CN116011825A - Multi-dimensional evaluation method for operation risk of distribution cable line - Google Patents
Multi-dimensional evaluation method for operation risk of distribution cable line Download PDFInfo
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- 238000013210 evaluation model Methods 0.000 claims abstract description 10
- 239000013598 vector Substances 0.000 claims description 29
- 239000011159 matrix material Substances 0.000 claims description 20
- 238000010606 normalization Methods 0.000 claims description 18
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention discloses a multi-dimensional evaluation method for the running risk of a distribution cable line, which comprises the steps of firstly establishing a multi-dimensional state characteristic data fusion algorithm of the distribution cable line, taking the loss of the running fault of the distribution cable line to a power grid as a standard for evaluating the running risk of the cable on the basis of the data fusion algorithm, setting the standard as four risks of serious loss, heavier loss, general loss and no loss, extracting different key characteristic values in different characteristic clusters, and forming a core characteristic evaluation index system for evaluating the risk level; then, a fuzzy analytic hierarchy process is introduced to the evaluation index system to form a loss evaluation model for the running risk of the distribution cable line; according to the method, risk evaluations of different dimensions are obtained from evaluation index systems of different dimensions, reliability and accuracy of operation risk evaluations of the distribution cable lines are effectively improved, risk tracking and management and control capabilities of the power cable are practically assisted, and operation, maintenance and overhaul decisions of the power cable are guided.
Description
Technical Field
The invention belongs to the technical field of power cable operation risk evaluation, and particularly relates to a multidimensional evaluation method for power distribution cable line operation risk.
Background
At present, the management strategy of the power cable circuit is mainly scheduled maintenance and post-maintenance, so that insulation defects and potential faults cannot be predicted in advance, and the running state and reliability of the cable cannot be timely and comprehensively known, so that the contradiction between the traditional scheduled maintenance mode and the construction of the strong smart grid is increasingly prominent.
At present, common comprehensive evaluation methods for researching the running risk of the distribution cable at home and abroad include expert scoring, probability risk evaluation (PRA), delphi, dynamic probability risk evaluation (DPRA), cost benefit method, analytic hierarchy process, association tree method, fuzzy comprehensive evaluation method, neural network and matter element analysis method and the like; however, the state evaluation of the cable is mainly focused on a certain index, the comprehensive evaluation of the cable state is less performed from multi-dimensions such as multi-index, multi-angle and the like, the state detection and evaluation technology of the cable has bottlenecks, and the defect characteristic rules under the condition that comprehensive factors influence the working condition cannot be systematically simulated.
In order to solve the above problems, it is necessary to develop a multidimensional evaluation method for the running risk of the distribution cable line.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a multidimensional evaluation method for the running risk of a distribution cable line, and by the method, the risk evaluation of different dimensions is obtained from an evaluation index system of different dimensions, so that the reliability and the accuracy of the running risk evaluation of the distribution cable line are effectively improved.
The purpose of the invention is realized in the following way: a multi-dimensional evaluation method for the running risk of a distribution cable line comprises the following steps:
firstly, a multidimensional state characteristic data fusion algorithm of a distribution cable line is established, loss of a distribution cable line operation fault to the cable is used as a standard of cable operation risk evaluation on the basis of the data fusion algorithm, four risk levels of serious loss, heavier loss, general loss and no loss are set, different key characteristic values in different characteristic clusters are extracted, and a core characteristic evaluation index system for evaluating the risk levels is formed;
then, a fuzzy analytic hierarchy process is introduced for a core characteristic evaluation index system to form a loss evaluation model for the running risk of the distribution cable line.
Preferably, the data fusion algorithm comprises the steps of constructing a multi-dimensional operation state characterization index set of the distribution cable line and extracting key indexes of the multi-dimensional operation state of the distribution cable line;
the construction of the multidimensional operation state characterization index set of the distribution cable line comprises the following steps: the initial index set is formed by analyzing the sensing data acquired by various sensors, the record data of the operation and detection workers and the specification data of the cable, and the index names and the index types are given as shown in the following table:
the key index extraction of the multidimensional operation state of the distribution cable circuit is specifically as follows: optimizing the index set by adopting a feature selection algorithm, and selecting a subset which enables the risk evaluation result to be optimal from the feature index set; firstly, carrying out normalization processing on feature index data, clustering all non-target features of a data set by calculating correlation between every two features and taking the feature correlation strength as a basis, and then selecting representative features from the non-target features;
by using
Representation feature->
Get the->
Probability of individual value ∈>
Representation feature->
Get the->
Probability of individual value->
Representation feature->
The value is +.>
Time characteristic value->
The value is +.>
Probability of->
Information entropy of->
Known as follows
After taking the value of->
Conditional information entropy of->
The calculation formula is as follows:
features (e.g. a character)
And->
Mutual information->
The calculation formula of (2) is as follows:
thus obtaining
And->
The calculation formula of the correlation between the two is as follows:
based on the above, the correlation between every two features in the table is obtained, and then the correlation value between every two features is obtained
Features greater than a threshold are classified into a class, and new index feature clusters are rearranged and summarized.
