CN112085333A - Power distribution network construction control index incidence relation research method based on incidence algorithm - Google Patents

Power distribution network construction control index incidence relation research method based on incidence algorithm Download PDF

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CN112085333A
CN112085333A CN202010785079.9A CN202010785079A CN112085333A CN 112085333 A CN112085333 A CN 112085333A CN 202010785079 A CN202010785079 A CN 202010785079A CN 112085333 A CN112085333 A CN 112085333A
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power distribution
distribution network
data
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CN112085333B (en
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于昊正
陈鹏浩
何永秀
郁晋雄
潘肇伦
孙慧君
王可蕙
李科
周鹏
郭新志
杨卓
李会涛
王利利
贾书艳
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North China Electric Power University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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North China Electric Power University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention relates to a power distribution network construction management and control index incidence relation research method based on an incidence algorithm, which comprises the following steps: step 1: collecting and controlling key index data in the whole process of a power distribution network construction project to form an index system; step 2: preprocessing data; and step 3: finding out association rules by using an Apriori algorithm; and 4, step 4: and analyzing the calculated association rule, and combing the association relation among the indexes in the index system. The construction data of the power distribution network has the characteristics of large volume, complex attributes, difficult collection and the like, is lack of data overall analysis, has low decision level, and disperses a large amount of data in different works, processes and systems. The relation between the determined power distribution network construction control indexes is difficult to determine, and the relation between the indexes is likely to have larger deviation only by experience judgment.

Description

Power distribution network construction control index incidence relation research method based on incidence algorithm
Technical Field
The invention belongs to the field of overall process management and control of power distribution network construction, is suitable for finding out an incidence relation between key management and control indexes, and particularly relates to a power distribution network construction management and control index incidence relation research method based on an incidence algorithm.
Background
The power distribution network has huge construction projects and different types, and a large amount of complex data relations are generated in the construction process. And forming a management and control index system of each stage of the project by combing key management and control indexes of the power distribution network construction project. And mining the incidence relation among all key indexes based on an Apriori algorithm so as to better realize the project group management of the power distribution network.
At present, the theory of the method for mining association rules based on the association algorithm is relatively mature, such as DHP algorithm and FP-Growth algorithm. However, when the DHP algorithm needs to spend a lot of time building the hash table and uses the number recorded by the hash hierarchy to estimate the support of the candidate item set, the support of some item sets is overestimated, resulting in a high false positive rate in the initial stage; the excavated result of the FP-Growth algorithm is too detailed, sometimes the result is not required to be so detailed, and in addition, the algorithm requires much time and space to construct an FP-tree in the excavating process; the Apriori algorithm uses the prior property, so that the efficiency of the layer-by-layer generation of the frequent item set is greatly improved. The number of power distribution network construction projects is huge, the types of the power distribution network construction projects are different, a large amount of complex data are generated in the construction process, and if the projects are managed one by one, a large amount of manpower is consumed, so that the cleaning of the association relation among key indexes of the power distribution network construction projects is very necessary for the management of the power distribution network construction projects, and no technology relates to the field at present.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a power distribution network construction management and control index association relation research method based on an association algorithm, and aims to analyze key management and control indexes of the whole process of construction of a plurality of power distribution network projects by using an Apriori algorithm, calculate the support degree, the confidence degree and the promotion degree among the indexes, compare the support degree, the confidence degree and the promotion degree with a set threshold value, mine the association relation in the indexes and provide reference for power distribution network construction project management.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a power distribution network construction management and control index incidence relation research method based on an incidence algorithm comprises the following steps:
step 1: collecting control key index data of the whole process of a power distribution network construction project to form an index system;
step 2: preprocessing data;
and step 3: finding out association rules by using an Apriori algorithm;
and 4, step 4: and analyzing the found association rule, and combing the association relation among the indexes in the index system.
On the basis of the scheme, the specific steps of the step 2 are as follows:
(1) deleting invalid records in the management and control key index data:
the data of the control key indexes are 0 or 1, 0 represents that the control key indexes are not deviated, 1 represents that the control key indexes are deviated, and the data which are not 0 or 1 are deleted;
(2) deleting the management and control key index data with missing values:
27 pieces of control key index data are drawn, and data with missing values are deleted in an index system.
