CN116109133A - Risk early warning method for thunderstorm weather power transmission line - Google Patents
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Abstract
The invention discloses a risk early warning method for a thunderstorm weather power transmission line, which comprises the following steps: s1, predicting a future thunderstorm cloud cluster according to near-real-time lightning positioning data to obtain key characteristic information for representing the future thunderstorm cloud cluster; s2, calculating the probability of lightning stroke faults of each section of the power transmission line in the future according to the key characteristic information; s3, weighting calculation is carried out on the lightning strike fault probability of each section of the power transmission line to obtain the lightning strike fault probability of the whole power transmission line; and S4, displaying the lightning strike risk information of the transmission line by combining a gis map and performing advanced early warning. The scheme adopts a method combining machine learning analysis prediction and physical model calculation early warning, has the advantages of high response speed, high calculation accuracy and wide early warning range, can improve the early warning and pre-control capability of the power grid running risk in thunderstorm disaster weather, and provides important decision support for the power grid dispatching running risk pre-control.
Description
Technical Field
The invention relates to the technical field of power failure early warning, in particular to a risk early warning method for a thunderstorm weather power transmission line.
Background
The power transmission line is easily tripped or unplanned to be forced to stop operating under the influence of thunderstorm meteorological environment, the lightning stroke not only can cause the tripping of the line to cause power interruption, but also can cause the insulation damage of primary power equipment or damage secondary equipment when lightning waves invade a transformer substation, thereby causing regional power failure and seriously threatening the safety of a large power grid. The existing power grid lightning protection is mainly used for researching and defining a high-risk lightning strike area according to historical lightning data and a lightning strike fault case of a power transmission line, then measures such as erecting a lightning conductor, installing a lightning arrester, reducing the grounding resistance of a pole tower and the like are adopted for passive defense, future moving paths and thunderstorm intensity prediction cannot be carried out aiming at real-time thunderstorm cloud clusters, and advanced quantitative early warning of lightning strike faults of the power transmission line is further developed.
The above information disclosed in the background section is only for enhancement of understanding of the background of the application and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims at solving the problem that the prior art does not analyze the key characteristics of the future thunderstorm cloud cluster to cause poor accuracy in estimating the probability prediction of the lightning stroke of the power transmission line, realizes the prediction of the key characteristics of the future thunderstorm cloud cluster according to the quasi-real-time lightning positioning data, further establishes a physical calculation model of the lightning stroke fault of the power transmission line, acquires the probability of the future lightning stroke fault of the power transmission line and carries out advanced early warning, adopts a scheme combining machine learning analysis prediction and physical model calculation early warning, has the advantages of high response speed, high calculation accuracy and wide early warning range, can improve the early warning and pre-controlling capability of the running risk of the power grid in thunderstorm disaster weather, and provides important decision support for the pre-controlling of the scheduling running risk of the power grid.
In a first aspect, a technical solution provided in an embodiment of the present invention is a risk early warning method for a power transmission line in thunderstorm weather, including the following steps:
s1, predicting a future thunderstorm cloud cluster according to near-real-time lightning positioning data to obtain key characteristic information for representing the future thunderstorm cloud cluster;
s2, calculating the probability of lightning stroke faults of each section of the power transmission line in the future according to the key characteristic information;
s3, weighting calculation is carried out on the lightning strike fault probability of each section of the power transmission line to obtain the lightning strike fault probability of the whole power transmission line;
s4, displaying lightning strike risk information of the transmission line by combining a gis map and performing advanced early warning;
the step S1 comprises the following steps:
s11, acquiring near-real-time lightning positioning data, and performing data preprocessing to generate a lightning data sample;
s12, analyzing the lightning data sample by adopting a clustering algorithm to generate a quasi-real-time thunderstorm cloud cluster;
s13, calculating and obtaining key characteristic information of each quasi-real-time thunderstorm cloud cluster;
s14, identifying the same quasi-real-time thunderstorm cloud cluster in different time periods;
s15, predicting key characteristic information of future thunderstorm cloud clusters;
the step S2 comprises the following steps:
s21, modeling the fixed characteristics of the power transmission line;
s22, calculating and obtaining lightning resistance levels of all sections of the power transmission line;
s23, calculating the probability of lightning stroke faults of each section of the power transmission line in the future according to the lightning resistance level of each section of the power transmission line.
Preferably, step S11 includes the steps of:
dividing the quasi-real-time lightning positioning data into different time periods for sampling to obtain lightning sample data;
specifically, the near-real-time lightning location data comprises lightning location data which is continuous for 5 minutes before 60 minutes at the current moment, lightning location data which is continuous for 5 minutes before 30 minutes at the current moment, and lightning location data which is continuous for 5 minutes at the current moment;
the lightning sample data comprises longitude and latitude coordinates of a lightning stroke occurrence place, lightning current intensity, lightning strike back steepness and lightning strike back times.
