CN114139988A - Power transmission line windage yaw flashover risk evaluation method based on matter element extension model - Google Patents
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Abstract
The invention belongs to the technical field of windage yaw evaluation of power transmission lines, and particularly relates to a windage yaw flashover risk evaluation method of a power transmission line based on a matter element extension model, which comprises the following steps: analyzing the influence factors of the windage yaw flashover risk of the power transmission line, and selecting an evaluation index; calculating the weight value of the selected windage yaw flashover risk evaluation index by adopting an analytic hierarchy process-entropy weight method; correcting the data of the evaluation index by combining the micro-terrain data; constructing a windage yaw flashover risk assessment model based on the matter element extension model for risk assessment; and judging the early warning level. The evaluation method can be used for comprehensively evaluating the risk condition of the tower unit by combining qualitative analysis and quantitative analysis, and the risk evaluation result is objective and accurate.
Description
Technical Field
The invention relates to the technical field of windage yaw evaluation of power transmission lines, in particular to a windage yaw flashover risk evaluation method of a power transmission line based on a matter element extension model.
Background
Under the influence of global climate change aggravation, in recent years, abnormal weather increases, the design wind speed of a line is exceeded in strong wind brought by strong convection weather, and power transmission line faults caused by wind disasters bring huge losses to a power system. The wind causing harm to the power transmission line mainly comprises typhoon, squall wind, tornado and local cyclone; the line faults caused by wind damage comprise windage yaw tripping, pole tower damage and the like, and the wind-blown foreign matter is easy to cause line external broken tripping. When extreme weather comes, due to the lack of an effective method for managing the windage yaw of the power transmission line, the windage yaw of the power transmission line cannot be effectively managed.
Under strong convection weather, strong wind and strong rainfall can be frequently caused, and major accidents caused by meteorological factors often happen to a power transmission line, such as: lightning trip, windage yaw flashover, strand breakage, tower collapse, disconnection and other events. The line is tripped due to windage flashover caused by windage weather, and the normal operation of the power system is seriously influenced.
At present, the research on the windage yaw of the power transmission line at home and abroad has less evaluation on the windage yaw flashover of a certain tower unit, and mainly focuses on the calculation of a windage yaw angle and a minimum air gap. For example, an expert scholars provides a method for calculating the minimum air gap of a strain tower based on a two-dimensional cartesian coordinate system, and a rainfall correction coefficient is combined to perform windage yaw early warning, but the method cannot perform windage yaw early warning on other types of towers; an expert scholars randomly samples wind speed and wind direction by adopting a Monte Carlo method, calculates a wind deflection angle by combining designed line parameters and corrects the wind deflection angle under the rainfall condition based on an S-shaped fuzzy membership function to establish a wind deflection tripping probability model; under the condition that rainfall is not considered, an expert scholar calculates a wind deflection angle based on a probability model for predicting the wind direction and the wind speed, and carries out wind deflection discharge probability early warning; an expert scholars adopts a mapping method to calculate the clearance between a lead at the lower end of the suspension insulator string and a building under the action of strong wind without considering the problem of windage yaw flashover evaluation; an expert scholars provides a time sequence Monte Carlo method based on a time storm sampling strategy, and can accurately evaluate power distribution systems under six types of storms with different danger levels; an expert scholar analyzes the influence factors of the windage yaw of the power transmission line, and establishes a windage yaw flashover risk assessment model based on a BP neural network, wherein the influence factors considered by the model are not comprehensive; an expert scholars predicts the wind speed based on a Doppler weather radar combined with a two-layer support vector machine method to obtain a critical wind bias angle and a critical wind speed of a strong convection influence area, and calculates the wind bias discharge probability by combining a generalized extreme value distribution diagram of the wind speed.
The research aiming at the windage yaw evaluation and treatment of the power transmission line obtains certain results, but still has certain limitations. Most of the evaluation methods cannot be applied to all types of towers, and the wind deflection angle and the minimum air gap are calculated only on the basis of design data in many documents, so that the evaluation result cannot be combined with real-time data and wind deflection influence factors are not fully considered.
Therefore, how to provide a transmission line windage yaw flashover risk assessment method which can comprehensively assess risk conditions of tower units by combining qualitative analysis and quantitative analysis and has objective and accurate risk assessment results is a problem to be solved urgently at present.
Disclosure of Invention
In order to solve the technical problem, an embodiment of the invention provides a power transmission line windage yaw flashover risk assessment method based on a matter element extension model. The micro-terrain correction coefficient is introduced in consideration of influence of terrain factors on actual wind speed, and the micro-terrain correction coefficient has high feasibility and effectiveness.
According to the embodiment of the invention, the invention provides a power transmission line windage yaw flashover risk assessment method based on a matter element extension model, which comprises the following steps:
selecting an evaluation index according to the influence factors of the windage yaw flashover risk of the power transmission line;
calculating the weight value of the selected windage yaw flashover risk evaluation index by adopting an analytic hierarchy process-entropy weight method;
correcting the weight value of the evaluation index by combining the micro-terrain data;
and constructing a windage yaw flashover risk assessment model based on the matter element extension model for risk assessment.
Further, the influence factors of the windage yaw flashover of the power transmission line include: weather, terrain, and line parameters.
Further, the evaluation indexes include wind speed, wind direction, rainfall intensity and humidity.
