A thunderstorm weather transmission line risk early warning method

Abstract

The application discloses a thunderstorm weather power transmission line risk early warning method, comprising the following steps: S1, according to the quasi-real-time lightning location data, future thunderstorm cloud cluster prediction is carried out to obtain key characteristic information representing the future thunderstorm cloud cluster; S2, according to the key characteristic information, the future lightning stroke failure probability of each section of the power transmission line is calculated; S3, the lightning stroke failure probability of each section of the power transmission line is weighted and calculated to obtain the lightning stroke failure probability of the whole power transmission line; S4, the lightning stroke risk information of the power transmission line is displayed in combination with the gis map and advanced early warning is carried out. The scheme adopts a method combining machine learning analysis and prediction and physical model calculation and early warning, has the advantages of fast response speed, high calculation accuracy and wide early warning range, can improve the power grid operation risk early warning and pre-control capability under thunderstorm disaster weather, and provides important decision support for power grid dispatching operation risk pre-control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power failure early warning, in particular, to a thunderstorm weather power transmission line risk early warning method. BACKGROUND

[0002] The power transmission line is prone to trip or unplanned forced outage under the influence of thunderstorm weather environment. Lightning will not only cause line trip and lead to power interruption, but also cause damage to primary equipment insulation and secondary equipment in the substation, resulting in regional power outage and serious threat to the safety of the power grid. The existing power grid lightning protection mainly studies the high-risk lightning area according to historical lightning data and power transmission line lightning fault cases, and then takes measures such as erecting lightning arresters, installing lightning arresters, and reducing tower grounding resistance to passively defend, which cannot predict the future moving path and thunderstorm intensity of real-time thunderstorm clouds and further carry out the advanced quantitative early warning of lightning failure of power transmission lines.

[0003] The above information disclosed in the background section is only used to enhance the understanding of the background of the present application, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0004] The purpose of the present application is to solve the problem that the prior art does not analyze the key features of future thunderstorm clouds, resulting in poor prediction accuracy of the probability of power transmission line lightning. The present application realizes the prediction of the key features of future thunderstorm clouds according to quasi-real-time lightning positioning data, further establishes a lightning failure physical calculation model of power transmission line, obtains the probability of future lightning failure of power transmission line and carries out advanced early warning. The present application combines machine learning analysis and prediction with physical model calculation and early warning, has the advantages of fast response speed, high calculation accuracy, wide early warning range, and can improve the power grid operation risk early warning and pre-control ability under thunderstorm disaster weather, and provide important decision support for power grid dispatching operation risk pre-control.

[0005] In the first aspect, a technical scheme provided by an embodiment of the present application is a thunderstorm weather power transmission line risk early warning method, comprising the following steps:

[0006] S1. According to the quasi-real-time lightning positioning data, the future thunderstorm cloud is predicted to obtain the key feature information representing the future thunderstorm cloud;

[0007] S2. According to the key feature information, the future lightning failure probability of each section of the power transmission line is calculated;

[0008] S3. The lightning failure probability of each section of the power transmission line is weighted to obtain the lightning failure probability of the whole power transmission line;

[0009] S4, combining the gis map to show the lightning risk information of the transmission line and to make early warning;

[0010] The S1 comprises the following steps:

[0011] S11, collecting the quasi-real-time lightning location data to generate lightning data samples through data preprocessing;

[0012] S12, using a clustering algorithm to analyze the lightning data samples to generate quasi-real-time thunderstorm clusters;

[0013] S13, calculating the key feature information of each quasi-real-time thunderstorm cluster;

[0014] S14, identifying the same quasi-real-time thunderstorm cluster in different time periods;

[0015] S15, predicting the key feature information of the future thunderstorm cluster;

[0016] The S2 comprises the following steps:

[0017] S21, modeling the fixed features of the transmission line;

[0018] S22, calculating the lightning withstand level of each section of the transmission line;

[0019] S23, calculating the probability of lightning strike failure of each section of the transmission line according to the lightning withstand level of each section of the transmission line.

[0020] Preferably, in step S11, the following steps are included:

[0021] The quasi-real-time lightning location data is divided into different time periods to obtain lightning sample data;

[0022] Specifically, the quasi-real-time lightning location data includes lightning location data of 5 consecutive minutes 60 minutes ago, lightning location data of 5 consecutive minutes 30 minutes ago, and lightning location data of 5 consecutive minutes at the current time.

[0023] The lightning sample data includes the longitude and latitude coordinates of the lightning strike location, the lightning current intensity, the lightning return stroke steepness, and the number of lightning return strokes.

[0024] Preferably, step S12 comprises the following steps:

[0025] S121, specifying the minimum number of lightning strike points of a thunderstorm cluster as the minimum number of points of the clustering algorithm;

[0026] S122, specifying the minimum radius of the thunderstorm cluster as the domain radius of the clustering algorithm;

[0027] S123, calling the DBSCAN clustering algorithm to generate thunderstorm cloud clusters with lightning data samples, the minimum number of clusters, and the cluster field radius as input parameters;

[0028] S124, generating a unique number identification for each thunderstorm cloud cluster.