Preferably, step S12 includes the steps of:
s121, designating the minimum value of the number of lightning drop points forming a thunderstorm cloud cluster as the minimum number of points of a clustering algorithm;
s122, designating the minimum radius of the thunderstorm cloud cluster as the field radius of a clustering algorithm;
s123, calling a DBSCAN clustering algorithm to generate thunderstorm cloud clusters by taking a thunder and lightning data sample, the clustering minimum point number and the clustering field radius as input parameters;
s124, generating a unique number identification for each thunderstorm cloud cluster.
Preferably, in step S13, the key characteristic information of the real-time thunderstorm cloud includes: the longitude and latitude of the center of the thunderstorm cloud, the radius of the thunderstorm cloud, the thunderstorm cloud thunder-falling number, the maximum lightning stroke intensity of the thunderstorm cloud, the minimum lightning stroke intensity of the thunderstorm cloud and the average lightning stroke intensity of the thunderstorm cloud;
the calculation formula of the longitude and latitude of the center of the thunderstorm cloud cluster is as follows:
wherein: LON is the longitude of the center position of the current thunderstorm cloud cluster; LAT is the latitude of the center position of the current thunderstorm cloud cluster; lon (lon) i The longitude of the lightning point in the current thunderstorm cloud cluster; lat i The latitude of the lightning point in the current thunderstorm cloud cluster range is determined; n is the total number of lightning strokes generated by thunderstorm cloud clusters;
after acquiring the longitude and latitude of the central position of the thunderstorm cloud cluster, traversing all thunder-drop information of the thunderstorm cloud cluster, and calculating the distance D from the thunder-drop point to the central position of the thunderstorm cloud cluster according to the longitude and latitude of the thunder-drop point, wherein the formula is as follows:
D＝R*cos -1 [cos(y1)*cos(y2)*cos(x1-x2)+sin(y1)*sin(y2)]
wherein R is the earth radius, x1 and y1 are radians corresponding to the longitude and latitude of the center of the thunderstorm cloud cluster, and x2 and y2 are radians corresponding to the longitude and latitude of a certain lightning point of the thunderstorm cloud cluster;
and (3) evaluating the lightning stroke intensity of the thunderstorm cloud cluster according to the lightning current intensity equivalence, wherein the calculation formula of the average lightning stroke intensity of the thunderstorm cloud cluster is as follows:
wherein: p is the average intensity of the thunderstorm cloud;p i is the absolute value of the intensity of a single lightning strike in the cloud cluster range; n is the thunderstorm cloud thunder number.
Preferably, in step S14, the different time periods refer to three time periods of 60 minutes before the current time, 30 minutes before the current time and the current time, and the identification of the same quasi-real-time thunderstorm cloud cluster in the different time periods includes the following steps:
s141, initializing and defining the maximum running distance S of the same thunderstorm cloud cluster in a continuous period;
s142, searching a thunderstorm cloud cluster B1 corresponding to a thunderstorm cloud cluster A1 with the closest center distance and the center distance smaller than the maximum running distance S in a period from the center longitude and latitude of the thunderstorm cloud cluster A1 to the period from the current time of 30 minutes to the period from the current time of 60 minutes, recognizing that the thunderstorm cloud cluster A1 and the thunderstorm cloud cluster B1 are the same thunderstorm cloud cluster, assigning the serial number identification of the B1 cloud cluster to the A1 cloud cluster, completing the recognition of the thunderstorm cloud cluster in the period from the current time of 30 minutes, and if the thunderstorm cloud cluster A1 is not matched with the corresponding cloud cluster, recognizing that the thunderstorm cloud cluster A1 is a newly generated thunderstorm cloud cluster and assigning a new serial number identification to the thunderstorm cloud cluster A1;
s143, searching a thunderstorm cloud cluster B2 corresponding to a period from the longitude and latitude of the center of the thunderstorm cloud cluster A2 to 30 minutes before the current moment, wherein the center distance is nearest and is smaller than the maximum running distance S, recognizing that the thunderstorm cloud cluster A2 and the thunderstorm cloud cluster B2 are the same thunderstorm cloud cluster, assigning the serial number identification of the thunderstorm cloud cluster B2 to the thunderstorm cloud cluster A2 cloud cluster, completing the recognition of the thunderstorm cloud cluster in the current moment, and if the thunderstorm cloud cluster A is not matched with the corresponding thunderstorm cloud cluster, recognizing that the thunderstorm cloud cluster A is a newly generated thunderstorm cloud cluster and assigning a new serial number identification to the thunderstorm cloud cluster A;
s144, putting the Lei Tuan cloud cluster key characteristic information with the same number identification in different time periods into the same data set according to the time period sequence, and combining the same data set into a prediction sample set.
Preferably, step S15 includes the steps of:
s151, performing fitting calculation on the central longitude and latitude of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the central longitude and latitude of the thunderstorm cloud cluster A after 30 minutes;
s152, performing fitting calculation on the radius of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the radius of the thunderstorm cloud cluster A after 30 minutes;
s153, performing fitting calculation on the thunder number of the thunderstorm cloud cluster A in the prediction sample set by using polynomial fitting, and obtaining the thunder number of the thunderstorm cloud cluster A after 30 minutes;
s154, performing fitting calculation on the maximum lightning stroke intensity of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the maximum lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes;
s155, performing fitting calculation on the minimum lightning stroke intensity of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the minimum lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes;
and S156, performing fitting calculation on the minimum lightning stroke intensity of the thunderstorm cloud cluster A in the predicted sample set by adopting polynomial fitting, and obtaining the average lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes.