Further, the risk of windage yaw flashover of the power transmission line is divided into 4 grades: safe, low, medium, high.
Further, the weight value is obtained by:
combining qualitative analysis and quantitative analysis by adopting an analytic hierarchy process, and calculating a weight value of an evaluation index;
and correcting the index weight value by adopting an entropy weight method.
Preferably, with the analytic hierarchy process, the step of calculating the weight value is as follows:
step (11), determining a target layer, a criterion layer and an index layer, and constructing an index system;
step (12), a judgment matrix is established according to the importance degree of the evaluation index relative to the target, and the element values in the judgment matrix are assigned, wherein the judgment matrix A is (a)ij)n×n，aijC for indicating index layeriAnd CjDegree of importance relative to the target layer; wherein, CiAnd CjRespectively representing two different indexes of the index layer, wherein n represents the number of evaluation indexes in the index layer;
and (13) consistency check is carried out, and errors existing in the judgment matrix are reduced:
calculating an eigenvalue vector lambda according to the judgment matrix A, and solving a consistency index CI:
and (3) performing consistency check of each level of single sequencing by adopting the following formula:
in the formula: n represents the number of evaluation indexes in the index layer, CR represents the result of single-sequencing consistency check of each layer, and the value of the random consistency index RI is related to the value of n;
relative weight value ω of evaluation indexiThe calculation formula is shown as follows:
j＝1,2,…,n；
step (14), checking the total sorting of layers, specifically as follows:
in the formula, m represents the number of row vectors of the judgment matrix; a isjA relative weight value ω representing the evaluation indexiA value of (d); when CR is reached<When the time is 0.1, the judgment matrix A shows that the sequencing has consistency; and (3) when CR is more than or equal to 0.1, returning to the step (2) to reconstruct the element values in the judgment matrix.
Preferably, the step of modifying the index weight value by using the entropy weight method is as follows:
step (21), the evaluation indexes in the judgment matrix A are processed as follows:
in the formula, vijA value representing an evaluation index in the judgment matrix a; pijRepresenting the calculated value thereof and reflecting the influence degree of the index on the windage yaw of the power transmission line;
step (22) of calculating an evaluation index entropy value ej：
Step (23) of calculating a difference coefficient gj：
Wherein g is not less than 0j≤1；
Step (24), calculating the weight value of the evaluation index:
step (25), using yjCorrecting the weight value calculated by the analytic hierarchy process AHP, and calculating the weight value of the correction evaluation index based on the following formula:
in the formula, wjRepresenting the weight values of all the indexes in the index layer.
Further, the wind speed data is the actual wind speed obtained by correcting the weather forecast wind speed value according to the micro-terrain factor data of the object element to be evaluated.
Preferably, the formula for correcting the weather forecast wind speed value and the micro-terrain factor data into the actual wind speed is as follows:
VP＝α×Vq
in the formula, VqForecasting wind speed, V, for the weatherpAlpha is a micro-terrain correction coefficient for the corrected actual wind speed.
Further, when the microtopography is a hillside and a mountain peak, the microtopography correction coefficient of the hillside and the mountain peak is according to the following formula:
in the formula (I), the compound is shown in the specification,
the included angle between the slope of the mountain on the windward side and the flat ground; k is a correction coefficient; h is the vertical height of the mountain and the flat ground; z is the breath height of the matter element to be evaluated.
Preferably, the steps of constructing the windage yaw flashover risk assessment model based on the physical element extension model for risk assessment are as follows:
and (31) constructing a classical domain of the evaluation index according to the matter element R ═ N, C and V:
n is the windage yaw flashover risk level in the tower unit, C is a risk evaluation index set, and V is the magnitude of the risk evaluation index; in the formula, NjRepresenting the divided j levels, CiIndicating quality class NjCharacteristic value of vjiThe value range of the characteristic value under the grade is shown, namely a classical domain; a isj1…ajnRepresenting the minimum value of the presence of different evaluation indices in the index layer, bj1……bjnRepresenting the maximum value of different evaluation indexes in the index layer;
step (32), constructing a section domain:
in the formula, NPSet representing all risk classes, vpiIs CiValue range of (1), i.e. section range, its classical range vjiAs section area vpiA subset ofp1…apnRepresenting the minimum value of the presence of different evaluation indices in the index layer, bp1……bpnRepresenting the maximum value of different evaluation indexes in the index layer;
step (33), determining the object elements to be evaluated:
the object element to be evaluated refers to a tower unit of the power transmission line to be evaluated, and the measured data of the tower unit is correspondingly assigned according to the evaluation index, namely:
in the formula, R0Representing the elements to be evaluated, i.e. the tower units of the transmission line to be evaluated, N0Representing the object to be evaluated, namely the wind deflection flashover risk grade in the tower unit, CiRepresenting things to be evaluated N0Characteristic value of v0iIs N0With respect to CiThe magnitude of (d);
step (34), calculating the association degree data of the real-time data of the evaluation indexes and each risk level;
and (35) calculating the comprehensive association degree of each risk level of the tower unit according to the association degree data and the weight value of the real-time data of the evaluation index and each risk level.
Preferably, the step of constructing the classical domain of the evaluation index is:
determining an evaluation index;
dividing the risk of windage yaw flashover into a plurality of grades;
determining a plurality of grades of classical domains of the risk of windage flashover corresponding to each evaluation index.
