Early warning method for fault state of power transmission line in extreme weather

Abstract

The invention discloses a power transmission line fault state early warning method in extreme weather, which belongs to the technical field of electric power and specifically comprises the following steps: S1, acquiring inspection track data; s2, preprocessing inspection data; s3, carrying out digital twin three-dimensional modeling; s4, establishing a line state prediction model under the influence of typhoon; and S5, dividing multiple layers of early warning levels. According to the early warning method for the fault state of the power transmission line in the extreme weather, a power transmission line breakage fault probability prediction model is established by relying on advanced technologies such as unmanned aerial vehicle inspection and digital twin modeling, layered early warning levels are divided, danger response fine processing is achieved, and resource waste and manpower loss are effectively avoided.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method for early warning of a power transmission line fault state under extreme weather conditions. Background Art

[0002] As global climate change intensifies, extreme weather such as typhoons occur frequently, posing a major threat to the safe operation of transmission lines. Traditional monitoring methods mainly rely on manual inspections and fixed monitoring equipment, which are unable to finely monitor the status of transmission lines under extreme weather conditions, resulting in inaccurate disaster data analysis and untimely early warning information. Therefore, it is important to study early warning methods for transmission line fault conditions under extreme weather conditions. Summary of the invention

[0003] The purpose of this invention is to provide a method for early warning of power transmission line fault status under extreme weather conditions. Relying on cutting-edge technologies such as drone inspection and digital twin modeling, a transmission line disconnection failure probability prediction model is established, hierarchical early warning levels are divided, and refined processing of dangerous responses is achieved, effectively avoiding waste of resources and manpower loss.

[0004] To achieve the above object, the present invention provides a method for early warning of power transmission line fault status under extreme weather conditions, which specifically includes the following steps: S1. Obtain inspection trajectory data; S2, preprocessing inspection data; S3, perform digital twin 3D modeling; S4. Establish a line status prediction model under the influence of typhoon; S5. Divide into multiple layers of warning levels.

[0005] Preferably, in S1, according to the three-side law, the relationship between the ea and eb values ​​and the set threshold is analyzed to obtain the UAV inspection trajectory data, wherein e is the UAV position, a and b are the base station positions, c is an arbitrarily selected point, and eabc is the UAV inspection trajectory range; when ea is greater than the set threshold, the UAV patrols to the left and obtains the left inspection trajectory data; when eb is less than the set threshold, the UAV patrols to the right and obtains the right inspection trajectory data; when the set threshold is greater than ea and less than eb, the UAV patrols forward and obtains the front inspection trajectory data.

[0006] Preferably, in S2, by scanning the laser radar data, the plane coordinates of the drone are used as index values, and the corresponding multispectral stitching image data is searched according to the index value, and the corresponding points are interpolated using the index value to improve the accuracy of data aggregation, and the interpolation processing is performed using the bilinear interpolation method, where point O is the pixel coordinate value of the drone inspection image (x 0 ,y 0), that is, (0,0), A, B, C, D are adjacent points, and the pixel at point O is affected by the pixels at other adjacent points. The closer the distance, the greater the weight. , is the spectral information point, and the interpolation calculation formula is as follows: (1); (2); (3) in, represents the interpolation symbol, Represents spectral information points The interpolation of Indicates the horizontal coordinate values ​​of points A and B. Indicates the horizontal coordinate values ​​of points C and D. represents the interpolation of the spectral information point A, represents the interpolation of the spectral information point D, Represents spectral information points The interpolation of represents the interpolation of spectral information point B, represents the interpolation of the spectral information point C, represents the interpolation of the spectral information point O, Indicates the ordinate values ​​of points A and D. Indicates the ordinate values ​​of points B and C; Combining the bilinear interpolation method and using the LAS data format of the laser point cloud, an accurate spectrum is obtained and stored as the spectrum data of the laser point cloud to realize the preprocessing of UAV inspection data.

