CN113010559A - Association mining method for micro-terrain and lightning damage characteristic parameters of power transmission corridor area - Google Patents
Association mining method for micro-terrain and lightning damage characteristic parameters of power transmission corridor area Download PDFInfo
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Abstract
The invention provides a method for mining association of a micro-terrain and lightning damage characteristic parameters of a power transmission corridor area. The method comprises the steps of firstly obtaining tower information of a power transmission line, lightning activity parameters of a region where the power transmission line is located and regional elevation data, and obtaining a tower ground lightning density value and a lightning current amplitude mean value by adopting a new lightning activity parameter statistical method in consideration of requirements of micro-terrain analysis on region subdivision. Secondly, various micro-terrain characteristic parameters are analyzed and extracted through geographic information software, and the characteristics of the micro-terrain are comprehensively and accurately reflected. And then, preprocessing the parameters by adopting different data generalization methods to construct a transaction table to be mined. And then, performing relevance analysis based on a relevance rule algorithm to obtain micro-terrain features with strong relevance to lightning activities. The method is suitable for analyzing the relevance between the complex microtopography of the power transmission line and the lightning activity, and has important significance for defining key lines and key towers for lightning protection, power transmission line construction and lightning protection measure transformation work.
Description
Technical Field
The application relates to the technical field of lightning damage protection of power transmission lines, in particular to a method for mining association of micro-terrains and lightning damage characteristic parameters in a power transmission corridor area.
Background
The transmission line is a basic component of a power grid, and the safety, reliability and stability of the transmission line play a vital role in ensuring the stable operation of a power system. The landform of the power transmission corridor is quite complex, the landform is extremely diversified, most overhead power transmission lines with the voltage class of 110kV and above are subjected to severe tests in the thunderstorm season, and lightning stroke faults frequently occur. The lightning stroke fault of the power transmission line accounts for a high proportion of the fault rate of the whole power system, and the fault caused by lightning is the most serious, so that the safe and stable operation of a power grid is seriously threatened.
The existing analysis of the lightning activity of the power transmission line only stays in the situation that the ground lightning density and the lightning activity intensity along the line are counted by utilizing a grid method along with the time change of seasons, months and the like, the method lacks consideration on field factors such as terrain and landform, cannot comprehensively reflect the influence of micro-terrain in a line erection area on line lightning stroke faults, cannot carry out protection design aiming at specific scene lightning damage characteristics, and causes that the effectiveness and the economy of the lightning damage protection measures of the power transmission line cannot be considered. And the lightning activity has the characteristics of strong dispersity and strong randomness, the influence factors are various, and the factors occupying the dominant action are not existed, so that the accurate and effective research result is difficult to obtain by quantitatively describing the lightning activity by adopting the traditional statistical method through a calculation method.
Disclosure of Invention
The application aims to provide a method for mining association between micro-terrain and lightning damage characteristic parameters of a power transmission corridor area, which comprises the following steps:
acquiring parameters of a power transmission line tower, lightning activity parameters and elevation data of an area where the power transmission line tower is located, wherein the parameters of the power transmission line tower comprise longitude and latitude coordinates of the tower, and the lightning activity parameters comprise lightning falling time, longitude and latitude position coordinates and lightning current amplitude;
calculating the lightning density distribution and the lightning current amplitude mean value characteristic parameters in the line corridor of the tower pole according to the tower pole parameters and the lightning activity parameters of the power transmission line;
acquiring terrain parameter attribute values of the altitude, the gradient, the slope direction, the slope shape, the slope change rate, the distance from a ridge line and the distance from a valley line of a tower line corridor area according to the area elevation data;
adopting a data generalization processing method to carry out data preprocessing on the lightning activity parameters and the micro-terrain characteristic parameters and construct a transaction table to be mined;
and establishing a relevance analysis model based on an Apriori relevance rule algorithm, and forming a relevance rule between the micro-terrain factor and the thunder damage characteristic by accessing and calling a transaction table to obtain the micro-terrain characteristic with strong relevance to the thunder and lightning activity.