Preferably, the evaluation indexes include wind speed, wind direction, rainfall intensity and humidity; the risk of windage yaw flashover is divided into 4 grades: safe, low, medium, high;
in four safe, low, medium and high risk levels of the windage yaw flashover risk, the classical domains of the wind speed are 0-10, 10-20, 20-30 and 30-40 respectively; the classical domains of the wind directions are 0-22.5, 22.5-45, 45-67.5 and 67.5-90 respectively; the rainfall intensity is respectively 0-2, 2-4, 4-6 and 6-8 according to the classical domains determined by the relation between the rainfall intensity and the rainfall level; the classical domains of humidity are respectively 0-25, 25-50, 50-75 and 75-100;
classical domain R corresponding to four risk classes in a risk assessment modeli(i ═ 1,2,3,4) as follows:
wherein N is1、N2、N3And N4Safe, low, medium and high corresponding to windage yaw flashover risk level of the object element to be evaluated respectively, c1Is the wind speed, c2Is the wind direction, c3For intensity of rainfall, c4Is humidity.
Preferably, the classical domains R corresponding to the four risk classes in the risk assessment modeli(i ═ 1,2,3,4) corresponding node region RPAs follows:
wherein N ispRepresents the set of all risk classes, p ═ 1,2,3, 4; c. C1Is the wind speed, c2Is the wind direction, c3For intensity of rainfall, c4Is humidity.
Preferably, the step of calculating the comprehensive association degree between the real-time data of the evaluation index and each risk level is as follows:
and quantitatively describing the degree of association between the real-time data of each evaluation index in the risk assessment and the risk level by adopting an association function, wherein the association function is as follows:
the distance between a point and a point is extended to the distance between a point and an interval, called distance, where p (v) is0i,vji) Indicates v in the evaluation index0iAnd classicField vjiDistance between p (v)0i,vpi) Denotes v0iAnd section area vpiDistance between p (v)0i,vji)、p(v0i,vpi) As follows:
in the formula, section area vpiRepresenting the value range of the characteristic value at the grade; a ispiRepresenting the minimum value of the presence of different evaluation indices in the index layer, bpiRepresenting the maximum value of different evaluation indexes in the index layer;
the value of the degree of association is the whole real number axis, if kj(v0i)＝maxkj(v0i) J 1, 2.. times.m, the evaluation index CiAt evaluation level j; things to be evaluated N0Comprehensive degree of association with respect to each evaluation level
In which ω isiThe weight value corresponding to each evaluation index; if k isj(N0)＝maxkj(N0) J is 1,2,.. m, the object to be evaluated is in an evaluation grade j; j represents the evaluation grade of the object to be evaluated.
According to a second aspect of embodiments of the present invention, there is provided a computer apparatus.
In some embodiments, the computer device comprises a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the invention provides a power transmission line windage yaw flashover risk assessment method based on a matter element extension model, which comprises the steps of firstly, selecting an evaluation index according to an influence factor of the windage yaw of the power transmission line; then, calculating the weight value of the evaluation index by adopting an analytic hierarchy process-entropy weight method; and finally, according to the windage yaw evaluation model, combining real-time meteorological data and micro-terrain data around the tower unit, carrying out windage yaw flashover risk evaluation and determining the early warning level of the tower unit. As the whole transmission line has numerous towers and various tower types, all tower units on the transmission line can carry out risk prediction according to the method under the condition of enough data.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart illustrating a method for assessing risk of windage yaw flashover of a power transmission line according to an exemplary embodiment;
FIG. 2 is a flow chart illustrating a method for assessing risk of windage yaw flashover of a power transmission line according to another exemplary embodiment;
FIG. 3 is a schematic view of the tower position;
FIG. 4 is a schematic diagram illustrating the structure of a computer device according to an example embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the embodiments herein includes the full ambit of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like, herein are used solely to distinguish one element from another without requiring or implying any actual such relationship or order between such elements. In practice, a first element can also be referred to as a second element, and vice versa. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a structure, device or apparatus that comprises the element. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein, as used herein, are defined as orientations or positional relationships based on the orientation or positional relationship shown in the drawings, and are used for convenience in describing and simplifying the description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may include, for example, mechanical or electrical connections, communications between two elements, direct connections, and indirect connections via intermediary media, where the specific meaning of the terms is understood by those skilled in the art as appropriate.
Herein, the term "plurality" means two or more, unless otherwise specified.
Herein, the character "/" indicates that the preceding and following objects are in an "or" relationship. For example, A/B represents: a or B.
Herein, the term "and/or" is an associative relationship describing objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
The invention provides a power transmission line windage yaw flashover risk assessment method based on a matter element extension model, wherein an assessment process mainly comprises two parts: (1) calculating the weight value of the selected windage yaw flashover risk evaluation index; (2) and constructing a risk assessment model based on the matter element extension model. As shown in fig. 1, the method for assessing risk of windage yaw flashover of a power transmission line based on an object element extension model includes the following steps: firstly, selecting an evaluation index according to the influence factors of the windage yaw flashover risk of the power transmission line; then, calculating a weighted value of the evaluation index by adopting an analytic hierarchy process-entropy weight method; then, correcting the data of the evaluation index by combining the micro-terrain data; and finally, constructing a windage yaw flashover risk assessment model based on the object element extension model for risk assessment, and performing windage yaw flashover risk assessment according to the risk assessment model and the real-time meteorological data and the micro-terrain data around the tower unit to determine the early warning level of the tower unit. As the whole transmission line has numerous towers and various tower types, all tower units on the transmission line can carry out risk prediction according to the method under the condition of enough data.