[0007] Preferably, digital twin technology is used in S3 to fully cover the working area, transmission lines, towers and their surrounding areas, ensure real-time transmission of high-definition video, establish a three-dimensional model of the drone inspection site based on the Beidou grid method, and input the pre-processed data into the digital twin module.

[0008] Preferably, S3 is combined with the digital twin module, given a data set ,Depend on A data set consisting of samples is selected to construct a kernel function using a nonparametric regression model. Nonparametric analysis is performed on the data. After determining the drone inspection data, if you want to obtain a global function, you need to perform model fitting to reduce data aggregation loss. The least squares fitting method is used to find the function for the data set. The fit is as follows: (4); (5); in, represents the number of fittings, represents the result of Gaussian function superposition, Indicates Datasets, represents the fitting result, express to Datasets; The calculation formula of Gaussian function superposition result is as follows: (6); in, represents the full vector of Gaussian function, and Respectively represent the mean and variance of the Gaussian function. In the least squares fitting method, each data in a given data set is composed of a combination of Gaussian functions. Based on this, the data aggregation model for the single-core problem is constructed, which can be expressed as: (7); in, represents a sample cluster, represents the kernel matrix, represents a set of adjacent samples; for Kernel matrix, construction The regression coefficient matrix can be expressed as: (8); in, express The regression coefficient matrix The regression coefficient matrix, express In the data set Datasets; For the kernel matrix, its kernel regression neighborhood and regression coefficient can be adapted according to the kernel evidence. For the data aggregation model constructed for multi-kernel problems, the model is fused by the linear weighting method, which is expressed as: (9); in, Is indivual The data aggregation set composed of samples, represents the multi-core aggregation weight, express The regression coefficient matrix The transpose of the regression coefficient matrix, represents the linearly weighted fusion model, Represents the constraint conditions, thereby realizing the construction of the UAV inspection data aggregation model.

[0009] Preferably, S4 specifically comprises the following steps: S4.1. Carry out typhoon wind field simulation under extreme weather conditions, generate wind speed, wind direction and rainfall intensity correction samples that reflect the instantaneous dynamic characteristics of typhoons under extreme weather conditions, and perform grid processing on the areas involved in overhead lines to simulate the meteorological data of the corresponding grids; S4.2. Calculate the load on the line and the real-time wind deviation distance of the line using the line structure and location information, and describe the real-time impact process of typhoons under extreme weather conditions; S4.3, establish the MSSSIM function corresponding to the disconnection fault mode, and then establish the fault probability model; S4.4. Based on the series reliability principle, the overhead line is equivalent to a series structure system composed of several tension sections, and a comprehensive fault probability model of the overhead line taking into account line breakage is obtained.

[0010] Preferably, the specific steps of constructing the fault probability model in S4.3 are: The probability of overhead line failure is directly related to the instantaneous impact process of the typhoon. The impact process is the extreme effect of wind and rain loads on overhead lines, which is reflected in the form of "stress" of wind and rain. The comprehensive load and real-time gap distance of the line are regarded as "stress", and the "intensity" corresponding to each "stress" is clarified. Then, based on the MSSSIM modeling idea, the fault probability model of the broken line is constructed in sequence; The impact on overhead lines during typhoon disasters is composed of wind load and rain load. The wind load includes average wind load and pulsating wind load. The angle between the line direction and the wind direction is , , is the wind direction angle, is the line strike angle, the wind and rain load on the line can be expressed as: 1) Line wind load The average wind load and pulsating wind load acting on the line are: (10); (11); in, represents the average wind load on the line, and They represent the wind pressure unevenness coefficient and height variation coefficient respectively. represents the shape factor of the conductor, Indicates the outer diameter of the conductor / m, Indicates the number of wire splits, represents the horizontal span / m, then the wind load acting on the line is: (12); in, Indicates the average fluctuating wind load on the line; 2) Line rain load The horizontal speed of a raindrop as it falls is The average wind speed of the typhoon at the height of the transmission line The relationship is: (13); (14); in, represents the ratio of the horizontal components of raindrop and wind speed, represents the raindrop diameter, Indicates height, It represents the correlation coefficient between horizontal velocity and average typhoon wind speed. The theoretical maximum diameter of raindrops is 7mm. The number of raindrops per unit volume can be approximately calculated as: (15); in, , Indicates rainfall intensity; After a raindrop collides with an overhead line or a pole tower, its speed drops to 0 in a very short time. According to the law of conservation of momentum: (16); in, represents the horizontal force of a single raindrop, represents the mass of a raindrop, which can be calculated by approximating the raindrop as a sphere. represents the collision time of raindrops, Indicates density; The spatial occupancy of all raindrops per unit volume is: (17); in, Indicates the spatial occupancy of all raindrops within a unit volume; The rain load on the line is: (18); in, represents the cross-sectional area of ​​a raindrop, , Indicates the rain-facing area of ​​the line; Substituting equations (16) and (17) into equation (18), we can obtain: (19); The comprehensive wind and rain load on the line is: (20); Considering the design load of the line as “strength” and assuming that it obeys normal distribution, the probability of line disconnection under typhoon disaster is: (twenty one); in, The function representing the disconnection fault model, represents the design load of the line, Indicates the failure probability of line break.