Further, the calculation steps of the characteristic parameters of the lightning density distribution and the lightning current amplitude mean value in the line corridor of the tower pole are as follows,
numbering the towers in sequence according to the line trend, taking the position of each base tower as the center, drawing a circle by taking a certain distance as a radius,
and counting the lightning frequency in the circle and the lightning current amplitude of each lightning, and calculating the regional lightning density value and the average value of the lightning current amplitude of the tower.
Furthermore, the tower ground lightning density calculation formula is as follows,
wherein N isGThe unit is the number/(km 2 & a) for the density of the lightning, m is the statistical period, the unit is year (a), R is the radius of the circular statistical area, the unit is km, and Pi is the lightning frequency in the statistical area of the ith year.
Furthermore, the calculation formula of the tower lightning current amplitude mean value is as follows,
in the formula, Iav is the average value of lightning current amplitude, and the unit is kA; n is the lightning strike frequency of the ith year in the circular statistical area; iij is the lightning current amplitude of a single lightning strike in a circular statistical area, and the unit is kA.
Furthermore, the micro-terrain characteristic parameter extraction method comprises the following steps,
performing terrain analysis through geographic information software based on digital elevation data, and calculating the attribute values of the altitude, the slope direction, the slope shape and the slope change of the whole area;
and importing each attribute value into tower position information, extracting each attribute value to a tower point, and obtaining the attribute value of each tower from the distance between the ridge line and the valley line through hydrological analysis and neighbor analysis.
Furthermore, the data generalization processing method comprises the following steps,
respectively discretizing the lightning activity characteristic parameters and the micro-terrain characteristic parameters;
and after the parameter discretization is finished, carrying out data binarization processing, and constructing a to-be-mined transaction table in a binary matrix form.
Further, the method for discretizing the special city parameter of the lightning activity is to divide the grade of the lightning density and discretize the lightning density further.
Furthermore, the association analysis model established based on the Apriori association rule algorithm is that,
setting a minimum support and a minimum confidence threshold,
forming an association rule between the micro-terrain factor and the thunderstorm feature by accessing a calling transaction table,
and screening out rules meeting the support degree and confidence degree threshold values to obtain the micro-terrain features with strong correlation with lightning activities.
The method for mining the association of the micro-terrain and the lightning damage characteristic parameters in the power transmission corridor area has the following advantages,
as the complex micro-terrain and landform environment through which the power transmission line passes can obviously influence the lightning activity, the factors of the micro-terrain and landform need to be fully considered for analyzing the lightning activity. Considering the influence of micro-terrain and micro-landform factors, lines need to be divided more finely, so when the lightning activity characteristic parameters are counted, a brand new region division mode is adopted for counting, and when the micro-terrain characteristic parameters are extracted, the parameter attribute values of regions where the base-pole towers are located one by one are also extracted according to the pole towers.
Due to the fact that the micro-terrain has different influences on lightning activities, the lightning activities are described by the aid of two characteristic rates of the ground lightning density and the average value of lightning current amplitude, and a new statistical region division method with the tower as the center is adopted, so that the statistical result can reflect actual lightning activity level around the tower better.
By adopting a new microtopography description method and based on high-resolution DEM data, microtopography characteristic parameter values of the position of the tower, such as altitude, gradient, slope direction, slope shape and gradient change rate are analyzed and extracted, and the distance between the tower and a ridge line and the distance between the tower and a valley line are analyzed and calculated to serve as new microtopography factors, so that the considered microtopography characteristic parameters are more comprehensive, more reasonable and more accurate.
Due to the fact that lightning activities have strong dispersity and randomness and the micro-terrain influence factors are various, when the relevance between the micro-terrain and the lightning activities is analyzed, a data mining technology is adopted, a relevance analysis model based on a relevance rule is established, the efficiency of lightning hazard data mining analysis can be improved, the 'causal thinking' of a traditional statistical method is eliminated, the relevance relation between the micro-terrain which is easy to ignore and the lightning activities is mined, and the objectivity of the relevance analysis is guaranteed to a higher degree.