[0029] As a preferred, in step S13, the key feature information of the real-time thunderstorm cloud cluster includes: the longitude and latitude of the thunderstorm cloud cluster center, the thunderstorm cloud cluster radius, the number of lightning strikes of the thunderstorm cloud cluster, the maximum lightning strike intensity of the thunderstorm cloud cluster, the minimum lightning strike intensity of the thunderstorm cloud cluster, and the average lightning strike intensity of the thunderstorm cloud cluster;

[0030] The calculation formula of the longitude and latitude of the thunderstorm cloud cluster center is as follows:

[0031]

[0032] In the formula, LON is the current longitude of the thunderstorm cloud cluster center position; LAT is the current latitude of the thunderstorm cloud cluster center position; lon i The current thunderstorm cloud cluster range lightning point longitude; lat i The current thunderstorm cloud cluster range lightning point latitude; n is the total number of lightning strikes of the thunderstorm cloud cluster;

[0033] After obtaining the longitude and latitude of the thunderstorm cloud cluster center position, all lightning information of the thunderstorm cloud cluster is traversed, and the distance D from the lightning point to the thunderstorm cloud cluster center position is calculated according to the longitude and latitude of the lightning point. The formula is as follows:

[0034] D = R*cos -1 [cos(y1)*cos(y2)*cos(x1-x2)+sin(y1)*sin(y2)]

[0035] In the formula, R is the radius of the earth, x1 and y1 are the radian corresponding to the longitude and latitude of the thunderstorm cloud cluster center respectively, and x2 and y2 are the radian corresponding to the longitude and latitude of a lightning point of the thunderstorm cloud cluster respectively;

[0036] The lightning strike intensity of the thunderstorm cloud cluster is evaluated according to the lightning current intensity, and the average lightning strike intensity calculation formula of the thunderstorm cloud cluster is as follows:

[0037]

[0038] In the formula, P is the average intensity of the thunderstorm cloud cluster; p i The absolute value of the single lightning strike intensity in the cloud cluster range; n is the number of lightning strikes of the thunderstorm cloud cluster.

[0039] As a preferred, in step S14, the different time periods refer to the current time 60 minutes ago, the current time 30 minutes ago, and the current time three time periods, and the identification of the same quasi-real-time thunderstorm cloud cluster in the different time periods includes the following steps:

[0040] S141, initialize the maximum running distance S of the same thunderstorm cluster in the defined connected period;

[0041] S142, according to the center longitude and latitude of thunderstorm cluster A1 in the period 30 minutes ago at the current time, find the thunderstorm cluster B1 corresponding to the closest center distance and the center distance less than the maximum running distance S in the period 60 minutes ago at the current time, determine that thunderstorm cluster A1 and thunderstorm cluster B1 are the same thunderstorm cluster, assign the number identification of B1 cluster to A1 cluster, complete the identification of thunderstorm cluster in the period 30 minutes ago at the current time, if thunderstorm cluster A1 does not match the corresponding cluster, consider that thunderstorm cluster A1 is a newly generated thunderstorm cluster and assign a new number identification to thunderstorm cluster A1;

[0042] S143, according to the center longitude and latitude of thunderstorm cluster A2 in the period at the current time, find the thunderstorm cluster B2 corresponding to the closest center distance and the center distance less than the maximum running distance S in the period 30 minutes ago at the current time, determine that thunderstorm cluster A2 and thunderstorm cluster B2 are the same thunderstorm cluster, assign the number identification of thunderstorm cluster B2 to thunderstorm cluster A2 cluster, complete the identification of thunderstorm cluster in the period at the current time, if thunderstorm cluster A does not match the corresponding thunderstorm cluster, consider that thunderstorm cluster A is a newly generated thunderstorm cluster and assign a new number identification to thunderstorm cluster A;

[0043] S144, put the key feature information of the thunderstorm clusters with the same number identification in different periods into the same data set in time sequence, and combine to form a prediction sample set.