Preferably, in step S21, the modeling of the transmission line fixed characteristic includes the steps of:
s211, dividing the power transmission line into a plurality of sections by taking each tower of the power transmission line as an interval;
s212, calculating insulator strings U of all sections of the power transmission line 50％ The discharge voltage is given by:
U 50％ ＝531*L k +31
wherein L is k The length of the line insulator string is as long as the line insulator string;
s213, calculating the rod striking rate g of each section of the transmission line,
s214, calculating shielding failure rate P of each section of the power transmission line according to different terrains of the line α The formula is as follows:
plain area:
mountain area:
wherein alpha is the protection angle of the line lightning conductor, and h is the average suspension height of the lead.
Preferably, in step S22, the lightning withstand level includes a lightning withstand level I at the top of the lightning striking tower 1 And lightning-proof level I of lightning shielding failure wire 2 ，
Wherein:
lightning-proof level I of lightning striking tower top 1 The calculation formula is as follows:
wherein k is a coupling coefficient, and the value is 0.256; beta is a shunt coefficient, and the value is 0.88; r is R ch A tower impact grounding resistor; l (L) gt The inductor is a tower inductor; h is a d Average hanging height for the wire;
lightning-proof level I of lightning shielding failure conductor 2 The calculation formula is as follows:
where Z is the line wave impedance.
Preferably, step S23 includes the steps of:
s231, determining a line section affected by the thunderstorm in the future according to the longitude and latitude of the center of the thunderstorm cloud cluster and the radius of the thunderstorm cloud cluster in the future;
s232 based on lightning current amplitude probability distribution F lc Calculating probability P that lightning current amplitude of line section affected by future thunderstorm cloud cluster exceeds lightning resistance level I1 and lightning resistance level I2 of lightning striking tower top 1 、P 2 ：
Wherein: probability distribution F of lightning current amplitude lc The calculation formula is as follows:
wherein P (I is greater than or equal to I) represents the probability that the lightning current amplitude I exceeds I; a, the value of the parameter a is 17.85; b parameter value 1.926;
wherein I is 1 、I 2 The lightning-proof level of the lightning striking tower top and the lightning-proof level of the lightning shielding failure lead of the line section affected by the future thunderstorm cloud cluster are respectively; i max 、I min Respectively the maximum lightning stroke intensity and the minimum lightning stroke intensity of the future thunderstorm cloud cluster; when lightning-resistant level I i <I min At the time, correspond to P i Should be taken as 1; when I i >I max At the time, correspond to P i Should be taken as 0;
s233, calculating lightning trip probability P of line section affected by future thunderstorm cloud t The formula is as follows:
P t ＝η(gP 1 +P α P 2 )
wherein, eta is the arc establishment rate and the value is 1; g is the striking rate; p (P) α The shielding failure rate is the shielding failure rate.
Preferably, in step S3, the probability of lightning strike failure of each section of the power transmission line is obtained by weighting calculation, and the formula is as follows:
wherein P is T The lightning strike fault probability of the whole transmission line is P tj The probability of a lightning strike failure for line segment j, z, is the number of line segments affected by future thunderstorm clouds.
Preferably, in step S4, the displaying the lightning strike risk information of the transmission line in combination with the gis map and performing advanced warning includes the following steps:
s41, drawing complete paths of all power transmission lines in a power grid in a gis map;
s42, carrying out color rendering on the power transmission line with the probability of lightning stroke faults, which is influenced by the future thunderstorm cloud, in a probability distribution grade form, and marking the power transmission line in a gis map; meanwhile, the name, fault reason, fault probability and fault moment of the power transmission line are displayed, and advanced early warning is carried out.
The invention has the beneficial effects that: according to the invention, a machine learning algorithm (DBSCAN clustering algorithm) is adopted to realize the prediction of key characteristics of future thunderstorm cloud clusters according to the quasi-real-time thunderstorm positioning data, a physical calculation model of the lightning faults of the power transmission line is further established, the probability of the future lightning faults of the power transmission line is obtained, advanced early warning is carried out, and a scheme combining the machine learning analysis prediction and the physical model calculation early warning is adopted.
The foregoing summary is merely an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more fully understood, and in order that the same or additional objects, features and advantages of the present invention may be more fully understood.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures.
Fig. 1 is a flowchart of a risk early warning method for a thunderstorm weather power transmission line.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and examples, it being understood that the detailed description herein is merely a preferred embodiment of the present invention, which is intended to illustrate the present invention, and not to limit the scope of the invention, as all other embodiments obtained by those skilled in the art without making any inventive effort fall within the scope of the present invention.
Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations (or steps) as a sequential process, many of the operations (or steps) can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures; the processes may correspond to methods, functions, procedures, subroutines, and the like.
Examples: as shown in fig. 1, a risk early warning method for a thunderstorm weather power transmission line includes the following steps:
s1, predicting a future thunderstorm cloud cluster according to near-real-time lightning positioning data to obtain key characteristic information representing the future thunderstorm cloud cluster.