[0011] Preferably, in S4.4, a line break in each span of an overhead line will cause the entire line to stop operating. Then the comprehensive failure probability of the overhead line in a certain span is for: (twenty two); Preferably, in S5, the entire overhead line is divided into span, the comprehensive failure probability of the line under typhoon disaster is: (twenty three); in, represents the comprehensive failure probability of overhead lines under typhoon disasters, Indicates The comprehensive failure probability of the file.

[0012] Therefore, the present invention adopts the above-mentioned transmission line fault status warning method under extreme weather conditions, and relies on cutting-edge technologies such as drone inspection and digital twin modeling to establish a transmission line break failure probability prediction model, divide the hierarchical warning levels, and realize the refined processing of dangerous responses, effectively avoiding waste of resources and manpower loss.

[0013] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is the inspection trajectory tracking diagram of the present invention based on the three-side positioning of the digital twin technology; Figure 2 is a bilinear interpolation graph of two-dimensional data of the present invention; Figure 3 It is a structural diagram of the digital twin module of the present invention; Figure 4 This is a conceptual diagram of the refined modeling of the overhead line failure probability under typhoon disasters according to the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0016] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0017] Embodiment 1 The present invention provides a method for early warning of power transmission line fault status under extreme weather conditions, which specifically comprises the following steps: S1. Obtain inspection trajectory data; like Figure 1 As shown in the figure, according to the three-side law, the relationship between the ea and eb values ​​and the set threshold is analyzed to obtain the drone inspection trajectory data, where e is the drone position, a and b are the base station positions, c is an arbitrarily selected point, and eabc is the drone inspection trajectory range. When ea is greater than the set threshold, the drone inspects to the left and obtains the left inspection trajectory data; when eb is less than the set threshold, the drone inspects to the right and obtains the right inspection trajectory data; when the set threshold is greater than ea and less than eb, the drone inspects forward and obtains the front inspection trajectory data.

[0018] S2, preprocessing inspection data; By scanning the lidar data, the plane coordinates of the drone are used as index values, and the corresponding multispectral mosaic image data is found according to the index value. The corresponding points are interpolated using the index value to improve the accuracy of data aggregation, and the bilinear interpolation method is used for interpolation processing, such as Figure 2 shown.

[0019] The O point is the pixel coordinate value of the drone inspection image (x 0 ,y 0 ), that is, (0,0), A, B, C, D are adjacent points, and the pixel at point O is affected by the pixels at other adjacent points. The closer the distance, the greater the weight. , is the spectral information point, and the interpolation calculation formula is as follows: (1); (2); (3) in, represents the interpolation symbol, Represents spectral information points The interpolation of Indicates the horizontal coordinate values ​​of points A and B. Indicates the horizontal coordinate values ​​of points C and D. represents the interpolation of the spectral information point A, represents the interpolation of the spectral information point D, Represents spectral information points The interpolation of represents the interpolation of spectral information point B, represents the interpolation of the spectral information point C, represents the interpolation of the spectral information point O, Indicates the ordinate values ​​of points A and D. Indicates the vertical coordinate values ​​of points B and C.

