CN111680074A - Clustering algorithm-based electric power collection load leakage point feature mining method - Google Patents
Clustering algorithm-based electric power collection load leakage point feature mining method Download PDFInfo
- Publication number
- CN111680074A CN111680074A CN201911403417.1A CN201911403417A CN111680074A CN 111680074 A CN111680074 A CN 111680074A CN 201911403417 A CN201911403417 A CN 201911403417A CN 111680074 A CN111680074 A CN 111680074A
- Authority
- CN
- China
- Prior art keywords
- point
- leakage
- points
- data
- missing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a clustering algorithm-based electric power acquisition load leakage point feature mining method, and relates to the field of electric power information acquisition. At present, in the process of acquiring power load data, the condition of unsuccessful data acquisition often occurs, user load data is lost at certain moments to form a leakage point phenomenon, the electric leakage acquisition accuracy in the existing system is not high, the acquisition success rate is relatively low, and the information of mastering the leakage point condition is incomplete. The method comprises the following steps: collecting load data; extracting missing point data; extracting a missing point characteristic index through the missing point data; performing clustering analysis on the missing point data indexes to form clustering class characteristics; and finally extracting the missing point characteristics of the user. Through analyzing the load collection missing point result data and the collection process behavior data, the user missing point condition can be deeply mastered, the equipment missing point characteristics can be timely found, and the collection success rate is improved.
Description
Technical Field
The invention relates to the field of electric power information acquisition, in particular to an electric power acquisition load leakage point characteristic mining method based on a clustering algorithm.
Background
In the electric power collection service, the collection of user load data is a basic work in electric power data collection, and has great significance for the collection of user electric quantity loads and other data. At present, in the process of acquiring power load data, the condition of unsuccessful data acquisition often occurs, and the phenomenon of missing of user load data at certain moments occurs to form a missing point phenomenon. The current leakage collecting accuracy in the existing system is not high, the collecting success rate is relatively low, and the information for mastering the leakage point condition is incomplete.
Disclosure of Invention
The technical problem to be solved and the technical task to be solved by the invention are to perfect and improve the prior technical scheme, and provide a clustering algorithm-based electric power acquisition load leakage point characteristic mining method, aiming at deeply mastering the leakage point condition and improving the success rate of leakage point acquisition. Therefore, the invention adopts the following technical scheme.
A clustering algorithm-based electric power collection load leakage point feature mining method comprises the following steps:
1) collecting load data from a collection system;
2) extracting corresponding metering point missing point data;
3) extracting a missing point characteristic index through the missing point data;
4) analyzing the data indexes of the missing points through a clustering algorithm to form clustering class characteristics of the metering points;
5) and extracting the missing point characteristics of each user through the clustering category characteristics of the metering points.
The device has the advantages that the device can deeply master the leakage point condition of the user by analyzing the load collection leakage point result data and the collection process behavior data, the collection success rate is improved, reference is provided for collection personnel, collection work is convenient to carry out, the auxiliary collection rate is improved, and timely normal operation of the device is guaranteed.
As a preferable technical means: in step 3), counting indexes of each metering point of the leakage point data, wherein the index content comprises a total number of the leakage points, a continuity characteristic of the leakage points, a week characteristic of the leakage points and a moment characteristic of the leakage points, the total number of the leakage points is used for counting the whole leakage point condition of the metering point, the continuity characteristic of the leakage points is used for describing the number of times of 2 or more continuous leakage points of the metering points and reflecting the serious condition of the leakage points, the week characteristic of the leakage points is used for describing the time law of the leakage points of the metering points in a week period, and the moment characteristic of the leakage points is used for describing the moment law of the leakage points of the metering points of the leakage points 24 hours a day. And the extraction of the characteristic indexes of the missing points is effectively realized.
As a preferable technical means: in step 4), passing through the distance function
Wherein
And representing each calculation index of any two metering points to calculate the distance of each metering point, and gathering the points with similar distances together to form each characteristic category. Effectively realizing the clustering class characteristics of the metering points.
Has the advantages that: the device has the advantages that the device can deeply master the leakage point condition of the user by analyzing the load collection leakage point result data and the collection process behavior data, the collection success rate is improved, reference is provided for collection personnel, collection work is convenient to carry out, the auxiliary collection rate is improved, and timely normal operation of the device is guaranteed.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
As shown in fig. 1, a method for mining characteristics of a power collection load leakage point based on a clustering algorithm includes the following steps:
1) acquiring the collection load data of the past month of the specific transformer user from the current date from the collection system.
2) And extracting corresponding measuring point missing point data by matching the acquired measuring points, time and related load data with the complete measuring points and time.
3) The data indexes of the leakage point data are summarized to be used as input of analysis and mining, each index of each metering point in the past month is counted, the index content comprises total leakage points, continuity characteristics of the leakage points, week characteristics of the leakage points and time characteristics of the leakage points, the total leakage points are used for counting the whole leakage point condition of the metering points, the continuity characteristics of the leakage points are used for describing 2 times or more of continuous leakage points of the metering points and reflecting the serious condition of the leakage points, the week characteristics of the leakage points are used for describing the time law of the leakage points in one week period and are totally divided into 7 leakage point indexes, the time characteristics of the leakage points are used for describing the time law of the leakage points 24 hours a day, and are totally divided into 24 leakage point indexes, and the table is specifically shown in the following table.
