CN112884352A - Lightning stroke fault risk assessment method for overhead transmission line - Google Patents
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Abstract
The invention discloses a lightning stroke fault risk assessment method for an overhead transmission line, which comprises the following steps of: s1, collecting impact factor data of the lightning stroke fault of the overhead transmission line, and sorting the data; s2, calculating tower lightning stroke fault confidence coefficients under each influence factor by using the collected data to form a lightning stroke fault association matrix; s3, determining the weight of the influence factor of the lightning stroke fault incidence matrix to obtain a weighted lightning stroke fault incidence matrix; s4, calculating and fusing Mass functions based on the weighted lightning stroke fault incidence matrix to obtain a confidence function; and S5, mapping the confidence function into the lightning trip-out rate of the overhead transmission line, and comparing the obtained lightning trip-out rate with the divided lightning fault risk grades to realize the assessment and early warning of the lightning fault risk of the overhead transmission line. The lightning stroke fault risk assessment method for the overhead transmission line has the characteristics of strong pertinence, comprehensive analysis and accurate result.
Description
Technical Field
The invention relates to the technical field of lightning risk assessment of power transmission lines, in particular to a lightning stroke fault risk assessment method of an overhead power transmission line.
Background
Lightning stroke is one of the main reasons for causing the faults of the power transmission line, and lightning stroke tripping faults cause power failure accidents of a power grid and are not beneficial to stable operation of the power grid, so that lightning stroke fault risk assessment of the power transmission line has important significance. At present, methods for assessing lightning stroke fault risks of overhead transmission lines mainly comprise an electrical geometric model method, an improved electrical geometric model method, a Monte Carlo method, a pilot discharge development method and the like, and the methods are based on lightning stroke fault mechanism analysis and are researched by using a physical or mathematical model. In addition, historical lightning stroke fault data of the overhead transmission line can be utilized, data mining algorithms are adopted for data analysis, and incidence relations among the data are researched, so that lightning stroke fault risks of the overhead transmission line are evaluated, for example, a statistical method. However, compared with a method for analyzing the overhead line lightning stroke fault historical data in a certain area by using a data mining algorithm, the method for analyzing the lightning stroke mechanism is lack of pertinence; the statistical method only analyzes single influence factors and lightning faults, and the considered factors are single, so that the analysis result is incomplete. Based on the above problems, it is urgently needed to provide a new lightning stroke fault risk assessment method for an overhead transmission line.
Disclosure of Invention
The invention aims to provide a lightning stroke fault risk assessment method for an overhead transmission line, which integrates several influence factors, can accurately assess the lightning stroke fault risk of the transmission line and has the characteristics of strong pertinence, comprehensive analysis and accurate result.
In order to achieve the purpose, the invention provides the following scheme:
a lightning stroke fault risk assessment method for an overhead transmission line comprises the following steps:
s1, collecting impact factor data of the lightning stroke fault of the overhead transmission line, and sorting the data;
s2, calculating tower lightning stroke fault confidence coefficients under each influence factor by using the collected data to form a lightning stroke fault association matrix;
s3, determining the weight of the influence factor of the lightning stroke fault incidence matrix to obtain a weighted lightning stroke fault incidence matrix;
s4, calculating and fusing Mass functions based on the weighted lightning stroke fault incidence matrix to obtain a confidence function;
and S5, mapping the confidence function into the lightning trip-out rate of the overhead transmission line, and comparing the obtained lightning trip-out rate with the divided lightning fault risk grades to realize the assessment and early warning of the lightning fault risk of the overhead transmission line.
Optionally, the influence factors in step S1 include weather conditions, tower height, tower model, voltage level, altitude, and terrain.
Optionally, in step S2, the tower lightning stroke fault confidence coefficient under each influence factor is calculated by using the collected data, so as to form a lightning stroke fault association matrix, where the confidence coefficient calculation formula is:
wherein, gijTo determine the probability of lightning strike failure of the ith tower under the influence of the jth influencing factor, C (A)j→B1) For transaction Aj→B1Confidence of (A)jFor towers under the jth influence factor, B1For lightning strike failure of tower, sigma (A)j∪B1) The number of towers with lightning stroke faults under the jth influence factor, sigma (A)j) To be at the j influence factorThe number of lower towers;
the lightning stroke fault incidence matrix G consists of GijThe lightning stroke fault incidence matrix G is normalized to obtain a normalized lightning stroke fault incidence matrix Y, and the calculation formula is as follows:
wherein m is the number of the towers, and n is the number of the influence factors.