[0020] Combining the bilinear interpolation method and using the LAS data format of the laser point cloud, an accurate spectrum is obtained and stored as the spectrum data of the laser point cloud to realize the preprocessing of UAV inspection data.

[0021] S3, perform digital twin 3D modeling; Digital twin technology is used to fully cover the work area, transmission lines, towers and their surrounding areas, ensuring real-time transmission of high-definition 4K video and improving the overall perception of transmission lines and surrounding areas. A three-dimensional model of the drone inspection site is established based on the Beidou grid method. During the drone inspection process, the positioning code is applied to the system using the Beidou positioning mode to achieve precise positioning. Grid model technology is used to digitally simulate drone inspections of transmission lines, realizing positioning control of towers, cables and other equipment based on the real three-dimensional model of the earth. The pre-processed data is input into the digital twin module, such as Figure 3 shown.

[0022] Combined with the digital twin module, given a data set ,Depend on A data set consisting of samples is selected, and a non-parametric regression model is constructed to construct a kernel function, and a non-parametric analysis is performed on the data. After determining the drone inspection data, if you want to obtain a global function, you need to perform model fitting to reduce data aggregation loss. The least squares fitting method is used to find the function for the data set. The fit is as follows: (4); (5); in, represents the number of fittings, represents the result of Gaussian function superposition, Indicates Datasets, represents the fitting result, express to Dataset.

[0023] The calculation formula of Gaussian function superposition result is as follows: (6); in, represents the full vector of Gaussian function, and Represent the mean and variance of the Gaussian function respectively. In the least squares fitting method, each data in a given data set is composed of a combination of Gaussian functions. Based on this, the data aggregation model for the single-core problem is constructed, which can be expressed as: (7); in, represents a sample cluster, represents the kernel matrix, represents a set of adjacent samples. Kernel matrix, construction The regression coefficient matrix can be expressed as: (8); in, express The regression coefficient matrix The regression coefficient matrix, express In the data set Datasets.

[0024] For the kernel matrix, its kernel regression neighborhood and regression coefficient can be adapted according to the kernel evidence. For the data aggregation model constructed for multi-kernel problems, the model is fused by the linear weighting method, which is expressed as: (9); in, Is indivual The data aggregation set composed of samples, represents the multi-core aggregation weight, express The regression coefficient matrix The transpose of the regression coefficient matrix, represents the linearly weighted fusion model, Represents the constraint conditions, thereby realizing the construction of the UAV inspection data aggregation model.

[0025] S4. Establish a line status prediction model under the influence of typhoon; The overall modeling idea is as follows Figure 4 As shown, the specific steps include: S4.1. Carry out typhoon wind field simulation under extreme weather conditions, generate wind speed, wind direction and rainfall intensity correction samples that reflect the instantaneous dynamic characteristics of typhoons under extreme weather conditions, and grid the areas involved in overhead lines to simulate the meteorological data of the corresponding grids.

[0026] S4.2. Utilize the line structure and location information to calculate the load on the line and the real-time wind deviation distance of the line, and describe the real-time impact process of typhoons under extreme weather conditions.

[0027] S4.3. Establish the MSSSIM function corresponding to the disconnection fault mode, and then establish the fault probability model.

[0028] The specific steps to build a failure probability model are: The probability of overhead line failure is directly related to the instantaneous impact process of the typhoon. The impact process is the extreme effect of wind and rain loads on overhead lines, which is reflected in the form of "stress" of wind and rain. The comprehensive load and real-time gap distance of the line are regarded as "stress", and the "intensity" corresponding to each "stress" is clarified. Then, based on the MSSSIM modeling idea, the failure probability model of the broken line is constructed in turn.