On the basis of the scheme, the specific steps of the step 3 are as follows:
assuming that n power distribution network construction project data are collected, each project data comprises the completion condition of m control key indexes, and each of the n power distribution network construction project data records that at least one control key index in m control key indexes has deviation;
Figure BDA0002621648800000021
in the formula: x is the number ofijThe implementation condition of the jth index of the ith power distribution network construction project is shown; 1,2, …, n; j is 1,2, …, m;
calculating the support degree between the jth index and the kth index
Figure BDA0002621648800000031
Confidence level
Figure BDA0002621648800000032
Degree of lifting
Figure BDA0002621648800000033
As shown in formulas (2) to (6):
Figure BDA0002621648800000034
Figure BDA0002621648800000035
Figure BDA0002621648800000036
Figure BDA0002621648800000037
Figure BDA0002621648800000038
in the formula: sigmajkThe number of items is that the k index also has deviation when the j index has deviation in n power distribution network construction items, and k is 1,2, …, m; sigmak、σjRespectively representing the number of items with deviation of the kth index and the jth index in n power distribution network construction items; support (j) represents the support of the jth index; support (k) indicates the support of the kth index.
If the calculated support degree between the jth index and the kth index
Figure BDA0002621648800000039
Confidence level
Figure BDA00026216488000000310
Degree of lifting
Figure BDA00026216488000000311
If the three conditions of the formulas (7) to (9) are satisfied simultaneously, the k-th index is considered to have deviation when the j-th index is judged to have deviationThe probability of deviation of each index is increased, so that the k index is considered to be related to the j index; otherwise, the k index and the j index are considered to have no association relation;
Figure BDA00026216488000000312
Figure BDA00026216488000000313
Figure BDA00026216488000000314
in the formula, T1，T2，T3Respectively threshold values of support degree, confidence degree and promotion degree;
in the first iteration, calculating the support degree, the confidence degree and the promotion degree of each item in an index system, comparing the support degree, the confidence degree and the promotion degree with a set threshold value, and recording all found frequent item sets as 1-; in the 2 nd iteration, taking the 1-frequent item set as a seed item set of the 2 nd iteration so as to generate a candidate 2-item set; in the 2 nd iteration, in order to mine all 2-frequent item sets as seed item sets of the next iteration, the actual support degree, confidence degree and promotion degree of each candidate 2-item set need to be calculated and compared with a set threshold value, and the 2-frequent item sets are found out; the iterative process continues until a new set of frequent items cannot be generated.
The invention has the beneficial effects that:
the construction data of the power distribution network has the characteristics of large volume, complex attributes, difficult collection and the like, is lack of data overall analysis, has low decision level, and disperses a large amount of data in different works, processes and systems. The relation between the determined power distribution network construction control indexes is difficult to determine, and the relation between the indexes is likely to have larger deviation only by experience judgment.
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The invention has the following drawings:
FIG. 1 is a flow chart of the present invention.
FIG. 2 shows a raw index data preprocessing process.
Fig. 3Apriori algorithm flow chart.
FIG. 4 is a diagram of a power distribution network construction project management and control key index association relationship.
Detailed Description
The invention is described in further detail below with reference to figures 1-4.
A power distribution network construction management and control index incidence relation research method based on an incidence algorithm comprises the following steps:
step 1:
in the step 1, collecting management and control data of a power distribution network construction project to form an index system;
the remarks are as follows:
when collecting load data, the integrity and accuracy of the data are guaranteed as much as possible.
Step 2:
in step 2, the data preprocessing method is as follows, and the specific process is shown in fig. 2:
during data preprocessing, the correctness of the calculation result is affected by the absence or the mistiming of the index data, so the index data should be preprocessed before calculation, and the method is as follows:
(1) deleting invalid records in index data
The control key index data required by the research is 0 or 1. 0 indicates that the index has no deviation, and 1 indicates that the index has deviation. Data other than 0 or 1 is deleted.