The invention carries out more comprehensive and reasonable statistical analysis on the lightning activities and the micro-terrain landforms of the base-by-base towers of the power transmission line, and carries out mining on the relevance between the lightning activities and the micro-terrain by combining a data mining method, thereby ensuring the general applicability and the accuracy of the method.
The method is suitable for analyzing the relevance between the complex microtopography of the power transmission line and the lightning activity.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is an overall framework and concise calculation steps of a correlation mining method of micro-terrain characteristic parameters and thunderstorm characteristic parameters;
FIG. 2 is a schematic diagram of the statistical area division of lightning parameters;
FIG. 3 is a micro-terrain feature parameter extraction process;
FIG. 4 is a correlation analysis process based on a correlation rule algorithm;
in the figure, min _ sup is the set minimum support degree, and min _ conf is the set minimum confidence.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1, a method for mining association between micro-terrain characteristic parameters and lightning damage characteristic parameters in a power transmission corridor area in an embodiment of the present invention includes the following steps:
the method comprises the steps of firstly, acquiring parameters of a power transmission line tower, lightning activity parameters and elevation data of an area where the power transmission line tower is located, wherein the parameters of the power transmission line tower comprise longitude and latitude coordinates of the tower, and the lightning activity parameters comprise lightning falling time, longitude and latitude position coordinates and lightning current amplitude.
And step two, according to the lightning location data and the parameters of the line towers, counting characteristic parameters of the ground lightning density and the lightning current amplitude (mean value) of each base tower. Due to the fact that the influence range of the micro-terrain on lightning activities is small, the situation is more complex, a statistical method of lightning parameters of a circular area with a tower as a center is adopted, and a schematic diagram of the statistical method is shown in figure 2.
And step three, extracting the micro-terrain characteristic parameters according to the tower. Based on digital elevation data, performing terrain analysis through geographic information software, calculating attribute values of the altitude, the gradient, the slope direction, the slope shape and the gradient change rate of the whole area, importing tower position information, extracting each attribute value to a tower point, and obtaining the attribute values of the distance between each tower and a ridge line and the distance between each tower and a valley line through hydrological analysis and neighbor analysis. The micro-terrain parameter extraction process is shown in fig. 3.
The gradient represents the degree of inclination of the ground surface at that point, and when gradient extraction is performed, the following difference formula can be used:
in the formula: slope — Slope, degrees (°);
fx-rate of elevation change in the east-west direction;
fy-elevation change rate in the north-south direction;
the slope value is defined as follows: the north direction is 0 degree, and the value range is 0-360 degree according to the calculation of the clockwise direction. The slope calculation formula is as follows:
in the formula: aspect — slope, in degrees (°);
the extraction formula of the slope shape is as follows:
in the formula: p-slope curvature;
ha-elevation of the spot to be analyzed, in meters (m);
b, the number of neighborhood points in different directions near the point to be analyzed;
hi-elevation of the field point, in meters (m);
when P is greater than 0, the slope is a convex slope; when P is 0, the slope is a straight slope; when P <0, the slope is a concave slope.
The gradient change rate is calculated by calculating the gradient again.
And after the calculation of the gradient, the slope direction, the slope shape and the slope change rate is completed, importing the longitude and latitude coordinates of the tower, and extracting the attribute values of the altitude, the gradient, the slope direction, the slope shape and the slope change rate according to the tower.
Hydrologic analysis is performed on the elevation data of the area where the power transmission line is located through GIS software, terrain feature line image layers of valley lines and ridge lines are respectively constructed, tower coordinates are led into the terrain feature line image layers, and the distance value of each tower from the nearest ridge line and the nearest valley line is calculated.
And fourthly, taking each foundation pole tower as a transaction, taking the lightning activity characteristic parameters and the micro-terrain characteristic parameters as transaction attribute items, constructing a transaction table to be mined, and performing data preprocessing on the lightning activity characteristic parameters and the micro-terrain characteristic parameters by adopting different data generalization processing methods according to the requirements of an associated mining algorithm on data.
