Method, device and apparatus for evaluating cross-linked polyethylene insulation of cable-gis termination

Abstract

The application discloses a cable-GIS terminal cross-linked polyethylene insulation evaluation method, device and equipment, the method comprises the following steps: obtaining a time series data matrix; traversing the signal acquisition channel in each time series data matrix, respectively performing variational mode decomposition and kurtosis reconstruction, and then combining a preset energy operator to calculate a pulse arrival time; performing three-dimensional coordinate reconstruction based on each pulse arrival time, calculating spatial aggregation information entropy based on the reconstructed coordinate set; creating a three-dimensional state feature vector of a local pulse, and determining a time evolution parameter according to the deviation degree between the three-dimensional state feature vector and a reference feature vector; performing nonlinear fusion on the spatial aggregation information entropy and the time evolution parameter, determining a space-time composite factor, and determining the insulation aging state of the cross-linked polyethylene based on the numerical interval of the space-time composite factor, and generating system response information, so as to effectively fuse the spatial distribution information and the time evolution trend of the discharge signal, and improve the evaluation accuracy of the cross-linked polyethylene insulation state.

Description

Technical Field

[0001] This invention relates to the field of condition assessment technology, and in particular to a method, apparatus and equipment for assessing the cross-linked polyethylene insulation of cable-GIS terminals. Background Technology

[0002] High-voltage cable-GIS (Geographic Information System) terminals are crucial equipment in modern power grid transmission and distribution systems, serving as the electrical transition and insulation isolation between high-voltage cable lines and gas-insulated metal-enclosed switchgear. Cross-linked polyethylene (XLPE), as the main solid insulation material, is susceptible to microscopic defects and gradual deterioration during long-term operation due to the combined effects of high-intensity electric field stress, thermal stress, and mechanical vibration. This leads to the formation of water trees or electrical trees, ultimately causing partial discharge and even insulation breakdown, resulting in power grid outages.

[0003] For online monitoring of the operating status of XLPE insulation in cable-GIS terminals, the mainstream methods used in the industry are currently Ultra-High Frequency (UHF) and Acoustic Emission (AE) testing. These methods primarily focus on statistical analysis of the amplitude, frequency, and phase information of the collected single-type detection signals, or on plotting two-dimensional partial discharge phase-resolved maps, which serve as the basis for insulation condition assessment.

[0004] When the above scheme lacks effective integration of spatial distribution information and temporal evolution trend of discharge signal, it is impossible to accurately and quantitatively assess the aging degree of XLPE insulation. Summary of the Invention

[0005] This invention provides a method, apparatus, and equipment for evaluating the cross-linked polyethylene insulation of cable-GIS terminals, which solves the technical problem that existing detection schemes for XLPE insulation status lack effective integration of spatial distribution information and temporal evolution trends of discharge signals, and cannot accurately and quantitatively assess the aging degree of XLPE insulation.

[0006] The first aspect of this invention provides a method for evaluating the cross-linked polyethylene insulation of a cable-GIS terminal, wherein the cable-GIS terminal is equipped with multiple ultra-high frequency sensors and multiple ultrasonic sensors, and the method includes:

[0007] According to the detection time window, the sensing data sequence is obtained from each of the ultra-high frequency sensor and each of the ultrasonic sensor, and a time series data matrix is ​​constructed respectively.

[0008] The signal acquisition channels within each of the time-series data matrices are traversed, and variational mode decomposition and kurtosis reconstruction are performed respectively. The pulse arrival time is calculated based on the reconstructed signal and the preset energy operator.

[0009] Three-dimensional coordinates are reconstructed based on the arrival time of each pulse, and spatial aggregation information entropy is calculated based on the reconstructed coordinate set.

[0010] Create a three-dimensional state feature vector corresponding to the local pulse, and determine the time evolution parameters based on the degree of deviation between the three-dimensional state feature vector and the reference feature vector;

[0011] The spatial aggregation information entropy and the temporal evolution parameter are nonlinearly fused to determine the spatiotemporal composite factor;

[0012] Based on the numerical range of the spatiotemporal composite factor, the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined and system response information is generated.

[0013] Optionally, the time-series data matrix includes a UHF time-series data matrix and an ultrasonic time-series data matrix; the step of traversing the signal acquisition channels within each of the time-series data matrices, performing variational mode decomposition and kurtosis reconstruction respectively, and calculating the pulse arrival time based on the reconstructed signal and a preset energy operator includes:

[0014] By traversing the signal acquisition channels within the time-series data matrix, multiple sets of single-channel signals are obtained;

[0015] Variational mode decomposition is performed on each group of single-channel signals to obtain multiple discrete intrinsic mode components;

[0016] Calculate the kurtosis value corresponding to each of the discrete intrinsic mode components;

[0017] The discrete intrinsic mode components with kurtosis values ​​greater than a preset kurtosis threshold are linearly superimposed according to the time sequence to obtain the reconstructed net signal;

[0018] The nonlinear envelope corresponding to the reconstructed net signal is calculated using a preset energy operator;

[0019] The first time the nonlinear envelope exceeds the limit is determined as the pulse arrival time.

[0020] Optionally, the discrete intrinsic mode components are:

[0021] ;

[0022] ;

[0023] in, For the discrete gradient operator, { } represents the set of all discrete eigenmode components obtained from the decomposition. Let T be the set of center angular frequencies corresponding to each discrete modal component, and T be the detection time window. It is a discrete frequency operator.

[0024] Optionally, the formula for calculating the kurtosis value is:

[0025] ;

[0026] in, Here, N is the kurtosis value, and N is the number of sampling points. This is the average value of the discrete intrinsic mode components of this set.

[0027] Optionally, the pulse arrival time includes the arrival time of a UHF pulse and the arrival time of an ultrasonic pulse; the step of reconstructing three-dimensional coordinates based on each of the pulse arrival times and calculating the spatial aggregation information entropy based on the reconstructed coordinate set includes:

[0028] The first arrival time difference between the arrival time of each of the ultra-high frequency pulses and the first reference arrival time is used;

[0029] The second arrival time difference between the arrival time of each ultrasonic pulse and the second reference arrival time is used;

[0030] Based on the first arrival time difference, a first set of spatial distance equations is constructed and solved to determine the first set of coordinates of all discharge pulses within the detection time window.

[0031] Based on the second arrival time difference, a second set of spatial distance equations is constructed and solved to determine the second set of coordinates of all discharge pulses within the detection time window;

[0032] Calculate the coordinate deviation based on the first coordinate set and the second coordinate set;

[0033] Select coordinates whose coordinate deviation is less than a preset deviation threshold, and construct a reconstructed coordinate set;

[0034] Density clustering is performed on the reconstructed coordinate set and discrete noise points are removed to obtain multiple effective discharge clusters;

[0035] The effective discharge frequency probability within each effective discharge cluster is calculated and accumulated to obtain the spatial clustering information entropy.

