Method for measuring a crossing line in an overhead power transmission line

Abstract

This invention relates to a measurement method for crossing lines in overhead transmission lines, comprising the following steps: collecting geographical data, meteorological data, and parameter data of the area where the crossing line is located, and preprocessing the data; calculating the conductor sag of the crossing line based on the meteorological data and parameter data, and calculating the conductor height based on the conductor sag; calculating the initial net distance of the crossing line based on the conductor height; determining the structure type of the crossing through the geographical data of the area where the crossing line is located, and correcting the initial net distance of the crossing line by setting a corresponding structure correction value according to the structure type, thereby obtaining the final net distance of the crossing line.

Description

Technical Field

[0001] This invention relates to a method for measuring crossing lines in overhead transmission lines, belonging to the field of transmission line measurement technology. Background Technology

[0002] With the continuous growth of electricity demand in modern society, the scale of power transmission line construction and renovation is expanding daily. Especially in environments with complex terrain and dense infrastructure, the planning and construction of overhead power transmission lines face severe challenges. During construction, transmission lines frequently need to cross important infrastructure such as highways, railways, high-voltage lines, and gas pipelines. These crossing areas impose extremely stringent safety clearance requirements on the lines; even the slightest error can lead to serious safety hazards. Therefore, how to accurately and reliably measure and predict the safety clearance of transmission lines in these crossing areas has become a key technical challenge in current power transmission line construction. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention proposes a method for measuring crossing lines in overhead transmission lines.

[0004] The technical solution of the present invention is as follows:

[0005] On one hand, the present invention provides a method for measuring crossing lines in overhead transmission lines, comprising the following steps:

[0006] Collect geographical data, meteorological data, and parameter data of the area where the crossing line is located, and preprocess the data.

[0007] Based on meteorological and parameter data, the sag of the conductors crossing the line is calculated, and the conductor height is calculated based on the sag.

[0008] Calculate the initial net distance between crossing lines based on the conductor height of the crossing lines;

[0009] The type of structure crossed is determined by the geographical data of the area where the crossing line is located, and the initial net distance of the crossing line is corrected by setting the corresponding structural correction value according to the structural type, so as to obtain the final net distance of the crossing line.

[0010] In a preferred embodiment of the present invention, the formula for calculating the conductor sag s of the crossing line is:

[0011]

[0012] Where: w represents the weight per unit length of the conductor; L represents the span distance; T represents the conductor tension; α represents the conductor expansion coefficient; ΔT represents the temperature change; q wIndicates the regional average wind pressure; v w This indicates the average wind speed in the area.

[0013] In a preferred embodiment of the present invention, the formula for calculating the conductor height H is:

[0014] H = H g -sH terrain

[0015] Wherein: H g H represents the height of the support point. terrain This indicates the average elevation undulation of the ground in the area.

[0016] In a preferred embodiment of the present invention, the initial net distance calculation formula for the crossing line is as follows:

[0017]

[0018] Where: d x Δx represents the horizontal distance on the ground, that is, the horizontal projection distance between the two support points of the conductor on the ground; Δx represents the wind deflection correction.

[0019] In a preferred embodiment of the present invention, the final net distance D of the crossing line is calculated using the following formula:

[0020] D = D ′ min +K struct

[0021] Where: K struct This is a structural correction value.

[0022] In a preferred embodiment of the present invention, a machine learning model is constructed. The machine learning model is trained using actual measurement data of the crossing lines, as well as corresponding geographical data, meteorological data, and parameter data. The trained machine learning model outputs a correction factor to correct the final net distance of the crossing lines, as shown in the following formula:

[0023] D = D ′ min +K struct +D ML

[0024] Where: D ML This is a correction factor.

[0025] On the other hand, the present invention also provides a measurement system for crossing lines in overhead transmission lines, including a data acquisition module, a conductor height calculation module, an initial net distance calculation module, and a correction module;

[0026] The data acquisition module is used to collect geographical data, meteorological data, and parameter data of the area where the crossing line is located, and to preprocess the data.

