Intelligent detection method for discharge site of high-voltage transmission line

Abstract

The application relates to the field of discharge site detection, and specifically discloses an intelligent detection method for discharge sites of high-voltage transmission lines, which comprises the following steps: arranging UHF sensors on multiple parallel high-voltage transmission lines in an equal-interval staggered manner, monitoring signals in real time, screening effective signals based on signal amplitudes, and judging whether the signals are local discharge signals through pulse characteristic matching; after preliminarily determining the discharge range based on the strength of the local discharge signals, constructing multiple triangular sensor arrays, calculating and determining the suspected discharge points corresponding to each sensor array by using a time-difference positioning method, and determining the discharge sites and their strengths through spatial clustering analysis; finally, collecting visible light images of the discharge sites, extracting physical defect information and analyzing the defect degree, combining the discharge strength and the defect degree to evaluate the discharge severity, and generating a detection report. The application can effectively eliminate interference, improve positioning accuracy and reliability, and realize efficient detection and evaluation of discharge sites of high-voltage transmission lines.

Description

Technical Field

[0001] This invention relates to the field of discharge point detection, and specifically to an intelligent detection method for discharge points in high-voltage transmission lines. Background Technology

[0002] During long-term operation, high-voltage transmission lines are prone to partial discharge due to factors such as insulation aging, mechanical damage, and environmental pollution. Discharge not only accelerates the deterioration of insulation materials but can also trigger line faults or even power outages, seriously affecting the safe and stable operation of the power grid. Therefore, accurate and efficient detection and location of discharge points on high-voltage transmission lines is of significant practical engineering importance.

[0003] Currently, discharge location often uses the time difference of arrival (TDOA) method. Its basic principle is to calculate the spatial location of the discharge point by measuring the time difference of the discharge signal reaching different sensors and combining it with the electromagnetic wave propagation speed.

[0004] Existing technologies often employ one-dimensional point localization, which involves deploying sensors on a single transmission line and locating the discharge point on that line based on the time difference between two sensors. However, this method does not consider the interference from discharges on different transmission lines, making the localization results susceptible to multipath effects and cross-interference, resulting in insufficient reliability. Furthermore, a single sensor group offers limited data support, has weak anti-interference capabilities, and struggles to meet the discharge detection requirements in complex electromagnetic environments, leading to a high risk of false alarms and missed alarms. Summary of the Invention

[0005] To address the above problems, this invention proposes an intelligent detection method for discharge points of high-voltage transmission lines. The specific technical solution is as follows: An intelligent detection method for discharge points of high-voltage transmission lines includes the following steps: S1, Sensor deployment: Several UHF sensors are uniformly deployed at equal intervals and staggered positions on multiple parallel high-voltage transmission lines, and each UHF sensor is numbered and its position is recorded.

[0006] S2. Discharge Identification: Real-time monitoring of the raw signals captured by each UHF sensor, filtering out valid signals based on signal amplitude, and determining whether the valid signal is a partial discharge signal based on the pulse characteristics of the valid signal. If it is, an early warning is triggered and the process proceeds to step S3; otherwise, the monitoring of sensor signals continues.

[0007] S3. Discharge Location: Based on the intensity of the partial discharge signal, the discharge range is initially determined. Multiple triangular sensor arrays are constructed within the discharge range. The time difference positioning method is used to analyze the suspected discharge points corresponding to each sensor array. Based on the analysis results of the suspected discharge points of each sensor array, the discharge location is determined, and the discharge intensity of the discharge location is determined.

[0008] S4. Severity Assessment: Visible light images of the discharge site are collected by drone, physical defect information is extracted and the degree of defect is analyzed. The severity of the discharge is assessed by combining the degree of defect of the discharge site and the discharge intensity, and a test report is generated.

[0009] The specific analysis process of step S1 is as follows: S11: Based on the measurable frequency of the UHF sensor, the electromagnetic signal attenuation characteristics and the positioning accuracy requirements, determine the distance between two adjacent UHF sensors as the deployment distance.

[0010] S12: Sort the transmission lines in order from one side to the other.

[0011] S13: On the first transmission line, multiple UHF sensors are evenly arranged according to the specified spacing.

[0012] S14: Based on the position of the UHF sensor on the first transmission line, multiple UHF sensors are evenly arranged on the second transmission line in a staggered manner according to the aforementioned arrangement spacing.

[0013] S15: Based on the position of the UHF sensor on the second transmission line, a plurality of UHF sensors are uniformly arranged on the third transmission line in a staggered manner according to the arrangement spacing, wherein the staggered direction of the UHF sensor on the third transmission line relative to the UHF sensor on the second transmission line is the same as or opposite to the staggered direction of the UHF sensor on the second transmission line relative to the UHF sensor on the first transmission line.

[0014] S16: Following the methods described in S13 to S15, install UHF sensors sequentially on each transmission line.

[0015] S17: Number all UHF sensors and record the location information of each sensor, including the transmission line number to which it belongs and its position coordinates on the transmission line.

[0016] The specific analysis process for screening valid signals in step S2 is as follows: obtain the amplitude of the original signal captured by the UHF sensor and compare it with the preset threshold of the signal amplitude. If the amplitude of the original signal is greater than the threshold of the signal amplitude, the original signal is determined to be a valid signal; otherwise, it is determined to be a noise signal.

