Power transmission line fault risk assessment method, device, medium and system

Abstract

The invention belongs to the technical field of power transmission, and particularly relates to a power transmission line fault risk assessment method, which comprises the following steps of: extracting influence factors related to a fault, finding an association rule in the influence factors by using an association rule mining algorithm, and positioning a fault point to obtain data of each influence factor during the fault; the method comprises the following steps: extracting indexes, weighting the extracted indexes by using a combined weighting method, calculating a membership degree by using trapezoidal distribution, and realizing fault risk assessment under different fault possibilities by calculating a weighted assessment matrix, and is suitable for operation and maintenance work of a long-distance power transmission line. Based on the same inventive concept, the invention further provides a power transmission line fault risk assessment device, a non-transient readable recording medium storing a program of the method and a system comprising the medium, and the program can be called through a processing circuit to execute the method.

Description

Technical Field

[0001] The invention belongs to the technical field of power transmission, and discloses a method, a device, a system for evaluating the risk of power transmission line faults, and a recording medium storing a program capable of executing the method. Background Art

[0002] At present, large regional power grids are interconnected, the power grid structure is complex, and the geographical environment of long transmission lines is complex. They are faced with multiple disasters such as wind deviation, icing, lightning strikes, bird damage, and wildfires. The frequent occurrence of extreme weather events poses a serious threat to transmission lines. Therefore, the risk assessment of transmission line faults has important research value. In order to ensure the safe and stable operation of the power grid, power experts in many countries have made efforts in the field of disaster prevention and mitigation of transmission lines. Traditional power system security defense measures focus on the continuous optimization of the power system's own operating mode and stable control system, such as optimizing the operating mode, optimizing the control strategy, and strengthening the insulation level of equipment. There is a lack of research on fault risk assessment of distributed transmission lines.

[0003] In this context, in order to reduce the negative impact of faults on transmission lines and improve the safety and stability of the power grid, on-site operation and maintenance personnel try to determine the possible faults by extracting the values ​​of related factors that cause disasters. However, the entire transmission line is long, and the values ​​of related factors are not the same at different locations. Which values ​​are strongly correlated, which values ​​are weakly correlated or unrelated, and how to screen data to improve the efficiency of research and judgment are the first issues that technicians need to consider during the sampling process. Secondly, the formation of a fault is often the result of the joint action of multiple factors. How to determine the weights of various factors is also a problem that needs to be considered. Letting experts determine the weights may be too subjective, and using general mathematical statistics methods will lead to Simpson's paradox due to ignoring the existence of potential variables. How to comprehensively use mathematical tools and use fewer preliminary samples to quickly obtain fuzzy judgments on fault types and risk levels with relatively high accuracy is an urgent problem for technical personnel in this field to solve. Summary of the invention

[0004] In view of the above problems, the present invention provides a method for assessing the risk of power transmission line faults, and the specific scheme includes the following steps:

[0005] S1. Mining the risk state thresholds of the associated factors of various types of faults through preset association rules, setting the intervals separated by each risk state threshold to correspond to the risk level of various types of faults; locating the fault point of the transmission line, and obtaining the measured associated factor values ​​at the fault location;

[0006] S2. Weight the associated factors corresponding to different risk levels according to the expert scores and entropy weight method, and combine the weighted results to obtain the comprehensive weights of the associated factors corresponding to different risk levels;

[0007] S3. Determine the critical value of each correlation factor based on the trapezoidal distribution method, calculate the membership of each measured correlation factor value to different risk levels according to the position of each measured correlation factor value in each trapezoidal distribution, and establish a membership matrix formed by all correlation factor combinations;

[0008] S4: The membership matrix is ​​weighted by comprehensive weights to obtain the probability of occurrence of different risk levels, and the fault type and risk level corresponding to the maximum probability are taken as the fault risk output of the assessment.

[0009] Preferably, the association rules include rules that the support, confidence and effect of the association factor X on the fault type Y should satisfy, which are as follows:

[0010] Support represents the probability of association between X and Y, and its calculation method is:

[0011]

[0012] Confidence indicates that under the condition that X occurs, the association rule The probability of Y occurring is calculated as follows:

[0013]

[0014] The effect degree indicates the degree to which the occurrence of X increases the probability of the occurrence of Y, and its calculation method is:

[0015]

[0016] The association rules should satisfy

[0017]

[0018] Where: Supmin is the preset support lower limit; Confmin is the preset confidence lower limit; Liftmin is the preset action lower limit; according to the probability of each fault type occurring, it is divided into four risk levels: ultra-high, high, medium, and low.