Optionally, in step S3, determining an influence factor weight of the lightning stroke fault association matrix to obtain a weighted lightning stroke fault association matrix, where a calculation process of the weighted lightning stroke fault association matrix X is:
calculating an entropy matrix E and a weight matrix W of the influence factors according to the normalized lightning stroke fault incidence matrix Y, wherein the calculation formula is as follows:
the calculation process of the weighted lightning stroke fault incidence matrix X is as follows:
X＝(xij)m×n＝(wj·yij)m×n
wherein, wjIs the weight of the jth influence factor.
Optionally, in the step S4, a Mass function is calculated and fused by using the weighted lightning stroke fault incidence matrix, so as to obtain a confidence function, and the specific steps are as follows:
s401, finding out an ideal optimal sequence X in the weighted lightning stroke fault correlation matrix X+With the ideal worst sequence X-The calculation formula is as follows:
s402, determining a Mass function by utilizing a gray correlation analysis method, and calculating a gray correlation coefficient r of the ith tower under the jth influence factorijThe calculation formula is as follows:
wherein the content of the first and second substances,
in order to optimize the correlation coefficient,
the correlation coefficient is the worst, and xi is a comprehensive gray resolution coefficient;
s403, calculating second-order uncertainty u of the jth influence factorjThe calculation formula is as follows:
s404, according to the weighted lightning stroke fault correlation matrix X and the second-order uncertainty u of each influence factorjCalculating a Mass function, correcting and fusing the Mass function to obtain a confidence function, wherein the Mass function mj(Ki) The calculation formula of (2) is as follows:
mj(Ki)＝(1-uj)xij
the Mass function has a certain uncertainty, i.e.
Therefore, the overall uncertainty Mass function m under the jth influence factor needs to be calculatedj(KΘ) The calculation formula is as follows:
and introducing a correction coefficient alpha to correct the Mass function, so that the evaluation accuracy is improved, and the calculation formula is as follows:
m'j(Ki)＝(1-αj)mj(Ki)
m'j(KΘ)＝(1-αj)mj(KΘ)+αj
fusing the corrected Mass functions to calculate the confidence function Bel (K)i) The calculation formula of (2) is as follows:
optionally, in the step S5, the mapping the confidence function to the lightning trip-out rate of the overhead transmission line includes:
n is the number of lightning stroke faults of the f-year overhead transmission line in the data area, f is the number of years, and P isiFor lightning trip-out rate, k and b are confidence function and lightning trip-outAnd (3) combining the two formulas to obtain the unknown numbers k and b by taking corresponding data into the unknown numbers k and b to obtain a relational expression of the credibility function and the lightning trip-out rate.
Optionally, the calculation process of the correction coefficient α in step S404 is as follows:
wmax＝max{w1,w2,...,wn}
W'＝W/wmax＝(w1,w2,...,wn)/wmax
αj＝1-(wj/wmax)
wherein W' is a relative weight matrix.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the method for evaluating the risk of the lightning stroke fault of the overhead transmission line, provided by the invention, various influence factors of the lightning stroke fault of the line are comprehensively considered and determined through collecting and sorting the historical data of the lightning stroke fault of the overhead transmission line, and weather condition factors are added, so that the data can be more comprehensively analyzed; by solving the Mass function by using an entropy weight theory and the like and adding correction parameters to correct the Mass function, the accuracy of data prediction is improved; and fusing all the influence factors by using an evidence theory, analyzing the incidence relation between the multiple influence factors and the lightning stroke fault of the overhead transmission line, and mapping the trust function calculated by fusing the influence factors into the lightning stroke trip-out rate to realize the evaluation and early warning of the lightning stroke fault of the overhead transmission line. The lightning stroke fault risk assessment method for the overhead transmission line has the characteristics of strong pertinence, comprehensive analysis and accurate result.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of the lightning stroke fault risk assessment method of the overhead transmission line of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
The invention aims to provide a lightning stroke fault risk assessment method for an overhead transmission line, which integrates several influence factors, can accurately assess the lightning stroke fault risk of the transmission line and has the characteristics of strong pertinence, comprehensive analysis and accurate result.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The evaluation model of the lightning stroke fault risk evaluation method of the overhead transmission line provided by the invention comprises the following steps: the system comprises a data management system, a multi-information fusion system and an early warning evaluation system;