[0044] As preferred, step S15 comprises the following steps:

[0045] S151, perform polynomial fitting on the center longitude and latitude of thunderstorm cluster A in the prediction sample set to obtain the center longitude and latitude of thunderstorm cluster A after 30 minutes;

[0046] S152, perform polynomial fitting on the radius of thunderstorm cluster A in the prediction sample set to obtain the radius of thunderstorm cluster A after 30 minutes;

[0047] S153, perform polynomial fitting on the number of lightning strokes of thunderstorm cluster A in the prediction sample set to obtain the number of lightning strokes of thunderstorm cluster A after 30 minutes;

[0048] S154, perform polynomial fitting on the maximum lightning stroke intensity of thunderstorm cluster A in the prediction sample set to obtain the maximum lightning stroke intensity of thunderstorm cluster A after 30 minutes;

[0049] S155, the minimum lightning stroke intensity of the thunderstorm cloud A in the prediction sample set is calculated by polynomial fitting, and the minimum lightning stroke intensity of the thunderstorm cloud A after 30 minutes is obtained;

[0050] S156, the minimum lightning stroke intensity of the thunderstorm cloud A in the prediction sample set is calculated by polynomial fitting, and the minimum lightning stroke intensity of the thunderstorm cloud A after 30 minutes is obtained;

[0051] As preferred, in step S21, the power transmission line fixed feature modeling comprises the following steps:

[0052] S211, the power transmission line is divided into several sections at intervals of each tower of the power transmission line;

[0053] S212, the insulator string U of each section of the power transmission line is calculated 50％ The discharge voltage is as follows:

[0054] U 50％ = 531 * L k + 31

[0055] In the formula, L k is the length of the line insulator string;

[0056] S213, the striking rate g of each section of the power transmission line is calculated,

[0057] S214, the shielding failure rate P of each section of the power transmission line is calculated according to different terrains where the line is located α , and the formula is as follows:

[0058] In the plain area:

[0059] In the mountain area:

[0060] In the formula, α is the protection angle of the line lightning conductor, and h is the average suspension height of the conductor.

[0061] As preferred, in step S22, the lightning withstand level includes a lightning shielding tower top lightning withstand level I1 and a lightning shielding conductor lightning withstand level I2,

[0062] Wherein:

[0063] The lightning shielding tower top lightning withstand level I1 is calculated according to the following formula:

[0064]

[0065] In the formula, k is the coupling coefficient, taking the value of 0.256; β is the shunt coefficient, taking the value of 0.88; R ch is the tower impulse grounding resistance; L gt is the tower inductance; h dThe average height of the conductor suspension;

[0066] The lightning shielding level I2 of the conductor is calculated by the following formula:

[0067]

[0068] Wherein, Z is the wave impedance of the line.

[0069] As preferred, the step S23 comprises the following steps:

[0070] S231, determining the line section affected by the future thunderstorm according to the longitude and latitude of the center of the future thunderstorm and the radius of the thunderstorm;

[0071] S232, determining the lightning current amplitude probability distribution F lc Calculate the probabilities P1, P2 that the lightning current amplitude of the line section affected by the future thunderstorm exceeds the lightning shielding level I1 of the lightning arrester at the top of the tower and the lightning shielding level I2 of the conductor:

[0072] Wherein, the lightning current amplitude probability distribution F lc The calculation formula is as follows:

[0073]

[0074] Wherein, P(I≥i) represents the probability that the lightning current amplitude I exceeds i; the value of the parameter a is 17.85; the value of the parameter b is 1.926;

[0075]

[0076] Wherein, I1 and I2 are the lightning shielding level of the lightning arrester at the top of the tower and the lightning shielding level of the conductor of the line section affected by the future thunderstorm, respectively; I max max and I min min are the maximum lightning stroke intensity and the minimum lightning stroke intensity of the future thunderstorm, respectively; when the lightning shielding level I i <I min , P i should be taken as 1; when I i >I max , P i should be taken as 0;

[0077] S233, calculate the lightning trip-out probability P t of the line section affected by the future thunderstorm, the formula is as follows:

[0078] P t =η(gP1+P α P2)

[0079] Wherein, η is the striking rate, which is taken as 1; g is the striking rate; P α is the shielding failure rate.

[0080] As preferred, in step S3, the lightning failure probability of each section of the power transmission line is weighted to obtain the lightning failure probability of the whole power transmission line, and the formula is as follows:

[0081]

[0082] In the formula, P is the lightning failure probability of the whole power transmission line, P is the lightning failure probability of the line section j, and z is the number of line sections affected by the future thunderstorm cloud. T In the formula, P is the lightning failure probability of the whole power transmission line, P is the lightning failure probability of the line section j, and z is the number of line sections affected by the future thunderstorm cloud. tj

[0083] As preferred, in step S4, the lightning risk information of the power transmission line is displayed on the gis map and advanced warning is performed, including the following steps:

[0084] S41, the complete path of all power transmission lines in the power grid is plotted on the gis map;

[0085] S42, the power transmission line affected by the future thunderstorm cloud and having the lightning failure probability is color rendered in the form of probability distribution level and labeled on the gis map; meanwhile, the name, failure cause, failure probability and failure time of the power transmission line are displayed for advanced warning.

