A power grid line icing disaster processing system, method and electronic device

Abstract

This invention relates to the field of icing disaster management technology, specifically providing a power grid line icing disaster management system, method, and electronic equipment. The system includes: a sensor assembly for collecting icing data and environmental data of the power grid line; a control system for extracting icing eccentricity features based on the icing and environmental data; calculating the potential galloping risk probability of each monitoring point by combining the dynamic matching relationship between the icing eccentricity features and the wind direction angle; fusing the potential galloping risk probability with the wind direction-eccentricity interaction coefficient to generate an icing disaster risk distribution map, extracting key disaster locations and characteristic parameters, and generating a de-icing strategy; and a de-icing device for performing de-icing operations on key disaster locations according to the de-icing strategy. This invention solves the problems of icing disaster management methods failing to reflect risk differences caused by uneven icing distribution, lacking mechanical analysis basis, and having insufficient precision in risk early warning.

Description

Technical Field

[0001] This invention relates to the field of icing disaster management technology, and in particular to a power grid line icing disaster management system, method and electronic equipment. Background Technology

[0002] Power grid lines often cross complex terrains such as mountains and rivers. In winter, they are susceptible to icing due to low temperatures and high humidity, which can lead to faults such as line breaks, tower collapses, and conductor galloping. This seriously threatens the safe and stable operation of the power grid and can even cause large-scale power outages. Therefore, dealing with power grid line icing disasters is of great urgency.

[0003] Existing methods for handling icing disasters on power grid lines mostly use topographic, meteorological, and historical icing data as core inputs. They construct predictive models or combine algorithms such as pattern recognition and probabilistic regression to predict and assess icing thickness, icing patterns, or icing risk levels. Some technologies improve prediction accuracy through model correction and rely on GIS platforms to achieve spatial visualization and early warning of icing risks. These existing technologies have the following defects: (1) They do not accurately quantify the icing eccentricity characteristics, focusing only on the prediction of total icing amount or thickness, and cannot reflect the risk differences caused by uneven icing distribution; (2) They lack dynamic matching mechanical analysis of icing and wind direction, and conduct risk prediction without the actual mechanical state of the conductor, so the prediction results are not supported by physical mechanisms; (3) They lack vibration coupling and energy accumulation analysis, and do not quantify the interaction between wind direction and eccentricity direction, resulting in insufficient precision in risk warning and poor targeted operation and maintenance.

[0004] There is currently no effective solution to the problems that existing technologies cannot reflect the risk differences caused by uneven icing distribution, lack mechanical analysis basis, and have insufficient precision in risk warning. Summary of the Invention

[0005] The present invention provides a power grid line icing disaster handling system, method and electronic equipment, which at least solves the problems of related technologies failing to reflect the risk differences caused by uneven icing distribution, lacking mechanical analysis basis and insufficient precision in risk early warning.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The first aspect of this invention provides a power grid line icing disaster handling system, the system comprising: a sensor assembly for collecting icing data and environmental data of the power grid line; a control system connected to the sensor assembly for analyzing the icing distribution characteristics of the cross-section of each monitoring point of the power grid line based on the icing data and the environmental data, calculating the deviation between the actual centroid of the cross-section and the geometric center of the standard conductor, and extracting icing eccentricity characteristics; combining the dynamic matching relationship between the icing eccentricity characteristics and the wind direction angle to extract the first coupling characteristic of wind speed and conductor vibration, performing mechanical state simulation analysis, and calculating the potential galloping risk probability of each monitoring point; fusing the potential galloping risk probability with the wind direction-eccentricity interaction coefficient, and generating an icing disaster risk distribution map that takes into account both dynamic mechanical risk and spatial distribution through geospatial visualization processing; extracting key disaster locations and characteristic parameters based on the icing disaster risk distribution map, and generating a de-icing strategy; and a de-icing device connected to the control system for performing de-icing operations on the key disaster locations according to the de-icing strategy.

[0008] The second aspect of this invention provides a method for handling icing disasters on power grid lines, applied to the aforementioned power grid line icing disaster handling system. The method includes: analyzing the icing distribution characteristics of the cross-sections at various monitoring points of the power grid line based on icing data and environmental data; calculating the deviation between the actual centroid of the cross-section and the geometric center of the standard conductor; extracting icing eccentricity features; combining the dynamic matching relationship between the icing eccentricity features and the wind direction angle; extracting the first coupling feature between wind speed and conductor vibration; performing mechanical state simulation analysis; calculating the potential galloping risk probability at each monitoring point; fusing the potential galloping risk probability with the wind direction-eccentricity interaction coefficient; and generating an icing disaster risk distribution map that considers both dynamic mechanical risk and spatial distribution through geospatial visualization processing; and based on the icing disaster risk distribution map, extracting key disaster locations and characteristic parameters; generating a de-icing strategy; and having a de-icing device perform de-icing operations on the key disaster locations according to the de-icing strategy.

[0009] Preferably, based on icing data and environmental data of the power grid line, the icing distribution characteristics of the cross-sections at various monitoring points of the power grid line are analyzed, the deviation between the actual centroid of the cross-section and the geometric center of the standard conductor is calculated, and the icing eccentricity features are extracted. This includes the following steps: sampling the icing data and environmental data to obtain the full-angle icing thickness value of the power grid line; fitting the full-angle icing thickness value to construct the icing profile curve of the cross-sections at various monitoring points of the power grid line; calculating the actual centroid position coordinates and icing mass of each cross-section based on the icing profile curves; performing vector operations on the actual centroid position coordinates and the geometric center position coordinates of the standard conductor corresponding to the cross-section to obtain the centroid offset vector and eccentric mass of each cross-section; and obtaining the mass eccentricity rate of each cross-section based on the ratio of the eccentric mass to the icing mass.

[0010] Preferably, calculating the actual centroid coordinates and icing mass of each cross-section based on the icing profile curve includes the following steps: dividing each cross-section into multiple corresponding sector units based on the icing profile curve; calculating the mass of each sector unit and the radial distance between the centroid of each sector unit and the geometric center of the standard conductor corresponding to the cross-section; summing the masses of each sector unit to obtain the icing mass of each cross-section; determining the actual centroid coordinates of each cross-section based on the ratio of the sum of the mass moments of each sector unit to the corresponding icing mass; wherein the sum of the mass moments is the sum of the products of the mass of each sector unit and the corresponding radial distance.

[0011] Preferably, by combining the dynamic matching relationship between the icing eccentricity feature and the wind direction angle, the first coupling feature between wind speed and conductor vibration is extracted, and a mechanical state simulation analysis is performed to calculate the potential galloping risk probability of each monitoring point. This includes the following steps: combining the dynamic matching relationship between the icing eccentricity feature and the wind direction angle, a mechanical state simulation analysis is performed on the power grid line to identify the torsional stress state of the power grid line and local stress concentration areas; when the peak torsional stress in the torsional stress state reaches a preset vibration risk association threshold, a time-series simulation is performed on the dynamic interaction between wind speed and the vibration frequency of the power grid line to extract the first coupling feature between wind speed and conductor vibration; based on the first coupling feature, combined with the periodic fluctuation data of wind direction, the second coupling feature between the vibration frequency of the power grid line under a specific wind direction and the vibration frequency caused by icing eccentricity is analyzed, and the instability state of the power grid line is determined by combining the energy balance principle; based on the instability state, the empirical probability is corrected by combining historical similar working condition data and current icing and wind direction related parameters, and the potential galloping risk probability of each monitoring point is calculated.

[0012] Preferably, by combining the dynamic matching relationship between the icing eccentricity feature and the wind direction angle, a mechanical state simulation analysis is performed on the power grid line to identify the torsional stress state and local stress concentration areas of the power grid line. This includes the following steps: constructing a mass distribution function for the cross-section of each monitoring point of the power grid line within the eccentricity-sensitive section by combining the dynamic matching relationship between the icing eccentricity feature and the wind direction angle; wherein, the eccentricity-sensitive section refers to a power grid line section where the relative angle between the wind direction and the icing eccentricity direction is within a preset threshold range; determining the eccentric arm of the power grid line within the eccentricity-sensitive section through vector calculation of the mass distribution function and the wind direction angle; calculating the torsional moment and torsional stress state of the power grid line within the eccentricity-sensitive section based on the eccentric arm and wind pressure data; and calculating the stress change rate distribution of the power grid line within the eccentricity-sensitive section based on the torsional stress state, and identifying the local stress concentration areas of the power grid line.

[0013] Preferably, based on the first coupling feature and combined with wind direction periodic fluctuation data, the second coupling feature of the vibration frequency of the power grid line under a specific wind direction and the vibration frequency caused by icing eccentricity is analyzed, and the instability state of the power grid line is determined by combining the energy balance principle. This includes the following steps: based on the first coupling feature, the wind direction periodic fluctuation data is decomposed into periodic components to extract the periodic features of the wind direction fluctuation; combining the periodic features and the first coupling feature, the second coupling feature of the vibration frequency of the power grid line under a specific wind direction and the vibration frequency caused by icing eccentricity in the local stress concentration area is analyzed; based on the second coupling feature, the energy accumulation and energy dissipation per unit time during the vibration process of the power grid line are calculated; and by combining the energy balance principle, the dynamic relationship between the energy accumulation and the energy dissipation is compared to determine the instability state of the power grid line.

[0014] Preferably, before fusing the potential galloping risk probability with the wind direction-eccentricity interaction coefficient, the method includes: based on real-time wind direction monitoring data of each monitoring point of the power grid line, statistically obtaining the wind direction change frequency per unit time of each monitoring point; performing a correlation calculation between the wind direction change frequency of each monitoring point and the eccentricity direction angle in the corresponding icing eccentricity feature to obtain a calculation result; dividing the calculation result by a preset normalization coefficient to calculate the wind direction-eccentricity interaction coefficient of each monitoring point of the power grid line.

