Early warning method for conductor galloping on transmission towers combined with a multi-factor coupling model

Abstract

This application relates to the field of transmission line galloping early warning technology, specifically to a method for early warning of transmission line conductor galloping based on a multi-factor coupling model. The method includes: comparing the differences between all class feature values ​​and their effective values ​​for each conductor at the current moment to determine the bearing deviation; comparing the deviation between the displacement velocity of each point on each conductor in any direction and the fitting result to determine the conductor galloping transmission value; analyzing the distance from each point on each conductor to the transmission tower at the current moment, and combining the bearing deviation and the conductor galloping transmission value to determine the conductor galloping judgment value, thereby obtaining corrected displacement data; and using sparse regression to obtain a conductor galloping model for early warning of conductor galloping. This application aims to improve the accuracy of early warning for transmission line conductor galloping.

Description

Technical Field

[0001] This application relates to the field of transmission line galloping early warning technology, specifically to a transmission tower conductor galloping early warning method that combines a multi-factor coupling model. Background Technology

[0002] Transmission towers are crucial structures in power systems used to support and secure transmission lines. Typically constructed of steel or concrete, they fulfill the critical task of transmitting electrical energy from power plants to end-users. The location of transmission towers is often determined by the specific terrain, and due to the limitations imposed by power plants, they are usually built in areas with significant elevation differences and undulating terrain. In cold climates, transmission lines are prone to icing due to rain, snow, and freezing rain. Icing alters the aerodynamic properties of the conductors, leading to conductor galloping under wind loads. Conductor galloping is a low-frequency, high-amplitude periodic vibration that can cause conductor fatigue damage, short circuits between adjacent conductors, arcing, and even structural damage to the transmission tower. Therefore, an early warning method based on conductor galloping of transmission towers is needed.

[0003] Currently, the analysis of conductor galloping on transmission towers only considers the vertical-horizontal coupled galloping mode, neglecting the axial galloping phenomenon under icing conditions. In actual operation, the failure to fully consider the influence of axial conductor galloping may result in low accuracy of conductor galloping analysis, leading to the early warning system's inability to accurately predict the dynamic behavior of the conductors and reducing the accuracy of conductor galloping early warning. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method for early warning of conductor galloping on transmission towers that incorporates a multi-factor coupling model, thereby resolving the existing issues.

[0005] The transmission tower conductor galloping early warning method combining a multi-factor coupling model in this application adopts the following technical solution:

[0006] One embodiment of this application provides a method for early warning of conductor galloping on transmission towers that incorporates a multi-factor coupling model. The method includes the following steps:

[0007] Data acquisition: Real-time acquisition of various characteristic values ​​of each conductor on the transmission tower, displacement data and displacement velocity of all points on each conductor in different directions, as well as real-time wind speed and wind direction;

[0008] Galloping analysis: This involves analyzing the distribution of various characteristic values ​​and displacement deviations of the conductor to determine the corrected displacement data. Specifically:

[0009] Analyze the distribution of the squares of various characteristic values ​​of each conductor within a preset time period before the current moment, determine the effective value of various characteristic values ​​of each conductor at the current moment, compare the differences between all characteristic values ​​of each conductor at the current moment and their effective values, and determine the load-bearing deviation of each conductor at the current moment.

[0010] Fit the displacement velocity of all points on each conductor in any direction at the current moment, compare the deviation of the displacement velocity of each point on each conductor in any direction with the fitting result, and determine the conductor galloping transmission value of each point on each conductor in any direction at the current moment.

[0011] Analyze the distance from each point on each conductor to the transmission tower at the current moment, and combine the bearing deviation and the conductor galloping transmission value to determine the conductor galloping judgment value in any direction at each point on each conductor at the current moment.

[0012] Based on the conductor galloping determination value, for any direction, abnormal position points are selected from all position points on each conductor at the current time, and the displacement of all abnormal position points in any direction is corrected to obtain the corrected displacement data of all position points on each conductor in any direction at the current time.

[0013] Conductor health management: Sparse regression is used to model the corrected displacement data of all points on each conductor in any direction at the current moment, all class feature values ​​of each conductor, as well as wind speed and wind direction, to obtain the conductor galloping model of each conductor at the current moment, and to provide early warning of conductor galloping.