Specifically, the step S1 includes the following steps:
s11, acquiring near-real-time lightning positioning data, and performing data preprocessing to generate a lightning data sample.
More specifically, in step S11, the following steps are included:
dividing the near-real-time lightning positioning data into different time periods for sampling to obtain lightning sample data.
Specifically, the near-real-time lightning location data comprises lightning location data which is continuous for 5 minutes before 60 minutes at the current moment, lightning location data which is continuous for 5 minutes before 30 minutes at the current moment, and lightning location data which is continuous for 5 minutes at the current moment.
The lightning sample data comprises longitude and latitude coordinates of a lightning stroke occurrence place, lightning current intensity, lightning strike back steepness and lightning strike back times.
And S12, analyzing the lightning data sample by adopting a clustering algorithm to generate a quasi-real-time thunderstorm cloud cluster.
More specifically, step S12 includes the steps of:
s121, designating the minimum value of the number of lightning drop points forming the thunderstorm cloud cluster as the minimum number of points of a clustering algorithm.
S122, designating the minimum radius of the thunderstorm cloud cluster as the field radius of the clustering algorithm.
S123, calling a DBSCAN clustering algorithm to generate thunderstorm cloud clusters by taking a thunder and lightning data sample, the clustering minimum point number and the clustering field radius as input parameters; the clustering algorithm employs a density-based clustering algorithm (density-based spatial clustering of applications with noise, DBSCAN).
S124, generating a unique number identification for each thunderstorm cloud cluster.
And S13, calculating and obtaining key characteristic information of each quasi-real-time thunderstorm cloud cluster.
More specifically, in step S13, the key characteristic information of the real-time thunderstorm cloud includes: the longitude and latitude of the center of the thunderstorm cloud, the radius of the thunderstorm cloud, the number of thunderstorm cloud thunderbolts, the maximum lightning strike intensity of the thunderstorm cloud, the minimum lightning strike intensity of the thunderstorm cloud and the average lightning strike intensity of the thunderstorm cloud.
The calculation formula of the longitude and latitude of the center of the thunderstorm cloud cluster is as follows:
wherein: LON is the longitude of the center position of the current thunderstorm cloud cluster; LAT is the latitude of the center position of the current thunderstorm cloud cluster; lon (lon) i The longitude of the lightning point in the current thunderstorm cloud cluster; lat i The latitude of the lightning point in the current thunderstorm cloud cluster range is determined; n is the total number of lightning strokes generated by thunderstorm cloud.
After acquiring the longitude and latitude of the central position of the thunderstorm cloud cluster, traversing all thunder-drop information of the thunderstorm cloud cluster, and calculating the distance D from the thunder-drop point to the central position of the thunderstorm cloud cluster according to the longitude and latitude of the thunder-drop point, wherein the formula is as follows:
D＝R*cos -1 [cos(y1)*cos(y2)*cos(x1-x2)+sin(y1)*sin(y2)]
wherein R is the earth radius which is about 6371kM, x1 and y1 are radians corresponding to the longitude and latitude of the center of the thunderstorm cloud cluster, and x2 and y2 are radians corresponding to the longitude and latitude of a certain lightning point of the thunderstorm cloud cluster.
And (3) evaluating the lightning stroke intensity of the thunderstorm cloud cluster according to the lightning current intensity equivalence, wherein the calculation formula of the average lightning stroke intensity of the thunderstorm cloud cluster is as follows:
wherein: p is the average intensity of the thunderstorm cloud; p is p i Is the absolute value of the intensity of a single lightning strike in the cloud cluster range; n is the thunderstorm cloud thunder number.
S14, identifying the same quasi-real-time thunderstorm cloud cluster in different time periods.
More specifically, in step S14, the different time periods refer to three time periods of 60 minutes before the current time, 30 minutes before the current time, and the identification of the same quasi-real-time thunderstorm cloud cluster in the different time periods includes the following steps:
s141, initializing and defining the maximum running distance S of the same thunderstorm cloud cluster in the connected time period.
S142, searching a thunderstorm cloud cluster B1 corresponding to a thunderstorm cloud cluster A1 with the closest center distance and the center distance smaller than the maximum running distance S in a period from the center longitude and latitude of the thunderstorm cloud cluster A1 to the period from the current time of 30 minutes to the period from the current time of 60 minutes, recognizing that the thunderstorm cloud cluster A1 and the thunderstorm cloud cluster B1 are the same thunderstorm cloud cluster, assigning the serial number identification of the B1 cloud cluster to the A1 cloud cluster, completing the recognition of the thunderstorm cloud cluster in the period from the current time of 30 minutes, and if the thunderstorm cloud cluster A1 is not matched with the corresponding cloud cluster, recognizing that the thunderstorm cloud cluster A1 is a newly generated thunderstorm cloud cluster and assigning a new serial number identification to the thunderstorm cloud cluster A1.