[0036] Optionally, the step of creating a three-dimensional state feature vector corresponding to a local impulse and determining the time evolution parameters based on the degree of deviation between the three-dimensional state feature vector and the reference feature vector includes:

[0037] The operating cycle of the cable-GIS terminal is divided into multiple continuous sliding analysis windows;

[0038] In each sliding analysis window, the average discharge quantity, pulse repetition rate and maximum single discharge energy corresponding to the local pulse are extracted to construct a three-dimensional state feature vector.

[0039] The time evolution parameters are determined based on the degree of deviation between the three-dimensional state feature vector and the reference feature vector.

[0040] Optionally, the formula for calculating the spatiotemporal composite factor is:

[0041] ;

[0042] in, As a spatiotemporal composite factor, For time evolution parameters, To aggregate information entropy in space, For time-deterioration sensitivity coefficient, The spatial dispersion penalty coefficient, To adjust the offset constant.

[0043] Optionally, the numerical range includes a first threshold range, a second threshold range, and a third threshold range; the step of determining the insulation aging state of the cross-linked polyethylene within the cable-GIS terminal and generating system response information according to the numerical range of the spatiotemporal composite factor includes:

[0044] If the spatiotemporal composite factor is within the first threshold range, then the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined to be healthy.

[0045] If the spatiotemporal composite factor is within the second threshold range, the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined to be a local deterioration state, and system response information is generated to increase the sampling frequency density and trigger waveform persistent storage.

[0046] If the spatiotemporal composite factor is within the third threshold range, the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined to be a critical breakdown state, and system response information for triggering the defense alarm protocol and power outage protection is generated.

[0047] A second aspect of the present invention provides a device for evaluating the cross-linked polyethylene insulation of a cable-GIS terminal. The cable-GIS terminal is equipped with multiple ultra-high frequency sensors and multiple ultrasonic sensors. The device includes:

[0048] The time-series data matrix creation module is used to acquire sensing data sequences from each of the ultra-high frequency sensors and each of the ultrasonic sensors according to the detection time window, and construct time-series data matrices accordingly.

[0049] The pulse arrival time calculation module is used to traverse the signal acquisition channels in each of the time-series data matrices, perform variational mode decomposition and kurtosis reconstruction respectively, and calculate the pulse arrival time based on the reconstructed signal and the preset energy operator.

[0050] The spatial aggregation information entropy calculation module is used to reconstruct three-dimensional coordinates based on the arrival time of each pulse, and to calculate the spatial aggregation information entropy based on the reconstructed coordinate set.

[0051] The time evolution parameter determination module is used to create a three-dimensional state feature vector corresponding to a local pulse, and determine the time evolution parameters based on the degree of deviation between the three-dimensional state feature vector and the reference feature vector.

[0052] The spatiotemporal composite factor determination module is used to nonlinearly fuse the spatial aggregation information entropy and the temporal evolution parameters to determine the spatiotemporal composite factor.

[0053] The insulation aging state matching module is used to determine the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal according to the numerical range of the spatiotemporal composite factor and generate system response information.

[0054] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the cable-GIS terminal cross-linked polyethylene insulation assessment method as described in any one of the first aspects of the present invention.

[0055] As can be seen from the above technical solutions, the present invention has the following advantages:

[0056] This invention deploys multiple UHF sensors and multiple ultrasonic sensors at the cable-GIS terminal. Sensing data sequences are acquired from each UHF sensor and each ultrasonic sensor according to a detection time window, and time-series data matrices are constructed accordingly. The signal acquisition channels within each time-series data matrix are traversed, and variational mode decomposition and kurtosis reconstruction are performed. The pulse arrival time is then calculated using a preset energy operator. Three-dimensional coordinate reconstruction is performed based on the pulse arrival time, and spatial aggregation information entropy is calculated based on the reconstructed coordinate set. A three-dimensional state feature vector for local pulses is created, and time evolution parameters are determined based on the deviation between the three-dimensional state feature vector and the reference feature vector. The spatial aggregation information entropy and time evolution parameters are nonlinearly fused to determine the spatiotemporal composite factor. Based on its numerical range, the insulation aging state of cross-linked polyethylene is determined, and system response information is generated. This effectively integrates the spatial distribution information and temporal evolution trend of the discharge signal, improving the accuracy of cross-linked polyethylene insulation state assessment. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating the steps of a method for evaluating cross-linked polyethylene insulation in cable-GIS terminals, provided in this embodiment of the invention;

[0059] Figure 2 This is a schematic diagram showing the placement of a UHF sensor and an AF sensor according to an embodiment of the present invention.

[0060] Figure 3 This is a schematic diagram of the spatial distribution pattern of insulation defects provided in an embodiment of the present invention;

[0061] Figure 4 This invention provides a spatiotemporal composite factor evolution curve and a schematic diagram of dual-threshold early warning.

[0062] Figure 5 A detailed flowchart of a method for evaluating cross-linked polyethylene insulation in cable-GIS terminals provided in this embodiment of the invention;

[0063] Figure 6 This is a structural block diagram of a cable-GIS terminal cross-linked polyethylene insulation evaluation device provided in an embodiment of the present invention. Detailed Implementation

[0064] This invention provides a method, apparatus, and equipment for evaluating the cross-linked polyethylene insulation of cable-GIS terminals, which addresses the technical problem that existing detection schemes for XLPE insulation status lack effective integration of spatial distribution information and temporal evolution trends of discharge signals, thus failing to accurately and quantitatively assess the aging degree of XLPE insulation.

[0065] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0066] Please see Figure 1 , Figure 1A flowchart illustrating the steps of a method for evaluating the cross-linked polyethylene insulation of a cable-GIS terminal, provided in an embodiment of the present invention.

[0067] This invention provides a method for evaluating the cross-linked polyethylene insulation of a cable-GIS terminal. The cable-GIS terminal is equipped with multiple ultra-high frequency sensors and multiple ultrasonic sensors. The method includes:

[0068] Step 101: Acquire sensor data sequences from each UHF sensor and each ultrasonic sensor according to the detection time window, and construct time-series data matrices respectively;

[0069] The detection time window refers to a continuous time segment set by the data acquisition system, during which the signal is continuously sampled to capture transient partial discharge pulses.

[0070] A time-series data matrix refers to a two-dimensional data structure formed by arranging discrete digital signals acquired synchronously by various UHF and ultrasonic sensors within a detection time window according to sensor channels and time sampling points. Rows correspond to discrete time sampling points, and columns correspond to different signal acquisition channels, i.e., different sensors.