[0014] Preferably, the feature values ​​include at least: tensile force, tilt angle, and axial angle.

[0015] Preferably, the effective value of each characteristic value of each conductor at the current moment is the square root of the mean of the squares of each characteristic value of each conductor within a preset time period before the current moment.

[0016] Preferably, the expression for the load-bearing deviation of each conductor at the current moment is: In the formula, This represents the load-bearing deviation of the j-th conductor at the current moment; This represents the effective value of the i-th type of characteristic value of the j-th conductor at the current time. This represents the i-th type of feature value of the j-th conductor at the current time; n represents the total number of feature value categories.

[0017] Preferably, the expression for the conductor galloping transmission value at each position point on each conductor in any direction at the current moment is: In the formula, This represents the conductor galloping transmission value of position k on the j-th conductor in direction m at the current moment; This represents the deviation between the displacement velocity of point k on the j-th conductor in direction m at the current moment and the fitting result; This represents the average deviation of the displacement velocity of point k on the j-th conductor in all directions from the fitted result at the current moment.

[0018] Preferably, the expression for the conductor galloping determination value of each position point on each conductor in any direction at the current moment is: In the formula, This represents the conductor galloping determination value of position point k on the j-th conductor in direction m at the current moment; represents the distance from point k on the j-th conductor to the transmission tower at the current moment; ln() represents the logarithmic function with the natural constant as the argument; norm[] represents the normalization function.

[0019] Preferably, the method for obtaining the abnormal location points is as follows:

[0020] The galloping judgment value of all positions on each conductor in any direction at the current moment is used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. Positions with galloping judgment values ​​greater than the segmentation threshold are recorded as abnormal position points of each conductor in any direction at the current moment.

[0021] Preferably, the method for obtaining the corrected displacement data of all positions on each conductor in any direction at the current moment is as follows:

[0022] At the current moment, among all positions on each conductor, the displacement data at non-abnormal positions remains unchanged, and the displacement data at abnormal positions in direction m is removed. The missing displacement data at abnormal positions is filled by nonlinear interpolation. The filled data is used as the corrected displacement data at abnormal positions. By traversing all directions, the corrected displacement data of all positions on each conductor in any direction at the current moment is obtained.

[0023] Preferably, the method for obtaining the conductor galloping model of each conductor at the current moment is as follows:

[0024] The corrected displacement data of all points on each conductor in any direction at the current moment, all class feature values ​​of each conductor, and wind speed and direction are used as inputs for sparse regression. The output equation serves as the conductor galloping model for each conductor at the current moment.

[0025] Preferably, the method for providing early warning of conductor galloping includes:

[0026] The neural network takes the conductor galloping model of all conductors at the current moment as input and outputs the distance between each pair of conductors. If the distance between each pair of conductors at the current moment is less than the preset safe distance threshold, a conductor galloping safety warning is triggered; otherwise, the conductor galloping safety warning is not triggered.

[0027] This application has at least the following beneficial effects:

[0028] This application collects relevant data on conductor galloping in real time, analyzes the distribution of conductor characteristic values ​​and displacement deviation, and constructs a bearing deviation to reflect the impact of conductor galloping amplitude on transmission towers. This allows for timely detection of abnormalities during conductor galloping, enabling corresponding measures to ensure the safe operation of transmission lines. Furthermore, by analyzing the displacement characteristics of the conductor in different directions, a conductor galloping transmission value is constructed, reflecting the galloping transmission characteristics between various locations. This helps in judging abnormalities in conductor galloping and improves the accuracy of conductor galloping early warning, thus ensuring the safe operation of transmission lines. Further, by integrating the bearing deviation, conductor galloping transmission value, and distance from the location point to the transmission tower, a conductor galloping judgment value is constructed, quantifying the severity of conductor galloping and enabling timely detection of abnormalities, thus improving the accuracy of conductor galloping early warning. Finally, based on the conductor galloping judgment value, displacement data is corrected to obtain a conductor galloping model, thereby predicting the development trend of conductor galloping and improving the accuracy of conductor galloping early warning. This application combines a multi-factor coupling model to predict and warn of conductor galloping on transmission towers, thereby improving the accuracy of early warning for conductor galloping on transmission towers. Attached Figure Description

[0029] 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.