4) Analyzing the data index of the leakage point by a clustering algorithm, counting each calculation index of the metering point, and then utilizing the clustering calculationThe method carries out clustering mining analysis on the indexes to form clustering classification characteristics of the metering points through a distance function
Wherein
And representing each calculation index of any two metering points to calculate the distance of each metering point, and gathering the points with similar distances together to form each characteristic category. Each category is specific to the calculation index, and for example, a certain category may be characterized by continuous missing points in the past month, a missing point occupation ratio of wednesday, and a missing point occupation ratio of 17 points.
5) And extracting the missing point characteristics of each user through the clustering category characteristics of the metering points, wherein for example, if a certain category characteristic is continuous missing points in the past month, the missing point occupancy ratio of the wednesday and the missing point occupancy ratio of 17 points, the user missing point characteristics in the category can be labeled as serious continuous missing points in the month, frequent missing points in the wednesday and frequent missing point moments in the 17 hours.
In this example, the clustering algorithm is an analysis process that groups a set of physical or abstract objects into a plurality of classes composed of similar objects, collects results of the load leakage points, and can classify the leakage point objects into different classes according to the similarity of the leakage point characteristics through clustering analysis, extract the leakage point information, and visually express the load leakage point characteristics. The distribution rule of load leakage points is researched currently, the quantity and the continuity of object leakage points are analyzed from the existing leakage point data, the distribution rule is distributed along with time, and the modeling is carried out on the leakage point condition according to different information by using a clustering algorithm.
The method for mining the characteristics of the power collection load leakage point based on the clustering algorithm shown in fig. 1 is a specific embodiment of the invention, has shown the outstanding substantive features and remarkable progress of the invention, and can modify the same in shape, structure and the like according to the practical use requirements and under the teaching of the invention, and the method is in the scope of protection of the scheme.
Claims (5)
1. A clustering algorithm-based electric power collection load leakage point feature mining method is characterized by comprising the following steps:
1) collecting load data from a collection system;
2) extracting corresponding metering point missing point data;
3) extracting a missing point characteristic index through the missing point data;
4) analyzing the data indexes of the missing points through a clustering algorithm to form clustering class characteristics of the metering points;
5) and extracting the missing point characteristics of each user through the clustering category characteristics of the metering points.
2. The clustering algorithm-based power collection load leakage point feature mining method according to claim 1, characterized in that: in the step 1), the time period for acquiring the acquired load data from the acquisition system is at least one month backward from the current date.
3. The clustering algorithm-based power collection load leakage point feature mining method according to claim 1, characterized in that: in the step 2), corresponding measurement point missing point data is extracted from the measurement point missing point data by matching the acquired measurement point, time and related load data with the complete measurement point and time.
4. The clustering algorithm-based power collection load leakage point feature mining method according to claim 1, characterized in that: in step 3), counting indexes of each metering point of the leakage point data, wherein the index content comprises a total number of the leakage points, a continuity characteristic of the leakage points, a week characteristic of the leakage points and a moment characteristic of the leakage points, the total number of the leakage points is used for counting the whole leakage point condition of the metering point, the continuity characteristic of the leakage points is used for describing the number of times of 2 or more continuous leakage points of the metering points and reflecting the serious condition of the leakage points, the week characteristic of the leakage points is used for describing the time law of the leakage points of the metering points in a week period, and the moment characteristic of the leakage points is used for describing the moment law of the leakage points of the metering points of the leakage points 24 hours a day.