[0019] Preferably, the fault location adopts the traveling wave double-end location method, and the distances from the fault point to the monitoring points M and N on both sides are obtained as follows:

[0020] L M =(L+v·(t l -t r )) / 2

[0021] L N =(Lv·(t l -t r )) / 2

[0022] Where L is the length between the monitoring points at both ends of the line, t l and t r is the time it takes for the traveling wave to reach both ends, L M , L N is the distance between the fault point and the two monitoring points on the left and right.

[0023] Preferably, calculating the comprehensive weight includes the following process:

[0024] According to the expert scoring weights, the judgment matrix a can be obtained based on the experts' scores on the importance of n related factors:

[0025]

[0026] Calculate the product of each row of elements and find the nth root:

[0027]

[0028] Normalization:

[0029]

[0030] After consistency check,

[0031] The details of using the entropy weight method to weight different fault indicators are as follows:

[0032] For any fault type, take m samples and n associated factors, and construct the original fault data matrix as follows:

[0033]

[0034] Where x ij It represents the value of the jth associated factor of the i-th sample;

[0035] The mean value method is used to make the values ​​of the correlation factors dimensionless, that is:

[0036]

[0037] In the formula is the mean of the data in this column;

[0038] After averaging, the mean of each correlation factor is 1, and the variance is:

[0039]

[0040] Perform positive processing, that is:

[0041] y ij ′=-y ij

[0042] The contribution of the i-th sample under the j-th indicator is:

[0043]

[0044] Then the information entropy value of the jth indicator is:

[0045]

[0046] Where: K is a constant, which is related to the number of samples m, K = 1 / ln(m), m∈N*(N * is a positive integer); calculate the degree of difference d j :

[0047] d j =1-E j

[0048] The weight Wj of the jth indicator is:

[0049]

[0050] According to the above steps, the weight coefficient vector of each correlation factor can be obtained The weights obtained by the two weighting methods are combined, and the formula is as follows:

[0051]

[0052] Where W′ j is the weight value obtained after combination;

[0053] Among them, α needs to satisfy the minimum sum of the squares of the difference between the subjective weight and the comprehensive weight and the difference between the objective weight and the comprehensive weight, which can be judged by the following formula:

[0054]

[0055] W′ j After normalization, we get the indicator weight vector W j =[W1,W2,W3,...,W n ].

[0056] To cooperate with the implementation of the above method, the present application also provides a transmission line fault risk assessment device, including the following functional modules:

[0057] The data screening module is used to mine the risk state thresholds of the associated factors of various faults through preset association rules, set the intervals separated by each risk state threshold to correspond to the risk level of various faults; locate the fault point of the transmission line and obtain the measured associated factor value at the fault location;

[0058] The data analysis module is used to weight the associated factors corresponding to different risk levels according to expert scores and entropy weight method, and to obtain the comprehensive weights of the associated factors corresponding to different risk levels by combining the weighting results;

[0059] Based on the trapezoidal distribution method, the critical value of each correlation factor is determined, and the membership of each measured correlation factor value to different risk levels is calculated according to the position of each measured correlation factor value in the respective trapezoidal distribution, and the membership matrix formed by all correlation factor combinations is established;

[0060] The statistical module is used to perform weighted operations on the membership matrix through comprehensive weights to obtain the probability of occurrence of different risk levels, and take the fault type and risk level corresponding to the maximum probability as the evaluated fault risk output.

[0061] Preferably, the association rules in the data screening module include rules that the support, confidence and effect of the association factor X on the fault type Y should satisfy, which are as follows:

[0062] Support represents the probability of association between X and Y, and its calculation method is:

[0063]

[0064] Confidence indicates that under the condition that X occurs, the association rule The probability of Y occurring is calculated as follows:

[0065]

[0066] The effect degree indicates the degree to which the occurrence of X increases the probability of the occurrence of Y, and its calculation method is:

[0067]

[0068] The association rules should satisfy

[0069]

[0070] Where: Supmin is the preset support lower limit; Confmin is the preset confidence lower limit; Liftmin is the preset action lower limit; according to the probability of each fault type occurring, it is divided into four risk levels: ultra-high, high, medium, and low.

