Method and Equipment for Defect Location in Power Grid Operation Lines Based on 3D Point Cloud

Abstract

This application relates to the field of point cloud defect localization technology, specifically to a method and equipment for locating defects in power grid operation lines based on three-dimensional point clouds. The method collects the original point cloud of the transmission line, conductor specifications and parameters of each tension section, and ambient temperature; calculates the horizontal span, vertical height difference, measured maximum sag, initial tension characteristic parameters, and distribution consistency weight of the span unit; calculates the theoretical sag of the span unit, constructs a total deviation function and iteratively solves it to determine the reference tension of the tension section; based on the difference between the theoretical and measured sag of the span unit, the horizontal span, conductor specifications and parameters, ambient temperature, and the original point cloud, it determines whether the clearance distance is sufficient and whether a defect exists in the span unit. If a defect exists in the span unit, defect localization is performed. This application can improve the accuracy of extracting the reference tension of the tension section.

Description

Technical Field

[0001] This application relates to the field of point cloud defect localization technology, specifically to a method and equipment for locating defects in power grid operation lines based on three-dimensional point clouds. Background Technology

[0002] Overhead transmission lines are the lifeline of the power grid, and their safe operation is significantly affected by ambient temperature and conductor tension. Increased grid load and frequent extreme weather events can lead to excessive conductor sag, causing ground discharge and tripping accidents. Conductor tension is the key factor determining sag, and accurately extracting the reference tension of the tension section is a core prerequisite for ensuring line safety.

[0003] Currently, airborne lidar has become the mainstream method for inspecting overhead transmission lines. However, its data processing methods have significant limitations, often resulting in the inaccurate extraction of the reference tension for tension sections. Specifically, within a tension section, each span of conductor is connected by a string of suspension insulators, theoretically ensuring a uniform horizontal tension. However, in actual measurements, the tension values ​​for each span are discretely distributed due to factors such as pulley friction, insulator sway, and measurement noise. Traditional methods typically use the average tension of each span as the reference tension for the tension section. However, this approach has two major drawbacks: first, a single LiDAR measurement can only reflect the specific operating conditions at the time of acquisition and cannot extrapolate the tension state under critical operating conditions such as high temperature and full load, making it difficult to support early warning systems; second, when a span has structural defects such as clamp slippage or loose fittings, the tension in that span will change abruptly. The average method will smooth out this abnormal tension, leading to missed detection of defective spans and misjudgment of normal spans.

[0004] Therefore, how to accurately extract the reference tension representing the mainstream mechanical state of the tension section from discrete tension measurement data containing noise and local fault interference, and then realize the precise location of defects and early warning of key operating conditions, has become an urgent technical problem to be solved in the operation and maintenance of overhead transmission lines. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method and equipment for locating defects in power grid operation lines based on three-dimensional point clouds. The specific technical solution adopted is as follows: In a first aspect, one embodiment of this application provides a method for locating defects in power grid operation lines based on three-dimensional point clouds. The method includes the following steps: The original point cloud of the transmission line, conductor specifications and ambient temperature of each tension section are collected. The hanging point cloud and conductor point cloud of each span unit are extracted from the original point cloud. Based on the hanging point cloud and conductor point cloud, the horizontal span, vertical height difference, measured maximum sag and initial tension characteristic parameters of the span unit are calculated. The corresponding distribution consistency weight is determined based on the initial tension characteristic parameters. Based on the conductor specifications, horizontal span, vertical height difference, measured maximum sag, initial tension characteristic parameters, and distribution consistency weight, the theoretical sag of the span unit is calculated, and a deviation summation function is constructed. The deviation summation function is used to evaluate the degree of agreement between the theoretical sag and the measured sag of all span units under the assumed tension. The deviation summation function is iteratively solved to determine the reference tension of the tension section. Based on the difference between the theoretical and measured sag of the span unit, the horizontal span of the span unit, the conductor specifications and parameters, the ambient temperature, and the original point cloud, it is determined whether the clearance distance is sufficient and whether there are defects in the span unit. If there are defects in the span unit, the defects are located.

