CN115907472A - Lead fault risk assessment method based on subjective and objective comprehensive weighting method - Google Patents
Lead fault risk assessment method based on subjective and objective comprehensive weighting method Download PDFInfo
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Abstract
The invention discloses a lead fault risk assessment method based on an subjective and objective comprehensive weighting method, which comprises the following steps: acquiring data information related to wire equipment in a power grid management information system; establishing an evaluation index set and a risk level of the wire fault influence factors based on the acquired data information; respectively calculating subjective weight and objective weight of the evaluation index by using a sequence relation method and an entropy weight method; carrying out weighted combination on the subjective weight and the objective weight, and carrying out optimized selection on a weighting coefficient; scoring each evaluation index of the wire, calculating a combined evaluation value, and dividing the line into three grades of low risk, medium risk and high risk according to the calculation result; the method solves the technical problems that in the prior art, a subjective analytic hierarchy process and an objective entropy weight process are combined for conducting wire fault risk assessment, the analytic hierarchy process is large in calculated amount and too subjective, consistency check needs to be carried out, weighting coefficients are difficult to select when weighting is carried out in combination, effectiveness is insufficient, and the like.
Description
Technical Field
The invention belongs to the technical field of risk assessment of power equipment, and particularly relates to a lead fault risk assessment method based on an objective comprehensive weighting method.
Background
With the continuous enlargement of modern electric power system scale, the voltage level is continuously improved, and rack structure is complicated day by day, and the electric wire netting operation also more and more faces latent risk. In recent years, extreme natural disaster events (such as ice disasters and mountain fires) are increased obviously, uncertainty of a power transmission side is increased greatly, and safe operation of a power system is seriously influenced. The wire is used as important equipment for conveying electric energy, the safety level of the wire directly influences the power supply reliability, and the wire fault is very easy to extend to the whole power system to cause large-area power failure, so that the risk fault of the wire needs to be evaluated.
Existing research has shown that aging failure of a wire has become a secondary factor in wire failure, resulting in higher failure rates when the wire crosses extreme terrain, and frequent lightning strikes and animal, human, higher wind speeds, higher pollution levels, etc. all cause line failures to occur more frequently. How to comprehensively consider various influence factors, the problem of integrated evaluation of the fault risk of the wire is still a difficult problem, the most common comprehensive evaluation method is the combination of a subjective analytic hierarchy process and an objective entropy weight process, although the entropy weight process is simple and effective, the analytic hierarchy process has large calculated amount and is too subjective, consistency check is required, and the problem of difficult selection of weighting coefficients exists in combined weighting, so that the effectiveness is insufficient.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method is used for solving the technical problems that in the prior art, a subjective analytic hierarchy process and an objective entropy weight process are combined for conducting wire fault risk assessment, although the entropy weight process is simple and effective, the analytic hierarchy process is large in calculation amount and too subjective, consistency check is needed, and in combination weighting, the weighting coefficient is difficult to select, so that effectiveness is insufficient and the like.
The technical scheme of the invention is as follows:
a lead fault risk assessment method based on an subjective and objective comprehensive weighting method, the method comprising:
step 1: acquiring data information related to lead equipment in a power grid management information system;
step 2: establishing an evaluation index set and a risk level of the wire fault influence factors based on the acquired data information;
and step 3: respectively calculating subjective weight and objective weight of the evaluation index by using an order relation method and an entropy weight method;
and 4, step 4: carrying out weighted combination on the subjective weight and the objective weight, and carrying out optimized selection on a weighting coefficient;
and 5: and scoring each evaluation index of the wire, calculating a combined evaluation value, and dividing the line into three grades of low risk, medium risk and high risk according to the calculation result.
Step 1, the data information comprises:
the technical parameters are as follows: the method comprises the steps of determining the type, the material, the diameter and the length of a wire device, the suspension height, the strand breakage condition, the maximum transmission capacity and the reference fault rate;
GIS information: including local terrain and basic environmental conditions;
weather information: including temperature, wind speed, relative humidity, lightning strike and ice damage conditions;
and (4) maintenance recording: including faulty components, fault location and fault causes;
other information, including animal exposure, human factors and pollution flashover.