As shown in fig. 2, a method for evaluating risk of windage yaw flashover of a power transmission line based on a physical element extension model specifically includes the following steps:
1. selection of wind deflection flashover risk evaluation index of power transmission line
The influence factors of windage yaw flashover of the power transmission line are analyzed from three aspects of weather, terrain and line parameters.
(1) And (4) meteorological factors. The wind speed and the wind direction indirectly influence the air gap between a line and a tower or surrounding objects due to the influence of wind load acting on the lead; rainfall and fog, humidity can reduce the air breakdown voltage of the gap.
(2) And (4) line parameters. The line parameters affect the maximum wind deflection angle and the minimum air gap between the two.
(3) A topographical factor. Altitude reduces the air breakdown voltage of the gap; the ground roughness can change the wind speed and indirectly influence the maximum wind deflection angle.
And for the operating power transmission line, the windage yaw flashover risk evaluation is carried out according to real-time data, and the evaluation cannot be carried out from the perspective of designing the power transmission line. The factors of height difference, span, wire parameters, altitude and sag are fully considered when designing the power transmission line, and only the wind speed, wind direction, rainfall intensity and humidity need to be considered at present. Therefore, the wind speed, wind direction, rainfall intensity, and humidity are used as evaluation indexes.
2. Calculation of evaluation index weight value
A weight refers to the degree of importance of an event or indicator relative to an event. Due to the selected evaluation index, the method is qualitative and quantitative. And combining qualitative and quantitative analysis by adopting an analytic hierarchy process, and calculating the weight value of the evaluation index. Based on the method, the entropy weight method is adopted to correct the index weight, so that the defect of artificial subjective factors existing in the analytic hierarchy process can be overcome.
By adopting an analytic hierarchy process, the steps of specifically evaluating the weighted value of the index are as follows:
(1) and determining a target layer, a standard layer and an index layer, and constructing an index system.
(2) And establishing a judgment matrix according to the importance degree of the evaluation index relative to the target, assigning the element values in the judgment matrix, and assigning the element values in the judgment matrix according to the scale list shown in the table 1.
TABLE 1 Scale Listing
Wherein the judgment matrix a ═ (a)ij)n×n，aijC for indicating index layeriAnd CjDegree of importance relative to the target layer; wherein, CiAnd CjRespectively representing two different indexes of the index layer, wherein n represents the number of evaluation indexes in the index layer;
(3) and (5) checking consistency. There are subjective factors in the analytic hierarchy process, and the consistency check aims to reduce errors in the judgment matrix. Calculating an eigenvalue vector lambda according to the judgment matrix A, and solving a consistency index CI:
in the formula: n represents the number of evaluation indexes;
and (3) performing consistency check of each level of single sequencing by adopting the following formula:
in the formula: n represents the number of evaluation indexes, CR represents the result of single-sequencing consistency check of each level, the value of a random consistency index RI is related to the value of n, and the relationship between the value of RI and n is shown in Table 2.
TABLE 2 RI values
Relative weight value ω of evaluation indexiThe calculation formula is shown as the following formula
j＝1,2,…,n；
(4) Checking the total hierarchical ordering, which is similar to the consistency checking method for performing the single hierarchical ordering in the step (3), and checking the total hierarchical ordering specifically as follows:
in the formula, m represents the number of row vectors of the judgment matrix; a isjA relative weight value representing the evaluation indexωiA value of (d); when CR is reached<When the time is 0.1, the judgment matrix A shows that the sequencing has consistency; and (3) when CR is more than or equal to 0.1, returning to the step (2) to reconstruct the element values in the judgment matrix.
3. Correcting index weight value by entropy weight method
The analytic hierarchy process can combine quantitative and qualitative analysis, on this basis, adopts entropy weight method correction index weight, can make up the analytic hierarchy process and have the not enough of artificial subjective factor, and then gets the strong point and offset the weak point.
In information theory, entropy is shown to be a measure of uncertainty. The entropy of information describes quantitatively how much information a piece of information contains. The stability and uncertainty of the system are positively correlated with the size of the entropy value, and the weight of the object is determined according to the characteristics of the entropy.
The steps of correcting the index weight value by adopting an entropy weight method are as follows:
(1) and (3) processing the evaluation indexes in the judgment matrix A as follows:
in the formula, vijA value representing an evaluation index in the judgment matrix a; pijRepresenting the calculated value, reflecting the influence degree of the index on the windage yaw of the power transmission line, and when the formula in the formula (4) is adopted for calculation, PijThe larger the value is, the smaller the influence of the windage yaw of the transmission line is; when calculated by the following formula in formula (4), PijSmaller values indicate smaller influence of windage yaw of the transmission line;
(2) calculating an evaluation index entropy value ej：
(3) Calculating the difference coefficient gi：
Wherein g is not less than 0j≤1
(4) Calculating the weight value of the evaluation index:
(5) by yjCorrecting the weight value calculated by the analytic hierarchy process AHP, and calculating the weight value of the correction evaluation index based on the following formula:
in the formula, wjRepresenting the weight values of all the indexes in the index layer.