[0017] The specific analysis process for determining whether a signal is a partial discharge signal in step S2 is as follows: Based on the phase-resolved partial discharge spectrum of the effective signal, its pulse characteristics are extracted. The pulse characteristics include pulse phase distribution characteristics, pulse amplitude distribution characteristics, pulse repetition rate, and pulse cross-correlation coefficient.

[0018] The extracted pulse features are compared with the typical pulse features of partial discharge signals pre-stored in the database to analyze the degree of agreement between the two.

[0019] The matching degree is compared with a preset matching degree threshold. If the matching degree is greater than or equal to the threshold, the valid signal is determined to be a partial discharge signal; otherwise, it is determined to be an interference signal.

[0020] The specific process of analyzing the intensity of the partial discharge signal in step S3 is as follows: obtain the amplitude index and energy index of the partial discharge signal. The amplitude index includes the peak amplitude and the equivalent apparent discharge quantity, and the energy index includes the pulse energy and the average power.

[0021] The amplitude and energy indices are input into a preset signal strength evaluation model to determine the intensity of the partial discharge signal. The signal strength evaluation model includes a quantitative mapping relationship between the amplitude and energy indices and the signal strength.

[0022] The specific analysis process for determining the discharge range in step S3 is as follows: statistically analyze all UHF sensors that captured partial discharge signals and their corresponding partial discharge signal intensities.

[0023] Extract the numerical range of partial discharge signal intensity corresponding to each intensity level from the pre-stored database.

[0024] The areas covered by the UHF sensor, whose partial discharge signal intensity is located in the ranges of high intensity, medium intensity, and low intensity, are respectively denoted as the discharge core region, transition region, and edge region.

[0025] The core region, transition region, and edge region of the discharge are integrated to determine the final discharge range.

[0026] The specific process of analyzing the suspected discharge points corresponding to each sensor array in step S3 is as follows: extract the spatial model of multiple parallel high-voltage transmission lines pre-stored in the database, cut out the high-voltage transmission line spatial model of the section corresponding to the discharge occurrence range, and mark the position of all UHF sensors within that range.

[0027] Three non-collinear UHF sensors in space are defined as a triangular sensor array.

[0028] All UHF sensors within the discharge range are combined in triplicate to construct multiple triangular sensor arrays: within the discharge range there are Calculate the number of combinations for each UHF sensor. This means selecting 3 elements from N distinct elements. Each selection corresponds to a potential triangular sensor array. The spatial positions of the three selected UHF sensors are checked to ensure they are not collinear. If they are not collinear, the three UHF sensors are defined and constructed into a triangular sensor array.

[0029] For each triangular sensor array, the time difference between every two sensors in the array capturing the same partial discharge signal is obtained. Combined with the electromagnetic wave propagation speed, the time difference positioning method is used to calculate and determine one or more suspected discharge points corresponding to the triangular sensor array.

[0030] The specific process of determining the suspected discharge point corresponding to the triangular sensor array using the time difference positioning method is as follows: The three sensors within the triangular sensor array are respectively denoted as... And obtain the time when each sensor captures the same partial discharge signal.

[0031] calculate and Time difference of signal acquisition Combined with the speed of electromagnetic wave propagation Through formula Calculate the discharge point To the sensor and Distance difference ,by , Draw a hyperbola that satisfies the distance difference around the focus, and denote it as the first hyperbola.

[0032] Similarly, based on , Time difference of signal acquisition and , Time difference of signal acquisition Draw the second and third hyperbolas respectively.

[0033] The intersection of the three hyperbolas is determined as the suspected discharge point corresponding to the triangular sensor array. There can be one or more suspected discharge points, and the spatial coordinates of the suspected discharge points are recorded.

[0034] The specific process of determining the discharge location and analyzing its discharge intensity in step S3 is as follows: T1: In the high-voltage transmission line spatial model of the section corresponding to the discharge occurrence range, mark all the suspected discharge points corresponding to each triangular sensor array, and remove abnormal points that are obviously far away from the transmission line according to preset rules to obtain the optimized spatial distribution of suspected discharge points.

[0035] T2: If the suspected discharge points converge at a single location in space, then that point is directly identified as the discharge site; otherwise, proceed to T3.

[0036] T3: Calculate the spatial distance between each suspected discharge point. If the distance between any two points is less than the set spatial distance threshold, then the two points are determined to belong to the same discharge point group.

[0037] Count the number of all discharge point groups.

[0038] If there is only one discharge point group, then the geometric center of that point group is determined as the discharge location; otherwise, proceed to T4.

[0039] T4: Count the number of suspected discharge points contained in each discharge point group, and calculate the average number of points contained in all point groups.

[0040] If the number of points in a certain discharge point group is higher than the average value and the excess reaches a set threshold, then the point group is defined as the main discharge point group.

[0041] Count the number of main discharge point groups: If there is only one main discharge point group, its center point is determined as the discharge location; if there are two main discharge point groups, the midpoint of the line connecting the center points of the two main discharge point groups is determined as the discharge location; if there are more than two main discharge point groups, proceed to T5.