The method for establishing the evaluation index set and the risk level in the step 2 comprises the following steps:
selecting 8 types of evaluation index sets as main influence factors from the data information, wherein the evaluation index sets comprise reference fault rate, terrain conditions, wind speed, lightning stroke, ice damage, maintenance records, animal contact and pollution flashover conditions;
the baseline failure rates are classified as: low risk, failure rate < 1 × 10 -4 Middle risk, 1X 10 -4 Less than or equal to 5 multiplied by 10 fault rate -4 High risk, failure rate ≥ 1 × 10 -4 ；
The wind speed is classified as: low risk, wind speed < 50mp/h; the wind speed is more than or equal to 50mp/h and less than 80mp/h; high risk, wind speed is more than or equal to 80mp/h;
lightning strikes are classified according to lightning strike density as: low risk, falling density < 1.05 pieces/(km) 2 A); risk of 1.05 pieces/(km) 2 A) is more than or equal to the thunderbolt density and less than 6.30/(km) 2 A); high risk, the falling density is more than or equal to 6.30 pieces/(km) 2 ·a)；
Ice damage is classified according to ice thickness: low risk, icing thickness =0; the risk is moderate, the ice coating thickness is more than 0 and less than 15mm; the risk is high, and the ice coating thickness is more than or equal to 15mm;
the maintenance records are classified according to the existing fault level of the line as follows: low risk, number of failures =0; the risk is moderate, the failure frequency is more than 0 and less than 3; high risk, the failure frequency is more than or equal to 3 times;
animal exposure is classified according to the influencing factors of animal exposure as: the risk is low, the fault proportion is less than 3 percent, the risk is medium, the fault proportion is more than or equal to 3 percent and less than or equal to 6 percent, the risk is high, and the fault proportion is more than 6 percent;
the pollution flashover adopts 2 indexes of equivalent salt deposit density and relative humidity to define the grade of the pollution flashover, and the classification is as follows: low risk, relative humidity less than 75%, equivalent salt density less than 0.02mg/cm 2 (ii) a Medium risk, relative humidity < 75%,0.02mg/cm 2 Equivalent salt density is not less than 0.05mg/cm 2 (ii) a High risk, relative humidity not less than 75% or equivalent salt density not less than 0.05mg/cm 2 。
The method for respectively calculating the subjective weight and the objective weight of the evaluation index by using the order relation method and the entropy weight method comprises the following steps:
step 3.1, subjective weighting method based on the sequence relation method:
1) Determining an order relation:
let x 1 ,x 2 ,…,x n (n is more than or equal to 2) is n indexes in a multilayer index system, and for a certain evaluation criterion, when x is i ≥x j When, the index x is explained i Is not less than x j . If there are n indexes x now 1 ,x 2 ,…,x n There is a relationship as shown below:
x 1 ≥x 2 ≥,…,≥x n
n indexes, x 1 ,x 2 ,…,x n Establishing an order relation according to the fact that the order relation is greater than or equal to;
2) Quantitative analysis of importance degree of each index:
index x k-1 And x k Is w k-1 /w k And is recorded as:
w k-1 /w k ＝r k (k＝n,n-1,n-2,…,3,2)
in the formula: w is a k Subjective weight as index k; r is k Assigning values to the order relation;
3) Calculation of subjective weight:
order relation assignment r k Calculating a subjective weight w k Comprises the following steps:
w k-1 ＝r k w k (k＝n,n-1,n-2,…,3,2)
obtaining the subjective weight w of the index n Post-calculation of w 1 ～w n-1 ；
Step 3.2, objective weighting method based on entropy weight method:
the entropy weight method solves the objective weight of the index by analyzing the information quantity carried by the index data, and is provided with m samples and n evaluation indexes, and for a certain index j, the index value of an evaluation object is x ij ；
1) Standardized matrix
2) Determining entropy of i index
k＝(lnm) -1 ,0＜e i ≤1
3) Determining objective weights for each index
Value h of utility of j-th index information i ＝1-e i Then the weight of the j-th index is:
the sequence relation establishing steps are as follows:
e) The expert is in the index set { x) according to the evaluation criterion 1 ,x 2 ,…,x n Selecting one index regarded as the first important index, and recording the index
f) The expert selects one index which is considered as the first important index from the index set containing n-1 indexes and records the index as the first important index
g) After selecting k times, the expert selects one index from the index set of the remaining n- (k-1) indexesIndex considered to be of first importance, and is recorded as
h) After n-1 times of selection, the only index in the residual index set is recorded as
Step 4, the method for performing weighted combination on the subjective weight and the objective weight and performing optimized selection on the weighting coefficient comprises the following steps:
step 4.1, combining weight:
λ i ＝αw i +βu i
wherein: alpha and beta are more than or equal to 0, alpha + beta =1,0 is more than or equal to alpha and less than or equal to 1,0 is more than or equal to beta and less than or equal to 1, and alpha and beta are subjective weight coefficients and objective weight coefficients respectively;
the level of agreement between the subjective and objective weighted attribute values of somewhere j:
in the formula: alpha w i x ij Subjectively weighting the attribute values; beta u i x ij An objective weighted attribute value;
4.2, establishing an optimization model by using a linear weighting method:
(α+β＝1,α·β≥0,0≤α≤1,0《β《1)
solving a binary optimization model of the weight coefficients alpha and beta by Lagrange multiplication to obtain a weighting coefficient of the subjective and objective weights of the evaluation index, and substituting the weighting coefficient into the formula to obtain a combined weight lambda of the evaluation index i 。
The method for scoring each evaluation index of the lead and calculating the combined evaluation value comprises the following steps:
step 5.1, carrying out sectional management on the transmission conductors, setting the transmission conductors into N sections, respectively evaluating 8 types of evaluation indexes of the N sections of lines, and constructing the following fuzzy matrix R by using a fuzzy mathematical method:
in the formula: f. of ij Is a normalized value of the total number of times that the ith index is rated as the jth comment, i.e., f ij ＝v ij /n，v ij Is an actual evaluation value; j =1 represents low risk, j =2 represents medium risk, j =3 represents high risk;
step 5.2, the expert sorts the importance of the 8 indexes, and obtains the subjective weight of the indexes by using a sequence relation method;
step 5.3, calculating objective weights of all indexes by utilizing an entropy weight method principle according to the numerical difference of the fuzzy matrix of the N sections of leads;
step 5.4, solving the combination weight lambda by adopting a Lagrange multiplier method;
step 5.5, calculating a comprehensive evaluation result vector according to the fuzzy matrix and the combined weight
B＝λ·R T 。
And performing comprehensive evaluation on the wire risks according to the evaluation vectors, and judging the wire risks to be low risk, medium risk or high risk.