4. Constructing a windage yaw flashover risk assessment model
And abstracting a certain tower unit into an object element model when risk assessment is carried out based on the object element extension model. And establishing a classical domain, a section domain and an evaluation grade according to the characteristics of the evaluation indexes, calculating the association degree of each evaluation index and the classical domain by combining a union function, and then calculating the comprehensive association degree to determine the maximum association degree of the to-be-evaluated object element with respect to the evaluation grade, thereby determining the risk grade of the tower. The windage yaw flashover risk assessment model is constructed, operation and maintenance staff can be informed of the windage yaw flashover risk level in advance to make precaution work, windage yaw treatment measures can be provided for the power transmission line in a targeted mode, and windage yaw faults of the power transmission line are reduced.
First, the concept related to the object element extension model is introduced:
(1) concept of matter element
The name of an object is N, the feature of the object is C, the magnitude of the feature is V, the ordered triple is used as a basic element for describing the object, the feature and the magnitude in the object constitute three elements of the object. Namely:
R＝(N，C,V) (9)
in the risk assessment of windage yaw flashover of the power transmission line, in the formula, N is the windage yaw flashover risk in a tower unit, C is a risk evaluation index set, and V is the magnitude of the risk evaluation index.
(2) Concept of classical domain
In the formula, NjRepresenting the divided j levels, CiIndicating quality class NjCharacteristic value of vjiThe value range of the characteristic value under the grade is shown, namely a classical domain; a isj1…ajnRepresenting the minimum value of the presence of different evaluation indices in the index layer, bj1……bjnRepresenting the maximum value of different evaluation indexes in the index layer;
(3) concept of section domain
In the formula, NPSet representing all risk classes, vpiIs CiValue range of (1), i.e. section range, its classical range vjiAs section area vpiA subset ofp1…apnRepresenting the minimum value of the presence of different evaluation indices in the index layer, bp1……bpnRepresenting the maximum value of different evaluation indexes in the index layer;
(4) matter element to be evaluated
And the object element to be evaluated refers to a certain tower unit of the power transmission line to be evaluated, and the actually measured data of the tower unit is subjected to corresponding assignment according to the evaluation index.
Namely:
in the formula, R0Representing the elements to be evaluated, i.e. the tower units of the transmission line to be evaluated, N0Representing the object to be evaluated, namely the wind deflection flashover risk grade in the tower unit, CiTo indicate a waitEvaluation things N0Characteristic value of v0iIs N0With respect to CiThe magnitude of (d);
(5) correlation function and comprehensive correlation degree
The correlation function is used for quantitatively describing the degree of correlation between each evaluation index in the risk assessment and the risk level, and the correlation function is as follows:
the distance between a point and a point is expanded to the distance between a point and an interval, which is called a distance. Wherein, p (v)0i,vji) Indicates v in the evaluation index0iAnd the classical domain vjiDistance between p (v)0i,vpi) Denotes v0iAnd section area vpiThe distance between them. p (v)0i,vji)p(v0i,vpi) As follows:
in the formula, section area vpiRepresenting the value range of the characteristic value at the grade; a ispiRepresenting the minimum value of the presence of different evaluation indices in the index layer, bpiRepresenting the maximum value of different evaluation indexes in the index layer;
the value of the degree of association is the whole real number axis, if kj(v0i)＝maxkj(v0i) J 1, 2.. times.m, the evaluation index CiAt evaluation level j; things to be evaluated N0Comprehensive degree of association with respect to each evaluation level
In which ω isiThe weight value corresponding to each evaluation index; if k isj(N0)＝maxkj(N0) J is 1,2,.. m, the object to be evaluated is in an evaluation grade j; j represents the evaluation grade of the object to be evaluated. K for all evaluation levelsj(N0) And less than or equal to 0, the evaluation grade of the object to be evaluated is not in each grade, and the evaluation grade is to be discarded.
In the formula j*The degree of the bias of the evaluated object to the adjacent grade can be deduced from the value of the variable characteristic value of the risk grade.
Then, a risk assessment model is established:
(1) a classical domain is constructed. The windage yaw flashover risk is classified herein into 4 classes: safety, low, medium and high, and the early warning grades are I, II, III and IV respectively. According to engineering experience, the classical domains of the wind speed in the four risk levels are 0-10, 10-20, 20-30 and 30-40 respectively. The classical domains of wind directions (included angles between wind and lines) are respectively 0-22.5, 22.5-45, 45-67.5 and 67.5-90. The rainfall intensity is respectively 0-2, 2-4, 4-6 and 6-8 according to the classical domains determined by the relation between the rainfall intensity and the rainfall level. The classical domains of humidity are 0-25, 25-50, 50-75, 75-100, respectively.
(2) And constructing a section domain. The section domain of the risk assessment index is a set of classical domains.
(3) And determining the matter element to be evaluated. And taking the real-time data of the tower unit as values of each risk evaluation index. The wind speed in the risk evaluation index is not the wind speed value near the tower unit to be evaluated, which is provided by the meteorological department when the microtopography is considered, and a microtopography correction coefficient is required to be introduced to correct the wind speed value.