5. The clustering algorithm-based power collection load leakage point feature mining method according to claim 1, characterized in that: in step 4), passing through the distance function
To calculate the distance of each metering point, and to group the points with similar distance together to form each characteristic class, wherein (A)
) Each calculation index representing any two measurement points.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911403417.1A CN111680074B (en) | 2019-12-31 | 2019-12-31 | Clustering algorithm-based power acquisition load leakage point feature mining method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911403417.1A CN111680074B (en) | 2019-12-31 | 2019-12-31 | Clustering algorithm-based power acquisition load leakage point feature mining method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111680074A true CN111680074A (en) | 2020-09-18 |
| CN111680074B CN111680074B (en) | 2023-07-04 |
Family
ID=72451247
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911403417.1A Active CN111680074B (en) | 2019-12-31 | 2019-12-31 | Clustering algorithm-based power acquisition load leakage point feature mining method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111680074B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756528A (en) * | 2023-08-18 | 2023-09-15 | 杭州鸿晟电力设计咨询有限公司 | Method, device, equipment and medium for complementing electricity load data |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102651116A (en) * | 2012-03-31 | 2012-08-29 | 上海市电力公司 | Power load data refining method |
| CN106447206A (en) * | 2016-10-09 | 2017-02-22 | 国网浙江省电力公司信息通信分公司 | Power utilization analysis method based on acquisition data of power utilization information |
| CN108681973A (en) * | 2018-05-14 | 2018-10-19 | 广州供电局有限公司 | Sorting technique, device, computer equipment and the storage medium of power consumer |
| CN108761196A (en) * | 2018-03-30 | 2018-11-06 | 国家电网公司 | A kind of intelligent electric meter user missing voltage data restorative procedure |
| JP2019054715A (en) * | 2017-09-15 | 2019-04-04 | 東京電力ホールディングス株式会社 | Power theft monitoring system, power theft monitoring device, power theft monitoring method and program |
| CN110022226A (en) * | 2019-01-04 | 2019-07-16 | 国网浙江省电力有限公司 | A kind of data collection system and acquisition method based on object-oriented |
| CN110188919A (en) * | 2019-04-22 | 2019-08-30 | 武汉大学 | A kind of load forecasting method based on shot and long term memory network |
-
2019
- 2019-12-31 CN CN201911403417.1A patent/CN111680074B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102651116A (en) * | 2012-03-31 | 2012-08-29 | 上海市电力公司 | Power load data refining method |
| CN106447206A (en) * | 2016-10-09 | 2017-02-22 | 国网浙江省电力公司信息通信分公司 | Power utilization analysis method based on acquisition data of power utilization information |
| JP2019054715A (en) * | 2017-09-15 | 2019-04-04 | 東京電力ホールディングス株式会社 | Power theft monitoring system, power theft monitoring device, power theft monitoring method and program |
| CN108761196A (en) * | 2018-03-30 | 2018-11-06 | 国家电网公司 | A kind of intelligent electric meter user missing voltage data restorative procedure |
| CN108681973A (en) * | 2018-05-14 | 2018-10-19 | 广州供电局有限公司 | Sorting technique, device, computer equipment and the storage medium of power consumer |
| CN110022226A (en) * | 2019-01-04 | 2019-07-16 | 国网浙江省电力有限公司 | A kind of data collection system and acquisition method based on object-oriented |
| CN110188919A (en) * | 2019-04-22 | 2019-08-30 | 武汉大学 | A kind of load forecasting method based on shot and long term memory network |
Non-Patent Citations (3)
| Title |
|---|
| CHENG Q 等: "Abnormal electricity consumption detection based on ensemble learning" * |
| 罗清雷 等: "基于增长模型的电力设备缺失数据筛查算法研究" * |
| 裴旭斌 等: "于蚁群算法改进One-Class SVM的电力离群用户检测算法研究" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756528A (en) * | 2023-08-18 | 2023-09-15 | 杭州鸿晟电力设计咨询有限公司 | Method, device, equipment and medium for complementing electricity load data |
| CN116756528B (en) * | 2023-08-18 | 2023-11-28 | 杭州鸿晟电力设计咨询有限公司 | Method, device, equipment and medium for complementing electricity load data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111680074B (en) | 2023-07-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104408547A (en) | Data-mining-based detection method for medical insurance fraud behavior | |
| CN109145097A (en) | A kind of judgement document's classification method based on information extraction | |
| CN102426590B (en) | Quality evaluation method and device | |
| CN103823896A (en) | Subject characteristic value algorithm and subject characteristic value algorithm-based project evaluation expert recommendation algorithm | |
| CN103605651A (en) | Data processing showing method based on on-line analytical processing (OLAP) multi-dimensional analysis | |
| CN107958043A (en) | A kind of electricity power engineering budget inventory automatic generation method | |
| CN103164537B (en) | A kind of method of search engine logs data mining of user oriented information requirement | |
| CN108021683A (en) | A kind of scale model retrieval implementation method based on three-dimensional labeling | |
| CN104992297A (en) | Electricity fee collection risk assessment device based on big data platform clustering algorithm and method thereof | |
| CN109033322A (en) | A kind of test method and device of multidimensional data | |
| CN105574265B (en) | Entire assembly model quantitative description towards model index | |
| CN106599138A (en) | Variety identification method for electrical appliances | |
| CN111427725A (en) | Big data storage system and method | |
| CN103955596A (en) | Accident hotspot comprehensive judging method based on traffic accident collection technology | |
| CN111680074A (en) | Clustering algorithm-based electric power collection load leakage point feature mining method | |
| CN107766983B (en) | Method for setting emergency rescue parking point of urban rail transit station | |
| CN104166756A (en) | Computation method for mass distribution of aircraft | |
| CN103700027A (en) | Method for achieving multi-dimensional bidirectional free-matching visual display of electric power data and graphics | |
| CN107844962A (en) | A kind of distribution Construction Cost Data based on standard data structure collects system | |
| CN104598713A (en) | Power grid theoretical line loss computation demand data synthesizing method | |
| CN105445577A (en) | Power quality disturbance source working condition identifying method | |
| CN102737254B (en) | Identification method of mark image | |
| CN107705194A (en) | Electricity power engineering project category Cost Comparison and Analysis method based on data analysis technique | |
| CN115587314A (en) | Platform region portrait drawing method based on cluster analysis method | |
| CN104200338A (en) | Line loss statistics and decision analysis system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |