group velocity values. The propagation time difference of the two group velocity values ​​on the transmission path is calculated using the estimated transmission distance, and the propagation time difference is defined as the group velocity dispersion value. The group velocity dispersion value and the signal attenuation factor are normalized and weighted, and the calculation result is output as the signal distortion compensation radius.

4. The system according to claim 3, characterized in that, The dynamic field construction unit is based on multiple signal uncertainty spheres on a time series, and uses a spatial envelope fitting algorithm to generate a closed real-time insulation state domain, including: All signal uncertainty spheres within the current monitoring time window are obtained from the time series, and feature centripetal convergence operation is performed on each signal uncertainty sphere to obtain the intrinsic feature points corresponding to each signal uncertainty sphere. A boundary centrifugal search is performed based on the coverage of all intrinsic feature points and all signal uncertainty spheres to identify several maximum distortion boundary points; A real-time insulation state domain is generated by performing spatial closed envelope processing based on all maximum distortion boundary points.

5. The system according to claim 4, characterized in that, The dynamic field construction unit performs a feature centripetal convergence operation on each signal uncertainty sphere, including: Calculate the geometric centroid of all real-time signal feature points within the current monitoring time window and define it as the centroid of the partial discharge cluster. For each sphere with signal uncertainty, construct a convergent vector line segment pointing from its center to the centroid of the partial discharge cluster; Calculate the intersection point of the convergent vector line segment and the surface of the corresponding signal uncertainty sphere, and define the intersection point as the intrinsic feature point corresponding to the signal uncertainty sphere; wherein, the intrinsic feature point is located on the line connecting the center of the signal uncertainty sphere and the centroid of the partial discharge cluster.

6. The system according to claim 5, characterized in that, The dynamic field building unit performs a boundary centrifugal search to identify several boundary points with maximum distortion, including: Curve fitting is performed on all intrinsic feature points to generate insulation state evolution curves, and multiple local distance maxima points of the insulation state evolution curves relative to the centroid of the partial discharge cluster are identified. Then, the multiple local distance maxima points are defined as the signal fluctuation extreme point set. For each extreme point of signal fluctuation in the set of extreme points of signal fluctuation, the direction from the centroid of the partial discharge cluster to the extreme point of signal fluctuation is defined as the centrifugal search direction. Starting from the extreme point of signal fluctuation, a step search is performed along the corresponding centrifugal search direction. At each search position, it is determined whether the effective coverage volume of all signal uncertainty spheres has been removed. When the search position first deviates from the effective coverage volume of all signal uncertainty spheres, the search stops, and the corresponding search position is marked as the maximum distortion boundary point in that centrifugal search direction.

7. The system according to claim 6, characterized in that, The dynamic field construction unit generates a real-time insulation state domain by performing spatial closed envelope processing based on all maximum distortion boundary points, including: A minimum closed surface capable of enclosing all maximum distortion boundary points is constructed, and the solid space enclosed by the minimum closed surface is defined as the real-time insulation state domain; the real-time insulation state domain characterizes the maximum physical distribution boundary of the partial discharge signal characteristics after dispersion compensation in the three-dimensional physical feature space under the current cable temperature and ambient humidity conditions.

8. The system according to claim 7, characterized in that, The process of constructing a standard fault feature domain includes: Construct solid models of typical defects corresponding to insulation gap discharge, metal tip discharge, floating potential discharge, and insulation surface discharge, respectively; For each typical defect entity model, multiple rounds of high-voltage partial discharge excitation experiments were performed within the temperature and humidity variation range covering the entire range. The pulse rise time, center frequency offset and apparent discharge quantity of the sample partial discharge pulse signal were extracted and mapped to the three-dimensional physical feature space to form the original sample feature point set. Calculate the spatial probability density field of the original sample feature point set in the three-dimensional physical feature space, and based on the preset probability density threshold gradient, divide the spatial probability density field from the inside out into nested high-density core region, medium-density transition region and low-density diffusion region. The high-density core region, medium-density transition region, and low-density diffusion region are encapsulated to generate the standard fault feature domain corresponding to this insulation defect type.

9. The system according to claim 8, characterized in that, The insulation defect diagnosis unit performs spatial projection coupling analysis on the real-time insulation state domain and multiple standard fault feature domains, calculating the overlap and coupling degree between the two, including: Multiple standard fault feature domains are retrieved, and coupling weight coefficients with successively decreasing values ​​are assigned to the high-density core region, medium-density transition region, and low-density diffusion region within each standard fault feature domain, thereby transforming each standard fault feature domain into a standard fault energy field with non-uniform density distribution characteristics. The real-time insulation state domain is projected onto the standard fault energy field, and the intersection volume of the real-time insulation state domain with the high-dense core region, the medium-dense transition region and the low-dense diffusion region is calculated respectively. The total intersection volume is obtained by weighting and summing the intersection space volumes of each level using coupling weight coefficients, and the ratio of the total intersection volume to the total volume of the real-time insulation state domain is defined as the overlap coupling degree.

10. The system according to claim 9, characterized in that, The insulation defect diagnosis unit calculates the fault location based on the geometric centroid of the overlapping region, including: In the standard fault feature domain with the highest degree of overlap and coupling, the solid part that overlaps with the high-density core region of the real-time insulation state domain is extracted, the geometric centroid of the solid part is calculated and defined as the fault feature anchor point. The coordinate values ​​of the fault feature anchor points are analyzed to obtain the first feature component corresponding to the pulse rise time and the second feature component corresponding to the center frequency offset. Based on the current cable temperature and ambient humidity parameters, the first and second characteristic components are mapped and converted into single-end equivalent propagation distances using a multi-parameter exponential correction model and the inverse solution logic of the transmission line equation. The single-end equivalent propagation distances at both ends of the cable line are obtained separately. A weighted average algorithm is then used to fuse and correct the single-end equivalent propagation distances at both ends, thereby determining the specific physical location of the fault point.

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