[0030] Figure 1 A flowchart illustrating the steps of a transmission tower conductor galloping early warning method combining a multi-factor coupling model, as provided in one embodiment of this application;

[0031] Figure 2 This is a schematic diagram illustrating the process of acquiring correction displacement data according to an embodiment of this application. Detailed Implementation

[0032] 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 transmission tower conductor galloping early warning method based on a multi-factor coupling model 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.

[0033] 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.

[0034] The following description, in conjunction with the accompanying drawings, details the specific scheme of the transmission tower conductor galloping early warning method provided in this application, which combines a multi-factor coupling model.

[0035] This application provides an embodiment of a transmission tower conductor galloping early warning method incorporating a multi-factor coupling model. Specifically, the method is described below. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0036] Step S1: Data Acquisition: Real-time acquisition of various characteristic values ​​of each conductor on the transmission tower, displacement data and displacement velocity of all points on each conductor in different directions, as well as real-time wind speed and wind direction.

[0037] To achieve accurate early warning of conductor galloping in transmission towers, data collection on the conductor conditions on the transmission towers is required, specifically:

[0038] Tension detectors, tilt detectors, and fiber optic gyroscopes are used to collect various characteristic values ​​of each conductor on the transmission tower in real time, namely tension value, tilt angle value, and axial angle. At the same time, position points are set at equal intervals on the conductor, and displacement sensors are used to acquire displacement data and displacement velocity of all position points on each conductor in different directions in real time. Wind speed and wind direction sensors are used to collect wind speed and wind direction in the environment where the conductor is located in real time, providing basic data support for conductor health management and galloping early warning.

[0039] It should be noted that, since the conductor galloping process is often a low-frequency galloping with a large amplitude, the data acquisition frequency in this embodiment is 1Hz and the interval between the location points is 0.5m. The implementer can also set the acquisition frequency and the interval between the location points according to the specific situation. This embodiment does not impose any special restrictions.

[0040] Thus, by collecting real-time data on conductor displacement, wind speed, and wind direction, the galloping state of the conductors is continuously monitored. Data analysis techniques are used to conduct in-depth analysis of various characteristic values ​​of the conductors to identify their normal operating range and potential abnormal patterns, thereby issuing early warnings to maintenance personnel to conduct health management of the conductors.

[0041] Step S2: Galloping Analysis: Analyze the distribution of various characteristic values ​​of the conductor and the displacement deviation to determine the correction displacement data.

[0042] Traditional conductor galloping modeling assumes the conductor has a circular cross-section. Ice accumulation only changes the conductor's weight; the actual aerodynamic conditions remain unchanged. The conductor is also considered rigid, resulting in only vertical and horizontal movement at the same location. However, in reality, adjacent transmission towers allow for a certain length of conductor during installation to mitigate the effects of thermal expansion and contraction. When the conductor is iced, gravity creates an ice-covered section on the back side, affecting its aerodynamic configuration. This leads to axial movement during actual conductor galloping, necessitating three-degree-of-freedom modeling of the conductor's movement.

[0043] Furthermore, limitations in data sensor acquisition accuracy and environmental noise lead to significant deviations between the accuracy of the conductor galloping model and the actual galloping situation. Therefore, it is necessary to analyze the distribution of various characteristic values ​​of the conductor and the displacement deviation to determine the correction displacement data, thereby improving the accuracy of model building. The specific process is as follows:

[0044] S201: Analyze the distribution of the squares of various characteristic values ​​of each conductor within a preset time period before the current moment, determine the effective value of various characteristic values ​​of each conductor at the current moment, compare the differences between all characteristic values ​​of each conductor at the current moment and their effective values, and determine the load-bearing deviation of each conductor at the current moment.

[0045] In the process of modeling conductor galloping, the accuracy of the final galloping model depends on the accuracy of the conductor displacement data. However, in the actual data acquisition process, the data is easily affected by the terrain distribution between transmission towers and wind vibration, resulting in low data accuracy when modeling conductor galloping. In particular, the axial motion of the conductor is often small during the galloping process, making it more susceptible to environmental noise. Therefore, it is necessary to preprocess the acquired data.