(2) Deleting missing data
27 key management and control indexes are drawn, and data with missing indexes are deleted in an index system.
And step 3:
in step 3, the method for determining the power distribution network construction project management and control index association relationship comprises the following steps:
assuming that n power distribution network construction project data are collected, each project data comprises the completion condition of m key indexes, and each piece of n power distribution network construction project data records that at least one control key index in m has deviation;
Figure BDA0002621648800000051
in the formula: x is the number ofijThe implementation condition of the jth index of the ith power distribution network construction project is shown; 1,2, …, n; j is 1,2, …, m.
Calculating the support degree between the jth index and the kth index
Figure BDA0002621648800000052
Confidence level
Figure BDA0002621648800000053
Degree of lifting
Figure BDA0002621648800000054
As shown in formulas (2) to (6).
Figure BDA0002621648800000055
Figure BDA0002621648800000056
Figure BDA0002621648800000061
Figure BDA0002621648800000062
Figure BDA0002621648800000063
In the formula: sigmajkThe number of items is that the k index also has deviation when the j index has deviation in n power distribution network construction items, and k is 1,2, …, m; sigmak、σjRespectively representing the number of items with deviation of the kth index and the jth index in n power distribution network construction items; support (j) represents the support of the jth index; support (k) indicates the support of the kth index.
If the calculated support degree between the jth index and the kth index
Figure BDA0002621648800000064
Confidence level
Figure BDA0002621648800000065
Degree of lifting
Figure BDA0002621648800000066
If the three conditions of the equations (7) - (9) are satisfied simultaneously, the probability that the k-th index has deviation is increased when the j-th index has deviation, so that the k-th index is considered to be associated with the j-th index; otherwise, the k index and the j index are not associated.
Figure BDA0002621648800000067
Figure BDA0002621648800000068
Figure BDA0002621648800000069
In the formula, T1，T2，T3Respectively, support, confidence and boost thresholds.
In the first iteration, the support degree, the confidence degree and the promotion degree of each item in the index system are calculated and compared with the set threshold value, and all the frequent items found are set as 1-. In the 2 nd iteration, the 1-frequent item set is taken as a seed item set of the 2 nd iteration, so as to generate a candidate 2-item set. In the 2 nd iteration, in order to mine all 2-frequent item sets as the seed item set of the next iteration, the actual support degree, confidence degree and promotion degree of each candidate 2-item set need to be calculated and compared with the set threshold value to find out the 2-frequent item set. The iterative process continues until a new set of frequent items cannot be generated. The algorithmic process is shown in fig. 3.
And 4, step 4:
in step 4, the incidence relation between the key management and control indexes of the whole process of the power distribution network construction project is analyzed according to the incidence relation result calculated in the step 3. Finally, the relationship between indexes mined according to the mining is shown in FIG. 4.
Detailed description of the preferred embodiment
1528 records of management and control key index data of a power distribution network construction project are collected firstly in the research. And secondly, deleting invalid data samples from the index data samples. After data processing, 94 invalid sample records and 1434 valid sample records exist in the sample data.
The data processed sample records are input to an Apriori algorithm model. According to a set threshold value (T)1＝0.3、T2＝0.6、T31) and since a large number of correlation relationships are calculated, a correlation relationship including two indexes in association with the rationality of addressing is shown in table 1.
TABLE 1 address selection rationality association
Figure BDA0002621648800000071
It can be seen that in the association relation of item 1,
Figure BDA0002621648800000072
Figure BDA0002621648800000073
38% of records in the description item setThe address rationality is deviated and the design progress is deviated, and the confidence coefficient 0.79 indicates that 79% of the project records with deviation in the address rationality are also deviated in the design progress. A degree of lift greater than 1 indicates that the two are positively correlated, with a greater value being more correlated.
The examination and approval progress, the labor cost, the mechanical cost, the equipment operation efficiency and the efficiency of constructors are also related to the rationality of site selection, and when the rationality of site selection is deviated, indexes related to the rationality of site selection are more prone to be deviated.
When a power distribution network construction project is managed in the future, if a certain index has deviation, the associated index can be managed and controlled in advance, the influence on project implementation is reduced, and the effect of prior control is achieved.