[0006] Furthermore, the hanging point cloud corresponds to the hanging point at the lower end of the insulator string of the tension section of the base tower, and the conductor cloud corresponds to the suspended portion of the conductor between the two hanging points.

[0007] Furthermore, the horizontal span is the Euclidean distance between the coordinates of the two hanging points at both ends of the span unit projected onto the horizontal plane, and the vertical height difference is the absolute value of the height difference between the coordinates of the two hanging points at both ends of the span unit in the vertical direction.

[0008] Furthermore, the method for obtaining the measured maximum sag is as follows: Project the conductor point cloud onto the vertical plane determined by the two hanging points and the gravity vector corresponding to the span unit. Fit the projected point cloud to the catenary equation to obtain the fitting curve. Record the maximum vertical distance of the fitting curve relative to the line connecting the two hanging points as the measured maximum sag of the corresponding span unit.

[0009] Furthermore, the method for calculating the initial tension characteristic parameter is as follows: The product of the square of the horizontal span of the span unit and the weight per unit length is used as the numerator, and the product of the measured maximum sag of the span unit and the number 8 is used as the denominator. The result of the fraction calculation is used as the initial tension characteristic parameter of the span unit.

[0010] Furthermore, the method for obtaining the distribution consistency weight is as follows: The sum of the standard deviation of the initial tension characteristic parameters of all the gear units and the preset small correction amount is denoted as the adaptive bandwidth. Based on the difference of the initial tension characteristic parameters of the gear units and the adaptive bandwidth, the affinity matrix of the eigenvector centrality algorithm is constructed based on the Gaussian kernel function. The affinity matrix is ​​eigenvalued and the eigenvector corresponding to the largest eigenvalue is normalized to obtain the distribution consistency weights of each range unit.

[0011] Furthermore, the method for determining whether the gear unit has a defect is as follows: The difference between the theoretical sag of the span unit and the measured maximum sag is recorded as the sag deviation value of the span unit. When the absolute value of the sag deviation of the gauge unit is greater than the abnormality judgment threshold, the gauge unit is judged to have a defect; otherwise, the gauge unit is judged not to have a defect.

[0012] Furthermore, the method for determining whether the clearance distance is sufficient is as follows: The regular span of the tension section is calculated based on the horizontal span of the span unit and used as the representative span of the state equation. Combining the conductor specification parameters and ambient temperature of the tension section, the state equation of the overhead conductor of the tension section is constructed from the ambient temperature condition to the design maximum temperature condition. The Newton iteration method is used to solve the state equation of the overhead conductor to obtain the predicted tension of the line under the high temperature condition of the design maximum temperature condition. Based on the horizontal span and vertical height difference of the span unit, generate the high-temperature working condition conductor profile of the span unit; calculate the minimum Euclidean distance from all points in the original point cloud to the high-temperature working condition conductor profile, and record it as the minimum obstacle distance. When the minimum distance to the obstacle is less than the preset minimum clearance distance, the clearance distance is insufficient; otherwise, the clearance distance is sufficient.

[0013] Furthermore, the defect is the location of the minimum obstacle distance in the original point cloud.

[0014] Secondly, another embodiment of this application provides a power grid operation line defect location device based on three-dimensional point clouds, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described power grid operation line defect location method based on three-dimensional point clouds.