[0015] Preferably, the potential galloping risk probability is fused with the wind direction-eccentricity interaction coefficient, and geospatial visualization is applied to generate an icing disaster risk distribution map that takes into account dynamic mechanics, risk level, and spatial distribution. This includes the following steps: If the potential galloping risk probability is within a preset probability range, the correlation coefficient between environmental gradient data and amplitude data is calculated; wherein the amplitude data is obtained based on the first coupling feature extraction; the environmental gradient data is gradient distribution data generated based on supplementary environmental variables; the correlation coefficient is compared with a preset threshold; if the correlation coefficient is greater than or equal to the preset threshold, the corresponding environmental gradient data is identified as a key influencing factor; the ratio of the peak torsional stress to the material yield strength corresponding to the key influencing factor is calculated, and risk levels are classified based on the magnitude of the ratio, and risky line segments in the power grid line segments are determined; the wind direction-eccentricity interaction coefficient is marked on the geographic coordinates of the risky line segments, and corresponding colors are assigned according to the numerical level of the wind direction-eccentricity interaction coefficient; a heat map is rendered on the geographic coordinates to generate an icing disaster risk distribution map.

[0016] Preferably, based on the icing disaster risk distribution map, key disaster locations and characteristic parameters are extracted to generate a de-icing strategy. The de-icing device then performs de-icing operations on the key disaster locations according to the de-icing strategy, including the following steps: Based on the icing disaster risk distribution map, the geographical coordinates of key disaster locations in the power grid line are extracted, along with the corresponding risk level, icing eccentricity characteristic parameters, and local stress concentration characteristic parameters, forming a basic de-icing parameter set; based on the basic de-icing parameter set, de-icing priorities are divided according to the risk association attributes of the key disaster locations, and targeted differentiated de-icing strategies adapted to each priority are formulated; the targeted differentiated de-icing strategies are parsed into executable operation instructions for the de-icing device, and the operation instructions are issued to the de-icing devices in the corresponding work areas; after receiving the operation instructions, the de-icing device performs precise de-icing operations on each key disaster location according to the de-icing strategy.

[0017] A third aspect of the present invention provides an electronic device, comprising: a processor and a memory storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the aforementioned method for handling power grid line icing disasters.

[0018] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0019] This invention provides a power grid line icing disaster handling system, method, and electronic equipment. Based on icing data and environmental data of the power grid line, it analyzes the icing distribution characteristics of the cross-section of each monitoring point of the power grid line and calculates the deviation between the actual center of gravity and the geometric center. It directly characterizes the inherent characteristics of uneven icing from the perspective of cross-sectional geometric offset, achieving accurate quantification of icing eccentricity and fundamentally solving the problem that the total amount of icing alone cannot reflect the distribution differences. By dynamically matching the icing eccentricity characteristics with the wind direction angle and combining the coupling relationship between wind speed and conductor vibration to carry out mechanical simulation, it calculates the galloping risk based on the actual stress state of the conductor, giving risk prediction a clear physical mechanism and making up for the lack of mechanical support. Furthermore, by integrating the galloping risk probability with the wind direction-eccentricity interaction, it forms a visualized risk map that takes into account both mechanical dynamics and spatial distribution, accurately locating key disaster locations and generating targeted de-icing strategies. This achieves refined risk warning and operation and maintenance, solving the problems that related technologies cannot reflect the risk differences caused by uneven icing distribution, lack mechanical analysis basis, and have insufficient refinement of risk warning. Attached Figure Description

[0020] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0021] Figure 1 This is a structural block diagram of a power grid line icing disaster handling system according to an embodiment of the present invention.

[0022] Figure 2 This is a schematic flowchart of a method for handling icing disasters on power grid lines, according to an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0025] To address the issues that related technologies cannot reflect the risk differences caused by uneven icing distribution, lack mechanical analysis basis, and have insufficient precision in risk warning, the embodiments of this invention provide a power grid line icing disaster handling system, method, and electronic equipment.

[0026] Among them, such as Figure 1 As shown, the embodiment of this invention provides a power grid line icing disaster handling system. The system includes: a sensor assembly for collecting icing data and environmental data of the power grid line; a control system connected to the sensor assembly for analyzing the icing distribution characteristics of the cross-section of each monitoring point of the power grid line based on the icing data and environmental data, calculating the deviation between the actual centroid of the cross-section and the geometric center of the standard conductor, and extracting the icing eccentricity feature; combining the dynamic matching relationship between the icing eccentricity feature and the wind direction angle to extract the first coupling feature of wind speed and conductor vibration, performing mechanical state simulation analysis, and calculating the potential galloping risk probability of each monitoring point; fusing the potential galloping risk probability with the wind direction-eccentricity interaction coefficient, and generating an icing disaster risk distribution map that takes into account both dynamic mechanical risk and spatial distribution through geospatial visualization processing; extracting key disaster locations and characteristic parameters based on the icing disaster risk distribution map, and generating a de-icing strategy; and a de-icing device connected to the control system for performing de-icing operations on key disaster locations according to the de-icing strategy.

[0027] Icing data can include parameters characterizing the physical morphology and load properties of icing itself, such as icing thickness, icing cross-sectional profile, icing density, and icing uniformity of conductors, ground wires, and insulators. Environmental data can include external environmental parameters such as wind speed, wind direction, ambient temperature and humidity, precipitation status, altitude, and geographical location of the monitoring area.

[0028] Sensor components are spaced out at different locations along the power grid lines, corresponding to different monitoring points. These components can include multi-source sensing units such as fiber optic sensors, tilt sensors, wind speed sensors, wind direction sensors, and temperature and humidity sensors. Through the collaborative work of multiple types of sensors, they simultaneously collect data on the icing itself and the external environment, achieving comprehensive and synchronous perception of the line's icing status and surrounding conditions.

[0029] Specifically, in a preferred embodiment, the system achieves precise measurement of ice thickness using an array of fiber optic sensors spaced apart on the conductor. The fiber optic sensors utilize the principle of fiber Bragg gratings; when ice on the conductor surface causes strain, the reflected wavelength of the grating shifts. By detecting the wavelength change using a demodulator, the ice thickness at the corresponding location can be calculated. The tilt sensor employs the principle of a triaxial accelerometer to monitor the change in the tilt angle of the conductor under the influence of ice in real time, providing attitude data support for subsequent center of gravity shift analysis.

[0030] The control system, which can be an industrial control host or a cloud-based intelligent analysis platform, can replace human experience in completing icing feature analysis, risk calculation, and strategy generation through digital algorithms and mechanical models.

[0031] Based on icing data and environmental data, the control system reconstructs the icing morphology of the cross-section of the power grid line at each monitoring point, and then analyzes the icing distribution characteristics of the cross-section of the power grid line at each monitoring point. The irregular icing is transformed into a quantifiable geometric model. By calculating the deviation between the actual centroid of the cross-section and the geometric center of the standard conductor, the icing eccentricity feature is quantitatively extracted. The eccentricity feature directly reflects the degree of asymmetrical distribution of icing and is a key geometric factor that induces line galloping.

[0032] By combining the dynamic matching relationship between the icing eccentricity characteristics and the wind direction angle, the control system extracts the first coupling characteristic between wind speed and conductor vibration. The relative angle between the icing eccentricity and the wind direction directly determines the effect of wind load, and the coupling between the two is the core mechanical cause driving line galloping. By reconstructing the vibration response of the line under wind-ice coupling through mechanical state simulation analysis, the potential galloping risk probability of each monitoring point can be calculated.

[0033] The control system integrates the probability of potential galloping risk with the wind direction-eccentricity interaction coefficient, and through geospatial visualization processing, generates an icing disaster risk distribution map that takes into account both dynamic mechanical risk and spatial distribution. By combining single-point mechanical risk with regional geographical distribution, it reflects both the dynamic danger of galloping and the spatial spread pattern of disasters, achieving an upgrade from "point monitoring" to "area assessment".

[0034] Based on the icing disaster risk distribution map, the control system automatically extracts key disaster locations and characteristic parameters to generate de-icing strategies. It prioritizes high-risk, high-impact key sections and develops differentiated plans based on icing thickness, galloping probability, and geographical conditions to ensure precise deployment of de-icing resources and efficient, targeted operations.

[0035] The de-icing device is the terminal execution mechanism that receives commands from the control system. It consists of two core hardware types: mechanical de-icing and thermal de-icing. The mechanical de-icing type is equipped with wire de-icing robots and impact de-icers that are adapted to different voltage levels. The thermal de-icing type is equipped with DC de-icing devices and electrically heated de-icing belts to meet the differentiated de-icing needs of high voltage / ultra-high voltage lines from 220kV to 1000kV.

[0036] The de-icing device follows the de-icing strategy issued by the control system. Based on the icing characteristics, risk level, and spatial location of key parts, it matches the corresponding de-icing method, work intensity, and execution sequence to achieve targeted and differentiated de-icing. While ensuring the de-icing effect, it avoids damage to the line body and improves the safety and economy of the operation.

[0037] The power grid icing disaster handling system provided by the present invention analyzes the icing distribution characteristics of the cross-section of each monitoring point of the power grid line based on the icing data and environmental data of the power grid line, calculates the deviation between the actual center of gravity and the geometric center, directly characterizes the inherent characteristics of uneven icing from the perspective of cross-sectional geometric offset, realizes the accurate quantification of icing eccentricity, and fundamentally solves the problem that the total amount of icing alone cannot reflect the distribution differences.

[0038] Furthermore, by dynamically matching the icing eccentricity characteristics with the wind direction angle, and combining the coupling relationship between wind speed and conductor vibration to carry out mechanical simulation, the galloping risk is calculated based on the actual stress state of the conductor, giving the risk prediction a clear physical mechanism and making up for the lack of mechanical support.

[0039] Furthermore, by integrating the probability of icing risk with the interaction between wind direction and eccentricity, a visualized risk map that takes into account both mechanical dynamics and spatial distribution is formed. This accurately identifies key disaster locations and generates targeted de-icing strategies, achieving refined risk warning and operation and maintenance. It solves the problems that related technologies cannot reflect the risk differences caused by uneven icing distribution, lack mechanical analysis basis, and have insufficient refinement of risk warning.

[0040] like Figure 2 As shown, an embodiment of the present invention also provides a method for handling power grid line icing disasters, applied to the power grid line icing disaster handling system described in the above embodiment. The method includes the following steps S1 to S4.

[0041] Step S1: Based on the icing data and environmental data of the power grid line, analyze the icing distribution characteristics of the cross-section of each monitoring point of the power grid line, calculate the deviation between the actual centroid of the cross-section and the geometric center of the standard conductor, and extract the icing eccentricity characteristics.