The technical key points and points to be protected of the invention are as follows:
1. a power distribution network construction management and control index association relation research method based on an Apriori algorithm is provided. According to the determined control indexes of the power distribution network construction project, the Apriori association algorithm can dig out association relations among the control indexes from massive power distribution network construction data.
2. And excavating the association relation between the control indexes based on an Apriori algorithm, comparing and analyzing the association relation with the association relation existing in the experience, and judging whether the excavated association relation is reasonable or making up for the defects of the past experience. And a reference is provided for the future power distribution network construction project management and control.
Those not described in detail in this specification are within the skill of the art.

Claims (3)

1. A power distribution network construction management and control index incidence relation research method based on an incidence algorithm is characterized by comprising the following steps:
step 1: collecting control key index data of the whole process of a power distribution network construction project, and forming an index system;
step 2: preprocessing data;
and step 3: finding out association rules by using an Apriori algorithm;
and 4, step 4: and analyzing the found association rule, and combing the association relation among the indexes in the index system.
2. The method for researching the association relationship of the construction management and control indexes of the power distribution network based on the association algorithm as claimed in claim 1, wherein the specific steps in step 2 are as follows:
(1) deleting invalid records in the management and control key index data:
the data of the control key indexes are 0 or 1, 0 represents that the control key indexes are not deviated, 1 represents that the control key indexes are deviated, and the data which are not 0 or 1 are deleted;
(2) deleting the management and control key index data with missing values:
27 pieces of control key index data are drawn, and data with missing values are deleted in an index system.
3. The method for researching the association relationship of the construction management and control indexes of the power distribution network based on the association algorithm as claimed in claim 1, wherein the specific steps in step 3 are as follows:
assuming that n power distribution network construction project data are collected, each project data comprises the completion condition of m control key indexes, and each of the n power distribution network construction project data records that at least one control key index in m control key indexes has deviation;
Figure FDA0002621648790000011
in the formula: x is the number ofijThe implementation condition of the jth index of the ith power distribution network construction project is shown; 1,2, …, n; j is 1,2, …, m;
calculating the support degree between the jth index and the kth index
Figure FDA0002621648790000012
Confidence level
Figure FDA0002621648790000013
Degree of lifting
Figure FDA0002621648790000014
As shown in formulas (2) to (6):
Figure FDA0002621648790000021
Figure FDA0002621648790000022
Figure FDA0002621648790000023
Figure FDA0002621648790000024
Figure FDA0002621648790000025
in the formula: sigmajkThe number of items is that the k index also has deviation when the j index has deviation in n power distribution network construction items, and k is 1,2, …, m; sigmak、σjRespectively representing the number of items with deviation of the kth index and the jth index in n power distribution network construction items; support (j) represents the support of the jth index; support (k) indicates the support of the kth index.
If the calculated support degree between the jth index and the kth index
Figure FDA0002621648790000026
Confidence level
Figure FDA0002621648790000027
Degree of lifting
Figure FDA0002621648790000028
If the three conditions of the equations (7) - (9) are satisfied simultaneously, the probability that the k-th index has deviation is increased when the j-th index has deviation, so that the k-th index is considered to be associated with the j-th index; otherwise, the k index and the j index are considered to have no association relation;
Figure FDA0002621648790000029
Figure FDA00026216487900000210
Figure FDA00026216487900000211
in the formula, T1，T2，T3Respectively threshold values of support degree, confidence degree and promotion degree;
in the first iteration, calculating the support degree, the confidence degree and the promotion degree of each item in an index system, comparing the support degree, the confidence degree and the promotion degree with a set threshold value, and recording all found frequent item sets as 1-; in the 2 nd iteration, taking the 1-frequent item set as a seed item set of the 2 nd iteration so as to generate a candidate 2-item set; in the 2 nd iteration, in order to mine all 2-frequent item sets as seed item sets of the next iteration, the actual support degree, confidence degree and promotion degree of each candidate 2-item set need to be calculated and compared with a set threshold value, and the 2-frequent item sets are found out; the iterative process continues until a new set of frequent items cannot be generated.
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