the data management system can perform data entry, data query, data modification and data export and is used for storing lightning stroke fault data, tower parameter data, terrain parameter data and meteorological data;
the multi-information fusion system fuses various data by using information entropy and evidence theory to establish an incidence relation between various factors and lightning stroke faults of the overhead transmission line; the method comprises the steps of performing information fusion by taking weather conditions, altitude, landform, tower height, voltage grade and tower model as influence factors, and calculating a lightning stroke fault confidence function of the overhead transmission line under the influence of multiple factors according to existing historical data; the altitude data is inquired by using Google Earth software according to the longitude and latitude in the lightning stroke fault historical data of the overhead transmission line;
the early warning evaluation system maps a lightning stroke fault confidence function after multi-information fusion into a lightning stroke trip-out rate, and carries out risk grade division on lightning stroke faults, so that the lightning stroke fault risk grade of the overhead transmission line is determined, and the assessment and early warning of the lightning stroke fault risk of the overhead transmission line are realized; the influence of meteorological conditions on line lightning stroke faults is considered, meteorological factors and various information are fused, and the accuracy of lightning stroke fault risk assessment of the overhead transmission line is improved;
the lightning stroke fault risk assessment method for the overhead transmission line, provided by the invention, has the specific steps as shown in figure 1, and the specific steps comprise:
s1, collecting impact factor data of the lightning stroke fault of the overhead transmission line, and sorting the data, wherein the impact factors comprise weather conditions, tower heights, tower models, voltage levels, altitude and landforms;
s2, calculating tower lightning stroke fault confidence coefficients under each influence factor by using the collected data to form a lightning stroke fault association matrix, wherein the confidence coefficient calculation formula is as follows:
wherein, gijTo determine the probability of lightning strike failure of the ith tower under the influence of the jth influencing factor, C (A)j→B1) For transaction Aj→B1Confidence of (A)jFor towers under the jth influence factor, B1For lightning strike failure of tower, sigma (A)j∪B1) The number of towers with lightning stroke faults under the jth influence factor, sigma (A)j) The number of the towers under the jth influence factor is obtained;
the lightning stroke fault incidence matrix G consists of GijThe lightning stroke fault incidence matrix G is normalized to obtain a normalized lightning stroke fault incidence matrix Y, and the calculation formula is as follows:
wherein m is the number of towers, and n is the number of influence factors;
s3, determining the influence factor weight of the lightning stroke fault incidence matrix to obtain a weighted lightning stroke fault incidence matrix, wherein the calculation process of the weighted lightning stroke fault incidence matrix X is as follows:
calculating an entropy matrix E and a weight matrix W of the influence factors according to the normalized lightning stroke fault incidence matrix Y, wherein the calculation formula is as follows:
the calculation process of the weighted lightning stroke fault incidence matrix X is as follows:
X＝(xij)m×n＝(wj·yij)m×n
wherein, wjIs the weight of the jth influence factor.
S4, calculating a Mass function and fusing the Mass function based on the weighted lightning stroke fault incidence matrix to obtain a confidence function; let identification frame Θ be { K ═ K1,K2,...,KmIf the function m: 2Θ→[0,1]Satisfy the following requirements
And is
Let m be the basic probability distribution function (Mass function) on Θ; the method comprises the following specific steps:
s401, finding out an ideal optimal sequence X in the weighted lightning stroke fault correlation matrix X+With the ideal worst sequence X-The calculation formula is as follows:
s402, determining a Mass function by utilizing a gray correlation analysis method, and calculating a gray correlation coefficient r of the ith tower under the jth influence factorijThe calculation formula is as follows:
wherein the content of the first and second substances,
in order to optimize the correlation coefficient,
the correlation coefficient is the worst, and xi is a comprehensive gray resolution coefficient;
s403, calculating second-order uncertainty u of the jth influence factorjThe calculation formula is as follows:
s404, according to the weighted lightning stroke fault correlation matrix X and the second-order uncertainty u of each influence factorjCalculating a Mass function, correcting and fusing the Mass function to obtain a confidence function, wherein the Mass function mj(Ki) The calculation formula of (2) is as follows:
mj(Ki)＝(1-uj)xij
the Mass function has a certain uncertainty, i.e.