[0027] The conductor height calculation module is used to calculate the conductor sag of crossing lines based on meteorological data and parameter data, and to calculate the conductor height based on the conductor sag.

[0028] The initial net distance calculation module is used to calculate the initial net distance of the crossing line based on the conductor height of the crossing line;

[0029] The correction module is used to determine the type of structure crossed by using geographical data of the area where the crossing line is located, and to correct the initial net distance of the crossing line by setting a corresponding structural correction value according to the structural type, so as to obtain the final net distance of the crossing line.

[0030] In a preferred embodiment of the present invention, the formula for calculating the conductor sag s of the crossing line is:

[0031]

[0032] Where: w represents the weight per unit length of the conductor; L represents the span distance; T represents the conductor tension; α represents the conductor expansion coefficient; ΔT represents the temperature change; q w Indicates the regional average wind pressure; v w This indicates the average wind speed in the area.

[0033] In another aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in any embodiment of the present invention.

[0034] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0035] The present invention has the following beneficial effects:

[0036] 1. This invention can dynamically adjust the prediction model according to different construction environments, such as intersections of highways, railways, and gas pipelines. At the same time, by inputting conductor parameters, terrain data, and meteorological information in real time, the machine learning model can adapt to different working conditions and continuously optimize the prediction results, ensuring the accuracy of the results. Attached Figure Description

[0037] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0038] 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.

[0039] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0040] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0041] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0042] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0043] Example 1:

[0044] See Figure 1 A method for measuring lines crossing each other in an overhead transmission line, comprising the following steps:

[0045] Collect geographical data, meteorological data, and parameter data of the area where the crossing line is located, and preprocess the data.

[0046] Based on meteorological and parameter data, the sag of the conductors crossing the line is calculated, and the conductor height is calculated based on the sag.

[0047] Calculate the initial net distance between crossing lines based on the conductor height of the crossing lines;

[0048] The type of structure crossed is determined by the geographical data of the area where the crossing line is located, and the initial net distance of the crossing line is corrected by setting the corresponding structural correction value according to the structural type, so as to obtain the final net distance of the crossing line.

[0049] In a preferred embodiment of this invention, the formula for calculating the conductor sag s of the crossing line is:

[0050]

[0051] Where: w represents the weight per unit length of the conductor; L represents the span distance; T represents the conductor tension; α represents the conductor expansion coefficient; ΔT represents the temperature change; q w Indicates the regional average wind pressure; v w This indicates the average wind speed in the area.

[0052] In a preferred embodiment of this invention, the formula for calculating the conductor height H is as follows:

[0053] H = H g -sH terrain

[0054] Wherein: H g Indicates the height of the conductor support point (such as a tower); H terrain This represents the average elevation difference of the ground surface in the area crossed by the transmission line, which is the vertical difference between the ground surface and the horizontal plane. This value reflects whether the terrain crossed by the line has slopes, hills, or other undulations. It is very important in calculating the clearance between the transmission line and the ground, buildings, etc., because complex terrain can affect the actual vertical clearance between the transmission line and the ground, buildings, etc.

[0055] In a preferred embodiment of this invention, the initial net distance calculation formula for the crossing line is as follows:

[0056]

[0057] Where: d x Δx represents the horizontal distance on the ground, which is the horizontal projection distance between two support points of the conductor on the ground. It does not consider the height undulation of the terrain and only measures the straight-line distance between the two points in the horizontal direction. In practical applications, the horizontal distance on the ground is used to evaluate the span of the conductor. Combined with parameters such as conductor height and sag, the horizontal distance on the ground helps to determine the safe clearance of the conductor in the crossing area; Δx represents the wind deflection correction amount.