[0086] The present application has the advantages that: the present application adopts the machine learning algorithm (DBSCAN clustering algorithm) to realize the prediction of the key features of the future thunderstorm cloud according to the quasi-real-time lightning positioning data, further establishes the lightning failure physical calculation model of the power transmission line, obtains the probability of the future lightning failure of the power transmission line and performs advanced warning, adopts the scheme combining the machine learning analysis and prediction with the physical model calculation and warning, has the advantages of fast response speed, high calculation accuracy and wide warning range, can improve the power grid operation risk warning and pre-control capability under the thunderstorm disaster weather, and provides important decision support for the power grid operation risk pre-control.

[0087] The above summary of the application is only a summary of the technical scheme of the present application, in order to more clearly understand the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described below. BRIEF DESCRIPTION OF DRAWINGS

[0088] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to designate the same parts.

[0089] ​Figure 1 A flow chart of a thunderstorm weather transmission line risk early warning method of the present application. DETAILED DESCRIPTION

[0090] To make the objectives, technical solutions, and advantages of the present application clearer, further detailed description will be given below in combination with the drawings and examples. It should be understood that the specific implementation described herein is only one of the best embodiments of the present application, which is only used to explain the present application and does not limit the protection scope of the present application. All other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0091] Before discussing the example embodiments in more detail, it should be mentioned that some example embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The processes can be terminated when their operations are completed, but can also have additional steps not included in the drawings; the processes can correspond to methods, functions, procedures, subroutines, subprograms, etc.

[0092] Embodiment: As shown in the figure, a thunderstorm weather transmission line risk early warning method includes the following steps: Figure 1

[0093] S1, according to the quasi-real-time lightning location data, the future thunderstorm cloud cluster prediction is obtained to represent the key feature information of the future thunderstorm cloud cluster.

[0094] Specifically, the S1 includes the following steps:

[0095] S11, collect quasi-real-time lightning location data for data preprocessing to generate lightning data samples.

[0096] More specifically, in step S11, the following steps are included:

[0097] The quasi-real-time lightning location data is divided into different time periods for sampling to obtain lightning sample data.

[0098] Specifically, the quasi-real-time lightning location data includes lightning location data for 5 minutes continuously 60 minutes before the current time, lightning location data for 5 minutes continuously 30 minutes before the current time, and lightning location data for 5 minutes continuously at the current time.

[0099] The lightning sample data includes the longitude and latitude coordinates of the lightning stroke location, the lightning current intensity, the lightning return stroke steepness, and the lightning return stroke number.

[0100] ​S12, generating quasi-real-time thunderstorm clusters by using a clustering algorithm to analyze the lightning data samples.

[0101] More specifically, step S12 includes the following steps:

[0102] S121, specifying the minimum number of lightning strike points constituting a thunderstorm cluster as the minimum point number of the clustering algorithm.

[0103] S122, specifying the minimum radius of the thunderstorm cluster as the field radius of the clustering algorithm.

[0104] S123, calling the DBSCAN clustering algorithm to generate the thunderstorm cluster by using the lightning data samples, the clustering minimum point number, and the clustering field radius as input parameters; the clustering algorithm uses the density-based spatial clustering of applications with noise (DBSCAN).

[0105] S124, generating a unique number identifier for each thunderstorm cluster.

[0106] S13, calculating the key feature information of each quasi-real-time thunderstorm cluster.

[0107] More specifically, in step S13, the key feature information of the real-time thunderstorm cluster includes the thunderstorm cluster center longitude and latitude, the thunderstorm cluster radius, the number of lightning strikes of the thunderstorm cluster, the maximum lightning strike intensity of the thunderstorm cluster, the minimum lightning strike intensity of the thunderstorm cluster, and the average lightning strike intensity of the thunderstorm cluster.

[0108] The calculation formula of the thunderstorm cluster center longitude and latitude is as follows:

[0109]

[0110] In the formula, LON is the current thunderstorm cluster center longitude, LAT is the current thunderstorm cluster center latitude, lon i is the longitude of the lightning strike point within the current thunderstorm cluster range, lat i is the latitude of the lightning strike point within the current thunderstorm cluster range, and n is the total number of lightning strikes of the thunderstorm cluster.

[0111] After obtaining the thunderstorm cluster center longitude and latitude, all lightning information of the thunderstorm cluster is traversed, and the distance D from the lightning strike point to the thunderstorm cluster center position is calculated according to the longitude and latitude of the lightning strike point. The formula is as follows:

[0112] D = R * cos -1 [cos(y1) * cos(y2) * cos(x1-x2) + sin(y1) * sin(y2)]

[0113] Wherein, R is the earth radius, about 6371kM, x1, y1 are the corresponding radian of the thunderstorm cloud center longitude and latitude, x2, y2 are the corresponding radian of the thunderstorm cloud center longitude and latitude of a lightning point.

[0114] According to the lightning current intensity, the lightning intensity of the thunderstorm cloud is evaluated, and the average lightning intensity calculation formula of the thunderstorm cloud is as follows:

[0115]

[0116] Wherein: P is the average intensity of the thunderstorm cloud; p i is the absolute value of the single lightning intensity in the cloud range; n is the number of lightning of the thunderstorm cloud.