[0071] In this embodiment, P ultra-high frequency (UHF) sensors and Q ultrasonic sensors can be distributed and arranged in a multi-source sensor array at key locations of the cable-GIS terminal, such as the outside of the epoxy resin sleeve or near the stress cone of the cable terminal. After system startup, the central processing unit synchronously triggers all sensors and the high-speed acquisition card to perform joint acoustic-electric detection within a set detection time window T, acquiring discrete sampling sequences. These discrete time-series data are arranged according to sensor number and sampling time to construct UHF time-series data matrices and ultrasonic time-series data matrices, respectively.

[0072] The ultra-high frequency time series data matrix can be represented as follows: The ultrasonic time series data matrix can be represented as , where n is the discrete-time sampling point sequence output by the high-speed hardware acquisition card.

[0073] In addition, when constructing the time-series data matrix, a power frequency voltage phase marker column can be added to associate the discharge pulse with the AC cycle.

[0074] It should be noted that the locations of its UHF and AF sensors can be found in [reference needed]. Figure 2 It is installed on the outside of the epoxy resin sleeve or near the cable terminal.

[0075] Step 102: Traverse the signal acquisition channels in each time series data matrix, perform variational mode decomposition and kurtosis reconstruction respectively, and calculate the pulse arrival time based on the reconstructed signal and the preset energy operator;

[0076] Variational mode decomposition refers to an algorithm that adaptively decomposes a single-channel signal into a finite number of eigenmode function components that oscillate narrowly around their respective center frequencies by constructing and solving a constrained variational problem.

[0077] Kurtosis reconstruction refers to a denoising reconstruction method that uses the kurtosis value of each modal component as a measure of the significance of the impact feature, removes modes with kurtosis close to Gaussian noise level, and linearly superimposes the remaining modes to restore the net signal.

[0078] The preset energy operator refers to the Tegel-Kaiser energy operator, which is used to obtain the instantaneous nonlinear envelope of discrete signals to enhance the energy mutation characteristics of the pulse leading edge.

[0079] In this embodiment, variational mode decomposition is performed on the discrete signal sequence of each channel in the time-series data matrix to obtain K intrinsic mode function components. The kurtosis value of each component is calculated, and modes with prominent impulse characteristics are selected by setting a kurtosis threshold for linear reconstruction to obtain a clean signal with smooth background noise removed. The Tegel-Kaiser energy operator is applied to the reconstructed signal to obtain a nonlinear energy envelope sequence. This envelope has an energy abrupt peak at the pulse start. An out-of-bounds threshold is set, and the sampling time that first exceeds the threshold is extracted as the pulse arrival time of that channel. Through the above kurtosis screening and energy operator sharpening, Gaussian white noise overlapping with the partial discharge pulse frequency band is effectively separated, and the arrival time is not affected by low-energy tail residual noise, providing high-precision time-stamped data for time difference positioning.

[0080] It should be noted that the variational mode decomposition algorithm can be replaced by ensemble empirical mode decomposition, empirical wavelet transform, or wavelet packet transform algorithm based on a specific mother wavelet. The impulse characteristic components can be obtained through different adaptive decomposition mechanisms. The energy operator can be replaced by analytical signal envelope extraction based on Hilbert transform, which can also extract the pulse arrival time.

[0081] In one example of the present invention, the time-series data matrix includes an ultra-high frequency time-series data matrix and an ultrasonic time-series data matrix; step 102 may include the following sub-steps:

[0082] By traversing the signal acquisition channels within the time-series data matrix, multiple sets of single-channel signals are obtained;

[0083] Variational mode decomposition is performed on each group of single-channel signals to obtain multiple discrete intrinsic mode components;

[0084] Calculate the kurtosis value corresponding to each discrete intrinsic mode component;

[0085] The discrete intrinsic mode components with kurtosis values ​​greater than a preset kurtosis threshold are linearly superimposed according to the time sequence to obtain the reconstructed net signal;

[0086] The nonlinear envelope corresponding to the reconstructed net signal is calculated using a preset energy operator;

[0087] The first time the nonlinear envelope exceeds the limit is defined as the pulse arrival time.

[0088] A single-channel signal refers to a discrete-time sampling sequence corresponding to a single acquisition channel extracted from a UHF time-series data matrix or an ultrasonic time-series data matrix according to the sensor channel index.

[0089] Intrinsic Mode Function (IMF) refers to the narrowband oscillating discrete sequence components with their own center frequencies output by variational mode decomposition after adaptively decomposing a single-channel signal.

[0090] Kurtosis is a fourth-order statistic that characterizes the steepness of discrete intrinsic mode components and their impact characteristics. The theoretical kurtosis of Gaussian white noise is constant, while the kurtosis of components containing partial discharge pulses is significantly higher.

[0091] The reconstructed net signal refers to the signal sequence obtained by linearly superimposing and reconstructing effective modal components with kurtosis values ​​exceeding a preset kurtosis threshold along the time axis, thus removing smooth background noise.

[0092] Nonlinear envelope refers to the instantaneous energy trajectory obtained by performing point-by-point operations on the reconstructed net signal using a preset energy operator, which forms a steep energy transition front at the beginning of the pulse.

[0093] The first time the limit is exceeded refers to the time value corresponding to the sampling point when the amplitude of the nonlinear envelope first exceeds the preset amplitude threshold, that is, the pulse arrival time detected by the channel.

[0094] In this embodiment, all signal acquisition channels within the UHF time-series data matrix and the ultrasonic time-series data matrix are traversed to extract multiple sets of single-channel signals corresponding one-to-one with each acquisition channel. Each set of single-channel signals represents one sensor. Variational mode decomposition is performed on each set of single-channel signals to obtain multiple mutually orthogonal discrete eigenmode components. The kurtosis value corresponding to each discrete eigenmode component is calculated one by one, and effective feature components with kurtosis values ​​greater than a preset kurtosis threshold are selected.

[0095] Linear superposition of all effective feature components according to the original sampling sequence yields a reconstructed net signal after removing background noise and interference. A preset Teager energy operator is then used to perform a nonlinear transformation on the reconstructed net signal. This operator characterizes local energy changes by the difference between the instantaneous squared value of the signal and the product of the preceding and following sampling points, significantly sharpening the energy abrupt changes at the pulse leading edge. The nonlinear envelope corresponding to the reconstructed net signal is extracted. Finally, the sampling time when the amplitude of the nonlinear envelope first exceeds a preset amplitude threshold is determined as the pulse arrival time corresponding to that acquisition channel. This achieves signal-noise separation through frequency domain decomposition, and then locks the effective components containing pulse characteristics through kurtosis statistics. After reconstruction, the masking of weak pulse characteristics by noise is eliminated. Finally, the pulse transient change characteristics are enhanced by the nonlinear energy operator, ultimately achieving accurate calibration of the arrival time of weak partial discharge pulses under strong interference environments.