[0015] The embodiments of this application have at least the following beneficial effects: To eliminate the influence of lateral wind deflection on sag measurement, this application introduces vertical plane projection to calculate the measured maximum sag of the span unit. Considering the force coupling of each span within the tension section, the horizontal tension of normal spans should tend to be consistent. Distribution consistency weights representing the mainstream mechanical state are automatically identified and extracted from discrete initial tension characteristic parameters. To find the unique optimal tension that can best explain the geometry of the entire section, the degree of agreement between the theoretical and measured sags of all spans under the assumed tension is measured. A total deviation function with the uniform tension of the tension section as the independent variable is constructed, and the total deviation function is iteratively solved to determine the reference tension of the tension section. The reference tension of the tension section excludes... The interference from local anomalies and random measurement errors can represent the true mechanical equilibrium state of the tension section at the corresponding acquisition time. Therefore, any geometric feature that significantly deviates from the reference tension of the tension section can be identified as a structural defect. Based on the extreme operating conditions derived from the reference tension of the tension section, the safety risks of the line under high summer temperatures or full load operation are assessed, initial state errors are eliminated, the accuracy of the prediction results is ensured, and the sufficiency of the clearance distance and the presence of defects in the span unit are determined. When defects are found in the span unit, the defect location is performed. This solves the problem that the discrete tension measurement data of the tension section of the power grid is affected by noise and local faults, which makes it impossible to accurately extract the reference tension of the tension section. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the steps of a method for locating power grid operation line defects based on three-dimensional point clouds, provided in one embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the power grid operation line defect location method and device based on three-dimensional point clouds proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the power grid operation line defect location method and equipment provided in this application based on three-dimensional point clouds.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a method for locating power grid line defects based on three-dimensional point clouds, according to an embodiment of this application. The method includes the following steps: Step S001: Collect the original point cloud of the transmission line, the conductor specifications and ambient temperature of each tension section, extract the hanging point cloud and conductor point cloud of each span unit from the original point cloud, calculate the horizontal span, vertical height difference, measured maximum sag and initial tension characteristic parameters of the span unit based on the hanging point cloud and conductor point cloud, and determine the corresponding distribution consistency weight based on the initial tension characteristic parameters.

[0022] A drone equipped with a LiDAR radar was used to scan the entire power transmission line to obtain the original point cloud of the power transmission line, and the ambient temperature at the time of the original point cloud acquisition was recorded simultaneously.

[0023] To ensure the validity of the mechanical model established later, the original point cloud was first divided into tension segments.

[0024] In this embodiment, the tension section refers to an independent mechanical section with tension towers at both ends and suspension towers in the middle. The conductors within this section are mechanically connected by suspension insulator strings, exhibiting mechanical connectivity. Data from jumpers outside the tension towers or adjacent tension sections are not included in this calculation. In other words, the tension section refers to an independent mechanical section with tension towers at both ends and suspension towers in the middle. The tension towers, acting as anchor points for mechanical stress, block the transmission of tension; therefore, the global consistency assumption of this application only holds within the tension section. If the input data spans across tension towers, for example, including multiple tension sections, the physical model will fail.

[0025] A point cloud classification algorithm based on elevation threshold and echo intensity is used to separate the hanging point cloud and the conductor point cloud of each span unit from the original point cloud.

[0026] Among them, the span unit refers to the spatial area corresponding to the conductor segment between the suspension insulator string suspension points or tension clamp suspension points of two adjacent towers within the tension section of an overhead transmission line, and all point cloud data units within that area; the suspension point point cloud corresponds to the lower suspension point of the insulator string of each tower within the tension section, and the three-dimensional spatial coordinates of these points represent the actual stress support points of the conductor at the time of acquisition; the conductor point cloud corresponds to the suspension portion of each span conductor between the two suspension points, that is, the point cloud of each span unit within the tension section is obtained.

[0027] Based on the identified transmission line number or ledger information, retrieve the conductor specifications corresponding to the tension section of the transmission line. The conductor specifications specifically include: weight per unit length, cross-sectional area, elastic modulus, coefficient of linear expansion, and design maximum temperature condition.

[0028] The unit of weight per unit length is N / m, used to calculate the catenary shape of the conductor under gravity; the unit of cross-sectional area is mm. 2 The unit of elastic modulus is N / mm. 2 The unit of the linear expansion coefficient is 1 / ℃. The cross-sectional area, elastic modulus, and linear expansion coefficient are all used as the basic parameters of the subsequent overhead conductor state equation. The unit of the design maximum temperature condition is ℃. The design maximum temperature condition can be used as the target temperature condition for extreme condition derivation.