Classical domain R for each risk class in a risk assessment modeli(i ═ 1,2,3,4) as follows:
in the formula, N1、N2、N3And N4The wind deflection flashover risk levels of the tower units are safe, low, medium and high respectively; c. C1Is the wind speed, c2Is the wind direction, c3For intensity of rainfall, c4Is humidity.
Classical domain R corresponding to four risk classes in a risk assessment modeli(i ═ 1,2,3,4) corresponding node region RPAs follows:
in the formula, NpRepresents the set of all risk classes, p ═ 1,2,3, 4; c. C1Is the wind speed, c2Is the wind direction, c3For intensity of rainfall, c4Is humidity.
Then, the wind speed value of the meteorological forecast is corrected by considering the terrain factors:
the wind speed provided by a meteorological department is generally the wind speed within a certain range of a meteorological station, and the wind speed far away from the meteorological station cannot be determined, so that the predicted wind speed provided by the meteorological department cannot accurately evaluate the risk of windage yaw flashover of a certain tower. When the micro-terrain where the tower is located is not considered, the meteorological forecast wind speed value cannot be directly used for the magnitude of the wind speed characteristic in the risk assessment model. Therefore, the wind speed value is corrected according to the terrain environment where the tower is located as shown in fig. 3, and the relevant parameters of the table 3 and the table 4 are referred to.
The micro-terrain correction coefficient of the hillside and the mountain peak is according to the following formula:
in the formula (I), the compound is shown in the specification,
the included angle between the slope of the mountain on the windward side and the flat ground; k is a correction coefficient, 2.2 is taken at the top of a mountain peak, and 1.4 is taken at a hillside; the vertical height of the H mountain and the flat ground;z is the nominal height of the tower unit.
When the mountain is located in different areas, H and
the values of (A) are different and are mainly classified into three categories as shown in Table 4.
TABLE 3 microrelief correction factor
TABLE 4H,
The relation between the value of (A) and the area of the mountain
The wind speed provided by the meteorological department is corrected by combining the micro-terrain correction coefficient, and the formula is as follows:
VP＝α×Vq (18)
in the formula, VqForecasting wind speed, V, for the weatherpIs the corrected actual wind speed.
The following provides a specific embodiment of the power transmission line windage yaw flashover risk evaluation method based on the matter element extension model.
According to data statistics of windage yaw faults of power transmission lines of Shandong power grids, the windage yaw flashover probability of 500KV power transmission lines is larger than that of 220KV power transmission lines, and towers of the 500KV power transmission lines are selected for example analysis. Therefore, data of the A-phase line in the #015, #016 and #017 tower units of the Yiyi city Mongolian I line and data of the B-phase jumper wire in the #168 tower unit of the cigarette platform city Juanshen II line are respectively selected. The #168 tower of the Jupiter II line is a corner tower and is used for verifying the jumper wire windage yaw condition of the corner tower; and #015, #016 and #017 of the Mongolian I line are all tangent towers and are used for verifying the windage yaw condition of the suspension insulator of the tangent towers. Wherein, the topography of the tower selected by the Mongolian I line is plain; the topography of the pole tower selected by the nuclear Shen II line is a hill; the respiratory height of #015 tower, #016 tower and #017 tower is 24m, and the respiratory height of #168 tower is 33 m. And modifying the wind speed value by combining the relevant parameters of the tables 3 and 4, and displaying the real-time data of the wind speed, the wind direction, the rainfall intensity and the humidity near the selected tower unit as shown in the table 5.
TABLE 5 Meteorological data in the vicinity of the selected Tower Unit
Because the Mongolian I lines #015, #016 and #017 towers are close to each other, the wind speed, rainfall intensity and humidity data are consistent, only the line trend changes, so that the included angle between wind and the line changes, and the wind directions of the #016 and #017 towers are 73 degrees and 66 degrees respectively. According to the meteorological data in the table 5, the association degree between the real-time data of the risk indexes of the tower units to be evaluated and the risk level is calculated and is shown in tables 6 and 7.
Table 6 Mongolian I line #015, #016 and #017 tower risk grade association table
TABLE 7 Risk class association table for Jushen II line #168 tower unit
The comprehensive association degrees of the risk levels of the Mongolian I line #015, #016 and #017 towers and the Jupiter II line #168 tower are calculated according to the data and the weight values of the risk levels of the evaluation indexes in the tables 6 and 7, as shown in the table 8.
TABLE 8 comprehensive association degree of each risk class and pole tower early warning class result
From the calculation results in table 8, it can be known that 015, #016 and #017 towers covering the I line # may cause different results of the windage yaw flashover risk assessment early warning level due to different wind directions. The early warning grade of the #016 tower covering the I line and the #168 tower covering the Shen II line is IV grade and is in a high risk condition, and actually, the A-phase lead of the #016 tower covering the I line and the B-phase jumper of the #168 tower covering the Shen II line are subjected to windage yaw flashover to cause a line tripping condition; and the early warning level of the A-phase line in the #015 tower and the #017 tower which cover the I line is III level, and the early warning level is in a medium risk condition, and the phenomenon of wind deflection flashover does not occur actually, so that the two tower units are known to be in a risk controllable state. Therefore, the estimated early warning level is consistent with the windage yaw flashover condition of the actual tower, and the effectiveness of the windage flashover risk estimation model can be verified.