The beneficial effects of the invention are:
based on various kinds of information which can be acquired by the existing power grid information management system, a lead evaluation index set considering multiple influence factors is established, and visual key monitoring on the running state of a lead by power system dispatching operation and maintenance personnel is facilitated; furthermore, subjective weight and objective weight of each evaluation index are calculated by using a sequence relation method and an entropy weight method, and weighting coefficients of the subjective weight and the objective weight are optimized and selected, so that the fault risk of the lead can be truly and effectively evaluated, and the scientificity of lead operation maintenance decision can be improved. The problems that the risk assessment of the fault of the lead is difficult and the decision scientificity is not enough are solved.
Drawings
FIG. 1 is a flow chart of the steps of the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
A lead fault risk assessment method based on a subjective and objective comprehensive weighting method comprises the following steps:
step 1: acquiring data related to the wire equipment in a power grid management information system, wherein the data comprises technical parameters, GIS information, weather information, maintenance records and other information;
step 2: establishing an evaluation index set and a risk level of main influence factors of the wire fault based on the acquired information;
and step 3: respectively calculating subjective weight and objective weight of the evaluation index by using a sequence relation method and an entropy weight method;
and 4, step 4: carrying out weighted combination on the subjective weight and the objective weight, and carrying out optimized selection on a weighting coefficient;
and 5: and scoring each evaluation index of the wire, calculating a combined evaluation value of the evaluation indexes, and dividing the line into three grades of low risk, medium risk and high risk according to a calculation result.
Further, the step 1 specifically comprises:
acquiring data associated with wire equipment in a power grid management information system, comprising: technical parameters including the type, material, diameter and length of the wire equipment, the suspension height, the strand breakage condition, the maximum transmission capacity and the reference fault rate; GIS information, including local terrain and basic environmental conditions; weather information including temperature, wind speed, relative humidity, lightning strike and ice damage conditions; the maintenance records comprise fault elements, fault location and fault reasons; other information, animal exposure, human factors, pollution flashover and other conditions.
Further, the step 2 specifically includes:
and establishing an evaluation index set and a risk level of main influence factors of the wire fault based on the acquired information.
Selecting 8 types of evaluation index sets as main influence factors from the indexes, wherein the evaluation index sets are respectively as follows: benchmark failure rate, terrain conditions, wind speed, lightning strike, ice damage, maintenance records, animal contact and pollution flashover conditions. These factors are not suitable for description using a continuous function model and are thus described discretized.
The baseline failure rate is an important technical parameter of the wire, and is also a general factor considering the failure of the wire, and is classified as follows: low risk (failure rate < 1X 10) -4 In case of stroke risk (1X 10) -4 Less than or equal to 5 multiplied by 10 fault rate -4 ) High risk (failure rate ≥ 5 × 10) -4 )。
The terrain conditions mainly need to consider special conditions such as plateaus and mountains, and leads are more prone to failure in high-altitude areas, and are classified as follows: low risk (altitude less than 500 m), medium risk (altitude less than or equal to 500m and less than 1000 m) and high risk (altitude more than or equal to 1000 m).
Wind speed is also a major consideration, with wires being more prone to failure with higher wind speeds, classified as: low risk (wind speed is less than 50 mp/h), medium risk (wind speed is less than or equal to 50mp/h and less than 80 mp/h) and high risk (wind speed is more than or equal to 80 mp/h).
Lightning strike is an important factor causing line faults, lightning strike density is an important index for analyzing the severity of the lightning strike in a region, and the lightning strike density is classified into the following factors according to the lightning strike density: low risk (thunderbolt density < 1.05 pieces/(km) 2 A)), risk (1.05 counts/(km) 2 A) is more than or equal to the thunderbolt density and less than 6.30/(km) 2 A)), high risk (lightning density ≥ 6.30/(km) 2 ·a))。
The formation of ice damage is complicated and is influenced by various factors such as air temperature, and the ice damage is classified into: low risk (icing thickness = 0), medium risk (0 < icing thickness < 15 mm), high risk (icing thickness ≧ 15 mm).
The maintenance record shows the historical fault level of the line, and the fault level is classified into the following types according to the existing fault level of the line: low risk (failure frequency = 0), medium risk (0 < failure frequency < 3), high risk (failure frequency > 3).
Line faults caused by animal contact, particularly bird activity, account for a considerable proportion of many areas in China, and are classified according to the influence factors of animal contact: low risk (fault ratio is less than 3%), medium risk (fault ratio is more than or equal to 3% and less than or equal to 6%), and high risk (fault ratio is more than 6%).