[0029] In particular, considering that conductors may age or creep during long-term operation, causing actual parameters to deviate from factory values, this embodiment preferably uses equivalent parameters corrected for line aging. For example, the elastic modulus is reduced based on the line's service life to improve calculation accuracy.

[0030] The horizontal span and vertical height difference of each span unit within the tension section are extracted from the point cloud of the hanging points. The horizontal span is the Euclidean distance of the coordinates of the hanging points at both ends of the span unit projected onto the horizontal plane, and the vertical height difference is the absolute value of the height difference between the coordinates of the hanging points at both ends of the span unit in the vertical direction.

[0031] To eliminate the influence of lateral wind deflection on sag measurement, a vertical plane projection is introduced. Specifically, a vertical plane is constructed, determined by the two end points of the span unit and the gravity vector. The traverse point cloud is projected onto this vertical plane, transforming the three-dimensional traverse point cloud into a two-dimensional planar point cloud. In the two-dimensional coordinate system, the least squares method is used to fit the projected point cloud to the catenary equation, obtaining the fitted curve. The maximum vertical distance of the fitted curve relative to the line connecting the two end points is denoted as the measured maximum sag of the corresponding span unit. Here, the gravity vector is the vertically downward direction.

[0032] Because the forces in each section of the tension section are coupled, the horizontal tension in the normal section should tend to be consistent. Therefore, the same idea as the eigenvector centrality algorithm can be used to automatically identify and extract the distribution consistency weights representing the mainstream mechanical state from the discrete initial tension characteristic parameters.

[0033] First, a projectile model is used as an engineering approximation for the catenary. The tension of each span unit within the tension section is estimated independently. The calculation method for the initial tension characteristic parameter of the span unit is as follows: the product of the square of the horizontal span of the span unit and the weight per unit length is used as the numerator, the product of the measured maximum sag of the span unit and the number 8 is used as the denominator, and the calculation result of the fraction is used as the initial tension characteristic parameter of the span unit.

[0034] The initial tension characteristic parameters of different span units are affected by measurement noise and local anomalies such as slip, exhibiting a multimodal distribution. Therefore, it is necessary to quantify the similarity of the initial tension characteristic parameters of different span units.

[0035] Specifically, an affinity matrix is ​​constructed based on the Gaussian kernel function. The sum of the standard deviations of the initial tension characteristic parameters of all span units and a preset small correction amount is denoted as the adaptive bandwidth. The tension similarity between two different span units is calculated. An affinity matrix is ​​constructed based on the tension similarity of all different span units. In the affinity matrix, the first... line, number The column value is the first Gear spacing unit and the first Tension similarity of the gap unit.

[0036] The formula for calculating the tension similarity of units with different spans is as follows: in, Indicates the first Gear spacing unit and the first Tension similarity of the unit spacing; , They represent the first File, No. Initial tension characteristic parameters of the span unit; Indicates adaptive bandwidth; This represents an exponential function with the natural constant as its base.

[0037] It is important to note that the diagonal elements of the affinity matrix are all set to 1 to ensure that the eigenvalue decomposition can extract a unique principal eigenvector in which all components are positive.

[0038] Then, identify which tension levels represent the mainstream state of the tension section, perform eigenvalue decomposition on the affinity matrix, extract the eigenvector corresponding to the largest eigenvalue in the eigenvalue decomposition result, and then process the extracted eigenvector. Norm normalization is performed to obtain the distribution consistency weights for each grade unit.

[0039] It is understandable that the values ​​in the normalized result of the feature vector are the distribution consistency weights of the corresponding gear units. The normalization process can ensure that the sum of the distribution consistency weights of all gear units is 1.

[0040] The distribution consistency weight can automatically identify the center of the data distribution. The distribution consistency weight of the span unit reflects the mainstream degree of the span unit in the overall data distribution. The larger the distribution consistency weight of the span unit, the closer the tension of the span unit is to the tension level of most normal spans in the tension section. The tension level of most normal spans is the mainstream mechanical state. The smaller the distribution consistency weight of the span unit, the more the tension of the span unit deviates from the mainstream mechanical state, and the more likely it is to correspond to an anomaly or noise.