[0042] T5: Connect the center points of each main discharge point group in sequence to form a polygonal region, and determine the geometric center of the polygonal region as the discharge location.

[0043] T6: The highest intensity among the partial discharge signals captured by all UHF sensors within the discharge occurrence range is determined as the discharge intensity of that discharge location.

[0044] The specific analysis process of step S4 is as follows: a visible light image of the area where the discharge site is located is collected by a drone, and physical defect information is extracted from the image based on image processing technology. The physical defect information includes deformation and damage area.

[0045] The degree of defect at the discharge location is determined based on the preset relationship function between deformation amount, damage area and defect degree.

[0046] A weighted fusion analysis of the defect severity and discharge intensity was performed to obtain a quantitative assessment of the discharge severity.

[0047] Compared with the prior art, the intelligent detection method for discharge points of high-voltage transmission lines described in this invention has the following advantages: 1. By scientifically setting the sensor deployment spacing and staggered arrangement, this invention balances monitoring sensitivity and positioning accuracy, avoids blind spots, and improves the coverage and monitoring efficiency of long transmission line sections.

[0048] 2. This invention constructs multiple triangular sensor arrays by deploying UHF sensors at equal intervals and staggered positions on multiple transmission lines, and uses time difference positioning method for multidimensional spatial positioning, which effectively avoids single-path interference and significantly improves the positioning accuracy and reliability of the discharge point.

[0049] 3. This invention uses a combination of signal amplitude threshold screening and pulse feature matching to effectively distinguish partial discharge signals from background noise or interference signals such as lightning, significantly reducing the false judgment rate and improving the anti-interference performance of the system.

[0050] 4. This invention integrates the discharge intensity during discharge and the physical defect information after discharge, and generates a quantitative assessment result of the severity of discharge through weighted fusion analysis, providing a more comprehensive and objective basis for operation and maintenance decisions. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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.

[0052] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0053] Figure 2 This is a schematic diagram of the sensor layout of the present invention.

[0054] Figure 3 This is a flowchart illustrating the identification of partial discharge signals according to the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Please see Figure 1 As shown, the present invention provides an intelligent detection method for discharge points of high-voltage transmission lines, which includes the following steps: S1, sensor deployment: a number of UHF sensors are uniformly deployed on multiple high-voltage transmission lines arranged in parallel with equal spacing and staggered arrangement, and each UHF sensor is numbered and its position is recorded.

[0057] As a preferred option, see [reference] Figure 2 As shown, the specific analysis process of step S1 is as follows: S11: Based on the measurable frequency of the UHF sensor, the electromagnetic signal attenuation characteristics and the positioning accuracy requirements, determine the distance between two adjacent UHF sensors as the deployment distance.

[0058] S12: Sort the transmission lines in order from one side to the other.

[0059] S13: On the first transmission line, multiple UHF sensors are evenly arranged according to the specified spacing.

[0060] S14: Based on the position of the UHF sensor on the first transmission line, multiple UHF sensors are evenly arranged on the second transmission line in a staggered manner according to the aforementioned arrangement spacing.

[0061] S15: Based on the position of the UHF sensor on the second transmission line, a plurality of UHF sensors are uniformly arranged on the third transmission line in a staggered manner according to the arrangement spacing, wherein the staggered direction of the UHF sensor on the third transmission line relative to the UHF sensor on the second transmission line is the same as or opposite to the staggered direction of the UHF sensor on the second transmission line relative to the UHF sensor on the first transmission line.

[0062] S16: Following the methods described in S13 to S15, install UHF sensors sequentially on each transmission line.

[0063] S17: Number all UHF sensors and record the location information of each sensor, including the transmission line number to which it belongs and its position coordinates on the transmission line.

[0064] It should be noted that the present invention uses the UHF method to locate the discharge point of high-voltage transmission lines, which has a long detection distance, high sensitivity, real-time online monitoring capability, and can cover long line sections.

[0065] It should be noted that a symmetrical and uniform layout of UHF sensors can improve the positioning accuracy of discharge points on high-voltage transmission lines.

[0066] It should be noted that the method for determining the spacing of UHF sensor deployment is as follows: based on the measurable frequency of the UHF sensor and the attenuation characteristics of electromagnetic waves in spatial transmission, its effective detection range is determined; at the same time, in order to meet the positioning accuracy requirements for sensor spatial resolution, the deployment spacing is set to be less than the effective detection distance to ensure that any discharge point can be effectively captured by at least multiple sensors.

[0067] It should be noted that higher frequency electromagnetic signals attenuate faster, limiting the monitoring range of a single sensor. Time-of-flight (TOF) positioning relies on the precise time difference between the arrival of signals at different sensors; smaller spacing between sensors can improve positioning accuracy and avoid blind spots. Determining the spacing between UHF sensors based on their measurable frequencies, electromagnetic signal attenuation characteristics, and positioning accuracy requirements balances monitoring sensitivity and positioning accuracy, laying the foundation for subsequent precise identification of discharge locations.

[0068] In this embodiment, the present invention scientifically sets the spacing and staggered arrangement of sensors to balance monitoring sensitivity and positioning accuracy, avoid blind spots, and improve the coverage and monitoring efficiency of long line sections.