The pollution flashover is related to a series of factors such as the withstand voltage, the humidity and the pollution levels of various regions of equipment, the method adopts 2 indexes of equivalent salt density and relative humidity to define the pollution flashover level and classifies the pollution flashover level into the following classes: low risk (relative humidity < 75%, equivalent salt density < 0.02 mg/cm) 2 ) In case of risk (relative humidity < 75%,0.02 mg/cm) 2 Equivalent salt density is not less than 0.05mg/cm 2 ) High risk (relative humidity is more than or equal to 75 percent or equivalent salt density is more than or equal to 0.05 mg/cm) 2 )。
Further, the step 3 specifically includes:
(1) Subjective weighting method based on sequence relation method
1) Determining order relationships
Let x 1 ,x 2 ,…,x n (n is more than or equal to 2) is n indexes in a multilayer index system, and for a certain evaluation criterion, when x is more than or equal to 2 i ≥x j When the index x is described i Is not less important than x j . If there are n indexes x now 1 ,x 2 ,…,x n There is a relationship as shown below:
x 1 ≥x 2 ≥,…,≥x n
n indexes, x 1 ,x 2 ,…,x n The order relation is established according to 'not less'. The steps for determining the order relationship are as follows: a) The expert is in the index set { x) according to the evaluation criterion 1 ,x 2 ,…,x n One index considered to be the first important index is selected,
is marked as
b) The expert selects one index which is considered as the first important index from the index set containing n-1 indexes and records the index as the first important index
c) After selecting k times, the expert selects one index from the remaining n- (k-1) indexes to be considered as the first indexThe desired index is recorded as
/>
d) After n-1 times of selection, the only index in the residual index set is recorded as
2) Quantitative analysis of importance of each index
Index x k-1 And x k Is w k-1 /w k And is recorded as:
w k-1 /w k ＝r k (k＝n,n-1,n-2,…,3,2)
in the formula: w is a k Subjective weight as index k; r is a radical of hydrogen k The order relationship assignment of (1) is shown in table 1.
3) Calculation of subjective weights
Assignment by expert r k Calculating a subjective weight w k Comprises the following steps:
w k-1 ＝r k w k (k＝n,n-1,n-2,…,3,2)
obtaining the subjective weight w of the index based on expert decision according to the formula n Post-calculation of w 1 ～w n-1 。
TABLE 1 order relation assignment table
| r k | Description of the invention |
| 1.0 | x k-1 And x k Of equal importance |
| 1.5 | x k-1 Ratio x k Of obvious importance |
| 2.0 | x k-1 Ratio x k Of extreme importance |
(2) Objective weighting method based on entropy weight method
The entropy weight method solves the objective weight of the index by analyzing the size of the information quantity carried by the index data. m samples, n evaluation indexes, for a certain index j, if the index value x of the evaluation object ij And if the difference is obvious, the evaluation effect of the index is obvious, otherwise, the evaluation effect is not obvious.
1) Standardized matrix
2) Determining entropy of i index
k＝(lnm) -1 ,0＜e i ≤1
3) Determining objective weights for indices
Value h of utility of j-th index information i ＝1-e i Then the weight of the jth index is
Further, the step 4 specifically includes:
based on order relation methodThe subjective reasonability of the index weight value obtained by subjective empowerment is high, but the randomness of artificial subjective factors is high, and objective information carried by the index is ignored; the entropy weight method calculates objective weight values of indexes by using original information of acquired data and combining with corresponding mathematical principles and models, ignores the experience and decision of experts, and leads the obtained weight values not to be the same under the actual condition. In view of the problems of the above 2 methods, a method for applying the subjective weight w is proposed i And objective weight mu i And the method for performing combination optimization solves the optimal weighting coefficient, so that the weighted value of the index has the advantages of both subjective weight and objective weight, the obtained result is more real and effective, and the actual condition is met.
Combining weight:
λ i ＝αw i +βu i
wherein: alpha beta is more than or equal to 0, alpha + beta =1,0 is more than or equal to alpha and less than or equal to 1,0 is more than or equal to beta and less than or equal to 1, and alpha and beta are subjective weight coefficients and objective weight coefficients respectively.
The level of agreement between the subjective and objective weighted attribute values of somewhere j:
in the formula: alpha w i x ij Subjectively weighting the attribute values; beta u i x ij The attribute values are objectively weighted.
In order to minimize the consistency level of the subjective and objective weighted attribute values, an optimization model is established by using a linear weighting method:
(α+β＝1,α·β≥0,0≤α≤1,0≤β≤1)
solving a binary optimization model of the weight coefficients alpha and beta by Lagrange multiplication to obtain a weighting coefficient of the subjective and objective weights of the evaluation index, and substituting the weighting coefficient into the formula to obtain a combined weight lambda of the evaluation index i 。
Further, the step 5 specifically includes:
(1) In practice, a transmission line is generally long, and is managed in a segmented manner, the transmission line is divided into N segments, 8 types of evaluation indexes of the N segments of lines are evaluated respectively, a unique comment (low risk, medium risk or high risk) can be obtained according to a definite interval division value, and the following fuzzy matrix R can be constructed by using a fuzzy mathematical method:
in the formula: f. of ij Is a normalized value of the total number of times that the ith index is rated as the jth comment, i.e., f ij ＝v ij /n，v ij Is an actual evaluation value; j =1 represents low risk, j =2 represents medium risk, and j =3 represents high risk.