Further preferably, the loss evaluation model comprises forming a loss evaluation index set, constructing a judgment matrix, carrying out normalization processing on evaluation indexes, establishing a fuzzy evaluation comment set, constructing a fuzzy evaluation matrix, constructing a variable weight function, constructing an evaluation index variable weight vector and evaluating the self loss grade of line operation;
the loss evaluation index set specifically includes: forming a loss evaluation index set aiming at the running risk of a distribution cable line
N is the number of core feature evaluation indexes;
the construction judgment matrix specifically comprises: a judgment matrix a is constructed as follows:
in the method, in the process of the invention,
for the importance ratio of the ith evaluation index to the jth evaluation index,/for>
The values are shown in the following table:
calculating the weight vector by using a proportion scale method of 1-9 to obtain a Chang Quan vector W of the index 0 ；
The normalization processing of the evaluation index specifically comprises the following steps: normalizing the evaluation index by adopting data standardization, wherein the data standardization is one of minimum-maximum standardization, Z-score standardization and decimal standard standardization;
the comment set for establishing the fuzzy evaluation is specifically as follows: establishing fuzzy comment sets
，V 1 For the serious loss of the cable line, V 2 For heavier loss of the cable line, V 3 For general loss of cable line itself, V 4 No loss is caused to the cable line;
the construction of the fuzzy judgment matrix specifically comprises the following steps: setting a membership function corresponding to the loss state of the cable line, wherein the formula is as follows:
in the method, in the process of the invention,
normalized value for evaluation index, +.>
Threshold value for classifying four evaluation states of the cable plant, < >>
The values of (2) are 0, 0.4, 0.7, 0.9, 1, c2, c3 and c4 are the evaluation interval +.>
Is a midpoint of (2);
calculating the membership degree of each evaluation index by a membership function, wherein the membership degree of each evaluation index comprises
A fuzzy evaluation matrix R is obtained as follows:
in the method, in the process of the invention,
a fuzzy judgment vector of a single factor of the ith evaluation index, namely a membership value of the index;
combining all single-factor fuzzy judgment vectors to construct a fuzzy judgment matrix R aiming at all evaluation factors;
the construction variable weight function specifically comprises the following steps: constructing a variable weight function S, wherein the formula is as follows:
in the method, in the process of the invention,
；
the construction evaluation index weight-changing vector is specifically: constructing an evaluation index weight-changing vector W, wherein the formula is as follows:
wherein j=1, 2,3 … n, n is the total number of evaluation indexes, W 0 In order to evaluate the vector of the index Chang Quan,
changing weight vector for evaluation index>
Is a state variable weight vector;
the evaluation of the line operation self-loss level is specifically as follows: and calculating a loss evaluation value B of the operation risk of the cable line, wherein the calculation formula is as follows:
the loss level of the cable line running is evaluated, aiming at
The medium value sequence is used for correspondingly judging the loss level of the cable line as serious loss, heavy loss, general loss or no loss.
Due to the adoption of the technical scheme, the invention has the beneficial effects that: the invention adopts the data fusion algorithm, the data fusion algorithm can extract key indexes in the multidimensional operation state indexes of the distribution cable and use the key indexes for operation risk evaluation, thereby realizing comprehensive evaluation of the operation state of the power cable from a plurality of index angles, and the system evaluates defect characteristic rules under the condition that comprehensive factors influence; according to the invention, the fuzzy analytic hierarchy process is adopted to quantify the index and select the optimal scheme, so that the accuracy and reliability of the loss evaluation model of the cable line are improved, the risk tracking and management and control capability of the power cable are improved, and the operation, maintenance and overhaul decision of the power cable is guided by assistance.
Detailed Description
The technical scheme of the invention is further specifically described by the following examples.