[0096] Optionally, the discrete eigenmode components are:

[0097] ;

[0098] ;

[0099] in, For the discrete gradient operator, { } represents the set of all discrete eigenmode components obtained from the decomposition. Let T be the set of center angular frequencies corresponding to each discrete modal component, and T be the detection time window. It is a discrete frequency operator.

[0100] It should be noted that the discrete intrinsic mode components in the above example can be derived through a continuous-time theoretical model. Specifically, taking any single-channel signal in the acquisition matrix as an example, its continuous-time theoretical model is denoted as x(t). To extract pure partial discharge characteristics, a constrained variational problem is constructed and solved, adaptively decomposing it into K signals with center frequencies ω. k The intrinsic mode function u k (t):

[0101] ;

[0102] The constraints are .

[0103] Optionally, the partial discharge signal physically manifests as a high-frequency impulse attenuated pulse, while the ambient interference is mostly smooth Gaussian white noise. To extract the effective partial discharge component, the kurtosis value is calculated using the following formula:

[0104] ;

[0105] in, Here, N is the kurtosis value, and N is the number of sampling points. This is the average value of the discrete intrinsic mode components of this set.

[0106] In this embodiment, a kurtosis threshold K is set. th Since the kurtosis of Gaussian white noise is theoretically always equal to 3, and the partial discharge pulse has a prominent impulse spike, its kurtosis is significantly greater than 3. Therefore, we take K... th ∈[3.5, 4.5], select K that satisfies the condition. u >K th The effective modal components are linearly added to obtain the reconstructed net signal y(n) after removing the smooth background noise.

[0107] It should be noted that direct timing errors are large because the initial oscillation point of the partial discharge pulse is often submerged by residual noise. The Teger-Kaiser energy operator is introduced into the reconstructed signal y(n) to obtain the nonlinear envelope E(n), thereby sharpening the energy abrupt change at the pulse leading edge.

[0108] ;

[0109] Set an amplitude threshold and extract the moment when E(n) first exceeds the threshold as the arrival time t of the signal in that channel. i .

[0110] Step 103: Reconstruct the three-dimensional coordinates based on the arrival time of each pulse, and calculate the spatial aggregation information entropy based on the reconstructed coordinate set;

[0111] The reconstructed coordinate set refers to the set of points formed by the spatial coordinates of all discharge pulses obtained within a single detection time window.

[0112] Spatial clustering information entropy refers to the information entropy measure calculated based on the proportion of pulse frequencies within each effective discharge cluster after performing density clustering on the reconstructed coordinate set. It is used to characterize the degree to which the spatial distribution of discharge sources changes from highly concentrated to diffuse.

[0113] In this embodiment, an ultra-high frequency (UHF) sensor and an ultrasonic sensor are selected as references. Using the device's preset three-dimensional spatial coordinate system as a benchmark, the pulse arrival time difference between multiple sensors is calculated based on the pulse arrival times of each channel of the UHF and ultrasonic sensors. A time-difference positioning algorithm is used to reconstruct the three-dimensional coordinates of the partial discharge source, obtaining the three-dimensional coordinates of the discharge source within a single detection time window. All three-dimensional coordinates of the discharge sources obtained in multiple consecutive detection cycles are summarized into a reconstructed coordinate set. The device's three-dimensional space is then divided into grids. The frequency of each coordinate point falling into the corresponding spatial grid within the reconstructed coordinate set is statistically analyzed, and the probability of each grid's coordinate occurrence is calculated. Based on the information entropy formula, the spatial aggregation information entropy, representing the degree of spatial aggregation of the coordinate space, is calculated. This quantifies the spatial distribution characteristics of the partial discharge source, distinguishing between fixed-location discharges caused by real insulation defects and discrete noise signals generated by random interference. When the insulation contains a single pointed defect, the number of discharge clusters is small, and the entropy value approaches zero. When large-area dendrite formation or moisture-induced dispersion deterioration occurs, the number of clusters increases and their distribution becomes more dispersed, significantly increasing the entropy value. This compresses the geometric topological characteristics of the defect into a single scalar parameter.

[0114] In one example of the present invention, the pulse arrival time includes the arrival time of a UHF pulse and the arrival time of an ultrasonic pulse; step 103 may include the following sub-steps:

[0115] The first arrival time difference between the arrival time of each ultra-high frequency pulse and the first reference arrival time is used;

[0116] The second arrival time difference between the arrival time of each ultrasonic pulse and the second reference arrival time is used;

[0117] Based on the first arrival time difference, a first set of spatial distance equations is constructed and solved to determine the first set of coordinates of all discharge pulses within the detection time window;

[0118] Based on the second arrival time difference, a second set of spatial distance equations is constructed and solved to determine the second set of coordinates of all discharge pulses within the detection time window;

[0119] Calculate the coordinate deviation based on the first coordinate set and the second coordinate set;

[0120] Select coordinates whose coordinate deviation is less than a preset deviation threshold, and construct a reconstructed coordinate set;

[0121] Density clustering was performed on the reconstructed coordinate set and discrete noise points were removed to obtain multiple effective discharge clusters;

[0122] The effective discharge frequency probability within each effective discharge cluster is calculated and accumulated to obtain the spatial clustering information entropy.

[0123] In this embodiment, for valid discharge pulse signals identified within the same detection time window, the arrival time of the UHF pulse corresponding to each UHF sensor and the arrival time of the ultrasonic pulse corresponding to each ultrasonic sensor are extracted respectively. The difference between the arrival time of each UHF pulse and a preset first reference arrival time is calculated to obtain the first arrival time difference. Simultaneously, the difference between the arrival time of each ultrasonic pulse and a preset second reference arrival time is calculated to obtain the second arrival time difference. Based on the propagation speed of UHF electromagnetic waves in the equipment medium, the three-dimensional deployment coordinates of each UHF sensor, and the first arrival time difference, a first spatial distance equation system is constructed and solved using a nonlinear optimization algorithm to obtain the first coordinate set of all discharge pulses within the detection time window. Simultaneously, based on the propagation speed of ultrasonic waves in the equipment medium, the three-dimensional deployment coordinates of each ultrasonic sensor, and the second arrival time difference, a second spatial distance equation system is constructed and solved to obtain the corresponding second coordinate set. Specifically, the unknown three-dimensional coordinates of the partial discharge source are (x, y, z), and the coordinates of the i-th sensor are (x, y, z). i ,y i ,z i The 0th UHF / ultrasonic sensor was selected as the reference for the first reference arrival time / second reference arrival time. The time difference between their arrival times is... The spatial distance equations for UHF / ultrasonic sensors can be:

[0124] ;

[0125] Where v is the equivalent propagation speed of electromagnetic waves or ultrasonic waves in XLPE.