S143, searching a thunderstorm cloud cluster B2 which is closest to the center distance and corresponds to the center distance which is smaller than the maximum running distance S in a period from the longitude and latitude of the center of the thunderstorm cloud cluster A2 to 30 minutes before the current moment, recognizing that the thunderstorm cloud cluster A2 and the thunderstorm cloud cluster B2 are the same thunderstorm cloud cluster, assigning the serial number identification of the thunderstorm cloud cluster B2 to the thunderstorm cloud cluster A2 cloud cluster, completing the recognition of the thunderstorm cloud cluster in the current moment, and if the thunderstorm cloud cluster A is not matched with the corresponding thunderstorm cloud cluster, recognizing that the thunderstorm cloud cluster A is a newly generated thunderstorm cloud cluster and assigning a new serial number identification to the thunderstorm cloud cluster A.
S144, putting the Lei Tuan cloud cluster key characteristic information with the same number identification in different time periods into the same data set according to the time period sequence, and combining the same data set into a prediction sample set.
S15, predicting key characteristic information of future thunderstorm cloud clusters.
More specifically, step S15 includes the steps of:
and S151, performing fitting calculation on the central longitude and latitude of the thunderstorm cloud cluster A in the prediction sample set by using polynomial fitting, and obtaining the central longitude and latitude of the thunderstorm cloud cluster A after 30 minutes.
And S152, performing fitting calculation on the radius of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the radius of the thunderstorm cloud cluster A after 30 minutes.
And S153, performing fitting calculation on the thunder number of the thunderstorm cloud cluster A in the prediction sample set by using polynomial fitting, and obtaining the thunder number of the thunderstorm cloud cluster A after 30 minutes.
And S154, performing fitting calculation on the maximum lightning stroke intensity of the thunderstorm cloud cluster A in the predicted sample set by adopting polynomial fitting, and obtaining the maximum lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes.
And S155, performing fitting calculation on the minimum lightning stroke intensity of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the minimum lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes.
And S156, performing fitting calculation on the minimum lightning stroke intensity of the thunderstorm cloud cluster A in the predicted sample set by adopting polynomial fitting, and obtaining the average lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes.
S2, calculating the probability of lightning stroke faults of each section of the power transmission line in the future according to the key characteristic information.
Specifically, the step S2 includes the following steps:
s21, modeling the fixed characteristics of the power transmission line.
More specifically, in step S21, the modeling of the fixed characteristic of the transmission line includes the following steps:
s211, dividing the power transmission line into a plurality of sections by taking each tower of the power transmission line as an interval.
S212, calculating insulator strings U of all sections of the power transmission line 50％ The discharge voltage is given by the following formula.
U 50％ ＝531*L k +31
Wherein L is k Is the length of the line insulator string.
S213, calculating the rod striking rate g of each section of the power transmission line, wherein the rod striking rate g can be obtained by adopting the data listed in the table 1 according to the DL/T620-1997 standard.
TABLE 1 comparison of the striking rates
| Number of lightning conductors | 1 | 2 |
| Striking rate g in plain area | 1/4 | 1/6 |
| Mountain area striking rate g | 1/3 | 1/4 |
S214, calculating shielding failure rate P of each section of the power transmission line according to different terrains of the line α The formula is as follows:
plain area:
mountain area:
wherein alpha is the protection angle of the line lightning conductor, and h is the average suspension height of the lead.
S22, calculating and obtaining lightning resistance levels of all sections of the power transmission line.
More specifically, in step S22, the lightning-proof level includes a lightning-proof level I at the top of the lightning striking tower 1 And lightning-proof level I of lightning shielding failure wire 2 ，
Wherein:
lightning-proof level I of lightning striking tower top 1 The calculation formula is as follows:
wherein k is a coupling coefficient, and the value is 0.256; beta is a shunt coefficient, and the value is 0.88; r is R ch A tower impact grounding resistor; l (L) gt The inductor is a tower inductor; h is a d The average suspension height for the wire.
Lightning-proof level I of lightning shielding failure conductor 2 The calculation formula is as follows:
where Z is the line wave impedance.
S23, calculating the probability of lightning stroke faults of each section of the power transmission line in the future according to the lightning resistance level of each section of the power transmission line.
More specifically, step S23 includes the steps of:
s231, determining a line section affected by the thunderstorm in the future according to the longitude and latitude of the center of the thunderstorm cloud cluster and the radius of the thunderstorm cloud cluster in the future.
S232 based on lightning current amplitude probability distribution F lc Calculating probability P that lightning current amplitude of line section affected by future thunderstorm cloud cluster exceeds lightning resistance level I1 and lightning resistance level I2 of lightning striking tower top 1 、P 2 。
Wherein: probability distribution F of lightning current amplitude lc The calculation formula is as follows:
wherein P (I is greater than or equal to I) represents the probability that the lightning current amplitude I exceeds I; a, the value of the parameter a is 17.85; b parameter value 1.926;
wherein I is 1 、I 2 The lightning-proof level of the lightning striking tower top and the lightning-proof level of the lightning shielding failure lead of the line section affected by the future thunderstorm cloud cluster are respectively; i max 、I min Respectively the maximum lightning stroke intensity and the minimum lightning stroke intensity of the future thunderstorm cloud cluster; when lightning-resistant level I i <I min At the time, correspond to P i Should be taken as 1; when I i >I max At the time, correspond to P i Should be taken as 0.