[0071] Preferably, the fault location in the data screening module adopts the traveling wave double-end location method, and the distances from the fault point to the monitoring points M and N on both sides are obtained as follows:

[0072] L M =(L+v·(t l -t r )) / 2

[0073] L N =(Lv·(t l -t r )) / 2

[0074] Where L is the length between the monitoring points at both ends of the line, t l and t r is the time it takes for the traveling wave to reach both ends, L M , L N is the distance between the fault point and the two monitoring points on the left and right.

[0075] Preferably, the calculation of the comprehensive weight in the data analysis module includes the following process:

[0076] According to the expert scoring weights, the judgment matrix a can be obtained based on the experts' scores on the importance of n related factors:

[0077]

[0078] Calculate the product of each row of elements and find the nth root:

[0079]

[0080] Normalization:

[0081]

[0082] After consistency check,

[0083] The details of using the entropy weight method to weight different fault indicators are as follows:

[0084] For any fault type, take m samples and n associated factors, and construct the original fault data matrix as follows:

[0085]

[0086] Where x ij It represents the value of the jth associated factor of the i-th sample;

[0087] The mean value method is used to make the values ​​of the correlation factors dimensionless, that is:

[0088]

[0089] In the formula is the mean of the data in this column;

[0090] After averaging, the mean of each correlation factor is 1, and the variance is:

[0091]

[0092] Perform positive processing, that is:

[0093] y′ ij =-y ij

[0094] The contribution of the i-th sample under the j-th indicator is:

[0095]

[0096] Then the information entropy value of the jth indicator is:

[0097]

[0098] Where: K is a constant, which is related to the number of samples m, K = 1 / ln(m), m∈N*(N * is a positive integer);

[0099] Calculate the degree of difference d j :

[0100] d j =1-E j

[0101] The weight Wj of the jth indicator is:

[0102]

[0103] According to the above steps, the weight coefficient vector of each correlation factor can be obtained

[0104] The weights obtained by the two weighting methods are combined, and the formula is as follows:

[0105]

[0106] Where W′ j is the weight value obtained after combination;

[0107] Among them, α needs to satisfy the minimum sum of the squares of the difference between the subjective weight and the comprehensive weight and the difference between the objective weight and the comprehensive weight, which can be judged by the following formula:

[0108]

[0109] W j 'After normalization, we get the indicator weight vector W j =[W1,W2,W3,...,W n ].

[0110] Another embodiment of the present invention is to provide a non-transitory readable recording medium for storing one or more programs including a plurality of instructions. When the instructions are executed, the processing circuit will execute the above-mentioned power transmission line fault risk assessment method.

[0111] The present invention also provides a transmission line fault risk assessment system, comprising a processing circuit and a memory electrically coupled thereto, wherein the memory is configured to store at least one program, the program comprising a plurality of instructions, and the processing circuit runs the program to execute the above-mentioned transmission line fault risk assessment method.

[0112] Beneficial effects of the present invention:

[0113] The present invention completes data screening through association rules and fault location, greatly compresses the amount of data involved in fault analysis, ensures accuracy, and improves efficiency. By combining expert scoring weighting and entropy weighting method, it effectively avoids the subjective bias of experts, makes up for the deviation caused by omitting potential variables in fuzzy mathematical tools, and improves the accuracy of weighting of various associated factors; then, through membership matrix calculation, objectively obtains the results of fault type and possibility, and the reliability and validity are better than the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figure 1 1 is a schematic diagram of a method for assessing the risk of power transmission line faults in an embodiment of the present invention. DETAILED DESCRIPTION

[0115] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making innovative efforts are within the scope of protection of the present invention.