[0117] S14, the same quasi-real-time thunderstorm cloud in different time periods is identified.

[0118] More specifically, in step S14, the different time periods refer to the current time 60 minutes ago, the current time 30 minutes ago, and the current time three time periods, and the identification of the same quasi-real-time thunderstorm cloud in different time periods includes the following steps:

[0119] S141, initialize the maximum running distance S of the same thunderstorm cloud in the connected period.

[0120] S142, according to the center longitude and latitude of the thunderstorm cloud A1 in the period 30 minutes ago from the current time, find the thunderstorm cloud B1 corresponding to the nearest center distance and the center distance less than the maximum running distance S in the period 60 minutes ago from the current time, determine that the thunderstorm cloud A1 and the thunderstorm cloud B1 are the same thunderstorm cloud, assign the number identification of the B1 cloud to the A1 cloud, complete the identification of the thunderstorm cloud in the period 30 minutes ago from the current time, if the thunderstorm cloud A1 does not match the corresponding cloud, it is considered that the thunderstorm cloud A1 is a newly generated thunderstorm cloud and a new number identification is assigned to the thunderstorm cloud A1.

[0121] S143, according to the center longitude and latitude of the thunderstorm cloud A2 in the current time period, find the thunderstorm cloud B2 corresponding to the nearest center distance and the center distance less than the maximum running distance S in the period 30 minutes ago from the current time, determine that the thunderstorm cloud A2 and the thunderstorm cloud B2 are the same thunderstorm cloud, assign the number identification of the thunderstorm cloud B2 to the thunderstorm cloud A2 cloud, complete the identification of the thunderstorm cloud in the current time period, if the thunderstorm cloud A does not match the corresponding thunderstorm cloud, it is considered that the thunderstorm cloud A is a newly generated thunderstorm cloud and a new number identification is assigned to the thunderstorm cloud A.

[0122] S144, the key feature information of the thunderstorm cloud with the same number identification in different time periods is put into the same data set in time sequence, and combined into a prediction sample set.

[0123] S15, predicting key characteristic information of the future thunderstorm cluster.

[0124] More specifically, step S15 includes the following steps:

[0125] S151, using polynomial fitting to calculate the center longitude and latitude of the thunderstorm cluster A in the prediction sample set, to obtain the center longitude and latitude of the thunderstorm cluster A after 30 minutes.

[0126] S152, using polynomial fitting to calculate the radius of the thunderstorm cluster A in the prediction sample set, to obtain the radius of the thunderstorm cluster A after 30 minutes.

[0127] S153, using polynomial fitting to calculate the number of lightning strokes of the thunderstorm cluster A in the prediction sample set, to obtain the number of lightning strokes of the thunderstorm cluster A after 30 minutes.

[0128] S154, using polynomial fitting to calculate the maximum lightning stroke intensity of the thunderstorm cluster A in the prediction sample set, to obtain the maximum lightning stroke intensity of the thunderstorm cluster A after 30 minutes.

[0129] S155, using polynomial fitting to calculate the minimum lightning stroke intensity of the thunderstorm cluster A in the prediction sample set, to obtain the minimum lightning stroke intensity of the thunderstorm cluster A after 30 minutes.

[0130] S156, using polynomial fitting to calculate the minimum lightning stroke intensity of the thunderstorm cluster A in the prediction sample set, to obtain the average lightning stroke intensity of the thunderstorm cluster A after 30 minutes.

[0131] S2, calculating the probability of lightning stroke failure of each section of the transmission line according to the key characteristic information.

[0132] Specifically, S2 includes the following steps:

[0133] S21, modeling the fixed characteristics of the transmission line.

[0134] More specifically, in step S21, the modeling of the fixed characteristics of the transmission line includes the following steps:

[0135] S211, dividing the transmission line into sections at intervals of each tower of the transmission line.

[0136] S212, calculating the insulator string U 50％ discharge voltage of each section of the transmission line, according to the formula.

[0137] U 50％ = 531 * L k + 31

[0138] In the formula, L k is the length of the insulator string of the line.

[0139] S213, calculate the striking rate g of each section of the transmission line, according to the DL / T 620-1997 standard, the striking rate g can be obtained by using the data listed in Table 1.

[0140] Table 1. Striking rate table

[0141] Number of ground wires 1 2 Ground rod rate g in plain area 1 / 4 1 / 6 Ground rod rate g in hilly area 1 / 3 1 / 4

[0142] S214, calculate the striking rate P of each section of the transmission line according to different terrains where the line is located α , the formula is as follows:

[0143] Plain area:

[0144] Mountainous area:

[0145] In the formula, a is the protection angle of the line lightning conductor, and h is the average suspension height of the conductor.