S233, calculating lightning trip probability P of line section affected by future thunderstorm cloud t The formula is as follows:
P t ＝η(gP 1 +P α P 2 )
wherein, eta is the arc establishment rate and the value is 1; g is the striking rate; p (P) α The shielding failure rate is the shielding failure rate.
And S3, weighting calculation is carried out on the lightning strike fault probability of each section of the power transmission line to obtain the lightning strike fault probability of the whole power transmission line.
More specifically, in step S3, the probability of lightning strike failure of the whole power transmission line is obtained by weighting and calculating the probability of lightning strike failure of each section of the power transmission line, and the formula is as follows:
wherein P is T The lightning strike fault probability of the whole transmission line is P tj The probability of a lightning strike failure for line segment j, z, is the number of line segments affected by future thunderstorm clouds.
And S4, displaying the lightning strike risk information of the transmission line by combining a gis map and performing advanced early warning.
More specifically, in step S4, the displaying the lightning strike risk information of the transmission line in combination with the gis map and performing advanced warning includes the following steps:
and S41, drawing complete paths of all power transmission lines in the power grid in a gis map.
S42, carrying out color rendering on the power transmission line with the probability of lightning stroke faults, which is influenced by the future thunderstorm cloud, in a probability distribution grade form, and marking the power transmission line in a gis map; meanwhile, the name, fault reason, fault probability and fault moment of the power transmission line are displayed, and advanced early warning is carried out. In this embodiment, the probability distribution classification may be set with 5% as a class, and the higher the probability, the darker the color it renders.
The above embodiments are preferred embodiments of the risk early warning method for a thunderstorm weather power transmission line according to the present invention, and the scope of the present invention is not limited to the preferred embodiments, and all equivalent changes of the shape and structure according to the present invention are within the scope of the present invention.
Claims (11)
1. The risk early warning method for the thunderstorm weather power transmission line is characterized by comprising the following steps of:
s1, predicting a future thunderstorm cloud cluster according to near-real-time lightning positioning data to obtain key characteristic information for representing the future thunderstorm cloud cluster;
s2, calculating the probability of lightning stroke faults of each section of the power transmission line in the future according to the key characteristic information;
s3, weighting calculation is carried out on the lightning strike fault probability of each section of the power transmission line to obtain the lightning strike fault probability of the whole power transmission line;
s4, displaying lightning strike risk information of the transmission line by combining a gis map and performing advanced early warning;
the step S1 comprises the following steps:
s11, acquiring near-real-time lightning positioning data, and performing data preprocessing to generate a lightning data sample;
s12, analyzing the lightning data sample by adopting a clustering algorithm to generate a quasi-real-time thunderstorm cloud cluster;
s13, calculating and obtaining key characteristic information of each quasi-real-time thunderstorm cloud cluster;
s14, identifying the same quasi-real-time thunderstorm cloud cluster in different time periods;
s15, predicting key characteristic information of future thunderstorm cloud clusters;
the step S2 comprises the following steps:
s21, modeling the fixed characteristics of the power transmission line;
s22, calculating and obtaining lightning resistance levels of all sections of the power transmission line;
s23, calculating the probability of lightning stroke faults of each section of the power transmission line in the future according to the lightning resistance level of each section of the power transmission line.
2. The method for warning risk of a thunderstorm weather transmission line according to claim 1, wherein,
the step S11 includes the steps of:
dividing the quasi-real-time lightning positioning data into different time periods for sampling to obtain lightning sample data;
specifically, the near-real-time lightning location data comprises lightning location data which is continuous for 5 minutes before 60 minutes at the current moment, lightning location data which is continuous for 5 minutes before 30 minutes at the current moment, and lightning location data which is continuous for 5 minutes at the current moment;
the lightning sample data comprises longitude and latitude coordinates of a lightning stroke occurrence place, lightning current intensity, lightning strike back steepness and lightning strike back times.
3. The method for warning risk of a thunderstorm weather transmission line according to claim 1, wherein,
step S12 includes the steps of:
s121, designating the minimum value of the number of lightning drop points forming a thunderstorm cloud cluster as the minimum number of points of a clustering algorithm;
s122, designating the minimum radius of the thunderstorm cloud cluster as the field radius of a clustering algorithm;
s123, calling a DBSCAN clustering algorithm to generate thunderstorm cloud clusters by taking a thunder and lightning data sample, the clustering minimum point number and the clustering field radius as input parameters;
s124, generating a unique number identification for each thunderstorm cloud cluster.