For discretization of the ground flash density, a priori knowledge division method is adopted, and the ground flash density is divided into 4 grade areas from weak to strong according to experience of an electric power expert:
and (3) a lightning area: NG <0.78 times/(km 2 & a);
a middle thunder area: NG is more than or equal to 0.78 times/(km 2 & a) and less than 2.78 times/(km 2 & a);
a thunderstorm region: 2.78 times/(km 2 & a) is more than or equal to NG <7.98 times/(km 2 & a);
strong thunder area: NG is more than or equal to 7.98 times/(km 2 & a);
and for the lightning current amplitude mean value and the micro-terrain characteristic parameters, dividing the sample object into intervals with the same number of instances by adopting an equal-frequency boxing method.
And after the discretization of the characteristic parameters is finished, carrying out data binarization processing, namely taking the interval of each characteristic parameter as an independent attribute, judging whether each base pole tower contains the attribute, if so, the attribute value is 1, if not, the attribute value is 0, and finally constructing a transaction table to be mined in a binary matrix form.
And fifthly, establishing a relevance analysis model based on an Apriori relevance rule algorithm, setting a minimum support degree and a minimum confidence coefficient threshold value, forming a relevance rule between the micro-terrain factor and the lightning damage characteristic by accessing and calling a transaction table, and screening out a rule meeting the support degree and the confidence coefficient threshold value to obtain the micro-terrain characteristic with strong relevance to the lightning activity. The specific flow of association analysis is shown in fig. 4. The method comprises the following specific steps:
setting a proper minimum support degree min _ sup and a proper minimum confidence coefficient min _ conf according to the scale size and the data size of the transaction table;
accessing a transaction table to read all transactions, taking each transaction attribute item as a candidate set of 1 item, calculating the support degree of each item, wherein the support degree represents the proportion of the transactions containing a certain incidence relation in the transaction table and reflects the magnitude of the incidence degree between the transaction attributes, and the support degree calculation formula is as follows:
in the formula: a-rule antecedent of association rule;
b-rule back-parts of association rules;
σ (AuB) -the number of transactions containing both A and B;
θ -total number of transactions;
comparing the current support degree of the candidate set with the set minimum support degree, and if the current support degree is greater than or equal to the minimum support degree, determining that the candidate set is a frequent 1 item set;
and generating a candidate 2 item set from the frequent 1 item set, repeatedly scanning the transaction table and comparing with the minimum support degree to generate a higher-level frequent item set, and knowing to find all frequent item sets meeting the support degree threshold.
Calculating a confidence coefficient for each item in the frequent item set, wherein the confidence coefficient reflects the confidence coefficient of the association rule, and the confidence coefficient calculation formula is as follows:
if the rule also meets the set minimum confidence threshold, the rule is considered to be a strong association rule, namely the rule front piece and the rule back piece of the rule have strong association.
And step six, screening out strong association rules respectively taking the high grade of the lightning density area, the strong lightning area and the lightning current amplitude mean value as rule back parts, summarizing and summarizing the micro-terrain characteristic attributes in the rule front parts, namely the micro-terrain characteristics with strong association with lightning activities, so as to obtain the micro-terrain environment characteristics easy to cause lightning stroke.
In the first step, the path of the power transmission line and longitude and latitude coordinates of base-by-base towers are determined according to a geographic information system, the longitude and latitude and the lightning strike time of each lightning strike are determined according to the recorded data of a lightning positioning system, and 30m resolution digital elevation grid data of a research area are obtained.
The present application has been described in detail with reference to specific embodiments and illustrative examples, but the description is not intended to limit the application. Those skilled in the art will appreciate that various equivalent substitutions, modifications or improvements may be made to the presently disclosed embodiments and implementations thereof without departing from the spirit and scope of the present disclosure, and these fall within the scope of the present disclosure. The protection scope of this application is subject to the appended claims.