A multi-dimensional evaluation method for the running risk of a distribution cable line comprises the following steps: firstly, a multidimensional state characteristic data fusion algorithm of a distribution cable line is established, the data fusion algorithm can fuse and extract related data of the cable line with different dimensions, loss of the distribution cable line operation fault to the cable is used as a standard of cable operation risk evaluation on the basis of the data fusion algorithm, the loss is set to be four risk levels of serious loss, heavy loss, general loss and no loss, different key characteristic values in different characteristic clusters are extracted, and a core characteristic evaluation index system for evaluating the risk levels is formed; then, a fuzzy analytic hierarchy process is introduced for a core characteristic evaluation index system to form a loss evaluation model for the running risk of the distribution cable line.
The data fusion algorithm comprises the steps of constructing a distribution cable line multidimensional operation state representation index set and extracting a distribution cable line multidimensional operation state key index.
The initial index set is formed by analyzing the sensing data acquired by various sensors, the record data of the operation and detection workers and the specification data of the cable, and the index names and the index types are given as shown in the following table:
optimizing the index set by adopting a feature selection algorithm, and selecting a subset which enables the risk evaluation result to be optimal from the feature index set; firstly, carrying out normalization processing on feature index data, clustering all non-target features of a data set by calculating correlation between every two features and taking the feature correlation strength as a basis, and then selecting representative features from the non-target features.
By using
Representation feature->
Get the->
Probability of individual value ∈>
Representation feature->
Get the->
The probability of the value of the code,
representation feature->
The value is +.>
Time characteristic value->
The value is +.>
Probability of->
Information entropy of->
Is known->
After taking the value of->
Conditional information entropy of->
The calculation formula is as follows:
features (e.g. a character)
And->
Mutual information->
The calculation formula of (2) is as follows:
thus obtaining
And->
The calculation formula of the correlation between the two is as follows:
based on the above, the correlation between every two features in the table is obtained, and then the correlation value between every two features is obtained
Features greater than a threshold are classified into a class, and new index feature clusters are rearranged and summarized. />
Aiming at multi-source heterogeneous information data of a cable running state, the invention surrounds a core task of evaluating the running state of a distribution cable line, constructs a multi-dimensional running state representation index set of the distribution cable line, and mainly comprises an on-line monitoring information subset of grounding current, joint temperature, partial discharge capacity and the like and an operating condition information subset of running current, running voltage, running years and the like; through the normalization processing of the evaluation indexes, an information entropy theory is introduced, the correlation between any two evaluation indexes in the index set is analyzed, and a new cable line running state evaluation index feature cluster is constructed; the method for defining the running state level of the distribution cable line is provided, the correlation between each index in the index feature cluster and the running state level of the distribution cable line is researched, the core feature parameters of each index feature cluster are extracted, further core feature data for evaluating the running state of the cable line are obtained, and finally a multidimensional state feature data fusion algorithm of the distribution cable line is formed.
The loss evaluation model comprises the steps of forming a loss evaluation index set, constructing a judgment matrix, carrying out normalization processing on evaluation indexes, establishing a fuzzy evaluation comment set, constructing a fuzzy judgment matrix, constructing a variable weight function, constructing an evaluation index variable weight vector and evaluating the self loss level of line operation.
Forming a loss evaluation index set aiming at the running risk of a distribution cable line
N is the number of core feature evaluation indexes.
A judgment matrix a is constructed as follows:
in the method, in the process of the invention,
for the importance ratio of the ith evaluation index to the jth evaluation index,/for>
The values are shown in the following table:
calculating the weight by using a proportion scale method of 1-9Vector, chang Quan vector W of index is obtained 0 。
And normalizing the evaluation index by adopting data normalization, wherein the data normalization is one of minimum-maximum normalization, Z-score normalization and decimal scale normalization.
Wherein, the min-max normalization is to perform linear transformation on the original data, map one original data of the data set to new data in the interval [0,1] through the min-max normalization, and the normalization formula is: new data= (original data-minimum)/(maximum-minimum).
Wherein, Z-score normalization is data normalization based on the mean and standard deviation of the raw data, and the normalization formula is: new data= (raw data-mean)/standard deviation, applicable to cases where maximum and minimum values are unknown or there is outlier data outside the range of values.
The decimal point calibration and standardization is performed by moving decimal points of data, and the movement of the decimal points depends on the maximum absolute value in the value of a data set, wherein a standardization formula is as follows: new data=original data/(10×j), j being the smallest integer satisfying the condition.
