CN112884352B - 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 influence factor data of lightning faults of an overhead transmission line, and arranging the data; s2, calculating the lightning stroke fault confidence coefficient of the tower under each influence factor by using the collected data to form a lightning stroke fault incidence matrix; s3, determining the influence factor weight of the lightning stroke fault incidence matrix to obtain a weighted lightning stroke fault incidence matrix; s4, calculating a Mass function based on the weighted lightning stroke fault incidence matrix and fusing to obtain a trust degree function; and S5, mapping the trust function into the lightning trip-out rate of the overhead transmission line, and comparing the obtained lightning trip-out rate with the classified lightning fault risk level to realize evaluation 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 being strong in pertinence, comprehensive in analysis and accurate in result.
Description
Technical Field
The invention relates to the technical field of lightning risk assessment of transmission lines, in particular to a lightning risk assessment method for overhead transmission lines.
Background
Lightning stroke is one of main reasons for causing faults of the power transmission line, and lightning stroke tripping faults cause power grid power failure accidents, so that stable operation of the power grid is not facilitated, and therefore, the power transmission line lightning stroke fault risk assessment has important significance. At present, the lightning stroke fault risk assessment method of the overhead transmission line mainly comprises 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, the lightning stroke fault history data of the overhead transmission line can be utilized, data analysis is carried out by adopting some data mining algorithms, and the association relation between the data is researched, so that the lightning stroke fault risk of the overhead transmission line is evaluated, for example, a statistical method is adopted. However, compared with a method for analyzing lightning stroke fault history data of overhead lines in a certain area by using a data mining algorithm, the lightning stroke mechanism analysis method has no pertinency; the statistical method only analyzes the single influencing factors and the lightning stroke faults, and the considered factors are single, so that the analysis result is incomplete. Based on the above problems, a new lightning strike fault risk assessment method for overhead transmission lines is needed.
Disclosure of Invention
The invention aims to provide the lightning stroke fault risk assessment method for the overhead transmission line, which is characterized by being capable of integrating a plurality of influence factors, accurately assessing the lightning stroke fault risk of the transmission line, and having the characteristics of strong pertinence, comprehensive analysis and accurate result.
In order to achieve the above object, the present invention provides the following solutions:
a lightning stroke fault risk assessment method for an overhead transmission line comprises the following steps:
s1, collecting influence factor data of lightning faults of an overhead transmission line, and arranging the data;
s2, calculating the lightning stroke fault confidence coefficient of the tower under each influence factor by using the collected data to form a lightning stroke fault incidence matrix;
s3, determining the influence factor weight of the lightning stroke fault incidence matrix to obtain a weighted lightning stroke fault incidence matrix;
s4, calculating a Mass function based on the weighted lightning stroke fault incidence matrix and fusing to obtain a trust degree function;
and S5, mapping the trust function into the lightning trip-out rate of the overhead transmission line, and comparing the obtained lightning trip-out rate with the classified lightning fault risk level to realize evaluation and early warning of the lightning fault risk of the overhead transmission line.
Optionally, the influencing factors in the step S1 include weather conditions, tower heights, tower models, voltage levels, altitude, and topography.
Optionally, in the step S2, the confidence coefficient of the lightning stroke fault of the tower under each influence factor is calculated by using the collected data to form a lightning stroke fault association matrix, and the confidence coefficient calculation formula is as follows:
wherein g ij To the possibility of lightning strike failure of the ith tower under the influence of the jth influence factor, C (A j →B 1 ) For transaction A j →B 1 Confidence of A j For towers under the j-th influencing factor, B 1 For the tower to strike a lightning failure, σ (A j ∪B 1 ) For the number of towers that have a lightning strike failure at the jth impact factor, σ (A) j ) Number of towers under the j-th influence factor;
the lightning stroke fault incidence matrix G is formed by G ij The 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 influencing factors.