According to the power transmission line windage yaw flashover risk evaluation method based on the matter element extension model, the risk evaluation index is selected according to the influence factors of the windage yaw flashover of the power transmission line. In consideration of the fact that subjective factors in the analytic hierarchy process are heavy, the evaluation index weight is calculated based on the analytic hierarchy process, and then the evaluation index weight is corrected by an entropy weight method.
According to the power transmission line windage yaw flashover risk evaluation method based on the matter element extension model, the windage yaw flashover risk evaluation model based on the matter element extension model is constructed, the risk condition of the tower unit can be comprehensively evaluated by combining qualitative analysis and quantitative analysis, and the method has the advantages of objective and accurate risk evaluation result and the like. In consideration of the influence of the micro-terrain in the area where the tower is located on the suspension insulator string or jumper wind deflection, a micro-terrain correction coefficient alpha is introduced to correct the wind speed value forecasted by a meteorological department, so that the risk evaluation grade is closer to the actual condition.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing static information and dynamic information data. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program is executed by a processor to carry out the steps in the above-described method embodiments.
Those skilled in the art will appreciate that the configuration shown in fig. 4 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing devices to which aspects of the present invention may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The present invention is not limited to the structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (16)
1. A power transmission line windage yaw flashover risk evaluation method based on a matter element extension model is characterized by comprising the following steps:
selecting an evaluation index according to the influence factors of the windage yaw flashover risk of the power transmission line;
calculating the weight value of the selected windage yaw flashover risk evaluation index;
correcting the weight value of the evaluation index by combining the micro-terrain data;
and constructing a windage yaw flashover risk assessment model based on the matter element extension model for risk assessment.
2. The risk assessment method according to claim 1,
the influence factors of the windage yaw flashover of the power transmission line comprise: weather, terrain, and line parameters.
3. The risk assessment method according to claim 1,
the evaluation indexes comprise wind speed, wind direction, rainfall intensity and humidity.
4. The risk assessment method according to claim 1,
the risk of windage yaw flashover of the power transmission line is divided into 4 grades: safe, low, medium, high.
5. The risk assessment method according to claim 1,
the weight value is obtained by the following steps:
calculating the weight value of the evaluation index by adopting an analytic hierarchy process;
and correcting the weight value of the evaluation index by adopting an entropy weight method.
6. The risk assessment method according to claim 5,
the method for calculating the weight value of the evaluation index by adopting the analytic hierarchy process comprises the following steps:
step (11), determining a target layer, a criterion layer and an index layer, and constructing an index system;
step (12), a judgment matrix is established according to the importance degree of the evaluation index relative to the target, and the element values in the judgment matrix are assigned, wherein the judgment matrix A is (a)ij)n×n，aijC for indicating index layeriAnd CjDegree of importance relative to the target layer; wherein, CiAnd CjRespectively representing two different indexes of the index layer, wherein n represents the number of evaluation indexes in the index layer;
and (13) consistency check is carried out, and errors existing in the judgment matrix are reduced:
calculating an eigenvalue vector lambda according to the judgment matrix A, and solving a consistency index CI:
and (3) performing consistency check of each level of single sequencing by adopting the following formula:
in the formula, n represents the number of evaluation indexes in an index layer, CR represents the result of single-sequencing consistency check of each layer, and the value of a random consistency index RI is related to the value of n;
relative weight value ω of evaluation indexiThe calculation formula is shown as follows:
j＝1,2,…,n；
step (14), checking the total sorting of layers, specifically as follows:
in the formula, m represents the number of row vectors of the judgment matrix; a isjA relative weight value ω representing the evaluation indexiA value of (d); when CR is reached<When the time is 0.1, the judgment matrix A shows that the sequencing has consistency; when CR is more than or equal to 0.1, the step (12) is returned to reconstruct the element values in the judgment matrix.
7. The risk assessment method according to claim 5,
the method for correcting the weight value of the evaluation index by adopting the entropy weight method comprises the following steps:
step (21), the evaluation indexes in the judgment matrix A are processed as follows:
in the formula, vijRepresentative judgmentBreaking the value of the evaluation index in the matrix A; pijRepresenting the calculated value thereof and reflecting the influence degree of the index on the windage yaw of the power transmission line;
step (22) of calculating an evaluation index entropy value ej：
Step (23) of calculating a difference coefficient gj：
Wherein g is not less than 0j≤1；
Step (24), calculating the weight value of the evaluation index:
step (25), using yjCorrecting the weight value calculated by the analytic hierarchy process AHP, and calculating the weight value of the correction evaluation index based on the following formula:
in the formula, wjRepresenting the weight values of all the indexes in the index layer.
8. The risk assessment method according to claim 3,
and the wind speed data is the actual wind speed obtained by correcting the weather forecast wind speed value according to the micro-terrain factor data of the object element to be evaluated.
9. The risk assessment method according to claim 8,
the formula for correcting the weather forecast wind speed value and the micro-terrain factor data into the actual wind speed is as follows:
VP＝α×Vq
in the formula, VqForecasting wind speed, V, for the weatherpAlpha is a micro-terrain correction coefficient for the corrected actual wind speed.