[0058] In a preferred embodiment of this invention, the final net distance D of the crossing line is calculated using the following formula:

[0059] D = D ′ min +K struct

[0060] Where: K struct As a structural correction value, in this embodiment, K struct Determined based on the type of structure being crossed, specifically:

[0061] Conventional highway: K struct = 1.5 meters;

[0062] Highway: K struct = 1 meter;

[0063] Conventional railway: K struct = 1.5 meters;

[0064] High-speed rail: K struct = 2 meters;

[0065] High-voltage line: K struct = 1 meter;

[0066] Gas pipeline: K struct = 0.8 meters;

[0067] In a preferred embodiment of this invention, a machine learning model is constructed. This model is trained using actual measurement data of the crossing lines, along with corresponding geographical, meteorological, and parameter data. The trained machine learning model outputs a correction factor to adjust the final net distance of the crossing lines, as shown in the following formula:

[0068] D = D ′ min +K struct +D ML

[0069] Where: D ML As a correction factor;

[0070] The training steps for this machine learning model are as follows:

[0071] 1. Data Collection

[0072] First, construct a dataset containing multiple features. The collected feature data may include:

[0073] Physical characteristics: conductor tension, sag, span, conductor weight, expansion coefficient, wind deflection, etc.

[0074] Meteorological characteristics: real-time wind speed, temperature, humidity, air pressure, etc.

[0075] Topographic features: changes in elevation and surface types (such as highways, railways, buildings, etc.) across the region.

[0076] Structural characteristics of crossings: the specific type of crossing (highway, railway, gas pipeline, etc.) and the required safety clearance specifications.

[0077] Simultaneously, a large amount of historical measurement data was collected, including net distances calculated using physical formulas and actual measured net distances. This data will provide sufficient samples for machine learning models.

[0078] 2. Machine learning model training

[0079] 2.1 Model Selection

[0080] Based on the characteristics of the net distance prediction problem, the following machine learning models can be selected:

[0081] Regression model: used to predict the magnitude of the correction factor.

[0082] Random forest regression: It can handle non-linear relationships and is suitable for handling a large number of features.

[0083] Gradient Boosting Decision Tree (GBDT): A high-precision tree model capable of handling complex input features.

[0084] Support Vector Regression (SVR): Suitable for medium-sized datasets, capable of handling both linear and nonlinear regression problems.

[0085] 2.2 Feature Engineering

[0086] Constructing and selecting effective features is a crucial step in training machine learning models. Common features include:

[0087] Conductor parameters: such as conductor tension, weight per unit length, span length, etc.

[0088] Meteorological conditions: factors such as wind speed and temperature that affect the sag and height of the conductor.

[0089] Topographic parameters: terrain elevation and terrain undulation across the region.

[0090] Structural characteristics: The safety clearance required by the structure type (such as highways, high-voltage lines, gas pipelines, etc.) and its specifications.

[0091] By using feature selection and feature interaction (such as the interaction between wind speed and guide sag), key features that can significantly affect net distance are extracted, thereby improving the model's predictive ability.

[0092] 2.3 Model Training

[0093] The input feature data and the actual measured safety clearance (label) are used as the training set, and a machine learning model learns the relationship between the features and the clearance. Through training, the model will gradually find the optimal combination of parameters and be able to predict the clearance correction value.

[0094] 2.4 Model Evaluation and Optimization

[0095] The predictive performance of a model is evaluated using methods such as cross-validation and test sets. Commonly used evaluation metrics include:

[0096] Mean Squared Error (MSE): Measures the error between the predicted value and the actual value.

[0097] Mean Absolute Error (MAE): Measures the absolute error between the predicted and actual values.

[0098] R 2 Scoring: Measures the model's explanatory power.

[0099] The predictive performance of the model can be optimized by adjusting its hyperparameters (such as tree depth, number of leaf nodes, etc.).

[0100] After the model is trained, new real-time data (such as line parameters, weather conditions, terrain features, etc.) are input, and the machine learning model will predict the correction factor, which is the value that compensates or corrects the physical calculation results.

[0101] Example 2:

[0102] A measurement system for crossing lines in an overhead transmission line, characterized in that it includes a data acquisition module, a conductor height calculation module, an initial net distance calculation module, and a correction module;

[0103] The data acquisition module is used to collect geographical data, meteorological data, and parameter data of the area where the crossing line is located, and to preprocess the data.