[0041] When most span units within a tension section are operating normally, the tension values ​​of these normal span units are close to each other, forming a high-density data cluster in the affinity matrix. The corresponding distribution consistency weight in the principal eigenvector is relatively large, indicating that the tension of these span units can effectively represent the mainstream mechanical state of the tension section. When the conductor corresponding to a span unit experiences structural defects such as clamp slippage, leading to a sudden change in tension, the tension value of that span unit will be far away from the mainstream data cluster, becoming an isolated point in the data distribution. Its affinity with the mainstream normal span is extremely low, resulting in a very small component in the principal eigenvector. The corresponding distribution consistency weight approaches 0, suppressing the interference of span units with sudden tension changes on subsequent benchmark calculations.

[0042] The above process achieves automatic weighting based on data distribution density, without the need for preset fixed judgment thresholds. It can adaptively adapt to overhead transmission lines with different voltage levels and different terrain conditions, effectively avoiding the defects of the traditional average value method, significantly improving the robustness and accuracy of the benchmark tension extraction. At the same time, it realizes the automatic identification of defective spans, providing reliable support for the subsequent accurate calculation of benchmark tension and safe operation and maintenance of the line.

[0043] At this point, the distribution consistency weights of all range units are obtained.

[0044] Step S002: Based on the conductor specification parameters, the horizontal span distance, vertical height difference, measured maximum sag, initial tension characteristic parameters, and distribution consistency weight of the span unit, calculate the theoretical sag of the span unit and construct a deviation summation function. The deviation summation function is used to evaluate the degree of agreement between the theoretical sag and the measured sag of all span units under the assumed tension. Iteratively solve the deviation summation function to determine the reference tension of the tension section.

[0045] In order to find the unique optimal tension that can best explain the geometry of the entire segment, a unified tension for the tension segment is constructed. The sum of deviations of the independent variable The sum of deviations function measures the stress under the assumption of tension. Below, the degree of agreement between the theoretical sag and the measured sag for all gears in the entire section.

[0046] Specifically, the total deviation function is as follows: in, Represents the sum of deviations function; This indicates a uniform tension in the tension section; Indicates the number of gear interval units; Indicates the first The distribution consistency weight of each grade unit; Indicates the first The horizontal spacing of each spacing unit; Indicates the first Vertical height difference of each span unit; This indicates that the tension is uniform in the tension section. and rigid boundary Under the constraints, the first The theoretical sag of each span unit; Indicates the first The measured maximum sag of each span unit; Indicates weight per unit length; Represents the hyperbolic cosine function; This represents the preset denominator parameter. The purpose of the denominator parameter is to prevent the denominator from being 0. In this embodiment, the value of the denominator parameter is 0.001.

[0047] The effects of conductor weight, tension, and elevation difference on sag were comprehensively considered.

[0048] The LM Levenberg-Marquardt iterative algorithm is used to find the optimal tension that minimizes the sum of deviations.

[0049] The LM algorithm is a nonlinear least squares optimization algorithm. The optimal values ​​of the independent variables for solving the function using the LM algorithm are well-known techniques and will not be elaborated further. During the iterative solution process, the distribution consistency weight of the span units is used as the weight of the initial tension characteristic parameters. The initial tension characteristic parameters of all span units are weighted and summed. The weighted sum is used as the initial value of the uniform tension of the tension section during iteration, helping the algorithm converge quickly. During the iterative solution process, the lower bound of the tension search is set to 0, and the upper bound is the rated breaking force of the conductor. When the iteration exceeds the bounds, the midpoint between the upper and lower bounds is used for re-iteration to prevent the algorithm from diverging when encountering extreme abnormal data. Iteration stops when the absolute value of the difference between the sum of deviations of two consecutive iterations is less than a preset threshold, or when the number of iterations reaches the upper limit. In this embodiment, the preset threshold is set to... The maximum number of iterations is set to 100.