[0116] An embodiment of a transmission line fault risk assessment method comprises the following steps:

[0117] S1. The risk state thresholds of the associated factors of various types of failures are mined through preset association rules, and the intervals separated by each risk state threshold are set to correspond to the risk levels of various types of failures;

[0118] The association rules include the rules that the support, confidence and effect of the association factor X on the fault type Y should satisfy, as follows:

[0119] Support represents the probability of association between X and Y, and its calculation method is:

[0120]

[0121] Confidence indicates that under the condition that X occurs, the association rule The probability of Y occurring is calculated as follows:

[0122]

[0123] The effect degree indicates the degree to which the occurrence of X increases the probability of the occurrence of Y, and its calculation method is:

[0124]

[0125] The association rules should satisfy

[0126]

[0127] Where: Supmin is the preset support lower limit; Confmin is the preset confidence lower limit; Liftmin is the preset action lower limit; according to the probability of each fault type occurring, it is divided into four risk levels: ultra-high, high, medium, and low.

[0128] The fault point of the transmission line is located to obtain the measured correlation factor value of the fault location; the fault location adopts the traveling wave double-end positioning method to obtain the distances from the fault point to the monitoring points M and N on both sides respectively:

[0129] L M =(L+v·(t l -t r )) / 2

[0130] L N =(Lv·(t l -t r )) / 2

[0131] Where L is the length between the monitoring points at both ends of the line, t l and t r is the time it takes for the traveling wave to reach both ends, L M , L N is the distance between the fault point and the two monitoring points on the left and right.

[0132] S2. According to the expert scoring and entropy weight method, weights are assigned to the associated factors corresponding to different risk levels. The weighted results are combined to obtain the comprehensive weights of the associated factors corresponding to different risk levels. The details are as follows:

[0133] According to the expert scoring weights, the judgment matrix a can be obtained based on the experts' scores on the importance of n related factors:

[0134]

[0135] Calculate the product of each row of elements and find the nth root:

[0136]

[0137] Normalization:

[0138]

[0139] After consistency check,

[0140] The details of using the entropy weight method to weight different fault indicators are as follows:

[0141] For any fault type, take m samples and n associated factors, and construct the original fault data matrix as follows:

[0142]

[0143] Where x ij It represents the value of the jth associated factor of the i-th sample;

[0144] The mean value method is used to make the values ​​of the correlation factors dimensionless, that is:

[0145]

[0146] In the formula is the mean of the data in this column;

[0147] After averaging, the mean of each correlation factor is 1, and the variance is:

[0148]

[0149] Perform positive processing, that is:

[0150] y′ ij =-y ij

[0151] The contribution of the i-th sample under the j-th indicator is:

[0152]

[0153] Then the information entropy value of the jth indicator is:

[0154]

[0155] Where: K is a constant, which is related to the number of samples m, K = 1 / ln(m), m∈N*(N * is a positive integer);

[0156] Calculate the degree of difference d j :

[0157] d j =1-E j

[0158] The weight Wj of the jth indicator is:

[0159]

[0160] According to the above steps, the weight coefficient vector of each correlation factor can be obtained

[0161] The weights obtained by the two weighting methods are combined, and the formula is as follows:

[0162]

[0163] Where W′ j is the weight value obtained after combination;

[0164] Among them, α needs to satisfy the minimum sum of the squares of the difference between the subjective weight and the comprehensive weight and the difference between the objective weight and the comprehensive weight, which can be judged by the following formula:

[0165]

[0166] W j 'After normalization, we get the indicator weight vector W j =[W1,W2,W3,...,W n ].

[0167] S3. Determine the critical value of each correlation factor based on the trapezoidal distribution method, calculate the membership of each measured correlation factor value to different risk levels according to the position of each measured correlation factor value in each trapezoidal distribution, and establish a membership matrix formed by all correlation factor combinations;

[0168] The description formula of the trapezoidal distribution is as follows:

[0169]

[0170] Where a, b, c, and d are the values ​​of the critical points in the trapezoidal distribution.

[0171] The obtained membership degrees are organized into a matrix to obtain the fault risk assessment matrix G:

[0172]

[0173] Where: s is the risk assessment index number, γ st is the membership degree of the sth indicator to the risk possibility t.

[0174] S4: The probability of occurrence of different risk levels is obtained by performing weighted operation on the membership matrix through comprehensive weights:

[0175] A=W j ·G

[0176] The Ai that satisfies the following formula is the final fault assessment result.

[0177] A i =max{A n},(n=1,2,...,t)

[0178] Where t is the number of fault risk levels.

[0179] Compare the fault evaluation results under each fault type, and take the fault type corresponding to the maximum value as the final evaluated fault type.

