CN110070111A - A kind of distribution line classification method and system - Google Patents
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Abstract
The invention discloses a kind of distribution line classification methods, including several distribution network line data of acquisition, and are divided into test sample collection, training sample set and verifying sample set；Based on training sample set, K central cluster algorithm and Decision Classfication algorithm are successively used, generates K member classifiers；Obtain several nearest-neighbors of each test sample in verifying sample set；K member classifiers are traversed, the nearest-neighbors of all test samples if member classifiers can correctly classify the member classifiers are added in optimum classifier set；Classified with optimum classifier collection to distribution line to be sorted.Also disclose corresponding system.The present invention solves the problems, such as that lower existing distribution line classification accuracy, low efficiency, robustness are bad.
Description
Technical field
The present invention relates to a kind of distribution line classification method and systems, belong to power grid operation field of automation technology.
Background technique
Requirement with user to power distribution network is higher and higher, and State Grid Corporation of China proposes that accelerating urban power distribution network builds in recent years
If transformation, makes great efforts to improve urban distribution network safety operation level, improve power quality, reduce line loss, while proposing optimization rack knot
Structure and raising city power distribution automation and management level.
One important component of distribution network construction is exactly the O&M of distribution line, the principle of O&M be " important equipment,
Emphasis O&M ", it is therefore desirable to grade assessment is carried out to distribution line, and the premise of grade assessment is divided distribution line
Class, the method used in existing power grid operational system is mainly include the following types: one, single classifier disaggregated model；Two, static point
Class device model；But the accuracy rate of these common methods is lower, low efficiency, robustness are bad.
Summary of the invention
The present invention provides a kind of distribution line classification method and system, solve existing distribution line classification accuracy compared with
The bad problem of low, low efficiency, robustness.
In order to solve the above-mentioned technical problem, the technical scheme adopted by the invention is that:
A kind of distribution line classification method, including,
Several distribution network line data are acquired, and are divided into test sample collection, training sample set and verifying sample set；
Based on training sample set, K central cluster algorithm and Decision Classfication algorithm are successively used, generates K member classifiers；
Obtain several nearest-neighbors of each test sample in verifying sample set；
Traverse K member classifiers, the nearest-neighbors of all test samples if member classifiers can correctly classify, this
Member classifiers are added in optimum classifier set；
Classified with optimum classifier collection to distribution line to be sorted.
Each sample of test sample collection, training sample set and verifying sample set is a distribution network line data, distribution
Cable circuit-switched data includes the put into operation time limit, monthly average load factor, monthly average line loss per unit, the annual number of stoppages, the year for being associated with distribution transforming
The number of stoppages, be associated with distribution transforming Rate of average load and whether include responsible consumer.
Generate K member classifiers process be,
Training sample set is divided using PAM, generates K training sample subset；
Using C4.5 sorting algorithm, K training subset is trained, generates K member classifiers.
Test sample is calculated to the Euclidean distance of each verifying sample, Euclidean distance is selected to be less than several verifying samples of threshold value
As nearest-neighbors.
A kind of distribution line categorizing system, including,
Acquisition module: several distribution network line data of acquisition, and be divided into test sample collection, training sample set and test
Demonstrate,prove sample set；
Member classifiers' generation module: being based on training sample set, is successively calculated using K central cluster algorithm and Decision Classfication
Method generates K member classifiers；
Nearest-neighbors obtain module: obtaining several nearest-neighbors of each test sample in verifying sample set；
Optimum classifier set generation module: K member classifiers of traversal, all surveys if member classifiers can correctly classify
The nearest-neighbors of sample sheet then the member classifiers are added in optimum classifier set；
Categorization module: classified with optimum classifier collection to distribution line to be sorted.
Each sample of test sample collection, training sample set and verifying sample set is a distribution network line data, distribution
Cable circuit-switched data includes the put into operation time limit, monthly average load factor, monthly average line loss per unit, the annual number of stoppages, the year for being associated with distribution transforming
The number of stoppages, be associated with distribution transforming Rate of average load and whether include responsible consumer.
Member classifiers' generation module includes PAM module and C4.5 module；
PAM module: dividing training sample set using PAM, generates K training sample subset；
C4.5 module: using C4.5 sorting algorithm, be trained to K training subset, generates K member classifiers.
Nearest-neighbors obtain the process that module obtains nearest-neighbors are as follows:
Test sample is calculated to the Euclidean distance of each verifying sample, Euclidean distance is selected to be less than several verifying samples of threshold value
As nearest-neighbors.