[0069] S2. Discharge Identification: Real-time monitoring of the raw signals captured by each UHF sensor, filtering out valid signals based on signal amplitude, and determining whether the valid signal is a partial discharge signal based on the pulse characteristics of the valid signal. If it is, an early warning is triggered and the process proceeds to step S3; otherwise, the monitoring of sensor signals continues.

[0070] As a preferred option, see [reference] Figure 3 As shown, the specific analysis process for screening valid signals in step S2 is as follows: obtain the amplitude of the original signal captured by the UHF sensor and compare it with the preset threshold of the signal amplitude. If the amplitude of the original signal is greater than the threshold of the signal amplitude, the original signal is determined to be a valid signal; otherwise, it is determined to be a noise signal.

[0071] It should be noted that the threshold value of the signal amplitude is determined by collecting and statistically analyzing the background electromagnetic signals of the high-voltage transmission line under normal discharge-free conditions over a long period of time, selecting the upper limit of the statistical distribution of the background signal amplitude as an initial reference, and then calibrating and verifying it in combination with the amplitude characteristics of typical partial discharge signals, thereby setting a threshold value that can effectively distinguish between background noise and potential discharge signals.

[0072] It should be noted that the amplitude of the UHF electromagnetic signal generated by partial discharge, when propagating to the sensor, is typically significantly higher than the average level of electromagnetic noise introduced by the environment and system background during normal operation. By setting a reasonable amplitude threshold, this amplitude difference can be used for preliminary screening. Its function is to quickly eliminate the vast majority of background noise from massive amounts of monitoring data, significantly reducing the amount of data and computational load in subsequent signal processing and pattern recognition stages, and providing a preprocessing basis for efficiently and accurately identifying real partial discharge pulses.

[0073] As a preferred embodiment, the specific analysis process for determining whether a signal is a partial discharge signal in step S2 is as follows: based on the phase-resolved partial discharge spectrum of the effective signal, its pulse characteristics are extracted. The pulse characteristics include pulse phase distribution characteristics, pulse amplitude distribution characteristics, pulse repetition rate, and pulse cross-correlation coefficient.

[0074] The extracted pulse features are compared with the typical pulse features of partial discharge signals pre-stored in the database to analyze the degree of agreement between the two.

[0075] The matching degree is compared with a preset matching degree threshold. If the matching degree is greater than or equal to the threshold, the valid signal is determined to be a partial discharge signal; otherwise, it is determined to be an interference signal.

[0076] It should be noted that the specific analysis process for analyzing the consistency between the effective signal pulse characteristics and the typical pulse characteristics of the partial discharge signal is as follows: D1: Obtain the pulse phase distribution characteristics of the effective signal, which include the number of pulse clusters, the average phase angle of the clusters, the phase span of the clusters, and the ratio of the number of positive and negative half-cycle pulses. Compare the pulse phase distribution characteristics of the effective signal with the typical pulse phase distribution characteristics of the partial discharge signal to obtain the relative deviation between each sub-item of the effective signal pulse phase distribution characteristics and the corresponding sub-item of the typical pulse phase distribution characteristics of the partial discharge signal. Combine the preset relationship function between the relative deviation of each sub-item of the pulse phase distribution characteristics and the consistency factor to determine the consistency factor between the effective signal pulse phase distribution characteristics and the typical pulse phase distribution characteristics of the partial discharge signal, and record it as the first consistency factor. The relationship function is a linear negative correlation function.

[0077] D2: Obtain the pulse amplitude distribution characteristics of the effective signal, which include skewness, kurtosis and amplitude variation. Compare the pulse amplitude distribution characteristics of the effective signal with the typical pulse amplitude distribution characteristics of the partial discharge signal, analyze the matching factor between the pulse amplitude distribution characteristics of the effective signal and the typical pulse amplitude distribution characteristics of the partial discharge signal, and record it as the second matching factor.

[0078] D3: The relative deviation is obtained by comparing the pulse repetition rate of the effective signal with the typical pulse repetition rate of the partial discharge signal. Combined with the preset mapping relationship between the relative deviation and the matching factor, the matching factor between the pulse repetition rate of the effective signal and the typical pulse repetition rate of the partial discharge signal is determined and denoted as the third matching factor.

[0079] D4: The relative deviation is obtained by comparing the pulse cross-correlation coefficient of the effective signal with the typical pulse cross-correlation coefficient of the partial discharge signal. The coincidence factor between the pulse cross-correlation coefficient of the effective signal and the typical pulse cross-correlation coefficient of the partial discharge signal is analyzed and denoted as the fourth coincidence factor.

[0080] D5: The first, second, third, and fourth matching factors are summed to obtain the degree of agreement between the effective signal pulse characteristics and the typical pulse characteristics of the partial discharge signal.

[0081] It should be noted that the consistency threshold was determined through statistical analysis of the pulse characteristics of a large number of known types of partial discharge signals and typical interference signals. First, a training set containing samples of various discharge modes and interference signals was constructed. The pulse characteristics in their PRPD spectra were extracted, and the consistency with standard discharge characteristics was calculated, thereby obtaining the consistency distributions for discharge samples and interference samples respectively. Then, based on the discriminative power of the two types of samples, a critical value that optimally distinguishes between partial discharge and interference signals was selected as the threshold. This threshold was then calibrated and optimized using a validation set in practical applications to ensure the accuracy and reliability of the judgment.