[0050] To prevent the algorithm from failing to converge or the calculation results from deviating from physical laws due to extreme data anomalies, a convergence check is performed. Such extreme data anomalies include severe slippage in all gear positions.

[0051] The convergence verification specifically involves determining whether the optimal tension is within 10% to 40% of the conductor's rated breaking force. If it is, the optimal tension passes the convergence verification and is used as the reference tension for the tension section. If it is not, the optimal tension fails the convergence verification, an alarm signal is output, prompting maintenance personnel to check the original point cloud and conductor specifications, and the analysis is terminated to avoid outputting incorrect warning results.

[0052] Convergence verification ensures high reliability and robustness in engineering applications, avoiding the risk of logic collapse.

[0053] It should be noted that in practical engineering applications, there may be situations where the sling cannot converge or yields invalid results due to extremely poor data quality. Therefore, a degradation handling logic is implemented: 1. The number of span units within the tension section is greater than or equal to 4. If it is less than 4, output a short tension section prompt and terminate the algorithm.

[0054] 2. If the number of iterations reaches the upper limit and convergence is still not achieved, or if the optimal tension fails the convergence check, an alarm for calculation anomaly will be output, the analysis will be terminated, and manual intervention will be requested to avoid outputting erroneous warning results.

[0055] At this point, the reference tension of the tension section is obtained.

[0056] Step S003: Based on the difference between the theoretical sag and the measured sag of the span unit, the horizontal span of the span unit, the conductor specifications and parameters, the ambient temperature, and the original point cloud, determine whether the clearance distance is sufficient and whether there is a defect in the span unit. If there is a defect in the span unit, locate the defect.

[0057] The reference tension of the tension section eliminates interference from local anomalies and random measurement errors, and can represent the true mechanical equilibrium state of the tension section at the corresponding acquisition time. Therefore, any geometric feature that significantly deviates from the reference tension of the tension section can be identified as a structural defect. The extreme working condition derived based on the reference tension of the tension section improves the reliability of the derivation.

[0058] The difference between the theoretical sag of the span unit and the measured maximum sag is recorded as the sag deviation value of the span unit.

[0059] The magnitude of the sag deviation value reflects the degree to which the span unit deviates from the reference state, and the positive or negative sign of the sag deviation value reflects the direction of the deviation.

[0060] An anomaly detection threshold is set. In this embodiment, the anomaly detection threshold is set to three times the nominal ranging accuracy of the lidar device. For example, if the ranging accuracy is... Then the anomaly detection threshold will be set to .

[0061] When the absolute value of the sag deviation of the span unit is less than or equal to the abnormal judgment threshold, the span unit is judged to be in normal state, and the deviation originates from random measurement error.

[0062] When the sag deviation of the span unit is less than the negative of the anomaly judgment threshold, the measured maximum sag is significantly less than the theoretical value. It is determined that the span unit has a non-gravity morphological anomaly. At this time, the conductor corresponding to the span unit is subjected to an upward supporting force, which may be affected by tree tops or bird interference. A diagnostic report of foreign object risk is output for the span unit.

[0063] When the sag deviation of the span unit exceeds the abnormal judgment threshold, the measured maximum sag is significantly greater than the theoretical value. It is determined that the span unit has a defect of abnormal line elongation. The actual length of the conductor corresponding to the conductor of the span unit is longer than the theoretical length. It is possible that the clamp slipped, causing the conductor to slip out of the clamp, or there may be a problem of conductor plastic elongation. A diagnostic report of slippage risk of span unit is output.

[0064] To assess the safety risks of the railway line operating under high summer temperatures or full load, the line's morphology under the highest design temperature condition is extrapolated using the reference tension of the tension section. Compared to directly using transient tension measured in a single instance, using the reference tension of the tension section as the starting point for the extrapolation eliminates initial state errors and ensures the accuracy of the prediction results.