A kind of computer readable storage medium storing one or more programs, one or more of programs include referring to
Enable, described instruction when executed by a computing apparatus so that the calculatings equipment execution distribution line classification method.
A kind of calculating equipment, including one or more processors, memory and one or more program, one of them or
Multiple programs store in the memory and are configured as being executed by one or more of processors, one or more of
Program includes the instruction for executing distribution line classification method.
Advantageous effects of the invention: the present invention solve lower existing distribution line classification accuracy, low efficiency,
The bad problem of robustness.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is the comparison figure of three kinds of classification methods.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, a kind of distribution line classification method, comprising the following steps:
Step 1, several distribution network line data are acquired, and are divided into test sample collection, training sample set and verifying
Sample set.
Training sample set, test sample collection and verifying sample set proportion are 8:1:1, test sample collection, training sample
Each sample of collection and verifying sample set is a distribution network line data, and distribution network line data include putting into operation to put down the time limit, the moon
Equal load factor, monthly average line loss per unit, the annual number of stoppages, the annual number of stoppages for being associated with distribution transforming, the average load for being associated with distribution transforming
Rate and whether include responsible consumer.
Put into operation the time limit: obtaining distribution line date of putting into operation from production management system, put into operation the time limit=current run time-throwing
Transport the date；
Monthly average load factor: from dispatch automated system obtain real-time current, route is obtained from production management system
Rated current, load factor=real-time current/rated current；
Monthly average line loss per unit: distribution line this month loss electricity, of that month electricity, the moon are obtained from power information acquisition system
Average line loss per unit=this month loses electricity/this month electricity；
The annual number of stoppages: annual failure frequency is obtained from dispatch automated system；
It is associated with the annual number of stoppages of distribution transforming: obtaining distribution line from production management system and be associated with all distribution transforming information,
Annual failure frequency is obtained from dispatch automated system；
It is associated with the Rate of average load of distribution transforming: obtaining distribution line from production management system and be associated with all distribution transforming information, from
Load and capacity, load factor=load/capacity are obtained in power information acquisition system；
Whether include responsible consumer: obtaining distribution line from production management system and be associated with all distribution transforming information, from marketing
System is checked whether comprising responsible consumer.
Step 2, it is based on training sample set, successively uses K central cluster algorithm and Decision Classfication algorithm, generates K member
Classifier.
Detailed process are as follows: training sample set is divided using PAM, generates K training sample subset；Using C4.5 points
Class algorithm is trained K training subset, generates K member classifiers.
PAM is to carry out K central point clustering to training sample, according to Calinski-Harabasz criterion, by side between class
Difference, variance within clusters and complicated dynamic behaviour obtain K value, generate multiple sub- training sample sets.
PAM divides specific as follows:
(1) K training sample object arbitrarily is selected as initial representative object from training sample concentration；
It (2) will be in set representated by each remaining object assignment to nearest representative object；
(3) be randomly chosen one it is non-represent object, calculate the non-total cost for representing object exchange and representing objectWherein p is the point in space, for representative set CJIn non-represent object, OjTo represent set CJIn
Representative object, k be representative set CJIn represent number of objects；
(4) it if total cost is less than 0, is represented object replacement with non-and is represented object, and generated K and represent object set；
(5) (2), (3), (4) step are recycled, until each set no longer changes.
C4.5 sorting algorithm is a series of algorithms in the classification problem of machine learning and data mining, and target is
Supervised learning: giving a data set, each of these tuple can be described with one group of attribute value, each tuple belongs to
Certain in the classification of one mutual exclusion is a kind of；Target be by study, find a dependence value to classification mapping relations, and
This mapping can be used for the entity unknown to new classification and classify.
Step 3, several nearest-neighbors of each test sample in verifying sample set are obtained.
Detailed process are as follows: calculate test sample to the Euclidean distance of each verifying sample, Euclidean distance is selected to be less than threshold value
Several verifying samples are as nearest-neighbors.
Step 4, K member classifiers of traversal, the nearest-neighbors of all test samples if member classifiers can correctly classify,
Then the member classifiers are added in optimum classifier set.
Step 5, classified with optimum classifier collection to distribution line to be sorted.
According to the feature of distribution line to be sorted itself, from optimum classifier concentration be adaptive selected classifiers combination or
It assigns classifier weight to carry out final dynamic combined to classify.
In order to verify the above method, following experiment is done:
Each 1600 distribution lines operation data in 2018 of certain three province, training, verifying, survey as model are had chosen respectively
Data are tried, every distribution line picks the time limit that puts into operation, monthly average load factor, monthly average line loss per unit, the annual number of stoppages, association
The annual number of stoppages of distribution transforming, the Rate of average load for being associated with distribution transforming, whether including responsible consumer etc. totally seven characteristic variables.