10. The risk assessment method according to claim 9,
when the microtopography is a hillside or a mountain peak, the microtopography correction coefficient of the hillside or the mountain peak is according to the following formula:
in the formula (I), the compound is shown in the specification,
the included angle between the slope of the mountain on the windward side and the flat ground; k is a correction coefficient; h is the vertical height of the mountain and the flat ground; z is the breath height of the matter element to be evaluated.
11. The risk assessment method according to claim 1,
the steps of constructing a windage yaw flashover risk assessment model based on the matter element extension model for risk assessment are as follows:
and (31) constructing a classical domain of the evaluation index according to the matter element R ═ N, C and V:
in the formula, N is the windage yaw flashover risk level in the tower unit, C is a risk evaluation index set, and V is the magnitude of the risk evaluation index; n is a radical ofjRepresenting the divided j levels, CiIndicating quality class NjCharacteristic value of vjiThe value range of the characteristic value under the grade is shown, namely a classical domain; a isj1…ajnRepresenting the minimum value of the presence of different evaluation indices in the index layer, bj1……bjnRepresenting the maximum value of different evaluation indexes in the index layer;
step (32), constructing a section domain:
in the formula, NPSet representing all risk classes, vpiIs CiValue range of (1), i.e. section range, its classical range vjiAs section area vpiA subset ofp1…apnRepresenting the minimum value of the presence of different evaluation indices in the index layer, bp1……bpnRepresenting the maximum value of different evaluation indexes in the index layer;
step (33), determining the object elements to be evaluated:
the object element to be evaluated refers to a tower unit of the power transmission line to be evaluated, and the measured data of the tower unit is correspondingly assigned according to the evaluation index, namely:
in the formula, R0Representing the elements to be evaluated, i.e. the tower units of the transmission line to be evaluated, N0Representing the object to be evaluated, namely the wind deflection flashover risk grade in the tower unit, CiRepresenting things to be evaluated N0Characteristic value of v0iIs N0With respect to CiThe magnitude of (d);
step (34), calculating the association degree data of the real-time data of the evaluation indexes and each risk level;
and (35) calculating the comprehensive association degree of each risk level of the tower unit according to the association degree data and the weight value of the real-time data of the evaluation index and each risk level.
12. The risk assessment method according to claim 11,
the step of constructing the classical domain of the evaluation index comprises the following steps:
determining an evaluation index;
dividing the risk of windage yaw flashover into a plurality of grades;
determining a plurality of grades of classical domains of the risk of windage flashover corresponding to each evaluation index.
13. The risk assessment method according to claim 12,
the evaluation indexes comprise wind speed, wind direction, rainfall intensity and humidity; the risk of windage yaw flashover is divided into 4 grades: safe, low, medium, high;
in four safe, low, medium and high risk levels of the windage yaw flashover risk, the classical domains of the wind speed are 0-10, 10-20, 20-30 and 30-40 respectively; the classical domains of the wind directions are 0-22.5, 22.5-45, 45-67.5 and 67.5-90 respectively; the rainfall intensity is respectively 0-2, 2-4, 4-6 and 6-8 according to the classical domains determined by the relation between the rainfall intensity and the rainfall level; the classical domains of humidity are respectively 0-25, 25-50, 50-75 and 75-100;
classical domain R corresponding to four risk classes in a risk assessment modeli(i ═ 1,2,3,4) as follows:
in the formula, N1、N2、N3And N4Safe, low, medium and high windage yaw flashover risk levels respectively corresponding to the object elements to be evaluated，c1Is the wind speed, c2Is the wind direction, c3For intensity of rainfall, c4Is humidity.
14. The risk assessment method according to claim 13,
classical domain R corresponding to four risk classes in a risk assessment modeli(i ═ 1,2,3,4) corresponding node region RPAs follows:
in the formula, NpRepresents the set of all risk classes, p ═ 1,2,3, 4; c. C1Is the wind speed, c2Is the wind direction, c3For intensity of rainfall, c4Is humidity.
15. The risk assessment method according to claim 11,
the step of calculating the comprehensive association degree of the real-time data of the evaluation index and each risk level is as follows:
and quantitatively describing the degree of association between the real-time data of each evaluation index in the risk assessment and the risk level by adopting an association function, wherein the association function is as follows:
the distance between a point and a point is extended to the distance between a point and an interval, called distance, where p (v) is0i,vji) Indicates v in the evaluation index0iAnd the classical domain vjiDistance between p (v)0i,vpi) Denotes v0iAnd section area vpiDistance between p (v)0i,vji)、p(v0i,vpi) As follows:
in the formula, section area vpiRepresenting the value range of the characteristic value at the grade; a ispiRepresenting the minimum value of the presence of different evaluation indices in the index layer, bpiRepresenting the maximum value of different evaluation indexes in the index layer;
the value of the degree of association is the whole real number axis, if kj(v0i)＝max kj(v0i) J 1, 2.. times.m, the evaluation index CiAt evaluation level j; things to be evaluated N0Comprehensive degree of association with respect to each evaluation level
In which ω isiThe weight value corresponding to each evaluation index; if k isj(N0)＝max kj(N0) J is 1,2,.. m, the object to be evaluated is in an evaluation grade j; j represents the evaluation grade of the object to be evaluated.
16. A computer arrangement, characterized in that the computer arrangement comprises a memory, in which a computer program is stored, and a processor, which when executing the computer program realizes the steps of the method according to any of claims 1 to 15.
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