Optionally, in the step S3, the determining an impact factor weight of the lightning stroke fault correlation matrix obtains a weighted lightning stroke fault correlation matrix, and the calculating process of the weighted lightning stroke fault correlation matrix X is as follows:
according to the normalized lightning fault incidence matrix Y, calculating an entropy matrix E and a weight matrix W of an influence factor, wherein the calculation formula is as follows:
the calculation process of the weighted lightning stroke fault incidence matrix X comprises the following steps:
X＝(x ij ) m×n ＝(w j ·y ij ) m×n
wherein w is j Is the weight of the j-th influencing factor.
Optionally, in the step S4, the weighted lightning strike fault correlation matrix is used to calculate a Mass function and perform fusion to obtain a trust function, which specifically includes the steps of:
s401, finding out an ideal optimal sequence X in the weighted lightning stroke fault incidence matrix X + And ideal worst sequence X - The calculation formula is as follows:
s402, determining a Mass function by using a gray correlation analysis method, and calculating an ith rod under a jth influence factorGray correlation coefficient r of tower ij The calculation formula is as follows:
wherein,for the optimal association coefficient, +.>Being the worst correlation coefficient, ζ is the comprehensive gray resolution coefficient;
s403, calculating the second-order uncertainty u of the jth influence factor j The calculation formula is as follows:
s404, according to the weighted lightning fault incidence matrix X and the second-order uncertainty u of each influence factor j Calculating a Mass function, correcting and fusing the Mass function to obtain a trust degree function, wherein the Mass function m j (K i ) The calculation formula of (2) is as follows:
m j (K i )＝(1-u j )x ij
the Mass function has a certain uncertainty, namelyTherefore, the integral uncertainty Mass function m under the jth influence factor needs to be calculated j (K Θ ) Calculation ofThe formula is:
and a correction coefficient alpha is introduced to correct the Mass function, so that the evaluation accuracy is improved, and the calculation formula is as follows:
m' j (K i )＝(1-α j )m j (K i )
m' j (K Θ )＝(1-α j )m j (K Θ )+α j
the above-mentioned modified Mass functions are fused, and the trust function Bel (K i ) The calculation formula of (2) is as follows:
optionally, in the step S5, the mapping the trust function to the lightning trip rate of the overhead transmission line includes the following calculation process:
wherein N is the lightning strike fault frequency of the f-year overhead transmission line in the data area, f is the year number and P i For the lightning trip rate, k and b are unknown numbers to be solved of the relation between the trust function and the lightning trip rate, the two equations are combined, corresponding data are brought into the unknown numbers k and b to obtain the relation between the trust function and the lightning trip rate.
Optionally, the correction coefficient α calculation process in step S404 is:
w max ＝max{w 1 ,w 2 ,...,w n }
W'＝W/w max ＝(w 1 ,w 2 ,...,w n )/w max
α j ＝1-(w j /w max )
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 lightning stroke fault risk assessment method for the overhead transmission line, through collecting and arranging lightning stroke fault history data of the overhead transmission line, various influencing factors of the lightning stroke fault of the line are comprehensively considered and determined, and weather condition factors are added, so that the data can be analyzed more comprehensively; the Mass function is obtained by utilizing entropy weight theory and the like, and the Mass function is corrected by adding correction parameters, so that the accuracy of data prediction is improved; and (3) fusing all influence factors by utilizing an evidence theory, analyzing the association relation between the multiple influence factors and the lightning stroke faults of the overhead transmission line, mapping the trust function calculated by the fused influence factors into the lightning stroke tripping rate, and realizing evaluation and early warning of the lightning stroke faults of the overhead transmission line. The lightning stroke fault risk assessment method for the overhead transmission line has the characteristics of being strong in pertinence, comprehensive in analysis and accurate in result.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are 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 other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a lightning strike fault risk assessment method for an overhead transmission line.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide the lightning stroke fault risk assessment method for the overhead transmission line, which is characterized by being capable of integrating a plurality of influence factors, accurately assessing the lightning stroke fault risk of the transmission line, and having the characteristics of strong pertinence, comprehensive analysis and accurate result.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