4. A method for warning risk of a thunderstorm weather transmission line according to claim 1 or 3, wherein in step S13, the key feature information of the real-time thunderstorm cloud cluster includes: the longitude and latitude of the center of the thunderstorm cloud, the radius of the thunderstorm cloud, the thunderstorm cloud thunder-falling number, the maximum lightning stroke intensity of the thunderstorm cloud, the minimum lightning stroke intensity of the thunderstorm cloud and the average lightning stroke intensity of the thunderstorm cloud;
the calculation formula of the longitude and latitude of the center of the thunderstorm cloud cluster is as follows:
wherein: LON is the longitude of the center position of the current thunderstorm cloud cluster; LAT is the latitude of the center position of the current thunderstorm cloud cluster; lon (lon) i The longitude of the lightning point in the current thunderstorm cloud cluster; lat i The latitude of the lightning point in the current thunderstorm cloud cluster range is determined; n is the total number of lightning strokes generated by thunderstorm cloud clusters;
after acquiring the longitude and latitude of the central position of the thunderstorm cloud cluster, traversing all thunder-drop information of the thunderstorm cloud cluster, and calculating the distance D from the thunder-drop point to the central position of the thunderstorm cloud cluster according to the longitude and latitude of the thunder-drop point, wherein the formula is as follows:
D＝R*cos -1 [cos(y1)*cos(y2)*cos(x1-x2)+sin(y1)*sin(y2)]
wherein R is the earth radius, x1 and y1 are radians corresponding to the longitude and latitude of the center of the thunderstorm cloud cluster, and x2 and y2 are radians corresponding to the longitude and latitude of a certain lightning point of the thunderstorm cloud cluster;
and (3) evaluating the lightning stroke intensity of the thunderstorm cloud cluster according to the lightning current intensity equivalence, wherein the calculation formula of the average lightning stroke intensity of the thunderstorm cloud cluster is as follows:
wherein: p is the average intensity of the thunderstorm cloud; p is p i Is the absolute value of the intensity of a single lightning strike in the cloud cluster range; n is the thunderstorm cloud thunder number.
5. The method for warning risk of a thunderstorm weather transmission line according to claim 1, wherein,
in step S14, the different time periods refer to three time periods of 60 minutes before the current time, 30 minutes before the current time and the current time, and the identification of the same quasi-real-time thunderstorm cloud cluster in the different time periods includes the following steps:
s141, initializing and defining the maximum running distance S of the same thunderstorm cloud cluster in a continuous period;
s142, searching a thunderstorm cloud cluster B1 corresponding to a thunderstorm cloud cluster A1 with the closest center distance and the center distance smaller than the maximum running distance S in a period from the center longitude and latitude of the thunderstorm cloud cluster A1 to the period from the current time of 30 minutes to the period from the current time of 60 minutes, recognizing that the thunderstorm cloud cluster A1 and the thunderstorm cloud cluster B1 are the same thunderstorm cloud cluster, assigning the serial number identification of the B1 cloud cluster to the A1 cloud cluster, completing the recognition of the thunderstorm cloud cluster in the period from the current time of 30 minutes, and if the thunderstorm cloud cluster A1 is not matched with the corresponding cloud cluster, recognizing that the thunderstorm cloud cluster A1 is a newly generated thunderstorm cloud cluster and assigning a new serial number identification to the thunderstorm cloud cluster A1;
s143, searching a thunderstorm cloud cluster B2 corresponding to a period from the longitude and latitude of the center of the thunderstorm cloud cluster A2 to 30 minutes before the current moment, wherein the center distance is nearest and is smaller than the maximum running distance S, recognizing that the thunderstorm cloud cluster A2 and the thunderstorm cloud cluster B2 are the same thunderstorm cloud cluster, assigning the serial number identification of the thunderstorm cloud cluster B2 to the thunderstorm cloud cluster A2 cloud cluster, completing the recognition of the thunderstorm cloud cluster in the current moment, and if the thunderstorm cloud cluster A is not matched with the corresponding thunderstorm cloud cluster, recognizing that the thunderstorm cloud cluster A is a newly generated thunderstorm cloud cluster and assigning a new serial number identification to the thunderstorm cloud cluster A;
s144, putting the Lei Tuan cloud cluster key characteristic information with the same number identification in different time periods into the same data set according to the time period sequence, and combining the same data set into a prediction sample set.
6. The method for warning risk of a thunderstorm weather transmission line according to claim 1, wherein,
step S15 includes the steps of:
s151, performing fitting calculation on the central longitude and latitude of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the central longitude and latitude of the thunderstorm cloud cluster A after 30 minutes;
s152, performing fitting calculation on the radius of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the radius of the thunderstorm cloud cluster A after 30 minutes;
s153, performing fitting calculation on the thunder number of the thunderstorm cloud cluster A in the prediction sample set by using polynomial fitting, and obtaining the thunder number of the thunderstorm cloud cluster A after 30 minutes;
s154, performing fitting calculation on the maximum lightning stroke intensity of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the maximum lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes;
s155, performing fitting calculation on the minimum lightning stroke intensity of the thunderstorm cloud cluster A in the predicted sample set by using polynomial fitting, and obtaining the minimum lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes;
and S156, performing fitting calculation on the minimum lightning stroke intensity of the thunderstorm cloud cluster A in the predicted sample set by adopting polynomial fitting, and obtaining the average lightning stroke intensity of the thunderstorm cloud cluster A after 30 minutes.