[0126] After constructing the spatial distance equations, the Newton-Raphson iterative method was used to solve the equations, obtaining the first and second coordinate sets of all discharge pulses within the detection time window T. The three-dimensional spatial coordinate deviation between the UHF coordinates and the ultrasonic coordinates corresponding to the same discharge pulse was calculated group by group. Valid coordinates with coordinate deviations less than a preset deviation threshold were selected, and a reconstructed coordinate set was constructed. This cross-validation step can directly filter out false positioning points caused solely by electromagnetic interference from the UHF channel or mechanical vibration from the ultrasonic channel, because these interference sources cannot simultaneously produce spatially consistent positioning results in both physical fields.

[0127] Subsequently, a density clustering algorithm was used to cluster the reconstructed coordinate set. This involved defining a neighborhood radius ε and a minimum number of contained points MinPts, followed by DBSCAN density clustering to remove discrete noise points, resulting in multiple effective discharge clusters corresponding to independent discharge sources. The probability of effective discharge frequencies within each cluster was statistically analyzed, and the spatial clustering information entropy was obtained by accumulating these probabilities according to the information entropy formula. Specifically:

[0128] ;

[0129] in, denoted as , where is the probability of the discharge frequency within the j-th cluster relative to the total effective frequency, and m is the total number of effective discharge clusters.

[0130] Please see Figure 3 If the insulation defect is caused by a single sharp burr leading to discharge, m≈1, H s Approaching 0 (extremely concentrated space), such as Figure 3 As shown in (a); if the insulation becomes damp or undergoes large-area dendrite aging due to long-term operation, multiple diffuse discharge clusters will form, with a relatively large m and H. s Significantly increased, such as Figure 3 As shown in (b), this parameter enables a dimensionality reduction representation of the defect topology type.

[0131] Step 104: Create a three-dimensional state feature vector corresponding to the local pulse, and determine the time evolution parameters based on the degree of deviation between the three-dimensional state feature vector and the reference feature vector;

[0132] The three-dimensional state feature vector refers to a feature array composed of the average partial discharge quantity, pulse repetition rate, and maximum single discharge energy extracted within a single sliding analysis window.

[0133] The baseline eigenvector refers to the mean vector and covariance matrix of the eigenvectors of several time windows during the initial normal operation of the equipment, representing the statistical distribution center and dispersion characteristics under healthy conditions.

[0134] The degree of deviation refers to the magnitude of the difference between the three-dimensional state feature vector and the reference feature vector in the vector space, which can be quantified by indicators such as Mahalanobis distance, vector space distance, and cosine similarity.

[0135] In this embodiment, a sliding analysis window is set during the operation cycle of the cable-GIS terminal. The average discharge amount, pulse repetition count, and maximum single discharge energy of all partial discharge pulses within the window are statistically analyzed to form the current three-dimensional state feature vector. Several pre-set reference feature vectors of the device are invoked to calculate the mean vector and covariance matrix of the reference state.

[0136] The Mahalanobis distance between the current three-dimensional state feature vector and the reference feature vector is calculated. Since the Mahalanobis distance is sensitive to both the increase in discharge and the structural deviation between features, this parameter shows a monotonically increasing trend during the accelerated growth period of electrical trees. It can reflect the dynamic process of the insulation state from slow quantitative change to accelerated qualitative change, and avoids misjudgment caused by the fluctuation of a single parameter with load. Therefore, the Mahalanobis distance is used as the time evolution parameter to characterize the degree of degradation of the insulation aging state of cross-linked polyethylene in the cable-GIS terminal over time.

[0137] In one example of the present invention, step 104 may include the following sub-steps:

[0138] The operating cycle of the cable-GIS terminal is divided into multiple continuous sliding analysis windows;

[0139] In each sliding analysis window, the average discharge quantity, pulse repetition rate and maximum single discharge energy corresponding to the local pulse are extracted to construct a three-dimensional state feature vector.

[0140] The time evolution parameters are determined based on the degree of deviation between the three-dimensional state feature vector and the baseline feature vector.

[0141] A sliding analysis window refers to an analysis interval that slides across the operating cycle of a cable-GIS terminal in fixed time increments. Adjacent windows may have partial time overlap, which is used to track continuous temporal changes in insulation status.

[0142] The average discharge quantity refers to the arithmetic mean of the apparent discharge quantities of all partial discharge pulses within a single sliding analysis window.

[0143] Pulse repetition rate refers to the number of partial discharge pulses per second that occur within a single sliding analysis window.

[0144] The maximum single discharge energy refers to the maximum energy released by a single pulse within a single sliding analysis window, which is determined by both the discharge quantity and the instantaneous voltage.

[0145] In this embodiment, the entire operating cycle of the cable-GIS terminal is used as the baseline time axis. According to the preset window length and sliding step size, the continuous operating cycle is divided into multiple continuous sliding analysis windows w. For all valid partial discharge pulses verified by dual-modal analysis within each sliding analysis window, three core feature parameters are statistically extracted: average discharge quantity, pulse repetition rate, and maximum single discharge energy. After dimensionless standardization of these three feature parameters to eliminate dimensional differences, a three-dimensional state feature vector corresponding to the sliding analysis window is constructed. .in, Where N is the average discharge rate and N is the pulse repetition rate. This represents the maximum energy of a single discharge.

[0146] Based on the degree of deviation between the three-dimensional state eigenvectors and the baseline eigenvectors, the time evolution parameters are determined, which involves solving for the square root of the quadratic form of the vector difference after weighting by the inverse covariance matrix. The specific calculation formula is as follows:

[0147] ;

[0148] in, The Mahalanobis distance; The mean vector of the baseline feature vector can be obtained by calculating the mean value of the feature vectors of N time windows when the equipment is in normal operation and the insulation is not deteriorated; To convert column vectors The matrix transpose notation for converting to row vectors. This refers to the covariance matrix of the baseline eigenvectors. The inverse matrix.

[0149] In addition, Mahalanobis distance can also be replaced by relative entropy, cosine similarity, or support vector data to describe hyperspherical distance, which can also quantify the degree to which the current state feature vector deviates from the initial healthy baseline manifold.

[0150] Step 105: Nonlinearly fuse the spatial aggregation information entropy and temporal evolution parameters to determine the spatiotemporal composite factor;

[0151] The spatiotemporal composite factor refers to a comprehensive health index that intuitively represents the degree of insulation aging by fusion, and includes both defect spatial topology information and time-accumulated degradation trajectory.