[0042] Step S2: Combining the dynamic matching relationship between the icing eccentricity characteristics and the wind direction angle, the first coupling characteristics of wind speed and conductor vibration are extracted, and mechanical state simulation analysis is performed to calculate the potential galloping risk probability of each monitoring point.

[0043] Step S3: The potential icing risk probability is fused with the wind direction-eccentricity interaction coefficient, and after geospatial visualization processing, an icing disaster risk distribution map that takes into account both dynamic mechanical risk and spatial distribution is generated.

[0044] Step S4: Based on the icing disaster risk distribution map, extract key disaster locations and characteristic parameters, generate a de-icing strategy, and have the de-icing device perform de-icing operations on the key disaster locations according to the de-icing strategy.

[0045] Steps S1 to S3 in the embodiments of the present invention are configured to be executed by the control system in the power grid line icing disaster handling system, and step S4 is configured to be executed by the de-icing device.

[0046] Furthermore, step S1 in the method provided by the present invention preferably includes: sampling icing data and environmental data to obtain the icing thickness value of the power grid line at all angles; fitting the icing thickness value at all angles to construct the icing profile curve of the cross-section of each monitoring point of the power grid line; calculating the actual centroid position coordinates and icing mass of each cross-section based on the icing profile curve; performing vector calculations on the actual centroid position coordinates and the geometric center position coordinates of the standard conductor corresponding to the cross-section to obtain the centroid offset vector and eccentric mass of each cross-section; and obtaining the mass eccentricity of each cross-section based on the ratio of eccentric mass to icing mass.

[0047] Specifically, the sampling of icing and environmental data includes: dividing the entire circumference into several angular intervals, starting from the top of the conductor and numbering them clockwise. Icing thickness values ​​at each angular position are continuously collected at a preset sampling frequency to form time-series data.

[0048] By fitting the thickness values ​​at different angles at the same time, the Fourier series expansion method is used to construct the ice accretion profile curve equation. This equation describes the continuous functional relationship between the ice accretion thickness and the angle, realizing the transformation from discrete measurement points to a continuous profile.

[0049] The actual center of gravity position refers to the coordinates of the mass center of the combination of ice and conductor after the cross-section of the power grid line is covered with ice. It can be obtained by dividing the cross-section into sector elements, calculating the sum of the products of the mass of each element and the radial distance from the centroid of the element to the geometric center of the standard conductor, and then comparing the total mass moment with the total mass of the ice to determine the coordinates; or it can be calculated by combining the ice profile curve with general numerical analysis methods such as numerical integration.

[0050] Icing mass refers to the total mass of ice adhering to a unit length of power grid line cross-section. It can be obtained by dividing the cross-section into sector units, calculating the icing mass of each sector unit and then summing the results; or it can be calculated by integrating the area after fitting the icing profile curve, combined with the ice density and the unit length of the line.

[0051] The centroid offset vector refers to a directed line segment originating from the geometric center of the standard conductor's cross-section and ending at the actual centroid after icing. It includes two key parameters: offset distance and offset direction. It can be obtained by subtracting the coordinates of the actual centroid from the coordinates of the geometric center; or it can be solved by combining the spatial relationship between the two through polar coordinate transformation or other methods.

[0052] Eccentric mass refers to the equivalent offset mass that characterizes the non-uniform distribution of icing and causes the center of gravity shift. It is a key parameter connecting icing eccentricity and the mechanical effects of the track. It can be obtained by the ratio of the total mass moment to the magnitude of the center of gravity shift vector; or it can be solved by moment analysis after fitting the mass distribution function.

[0053] Mass eccentricity ratio refers to the dimensionless ratio of eccentric mass to total icing mass, used to standardize and quantify the relative degree of icing eccentricity; it can be obtained by directly calculating the ratio of the two, or by standardization methods such as analysis of variance of mass distribution.

[0054] Offset distance, offset direction, and mass eccentricity together constitute the icing eccentricity feature, which can accurately capture the spatial geometric features and relative degree of icing eccentricity, realizing a comprehensive and quantitative characterization of icing eccentricity. This provides core quantitative parameters for establishing a dynamic matching relationship between icing eccentricity and wind direction angle and simulating the mechanical state of the line, breaking through the limitation of judging risk based on the total amount of icing.

[0055] The above-mentioned step S1 provided by the embodiments of the present invention, by obtaining the icing eccentricity characteristic parameters, realizes a multi-dimensional and accurate quantitative characterization of the non-uniform distribution state of icing on power grid lines. It breaks through the limitation of judging the impact of icing solely by the total amount of icing. It provides a core quantitative basis for establishing a dynamic matching relationship between icing eccentricity and wind direction, simulating the mechanical state of the line, and calculating the probability of galloping risk. At the same time, it lays a key data foundation for refined risk prediction of icing disasters and the formulation of targeted de-icing strategies.

[0056] Furthermore, the method described above in the embodiments of the present invention, which calculates the actual centroid coordinates and icing mass of each cross-section based on the icing profile curve, preferably includes: dividing each cross-section into multiple corresponding sector units based on the icing profile curve; calculating the mass of each sector unit and the radial distance between the centroid of each sector unit and the geometric center of the standard conductor corresponding to the cross-section; summing the masses of each sector unit to obtain the icing mass of each cross-section; and determining the actual centroid coordinates of each cross-section based on the ratio of the sum of the mass moments of each sector unit to the corresponding icing mass; wherein the sum of the mass moments is the sum of the products of the mass of each sector unit and the corresponding radial distance.

[0057] Specifically, taking the standard geometric center of the conductor's cross-section as the center, the entire circumference is divided into N equal sector units in a clockwise or counterclockwise direction. The central angle of each sector unit is in the range of 15° to 20°.

[0058] The mass of each sector unit is the product of the ice volume within that unit and the ice density. The ice volume is calculated by multiplying the area of ​​the sector unit by the unit length of the conductor.

[0059] The method provided by the embodiments of this invention takes the fine division of fan-shaped units as its core, and through step-by-step quantization and precise calculation, achieves high-precision measurement of icing mass, actual center of gravity and various eccentric characteristic parameters. It can accurately capture the detailed differences in the non-uniform distribution of icing on different cross-sections. The obtained multi-dimensional quantified parameters provide highly reliable basic data for the subsequent dynamic matching of icing and wind direction mechanical state simulation. Moreover, the standardized parameter calculation method makes the degree of icing eccentricity at each monitoring point comparable, effectively supporting the accurate identification of icing disaster risks and the scientific formulation of subsequent differentiated prevention and control strategies.

[0060] Furthermore, the method provided in the embodiments of the present invention, after extracting the icing eccentricity features, further includes: determining the relative angle between the wind direction and the eccentricity direction based on the centroid offset vector and wind direction data collected by the wind speed sensor; if the relative angle is within the range of a first preset angle threshold and a second preset angle threshold, then marking the line segment as an eccentricity-sensitive segment, and recording a set of icing eccentricity feature parameters including eccentricity distance, eccentricity direction angle, and mass eccentricity rate. Using the icing eccentricity features and the marking of eccentricity-sensitive segments, monitoring units are divided along the conductor length direction at preset intervals. The eccentricity distance and eccentricity direction angle of each monitoring unit are continuously processed using cubic spline interpolation to generate a continuous eccentricity distribution curve, resulting in a complete conductor eccentricity feature distribution map.

[0061] Specifically, when identifying eccentrically sensitive sections, the wind direction data collected by the wind speed sensor is first extracted and expressed as the angle between the wind direction and true north. Simultaneously, the eccentricity angle, i.e., the angle between the eccentricity direction and the horizontal baseline of the conductor, is calculated based on the center of gravity offset vector. The relative angle between the wind direction and the eccentricity direction is determined through vector dot product calculation. When this relative angle meets preset conditions, it indicates that the wind direction and the icing eccentricity direction form an unfavorable combination, easily inducing conductor vibration.

[0062] The values ​​of the first and second preset angle thresholds follow the mechanical mechanism of wind-ice coupled vibration. The core angle of the most unfavorable coupling is 90°, which is perpendicular to the wind direction and the eccentric direction. The angle is used as the center of the interval to set the upper and lower floating angle interval. The first preset angle threshold is the lower limit of the interval, and the second preset angle threshold is the upper limit of the interval. The thresholds can be dynamically adapted according to the voltage level of the power grid line, the terrain and wind field characteristics, so as to balance the accuracy of risk identification and the sensitivity under complex working conditions.

[0063] For example: for 220kV / 500kV conventional lines in plain areas, the first preset angle threshold is 60° and the second preset angle threshold is 120°; for 1000kV UHV lines, the first preset angle threshold is 75° and the second preset angle threshold is 105°.

[0064] Preferably, when generating the eccentricity feature distribution map, the system comprehensively represents the eccentricity distance curve and the eccentricity direction angle curve. The map uses a two-dimensional coordinate system, with the horizontal axis representing the position coordinates along the length of the conductor, and the vertical axis using polar coordinates to simultaneously represent the eccentricity distance and eccentricity direction angle. The spatial distribution characteristics of the eccentricity degree are visually displayed through color coding or contour lines, where warm-colored areas represent high-risk sections with greater eccentricity, and cool-colored areas represent low-risk sections with less eccentricity.

[0065] Understandably, the generation of the eccentricity feature distribution map realizes the transformation from discrete measurement data to continuous distribution characteristics, laying a data foundation for the accurate assessment of conductor galloping risk. This map not only reflects the static eccentricity distribution at a certain moment, but also identifies dangerous sections with rapidly increasing eccentricity through time-series comparative analysis of the dynamic evolution of icing eccentricity, thereby achieving early warning and prediction of conductor galloping risk.

[0066] The method provided by the embodiments of the present invention determines the relative angle by combining the center of gravity offset vector with wind direction data, marks the eccentric sensitive section, records the characteristic parameter group, and then divides the monitoring unit into cubic spline interpolation continuous processing to generate a conductor eccentricity feature distribution map. It accurately identifies the highly sensitive eccentricity section and realizes the transformation of the eccentricity features of discrete monitoring points into a continuous distribution of the entire conductor. The generated visualization map intuitively presents the spatial distribution law of icing eccentricity.