(2) And (3) a plurality of experts of power grid scheduling, overhauling, operation and maintenance are requested to sequence the importance of the 8 indexes, and the subjective weight of the indexes is obtained by using a sequence relation method.
(3) And calculating objective weights of all indexes by utilizing an entropy weight method principle according to the numerical difference of the fuzzy matrix of the n sections of leads.
(4) And solving the combination weight lambda by adopting a Lagrange multiplier method.
(5) Calculating a comprehensive evaluation result vector according to the fuzzy matrix and the combined weight
B＝λ·R T
(6) And performing comprehensive evaluation on the wire risk according to the evaluation vector, and judging the wire risk to be low risk, medium risk or high risk.
A lead fault risk assessment method based on an subjective and objective comprehensive weighting method comprises the following steps:
step 1: acquiring data related to wire equipment in a power grid management information system, wherein the data comprises technical parameters, GIS information, weather information, maintenance records and other information;
step 2: establishing an evaluation index set and a risk level of main influence factors of the wire fault based on the acquired information;
and step 3: respectively calculating subjective weight and objective weight of the evaluation index by using a sequence relation method and an entropy weight method;
and 4, step 4: carrying out weighted combination on the subjective weight and the objective weight, and carrying out optimized selection on a weighting coefficient;
and 5: and scoring each evaluation index of the wire, calculating a combined evaluation value of the evaluation indexes, and dividing the line into three grades of low risk, medium risk and high risk according to a calculation result.
Further, the step 1 specifically comprises:
acquiring data associated with wire equipment in a power grid management information system, including: technical parameters including the type, material, diameter and length of the wire equipment, the suspension height, the strand breakage condition, the maximum transmission capacity and the reference fault rate; GIS information, including local terrain and basic environmental conditions; weather information including temperature, wind speed, relative humidity, lightning strike and ice damage conditions; the maintenance records comprise fault elements, fault location and fault reasons; other information, animal exposure, human factors, pollution flashover and other conditions.
Further, the step 2 specifically includes:
and establishing an evaluation index set and a risk level of main influence factors of the wire fault based on the acquired information.
Selecting 8 types of evaluation index sets as main influence factors from the indexes, wherein the evaluation index sets are respectively as follows: benchmark failure rate, terrain conditions, wind speed, lightning strike, ice damage, maintenance records, animal contact and pollution flashover conditions. These factors are not suitable for being described by a continuous function model, and thus are described in a discretization manner.
The reference failure rate is an important technical parameter of the wire, and is also a general factor considering the failure of the wire, and is classified as follows: low risk (failure rate < 1X 10) -4 ) In case of stroke risk (1X 10) -4 The failure rate is less than or equal to 5 multiplied by 10 -4 ) High risk (failure rate ≥ 5 × 10) -4 )。
The terrain conditions mainly need to consider special conditions such as plateaus and mountains, and leads are more prone to failure in high-altitude areas, and are classified as follows: low risk (altitude less than 500 m), medium risk (altitude less than or equal to 500m and less than 1000 m) and high risk (altitude more than or equal to 1000 m).
Wind speed is also a major consideration, with wires being more prone to failure with higher wind speeds, classified as: low risk (wind speed is less than 50 mp/h), medium risk (wind speed is less than or equal to 50mp/h and less than 80 mp/h) and high risk (wind speed is more than or equal to 80 mp/h).
Lightning strike is an important factor causing line faults, lightning strike density is an important index for analyzing the severity of the lightning strike in a region, and the lightning strike density is classified into the following factors according to the lightning strike density: low risk (thunderbolt density < 1.05 pieces/(km) 2 A)), risk of stroke (1.05 counts/(km) 2 A) is more than or equal to the lightning fall density and less than 6.30 pieces per km 2 A)), high risk (lightning density ≥ 6.30/(km) 2 ·a))。
The formation of ice damage is complicated and is influenced by various factors such as air temperature, and the ice damage is classified into: low risk (icing thickness = 0), medium risk (0 < icing thickness < 15 mm), high risk (icing thickness ≧ 15 mm).
The maintenance records reflect the historical fault level of the line, and the fault level is classified into the following types according to the existing fault level of the line: low risk (failure frequency = 0), medium risk (0 < failure frequency < 3), high risk (failure frequency > 3).
Line faults caused by animal contact, particularly bird activity, account for a considerable proportion of many areas in China, and are classified according to the influence factors of animal contact: low risk (fault ratio is less than 3%), medium risk (fault ratio is more than or equal to 3% and less than or equal to 6%), and high risk (fault ratio is more than 6%).