7. The method for warning risk of a thunderstorm weather transmission line according to claim 1, wherein,
in step S21, the modeling of the fixed characteristic of the power transmission line includes the following steps:
s211, dividing the power transmission line into a plurality of sections by taking each tower of the power transmission line as an interval;
s212, calculating insulator strings U of all sections of the power transmission line 50％ The discharge voltage is given by:
U 50％ ＝531*L k +31
wherein L is k The length of the line insulator string is as long as the line insulator string;
s213, calculating the rod striking rate g of each section of the transmission line,
s214, calculating shielding failure rate P of each section of the power transmission line according to different terrains of the line α The formula is as follows:
plain area:
mountain area:
wherein alpha is the protection angle of the line lightning conductor, and h is the average suspension height of the lead.
8. The method for warning risk of a thunderstorm weather transmission line according to claim 1 or 7, wherein,
in step S22, the lightning resistant level comprises a lightning resistant level I at the top of the lightning striking tower 1 And lightning-proof level I of lightning shielding failure wire 2 ，
Wherein:
lightning-proof level I of lightning striking tower top 1 The calculation formula is as follows:
wherein k is a coupling coefficient, and the value is 0.256; beta is a shunt coefficient, and the value is 0.88; r is R ch A tower impact grounding resistor; l (L) gt The inductor is a tower inductor; h is a d Average hanging height for the wire;
lightning-proof level I of lightning shielding failure conductor 2 The calculation formula is as follows:
where Z is the line wave impedance.
9. The method for warning risk of a thunderstorm weather transmission line according to claim 1, wherein,
step S23 includes the steps of:
s231, determining a line section affected by the thunderstorm in the future according to the longitude and latitude of the center of the thunderstorm cloud cluster and the radius of the thunderstorm cloud cluster in the future;
s232 based on lightning current amplitude probability distribution F lc Calculating probability P that lightning current amplitude of line section affected by future thunderstorm cloud cluster exceeds lightning resistance level I1 and lightning resistance level I2 of lightning striking tower top 1 、P 2 ：
Wherein: probability distribution F of lightning current amplitude lc The calculation formula is as follows:
wherein P (I is greater than or equal to I) represents the probability that the lightning current amplitude I exceeds I; a, the value of the parameter a is 17.85; b parameter value 1.926;
wherein I is 1 、I 2 The lightning-proof level of the lightning striking tower top and the lightning-proof level of the lightning shielding failure lead of the line section affected by the future thunderstorm cloud cluster are respectively; i max 、I min Respectively the maximum lightning stroke intensity and the minimum lightning stroke intensity of the future thunderstorm cloud cluster; when lightning-resistant level I i <I min At the time, correspond to P i Should be taken as 1; when I i >I max At the time, correspond to P i Should be taken as 0;
s233, calculating lightning trip probability P of line section affected by future thunderstorm cloud t The formula is as follows:
P t ＝η(gP 1 +P α P 2 )
wherein, eta is the arc establishment rate and the value is 1; g is the striking rate; p (P) α The shielding failure rate is the shielding failure rate.
10. The method for warning risk of a thunderstorm weather transmission line according to claim 1 or 9, wherein,
in step S3, the lightning strike fault probability of the whole power transmission line is obtained by weighting and calculating the lightning strike fault probability of each section of the power transmission line, and the formula is as follows:
wherein P is T The lightning strike fault probability of the whole transmission line is P tj The probability of a lightning strike failure for line segment j, z, is the number of line segments affected by future thunderstorm clouds.
11. The method for warning risk of a thunderstorm weather transmission line according to claim 1, wherein,
in step S4, the step of displaying the lightning strike risk information of the transmission line and performing advanced warning by combining with the gis map includes the following steps:
s41, drawing complete paths of all power transmission lines in a power grid in a gis map;
s42, carrying out color rendering on the power transmission line with the probability of lightning stroke faults, which is influenced by the future thunderstorm cloud, in a probability distribution grade form, and marking the power transmission line in a gis map; meanwhile, the name, fault reason, fault probability and fault moment of the power transmission line are displayed, and advanced early warning is carried out.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116957343A (en) * | 2023-09-19 | 2023-10-27 | 四川雅韵能源开发有限责任公司 | Natural gas transportation safety risk analysis method and system |
| CN117131783A (en) * | 2023-10-20 | 2023-11-28 | 合肥工业大学 | Multi-mode learning-based power transmission line risk prediction model, method and system |
| CN117349692A (en) * | 2023-12-04 | 2024-01-05 | 国网江西省电力有限公司南昌供电分公司 | Distribution line lightning early warning method integrating multiple lightning early warning factors |
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| CN116957343A (en) * | 2023-09-19 | 2023-10-27 | 四川雅韵能源开发有限责任公司 | Natural gas transportation safety risk analysis method and system |
| CN116957343B (en) * | 2023-09-19 | 2023-12-19 | 四川雅韵能源开发有限责任公司 | Natural gas transportation safety risk analysis method and system |
| CN117131783A (en) * | 2023-10-20 | 2023-11-28 | 合肥工业大学 | Multi-mode learning-based power transmission line risk prediction model, method and system |
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| CN117349692A (en) * | 2023-12-04 | 2024-01-05 | 国网江西省电力有限公司南昌供电分公司 | Distribution line lightning early warning method integrating multiple lightning early warning factors |
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