Claims (8)
1. A method for mining association of micro-terrain and lightning damage characteristic parameters of a power transmission corridor area is characterized by comprising the following steps:
acquiring parameters of a power transmission line tower, lightning activity parameters and elevation data of an area where the power transmission line tower is located, wherein the parameters of the power transmission line tower comprise longitude and latitude coordinates of the tower, and the lightning activity parameters comprise lightning falling time, longitude and latitude position coordinates and lightning current amplitude;
calculating the lightning density distribution and the lightning current amplitude mean value characteristic parameters in the line corridor of the tower pole according to the tower pole parameters and the lightning activity parameters of the power transmission line;
acquiring terrain parameter attribute values of the altitude, the gradient, the slope direction, the slope shape, the slope change rate, the distance from a ridge line and the distance from a valley line of a tower line corridor area according to the area elevation data;
adopting a data generalization processing method to carry out data preprocessing on the lightning activity parameters and the micro-terrain characteristic parameters and construct a transaction table to be mined;
and establishing a relevance analysis model based on an Apriori relevance rule algorithm, and forming a relevance rule between the micro-terrain factor and the thunder damage characteristic by accessing and calling a transaction table to obtain the micro-terrain characteristic with strong relevance to the thunder and lightning activity.
2. The method for mining association between micro-terrain and lightning damage characteristic parameters in power transmission corridor areas according to claim 1, wherein the calculation steps of characteristic parameters of lightning density distribution and lightning current amplitude mean value in the corridor of the tower pole are as follows,
numbering the towers in sequence according to the line trend, taking the position of each base tower as the center, drawing a circle by taking a certain distance as a radius,
and counting the lightning frequency in the circle and the lightning current amplitude of each lightning, and calculating the regional lightning density value and the average value of the lightning current amplitude of the tower.
3. The method for mining the association between the microtopography and the lightning damage characteristic parameters in the transmission corridor area according to claim 2, wherein the lightning density calculation formula of the tower and the ground is as follows,
wherein N isGThe unit is the number/(km 2 & a) for the density of the lightning, m is the statistical period, the unit is year (a), R is the radius of the circular statistical area, the unit is km, and Pi is the lightning frequency in the statistical area of the ith year.
4. The method for mining association between micro-terrain and lightning damage characteristic parameters in a transmission corridor area according to claim 2, wherein the calculation formula of the tower lightning current amplitude mean value is,
in the formula, Iav is the average value of lightning current amplitude, and the unit is kA; n is the lightning strike frequency of the ith year in the circular statistical area; iij is the lightning current amplitude of a single lightning strike in a circular statistical area, and the unit is kA.
5. The method for mining association between micro-terrain and lightning damage characteristic parameters in power transmission corridor areas according to claim 1, wherein the micro-terrain characteristic parameter extraction method comprises,
performing terrain analysis through geographic information software based on digital elevation data, and calculating the attribute values of the altitude, the slope direction, the slope shape and the slope change of the whole area;
and importing each attribute value into tower position information, extracting each attribute value to a tower point, and obtaining the attribute value of each tower from the distance between the ridge line and the valley line through hydrological analysis and neighbor analysis.
6. The method for mining association between micro-terrain and lightning damage characteristic parameters in power transmission corridor areas according to claim 1, wherein the data generalization processing method is,
respectively discretizing the lightning activity characteristic parameters and the micro-terrain characteristic parameters;
and after the parameter discretization is finished, carrying out data binarization processing, and constructing a to-be-mined transaction table in a binary matrix form.
7. The method for mining the association between the micro-terrain and the lightning damage characteristic parameters in the power transmission corridor area according to claim 6, wherein the discretization method for the lightning activity characteristic parameters is dividing the grade of the lightning density and performing progressive discretization on the lightning density.
8. The method for mining association between micro-terrain and lightning damage characteristic parameters in power transmission corridor areas according to claim 1, wherein the association analysis model is established based on Apriori association rule algorithm,
setting a minimum support and a minimum confidence threshold,
forming an association rule between the micro-terrain factor and the thunderstorm feature by accessing a calling transaction table,
and screening out rules meeting the support degree and confidence degree threshold values to obtain the micro-terrain features with strong correlation with lightning activities.
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