[0146] S22, calculate the lightning withstand level of each section of the transmission line.

[0147] More specifically, in step S22, the lightning withstand level includes a lightning division striking tower top lightning withstand level I1 and a lightning striking conductor lightning withstand level I2,

[0148] Wherein:

[0149] The lightning division striking tower top lightning withstand level I1 is calculated according to the following formula:

[0150]

[0151] In the formula, k is the coupling coefficient, which is 0.256; β is the shunt coefficient, which is 0.88; R ch is the impulse grounding resistance of the tower; L gt is the inductance of the tower; h d is the average suspension height of the conductor.

[0152] The lightning striking conductor lightning withstand level I2 is calculated according to the following formula:

[0153]

[0154] In the formula, Z is the line wave impedance.

[0155] S23, calculate the lightning strike failure probability of each section of the transmission line in the future according to the lightning withstand level of each section of the transmission line.

[0156] More specifically, step S23 includes the following steps:

[0157] S231, determine the line section affected by the future thunderstorm based on the future thunderstorm center longitude and latitude and the thunderstorm radius.

[0158] S232, determine the lightning current amplitude probability distribution F lc Calculate the probability P1, P2 that the lightning current amplitude of the line section affected by the future thunderstorm exceeds the lightning withstand level I1 of the lightning arrester at the top of the tower and the lightning withstand level I2 of the lightning arrester at the top of the tower.

[0159] Where: the lightning current amplitude probability distribution F lc The calculation formula is as follows:

[0160]

[0161] Where, P(I≥i) represents the probability that the lightning current amplitude I exceeds i; the value of parameter a is 17.85; the value of parameter b is 1.926;

[0162]

[0163] Where, I1, I2 are the lightning withstand level of the lightning arrester at the top of the tower and the lightning withstand level of the lightning arrester at the top of the tower of the line section affected by the future thunderstorm, respectively; I max , I min are the maximum lightning stroke intensity and the minimum lightning stroke intensity of the future thunderstorm, respectively; when the lightning withstand level I i <I min , P i should be taken as 1; when I i >I max , P i should be taken as 0.

[0164] S233, calculate the lightning trip-out probability P t of the line section affected by the future thunderstorm, and the formula is as follows:

[0165] P t =η(gP1+P α P2)

[0166] Where, η is the arc establishment rate, taken as 1; g is the striking rate; P α is the shielding failure rate.

[0167] S3, calculate the lightning trip-out probability of the entire transmission line by weighting the lightning trip-out probability of each section of the transmission line.

[0168] More specifically, in step S3, the lightning trip-out probability of the entire transmission line is calculated by weighting the lightning trip-out probability of each section of the transmission line, and the formula is as follows:

[0169]

[0170] In the formula, P T P represents the probability of a lightning strike failure along the entire transmission line. tj Let be the probability of line segment j being struck by lightning, and z be the number of line segments affected by future thunderstorm clouds.

[0171] S4. Combine GIS maps to display lightning strike risk information for power transmission lines and provide early warnings.

[0172] More specifically, in step S4, the step of combining GIS maps to display lightning strike risk information of transmission lines and providing early warning includes the following steps:

[0173] S41. Draw the complete path of all transmission lines in the power grid on the GIS map.

[0174] S42. Transmission lines affected by future thunderstorm clouds and with a probability of lightning strikes are rendered in color according to probability distribution levels and marked on a GIS map; simultaneously, the name of the transmission line, the cause of the fault, the probability of the fault, and the time of the fault are displayed to provide early warning. In this embodiment, the probability distribution levels can be set in 5% increments, with higher probabilities resulting in darker colors.