[0152] In this embodiment, when a sudden increase in time-related degradation occurs within the insulation and spatial discharge becomes highly concentrated, the exponential term of the mapping function forces the factor to rapidly climb along an S-shaped curve to a high saturation level. Conversely, if the spatial information entropy is large, it indicates that the discharge point is diffuse or is due to external interference. The positive weight term offsets the increase in the time parameter, and the factor remains in a low-level safe zone. Therefore, the dimensional differences and numerical range differences between the two parameters can be eliminated by first normalizing the spatial aggregation information entropy and the time evolution parameter. A preset nonlinear fusion model is then used to perform nonlinear transformation and coupling calculations on the two normalized parameters. The nonlinear fusion model uses a nonlinear mapping function that can characterize the contribution of the two parameters to insulation aging. Through fusion calculation, a spatiotemporal composite factor that simultaneously covers spatial aggregation characteristics and time evolution characteristics is obtained, thereby reducing the multidimensional partial discharge characteristics to a percentage exponent.

[0153] The nonlinear fusion model can be a Logistic nonlinear regression function, or it can be replaced by a fuzzy inference system, a Sigmoid function, or a support vector machine probability output interface, in order to map spatial information entropy and time evolution parameters into a health index with a closed interval.

[0154] Optionally, in order to output an intuitive [0,100] health evaluation system to the substation operation and maintenance system, it can be represented by calculating a spatiotemporal composite factor. The calculation formula for this spatiotemporal composite factor is as follows:

[0155] ;

[0156] in, As a spatiotemporal composite factor, For time evolution parameters, To aggregate information entropy in space, For time-deterioration sensitivity coefficient, The spatial dispersion penalty coefficient, To adjust the offset constant.

[0157] It should be noted that both the time degradation sensitivity coefficient and the spatial dispersion penalty coefficient are positive values. When the partial discharge of the device shows a strong deviation from the reference (M) in time, the device is considered to be in a critical state. t (increases), and spatially exhibits a high concentration at a specific needle tip or air gap (H). s When the exponential term (-αM) decreases, the exponential term (-αM) t +βH s This will become a very large negative number, prompting The value rapidly approaches 100 along an S-shaped curve, issuing a strong warning of local breakdown. Conversely, if it is a diffuse discharge with high spatial information entropy or external noise interference, a larger H... s M will be canceled out by the negative sign. t The growth of this index keeps it stable at a low level, thus achieving adaptive smoothing against external disturbances.

[0158] Step 106: Determine the insulation aging state of cross-linked polyethylene in the cable-GIS terminal according to the numerical range of the spatiotemporal composite factor and generate system response information.

[0159] The numerical range refers to the pre-divided spatiotemporal composite factor segment range, which is used to distinguish different development levels of insulation aging.

[0160] System response information refers to the corresponding output information generated based on the insulation aging status level and used for equipment operation and maintenance management, including status prompts, early warning information, operation and maintenance suggestions, maintenance strategies, etc.

[0161] After obtaining the spatiotemporal composite factor, it can be matched with various numerical intervals to determine the insulation aging state level corresponding to the numerical interval into which the factor falls. Based on the insulation aging state level obtained by matching, the corresponding system response information is generated.

[0162] In one example of the present invention, the numerical range includes a first threshold range, a second threshold range, and a third threshold range; step 106 may include the following sub-steps:

[0163] If the spatiotemporal composite factor is within the first threshold range, the insulation aging state of the cross-linked polyethylene inside the cable-GIS terminal is determined to be healthy.

[0164] If the spatiotemporal composite factor is within the second threshold range, the insulation aging state of the cross-linked polyethylene inside the cable-GIS terminal is determined to be a local deterioration state, and system response information is generated to increase the sampling frequency density and trigger waveform persistence storage.

[0165] If the spatiotemporal composite factor is in the third threshold range, the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined to be critical breakdown state, and system response information is generated to trigger the defense alarm protocol and power outage protection.

[0166] Please see Figure 4 In this embodiment, the first threshold is used. =30 and the second threshold The system divides the data into a first threshold interval, a second threshold interval, and a third threshold interval. It then matches the spatiotemporal composite factor with the numerical interval. If the factor falls within the first threshold interval, then... At this time, it is determined to be in a healthy state, and a prompt message indicating that the equipment insulation status is normal is generated. The insulation is in a controllable slow change period. The system maintains the established 24-hour routine inspection cycle, only recording feature vectors and not storing waveforms.

[0167] If it is in the second threshold range, that is When a localized degradation state is identified, system response information can be generated to increase the sampling frequency density and trigger persistent waveform storage. This increases the sampling frequency of the UHF and ultrasonic sensors to several times the conventional configuration and persistently stores the original discharge waveform for the entire time period. Specifically, the sampling frequency and time window density of the UHF and AE sensors can be increased to three times the original density via the underlying communication bus, and the original waveform file of the pathological discharge for the entire time period can be forcibly triggered and persistently stored in the background for manual review.

[0168] If it is in the third threshold range, that is When the S-curve reaches a certain value, it indicates that the XLPE insulation is about to experience thermal breakdown or electrical tree penetration. At this point, the insulation aging state is determined to be critically broken down. The system immediately sends a high-voltage protection alarm message via the substation communication protocol and simultaneously outputs a dry contact control signal to trigger the circuit breaker to perform emergency power outage protection. Specifically, the system immediately triggers the substation's highest-level high-voltage protection alarm protocol, sends a dry contact action request to the main control room via the IEC61850 standard communication protocol, and triggers the circuit breaker to perform emergency power outage protection.

[0169] Please see Figure 5 , Figure 5 A detailed flowchart of a method for evaluating the cross-linked polyethylene insulation of a cable-GIS terminal is provided for embodiments of the present invention.

[0170] In this embodiment, UHF and ultrasonic AE signals from the cable-GIS terminal are acquired simultaneously. Then, based on the VMD-TKEO algorithm, adaptive decomposition and envelope extraction are performed on the acquired multi-source signals to obtain the high-precision arrival time of the pulses. TDOA three-dimensional positioning is then performed based on the time difference of arrival. After filtering out spatial noise using DBSCAN density clustering, the spatial aggregation information entropy H, which characterizes the topological features of the partial discharge spatial distribution, is calculated. s Simultaneously, a state feature vector containing the core features of partial discharge is constructed, and the Mahalanobis distance M between the current state and the device's initial health baseline is calculated. t As a parameter of temporal evolution, the spatially aggregated information entropy H is then used. s With time evolution parameter M t Nonlinear fusion using Logistic mapping outputs a spatiotemporal composite factor Φ that can intuitively characterize the degree of insulation aging. ST Ultimately based on Φ ST The numerical range completes the three-level hierarchical early warning and closed-loop control output, when Φ ST If the value is ≤30, it is considered healthy or slightly aged; routine inspections should be maintained. If the value is 30<Φ, it is considered healthy or slightly aged. ST When ≤75, it is determined to be a mid-term local degradation, the sampling density is automatically increased and waveform persistence is triggered; when Φ ST When the value is greater than 75, it is determined to be in a critical breakdown high-risk state, triggering the highest level alarm and linking to execute emergency power outage protection, thereby achieving accurate quantitative assessment and full-process adaptive control of the XLPE insulation status of the cable-GIS terminal.