[0029] The impact on overhead lines during typhoon disasters is composed of wind load and rain load. The wind load includes average wind load and pulsating wind load. The angle between the line direction and the wind direction is , , is the wind direction angle, is the line strike angle, the wind and rain load on the line can be expressed as: 1) Line wind load The average wind load and pulsating wind load acting on the line are: (10); (11); in, represents the average wind load on the line, and They represent the wind pressure unevenness coefficient and height variation coefficient respectively. represents the shape factor of the conductor, Indicates the outer diameter of the conductor / m, Indicates the number of wire splits, represents the horizontal span / m, then the wind load acting on the line is: (12); in, Represents the average fluctuating wind load on the line.

[0030] 2) Line rain load The horizontal speed of a raindrop as it falls is The average wind speed of the typhoon at the height of the transmission line The relationship is: (13); (14); in, represents the ratio of the horizontal components of raindrop and wind speed, represents the raindrop diameter, Indicates height, It represents the correlation coefficient between horizontal velocity and average typhoon wind speed. The theoretical maximum diameter of raindrops is 7mm. The number of raindrops per unit volume can be approximately calculated as: (15); in, , Indicates rainfall intensity.

[0031] After a raindrop collides with an overhead line or a pole tower, its speed drops to 0 in a very short time. According to the law of conservation of momentum: (16); in, represents the horizontal force of a single raindrop, represents the mass of a raindrop, which can be calculated by approximating the raindrop as a sphere. represents the collision time of raindrops, Indicates density.

[0032] The spatial occupancy of all raindrops per unit volume is: (17); in, It represents the spatial occupancy of all raindrops within a unit volume.

[0033] The rain load on the line is: (18); in, represents the cross-sectional area of ​​a raindrop, , Indicates the rain-facing area of ​​the line.

[0034] Substituting equations (16) and (17) into equation (18), we can obtain: (19); The comprehensive wind and rain load on the line is: (20); Considering the design load of the line as “strength” and assuming that it obeys normal distribution, the probability of line disconnection under typhoon disaster is: (twenty one); in, The function representing the disconnection fault model, represents the design load of the line, Indicates the failure probability of line break.

[0035] S4.4. Based on the series reliability principle, the overhead line is equivalent to a series structure system composed of several tension sections, and a comprehensive fault probability model of the overhead line taking into account line breakage is obtained.

[0036] If a line break occurs in each span of an overhead line, the entire line will be shut down. The comprehensive failure probability of the overhead line in a certain span is for: (twenty two); S5. Divide into multiple warning levels; The entire overhead line is divided into span, the comprehensive failure probability of the line under typhoon disaster is: (twenty three); in, represents the comprehensive failure probability of overhead lines under typhoon disasters, Indicates The comprehensive failure probability of the file.

[0037] The probability of overhead line failure under typhoon disasters is divided into 5 danger levels from high to low according to the following table. Based on this early warning classification table, the comprehensive failure probability danger level can be assigned according to the risk level. Then, the comprehensive failure rate is calculated based on the data of drone inspection, and the risk level is judged by the obtained comprehensive failure rate, and an early warning is issued.

[0038] Table 1 Overhead line danger warning table ;

[0039] Therefore, the present invention adopts the above-mentioned transmission line fault status warning method under extreme weather conditions, and relies on cutting-edge technologies such as drone inspection and digital twin modeling to establish a transmission line break failure probability prediction model, divide the hierarchical warning levels, and realize the refined processing of dangerous responses, effectively avoiding waste of resources and manpower loss.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for early warning of power transmission line fault status under extreme weather conditions, characterized in that: The specific steps include: S1. Obtain inspection trajectory data; S2, preprocessing inspection data; S3, perform digital twin 3D modeling; S4. Establish a line status prediction model under the influence of typhoon; S5. Divide into multiple layers of warning levels.