[0046] It should be noted that, under windless, stationary, and icing-free conditions, the shape and weight of the six-split conductor are assumed to be consistent, and the weather and rainfall conditions meet the basic conditions for conductor icing.

[0047] Taking a single conductor as an example, the characteristic values ​​collected at the connection points between each conductor and the transmission tower include tension, tilt angle, and axial angle. These characteristic values ​​reflect the degree of conductor galloping. When the conductor is subjected to wind force and gallops, it exhibits an approximately periodic galloping characteristic, causing the characteristic values ​​to show approximately periodic fluctuations. Therefore, the greater the fluctuation of the characteristic values ​​over a period of time, and the greater the difference from the initial value of the conductor's characteristics, the greater the conductor galloping.

[0048] Therefore, based on the above analysis, by analyzing the distribution of various characteristic values, the load-bearing deviation of each conductor at the current moment is determined as follows:

[0049] Take the square root of the mean of the squares of all characteristic values ​​of each conductor within the preset time period before the current time, and record it as the effective value of all characteristic values ​​of each conductor at the current time.

[0050] It should be noted that the preset duration is set manually. In this embodiment, the preset duration is 5 minutes. Implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0051] Furthermore, based on the differences between the various characteristic values ​​and their effective values ​​of each conductor at the current moment, the load-bearing deviation of each conductor at the current moment is determined, specifically expressed as follows:

[0052] Bearing deviation of the j-th conductor at the current moment The expression is: In the formula, This represents the effective value of the i-th type of characteristic value of the j-th conductor at the current time. This represents the i-th type of feature value of the j-th conductor at the current time; n represents the total number of feature value categories.

[0053] Analysis of the load-bearing deviation of each conductor at the current moment reveals the following: The greater the difference between the effective value of the i-th type of characteristic value of the j-th conductor and its corresponding characteristic value at the current moment, the greater the load-bearing deviation, meaning a larger amplitude of conductor galloping, which may cause a greater dynamic load on the tower structure. Conversely, the smaller the difference between the effective value of the i-th type of characteristic value of the j-th conductor and its corresponding characteristic value at the current moment, the smaller the load-bearing deviation, meaning a smaller amplitude of conductor galloping, less impact on the tower structure, and higher safety.

[0054] Thus, by accurately calculating the conductor's load-bearing deviation, a quantitative indicator for conductor galloping early warning is provided, which helps to identify potential safety risks of conductor galloping, conduct timely health management of the conductor, and ensure the stable operation of transmission tower conductors.

[0055] S202: Fit the displacement velocity of all points on each conductor in any direction at the current moment, compare the deviation of the displacement velocity of each point on each conductor in any direction with the fitting result, and determine the conductor galloping transmission value of each point on each conductor in any direction at the current moment.

[0056] During the actual galloping of conductors between transmission towers, since the conductor is a whole, the galloping displacement and velocity between different points on a single conductor have certain transmission characteristics. That is, the galloping situation at a certain point on a single conductor is at a certain moment in the entire galloping cycle.

[0057] Based on the above analysis, by comparing the deviation between the displacement velocity of each point on each conductor in any direction and the fitting result, the conductor galloping transmission value of each point on each conductor in any direction at the current moment is determined, specifically:

[0058] The displacement velocity of all points on each conductor in any direction at the current moment is fitted. In this embodiment, the least squares fitting method is used to fit the displacement velocity. In practical applications, as other implementation methods, implementers may also use other fitting methods such as polynomial fitting according to specific circumstances. This embodiment does not impose any special restrictions on the selection of fitting methods.

[0059] The least squares fitting method is a well-known technique, and its specific fitting principle and process will not be elaborated here.

[0060] Furthermore, the conductor galloping transmission value is determined, and the specific expression is as follows:

[0061] The conductor galloping transfer value at position k on the j-th conductor in direction m at the current moment. The expression is: In the formula, This represents the deviation between the displacement velocity of point k on the j-th conductor in direction m at the current moment and the fitting result; This represents the average deviation of the displacement velocity of point k on the j-th conductor in all directions from the fitted result at the current moment.