Therefore, the overall uncertainty Mass function m under the jth influence factor needs to be calculatedj(KΘ) The calculation formula is as follows:
the evidence theory is an uncertainty reasoning method, can represent the concept of uncertainty, and can improve the prediction precision and reduce the uncertainty by fusing the Mass function by using the evidence theory;
in order to ensure the accuracy of the confidence function after information fusion, a correction parameter alpha is introduced to correct the Mass function; aiming at the correction of the Mass function, a Yager method and a Murphy method are mainly adopted, the Yager method converts conflict information into integral uncertainty, and the information is considered to be completely useless; the Murphy method improves the convergence rate of the synthetic result, but the calculation process is more complex; at present, a correction method for a Mass function is mostly applied to data analysis and processing with numerous influence factors, so that according to the characteristics of relevant data in the field of the present invention, the Mass function is corrected by calculating correction parameters by using the weight matrix obtained in the above steps; the correction method performs weight distribution on each Mass function, reduces the possibility of information conflict, is simple in calculation and high in calculation speed, and ensures the accuracy of the confidence function after information fusion; for the weight matrix W, find WmaxAnd calculating a relative weight matrix W', wherein the calculation formula is as follows:
wmax＝max{w1,w2,...,wn}
W'＝W/wmax＝(w1,w2,...,wn)/wmax
αj＝1-(wj/wmax)
and introducing a correction coefficient alpha to correct the Mass function, so that the evaluation accuracy is improved, and the calculation formula is as follows:
m'j(Ki)＝(1-αj)mj(Ki)
m'j(KΘ)＝(1-αj)mj(KΘ)+αj
fusing the corrected Mass functions to calculate the confidence function Bel (K)i) The calculation formula is as follows:
calculating the integral uncertainty confidence function Bel (K)Θ) The calculation formula is as follows:
s5, mapping the confidence function to the lightning trip-out rate of the overhead transmission line, comparing the obtained lightning trip-out rate with the divided lightning fault risk grades to realize the assessment and early warning of the lightning fault risk of the overhead transmission line, mapping the confidence function to the lightning trip-out rate of the overhead transmission line, and calculating the following steps:
n is the number of lightning stroke faults of the f-year overhead transmission line in the data area, f is the number of years, and P isiAnd (4) obtaining an unknown number to be solved by a relation between the credibility function and the lightning trip-out rate by combining the two formulas and taking corresponding data to solve the unknown number k and b so as to obtain the relation between the credibility function and the lightning trip-out rate.
According to the method for evaluating the risk of the lightning stroke fault of the overhead transmission line, provided by the invention, various influence factors on the line lightning stroke fault are comprehensively considered and determined through collecting and sorting the historical data of the lightning stroke fault of the overhead transmission line, and weather condition factors are added, so that the data can be more comprehensively analyzed; by solving the Mass function by using an entropy weight theory and the like and adding correction parameters to correct the Mass function, the accuracy of data prediction is improved; and fusing all the influence factors by using an evidence theory, analyzing the incidence relation between the multiple influence factors and the lightning stroke fault of the overhead transmission line, and mapping the trust function calculated by fusing the influence factors into the lightning stroke trip-out rate to realize the evaluation and early warning of the lightning stroke fault of the overhead transmission line. The lightning stroke fault risk assessment method for the overhead transmission line has the characteristics of strong pertinence, comprehensive analysis and accurate result.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (7)
1. A lightning stroke fault risk assessment method for an overhead transmission line is characterized by comprising the following steps:
s1, collecting impact factor data of the lightning stroke fault of the overhead transmission line, and sorting the data;
s2, calculating tower lightning stroke fault confidence coefficients under each influence factor by using the collected data to form a lightning stroke fault association matrix;
s3, determining the weight of the influence factor of the lightning stroke fault incidence matrix to obtain a weighted lightning stroke fault incidence matrix;
s4, calculating and fusing Mass functions based on the weighted lightning stroke fault incidence matrix to obtain a confidence function;
and S5, mapping the confidence function into the lightning trip-out rate of the overhead transmission line, and comparing the obtained lightning trip-out rate with the divided lightning fault risk grades to realize the assessment and early warning of the lightning fault risk of the overhead transmission line.