Sample data is divided into training, verifying, three groups of test, and training dataset accounts for 80 the percent of total data sample, altogether
1280, test data set and validation data set respectively account for the 10 of sum, and each 160, and data set has been carried out discrete
Change processing.Experiment comparative approach includes the above method and model method conventional at present, i.e. dynamic combined classifies DDC-CD (i.e.
Method of the invention), single classifier method (C4.5), static combined method AdaBoost (be that member classifiers learn with C4.5
Algorithm).Three kinds of methods are compared in terms of data classification precision, classification effectiveness, robustness three respectively, specific such as table 1,
Shown in 2 and Fig. 2.
The nicety of grading of 1 three kinds of table square classification compares (%)
The classification robustness of 2 three kinds of table square classification compares (secondary)
Table 1 the result shows that, the nicety of grading of AdaBoost is better than C4.5, the nicety of grading of DCC-CD better than AdaBoost and
C4.5.This illustrates that DCC-CD can make full use of predictive information provided by base classifier, effectively improves nicety of grading, combination
Superiority is much larger than AdaBoost.
Fig. 2 the result shows that, classification effectiveness of the AdaBoost in three groups of data sources be better than C4.5, dynamically divide DCC-CD's
Classification effectiveness is better than AdaBoost and base classifier C4.5.
Furthermore the 10 affiliated sections of subseries accuracy rate of cross validation method are counted, accuracy rate interval selection is
(0, R-3) and (R+3,100), wherein R is Average Accuracy.From in table 2 it can be seen that C4.5 ten times verifying in have 5 times, and
AdaBoost has 3 times, and DCC-CD only has 1 time.The result shows that DCC-CD is better than C4.5 and AdaBoost in terms of robustness of classifying.
In conclusion the above method uses assembled classification, solve that existing distribution line classification accuracy is lower, efficiency
Problem low, robustness is bad.
A kind of distribution line categorizing system, comprising:
Acquisition module: several distribution network line data of acquisition, and be divided into test sample collection, training sample set and test
Demonstrate,prove sample set.Each sample of test sample collection, training sample set and verifying sample set is a distribution network line data, distribution
Cable circuit-switched data includes the put into operation time limit, monthly average load factor, monthly average line loss per unit, the annual number of stoppages, the year for being associated with distribution transforming
The number of stoppages, be associated with distribution transforming Rate of average load and whether include responsible consumer.
Member classifiers' generation module: being based on training sample set, is successively calculated using K central cluster algorithm and Decision Classfication
Method generates K member classifiers.
Member classifiers' generation module includes PAM module and C4.5 module；PAM module: using PAM to training sample set into
Row divides, and generates K training sample subset；C4.5 module: using C4.5 sorting algorithm, be trained to K training subset, raw
At K member classifiers.
Nearest-neighbors obtain module: obtaining several nearest-neighbors of each test sample in verifying sample set.
Nearest-neighbors obtain module obtain nearest-neighbors process are as follows: calculate test sample to it is each verify sample it is European away from
From, select Euclidean distance be less than threshold value several verifying samples as nearest-neighbors.
Optimum classifier set generation module: K member classifiers of traversal, all surveys if member classifiers can correctly classify
The nearest-neighbors of sample sheet then the member classifiers are added in optimum classifier set.
Categorization module: classified with optimum classifier collection to distribution line to be sorted.
A kind of computer readable storage medium storing one or more programs, one or more of programs include referring to
Enable, described instruction when executed by a computing apparatus so that the calculatings equipment execution distribution line classification method.
A kind of calculating equipment, including one or more processors, memory and one or more program, one of them or
Multiple programs store in the memory and are configured as being executed by one or more of processors, one or more of
Program includes the instruction for executing distribution line classification method.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above is only the embodiment of the present invention, are not intended to restrict the invention, all in the spirit and principles in the present invention
Within, any modification, equivalent substitution, improvement and etc. done, be all contained in apply pending scope of the presently claimed invention it
It is interior.
Claims (10)
1. a kind of distribution line classification method, it is characterised in that: including,
Several distribution network line data are acquired, and are divided into test sample collection, training sample set and verifying sample set；
Based on training sample set, K central cluster algorithm and Decision Classfication algorithm are successively used, generates K member classifiers；
Obtain several nearest-neighbors of each test sample in verifying sample set；
Traverse K member classifiers, the nearest-neighbors of all test samples if member classifiers can correctly classify, the member
Classifier is added in optimum classifier set；
Classified with optimum classifier collection to distribution line to be sorted.