2. The method for early warning of power transmission line fault status under extreme weather conditions according to claim 1, characterized in that: In S1, according to the three-side law, the relationship between the ea and eb values ​​and the set threshold is analyzed to obtain the drone inspection trajectory data, where e is the drone position, a and b are the base station positions, c is an arbitrarily selected point, and eabc is the drone inspection trajectory range; when ea is greater than the set threshold, the drone inspects to the left and obtains the left inspection trajectory data; when eb is less than the set threshold, the drone inspects to the right and obtains the right inspection trajectory data; When the set threshold is greater than ea and less than eb, the drone patrols forward and obtains the front patrol trajectory data.

3. The method for early warning of power transmission line fault status under extreme weather conditions according to claim 1 is characterized by: In S2, the laser radar data is scanned, the plane coordinates of the drone are used as the index value, the corresponding multispectral stitching image data is searched according to the index value, and the corresponding points are interpolated using the index value to improve the accuracy of data aggregation. The bilinear interpolation method is used for interpolation processing, where point O is the pixel coordinate value (x0, y0) of the drone inspection image, that is, (0, 0), A, B, C, and D are adjacent points, and the pixel at point O is affected by the pixels of the other adjacent points. The closer the distance, the greater the weight. , is the spectral information point, and the interpolation calculation formula is as follows: （1）； （2）； （3） in, represents the interpolation symbol, Represents spectral information points The interpolation of Indicates the horizontal coordinate values ​​of points A and B. Indicates the horizontal coordinate values ​​of points C and D. represents the interpolation of the spectral information point A, represents the interpolation of the spectral information point D, Represents spectral information points The interpolation of represents the interpolation of spectral information point B, represents the interpolation of the spectral information point C, represents the interpolation of the spectral information point O, Indicates the ordinate values ​​of points A and D. Indicates the ordinate values ​​of points B and C; Combining the bilinear interpolation method and using the LAS data format of the laser point cloud, an accurate spectrum is obtained and stored as the spectrum data of the laser point cloud to realize the preprocessing of UAV inspection data.

4. The method for early warning of power transmission line fault status under extreme weather conditions according to claim 1 is characterized by: S3 uses digital twin technology to fully cover the work area, transmission lines, towers and their surrounding areas, ensure real-time transmission of high-definition video, establish a three-dimensional model of the drone inspection site based on the Beidou grid method, and input the pre-processed data into the digital twin module.

5. The method for early warning of power transmission line fault status under extreme weather conditions according to claim 1 is characterized by: Combined with the digital twin module in S3, given a data set ,Depend on A data set consisting of samples is selected to construct a kernel function using a nonparametric regression model. Nonparametric analysis is performed on the data. After determining the drone inspection data, if you want to obtain a global function, you need to perform model fitting to reduce data aggregation loss. The least squares fitting method is used to find the function for the data set. The fit is as follows: （4）； （5）； in, represents the number of fittings, represents the result of Gaussian function superposition, Indicates Datasets, represents the fitting result, express to Datasets; The calculation formula of Gaussian function superposition result is as follows: （6）； in, represents the full vector of Gaussian function, and Respectively represent the mean and variance of the Gaussian function. In the least squares fitting method, each data in a given data set is composed of a combination of Gaussian functions. Based on this, the data aggregation model for the single-core problem is constructed, which can be expressed as: （7）； in, represents a sample cluster, represents the kernel matrix, represents a set of adjacent samples; for Kernel matrix, construction The regression coefficient matrix can be expressed as: （8）； in, express The regression coefficient matrix The regression coefficient matrix, express In the data set Datasets; For the kernel matrix, its kernel regression neighborhood and regression coefficient can be adapted according to the kernel evidence. For the data aggregation model constructed for multi-kernel problems, the model is fused by the linear weighting method, which is expressed as: （9）； in, Is indivual The data aggregation set composed of samples, represents the multi-core aggregation weight, express The regression coefficient matrix The transpose of the regression coefficient matrix, represents the linearly weighted fusion model, Represents the constraint conditions, thereby realizing the construction of the UAV inspection data aggregation model.

6. The method for early warning of power transmission line fault status under extreme weather conditions according to claim 1 is characterized by: S4 specifically includes the following steps: S4.