The pollution flashover is related to a series of factors such as the withstand voltage, the humidity and the pollution levels of various regions of equipment, and the method adopts 2 indexes of equivalent salt density and relative humidity to define the pollution flashover level and classifies the pollution flashover level into the following classes: low risk (relative humidity < 75%, equivalent salt density < 0.02 mg/cm) 2 ) In case of risk (relative humidity < 75%,0.02 mg/cm) 2 Equivalent salt density is less than or equal to 0.05mg/cm 2 ) High risk (relative humidity is more than or equal to 75 percent or equivalent salt density is more than or equal to 0.05 mg/cm) 2 )。
Further, the step 3 specifically includes:
(1) Subjective weighting method based on sequence relation method
1) Determining order relationships
Let x 1 ,x 2 ,…,x n (n is more than or equal to 2) is n indexes in a multilayer index system, and for a certain evaluation criterion, when x is more than or equal to 2 i ≥x j When, the index x is explained i Is not less than x j . If there are n indexes x now 1 ,x 2 ,…,x n There is a relationship as shown below:
x 1 ≥x 2 ≥,…,≥x n
n indexes, x 1 ,x 2 ,…,x n The order relation is established according to 'not less'. The steps for determining the order relationship are as follows:
i) The expert is in the index set { x) according to the evaluation criterion 1 ,x 2 ,…,x n Selecting one index considered as the first important index and recording the index as the first important index
j) The expert selects one index which is considered as the first important index from the index set containing n-1 indexes and records the index as the first important index
k) After k times of selection, the expert selects one index which is considered as the first important index from the index set of the remaining n- (k-1) indexes and records the index as the first important index
l) after n-1 times of selection, recording the only index in the residual index set as
2) Quantitative analysis of importance of each index
Index x k-1 And x k Is w k-1 /w k And is recorded as:
w k-1 /w k ＝r k (k＝n,n-1,n-2,…,3,2)
in the formula: w is a k Subjective weight as index k; r is k The order relationship assignment of (1) is shown in table 1.
3) Calculation of subjective weights
Assignment by expert r k Calculating a subjective weight w k Comprises the following steps:
w k-1 ＝r k w k (k＝n,n-1,n-2,…,3,2)
obtaining the subjective weight w of the index based on expert decision according to the formula n Post-calculation of w 1 ～w n-1 。
TABLE 1 order relation assignment table
| r k | Description of the invention |
| 1.0 | x k-1 And x k Of equal importance |
| 1.5 | x k-1 Ratio x k Of obvious importance |
| 2.0 | x k-1 Ratio x k Of extreme importance |
(2) Objective weighting method based on entropy weight method
Entropy weight method by analyzing the information carried by the index dataAnd solving the objective weight of the index according to the information amount. m samples, n evaluation indexes, for a certain index j, if the index value x of the evaluation object ij And if the difference is obvious, the evaluation effect of the index is obvious, otherwise, the evaluation effect is not obvious.
1) Standardized matrix
2) Determining entropy of i index
k＝(lnm) -1 ,0＜e i ≤1
3) Determining objective weights for each index
Value h of utility of j-th index information i ＝1-e i Then the weight of the jth index is
Further, the step 4 specifically includes:
the index weight value obtained based on the subjective weighting of the order relation method has higher subjective reasonability, but the randomness of artificial subjective factors is higher, and objective information carried by the index is ignored; the entropy weight method calculates objective weight values of indexes by using original information of acquired data and combining corresponding mathematical principles and models, ignores the experience and decision of experts, and thus the obtained weight values may not be the same in practical situations. In view of the problems of the above 2 methods, a method for applying subjective weight w is proposed i And objective weight mu i And the method for combination optimization solves the optimal weighting coefficient, so that the weighted value of the index has the advantages of both subjective weight and objective weight, the obtained result is more real and effective, and the result accords with the actual condition.
Combining weight:
λ i ＝αw i +βu i
wherein: alpha beta is more than or equal to 0, alpha + beta =1,0 is more than or equal to alpha and less than or equal to 1,0 is more than or equal to beta and less than or equal to 1, and alpha and beta are subjective weight coefficients and objective weight coefficients respectively.
The level of agreement between the subjective and objective weighted attribute values of somewhere j:
in the formula: alpha w i x ij Subjectively weighting the attribute values; beta u i x ij Is an objective weighted attribute value.
In order to minimize the consistency level of the subjective and objective weighted attribute values, an optimization model is established by using a linear weighting method:
(α+β＝1,α·β≥0,0≤α≤1,0≤β≤1)
solving a binary optimization model of the weight coefficients alpha and beta by Lagrange multiplication to obtain a weighting coefficient of the subjective and objective weights of the evaluation index, and substituting the weighting coefficient into the formula to obtain a combined weight lambda of the evaluation index i 。
Further, the step 5 specifically includes:
(1) In practice, a transmission line is generally long, and is managed in a segmented manner, the transmission line is divided into N segments, 8 types of evaluation indexes of the N segments of lines are evaluated respectively, a unique comment (low risk, medium risk or high risk) can be obtained according to a definite interval division value, and the following fuzzy matrix R can be constructed by using a fuzzy mathematical method:
in the formula: f. of ij Is a normalized value of the total number of times that the ith index is rated as the jth comment, i.e., f ij ＝v ij /n，v ij Is an actual evaluation value; j =1 represents low risk, j =2 represents medium risk, and j =3 represents high risk.
(2) And (3) a plurality of experts of power grid scheduling, overhauling, operation and maintenance are requested to sequence the importance of the 8 indexes, and the subjective weight of the indexes is obtained by using a sequence relation method.