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 input, data inquiry, data modification and data export and is used for storing lightning fault data, tower parameter data, topographic parameter data and meteorological data;
the multi-information fusion system fuses various data by utilizing information entropy and evidence theory, and establishes an association relation between various factors and lightning stroke faults of the overhead transmission line; information fusion is carried out by taking weather conditions, altitude, topography, pole tower height, voltage level and pole tower model as influence factors, and an overhead transmission line lightning stroke fault trust function under the influence of multiple factors is calculated through existing historical data; the altitude data are queried by utilizing Google Earth software according to the longitude and latitude in the lightning stroke fault history data of the overhead transmission line;
the early warning and evaluating system maps the lightning stroke fault trust function after the multi-information fusion into lightning stroke tripping rate and classifies lightning stroke faults into risk classes, so that the lightning stroke fault risk class of the overhead transmission line is determined, and the evaluation and early warning of the lightning stroke fault risk of the overhead transmission line are realized; according to the model, the influence of meteorological conditions on the lightning stroke faults of the line is considered, meteorological factors are fused with various information, and the accuracy of lightning stroke fault risk assessment of the overhead transmission line is improved;
the specific steps of the lightning stroke fault risk assessment method for the overhead transmission line provided by the invention are shown in figure 1, and the specific steps comprise:
s1, collecting influence factor data of lightning faults of an overhead transmission line, and sorting the data, wherein the influence factors comprise weather conditions, pole heights, pole tower models, voltage grades, altitude heights and topography;
s2, calculating the tower lightning stroke fault confidence coefficient under each influence factor by using the collected data to form a lightning stroke fault incidence matrix, wherein the confidence coefficient calculation formula is as follows:
wherein g ij To the possibility of lightning strike failure of the ith tower under the influence of the jth influence factor, C (A j →B 1 ) For transaction A j →B 1 Confidence of A j For towers under the j-th influencing factor, B 1 For the tower to strike a lightning failure, σ (A j ∪B 1 ) For the number of towers that have a lightning strike failure at the jth impact factor, σ (A) j ) Number of towers under the j-th influence factor;
the lightning stroke fault incidence matrix G is formed by G ij The 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 influencing 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:
according to the normalized lightning fault incidence matrix Y, calculating an entropy matrix E and a weight matrix W of an influence factor, wherein the calculation formula is as follows:
the calculation process of the weighted lightning stroke fault incidence matrix X comprises the following steps:
X＝(x ij ) m×n ＝(w j ·y ij ) m×n
wherein w is j Is the weight of the j-th influencing factor.
S4, calculating a Mass function based on the weighted lightning stroke fault incidence matrix and fusing to obtain a trust degree function; let the recognition frame Θ= { K 1 ,K 2 ,...,K m If the function m:2 Θ →[0,1]Satisfies the following conditionsAnd->The m is called a 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 incidence matrix X + And ideal worst sequence X - The calculation formula is as follows:
s402, determining a Mass function by using a gray correlation analysis method, and calculating the j-th influence factorGray correlation coefficient r of the ith tower ij The calculation formula is as follows:
wherein,for the optimal association coefficient, +.>Being the worst correlation coefficient, ζ is the comprehensive gray resolution coefficient;
s403, calculating the second-order uncertainty u of the jth influence factor j The calculation formula is as follows:
s404, according to the weighted lightning fault incidence matrix X and the second-order uncertainty u of each influence factor j Calculating a Mass function, correcting and fusing the Mass function to obtain a trust degree function, wherein the Mass function m j (K i ) The calculation formula of (2) is as follows:
m j (K i )＝(1-u j )x ij
the Mass function has a certain uncertainty, namelyTherefore, the integral uncertainty Mass function m under the jth influence factor needs to be calculated j (K Θ ) The calculation formula is as follows:
the evidence theory is an uncertainty reasoning method, can represent the concept of uncertainty, and uses the evidence theory to fuse Mass functions, so that the prediction precision can be improved, the uncertainty can be reduced, when the evidence theory is utilized to fuse multiple information, the multiple influence factors can cause information conflict due to the complexity of the influence factors, and the precision is reduced after the information is fused;
in order to ensure the accuracy of the trust function after information fusion, introducing a correction parameter alpha to correct the Mass function; for the correction of the Mass function, mainly there are a Yager method and a Murphy method, and the Yager method converts conflict information into integral uncertainty and considers the information to be completely useless; the Murphy method improves the convergence rate of the synthesized result, but the calculation process is more complex; at present, the correction method for the Mass function is mostly applied to the analysis and the processing of data with a plurality of influencing factors, so that the Mass function is corrected by calculating the correction parameters by using the weight matrix obtained in the steps according to the characteristics of related data in the field; the correction method carries out weight distribution on each Mass function, reduces the possibility of information conflict, has simple calculation and high calculation speed, and ensures the accuracy of the trust function after information fusion; for the weight matrix W, find W max The relative weight matrix W' is calculated, and the calculation formula is as follows:
w max ＝max{w 1 ,w 2 ,...,w n }
W'＝W/w max ＝(w 1 ,w 2 ,...,w n )/w max
α j ＝1-(w j /w max )
and a correction coefficient alpha is introduced to correct the Mass function, so that the evaluation accuracy is improved, and the calculation formula is as follows:
m' j (K i )＝(1-α j )m j (K i )
m' j (K Θ )＝(1-α j )m j (K Θ )+α j
the above-mentioned modified Mass functions are fused, and the trust function Bel (K i ) The calculation formula is as follows:
calculating the global uncertainty trust function Bel (K Θ ) The calculation formula is as follows:
s5, mapping the trust function into the lightning trip rate of the overhead transmission line, and comparing the obtained lightning trip rate with the classified lightning fault risk level to realize evaluation and early warning of the lightning fault risk of the overhead transmission line, wherein the trust function is mapped into the lightning trip rate of the overhead transmission line, and the calculation process is as follows:
wherein N is the lightning strike fault frequency of the f-year overhead transmission line in the data area, f is the year number and P i For the lightning trip rate, k and b are unknown numbers to be solved of the relation between the trust function and the lightning trip rate, the two equations are combined, corresponding data are brought into the unknown numbers k and b to obtain the relation between the trust function and the lightning trip rate.
According to the lightning stroke fault risk assessment method for the overhead transmission line, through collecting and arranging the lightning stroke fault history data of the overhead transmission line, various influencing factors of the lightning stroke fault of the line are comprehensively considered and determined, and weather condition factors are added, so that the data can be analyzed more comprehensively; the Mass function is obtained by utilizing entropy weight theory and the like, and the Mass function is corrected by adding correction parameters, so that the accuracy of data prediction is improved; and (3) fusing all influence factors by utilizing an evidence theory, analyzing the association relation between the multiple influence factors and the lightning stroke faults of the overhead transmission line, mapping the trust function calculated by the fused influence factors into the lightning stroke tripping rate, and realizing evaluation and early warning of the lightning stroke faults of the overhead transmission line. The lightning stroke fault risk assessment method for the overhead transmission line has the characteristics of being strong in pertinence, comprehensive in analysis and accurate in result.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (1)
1. The lightning stroke fault risk assessment method for the overhead transmission line is characterized by comprising the following steps of:
s1, collecting influence factor data of lightning faults of an overhead transmission line, and arranging the data;
s2, calculating the lightning stroke fault confidence coefficient of the tower under each influence factor by using the collected data to form a lightning stroke fault incidence matrix;
s3, determining the influence factor weight of the lightning stroke fault incidence matrix to obtain a weighted lightning stroke fault incidence matrix;