Establishing fuzzy comment sets
，V 1 For the serious loss of the cable line, V 2 For heavier loss of the cable line, V 3 For general loss of cable line itself, V 4 No loss for the cable line
Setting a membership function corresponding to the loss state of the cable line, wherein the formula is as follows:
in the method, in the process of the invention,
normalized value for evaluation index, +.>
Threshold value for classifying four evaluation states of the cable plant, < >>
The values of (2) are 0, 0.4, 0.7, 0.9, 1, c2, c3 and c4 are the evaluation interval +.>
Is defined by a central point of the lens.
Calculating the membership degree of each evaluation index by a membership function, wherein the membership degree of each evaluation index comprises
A fuzzy evaluation matrix R is obtained as follows:
in the method, in the process of the invention,
the fuzzy judgment vector is the single factor of the ith evaluation index, namely the membership value of the index.
And combining all single-factor fuzzy judgment vectors to construct a fuzzy judgment matrix R aiming at all the evaluation factors.
Constructing a variable weight function S, wherein the formula is as follows:
in the method, in the process of the invention,
。
constructing an evaluation index weight-changing vector W, wherein the formula is as follows:
wherein j=1, 2,3 … n, n is the total number of evaluation indexes, W 0 In order to evaluate the vector of the index Chang Quan,
changing weight vector for evaluation index>
Is a state variable weight vector.
On the basis of the evaluation index weight-changing vector W and the fuzzy evaluation matrix R, a loss evaluation value B of the cable line operation risk is calculated, and the calculation formula is as follows:
the loss level of the cable line running is evaluated, aiming at
The medium value sequence is used for correspondingly judging the loss level of the cable line as serious loss, heavy loss, general loss or no loss.
Finally, aiming at the judged risk level of loss of the four cable lines, namely serious loss, heavy loss, general loss or no loss, different overhaul decisions are adopted to assist the operation and maintenance overhaul work of the power cable.
According to the invention, based on a multidimensional state characteristic data fusion algorithm of the distribution cable line, risk evaluation characteristics of different dimensions are defined, and an optimized multidimensional evaluation index system of the distribution cable line operation loss risk is established through correlation analysis between the distribution cable line operation characteristic parameters and the risk evaluation characteristics of different dimensions; and forming a distribution cable line operation risk local loss evaluation index system by introducing a fuzzy analytic hierarchy process and researching the influence analysis of each cable line operation state parameter on the loss thereof, researching a distribution cable line operation risk loss evaluation model based on a variable weight function, realizing a distribution cable line operation risk multidimensional evaluation system and forming a distribution cable line operation risk multidimensional evaluation model.
Finally, it should be noted that the above-mentioned embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the specific embodiments of the present invention without departing from the spirit and scope of the present invention, and any modifications and equivalents are intended to be encompassed in the scope of the claims of the present invention.
Claims (3)
1. The multidimensional evaluation method for the running risk of the distribution cable line is characterized by comprising the following steps of:
firstly, a multidimensional state characteristic data fusion algorithm of a distribution cable line is established, loss of a distribution cable line operation fault to the cable is used as a standard of cable operation risk evaluation on the basis of the data fusion algorithm, four risk levels of serious loss, heavier loss, general loss and no loss are set, different key characteristic values in different characteristic clusters are extracted, and a core characteristic evaluation index system for evaluating the risk levels is formed;
then, a fuzzy analytic hierarchy process is introduced for a core characteristic evaluation index system to form a loss evaluation model for the running risk of the distribution cable line.
2. The distribution cable line operation risk multi-dimensional evaluation method according to claim 1, wherein: the data fusion algorithm comprises the steps of constructing a distribution cable line multidimensional operation state representation index set and extracting a distribution cable line multidimensional operation state key index;
the construction of the multidimensional operation state characterization index set of the distribution cable line comprises the following steps: the initial index set is formed by analyzing the sensing data acquired by various sensors, the record data of the operation and detection workers and the specification data of the cable, and the index names and the index types are given as shown in the following table:
the key index extraction of the multidimensional operation state of the distribution cable circuit is specifically as follows: optimizing the index set by adopting a feature selection algorithm, and selecting a subset which enables the risk evaluation result to be optimal from the feature index set; firstly, carrying out normalization processing on feature index data, clustering all non-target features of a data set by calculating correlation between every two features and taking the feature correlation strength as a basis, and then selecting representative features from the non-target features;
by using
Representation feature->
Get the->
Probability of individual value ∈>
Representation feature->
Get the->
The probability of the value of the code,
representation feature->
The value is +.>
Time characteristic value->
The value is +.>
Probability of->
Information entropy of->
Is known->
After taking the value of->
Conditional information entropy of->
The calculation formula is as follows:
features (e.g. a character)
And->
Mutual information->
The calculation formula of (2) is as follows:
thus obtaining
And->
The calculation formula of the correlation between the two is as follows:
based on the above, the correlation between every two features in the table is obtained, and then the correlation value between every two features is obtained
Features greater than a threshold are classified into a class, and new index feature clusters are rearranged and summarized.