(3) And calculating objective weights of all indexes by using an entropy weight method principle according to the numerical difference of the fuzzy matrix of the n sections of leads.
(4) And solving the combination weight lambda by adopting a Lagrange multiplier method.
(5) Calculating a comprehensive evaluation result vector according to the fuzzy matrix and the combined weight
B＝λ·R T
(6) And performing comprehensive evaluation on the wire risk according to the evaluation vector, and judging that the wire risk is low risk, medium risk or high risk.
In the embodiment, a certain transmission conductor of a power grid in a certain area is adopted for digital twin modeling and safety evaluation, the type of the conductor is LGJ-400/35, the conductor is divided into 10 sections, and required conductor data information can be obtained from an asset management system, a geographic information system and the like.
The fuzzy matrix of 8 indexes is
The calculated subjective weight, objective weight and combination weight are shown in table 2.
Table 2 subjective, objective and combination weights of the indices
| Index (I) | Subjective weighting | Objective weight | Combining weights |
| Reference failure rate | 0.160 | 0.131 | 0.150 |
| Topographic condition | 0.067 | 0.049 | 0.061 |
| Wind speed | 0.155 | 0.144 | 0.151 |
| Lightning stroke | 0.209 | 0.309 | 0.243 |
| Damage by ice | 0.144 | 0.085 | 0.124 |
| Maintenance record | 0.076 | 0.104 | 0.086 |
| Animal contact | 0.075 | 0.073 | 0.074 |
| Pollution flashover conditions | 0.114 | 0.105 | 0.111 |
The finally obtained comprehensive evaluation vector is B = [0.4946,0.371,0.1344], and in conclusion, the comprehensive fault risk of the wire is considered to be low.
Claims (8)
1. A lead fault risk assessment method based on an subjective and objective comprehensive weighting method is characterized by comprising the following steps: the method comprises the following steps:
step 1: acquiring data information related to wire equipment in a power grid management information system;
step 2: establishing an evaluation index set and a risk level of the wire fault influence factors based on the acquired data information;
and 3, step 3: respectively calculating subjective weight and objective weight of the evaluation index by using a sequence relation method and an entropy weight method;
and 4, step 4: carrying out weighted combination on the subjective weight and the objective weight, and carrying out optimized selection on a weighting coefficient;
and 5: and scoring each evaluation index of the wire, calculating a combined evaluation value, and dividing the line into three grades of low risk, medium risk and high risk according to the calculation result.
2. The lead fault risk assessment method based on subjective and objective comprehensive empowerment method according to claim 1, characterized in that: step 1, the data information comprises:
the technical parameters are as follows: the method comprises the steps of determining the type, the material, the diameter and the length of a wire device, the suspension height, the strand breakage condition, the maximum transmission capacity and the reference fault rate;
and GIS information: including local terrain and basic environmental conditions;
weather information: including temperature, wind speed, relative humidity, lightning strike and ice damage conditions;
and (4) maintenance recording: including faulty components, fault location and fault causes;
other information, including animal exposure, human factors and pollution flashover.
3. The lead fault risk assessment method based on the subjective and objective comprehensive weighting method according to claim 1, characterized in that: the method for establishing the evaluation index set and the risk level in the step 2 comprises the following steps:
selecting 8 types of evaluation index sets as main influence factors from the data information, wherein the evaluation index sets comprise reference fault rate, terrain conditions, wind speed, lightning stroke, ice damage, maintenance records, animal contact and pollution flashover conditions;
the baseline failure rates are classified as: low risk, failure rate < 1X 10 -4 Middle risk, 1X 10 -4 Less than or equal to 5 multiplied by 10 fault rate -4 High risk, failure rate greater than or equal to 5 x 10 -4 ；
The wind speed is classified as: low risk, wind speed < 50mp/h; the wind speed is more than or equal to 50mp/h and less than 80mp/h; high risk, wind speed is more than or equal to 80mp/h;
lightning strikes are classified according to lightning strike density as: low risk, falling density < 1.05 pieces/(km) 2 A); risk of stroke is 1.05/(km) 2 A) is more than or equal to the lightning fall density and less than 6.30 pieces per km 2 A); high risk, the falling density is more than or equal to 6.30 pieces/(km) 2 ·a)；
Ice damage is classified according to ice thickness: low risk, icing thickness =0; the risk is moderate, the ice coating thickness is more than 0 and less than 15mm; the risk is high, and the ice coating thickness is more than or equal to 15mm;
the maintenance records are classified according to the existing fault level of the line as follows: low risk, number of failures =0; the number of faults is more than 0 and less than 3; high risk, the failure frequency is more than or equal to 3 times;
animal exposure is classified according to the influencing factors of animal exposure as: the risk is low, the fault proportion is less than 3 percent, the risk is medium, the fault proportion is more than or equal to 3 percent and less than or equal to 6 percent, the risk is high, and the fault proportion is more than 6 percent;
the pollution flashover adopts 2 indexes of equivalent salt density and relative humidity to define the grade of the pollution flashover, and the pollution flashover is classifiedComprises the following steps: low risk, relative humidity less than 75%, equivalent salt density less than 0.02mg/cm 2 (ii) a Medium risk, relative humidity < 75%,0.02mg/cm 2 Equivalent salt density is less than or equal to 0.05mg/cm 2 (ii) a High risk, relative humidity not less than 75% or equivalent salt density not less than 0.05mg/cm 2 。