2. a kind of distribution line classification method according to claim 1, it is characterised in that: test sample collection, training sample
Each sample of collection and verifying sample set is a distribution network line data, and distribution network line data include putting into operation to put down the time limit, the moon
Equal load factor, monthly average line loss per unit, the annual number of stoppages, the annual number of stoppages for being associated with distribution transforming, the average load for being associated with distribution transforming
Rate and whether include responsible consumer.
3. a kind of distribution line classification method according to claim 1, it is characterised in that: generate K member classifiers'
Process is,
Training sample set is divided using PAM, generates K training sample subset；
Using C4.5 sorting algorithm, K training subset is trained, generates K member classifiers.
4. a kind of distribution line classification method according to claim 1, it is characterised in that: calculate test sample to each verifying
The Euclidean distance of sample selects Euclidean distance to be less than several verifying samples of threshold value as nearest-neighbors.
5. a kind of distribution line categorizing system, it is characterised in that: including,
Acquisition module: several distribution network line data of acquisition, and it is divided into test sample collection, training sample set and verifying sample
This collection；
Member classifiers' generation module: being based on training sample set, successively uses K central cluster algorithm and Decision Classfication algorithm, raw
At K member classifiers；
Nearest-neighbors obtain module: obtaining several nearest-neighbors of each test sample in verifying sample set；
Optimum classifier set generation module: K member classifiers of traversal, all test specimens if member classifiers can correctly classify
This nearest-neighbors then the member classifiers are added in optimum classifier set；
Categorization module: classified with optimum classifier collection to distribution line to be sorted.
6. a kind of distribution line categorizing system according to claim 5, it is characterised in that: test sample collection, training sample
Each sample of collection and verifying sample set is a distribution network line data, and distribution network line data include putting into operation to put down the time limit, the moon
Equal load factor, monthly average line loss per unit, the annual number of stoppages, the annual number of stoppages for being associated with distribution transforming, the average load for being associated with distribution transforming
Rate and whether include responsible consumer.
7. a kind of distribution line categorizing system according to claim 5, it is characterised in that: member classifiers' generation module packet
Include PAM module and C4.5 module；
PAM module: dividing training sample set using PAM, generates K training sample subset；
C4.5 module: using C4.5 sorting algorithm, be trained to K training subset, generates K member classifiers.
8. a kind of distribution line categorizing system according to claim 5, it is characterised in that: nearest-neighbors obtain module and obtain
The process of nearest-neighbors is,
Calculate test sample to it is each verifying sample Euclidean distance, select Euclidean distance less than threshold value several verifying samples as
Nearest-neighbors.
9. a kind of computer readable storage medium for storing one or more programs, it is characterised in that: one or more of journeys
Sequence include instruction, described instruction when executed by a computing apparatus so that the calculatings equipment execution according to claim 1 to 4 institutes
Method either in the method stated.
10. a kind of calculating equipment, it is characterised in that: including,
One or more processors, memory and one or more programs, wherein one or more programs are stored in described deposit
It in reservoir and is configured as being executed by one or more of processors, one or more of programs include for executing basis
The instruction of method either in method described in Claims 1-4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111062608A (en) * | 2019-12-14 | 2020-04-24 | 贵州电网有限责任公司 | Line loss monitoring method for 10kV line based on line loss classifier |
| CN111277464A (en) * | 2020-01-20 | 2020-06-12 | 平安科技（深圳）有限公司 | Internet of things equipment connection testing method and device and computer readable storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108416369A (en) * | 2018-02-08 | 2018-08-17 | 同济大学 | Based on Stacking and the random down-sampled sorting technique of overturning, system, medium and equipment |
-
2019
- 2019-03-29 CN CN201910247484.2A patent/CN110070111A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108416369A (en) * | 2018-02-08 | 2018-08-17 | 同济大学 | Based on Stacking and the random down-sampled sorting technique of overturning, system, medium and equipment |
Non-Patent Citations (2)
| Title |
|---|
| 于海平: "基于动态分类组合的财务欺诈识别关键技术研究", 《中国优秀硕士学位论文全文数据库（电子期刊）工程科技II辑》 * |
| 范莹等: "一种新的基于模糊聚类的组合分类器算法", 《计算机应用》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111062608A (en) * | 2019-12-14 | 2020-04-24 | 贵州电网有限责任公司 | Line loss monitoring method for 10kV line based on line loss classifier |
| CN111277464A (en) * | 2020-01-20 | 2020-06-12 | 平安科技（深圳）有限公司 | Internet of things equipment connection testing method and device and computer readable storage medium |
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