[0082] It should be noted that partial discharge signals exhibit pulse sequences with specific phase distributions, amplitude distributions, repetition rates, and correlations in PRPD maps. These patterns are closely related to the type and severity of insulation defects, while general electromagnetic interference often lacks stable and correlated pulse aggregation characteristics. By comparing the degree of agreement between pulse characteristics and typical discharge patterns, the physical nature and statistical regularities of discharge signals can be effectively utilized to achieve differentiation. Its role is to significantly improve the anti-interference capability and accuracy of discharge identification, reduce false alarms and false negatives, and provide a reliable signal source for subsequent localization and diagnosis.

[0083] In this embodiment, the present invention uses a combination of signal amplitude threshold screening and pulse feature matching to effectively distinguish partial discharge signals from background noise or interference signals such as lightning, thereby significantly reducing the false judgment rate and improving the anti-interference performance of the system.

[0084] S3. Discharge Location: Based on the intensity of the partial discharge signal, the discharge range is initially determined. Multiple triangular sensor arrays are constructed within the discharge range. The time difference positioning method is used to analyze the suspected discharge points corresponding to each sensor array. Based on the analysis results of the suspected discharge points of each sensor array, the discharge location is determined, and the discharge intensity of the discharge location is determined.

[0085] As a preferred embodiment, the specific process of analyzing the partial discharge signal intensity in step S3 is as follows: obtaining the amplitude index and energy index of the partial discharge signal, wherein the amplitude index includes peak amplitude and equivalent apparent discharge quantity, and the energy index includes pulse energy and average power.

[0086] The amplitude and energy indices are input into a preset signal strength evaluation model to determine the intensity of the partial discharge signal. The signal strength evaluation model includes a quantitative mapping relationship between the amplitude and energy indices and the signal strength.

[0087] It should be noted that the signal strength evaluation model is established as follows: First, UHF signal samples of different discharge types and intensities are collected through experimental simulation or historical data, and the amplitude and energy indices of each sample are extracted and their true discharge intensities are labeled; then, regression analysis, machine learning or other mathematical modeling methods are used to fit the quantitative mapping relationship between the amplitude index, energy index and discharge intensity to form an evaluation model; finally, the model is tested and optimized using a validation dataset to ensure its accuracy and generalization ability.

[0088] It should be noted that the severity of partial discharge is directly physically related to the amplitude of the electromagnetic signal, such as peak value, equivalent apparent discharge quantity, and energy, such as pulse energy and average power. Amplitude indicators reflect the instantaneous intensity of the discharge, while energy indicators characterize the sustained effect and overall level of the discharge. Combining the two can comprehensively and objectively characterize the actual intensity of the discharge. Its role is to provide a quantitative and reliable intensity basis for subsequent determination of the discharge range, assessment of defect severity, and warning level, avoiding misjudgments that may be caused by a single indicator.

[0089] As a preferred embodiment, the specific analysis process for determining the discharge range in step S3 is as follows: statistically analyze all UHF sensors that have captured partial discharge signals and their corresponding partial discharge signal intensities.

[0090] Extract the numerical range of partial discharge signal intensity corresponding to each intensity level from the pre-stored database.

[0091] The areas covered by the UHF sensor, whose partial discharge signal intensity is located in the ranges of high intensity, medium intensity, and low intensity, are respectively denoted as the discharge core region, transition region, and edge region.

[0092] The core region, transition region, and edge region of the discharge are integrated to determine the final discharge range.

[0093] It should be noted that the numerical ranges of partial discharge signal intensity corresponding to each intensity level were determined through statistical analysis of a large amount of historical discharge case data. The specific process includes: collecting partial discharge signal samples under different operating conditions and different defect types, measuring their signal intensity indicators such as peak amplitude and pulse energy, and performing cluster analysis or percentile division based on the actual discharge severity or expert experience. This divides the signal intensity range into numerical ranges corresponding to high, medium, and low intensity levels, and continuously optimizing and calibrating based on field feedback during application.

[0094] It should be noted that the intensity of partial discharge signals is closely related to the propagation distance; sensors closer to the discharge point receive higher signal strength. Therefore, the core area and influence range of the discharge activity can be inferred from the spatial distribution characteristics of the signal intensity. By dividing the sensors into core, transition, and edge regions according to signal intensity, the spatial range of the discharge can be preliminarily defined. This narrows the search area for subsequent precise positioning, significantly improves the computational efficiency and positioning accuracy of time-difference positioning algorithms, and provides spatial constraints for quickly and accurately locating the discharge site.

[0095] As a preferred embodiment, the specific process of analyzing the suspected discharge points corresponding to each sensor array in step S3 is as follows: extract the spatial model of multiple parallel high-voltage transmission lines pre-stored in the database, cut out the high-voltage transmission line spatial model of the section corresponding to the discharge occurrence range, and mark the position of all UHF sensors within that range.

[0096] Three non-collinear UHF sensors in space are defined as a triangular sensor array.