2. The overhead transmission line lightning stroke fault risk assessment method according to claim 1, wherein the influence factors in the step S1 include weather conditions, tower height, tower model, voltage level, altitude and terrain.
3. The overhead transmission line lightning stroke fault risk assessment method according to claim 1, wherein in step S2, tower lightning stroke fault confidence coefficients under each influence factor are calculated by using the collected data to form a lightning stroke fault association matrix, and the confidence coefficient calculation formula is as follows:
wherein, gijTo determine the probability of lightning strike failure of the ith tower under the influence of the jth influencing factor, C (A)j→B1) For transaction Aj→B1Confidence of (A)jFor towers under the jth influence factor, B1For lightning strike failure of tower, sigma (A)j∪B1) The number of towers with lightning stroke faults under the jth influence factor, sigma (A)j) The number of the towers under the jth influence factor is obtained;
the lightning stroke fault incidence matrix G consists of GijThe lightning stroke fault incidence matrix G is normalized to obtain a normalized lightning stroke fault incidence matrix Y, and the calculation formula is as follows:
wherein m is the number of the towers, and n is the number of the influence factors.
4. The overhead transmission line lightning stroke fault risk assessment method according to claim 3, wherein the influence factor weight of the lightning stroke fault incidence matrix is determined in the step S3 to obtain a weighted lightning stroke fault incidence matrix, and the calculation process of the weighted lightning stroke fault incidence matrix X is as follows:
calculating an entropy matrix E and a weight matrix W of the influence factors according to the normalized lightning stroke fault incidence matrix Y, wherein the calculation formula is as follows:
the calculation process of the weighted lightning stroke fault incidence matrix X is as follows:
X＝(xij)m×n＝(wj·yij)m×n
wherein, wjIs the weight of the jth influence factor.
5. The lightning stroke fault risk assessment method of the overhead transmission line according to claim 4, wherein in the step S4, a Mass function is calculated and fused by using the weighted lightning stroke fault incidence matrix to obtain a confidence function, and the specific steps are as follows:
s401, finding out an ideal optimal sequence X in the weighted lightning stroke fault correlation matrix X+With the ideal worst sequence X-The calculation formula is as follows:
s402, determining a Mass function by utilizing a gray correlation analysis method, and calculating a gray correlation coefficient r of the ith tower under the jth influence factorijThe calculation formula is as follows:
wherein the content of the first and second substances,
in order to optimize the correlation coefficient,
the correlation coefficient is the worst, and xi is a comprehensive gray resolution coefficient;
s403, calculating second-order uncertainty u of the jth influence factorjThe calculation formula is as follows:
s404, according to the weighted lightning stroke fault correlation matrix X and the second-order uncertainty u of each influence factorjCalculating a Mass function and matching the Mass functionCorrecting and fusing to obtain a confidence function, wherein the Mass function mj(Ki) The calculation formula of (2) is as follows:
mj(Ki)＝(1-uj)xij
the Mass function has a certain uncertainty, i.e.
Therefore, the overall uncertainty Mass function m under the jth influence factor needs to be calculatedj(KΘ) The calculation formula is as follows:
and introducing a correction coefficient alpha to correct the Mass function, so that the evaluation accuracy is improved, and the calculation formula is as follows:
m'j(Ki)＝(1-αj)mj(Ki)
m'j(KΘ)＝(1-αj)mj(KΘ)+αj
fusing the corrected Mass functions to calculate the confidence function Bel (K)i) The calculation formula of (2) is as follows:
6. the overhead transmission line lightning stroke fault risk assessment method according to claim 5, wherein in the step S5, the confidence function is mapped to the lightning stroke trip rate of the overhead transmission line, and the calculation process is as follows:
n is the number of lightning stroke faults of the f-year overhead transmission line in the data area, f is the number of years, and P isiAnd (4) obtaining an unknown number to be solved by a relation between the credibility function and the lightning trip-out rate by combining the two formulas and taking corresponding data to solve the unknown number k and b so as to obtain the relation between the credibility function and the lightning trip-out rate.
7. The overhead transmission line lightning stroke fault risk assessment method according to claim 5, wherein the calculation process of the correction coefficient α in the step S404 is as follows:
wmax＝max{w1,w2,...,wn}
W'＝W/wmax＝(w1,w2,...,wn)/wmax
αj＝1-(wj/wmax)
wherein W' is a relative weight matrix.
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