[0180] A transmission line fault risk assessment device only uses a virtual module to express the above method, and the basic content remains unchanged, and the embodiments of the device will not be described in detail here.

[0181] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computers, available storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0182] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0183] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0184] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0185] Compiling the above method steps into a program and then storing it on a hard disk or other non-transitory storage medium constitutes an embodiment of "a non-transitory readable recording medium" of the present invention; and electrically connecting the storage medium to a computer processor and completing the transmission line fault risk assessment through data processing constitutes an embodiment of "a transmission line fault risk assessment system" of the present invention.

[0186] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for assessing the risk of transmission line failure, characterized in that The following steps are involved: S1. Mining the risk state thresholds of the associated factors of various types of faults through preset association rules, setting the intervals separated by each risk state threshold to correspond to the risk level of various types of faults; locating the fault point of the transmission line, and obtaining the measured associated factor values ​​at the fault location; S2. Weight the associated factors corresponding to different risk levels according to the expert scores and entropy weight method, and combine the weighted results to obtain the comprehensive weights of the associated factors corresponding to different risk levels; S3. Determine the critical value of each correlation factor based on the trapezoidal distribution method, calculate the membership of each measured correlation factor value to different risk levels according to the position of each measured correlation factor value in each trapezoidal distribution, and establish a membership matrix formed by all correlation factor combinations; S4: The membership matrix is ​​weighted by comprehensive weights to obtain the probability of occurrence of different risk levels, and the fault type and risk level corresponding to the maximum probability are taken as the fault risk output of the assessment.

2. A transmission line fault risk assessment method according to claim 1, characterized in that: The association rules include the rules that the support, confidence and effect of the association factor X on the fault type Y should satisfy, which are as follows: Support represents the probability of association between X and Y, and its calculation method is: Confidence indicates that under the condition that X occurs, the association rule The probability of Y occurring is calculated as follows: The effect degree indicates the degree to which the occurrence of X increases the probability of the occurrence of Y, and its calculation method is: The association rules should satisfy Where: Supmin is the preset support lower limit; Confmin is the preset confidence lower limit; Liftmin is the preset action lower limit; according to the probability of each fault type occurring, it is divided into four risk levels: ultra-high, high, medium, and low.

3. A transmission line fault risk assessment method according to claim 2, characterized in that: The fault location adopts the traveling wave double-end positioning method, and the distances from the fault point to the monitoring points M and N on both sides are obtained as follows: L M =(L+v·(t l -t r )) / 2 L N =(L-v·(t l -t r )) / 2 Where L is the length between the monitoring points at both ends of the line, t l and t r is the time it takes for the traveling wave to reach both ends, L M , L N is the distance between the fault point and the two monitoring points on the left and right.

4. A transmission line fault risk assessment method according to claim 3, characterized in that: The calculation of comprehensive weight includes the following process: According to the expert scoring weights, the judgment matrix a can be obtained based on the experts' scores on the importance of n related factors: Calculate the product of each row of elements and find the nth root: Normalization: After consistency check, we get W j * ; The details of using the entropy weight method to weight different fault indicators are as follows: For any fault type, take m samples and n associated factors, and construct the original fault data matrix as follows: Where x ij It represents the value of the jth associated factor of the i-th sample; The mean value method is used to make the values ​​of the correlation factors dimensionless, that is: In the formula is the mean of the data in this column; After averaging, the mean of each correlation factor is 1, and the variance is: Perform positive processing, that is: and ij '=-y ij The contribution of the i-th sample under the j-th indicator is: Then the information entropy value of the jth indicator is: Where: K is a constant, which is related to the number of samples m, K = 1 / ln(m), m∈N*(N * is a positive integer); Calculate the degree of difference d j : d j =1-E j The weight Wj of the jth indicator is: According to the above steps, the weight coefficient vector W of each correlation factor can be obtained j ** =[w1,w2,...,w n ]; The weights obtained by the two weighting methods are combined, and the formula is as follows: W j '=αW j * +(1-α)W j ** Where W j ' is the weight value obtained after combination; Among them, α needs to satisfy the minimum sum of the squares of the difference between the subjective weight and the comprehensive weight and the difference between the objective weight and the comprehensive weight, which can be judged by the following formula: W j 'After normalization, we get the indicator weight vector W j =[W1,W2,W3,...,W n ].