[0067] Further, step S2 of the method provided in the embodiments of the present invention preferably includes: combining the dynamic matching relationship between the icing eccentricity characteristics and the wind direction angle, conducting a mechanical state simulation analysis of the power grid line to identify the torsional stress state of the power grid line and the local stress concentration area; when the peak value of the torsional stress in the torsional stress state reaches a preset vibration risk association threshold, performing a time-series simulation on the dynamic interaction between wind speed and the vibration frequency of the power grid line to extract the first coupling feature between wind speed and conductor vibration; based on the first coupling feature, combined with the periodic fluctuation data of wind direction, analyzing the second coupling feature between the vibration frequency of the power grid line under a specific wind direction and the vibration frequency caused by icing eccentricity, and determining the instability state of the power grid line based on the energy balance principle; based on the instability state, correcting the empirical probability by combining historical similar working condition data and current icing and wind direction related parameters, and calculating the potential galloping risk probability of each monitoring point.

[0068] The dynamic matching relationship between the icing eccentricity feature and the wind direction angle is a coupled state of the coordinated change of the icing eccentricity feature and the wind direction angle over time, which can reflect the dynamic correlation between their relative angles and rates of change.

[0069] The present invention can be implemented by first marking the eccentric sensitive section where the relative angle between the wind direction and the eccentric direction is within a preset threshold, constructing the mass distribution function of the cross-section of the monitoring point, and determining the length and direction of the eccentric lever arm by vector operation of the mass distribution function and the wind direction angle, thereby characterizing the dynamic matching relationship, or by establishing a time-series correlation model between the icing eccentricity parameter and the wind direction angle, and combining the wind field time-series data to fit the dynamic response relationship between the two to characterize it.

[0070] Furthermore, the mass distribution function is constructed using discrete point fitting. After dividing the conductor cross-section into several sector regions, the angular position of each sector region is used as the independent variable, and the corresponding mass value is used as the dependent variable. The continuous mass distribution function expression is obtained by fitting using the least squares method.

[0071] Torsional stress state refers to the overall magnitude and spatial distribution of torsional shear stress at various monitoring points and cross-sections along a power grid line under the coupled effects of icing eccentricity and wind load. It directly reflects the overall torsional mechanical stress condition of the line. Specifically, the torsional stress state can be derived by combining the eccentric lever arm obtained from the mass distribution function and the wind direction angle vector, and then calculating the torsional moment using wind pressure data; or by establishing a wind-ice coupled finite element mechanical model to directly simulate and solve the torsional stress state of the line.

[0072] Localized stress concentration areas refer to sections of power grid lines where, under the coupled effects of icing eccentricity and wind loads, torsional stress suddenly increases at localized locations, with stress values ​​significantly higher than those in surrounding sections. These are mechanically weak areas in the power grid's resistance to icing and street fighting. Specifically, areas where the stress change rate exceeds a preset threshold can be identified by calculating the stress change rate distribution under torsional stress conditions; or by performing gradient analysis on the line's stress field to pinpoint the localized areas corresponding to stress extrema.

[0073] The vibration risk correlation threshold is the critical value for the vibration risk triggered by the torsional stress of the line. It can be obtained by combining the characteristics of the conductor material, the line structural parameters, and the critical stress of historical galloping accidents.

[0074] When the peak torsional stress in the torsional stress state reaches the vibration risk correlation threshold, the power grid line is close to the vibration critical state. The dynamic interaction between wind speed and line vibration frequency will significantly amplify the galloping risk. At this time, time series simulation can accurately capture the coupling characteristics between the two.

[0075] Furthermore, the time-series simulation includes: constructing a nonlinear differential equation for the transverse vibration of the conductor based on the torsional stress state of the line and the wind speed gradient distribution, solving the equation by numerical integration, and analyzing the dynamic interaction between wind speed and line vibration.

[0076] Specifically, based on the torsional stress state of the line and the wind speed gradient distribution, the stress peak position and stress change period are extracted, and the frequency domain characteristics of the stress field are obtained through Fourier transform. The frequency domain characteristics are compared with the natural frequency of the conductor calculated based on the conductor material and structural parameters to determine whether the ratio of the two frequencies is within the preset resonance sensitive range.

[0077] If the frequency ratio is within the resonance-sensitive range, the wind speed and vibration coupling calculation process is initiated. Real-time wind speed data from the wind speed sensor array is acquired, and the wind speed gradient distribution along the conductor's length is calculated. Based on the spatial relationship between the wind speed gradient distribution and the torsional stress distribution, the phase difference between wind-induced vibration and stress response is determined through cross-correlation calculations. Based on this phase difference, a nonlinear differential equation describing the conductor's lateral vibration is constructed. The Runge-Kutta numerical integration method is used to solve the nonlinear differential equation, obtaining the vibration displacement time history curves at each point on the conductor. By extracting the envelope from the displacement time history curves, the amplitude evolution over time is obtained.

[0078] Based on the amplitude evolution pattern, the time period when the amplitude growth rate exceeds the preset critical value is identified and marked as the potential galloping trigger period. The maximum amplitude value and duration within this period are recorded to obtain the time-series simulation results of wind-induced vibration.

[0079] Furthermore, the natural frequency of the conductor is calculated based on the conductor's linear density, tension, and span length. For steel-cored aluminum stranded wire, the combined stiffness of the steel core and aluminum strands must also be considered. The resonance-sensitive range is preferably between 0.8 and 1.2. When the ratio of the stress dominant frequency to the conductor's natural frequency falls within the resonance-sensitive range, the system determines that there is a risk of resonance.

[0080] It should be noted that the wind speed gradient distribution involves the processing of multi-point synchronous measurement data. An array of wind speed sensors is arranged at equal intervals along the length of the conductor to collect instantaneous wind speed values ​​at each location. The difference in wind speed between adjacent measuring points is then divided by the spacing to obtain the local wind speed gradient value. Cubic spline interpolation is used to process the discrete gradient values ​​into a continuous function, forming a wind speed gradient distribution function along the entire length of the conductor. This function reflects the spatial non-uniformity of the wind field and is an important input for subsequent phase analysis.

[0081] In one possible implementation, the nonlinear differential equations are constructed based on the dynamic characteristics of the conductor. The equations consider three aspects: geometric nonlinearity, aerodynamic nonlinearity, and material nonlinearity. Geometric nonlinearity stems from the nonlinear strain-displacement relationship under large deformation conditions; aerodynamic nonlinearity is reflected in the nonlinear variation of the wind force coefficient with the angle of attack; and material nonlinearity considers discontinuous processes such as icing cracking and detachment. The equations are discretized using the finite element method, dividing the conductor into several elements. The motion equations of each element are established using the principle of virtual work. The elements are coupled through continuity conditions of nodal displacements and rotations, forming a unified set of nonlinear equations.

[0082] Preferably, the envelope extraction of the vibration displacement time history curve employs the Hilbert transform method. The displacement signal is subjected to a Hilbert transform to obtain an analytic signal, the magnitude of which is the instantaneous amplitude envelope. By performing a difference operation on the envelope curve, the time rate of change of amplitude, i.e., the amplitude growth rate, is calculated. When the amplitude growth rate is consistently positive and exceeds a preset threshold, it indicates that the vibration is in a divergent state, and the system marks this period as a potential galloping trigger period.

[0083] For example, in a monitoring case of an ultra-high voltage transmission line, when the dominant frequency of torsional stress caused by icing eccentricity was 0.95 Hz, while the natural frequency of the conductor was 1.0 Hz, the frequency ratio of 0.95 fell within the resonance-sensitive range. Wind speed gradient analysis showed a significant abrupt change in wind speed in the middle section of the line, with a phase difference of approximately 85 degrees from the torsional stress. By solving the nonlinear vibration equation, the obtained amplitude time history curve showed that after the wind speed reached 12 m / s, the amplitude growth rate exceeded the critical value of 10% per minute, and the system warned that the line section might experience galloping within the next 2 hours.

[0084] The first coupling feature, namely the coupling feature extracted from the time-series simulation results, is a set of core features that can reflect the multi-dimensional dynamic coupling relationship between wind speed and wind-induced vibration of power grid lines, including the evolution law of vibration dominant frequency and amplitude.

[0085] Wind direction periodic fluctuation data is a time-series signal of wind direction changes over time collected by wind direction sensors. It can be decomposed into multi-scale periodic components such as dominant period and secondary period through wavelet transform, reflecting the periodic fluctuation pattern of the wind field.

[0086] The second coupling characteristic is the beat vibration characteristic generated when the wind direction fluctuation frequency is close to the torsional vibration frequency caused by icing eccentricity, which includes the beat frequency value and the beat vibration amplitude growth rate.

[0087] Based on the first coupling feature, Morlet wavelet decomposition is used to decompose the periodic fluctuation data of wind direction, extract the wind direction change frequency corresponding to the dominant and secondary periods, and perform spectrum superposition calculation with the frequency of conductor torsional vibration caused by icing eccentricity. After determining that the two frequencies are close and produce beat vibration phenomenon, the core parameters such as beat frequency and beat vibration amplitude growth rate are extracted, which are the second coupling feature.

[0088] The unstable state is a mechanical state in which the accumulated energy of the power grid line vibration is continuously greater than the energy dissipation per unit time, the vibration shows a divergent trend, and even a small disturbance can trigger a large-scale galloping.

[0089] Based on the principle of energy balance, the vibration kinetic energy increment is first calculated based on the beat amplitude growth rate in the second coupling characteristic, combined with the equivalent mass of the conductor (including its own weight, icing mass, and additional mass). The cumulative energy value is obtained by time integration. Then, the energy dissipation value per unit time is calculated based on the conductor damping coefficient and vibration frequency. By comparing the dynamic relationship between the two, if the cumulative energy continues to exceed the energy dissipation value, the power grid line is determined to have entered an unstable state, and the time node of instability is determined.

[0090] Historical data on similar operating conditions, including historical monitoring and instability records of temperature, humidity, wind speed, and wind direction similar to the current operating conditions over historical periods. Current parameters: real-time icing eccentricity, wind direction and eccentricity matching degree, and real-time monitoring parameters related to icing and wind field.

[0091] The criteria for determining historically similar meteorological conditions are that the Euclidean distance in four dimensions—temperature, humidity, wind speed, and wind direction—is less than 5, and the historical data time range is limited to the last 5 years.