[0062] It should be noted that the deviation between the displacement velocity and the fitting result in the expression for the conductor galloping transmission value is the result of taking the absolute value of the difference between the displacement velocity and the fitting result.

[0063] It should be noted that a single location point may have three degrees of freedom for displacement. In this embodiment, at least the horizontal x-direction, the axial y-direction, and the vertical z-direction are considered.

[0064] Based on the conductor galloping transmission values ​​of each point on each conductor in any direction at the current moment, it can be understood that if the difference between the deviation of the displacement velocity of point k on the j-th conductor in direction m and the fitting result and the mean of the deviation is smaller, then the conductor galloping transmission value of point k on the j-th conductor in direction m is smaller, indicating that the displacement velocity measured at point k in direction m is normal; conversely, if the difference between the deviation of the displacement velocity of point k on the j-th conductor in direction m and the fitting result and the mean of the deviation is larger, then the conductor galloping transmission value of point k on the j-th conductor in direction m is larger, indicating that the displacement velocity measured at point k in direction m is abnormal.

[0065] Thus, by comparing the deviation between the actual displacement linear velocity and the fitting result, the conductor galloping transmission value is calculated, which helps to monitor the real-time status of the conductor. This is crucial for conductor galloping early warning and conductor health management, ensuring the safe and stable operation of transmission lines.

[0066] S203: Analyze the distance from each position point on each conductor to the transmission tower at the current moment, and combine the bearing deviation and the conductor galloping transmission value to determine the conductor galloping judgment value of each position point on each conductor in any direction at the current moment.

[0067] During the transmission of conductor galloping, the galloping parameters between individual points are similar across the entire conductor. The greater the deviation of the conductor galloping transfer function, the greater the possibility of anomalies in the measurement values ​​in that direction, requiring filtering and screening.

[0068] Therefore, by analyzing the distance from each point on each conductor to the transmission tower at the current moment, and combining the bearing deviation and the conductor galloping transmission value, the conductor galloping judgment value in any direction at each point on each conductor at the current moment is determined, specifically as follows:

[0069] The current moment's conductor galloping determination value at position k on the j-th conductor in direction m The expression is:

[0070] In the formula, represents the distance from point k on the j-th conductor to the transmission tower at the current moment; ln() represents the logarithmic function with the natural constant as the argument; norm[] represents the normalization function.

[0071] Based on the conductor galloping judgment values ​​at each position point on each conductor in any direction at the current moment, it can be understood that if the load-bearing deviation of the j-th conductor is larger at the current moment, it indicates abnormal tension of the conductor on the tower, which is more likely to be caused by abnormal conductor galloping. If the conductor galloping transmission value of position point k on the j-th conductor in direction m is larger at the current moment, it indicates abnormal conductor galloping at position point k in direction m. Moreover, the smaller the distance, the closer position point k is to the transmission tower, and the more the conductor galloping is constrained by the tower. Therefore, the larger the conductor galloping judgment value obtained in the summary, the more serious the galloping problem is. Conversely, if the load-bearing deviation of the j-th conductor is smaller at the current moment, it indicates that the tension of the conductor on the tower is close to normal. If the conductor galloping transmission value of position point k on the j-th conductor in direction m is smaller at the current moment, it indicates that the conductor galloping at position point k in direction m is normal. Moreover, the larger the distance, the farther position point k is from the transmission tower, and the more free the conductor galloping is. Therefore, the smaller the conductor galloping judgment value obtained in the summary, the less serious the galloping problem is.

[0072] Thus, based on three key factors—distance from the location point to the tower, load-bearing deviation, and conductor galloping transmission value—the degree of abnormal conductor galloping in any direction has been quantified, and conductor galloping judgment value has been obtained. This can effectively monitor the health status of conductors, provide a basis for conductor galloping early warning and health management, and ensure the safety of transmission lines.

[0073] S204: Based on the conductor galloping determination value, for any direction, select abnormal position points from all position points on each conductor at the current time, correct the displacement of all abnormal position points in any direction, and obtain the corrected displacement data of all position points on each conductor in any direction at the current time.

[0074] The galloping judgment value of all positions on each conductor in any direction at the current moment is used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. Positions with galloping judgment values ​​greater than the segmentation threshold are recorded as abnormal position points of each conductor in any direction at the current moment.