3. The distribution cabling run risk multi-dimensional evaluation method according to claim 2, wherein: the loss evaluation model comprises the steps of forming a loss evaluation index set, constructing a judgment matrix, carrying out normalization processing on evaluation indexes, establishing a fuzzy evaluation comment set, constructing a fuzzy judgment matrix, constructing a variable weight function, constructing an evaluation index variable weight vector and evaluating the self loss level of line operation;
the loss evaluation index set specifically includes: forming a loss evaluation index set aiming at the running risk of a distribution cable line
N is the number of core feature evaluation indexes;
the construction judgment matrix specifically comprises: a judgment matrix a is constructed as follows:
in the method, in the process of the invention,
for the importance ratio of the ith evaluation index to the jth evaluation index,/for>
The values are shown in the following table:
calculating the weight vector by using a proportion scale method of 1-9 to obtain a Chang Quan vector W of the index 0 ；
The normalization processing of the evaluation index specifically comprises the following steps: normalizing the evaluation index by adopting data standardization, wherein the data standardization is one of minimum-maximum standardization, Z-score standardization and decimal standard standardization;
the comment set for establishing the fuzzy evaluation is specifically as follows: establishing fuzzy comment sets
，V 1 For the serious loss of the cable line, V 2 For heavier loss of the cable line, V 3 For general loss of cable line itself, V 4 No loss is caused to the cable line;
the construction of the fuzzy judgment matrix specifically comprises the following steps: setting a membership function corresponding to the loss state of the cable line, wherein the formula is as follows:
in the method, in the process of the invention,
normalized value for evaluation index, +.>
Threshold value for classifying four evaluation states of the cable plant, < >>
The values of (a) are respectively 0, 0.4, 0.7, 0.9, 1 and c 1 、c 2 、c 3 、c 4 Respectively are evaluation intervals
Is a midpoint of (2);
calculating the membership degree of each evaluation index by a membership function, wherein the membership degree of each evaluation index comprises
A fuzzy evaluation matrix R is obtained as follows: />
In the method, in the process of the invention,
a fuzzy judgment vector of a single factor of the ith evaluation index, namely a membership value of the index;
combining all single-factor fuzzy judgment vectors to construct a fuzzy judgment matrix R aiming at all evaluation factors;
the construction variable weight function specifically comprises the following steps: constructing a variable weight function S, wherein the formula is as follows:
in the method, in the process of the invention,
；
the construction evaluation index weight-changing vector is specifically: constructing an evaluation index weight-changing vector W, wherein the formula is as follows:
wherein j=1, 2,3 … n, n is the total number of evaluation indexes, W 0 In order to evaluate the vector of the index Chang Quan,
changing weight vector for evaluation index>
Is a state variable weight vector;
the evaluation of the line operation self-loss level is specifically as follows: and calculating a loss evaluation value B of the operation risk of the cable line, wherein the calculation formula is as follows:
the loss level of the cable line running is evaluated, aiming at
The medium value sequence is used for correspondingly judging the loss level of the cable line as serious loss, heavy loss, general loss or no loss. />
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117214587A (en) * | 2023-11-07 | 2023-12-12 | 国网浙江省电力有限公司象山县供电公司 | Detection method and detection system for cable equipment |
| CN117767124A (en) * | 2023-11-10 | 2024-03-26 | 江苏诺金电气科技有限公司 | High-low voltage power distribution cabinet with fireproof monitoring function |
-
2023
- 2023-02-10 CN CN202310096301.8A patent/CN116011825A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117214587A (en) * | 2023-11-07 | 2023-12-12 | 国网浙江省电力有限公司象山县供电公司 | Detection method and detection system for cable equipment |
| CN117214587B (en) * | 2023-11-07 | 2024-03-29 | 国网浙江省电力有限公司象山县供电公司 | Detection method and detection system for cable equipment |
| CN117767124A (en) * | 2023-11-10 | 2024-03-26 | 江苏诺金电气科技有限公司 | High-low voltage power distribution cabinet with fireproof monitoring function |
| CN117767124B (en) * | 2023-11-10 | 2024-05-17 | 江苏诺金电气科技有限公司 | High-low voltage power distribution cabinet with fireproof monitoring function |
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