[0092] When calculating the potential galloping risk probability of each monitoring point, the ratio of the number of instability events in similar historical conditions to the total number of observations is first calculated to obtain the empirical probability. Then, the empirical probability is corrected by weighting factors such as the degree of icing eccentricity and the wind direction matching degree. The potential galloping risk probability of each monitoring point is obtained through weighted calculation.

[0093] Furthermore, the degree of icing eccentricity is quantified by the ratio of eccentricity to conductor radius; the larger the ratio, the higher the weighting factor. Wind direction matching is assessed by the cosine of the angle between the wind direction and the eccentricity direction; the closer the angle is to 90 degrees, the higher the matching degree, and the larger the corresponding weighting factor. The product of the two weighting factors serves as the total correction coefficient, which is multiplied by the empirical probability to obtain the corrected probability value.

[0094] For example, during winter monitoring of a 500 kV transmission line, wavelet decomposition revealed a dominant 3-minute period and an 8-minute secondary period for wind direction fluctuations. When the frequency corresponding to the 3-minute period (0.33 Hz) was close to the conductor torsional vibration frequency (0.35 Hz), a beat frequency of 0.02 Hz was generated. The beat amplitude increased by 8% per second, and the energy accumulation rate exceeded 1.5 times the damping dissipation rate. The system determined that the line would enter an unstable state after 20 minutes.

[0095] Understandably, the output of the potential trigger probability value provides a quantitative basis for operation and maintenance decisions. When the probability exceeds the preset warning value, the system automatically activates the emergency plan, including adjusting line operating parameters, activating anti-galloping devices, or issuing manual intervention commands, effectively reducing the risk of galloping accidents.

[0096] The method provided by the embodiments of the present invention enables accurate assessment of the wind-ice coupled mechanical state, vibration coupling characteristics and instability risk of power grid lines, and completes the quantitative calculation of the probability of galloping risk, providing reliable quantitative support for the prevention and control of icing and galloping.

[0097] Furthermore, the method provided in this invention utilizes the dynamic matching relationship between the icing eccentricity feature and the wind direction angle to conduct mechanical state simulation analysis of power grid lines, identify the torsional stress state and local stress concentration areas of the power grid lines, including the following steps: Constructing a mass distribution function for the cross-section of each monitoring point of the power grid line within the eccentricity-sensitive section by combining the dynamic matching relationship between the icing eccentricity feature and the wind direction angle; wherein, the eccentricity-sensitive section refers to a power grid line section where the relative angle between the wind direction and the icing eccentricity direction is within a preset threshold range; determining the eccentric arm of the power grid line within the eccentricity-sensitive section through vector calculation of the mass distribution function and the wind direction angle; calculating the torsional moment and torsional stress state of the power grid line within the eccentricity-sensitive section based on the eccentric arm and wind pressure data; and calculating the stress change rate distribution of the power grid line within the eccentricity-sensitive section based on the torsional stress state, and identifying local stress concentration areas of the power grid line.

[0098] The preset threshold range is based on the wind-ice coupling vibration mechanism. It takes 90°, which is perpendicular to the direction of the wind direction and the eccentricity of the ice cover, as the core center. It is set in combination with the voltage level of the power grid line and the characteristics of the terrain and wind field to determine the relative angle of the wind direction. Specifically, it can be 60° to 120° for conventional plain lines, 75° to 105° for ultra-high voltage lines, or 45° to 135° for lines in complex mountainous terrain.

[0099] Eccentricity sensitive sections are power grid sections where the relative angle between the wind direction and the eccentricity of the icing falls within the aforementioned preset threshold range.

[0100] Specifically, in one implementation, constructing the mass distribution function requires precise regional division of the conductor cross-section. In practice, a polar coordinate system is established with the conductor's axis as the origin. The cross-section is divided into several sector regions according to preset angular intervals. The central angle of each sector region is typically set to the same value to maintain computational uniformity. For each sector region, the icing mass is calculated using icing thickness data and the standard density value of ice, while simultaneously recording the radial distance from the centroid of that region to the conductor's center. The mass distribution function is constructed using discrete point fitting, with the angular position of each sector region as the independent variable and the corresponding mass value as the dependent variable. A continuous mass distribution function expression is obtained through least squares fitting.

[0101] More preferably, a polar coordinate system is established with the conductor axis as the origin, and the mass distribution function is expressed as the polar angle. function The wind direction angle is represented as the polar angle. .

[0102] Specifically, the vector operation of the mass distribution function and the wind direction angle is to take the geometric center of the conductor cross-section as the origin, transform the mass distribution function into the icing center of gravity offset vector, combine it with the wind direction vector determined by the wind direction angle, and simultaneously obtain the length of the eccentric lever arm, that is, the projection modulus of the center of gravity offset vector in the wind direction, and the direction, that is, the angle with the wind direction, through vector projection and modulus calculation.

[0103] By using the eccentric lever arm length and combining the wind pressure data obtained from the wind speed sensor, the wind pressure intensity on the surface of the conductor is calculated based on the quadratic relationship between wind speed and dynamic pressure. The torsional moment value is obtained by multiplying the eccentric lever arm and the wind pressure intensity. Curve fitting is performed on the torsional moment at each monitoring point along the conductor length to obtain a continuous torsional moment distribution curve.

[0104] Based on the torsional moment distribution curve, the torsional theory of mechanics of materials is applied. The shear modulus is calculated according to the elastic modulus and Poisson's ratio of the conductor material. The polar moment of inertia is determined by combining the geometric dimensions of the conductor cross-section. The shear stress values ​​at each position of the conductor are calculated by dividing the torsional moment by the polar moment of inertia and then multiplying by the radial distance, thus obtaining the torsional stress field.

[0105] By dividing the stress difference between adjacent points in the torsional stress field by the spatial distance, the stress change rate distribution is calculated, and regions where the stress change rate exceeds a preset threshold are identified and marked as local stress concentration regions. The stress peak value and influence range of each concentration region are recorded to obtain the local stress concentration state diagram of the conductor.

[0106] The core physical mechanism for the dynamic matching of icing eccentricity and wind direction angle is determined by the centroid offset vector. The deflection trend of the icing eccentricity direction, represented by the angle, remains synchronized with the real-time change trend of the wind direction angle α, that is, within any consecutive 10 seconds, Angle and The rate of change of the relative angle No more than When this dynamic matching condition is met, the direction of the eccentric lever arm will continuously face the unfavorable direction of the wind force, that is, the length of the eccentric lever arm will continuously increase or remain at a threshold greater than 1.5 times the conductor radius, which will continuously aggravate the uneven distribution of wind pressure on the conductor surface, and thus significantly amplify the torsional stress.

[0107] It should be noted that determining the point of application of wind force involves aerodynamic principles. When wind blows across an icy conductor, the irregular shape of the icing causes a non-uniform distribution of wind pressure on the conductor's surface. The point of application of wind force is defined as the point of application of the resultant force of all infinitesimal wind elements, and its location is obtained by integrating the wind pressure of each infinitesimal element on the conductor's surface. The center of gravity offset point is the actual center of gravity position mentioned earlier. The eccentric lever arm is the vector between these two points; its length characterizes the strength of the torsional effect, and its direction determines the direction of the torsion.

[0108] Furthermore, the preferred formula for calculating the coordinates of the wind force application point is: , The coordinates of the centroid offset point are: ,in, For eccentric distance, The eccentricity angle is the eccentricity arm length, which is the distance between the two points, and its direction is from the point of wind action to the point of center of gravity offset.

[0109] For example, wind pressure intensity is calculated based on the principle of dynamic pressure in fluid mechanics. The quadratic relationship between wind speed and dynamic pressure is expressed as: the dynamic pressure value is equal to half the product of air density and the square of wind speed. In practical applications, the instantaneous wind speed data measured by the wind speed sensor is first processed by time averaging to obtain a stable average wind speed value. The air density is determined according to the local altitude and temperature conditions, and the dynamic pressure value is calculated using the above relationship. Multiplying the dynamic pressure value by the windward projected area of ​​the conductor yields the total wind force acting on the conductor. The torsional moment is obtained by cross product of the total wind force and the eccentric lever arm; this moment value reflects the magnitude of the torsional load borne by the conductor.

[0110] Specifically, the fitting of the torsional moment distribution curve employs a piecewise processing method. Local torsional moment values ​​are calculated at each monitoring point along the conductor's length; these discrete moment values ​​constitute the original dataset. Considering the continuity of the conductor, polynomial fitting or spline interpolation methods are used to connect the discrete points into a smooth curve. The fitting process must satisfy boundary conditions, namely the torsional constraints at the suspension points at both ends of the conductor and the continuity requirements between adjacent segments.

[0111] In one possible implementation, the application of torsion theory in mechanics of materials involves determining multiple material parameters. The shear modulus is calculated using the elastic modulus and Poisson's ratio of the conductor material, with the relationship being that the shear modulus equals the elastic modulus divided by twice the Poisson's ratio plus 1. For composite conductors such as steel-cored aluminum stranded wire, the different material properties of the steel core and aluminum strands need to be considered, and the equivalent stiffness method is used to calculate the combined shear modulus. The polar moment of inertia reflects the cross-section's ability to resist torsional deformation. For a circular cross-section, the polar moment of inertia equals the product of pi and the fourth power of the diameter divided by 32. When the conductor forms a non-circular cross-section after icing, the actual polar moment of inertia value needs to be calculated using numerical integration. The calculation of shear stress follows the torsion formula in mechanics of materials, i.e., the shear stress equals the product of the torsional moment and the radial distance divided by the polar moment of inertia. By calculating shear stress at different radial positions of the conductor cross-section and along the conductor's length, a complete torsional stress field distribution is formed.

[0112] Preferably, the stress change rate is calculated using the finite difference method. In the torsional stress field, two adjacent spatial points are selected, the stress difference between the two points is calculated, and then divided by the spatial distance between the two points to obtain the stress change rate in that direction. To obtain comprehensive stress gradient information, the stress change rate needs to be calculated separately in the radial, circumferential, and axial directions to form a stress gradient tensor.