5. A transmission line fault risk assessment device, characterized in that Includes the following functional modules: The data screening module is used to mine the risk state thresholds of the associated factors of various faults through preset association rules, set the intervals separated by each risk state threshold to correspond to the risk level of various faults; locate the fault point of the transmission line and obtain the measured associated factor value at the fault location; The data analysis module is used to weight the associated factors corresponding to different risk levels according to expert scores and entropy weight method, and to obtain the comprehensive weights of the associated factors corresponding to different risk levels by combining the weighting results; Based on the trapezoidal distribution method, the critical value of each correlation factor is determined, and the membership of each measured correlation factor value to different risk levels is calculated according to the position of each measured correlation factor value in the respective trapezoidal distribution, and the membership matrix formed by all correlation factor combinations is established; The statistical module is used to perform weighted operations on the membership matrix through comprehensive weights to obtain the probability of occurrence of different risk levels, and take the fault type and risk level corresponding to the maximum probability as the evaluated fault risk output.

6. A transmission line fault risk assessment device according to claim 5, characterized in that: The association rules in the data screening module include the rules that the support, confidence and effect of the association factor X on the fault type Y should satisfy, which are as follows: Support represents the probability of association between X and Y, and its calculation method is: Confidence indicates that under the condition that X occurs, the association rule The probability of Y occurring is calculated as follows: The effect degree indicates the degree to which the occurrence of X increases the probability of the occurrence of Y, and its calculation method is: The association rules should satisfy Where: Supmin is the preset support lower limit; Confmin is the preset confidence lower limit; Liftmin is the preset action lower limit; according to the probability of each fault type occurring, it is divided into four risk levels: ultra-high, high, medium, and low.

7. A transmission line fault risk assessment device according to claim 6, characterized in that: The fault location in the data screening module adopts the traveling wave double-end location method, and the distances from the fault point to the monitoring points M and N on both sides are obtained as follows: L M =(L+v·(t l -t r )) / 2 L N =(L-v·(t l -t r )) / 2 Where L is the length between the monitoring points at both ends of the line, t l and t r is the time it takes for the traveling wave to reach both ends, L M , L N is the distance between the fault point and the two monitoring points on the left and right.

8. A transmission line fault risk assessment device according to claim 7, characterized in that: The calculation of comprehensive weight in the data analysis module includes the following processes: According to the expert scoring weights, the judgment matrix a can be obtained based on the experts' scores on the importance of n related factors: Calculate the product of each row of elements and find the nth root: Normalization: After consistency check, we get W j * ; The details of using the entropy weight method to weight different fault indicators are as follows: For any fault type, take m samples and n associated factors, and construct the original fault data matrix as follows: Where x ij It represents the value of the jth associated factor of the i-th sample; The mean value method is used to make the values ​​of the correlation factors dimensionless, that is: In the formula is the mean of the data in this column; After averaging, the mean of each correlation factor is 1, and the variance is: Perform positive processing, that is: The contribution of the i-th sample under the j-th indicator is: Then the information entropy value of the jth indicator is: Where: K is a constant, which is related to the number of samples m, K = 1 / ln(m), m∈N*(N * is a positive integer); Calculate the degree of difference d j : d j =1-E j The weight Wj of the jth indicator is: According to the above steps, the weight coefficient vector W of each correlation factor can be obtained j ** =[w1,w2,…,w n ]; The weights obtained by the two weighting methods are combined, and the formula is as follows: W j '=αW j * +(1-α)W j ** Where W j ' is the weight value obtained after combination; Among them, α needs to satisfy the minimum sum of the squares of the difference between the subjective weight and the comprehensive weight and the difference between the objective weight and the comprehensive weight, which can be judged by the following formula: W j 'After normalization, we get the indicator weight vector W j =[W1,W2,W3,...,W n ].

9. A non-transitory readable recording medium for storing one or more programs including a plurality of instructions, characterized in that: When the instruction is executed, the processing circuit will be caused to execute a power transmission line fault risk assessment method according to any one of claims 1 to 4.

10. A transmission line fault risk assessment system, characterized in that It comprises a processing circuit and a memory electrically coupled thereto, characterized in that the memory is configured to store at least one program, the program comprises a plurality of instructions, and the processing circuit runs the program to execute a program of a transmission line fault risk assessment method according to any one of claims 1-4.

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