1. Carry out typhoon wind field simulation under extreme weather conditions, generate wind speed, wind direction and rainfall intensity correction samples that reflect the instantaneous dynamic characteristics of typhoons under extreme weather conditions, and perform grid processing on the areas involved in overhead lines to simulate the meteorological data of the corresponding grids; S4.

2. Calculate the load on the line and the real-time wind deviation distance of the line using the line structure and location information, and describe the real-time impact process of typhoons under extreme weather conditions; S4.3, establish the MSSSIM function corresponding to the disconnection fault mode, and then establish the fault probability model; S4.

4. Based on the series reliability principle, the overhead line is equivalent to a series structure system composed of several tension sections, and a comprehensive fault probability model of the overhead line taking into account line breakage is obtained.

7. The method for early warning of power transmission line fault status under extreme weather conditions according to claim 6 is characterized by: The specific steps for constructing the failure probability model in S4.3 are: The probability of overhead line failure is directly related to the instantaneous impact process of the typhoon. The impact process is the extreme effect of wind and rain loads on overhead lines, which is reflected in the form of "stress" of wind and rain. The comprehensive load and real-time gap distance of the line are regarded as "stress", and the "intensity" corresponding to each "stress" is clarified. Then, based on the MSSSIM modeling idea, the fault probability model of the broken line is constructed in sequence. The impact on overhead lines during typhoon disasters is composed of wind load and rain load. The wind load includes average wind load and pulsating wind load. The angle between the line direction and the wind direction is , , is the wind direction angle, is the line strike angle, the wind and rain load on the line can be expressed as: 1) Line wind load The average wind load and pulsating wind load acting on the line are: （10）； （11）； in, represents the average wind load on the line, and They represent the wind pressure unevenness coefficient and height variation coefficient respectively. represents the shape factor of the conductor, Indicates the outer diameter of the conductor / m, Indicates the number of wire splits, represents the horizontal span / m, then the wind load acting on the line is: （12）； in, Indicates the average fluctuating wind load on the line; 2) Line rain load The horizontal speed of a raindrop as it falls is The average wind speed of the typhoon at the height of the transmission line The relationship is: （13）； （14）； in, represents the ratio of the horizontal components of raindrop and wind speed, represents the raindrop diameter, Indicates height, It represents the correlation coefficient between horizontal velocity and average typhoon wind speed. The theoretical maximum diameter of raindrops is 7mm. The number of raindrops per unit volume can be approximately calculated as: （15）； in, , Indicates rainfall intensity; After a raindrop collides with an overhead line or a pole tower, its speed drops to 0 in a very short time. According to the law of conservation of momentum: （16）； in, represents the horizontal force of a single raindrop, represents the mass of a raindrop, which can be calculated by approximating the raindrop as a sphere. represents the collision time of raindrops, Indicates density; The spatial occupancy of all raindrops per unit volume is: （17）； in, Indicates the spatial occupancy of all raindrops within a unit volume; The rain load on the line is: （18）； in, represents the cross-sectional area of ​​a raindrop, , Indicates the rain-facing area of ​​the line; Substituting equations (16) and (17) into equation (18), we can obtain: （19）； The comprehensive wind and rain load on the line is: （20）； Considering the design load of the line as "strength" and assuming that it obeys normal distribution, the probability of line disconnection under typhoon disaster is: （21）； in, The function representing the disconnection fault model, represents the design load of the line, Indicates the failure probability of line break.

8. The method for early warning of power transmission line fault status under extreme weather conditions according to claim 7 is characterized by: In S4.4, if a line break occurs in each span of an overhead line, the entire line will be shut down. Then the comprehensive failure probability of the overhead line in a certain span is for: （22）。 9. The method for early warning of power transmission line fault status under extreme weather conditions according to claim 1, characterized in that: In S5, the entire overhead line is divided into span, the comprehensive failure probability of the line under typhoon disaster is: （23）； in, represents the comprehensive failure probability of overhead lines under typhoon disasters, Indicates The comprehensive failure probability of the file.

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