[0171] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0172] The following describes the cable-GIS terminal cross-linked polyethylene insulation assessment device provided in the embodiments of the present invention. The cable-GIS terminal cross-linked polyethylene insulation assessment device described below can be referred to in correspondence with the cable-GIS terminal cross-linked polyethylene insulation assessment method described above.

[0173] Please see Figure 6This invention provides a device for evaluating the cross-linked polyethylene insulation of a cable-GIS terminal. The cable-GIS terminal is equipped with multiple ultra-high frequency sensors and multiple ultrasonic sensors. The device includes:

[0174] The time-series data matrix creation module 601 is used to acquire sensor data sequences from each ultra-high frequency sensor and each ultrasonic sensor according to the detection time window, and construct time-series data matrices respectively.

[0175] The pulse arrival time calculation module 602 is used to traverse the signal acquisition channels in each time series data matrix, perform variational mode decomposition and kurtosis reconstruction respectively, and calculate the pulse arrival time based on the reconstructed signal and the preset energy operator.

[0176] The spatial aggregation information entropy calculation module 603 is used to reconstruct three-dimensional coordinates based on the arrival time of each pulse, and to calculate the spatial aggregation information entropy based on the reconstructed coordinate set.

[0177] The time evolution parameter determination module 604 is used to create a three-dimensional state feature vector corresponding to the local pulse, and determine the time evolution parameters based on the degree of deviation between the three-dimensional state feature vector and the reference feature vector.

[0178] The spatiotemporal composite factor determination module 605 is used to nonlinearly fuse spatial aggregation information entropy and temporal evolution parameters to determine the spatiotemporal composite factor.

[0179] The insulation aging state matching module 606 is used to determine the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal according to the numerical range of the spatiotemporal composite factor and generate system response information.

[0180] Optionally, the time-series data matrix includes an ultra-high frequency time-series data matrix and an ultrasonic time-series data matrix; the pulse arrival time calculation module 602 is specifically used for:

[0181] By traversing the signal acquisition channels within the time-series data matrix, multiple sets of single-channel signals are obtained;

[0182] Variational mode decomposition is performed on each group of single-channel signals to obtain multiple discrete intrinsic mode components;

[0183] Calculate the kurtosis value corresponding to each discrete intrinsic mode component;

[0184] The discrete intrinsic mode components with kurtosis values ​​greater than a preset kurtosis threshold are linearly superimposed according to the time sequence to obtain the reconstructed net signal;

[0185] The nonlinear envelope corresponding to the reconstructed net signal is calculated using a preset energy operator;

[0186] The first time the nonlinear envelope exceeds the limit is defined as the pulse arrival time.

[0187] Optionally, the discrete eigenmode components are:

[0188] ;

[0189] ;

[0190] in, For the discrete gradient operator, { } represents the set of all discrete eigenmode components obtained from the decomposition. Let T be the set of center angular frequencies corresponding to each discrete modal component, and T be the detection time window. It is a discrete frequency operator.

[0191] Optionally, the formula for calculating the kurtosis value is:

[0192] ;

[0193] in, Here, N is the kurtosis value, and N is the number of sampling points. This is the average value of the discrete intrinsic mode components of this set.

[0194] Optionally, the pulse arrival time includes the arrival time of the ultra-high frequency pulse and the arrival time of the ultrasonic pulse; the spatial aggregation information entropy calculation module 603 is specifically used for:

[0195] The first arrival time difference between the arrival time of each ultra-high frequency pulse and the first reference arrival time is used;

[0196] The second arrival time difference between the arrival time of each ultrasonic pulse and the second reference arrival time is used;

[0197] Based on the first arrival time difference, a first set of spatial distance equations is constructed and solved to determine the first set of coordinates of all discharge pulses within the detection time window;

[0198] Based on the second arrival time difference, a second set of spatial distance equations is constructed and solved to determine the second set of coordinates of all discharge pulses within the detection time window;

[0199] Calculate the coordinate deviation based on the first coordinate set and the second coordinate set;

[0200] Select coordinates whose coordinate deviation is less than a preset deviation threshold, and construct a reconstructed coordinate set;

[0201] Density clustering was performed on the reconstructed coordinate set and discrete noise points were removed to obtain multiple effective discharge clusters;

[0202] The effective discharge frequency probability within each effective discharge cluster is calculated and accumulated to obtain the spatial clustering information entropy.

[0203] Optionally, the time evolution parameter determination module 604 is specifically used for:

[0204] The operating cycle of the cable-GIS terminal is divided into multiple continuous sliding analysis windows;

[0205] In each sliding analysis window, the average discharge quantity, pulse repetition rate and maximum single discharge energy corresponding to the local pulse are extracted to construct a three-dimensional state feature vector.

[0206] The time evolution parameters are determined based on the degree of deviation between the three-dimensional state feature vector and the baseline feature vector.

[0207] Optionally, the formula for calculating the spatiotemporal composite factor is:

[0208] ;

[0209] in, As a spatiotemporal composite factor, For time evolution parameters, To aggregate information entropy in space, For time-deterioration sensitivity coefficient, The spatial dispersion penalty coefficient, To adjust the offset constant.

[0210] Optionally, the numerical range includes a first threshold range, a second threshold range, and a third threshold range; the insulation aging state matching module 606 is specifically used for:

[0211] If the spatiotemporal composite factor is within the first threshold range, the insulation aging state of the cross-linked polyethylene inside the cable-GIS terminal is determined to be healthy.

[0212] If the spatiotemporal composite factor is within the second threshold range, the insulation aging state of the cross-linked polyethylene inside the cable-GIS terminal is determined to be a local deterioration state, and system response information is generated to increase the sampling frequency density and trigger waveform persistence storage.

[0213] If the spatiotemporal composite factor is in the third threshold range, the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined to be critical breakdown state, and system response information is generated to trigger the defense alarm protocol and power outage protection.

[0214] This invention provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the cable-GIS terminal cross-linked polyethylene insulation assessment method as described in any embodiment of this invention.