[0097] All UHF sensors within the discharge range are combined in groups of three to form multiple triangular sensor arrays.

[0098] It should be noted that the specific method for constructing multiple triangular sensor arrays by combining all UHF sensors within the discharge range in triplicate is as follows: assuming there are [number] UHF sensors within the discharge range... For each UHF sensor, systematically and comprehensively select all possible and distinct three-by-three combinations from all UHF sensors within the discharge occurrence range. This process is equivalent to calculating the number of combinations. That is, all ways to choose 3 elements from N different elements, and each choice corresponds to a potential triangular sensor array.

[0099] For each selected 3x3 combination, verify whether its spatial position satisfies the geometric condition of non-collinearity. This verification can be performed based on the three-dimensional coordinates of the sensor in the pre-stored high-voltage transmission line spatial model.

[0100] If the three points are not collinear, then the combination is defined and constructed into a triangular sensor array. This triangular array forms a unique spatial plane with the spatial positions of the three sensors as vertices.

[0101] Ultimately, all combinations that passed the verification were formally defined as valid "triangular sensor arrays".

[0102] For each triangular sensor array, the time difference between every two sensors in the array capturing the same partial discharge signal is obtained. Combined with the electromagnetic wave propagation speed, the time difference positioning method is used to calculate and determine one or more suspected discharge points corresponding to the triangular sensor array.

[0103] It should be noted that the spatial model of the high-voltage transmission line is constructed based on real-world images collected by UAV inspections and is dynamically updated.

[0104] As a preferred embodiment, the specific process of determining the suspected discharge point corresponding to the triangular sensor array using the time difference positioning method is as follows: The three sensors within the triangular sensor array are respectively denoted as... And obtain the time when each sensor captures the same partial discharge signal.

[0105] calculate and Time difference of signal acquisition Combined with the speed of electromagnetic wave propagation Through formula Calculate the discharge point To the sensor and Distance difference ,by , Draw a hyperbola that satisfies the distance difference around the focus, and denote it as the first hyperbola.

[0106] Similarly, based on , Time difference of signal acquisition and , Time difference of signal acquisition Draw the second and third hyperbolas respectively.

[0107] The intersection of the three hyperbolas is determined as the suspected discharge point corresponding to the triangular sensor array. There can be one or more suspected discharge points, and the spatial coordinates of the suspected discharge points are recorded.

[0108] It should be noted that the propagation speed of the electromagnetic wave was determined experimentally in advance.

[0109] As a preferred embodiment, the specific process of determining the discharge location and analyzing its discharge intensity in step S3 is as follows: T1: In the high-voltage transmission line spatial model of the section corresponding to the discharge occurrence range, mark all suspected discharge points corresponding to each triangular sensor array, and remove abnormal points that are obviously far away from the transmission line according to preset rules to obtain the optimized spatial distribution of suspected discharge points.

[0110] T2: If the suspected discharge points converge at a single location in space, then that point is directly identified as the discharge site; otherwise, proceed to T3.

[0111] T3: Calculate the spatial distance between each suspected discharge point. If the distance between any two points is less than the set spatial distance threshold, then the two points are determined to belong to the same discharge point group.

[0112] Count the number of all discharge point groups.

[0113] If there is only one discharge point group, then the geometric center of that point group is determined as the discharge location; otherwise, proceed to T4.

[0114] T4: Count the number of suspected discharge points contained in each discharge point group, and calculate the average number of points contained in all point groups.

[0115] If the number of points in a certain discharge point group is higher than the average value and the excess reaches a set threshold, then the point group is defined as the main discharge point group.

[0116] Count the number of main discharge point groups: If there is only one main discharge point group, its center point is determined as the discharge location; if there are two main discharge point groups, the midpoint of the line connecting the center points of the two main discharge point groups is determined as the discharge location; if there are more than two main discharge point groups, proceed to T5.

[0117] T5: Connect the center points of each main discharge point group in sequence to form a polygonal region, and determine the geometric center of the polygonal region as the discharge location.

[0118] T6: The highest intensity among the partial discharge signals captured by all UHF sensors within the discharge occurrence range is determined as the discharge intensity of that discharge location.

[0119] It should be noted that the specific process for eliminating abnormal points that are obviously far away from the transmission line is as follows: obtain the distance between the suspected power generation point and the transmission line and compare the distance with the set distance range. If the distance exceeds the range, it is determined to be an abnormal point and eliminated.

[0120] In this embodiment, the present invention constructs multiple triangular sensor arrays by deploying UHF sensors on multiple transmission lines in an equally spaced staggered manner, and uses the time difference positioning method for multi-dimensional spatial positioning, which effectively avoids single-path interference and significantly improves the positioning accuracy and reliability of the discharge point.

[0121] S4. Severity Assessment: Visible light images of the discharge site are collected by drone, physical defect information is extracted and the degree of defect is analyzed. The severity of the discharge is assessed by combining the degree of defect of the discharge site and the discharge intensity, and a test report is generated.

[0122] As a preferred embodiment, the specific analysis process of step S4 is as follows: a visible light image of the area where the discharge site is located is acquired by a drone, and physical defect information is extracted from the image based on image processing technology. The physical defect information includes deformation and damage area.