[0113] For example, during icing monitoring of a power transmission line in a mountainous area, a significant eccentric distribution was observed when the ice thickness on the north side of the conductor reached 20 mm while that on the south side was only 5 mm. The eccentric lever arm length calculated using the aforementioned method was 8 mm. Under a northwest wind of 15 m / s, the torsional moment borne by the conductor reached its peak. Stress analysis revealed stress concentration in the icing transition zone, with the local stress value being 2.5 times that of a uniformly distributed zone.

[0114] Understandably, the establishment of local stress concentration state diagrams provides crucial data support for conductor galloping early warning. By identifying the location and intensity of stress concentration areas, maintenance personnel can accurately pinpoint weak points in the conductors and take targeted anti-galloping measures, effectively reducing the risk of line faults.

[0115] The method provided by the embodiments of the present invention, by combining the eccentricity characteristics of icing and the wind direction angle to construct a mass distribution function and using vector operations to quantify the eccentric lever arm, accurately defines the eccentric sensitive section and realizes the quantitative characterization of the eccentric lever arm, providing core mechanical parameter support for the subsequent identification of torsional moment, torsional stress state and local stress concentration area.

[0116] Furthermore, the method provided in the embodiments of the present invention, based on the first coupling feature and combined with wind direction periodic fluctuation data, analyzes the second coupling feature of the vibration frequency of the power grid line under a specific wind direction and the vibration frequency caused by icing eccentricity, and determines the instability state of the power grid line by combining the energy balance principle. This includes the following steps: based on the first coupling feature, decomposing the wind direction periodic fluctuation data into periodic components to extract the periodic features of the wind direction fluctuations; combining the periodic features and the first coupling feature, analyzing the second coupling feature of the vibration frequency of the power grid line in the local stress concentration area under a specific wind direction and the vibration frequency caused by icing eccentricity; based on the second coupling feature, calculating the energy accumulation and energy dissipation per unit time during the vibration process of the power grid line; and combining the energy balance principle, comparing the dynamic relationship between the energy accumulation and energy dissipation to determine the instability state of the power grid line.

[0117] The periodic characteristics of wind direction fluctuations, namely the dominant and secondary periods reflecting the regular changes in the wind field in the periodic fluctuation data of wind direction, correspond to the fluctuation frequency and energy distribution of the wind field at different scales.

[0118] Based on the first coupling feature, wavelet transform is used to decompose the periodic fluctuation data of wind direction to obtain the periodic component, and then the frequency of the conductor vibration is superimposed on the spectrum to determine the beat vibration phenomenon and extract the second coupling feature.

[0119] Specifically, Morlet wavelets are used as basis functions. By changing the scale and translation parameters, the wind direction time series is decomposed into multi-resolution components, breaking the signal down into components of different frequency bands. Large scales correspond to low-frequency components, reflecting the slow trend of wind direction change; small scales correspond to high-frequency components, capturing rapid fluctuations in wind direction. Statistical analysis of the magnitudes of the coefficients at each scale identifies the scale ranges where energy is concentrated, and the corresponding frequencies represent the dominant period of wind direction fluctuations. Secondary periods are determined by finding secondary energy peaks, typically corresponding to periodic changes caused by vortex shedding in the wind field or topographic disturbances.

[0120] The physical significance of spectral superposition lies in analyzing the interaction between two periodic excitations. When the frequency of wind direction change is close to the frequency of torsional vibration caused by icing eccentricity, a beat vibration phenomenon occurs. Beat vibration is a periodic amplitude modulation phenomenon produced by the superposition of two simple harmonic vibrations with similar frequencies, and its beat frequency is equal to the absolute value of the difference between the two original frequencies. The system calculates the difference between the dominant frequency of wind direction change and the natural frequency of torsional vibration to determine whether it is less than a preset frequency difference threshold, which is usually set to 5% of the natural frequency. When the condition is met, the vibration amplitude will show a periodic increase and decrease. The energy accumulation within the increased period may cause the amplitude to exceed the safety limit.

[0121] It should be noted that the calculation of the beat vibration amplitude growth rate is based on envelope analysis. The system performs a Hilbert transform on the vibration time history curve to extract the instantaneous amplitude envelope, and obtains the amplitude change rate by differentiating the envelope. During the amplification phase of the beat vibration, the amplitude growth rate is positive and gradually increases; during the deceleration phase, the growth rate is negative. Specifically, the calculation of the beat vibration amplitude growth rate involves performing a Hilbert transform on the vibration displacement time history curve to obtain an analytic signal. The magnitude of the analytic signal is extracted as the instantaneous amplitude envelope, and the first-order numerical derivative of the amplitude envelope using the central difference method is performed to obtain the beat vibration amplitude growth rate. The system focuses on the maximum growth rate during the amplification phase, as this value directly relates to the rate of vibration energy accumulation.

[0122] The cumulative vibration energy is calculated based on the beat amplitude growth rate, and the energy dissipation per unit time is calculated based on the conductor damping coefficient and vibration frequency. If the cumulative energy continues to exceed the dissipated energy, the power grid line is determined to have entered an unstable state.

[0123] Specifically, based on the beat vibration amplitude growth rate, the relationship between the increase in vibration kinetic energy and time is expressed as the product of the square of the amplitude growth rate and the mass. The cumulative energy value is obtained by integrating this relationship over time. The energy dissipation value per unit time is calculated based on the conductor material damping coefficient and vibration frequency. If the cumulative energy exceeds the dissipated energy, it is marked as an unstable state, and the time node of instability is determined. For the instability time node, the ratio of the number of instability occurrences under similar historical meteorological conditions to the total number of observations is used as an empirical probability. This empirical probability is corrected based on the current degree of icing eccentricity and wind direction matching. A weighted average is then used to obtain the potential triggering probability value for conductor galloping.

[0124] More preferably, the energy balance method is used to analyze the accumulation and dissipation of vibration energy. The increase in vibration kinetic energy is calculated by multiplying the square of the amplitude growth rate by the equivalent mass, where the equivalent mass considers the combined effects of the conductor's self-weight, icing mass, and additional mass. The energy accumulation process is obtained by integrating the kinetic energy increase over time, with the upper limit of the integration being the current moment and the lower limit being the moment of vibration onset. The calculation of dissipated energy is based on structural damping theory, and the damping coefficient is determined through free decay experiments or empirical formulas, typically related to material properties, structural form, and environmental conditions. The energy dissipation per unit time is equal to the product of the damping coefficient, the square of the vibration frequency, and the square of the amplitude.

[0125] When the accumulated energy continuously exceeds the dissipated energy, the system is determined to enter an unstable state, at which point even minor disturbances can trigger large-scale fluctuations. Recording the instability time points provides crucial data support for subsequent probabilistic analysis. By statistically analyzing the instability occurrence times under different meteorological conditions, a correlation model between instability time and environmental factors can be established.

[0126] The method provided by the embodiments of the present invention, through the core means of extracting periodic features from wind direction periodic data based on the first coupling feature decomposition, analyzing vibration frequency coupling to obtain the second coupling feature based on the first coupling feature, calculating vibration energy accumulation and dissipation based on the second coupling feature, and judging the instability state based on the energy balance principle, accurately reveals the vibration coupling law of conductors under wind-ice coupling, and realizes the scientific and accurate determination of the instability state of power grid lines.

[0127] The potential icing risk probability is fused with the wind direction-eccentricity interaction coefficient, and then geospatial visualization is applied to generate an icing disaster risk distribution map that takes into account dynamic mechanics, risk level, and spatial distribution. This includes the following steps:

[0128] If the probability of potential dancing risk is within a preset probability range, then the correlation coefficient between the environmental gradient data and the amplitude data is calculated; wherein, the amplitude data is obtained based on the first coupling feature extraction; and the environmental gradient data is gradient distribution data generated based on supplementary environmental variables.

[0129] The correlation coefficient is compared with a preset threshold. If the correlation coefficient is greater than or equal to the preset threshold, the corresponding environmental gradient data is determined to be a key influencing factor.

[0130] Calculate the ratio of the peak torsional stress corresponding to the key influencing factor to the material yield strength, classify the risk level based on the magnitude of the ratio, and determine the risky line segments in the power grid line segments;

[0131] The wind direction-eccentricity interaction coefficient is marked on the geographical coordinates of the risk route segment, and the corresponding color is assigned according to the numerical level of the wind direction-eccentricity interaction coefficient. A heat map is rendered on the geographical coordinates to generate an ice accretion disaster risk distribution map.

[0132] Furthermore, the method provided in the embodiments of the present invention further includes, before step S3: based on real-time wind direction monitoring data of each monitoring point of the power grid line, statistically obtaining the wind direction change frequency of each monitoring point per unit time; performing correlation calculation between the wind direction change frequency of each monitoring point and the eccentricity direction angle in the corresponding icing eccentricity feature to obtain the calculation result; dividing the calculation result by a preset normalization coefficient to calculate the wind direction-eccentricity interaction influence coefficient of each monitoring point of the power grid line.

[0133] Risk change frequency refers to the number of times the wind direction deflection angle exceeds a preset threshold per unit time, obtained from statistics based on a sliding time window.

[0134] The system is set to a fixed time window, typically 1 hour, and counts the number of times the wind direction changes significantly within this time period. A significant change is defined as a wind direction deflection angle exceeding a preset threshold. The preset threshold can be 15° or 30°.

[0135] The frequency of wind direction change at each monitoring point is correlated with the eccentricity angle in the corresponding icing eccentricity feature. That is, the product of the frequency of wind direction change and the eccentricity angle is calculated to obtain the result. The eccentricity angle is expressed in radians.

[0136] The preset normalization coefficient is determined based on the maximum value of historical data, so that the interaction effect coefficient is kept between 0 and 1.

[0137] The wind direction-eccentricity interaction coefficient, which is the ratio of the calculated result to the normalized coefficient, is used to quantify the coupling interaction strength between wind direction change and icing eccentricity, providing a normalized quantitative indicator for dynamic assessment of galloping risk.

[0138] The method provided by the embodiments of this invention is a core means of calculating the wind direction-eccentricity interaction coefficient by statistically analyzing the frequency of wind direction changes at monitoring points, associating it with the eccentricity direction angle of icing, and normalizing the result. This achieves a quantitative characterization of the coupling effect between wind direction and icing eccentricity, providing key support for the refined dynamic assessment of power grid line galloping risk.