[0215] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0216] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0217] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0218] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0219] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating cable-GIS terminal crosslinked polyethylene insulation, characterized by, The cable-GIS terminal is equipped with multiple UHF sensors and multiple ultrasonic sensors. The method includes: According to the detection time window, the sensing data sequence is obtained from each of the ultra-high frequency sensor and each of the ultrasonic sensor, and a time series data matrix is ​​constructed respectively. The signal acquisition channels within each of the time-series data matrices are traversed, and variational mode decomposition and kurtosis reconstruction are performed respectively. The pulse arrival time is calculated based on the reconstructed signal and the preset energy operator. Three-dimensional coordinates are reconstructed based on the arrival time of each pulse, and spatial aggregation information entropy is calculated based on the reconstructed coordinate set. Create a three-dimensional state feature vector corresponding to the local pulse, and determine the time evolution parameters based on the degree of deviation between the three-dimensional state feature vector and the reference feature vector; The spatial aggregation information entropy and the temporal evolution parameter are nonlinearly fused to determine the spatiotemporal composite factor; Based on the numerical range of the spatiotemporal composite factor, the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined and system response information is generated.

2. The cable-GIS termination cross-linked polyethylene insulation assessment method according to claim 1, characterized in that, The time-series data matrix includes a UHF time-series data matrix and an ultrasonic time-series data matrix; the step of traversing the signal acquisition channels within each of the time-series data matrices, performing variational mode decomposition and kurtosis reconstruction respectively, and calculating the pulse arrival time based on the reconstructed signal and a preset energy operator includes: By traversing the signal acquisition channels within the time-series data matrix, multiple sets of single-channel signals are obtained; Variational mode decomposition is performed on each group of single-channel signals to obtain multiple discrete intrinsic mode components; Calculate the kurtosis value corresponding to each of the discrete intrinsic mode components; The discrete intrinsic mode components with kurtosis values ​​greater than a preset kurtosis threshold are linearly superimposed according to the time sequence to obtain the reconstructed net signal; The nonlinear envelope corresponding to the reconstructed net signal is calculated using a preset energy operator; The first time the nonlinear envelope exceeds the limit is determined as the pulse arrival time.

3. The cable-GIS termination cross-linked polyethylene insulation assessment method according to claim 2, characterized in that, The discrete intrinsic mode components are: ； ； wherein, is a discrete gradient operator, is a set of all discrete proper modal components decomposed, is a set of center angular frequencies corresponding to each discrete modal component, and T is a detection time window, is a discrete frequency operator.

4. The cable-GIS termination cross-linked polyethylene insulation assessment method of claim 2, wherein, The formula for calculating the kurtosis value is: ； in, Here, N is the kurtosis value, and N is the number of sampling points. This is the average value of the discrete intrinsic mode components of this set.

5. The method for evaluating the cross-linked polyethylene insulation of cable-GIS terminals according to claim 1, characterized in that, The pulse arrival time includes the arrival time of the ultra-high frequency pulse and the arrival time of the ultrasonic pulse; The step of reconstructing three-dimensional coordinates based on the arrival time of each pulse, and calculating the spatial aggregation information entropy based on the reconstructed coordinate set, includes: The first arrival time difference between the arrival time of each of the ultra-high frequency pulses and the first reference arrival time is used; The second arrival time difference between the arrival time of each ultrasonic pulse and the second reference arrival time is used; Based on the first arrival time difference, a first set of spatial distance equations is constructed and solved to determine the first set of coordinates of all discharge pulses within the detection time window. Based on the second arrival time difference, a second set of spatial distance equations is constructed and solved to determine the second set of coordinates of all discharge pulses within the detection time window; Calculate the coordinate deviation based on the first coordinate set and the second coordinate set; Select coordinates whose coordinate deviation is less than a preset deviation threshold, and construct a reconstructed coordinate set; Density clustering is performed on the reconstructed coordinate set and discrete noise points are removed to obtain multiple effective discharge clusters; The effective discharge frequency probability within each effective discharge cluster is calculated and accumulated to obtain the spatial clustering information entropy.

6. The method for evaluating the cross-linked polyethylene insulation of cable-GIS terminals according to claim 1, characterized in that, The steps of creating a three-dimensional state feature vector corresponding to a local impulse and determining the time evolution parameters based on the deviation between the three-dimensional state feature vector and the reference feature vector include: The operating cycle of the cable-GIS terminal is divided into multiple continuous sliding analysis windows; In each sliding analysis window, the average discharge quantity, pulse repetition rate and maximum single discharge energy corresponding to the local pulse are extracted to construct a three-dimensional state feature vector. The time evolution parameters are determined based on the degree of deviation between the three-dimensional state feature vector and the reference feature vector.

7. The method for evaluating the cross-linked polyethylene insulation of cable-GIS terminals according to claim 1, characterized in that, The formula for calculating the spatiotemporal composite factor is as follows: ； in, As a spatiotemporal composite factor, For time evolution parameters, To aggregate information entropy in space, For time-deterioration sensitivity coefficient, The spatial dispersion penalty coefficient, To adjust the offset constant.

8. The method for evaluating the cross-linked polyethylene insulation of cable-GIS terminals according to claim 1, characterized in that, The numerical range includes a first threshold range, a second threshold range, and a third threshold range; the step of determining the insulation aging state of the cross-linked polyethylene within the cable-GIS terminal and generating system response information according to the numerical range of the spatiotemporal composite factor includes: If the spatiotemporal composite factor is within the first threshold range, then the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined to be healthy. If the spatiotemporal composite factor is within the second threshold range, the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined to be a local deterioration state, and system response information is generated to increase the sampling frequency density and trigger waveform persistent storage. If the spatiotemporal composite factor is within the third threshold range, the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal is determined to be a critical breakdown state, and system response information for triggering the defense alarm protocol and power outage protection is generated.

9. A device for evaluating the cross-linked polyethylene insulation of a cable-GIS terminal, characterized in that, The cable-GIS terminal is equipped with multiple UHF sensors and multiple ultrasonic sensors. The device includes: The time-series data matrix creation module is used to acquire sensing data sequences from each of the ultra-high frequency sensors and each of the ultrasonic sensors according to the detection time window, and construct time-series data matrices accordingly. The pulse arrival time calculation module is used to traverse the signal acquisition channels in each of the time-series data matrices, perform variational mode decomposition and kurtosis reconstruction respectively, and calculate the pulse arrival time based on the reconstructed signal and the preset energy operator. The spatial aggregation information entropy calculation module is used to reconstruct three-dimensional coordinates based on the arrival time of each pulse, and to calculate the spatial aggregation information entropy based on the reconstructed coordinate set. The time evolution parameter determination module is used to create a three-dimensional state feature vector corresponding to a local pulse, and determine the time evolution parameters based on the degree of deviation between the three-dimensional state feature vector and the reference feature vector. The spatiotemporal composite factor determination module is used to nonlinearly fuse the spatial aggregation information entropy and the temporal evolution parameters to determine the spatiotemporal composite factor. The insulation aging state matching module is used to determine the insulation aging state of the cross-linked polyethylene in the cable-GIS terminal according to the numerical range of the spatiotemporal composite factor and generate system response information.

10. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the cable-GIS terminal cross-linked polyethylene insulation assessment method as described in any one of claims 1-8.

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