[0123] The degree of defect at the discharge location is determined based on the preset relationship function between deformation amount, damage area and defect degree.

[0124] A weighted fusion analysis of the defect severity and discharge intensity was performed to obtain a quantitative assessment of the discharge severity.

[0125] It should be noted that, by using sample images of physical defects caused by historical discharges of high-voltage transmission lines stored in the database, the physical defects at the discharge points are identified and quantified.

[0126] It should be noted that the weights of the defect severity and discharge intensity can be preset based on industry experience or determined through a limited number of test data. For example, historical data on the types of discharge defects in high-voltage transmission lines and their occurrence frequencies can be collected first, then the correlation coefficients of the defect severity and discharge intensity on the overall discharge severity can be calculated, and regression analysis or analytic hierarchy process can be used to determine the contribution of each indicator. Finally, after normalization, the contribution is converted into the weights of the defect severity and discharge intensity, and the sum of the weights is 1.

[0127] It should be noted that the test report is generated based on the location of the discharge site and the severity of the discharge.

[0128] In this embodiment, the present invention integrates the discharge intensity during discharge and the physical defect information after discharge, and generates a quantitative assessment result of the severity of discharge through weighted fusion analysis, providing a more comprehensive and objective basis for operation and maintenance decisions.

[0129] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0130] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0131] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0132] In addition, the functional modules in the various embodiments of this application 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.

[0133] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0134] Finally, 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 protection scope of the present invention.

Claims

1. A method for intelligent detection of discharge points in high-voltage transmission lines, characterized in that, Includes the following steps: S1. Sensor deployment: Several UHF sensors are evenly deployed on multiple parallel high-voltage transmission lines in a staggered manner with equal spacing. Each UHF sensor is numbered and its position is recorded. S2. Discharge identification: Real-time monitoring of the raw signals captured by each UHF sensor, filtering out valid signals based on the signal amplitude, and determining whether it is a partial discharge signal based on the pulse characteristics of the valid signal. If it is, an early warning is triggered and the process proceeds to step S3; otherwise, the sensor signals are monitored. S3. Discharge location: Based on the intensity of the partial discharge signal, the discharge range is initially determined. Multiple triangular sensor arrays are constructed within the discharge range. The time difference positioning method is used to analyze the suspected discharge points corresponding to each sensor array. Based on the analysis results of the suspected discharge points of each sensor array, the discharge location is determined, and the discharge intensity of the discharge location is determined. The specific process of analyzing the suspected discharge points corresponding to each sensor array in step S3 is as follows: Extract the spatial model of multiple parallel high-voltage transmission lines pre-stored in the database; extract the spatial model of the high-voltage transmission line segment corresponding to the discharge occurrence range; and mark the positions of all UHF sensors within this range; define three non-collinear UHF sensors in space as a triangular sensor array; combine all UHF sensors within the discharge occurrence range into three-by-three combinations to construct multiple triangular sensor arrays: within the discharge occurrence range there are Calculate the number of combinations for each UHF sensor. That is, all ways to select 3 elements from N different elements. Each selection corresponds to a potential triangular sensor array. Check whether the spatial positions of the three selected UHF sensors meet the geometric condition of non-collinearity. If they are not collinear, then the three UHF sensors are defined and constructed into a triangular sensor array. For each triangular sensor array, the time difference between every two sensors in the array capturing the same partial discharge signal is obtained. Combined with the electromagnetic wave propagation speed, the time difference positioning method is used to calculate and determine one or more suspected discharge points corresponding to the triangular sensor array. S4. Severity Assessment: Visible light images of the discharge site are collected by drone, physical defect information is extracted and the degree of defect is analyzed. The severity of the discharge is assessed by combining the degree of defect of the discharge site and the discharge intensity, and a test report is generated. The specific process of determining the suspected discharge point corresponding to the triangular sensor array using the time difference positioning method is as follows: The three sensors within the triangular sensor array are respectively denoted as... And obtain the time when each sensor captures the same partial discharge signal; calculate and Time difference of signal acquisition Combined with the speed of electromagnetic wave propagation Through formula Calculate the discharge point To the sensor and Distance difference ,by , Plot a hyperbola satisfying this distance difference around the focus, and denote it as the first hyperbola; similarly, based on... , Time difference of signal acquisition and , Time difference of signal acquisition Draw the second and third hyperbolas respectively; determine the intersection of the three hyperbolas as the suspected discharge point corresponding to the triangular sensor array. There can be one or more suspected discharge points, and record the spatial coordinates of the suspected discharge points.