[0065] First, the regular span of the tension section is calculated, and this regular span is used as the representative span in the state equation. The formula for calculation is: Based on the conductor specifications and ambient temperature of the tension section, the state equation for the overhead conductor in the ultimate state from ambient temperature condition to the design maximum temperature condition is constructed: in, Indicates weight per unit length; Indicates the cross-sectional area; Indicates the elastic modulus; Indicates the coefficient of linear expansion; Indicates the highest design temperature condition; Indicates ambient temperature; Indicates the reference tension of the tension section; This indicates the predicted tension under high-temperature conditions at the highest design temperature. This indicates the regular span of the tension section.

[0066] The equation of state for overhead conductors describes the tension-elongation relationship of the conductor under temperature changes. Specifically, the left side of the equation of state for overhead conductors represents the state term under the design maximum temperature condition, while the right side represents the difference between the state term and the thermal expansion term under the ambient temperature condition.

[0067] The state equation of the overhead conductor is solved using the Newton-Raphson iterative method to obtain the predicted tension of the line under high-temperature conditions at the design maximum temperature. .

[0068] The spatial morphology of each span unit under extreme high temperature is reconstructed by predicting the tension under high temperature conditions, and a safe distance scan is performed.

[0069] For the Gear spacing unit, combined with its hanging point spatial coordinate parameters A three-dimensional catenary with predicted tension under high-temperature conditions is generated as the high-temperature conductor profile of the span unit. This high-temperature conductor profile represents the position of the conductor corresponding to the span unit under the most dangerous operating condition.

[0070] Finally, the minimum Euclidean distance from all points in the original point cloud to the outline of the high-temperature operating condition conductor is calculated and denoted as the minimum obstacle distance. When the minimum obstacle distance is less than the preset minimum clearance distance, an insufficient clearance distance warning is output, and the corresponding position of the minimum obstacle distance in the original point cloud is marked. This position is the specific intrusion point location and intrusion type, which can be used by maintenance personnel for targeted handling. When the minimum obstacle distance is greater than or equal to the preset minimum clearance distance, a sufficient clearance distance judgment result is output.

[0071] In this embodiment, the minimum clearance distance is set to 5m.

[0072] It is important to note that in the aforementioned calculations, to ensure dimensional consistency, parameters involving physical quantities such as length, force, and stress must be pre-converted to the Standard International System of Units (SI units) to avoid calculation errors caused by dimensional inconsistencies and to ensure the clear physical meaning of the derived state equations. The specific units are as follows: 1. Length parameters are uniformly converted to meters (m) or square meters (m²). 2 For example, if the cross-sectional area is mm 2 It needs to be converted to m 2 Participate in the calculation.

[0073] 2. Force parameters are uniformly converted to Newtons (N) or Newtons per meter (N / m).

[0074] 3. Modulus parameters are uniformly converted to Pascals (Pa, i.e., N / m). 2 ).

[0075] This completes the location of defects in the power grid operation lines.

[0076] This application also proposes a power grid line defect location device based on three-dimensional point clouds, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps described above. Since the method for locating power grid line defects based on three-dimensional point clouds has been described in detail above, it will not be repeated here.

[0077] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0079] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for locating defects on power grid work lines based on three-dimensional point clouds, characterized in that, The method includes the following steps: The original point cloud of the transmission line, conductor specifications and ambient temperature of each tension section are collected. The hanging point cloud and conductor point cloud of each span unit are extracted from the original point cloud. Based on the hanging point cloud and conductor point cloud, the horizontal span, vertical height difference, measured maximum sag and initial tension characteristic parameters of the span unit are calculated. The corresponding distribution consistency weight is determined based on the initial tension characteristic parameters. Based on the conductor specifications, horizontal span, vertical height difference, measured maximum sag, initial tension characteristic parameters, and distribution consistency weight, the theoretical sag of the span unit is calculated, and a deviation summation function is constructed. The deviation summation function is used to evaluate the degree of agreement between the theoretical sag and the measured sag of all span units under the assumed tension. The deviation summation function is iteratively solved to determine the reference tension of the tension section. Based on the difference between the theoretical and measured sag of the span unit, the horizontal span of the span unit, the conductor specifications and parameters, the ambient temperature, and the original point cloud, it is determined whether the clearance distance is sufficient and whether there are defects in the span unit. If there are defects in the span unit, the defects are located.