[0175] The specific embodiments described above are preferred embodiments of the method for early warning of transmission line risks during thunderstorms according to the present invention, and are not intended to limit the specific scope of the present invention. The scope of the present invention includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A thunderstorm weather power transmission line risk early warning method, characterized in that, The method comprises the following steps: S1, obtaining key feature information of a future thunderstorm cloud according to quasi-real-time lightning positioning data; S2, calculating the future lightning failure probability of each section of the transmission line according to the key feature information; S3, calculating the lightning failure probability of the entire transmission line by weighting the lightning failure probability of each section of the transmission line; S4, combining the gis map to display the lightning risk information of the transmission line and to perform early warning; The S1 comprises the following steps: S11, collecting quasi-real-time lightning positioning data for data preprocessing to generate lightning data samples; S12, analyzing the lightning data samples by using a clustering algorithm to generate quasi-real-time thunderstorm clouds; S13, calculating the key feature information of each quasi-real-time thunderstorm cloud; S14, identifying the same quasi-real-time thunderstorm cloud in different time periods; S15, predicting the key feature information of the future thunderstorm cloud; The S2 comprises the following steps: S21, modeling the fixed features of the transmission line; S22, calculating the lightning withstand level of each section of the transmission line; S23, calculating the future lightning failure probability of each section of the transmission line according to the lightning withstand level of each section of the transmission line; In step S14, the different time periods refer to the current time 60 minutes ago, the current time 30 minutes ago, and the current time, and the identification of the same quasi-real-time thunderstorm cloud in different time periods comprises the following steps: S141, initializing the maximum running distance S of the same thunderstorm cloud in the connected time period; S142, searching for the thunderstorm cloud B1 corresponding to the closest center distance and smaller than the maximum running distance S in the time period 60 minutes ago from the center longitude and latitude of the thunderstorm cloud A1 in the time period 30 minutes ago from the current time, identifying the thunderstorm cloud A1 and the thunderstorm cloud B1 as the same thunderstorm cloud, assigning the number identifier of the thunderstorm cloud B1 to the thunderstorm cloud A1, completing the identification of the thunderstorm cloud in the time period 30 minutes ago from the current time, and if the thunderstorm cloud A1 is not matched with the corresponding cloud, considering the thunderstorm cloud A1 as a newly generated thunderstorm cloud and assigning a new number identifier to the thunderstorm cloud A1; S143, searching for the thunderstorm cloud B2 corresponding to the closest center distance and smaller than the maximum running distance S in the time period 30 minutes ago from the current time according to the center longitude and latitude of the thunderstorm cloud A2 in the current time period, identifying the thunderstorm cloud A2 and the thunderstorm cloud B2 as the same thunderstorm cloud, assigning the number identifier of the thunderstorm cloud B2 to the thunderstorm cloud A2, completing the identification of the thunderstorm cloud in the current time period, and if the thunderstorm cloud A2 is not matched with the corresponding cloud, considering the thunderstorm cloud A2 as a newly generated thunderstorm cloud and assigning a new number identifier to the thunderstorm cloud A2; S144, putting the key feature information of the thunderstorm clouds with the same number identifier in different time periods into the same data set in the time period order to form a prediction sample set.