[0139] Furthermore, step S3 of the method provided in the present invention preferably includes: if the probability of potential galloping risk is within a preset probability range, then calculating the correlation coefficient between environmental gradient data and amplitude data; wherein, amplitude data is obtained based on the extraction of the first coupling feature; environmental gradient data is gradient distribution data generated based on supplementary environmental variables; comparing the correlation coefficient with a preset threshold, and if the correlation coefficient is greater than or equal to the preset threshold, then determining the corresponding environmental gradient data as a key influencing factor; calculating the ratio of the peak torsional stress corresponding to the key influencing factor to the yield strength of the material, classifying the risk level based on the magnitude of the ratio, and determining the risk line segment in the power grid line segment; marking the wind direction-eccentricity interaction influence coefficient on the geographical coordinates of the risk line segment, assigning corresponding colors according to the numerical level of the wind direction-eccentricity interaction influence coefficient, rendering a heat map on the geographical coordinates, and generating an icing disaster risk distribution map.

[0140] The preset probability range is set based on historical galloping accident statistics, line voltage levels, terrain conditions, and conductor damping characteristics. The high-risk trigger range is defined using the correlation between empirical probability and risk triggering as a benchmark. For example, for conventional 220kV / 500kV plain lines, the preset probability range is 60% to 100%; for sensitive lines such as UHV and long-span lines in mountainous areas, the preset probability range is 50% to 100%.

[0141] Supplement environmental variables, including temperature and humidity data at various monitoring points along the conductor obtained through weather stations and micrometeorological sensors.

[0142] Environmental gradient data includes temperature gradient data and humidity gradient data. The temperature gradient data is derived from supplementary environmental variables by dividing the temperature difference between adjacent monitoring points by the distance. The humidity gradient data is calculated using the same method as the temperature gradient data. An environmental gradient distribution map is generated based on the environmental gradient data.

[0143] The amplitude data is obtained by extracting the conductor vibration displacement time history curve from the time-series simulation results corresponding to the first coupling feature, and obtaining the amplitude envelope and evolution law through Hilbert transform.

[0144] The correlation coefficient between environmental gradient data and amplitude data is used to quantify the degree of linear correlation between environmental gradient data such as local temperature difference and humidity gradient and conductor vibration amplitude. The Pearson correlation coefficient is preferred, and its calculation process includes dividing the covariance of the temperature gradient data and amplitude data by the product of their respective standard deviations.

[0145] When the absolute value of the correlation coefficient exceeds a preset threshold, the environmental gradient data corresponding to the correlation coefficient is identified as a key influencing factor. The preset threshold is statistically calibrated based on historical environmental and vibration correlation data, with a preferred value of 0.6 to 0.7. A correlation coefficient exceeding this threshold is considered a key influencing factor.

[0146] Based on the ratio of the peak torsional stress corresponding to the key influencing factors to the material yield strength, the power grid line segments are divided into three risk levels: high, medium, and low. A galloping early warning report is generated, which includes the risk level, location coordinates, influencing factor values, and recommendations for activating anti-galloping devices.

[0147] Specifically, in one implementation, the environmental gradient map is constructed based on a dense sensor network. Weather stations provide regional temperature and humidity baseline data, while micro-meteorological sensors are responsible for capturing local environmental changes around the conductor. Sensors are arranged at fixed intervals along the conductor, forming a linear monitoring array. The temperature gradient is calculated by dividing the temperature difference between two adjacent measuring points by the distance between the two points, in degrees Celsius per meter. The humidity gradient is calculated using the same method, in percentages per meter.

[0148] It should be noted that calculating the Pearson correlation coefficient requires a time-synchronized data sequence. The system first aligns the environmental gradient data and amplitude data in time to ensure that each gradient value has a corresponding amplitude value. The correlation coefficient calculation formula involves covariance and standard deviation. Covariance reflects the common trend of change between the two variables, while standard deviation is used for normalization. The correlation coefficient ranges from -1 to 1, with the absolute value closer to 1 indicating a stronger correlation. The preset threshold is usually set at 0.6; environmental factors exceeding this value are considered to have a significant impact on vibration.

[0149] Specifically, the risk level is determined based on the ratio of peak torsional stress to the material's yield strength. When the peak stress reaches 70% to 85% of the yield strength, it is classified as medium risk; above 85% is high risk; and below 70% is low risk. This classification takes into account the material's safety margin and the cumulative effect of fatigue. A high-risk line segment is a power grid line segment whose risk level reaches the high-risk level.

[0150] Preferably, the galloping warning report adopts a structured format and includes four core parts: the risk level is color-coded, with red representing high risk, yellow representing medium risk, and green representing low risk; the location coordinates are accurate to the tower number and span percentage; the influencing factor values ​​list the specific values ​​of temperature gradient, humidity gradient, and correlation coefficient; and the anti-galloping device activation recommendations are given according to the risk level, with high risk recommending immediate activation and medium risk recommending preheating preparation.

[0151] For example, during a cold wave, a local temperature gradient of 0.8 degrees Celsius per meter was measured on a certain mountain railway line, with a correlation coefficient of 0.72 with the vibration amplitude, which was identified as a key influencing factor. Combined with the fact that the peak stress in this section reached 88% of the material's yield strength, the system determined it to be a high-risk section, and the early warning report recommended the immediate activation of interphase spacers and line de-icing devices.

[0152] The wind direction-eccentricity interaction coefficient is marked on the geographic coordinates of the risk route segment, ensuring a one-to-one match between the geographic coordinates of the risk route segment and the wind direction-eccentricity interaction coefficient of the corresponding monitoring point. Corresponding colors are assigned according to the numerical level of the wind direction-eccentricity interaction coefficient, and a heat map is rendered on the geographic coordinates to generate an icing disaster risk distribution map.

[0153] The heatmap rendering employs a hierarchical coloring scheme. The system divides the wind direction-eccentricity interaction coefficient into five numerical levels, each corresponding to a specific color, gradually transitioning from dark blue for low risk to dark red for high risk. Geographic coordinates are represented using latitude and longitude, and the route location is overlaid with the actual map through a geographic information system to achieve a visual display of risk distribution. Based on the coloring scheme corresponding to the numerical levels of the wind direction-eccentricity interaction coefficient, a heatmap is rendered on the geographic coordinates to generate an icing disaster risk distribution map.

[0154] For example, the risk distribution map of ice accretion disaster for a cross-river transmission line shows that the area in the middle of the river is a red high-risk area due to frequent changes in wind direction and a large eccentric angle, while the areas on both banks are green low-risk areas due to the relatively stable wind direction caused by terrain shielding.

[0155] The method provided by the embodiments of the present invention identifies key influencing factors, classifies risk levels and risk lines by calculating the correlation coefficient between environmental gradient and amplitude, and then binds the wind direction-eccentricity interaction coefficient to geographical coordinates and renders a heat map. This achieves the identification of key causes of icing risk, the quantification of risk level and spatial visualization, providing an intuitive and accurate decision-making basis for the prevention and control of icing disasters.

[0156] Furthermore, step S4 of the method provided in the embodiments of the present invention preferably includes: extracting the geographical coordinates of key disaster locations in the power grid line, as well as the corresponding risk level, icing eccentricity characteristic parameters, and local stress concentration characteristic parameters, based on the icing disaster risk distribution map, to form a basic set of de-icing parameters; classifying de-icing priorities according to the risk association attributes of key disaster locations based on the basic set of de-icing parameters, and formulating targeted differentiated de-icing strategies adapted to each priority; parsing the targeted differentiated de-icing strategies into executable operation instructions for the de-icing devices, and issuing operation instructions to the de-icing devices in the corresponding work areas; after receiving the operation instructions, the de-icing devices perform precise de-icing operations on each key disaster location according to the de-icing strategy.

[0157] Key disaster areas are the core weak sections of power grid lines that are highly vulnerable in the icing disaster risk distribution map, characterized by high risk levels, significant icing eccentricity, and prominent local stress concentration, making them extremely prone to safety accidents such as line galloping and line breakage.

[0158] Based on the risk level, icing eccentricity, and local stress peak value of the basic parameters for de-icing, and combined with the importance of the line, key disaster areas are divided into three levels of de-icing priority: high, medium, and low, according to the degree of risk and hazard. The higher the risk, the higher the de-icing priority.

[0159] Differentiated de-icing operation plans are matched for different de-icing priorities: high-priority areas adopt high-power, high-frequency, and immediate de-icing strategies; medium-priority areas adopt conventional intensity and timed de-icing strategies; and low-priority areas adopt inspection-based and on-demand de-icing strategies, forming a targeted de-icing plan that is adapted to the risk level.

[0160] After receiving the operation command, the de-icing device locates the key disaster area based on the geographical coordinates, and performs precise de-icing operations in a fixed point, quantity, and direction according to the de-icing parameters set in the command for the target area with eccentric ice accumulation and local stress concentration.

[0161] Real-time data such as ice thickness, torsional stress, and vibration amplitude of power grid lines after de-icing are collected and fed back to the control system to achieve dynamic linkage between icing risk prediction and de-icing treatment, providing practical data support for parameter optimization of the risk prediction model.

[0162] The method provided by the embodiments of the present invention extracts parameters of key disaster locations to construct a basic parameter set for de-icing, prioritizes according to risks, and formulates targeted and differentiated de-icing strategies. It then parses the strategies into executable instructions for the device and issues them to execute precise de-icing. This is the core means to achieve accurate identification, priority ranking, and targeted treatment of power grid icing disasters, thereby improving the efficiency of de-icing operations and the safety assurance capability of power lines.

[0163] Embodiments of the present invention also provide a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0164] Embodiments of the present invention also provide a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the method of an embodiment of the present invention.

[0165] An embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method of the embodiment of the present invention.

[0166] refer to Figure 3 The present invention will now describe a structural block diagram of an electronic device that can serve as an embodiment of the present invention, serving as an example of a hardware device applicable to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0167] like Figure 3As shown, the electronic device includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 502 or loaded from storage unit 508 into RAM (Random Access Memory) 503. RAM 503 can also store various programs and data required for the operation of the electronic device. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An I / O interface (Input / Output Interface) 505 is also connected to bus 504.