s4, calculating a Mass function based on the weighted lightning stroke fault incidence matrix and fusing to obtain a trust degree function;
s5, mapping the trust function into the lightning trip-out rate of the overhead transmission line, and comparing the obtained lightning trip-out rate with the classified lightning fault risk level to realize evaluation and early warning of the lightning fault risk of the overhead transmission line;
the influence factors in the step S1 comprise weather conditions, tower heights, tower models, voltage levels, altitude heights and topography;
in the step S2, the confidence coefficient of the lightning stroke fault of the tower under each influence factor is calculated by utilizing the collected data to form a lightning stroke fault incidence matrix, and the confidence coefficient calculation formula is as follows:
wherein g ij To the possibility of lightning strike failure of the ith tower under the influence of the jth influence factor, C (A j →B 1 ) For transaction A j →B 1 Confidence of A j For towers under the j-th influencing factor, B 1 For the tower to strike a lightning failure, σ (A j ∪B 1 ) For the number of towers that have a lightning strike failure at the jth impact factor, σ (A) j ) Number of towers under the j-th influence factor;
the lightning stroke fault incidence matrix G is formed by G ij The 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 influencing factors;
in the step S3, the influence factor weight of the lightning stroke fault correlation matrix is determined, and a weighted lightning stroke fault correlation matrix is obtained, and the calculation process of the weighted lightning stroke fault correlation matrix X is as follows:
according to the normalized lightning fault incidence matrix Y, calculating an entropy matrix E and a weight matrix W of an influence factor, wherein the calculation formula is as follows:
the calculation process of the weighted lightning stroke fault incidence matrix X comprises the following steps:
X＝(x ij ) m×n ＝(w j ·y ij ) m×n
wherein w is j The weight of the j-th influence factor;
in the step S4, the weighted lightning strike fault association matrix is used to calculate and fuse a Mass function, so as to obtain a trust function, which specifically includes the steps of:
s401, finding out an ideal optimal sequence X in the weighted lightning stroke fault incidence matrix X + And ideal worst sequence X - The calculation formula is as follows:
s402, determining a Mass function by using a gray correlation analysis method, and calculating a gray correlation coefficient r of an ith tower under the jth influence factor ij The calculation formula is as follows:
wherein,for the optimal association coefficient, +.>Being the worst correlation coefficient, ζ is the comprehensive gray resolution coefficient;
s403, calculating the second-order uncertainty u of the jth influence factor j The calculation formula is as follows:
s404, according to the weighted lightning fault incidence matrix X and the second-order uncertainty u of each influence factor j Calculating a Mass function, correcting and fusing the Mass function to obtain a trust degree function, wherein the Mass function m j (K i ) The calculation formula of (2) is as follows:
m j (K i )＝(1-u j )x ij
the Mass function has a certain uncertainty, namelyTherefore, the integral uncertainty Mass function m under the jth influence factor needs to be calculated j (K Θ ) The calculation formula is as follows:
and a correction coefficient alpha is introduced to correct the Mass function, so that the evaluation accuracy is improved, and the calculation formula is as follows:
m' j (K i )＝(1-α j )m j (K i )
m' j (K Θ )＝(1-α j )m j (K Θ )+α j
the above-mentioned modified Mass functions are fused, and the trust function Bel (K i ) The calculation formula of (2) is as follows:
in the step S5, the trust function is mapped to a lightning trip rate of the overhead transmission line, and the calculation process is as follows:
wherein N is the lightning strike fault frequency of the f-year overhead transmission line in the data area, f is the year number and P i For the lightning trip rate, k and b are unknown numbers to be solved of a relation between the trust function and the lightning trip rate, the two formulas are combined, corresponding data are brought into the unknown numbers k and b to obtain the relation between the trust function and the lightning trip rate;
the correction coefficient α calculation process in step S404 is as follows:
w max ＝max{w 1 ,w 2 ,...,w n }
W'＝W/w max ＝(w 1 ,w 2 ,...,w n )/w max
α j ＝1-(w j /w max )
wherein W' is a relative weight matrix.
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| 基于区间直觉模糊和证据理论的军机研制风险评估;罗承昆;陈云翔;李大伟;朱强;;合肥工业大学学报(自然科学版)(11);全文 * |
| 基于多维关联信息融合的架空输电线路雷害风险评估方法;谢从珍等;《中国电机工程学报》;第38卷(第21期);第6234-6237页 * |
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