2. The method for locating power grid operation line defects based on three-dimensional point clouds according to claim 1, characterized in that, The hanging point cloud corresponds to the hanging point at the lower end of the insulator string of the tension section of the base tower, and the conductor cloud corresponds to the suspended portion of the conductor between the two hanging points.

3. The method for locating power grid operation line defects based on three-dimensional point clouds according to claim 1, characterized in that, The horizontal span is the Euclidean distance of the coordinates of the two hanging points at both ends of the span unit projected onto the horizontal plane, and the vertical height difference is the absolute value of the height difference between the coordinates of the two hanging points at both ends of the span unit in the vertical direction.

4. The method for locating power grid operation line defects based on three-dimensional point clouds according to claim 1, characterized in that, The method for obtaining the measured maximum sag is as follows: Project the conductor point cloud onto the vertical plane determined by the two hanging points and the gravity vector corresponding to the span unit. Fit the projected point cloud to the catenary equation to obtain the fitting curve. Record the maximum vertical distance of the fitting curve relative to the line connecting the two hanging points as the measured maximum sag of the corresponding span unit.

5. The method for locating power grid operation line defects based on three-dimensional point clouds according to claim 1, characterized in that, The method for calculating the initial tension characteristic parameter is as follows: The product of the square of the horizontal span of the span unit and the weight per unit length is used as the numerator, and the product of the measured maximum sag of the span unit and the number 8 is used as the denominator. The result of the fraction calculation is used as the initial tension characteristic parameter of the span unit.

6. The method for locating defects in power grid operation lines based on three-dimensional point clouds according to claim 1, characterized in that, The method for obtaining the distribution consistency weight is as follows: The sum of the standard deviation of the initial tension characteristic parameters of all the gear units and the preset small correction amount is denoted as the adaptive bandwidth. Based on the difference of the initial tension characteristic parameters of the gear units and the adaptive bandwidth, the affinity matrix of the eigenvector centrality algorithm is constructed based on the Gaussian kernel function. The affinity matrix is ​​eigenvalued and the eigenvector corresponding to the largest eigenvalue is normalized to obtain the distribution consistency weights of each range unit.

7. The method for locating defects in power grid operation lines based on three-dimensional point clouds according to claim 1, characterized in that, The method for determining whether the gear unit has a defect is as follows: The difference between the theoretical sag of the span unit and the measured maximum sag is recorded as the sag deviation value of the span unit. When the absolute value of the sag deviation of the gauge unit is greater than the abnormality judgment threshold, the gauge unit is judged to have a defect; otherwise, the gauge unit is judged not to have a defect.

8. The method for locating defects in power grid operation lines based on three-dimensional point clouds according to claim 1, characterized in that, The method for determining whether the clearance distance is sufficient is as follows: The regular span of the tension section is calculated based on the horizontal span of the span unit and used as the representative span of the state equation. Combining the conductor specification parameters and ambient temperature of the tension section, the state equation of the overhead conductor of the tension section is constructed from the ambient temperature condition to the design maximum temperature condition. The Newton iteration method is used to solve the state equation of the overhead conductor to obtain the predicted tension of the line under the high temperature condition of the design maximum temperature condition. Based on the horizontal span and vertical height difference of the span unit, the high-temperature operating condition conductor outline of the span unit is generated. Calculate the minimum Euclidean distance from all points in the original point cloud to the high-temperature working condition conductor profile, and denot it as the minimum obstacle distance; When the minimum distance to the obstacle is less than the preset minimum clearance distance, the clearance distance is insufficient; otherwise, the clearance distance is sufficient.

9. The method for locating power grid operation line defects based on three-dimensional point clouds according to claim 8, characterized in that, The defect is the location of the minimum obstacle distance in the original point cloud.

10. A power grid line defect location device based on three-dimensional point cloud, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the power grid operation line defect location method based on three-dimensional point cloud as described in any one of claims 1 to 9.

Images