4. The lead fault risk assessment method based on the subjective and objective comprehensive weighting method according to claim 1, characterized in that: the method for respectively calculating the subjective weight and the objective weight of the evaluation index by using the order relation method and the entropy weight method comprises the following steps:
step 3.1, subjective weighting method based on sequence relation method:
1) Determining an order relation:
let x 1 ,x 2 ,…,x n (n is more than or equal to 2) is n indexes in a multilayer index system, and for a certain evaluation criterion, when x is i ≥x j When, the index x is explained i Is not less than x j . If there are now n indices x 1 ,x 2 ,…,x n There is a relationship as shown below:
x 1 ≥x 2 ≥,…,≥x n
n indexes, x 1 ,x 2 ,…,x n Establishing an order relation according to 'not less than';
2) Quantitative analysis of importance degree of each index:
index x k-1 And x k Is w k-1 /w k And is recorded as:
w k-1 /w k ＝r k (k＝n,n-1,n-2,…,3,2)
in the formula: w is a k Is the subjective weight of the index k; r is k Assigning values to the order relation;
3) Calculation of subjective weight:
order relation assignment r k Calculating a subjective weight w k Comprises the following steps:
w k-1 ＝r k w k (k＝n,n-1,n-2,…,3,2)
obtaining the subjective weight w of the index n Post-calculation of w 1 ～w n-1 ；
Step 3.2, an entropy weight method-based objective weighting method:
the entropy weight method solves the objective weight of the index by analyzing the information quantity carried by the index data, and is provided with m samples and n evaluation indexes, and for a certain index j, the index value of an evaluation object is x ij ；
1) Standardized matrix
2) Determining entropy of i index
k＝(lnm) -1 ,0＜e i ≤1
3) Determining objective weights for each index
Value h of utility of j-th index information i ＝1-e i Then the weight of the j-th index is:
5. the lead fault risk assessment method based on the subjective and objective comprehensive weighting method according to claim 1, characterized in that: the sequence relation establishing steps are as follows:
a) The expert is in the index set { x) according to the evaluation criterion 1 ,x 2 ,…,x n Selecting one index considered as the first important index and recording the index as the first important index
b) The expert selects one index which is considered as the first important index from the index set containing n-1 indexes and records the index as the first important index
c) After k times of selection, the expert selects one index which is considered as the first important index from the index set of the remaining n- (k-1) indexes and records the index as the first important index
d) After n-1 times of selection, the only index in the residual index set is recorded as
6. The lead fault risk assessment method based on subjective and objective comprehensive empowerment method according to claim 1, characterized in that: step 4, the method for performing weighted combination on the subjective weight and the objective weight and performing optimized selection on the weighting coefficient comprises the following steps:
step 4.1, combining weight:
λ i ＝αw i +βu i
wherein: alpha and beta are more than or equal to 0, alpha + beta =1,0 is more than or equal to alpha and less than or equal to 1,0 is more than or equal to beta and less than or equal to 1, and the alpha and the beta are subjective weight coefficients and objective weight coefficients respectively;
the level of agreement between the subjective and objective weighted attribute values of somewhere j:
in the formula: alpha w i x ij Subjectively weighting the attribute values; beta u i x ij An objective weighted attribute value;
4.2, establishing an optimization model by using a linear weighting method:
(α+β＝1,α·β≥0,0≤α≤1,0≤β≤1)
solving a binary optimization model of the weight coefficients alpha and beta by Lagrange multiplication to obtain a weighting coefficient of the subjective and objective weights of the evaluation index, and substituting the weighting coefficient into the formula to obtain a combined weight lambda of the evaluation index i 。
7. The lead fault risk assessment method based on the subjective and objective comprehensive weighting method according to claim 1, characterized in that: the method for scoring each evaluation index of the lead and calculating the combined evaluation value comprises the following steps:
step 5.1, carrying out sectional management on the transmission conductors, setting the transmission conductors into N sections, respectively evaluating 8 types of evaluation indexes of the N sections of lines, and constructing the following fuzzy matrix R by using a fuzzy mathematical method:
in the formula: f. of ij Is a normalized value of the total number of times that the ith index is rated as the jth comment, i.e., f ij ＝v ij /n，v ij Is an actual evaluation value; j =1 represents low risk, j =2 represents medium risk, j =3 represents high risk;
step 5.2, the expert sorts the importance of the 8 indexes and obtains the subjective weight of the indexes by using a sequence relation method;
step 5.3, calculating objective weights of all indexes by utilizing an entropy weight method principle according to the numerical difference of the fuzzy matrix of the N sections of leads;
step 5.4, solving the combination weight lambda by adopting a Lagrange multiplier method;
step 5.5, calculating a comprehensive evaluation result vector according to the fuzzy matrix and the combined weight
B＝λ·R T 。
8. The lead fault risk assessment method based on the subjective and objective comprehensive weighting method according to claim 7, characterized in that: and performing comprehensive evaluation on the wire risk according to the evaluation vector, and judging the wire risk to be low risk, medium risk or high risk.
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