2. The thunderstorm weather transmission line risk warning method according to claim 1, wherein In step S11, the following steps are included: Divide the quasi-real-time lightning positioning data into different time periods to obtain lightning sample data. Specifically, the quasi-real-time lightning location data includes lightning location data of 5 consecutive minutes 60 minutes ago from the current time, lightning location data of 5 consecutive minutes 30 minutes ago from the current time, and lightning location data of 5 consecutive minutes from the current time. The lightning sample data includes longitude and latitude coordinates of a lightning stroke occurrence site, lightning current intensity, lightning return stroke steepness, and lightning return stroke number. 3.The thunderstorm weather transmission line risk early warning method of claim 1, characterized in that, Step S12 comprises the following steps: S121, specifying a minimum number of lightning stroke points of a thunderstorm cloud cluster as a minimum point number of a clustering algorithm; S122, specifying a minimum radius of the thunderstorm cloud cluster as a field radius of the clustering algorithm; S123, inputting the lightning data sample, the clustering minimum point number, and the clustering field radius as input parameters to call the DBSCAN clustering algorithm to generate the thunderstorm cloud cluster; S124, generating a unique number identification for each thunderstorm cloud cluster. 4.The thunderstorm weather transmission line risk early warning method of claim 1 or 3, characterized in that, In step S13, the key feature information of the real-time thunderstorm cloud cluster includes a thunderstorm cloud cluster center longitude and latitude, a thunderstorm cloud cluster radius, a thunderstorm cloud cluster lightning stroke number, a thunderstorm cloud cluster maximum lightning stroke intensity, a thunderstorm cloud cluster minimum lightning stroke intensity, and a thunderstorm cloud cluster average lightning stroke intensity. The calculation formula of the thunderstorm cloud cluster center longitude and latitude is as follows: ； In the formula: LON is the current thunderstorm cloud center position longitude; LAT is the current thunderstorm cloud center position latitude; The current thunderstorm cloud range within the lightning point longitude; The current thunderstorm cloud range within the lightning point latitude; n is the total number of thunderstorm cloud lightning; After obtaining the thunderstorm cloud cluster center position longitude and latitude, all lightning stroke information of the thunderstorm cloud cluster is traversed, and the distance D from a lightning stroke point to the thunderstorm cloud cluster center position is calculated according to the longitude and latitude of the lightning stroke point, and the formula is as follows: ； In the formula, R is the radius of the earth, x1 and y1 are the radian corresponding to the longitude and latitude of the thunderstorm cloud cluster center respectively, and x2 and y2 are the radian corresponding to the longitude and latitude of a certain lightning stroke point of the thunderstorm cloud cluster respectively; The lightning stroke intensity is equivalent to the evaluation of the lightning stroke intensity of the thunderstorm cloud cluster, and the calculation formula of the thunderstorm cloud cluster average lightning stroke intensity is as follows: ； wherein: is the average strength of the thunderstorm cloud; is the absolute value of the strength of a single lightning strike within the cloud range; n is the total number of lightning strikes produced by the thunderstorm cloud. 5.The thunderstorm weather transmission line risk early warning method of claim 1, characterized in that, Step S15 comprises the following steps: S151, performing polynomial fitting on the center longitude and latitude of the thunderstorm cloud cluster A in the prediction sample set to obtain the center longitude and latitude of the thunderstorm cloud cluster A after 30 minutes; S152, performing polynomial fitting on the radius of the thunderstorm cloud cluster A in the prediction sample set to obtain the radius of the thunderstorm cloud cluster A after 30 minutes; S153, performing polynomial fitting on the lightning stroke number of the thunderstorm cloud cluster A in the prediction sample set to obtain the lightning stroke number of the thunderstorm cloud cluster A after 30 minutes; S154, performing polynomial fitting on the maximum lightning stroke intensity of the thunderstorm cloud cluster A in the prediction sample set to obtain the maximum lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes; S155, performing polynomial fitting on the minimum lightning stroke intensity of the thunderstorm cloud cluster A in the prediction sample set to obtain the minimum lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes; S156, performing polynomial fitting on the minimum lightning stroke intensity of the thunderstorm cloud cluster A in the prediction sample set to obtain the average lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes. 6.The thunderstorm weather transmission line risk early warning method according to claim 1, characterized in that, In step S21, the transmission line fixed feature modeling comprises the following steps: S211, dividing the transmission line into several sections at intervals of each tower of the transmission line; S212, calculating insulator strings of each section of the power transmission line The discharge voltage is given by the formula: ； In the formula, is the length of the line insulator string; S213, calculating the tower striking rate g of each section of the transmission line, S214. Calculate the shielding failure rate of each section of the transmission line according to different terrains where the line is located The formula is as follows: Flat areas: ; Mountainous regions: ; In the formula, is the protection angle of the line lightning arrester, and h is the average suspension height of the conductor. 7.The thunderstorm weather transmission line risk early warning method according to claim 6, characterized in that, In step S22, the lightning withstand level includes a lightning tower top withstand level I1 and a lightning conductor withstand level I2, Wherein: The lightning tower top withstand level I1 is calculated according to the following formula: ； In the formula, is the coupling coefficient, and takes a value of 0.256; is the shunt coefficient, and takes a value of 0.88; is the tower impulse grounding resistance; is the tower inductance; is the average suspension height of the conductor; The lightning conductor withstand level I2 is calculated according to the following formula: ； In the formula, Z is the line wave impedance. 8.The thunderstorm weather transmission line risk early warning method according to claim 1, characterized in that, Step S23 comprises the following steps: S231, determining the line section affected by the future thunderstorm according to the longitude and latitude of the center of the future thunderstorm cloud cluster and the thunderstorm cloud cluster radius; S232, based on the lightning current amplitude probability distribution F lc The probabilities P1, P2 that the lightning current amplitude of a line section affected by a future thunderstorm cloud exceeds the lightning withstand level I1 of the lightning shield tower and the lightning withstand level I2 of the lightning shield conductor are calculated. Where: F is the lightning current amplitude probability distribution lc The calculation formula is as follows: ； wherein represents the probability that the lightning current amplitude I exceeds the current i; the parameter a takes the value 17.85; the parameter b takes the value 1.926; ； In the formula, I1 and I2 are the lightning protection levels of the lightning-divided tower top and the lightning shielding conductor, respectively, affected by the future thunderstorm cloud cluster; I max , min are the maximum and minimum lightning stroke intensities of the future thunderstorm cloud cluster, respectively; when the lightning protection level I x <I min , P x should be taken as 1; when I x >I max , P x should be taken as 0, wherein x is 1 or 2. S233, calculating a lightning trip-out probability P for a line section affected by a future thunderstorm cell t The formula is as follows: ； In the formula, is the build arc rate, which is 1; g is the hit rate; is the around hit rate. 9.The thunderstorm weather transmission line risk early warning method according to claim 1 or 8, characterized in that, In step S3, the lightning failure probability of each section of the transmission line is weighted to obtain the lightning failure probability of the entire transmission line, and the formula is as follows: ； wherein is the lightning fault probability for the whole transmission line, is the lightning fault probability for line section j, and z is the number of line sections affected by the future thunderstorm. 10.The thunderstorm weather transmission line risk early warning method according to claim 1, characterized in that, In step S4, the combination of gis map and lightning risk information of the transmission line for early warning comprises the following steps: S41, drawing the complete path of all transmission lines in the power grid in the gis map; S42, color rendering the transmission line affected by the future thunderstorm cloud cluster and having the lightning failure probability in the form of probability distribution level, and marking in the gis map; meanwhile, the name, failure cause, failure probability and failure time of the transmission line are displayed for early warning.

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