[0104] The conductor height calculation module is used to calculate the conductor sag of crossing lines based on meteorological data and parameter data, and to calculate the conductor height based on the conductor sag.

[0105] The initial net distance calculation module is used to calculate the initial net distance of the crossing line based on the conductor height of the crossing line;

[0106] The correction module is used to determine the type of structure crossed by using geographical data of the area where the crossing line is located, and to correct the initial net distance of the crossing line by setting a corresponding structural correction value according to the structural type, so as to obtain the final net distance of the crossing line.

[0107] In a preferred embodiment of this invention, the formula for calculating the conductor sag s of the crossing line is:

[0108]

[0109] Where: w represents the weight per unit length of the conductor; L represents the span distance; T represents the conductor tension; α represents the conductor expansion coefficient; ΔT represents the temperature change; q w Indicates the regional average wind pressure; v w This indicates the average wind speed in the area.

[0110] This system is used to implement the method in Embodiment 1, and will not be described in detail here.

[0111] Example 3:

[0112] This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described in any embodiment of the present invention.

[0113] Example 4:

[0114] This embodiment proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0115] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0116] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. 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.

[0117] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0118] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0119] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for measuring crossing lines in overhead transmission lines, characterized in that, Includes the following steps: Collect geographical data, meteorological data, and parameter data of the area where the crossing line is located, and preprocess the data. Based on meteorological and parameter data, the sag of the conductors crossing the line is calculated, and the conductor height is calculated based on the sag. Calculate the initial net distance between crossing lines based on the conductor height of the crossing lines; The type of structure crossing is determined by the geographical data of the area where the crossing line is located, and the initial net distance of the crossing line is corrected by setting the corresponding structural correction value according to the structural type, so as to obtain the final net distance of the crossing line. The conductor sag of the crossing line The calculation formula is: in: This indicates the weight per unit length of the conductor; Indicates the length of the distance spanned; Indicates conductor tension; Indicates the coefficient of thermal expansion of the conductor; Indicates the amount of temperature change; Indicates the regional average wind pressure; Indicates the average wind speed in the area; The height of the conductor The calculation formula is: in: Indicates the height of the support point; Indicates the average elevation of the ground in the area; The formula for calculating the initial net distance of the crossing line is: in: This indicates the horizontal distance on the ground, that is, the horizontal projection distance between two support points of the conductor on the ground; Indicates the windage correction amount; The final net distance of the crossing line The calculation formula is: in: This is a structural correction value; A machine learning model is constructed and trained using actual measurement data of the crossing route, as well as corresponding geographical, meteorological, and parameter data. The trained machine learning model outputs a correction factor to adjust the final net distance of the crossing route, as shown in the following formula: in: This is a correction factor.

2. A measurement system for crossing lines in an overhead transmission line, characterized in that, The system for implementing the method as described in claim 1 includes a data acquisition module, a conductor height calculation module, an initial net distance calculation module, and a correction module. The data acquisition module is used to collect geographical data, meteorological data, and parameter data of the area where the crossing line is located, and to preprocess the data. The conductor height calculation module is used to calculate the conductor sag of crossing lines based on meteorological data and parameter data, and to calculate the conductor height based on the conductor sag. The initial net distance calculation module is used to calculate the initial net distance of the crossing line based on the conductor height of the crossing line; The correction module is used to determine the type of structure crossed by using geographical data of the area where the crossing line is located, and to correct the initial net distance of the crossing line by setting a corresponding structural correction value according to the structural type, so as to obtain the final net distance of the crossing line.

3. The measurement system for crossing lines in an overhead transmission line according to claim 2, characterized in that, The conductor sag of the crossing line The calculation formula is: in: This indicates the weight per unit length of the conductor; Indicates the length of the distance spanned; Indicates conductor tension; Indicates the coefficient of thermal expansion of the conductor; Indicates the amount of temperature change; Indicates the regional average wind pressure; This indicates the average wind speed in the area.

4. An electronic device, 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 program, it implements the method as described in claim 1.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in claim 1.

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