[0075] It should be noted that the Otsu's algorithm is used to obtain the segmentation threshold in this embodiment. In actual applications, as other implementation methods, implementers may also use other methods such as cross-validation depending on the specific circumstances. This embodiment does not impose any special restrictions.

[0076] Furthermore, it should be understood that there are many commonly used nonlinear interpolation methods. In this embodiment, spline interpolation is used to fill in the missing data. In practical applications, as other implementation methods, implementers may also use other nonlinear interpolation methods such as radial basis function interpolation. This embodiment does not impose any special restrictions on the selection of nonlinear interpolation methods.

[0077] Among them, Otsu's inter-class variance and spline interpolation are well-known techniques, and their specific principles will not be elaborated here.

[0078] Furthermore, at the current moment, among all positions on each conductor, the displacement data at non-abnormal positions remains unchanged, the displacement data at abnormal positions in direction m is removed, and the missing displacement data at abnormal positions is filled by nonlinear interpolation. The filled data is used as the corrected displacement data at abnormal positions. By traversing all directions, the corrected displacement data of all positions on each conductor in any direction at the current moment is obtained.

[0079] Preferably, the schematic diagram of the correction displacement data acquisition process provided in this embodiment is as follows: Figure 2 As shown.

[0080] Thus, by correcting the displacement data of all points on each conductor in any direction, the quality of the data is improved, providing accurate basic data for conductor health management and galloping early warning. Corrected displacement data is key to ensuring the safe operation of transmission lines, enabling more accurate monitoring and analysis of conductor galloping, and allowing for timely measures to be taken for conductor galloping early warning and health management, thereby preventing potential risks.

[0081] Step S3: Conductor Health Management: Sparse regression is used to model the corrected displacement data of all points on each conductor in any direction at the current moment, all class feature values ​​of each conductor, as well as wind speed and wind direction, to obtain the conductor galloping model of each conductor at the current moment, and to provide early warning of conductor galloping.

[0082] The corrected displacement data of all points on each conductor in any direction at the current moment, all class feature values ​​of each conductor, and wind speed and direction are used as inputs to the Sparse Identification (SINDy) function of nonlinear dynamics. The output equation serves as the conductor galloping model for each conductor at the current moment. In this embodiment, the function library of Sparse Identification (SINDy) of nonlinear dynamics includes nonlinear basis functions such as polynomial functions, trigonometric functions, exponential functions, and power functions.

[0083] In this embodiment, a Long Short-Term Memory (LSTM) network is used to predict the conductor galloping model of each conductor at the current moment, and the distance between any two conductors at the current moment is obtained. If the distance between any two conductors at the current moment is less than a preset safe distance threshold, a conductor galloping safety warning is triggered, and a warning is issued to the maintenance personnel, so that the maintenance personnel can take measures to manage the conductor galloping healthily. Conversely, if the distance between any two conductors at the current moment is greater than or equal to the preset safe distance threshold, a conductor galloping safety warning is not triggered.

[0084] It should be noted that the preset safety distance threshold for conductors typically depends on various factors, including the conductor material, design standards, environmental conditions, line voltage level, and relevant standards. In this embodiment, it is assumed that the conductor operates at 110kV, in which case the preset safety distance is 2m. Implementers can also set this value according to specific circumstances; this embodiment does not impose any special restrictions.

[0085] Among them, Sparse identification based on nonlinear dynamics (SINDy) and Long Short-Term Memory (LSTM) networks are well-known technologies, and their specific principles and processes will not be elaborated here.

[0086] 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.

[0087] 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.