[0168] Multiple components in the electronic device are connected to I / O interface 505, including: input unit 506, output unit 507, storage unit 508, and communication unit 509. Input unit 506 can be any type of device capable of inputting information into the electronic device. Input unit 506 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 507 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 508 may include, but is not limited to, disks and optical discs. Communication unit 509 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0169] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU (Central Processing Unit), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as a computer program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 502 and / or communication unit 509. In some embodiments, the computing unit 501 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0170] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0171] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0172] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0173] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0174] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0175] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0176] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A power grid line icing disaster management system, characterized in that, The system includes: Sensor components are used to collect data on icing of power grid lines and environmental data; The control system, connected to the sensor assembly, is used to analyze the icing distribution characteristics of the cross-sections of each monitoring point of the power grid line based on the icing data and the environmental data, calculate the deviation between the actual centroid of the cross-section and the geometric center of the standard conductor, and extract the icing eccentricity feature; combine the dynamic matching relationship between the icing eccentricity feature and the wind direction angle to extract the first coupling feature between wind speed and conductor vibration, perform mechanical state simulation analysis, and calculate the potential galloping risk probability of each monitoring point; fuse the potential galloping risk probability with the wind direction-eccentricity interaction coefficient, and generate an icing disaster risk distribution map that takes into account both dynamic mechanical risk and spatial distribution through geospatial visualization processing; based on the icing disaster risk distribution map, extract key disaster locations and characteristic parameters, and generate a de-icing strategy; A de-icing device, connected to the control system, is used to perform de-icing operations on the critical disaster-prone areas according to the de-icing strategy.

2. A method for handling icing disasters on power grid lines, characterized in that, The method applied to the power grid line icing disaster handling system of claim 1 includes: Based on the icing data and environmental data of the power grid line, the icing distribution characteristics of the cross-section of each monitoring point of the power grid line are analyzed, the deviation between the actual centroid of the cross-section and the geometric center of the standard conductor is calculated, and the icing eccentricity characteristics are extracted. By combining the dynamic matching relationship between the icing eccentricity feature and the wind direction angle, the first coupling feature between wind speed and conductor vibration is extracted, and mechanical state simulation analysis is performed to calculate the potential galloping risk probability of each monitoring point. The potential galloping risk probability is fused with the wind direction-eccentricity interaction coefficient, and after geospatial visualization processing, an ice accretion disaster risk distribution map that takes into account both dynamic mechanical risk and spatial distribution is generated. Based on the icing disaster risk distribution map, key disaster locations and characteristic parameters are extracted to generate a de-icing strategy. The de-icing device then performs de-icing operations on the key disaster locations according to the de-icing strategy.

3. The method for handling power grid line icing disasters according to claim 2, characterized in that, Based on icing data and environmental data of the power grid line, the icing distribution characteristics of the cross-section at each monitoring point of the power grid line are analyzed, the deviation between the actual centroid of the cross-section and the geometric center of the standard conductor is calculated, and the icing eccentricity characteristics are extracted, including the following steps: The icing data and the environmental data are sampled to obtain the icing thickness value of the power grid line from all angles. The icing thickness values ​​at all angles are fitted to construct the icing profile curves of the cross-sections of each monitoring point of the power grid line. Based on the icing profile curve, calculate the actual centroid coordinates and icing mass of each cross section; By performing vector operations on the actual center of gravity coordinates and the geometric center coordinates of the standard conductor corresponding to the cross-section, the center of gravity offset vector and eccentric mass of each cross-section are obtained. The mass eccentricity of each cross section is obtained based on the ratio of the eccentric mass to the icing mass.

4. The method for handling power grid line icing disasters according to claim 3, characterized in that, Based on the icing profile curve, the actual centroid coordinates and icing mass of each cross-section are calculated, including the following steps: Based on the icing contour curve, each of the cross sections is divided into multiple corresponding sector units; Calculate the mass of each of the sector units, and the radial distance between the centroid of each of the sector units and the geometric center of the standard conductor corresponding to the cross-section; The mass of each of the sector units is summed to obtain the icing mass of each of the cross sections; Based on the ratio of the sum of the mass moments of each of the sector units to the corresponding ice-covered mass, the actual centroid position coordinates of each of the cross sections are determined; wherein, the sum of the mass moments is the sum of the products of the mass of each of the sector units and the corresponding radial distance.

5. The method for handling icing disasters on power grid lines according to claim 2, characterized in that, Based on the dynamic matching relationship between the icing eccentricity feature and the wind direction angle, the first coupling feature between wind speed and conductor vibration is extracted, and a mechanical state simulation analysis is performed to calculate the potential galloping risk probability of each monitoring point, including the following steps: Based on the dynamic matching relationship between the icing eccentricity characteristics and the wind direction angle, a mechanical state simulation analysis is conducted on the power grid line to identify the torsional stress state of the power grid line and the local stress concentration area. When the peak value of the torsional stress in the torsional stress state reaches the preset vibration risk association threshold, a time-series simulation is performed on the dynamic interaction between wind speed and the vibration frequency of the power grid line to extract the first coupling feature between wind speed and conductor vibration. Based on the first coupling feature, combined with the periodic fluctuation data of wind direction, the second coupling feature of the vibration frequency of the power grid line under a specific wind direction and the vibration frequency caused by icing eccentricity is analyzed, and the instability state of the power grid line is determined by combining the energy balance principle. Based on the aforementioned instability state, the empirical probability is corrected by combining historical similar working condition data with current icing and wind direction parameters, and the potential galloping risk probability of each monitoring point is calculated.

6. The method for handling power grid line icing disasters according to claim 5, characterized in that, Based on the dynamic matching relationship between the icing eccentricity characteristics and the wind direction angle, a mechanical state simulation analysis is conducted on the power grid line to identify the torsional stress state and local stress concentration areas of the power grid line, including the following steps: Based on the dynamic matching relationship between the icing eccentricity characteristics and the wind direction angle, a mass distribution function is constructed for the cross-section of each monitoring point of the power grid line within the eccentricity sensitive section; wherein, the eccentricity sensitive section refers to the power grid line section where the relative angle between the wind direction and the icing eccentricity direction is within a preset threshold range. The eccentric lever arm of the power grid line in the eccentric sensitive section is determined by vector calculation of the mass distribution function and the wind direction angle. Based on the eccentric lever arm and wind pressure data, the torsional moment and torsional stress state of the power grid line in the eccentric sensitive section are calculated. Based on the torsional stress state, the stress change rate distribution of the power grid line in the eccentric sensitive section is calculated, and the local stress concentration area of ​​the power grid line is identified.

7. The method for handling power grid line icing disasters according to claim 6, characterized in that, Based on the first coupling characteristic, combined with wind direction periodic fluctuation data, the second coupling characteristic of the vibration frequency of the power grid line under a specific wind direction and the vibration frequency caused by icing eccentricity is analyzed. The instability state of the power grid line is determined by combining the energy balance principle, including the following steps: Based on the first coupling feature, the periodic component decomposition of the wind direction periodic fluctuation data is performed to extract the periodic features of the wind direction fluctuation. Combining the periodic characteristics with the first coupling characteristics, a second coupling characteristic is obtained between the vibration frequency of the power grid line in the local stress concentration area under a specific wind direction and the vibration frequency caused by icing eccentricity. Based on the second coupling characteristic, the energy accumulation and energy dissipation per unit time during the vibration process of the power grid line are calculated. By combining the principle of energy balance and comparing the dynamic relationship between the energy accumulation and the energy dissipation, the instability state of the power grid line can be determined.

8. The method for handling icing disasters on power grid lines according to claim 2, characterized in that, Before fusing the potential galloping risk probability with the wind direction-eccentricity interaction coefficient, the method includes: Based on the real-time wind direction monitoring data of each monitoring point on the power grid line, the frequency of wind direction change per unit time at each monitoring point is statistically obtained. The wind direction change frequency at each monitoring point is correlated with the eccentricity angle in the corresponding icing eccentricity feature to obtain the calculation result; Divide the calculation result by a preset normalization coefficient to calculate the wind direction-eccentricity interaction influence coefficient of each monitoring point of the power grid line.

9. The method for handling power grid line icing disasters according to claim 8, characterized in that, The potential icing risk probability is fused with the wind direction-eccentricity interaction coefficient, and then geospatial visualization is applied to generate an icing disaster risk distribution map that takes into account dynamic mechanics, risk level, and spatial distribution. This includes the following steps: If the probability of potential dancing risk is within a preset probability range, then the correlation coefficient between the environmental gradient data and the amplitude data is calculated; wherein, the amplitude data is obtained based on the first coupling feature extraction; and the environmental gradient data is gradient distribution data generated based on supplementary environmental variables. The correlation coefficient is compared with a preset threshold. If the correlation coefficient is greater than or equal to the preset threshold, the corresponding environmental gradient data is determined to be a key influencing factor. Calculate the ratio of the peak torsional stress corresponding to the key influencing factor to the material yield strength, classify the risk level based on the magnitude of the ratio, and determine the risky line segments in the power grid line segments; The wind direction-eccentricity interaction coefficient is marked on the geographical coordinates of the risk route segment, and the corresponding color is assigned according to the numerical level of the wind direction-eccentricity interaction coefficient. A heat map is rendered on the geographical coordinates to generate an ice accretion disaster risk distribution map.

10. The method for handling icing disasters on power grid lines according to claim 2, characterized in that, Based on the icing disaster risk distribution map, key disaster locations and characteristic parameters are extracted to generate a de-icing strategy. The de-icing device then performs de-icing operations on the key disaster locations according to the de-icing strategy, including the following steps: Based on the icing disaster risk distribution map, the geographical coordinates of key disaster locations in the power grid lines, as well as the corresponding risk levels, icing eccentricity characteristic parameters, and local stress concentration characteristic parameters, are extracted to form a basic parameter set for de-icing. Based on the aforementioned basic de-icing parameter set, de-icing priorities are divided according to the risk association attributes of key disaster locations, and targeted differentiated de-icing strategies adapted to each priority are formulated. The targeted differentiated de-icing strategy is parsed into operational instructions that can be executed by the de-icing device, and the operational instructions are sent to the de-icing device in the corresponding work area. After receiving the operation command, the de-icing device performs precise de-icing operations on each critical disaster site according to the de-icing strategy.

11. An electronic device, comprising: A processor and a memory storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the power grid line icing disaster handling method according to any one of claims 2 to 10.

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