2. The intelligent detection method for discharge points of high-voltage transmission lines according to claim 1, characterized in that: The specific analysis process of step S1 is as follows: S11: Determine the spacing between two adjacent UHF sensors based on the measurable frequency, electromagnetic signal attenuation characteristics, and positioning accuracy requirements of the UHF sensor, and use this spacing as the deployment spacing. S12: Arrange the transmission lines in order from one side to the other; S13: On the first transmission line, multiple UHF sensors are evenly arranged according to the specified spacing; S14: Based on the position of the UHF sensor on the first transmission line, multiple UHF sensors are evenly arranged on the second transmission line in a staggered manner according to the arrangement spacing. S15: Based on the position of the UHF sensor on the second transmission line, a plurality of UHF sensors are evenly arranged on the third transmission line in a staggered manner according to the arrangement spacing, wherein the staggered direction of the UHF sensor on the third transmission line relative to the UHF sensor on the second transmission line is the same as or opposite to the staggered direction of the UHF sensor on the second transmission line relative to the UHF sensor on the first transmission line. S16: Following the methods described in S13 to S15, install UHF sensors sequentially on each transmission line; S17: Number all UHF sensors and record the location information of each sensor, including the transmission line number to which it belongs and its position coordinates on the transmission line.

3. The intelligent detection method for discharge points of high-voltage transmission lines according to claim 1, characterized in that: The specific analysis process for screening valid signals in step S2 is as follows: The amplitude of the raw signal captured by the UHF sensor is obtained and compared with a preset threshold for signal amplitude. If the amplitude of the raw signal is greater than the threshold, the raw signal is determined to be a valid signal; otherwise, it is determined to be a noise signal.

4. The intelligent detection method for discharge points of high-voltage transmission lines according to claim 1, characterized in that: The specific analysis process for determining whether a signal is a partial discharge signal in step S2 is as follows: Based on the phase-resolved partial discharge spectrum of the effective signal, its pulse characteristics are extracted. The pulse characteristics include pulse phase distribution characteristics, pulse amplitude distribution characteristics, pulse repetition rate, and pulse cross-correlation coefficient; The extracted pulse features are compared with the typical pulse features of partial discharge signals pre-stored in the database to analyze the degree of agreement between the two. The matching degree is compared with a preset matching degree threshold. If the matching degree is greater than or equal to the threshold, the valid signal is determined to be a partial discharge signal; otherwise, it is determined to be an interference signal.

5. The intelligent detection method for discharge points of high-voltage transmission lines according to claim 1, characterized in that: The specific process for analyzing the partial discharge signal intensity in step S3 is as follows: The amplitude and energy indices of the partial discharge signal are obtained, wherein the amplitude indices include peak amplitude and equivalent apparent discharge quantity, and the energy indices include pulse energy and average power. The amplitude and energy indices are input into a preset signal strength evaluation model to determine the intensity of the partial discharge signal. The signal strength evaluation model includes a quantitative mapping relationship between the amplitude and energy indices and the signal strength.

6. The intelligent detection method for discharge points of high-voltage transmission lines according to claim 1, characterized in that: The specific analytical process for determining the discharge range in step S3 is as follows: Collect statistics on all UHF sensors that captured partial discharge signals and their corresponding partial discharge signal intensities; Extract the numerical range of partial discharge signal intensity corresponding to each intensity level from the pre-stored database; The areas covered by the UHF sensor, whose partial discharge signal intensity is located in the range of high intensity, medium intensity and low intensity, are respectively denoted as the discharge core area, transition area and edge area. The core region, transition region, and edge region of the discharge are integrated to determine the final discharge range.

7. The intelligent detection method for discharge points of high-voltage transmission lines according to claim 1, characterized in that: The specific process of determining the discharge location and analyzing its discharge intensity in step S3 is as follows: T1: In the spatial model of the high-voltage transmission line corresponding to the discharge occurrence range, mark all the suspected discharge points corresponding to each triangular sensor array, and remove abnormal points that are obviously far away from the transmission line according to the preset rules to obtain the optimized spatial distribution of suspected discharge points. T2: If the suspected discharge points converge at a single location in space, then that point is directly identified as the discharge site; otherwise, proceed to T3. T3: Calculate the spatial distance between each suspected discharge point. If the distance between any two points is less than the set spatial distance threshold, then the two points are determined to belong to the same discharge point group. Count the number of all discharge point groups; If there is only one discharge point group, the geometric center of the point group is determined as the discharge location; otherwise, proceed to T4. T4: Count the number of suspected discharge points contained in each discharge point group, and calculate the average number of points contained in all point groups; If the number of points in a certain discharge point group is higher than the average value and the excess reaches a set threshold, then the point group is defined as the main discharge point group. Count the number of main discharge point groups: If there is only one main discharge point group, its center point is determined as the discharge location; if there are two main discharge point groups, the midpoint of the line connecting the center points of the two main discharge point groups is determined as the discharge location; if there are more than two main discharge point groups, proceed to T5. T5: Connect the center points of each main discharge point group in sequence to form a polygonal region, and determine the geometric center of the polygonal region as the discharge location. T6: The highest intensity among the partial discharge signals captured by all UHF sensors within the discharge occurrence range is determined as the discharge intensity of that discharge location.

8. The intelligent detection method for discharge points of high-voltage transmission lines according to claim 1, characterized in that: The specific analysis process of step S4 is as follows: The visible light image of the area where the discharge site is located is collected by a drone, and physical defect information is extracted from the image based on image processing technology. The physical defect information includes the amount of deformation and the damaged area. The degree of defect at the discharge site is determined based on the preset relationship function between deformation amount, damage area and defect degree. A weighted fusion analysis of the defect severity and discharge intensity was performed to obtain a quantitative assessment of the discharge severity.

Images