[0088] 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 power transmission tower and conductor galloping early warning method combined with a multi-factor coupling model, characterized in that, The method comprises the following steps: Data acquisition: real-time acquisition of various characteristic values of each conductor on the power transmission tower, displacement data and displacement velocity of all position points on each conductor in different directions, and real-time wind speed and wind direction; Dance analysis: analyze the distribution of various characteristic values of the conductor and the displacement deviation, and determine the corrected displacement data, specifically: Analyze the distribution of the square of each characteristic value of each conductor within a preset time length before the current time, determine the effective value of each characteristic value of each conductor at the current time, compare the difference between all characteristic values of each conductor at the current time and the effective value, and determine the load deviation of each conductor at the current time; Fit the displacement velocity of all position points on each conductor in any direction at the current time, compare the deviation of the displacement velocity of each position point on each conductor in any direction from the fitting result, and determine the conductor dance transmission value of each position point on each conductor in any direction at the current time; Analyze the distance from each position point on each conductor to the power transmission tower at the current time, and determine the conductor dance judgment value of each position point on each conductor in any direction at the current time in combination with the load deviation and the conductor dance transmission value; Based on the conductor dance judgment value, select abnormal position points from all position points on each conductor in any direction at the current time, correct the displacement of all abnormal position points in any direction, and obtain the corrected displacement data of all position points on each conductor in any direction at the current time; Conductor health management: model the corrected displacement data of all position points on each conductor in any direction at the current time, all characteristic values of each conductor, and wind speed and direction using sparse regression, obtain the conductor dance model of each conductor at the current time, and perform early warning on conductor dance; The expression of the load bias of each conductor at the current time is: ; in the formula, represents the load bias of the jth conductor at the current time; represents the effective value of the ith characteristic value of the jth conductor at the current time; represents the ith characteristic value of the jth conductor at the current time; n represents the total number of characteristic value categories. The expression of the conductor galloping transmission value of each position point on each conductor in any direction at the current time is: ; in the formula, represents the conductor galloping transmission value of position point k on the jth conductor in the mth direction at the current time; represents the deviation of the displacement velocity of position point k on the jth conductor in the mth direction at the current time from the fitting result; represents the average of the deviations of the displacement velocities of position point k on the jth conductor in all directions at the current time from the fitting result; The expression of the conductor galloping determination value of each position point on each conductor in any direction at the current time is: ; in the formula, represents the conductor galloping determination value of position point k on the jth conductor in the mth direction at the current time; represents the distance from position point k on the jth conductor to the power transmission tower at the current time; ln( ) represents the logarithmic function with natural constant as the true number; norm[ ] represents the normalization function.

2. The power transmission tower conductor galloping early warning method combined with multi-factor coupling model of claim 1, wherein, The characteristic values at least include tension value, inclination value and axial angle.

3. The power transmission tower conductor galloping early warning method combined with multi-factor coupling model of claim 1, wherein, The effective value of each characteristic value of each conductor at the current time is the square root of the mean of the square of each characteristic value of each conductor within a preset time length before the current time.

4. The power transmission tower and conductor galloping early warning method combined with multi-factor coupling model of claim 1, wherein, The method for obtaining the abnormal position points is: Take the conductor dance judgment value of all position points on each conductor in any direction at the current time as the input of the threshold segmentation algorithm, output the segmentation threshold, and record the position points with conductor dance judgment value greater than the segmentation threshold as the abnormal position points of each conductor in any direction at the current time.

5. The power transmission tower and conductor galloping early warning method combined with multi-factor coupling model of claim 1, wherein, The method for obtaining the corrected displacement data of all position points on each conductor in any direction at the current time is: Keep the displacement data at non-abnormal position points unchanged, remove the displacement data at abnormal position points in direction m, fill the missing displacement data at abnormal position points using nonlinear interpolation method, take the filled data as the corrected displacement data at abnormal position points, and traverse all directions to obtain the corrected displacement data of all position points on each conductor in any direction at the current time.

6. The power transmission tower and conductor galloping early warning method combined with multi-factor coupling model of claim 1, wherein, The method for obtaining the conductor dance model of each conductor at the current time is: The correction displacement data of all position points on each conductor in any direction at the current time, all class characteristic values of each conductor, and the wind speed and wind direction are taken as inputs of sparse regression, and the output equation is taken as a conductor galloping model of each conductor at the current time.

7. The power transmission tower and conductor galloping early warning method combined with multi-factor coupling model of claim 1, wherein, The method further comprises: The conductor galloping model of all conductors at the current time is taken as an input of a neural network, and the distance between two conductors is output. If the distance between two conductors at the current time is less than a preset safety distance threshold, a conductor galloping safety warning is triggered, otherwise, the conductor galloping safety warning is not triggered.

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