CN111680074B - Clustering algorithm-based power acquisition load leakage point feature mining method - Google Patents
Clustering algorithm-based power acquisition load leakage point feature mining method Download PDFInfo
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- CN111680074B CN111680074B CN201911403417.1A CN201911403417A CN111680074B CN 111680074 B CN111680074 B CN 111680074B CN 201911403417 A CN201911403417 A CN 201911403417A CN 111680074 B CN111680074 B CN 111680074B
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses a clustering algorithm-based power acquisition load leakage point feature mining method, and relates to the field of power information acquisition. At present, in the power load data acquisition process, the condition that data acquisition is unsuccessful often appears, user load data is missing at certain moments, a leakage point phenomenon is formed, the leakage acquisition accuracy in the existing system is not high, the acquisition success rate is relatively low, and the information of the leakage point situation is not perfect. The method comprises the following steps: collecting load data; extracting leakage point data; extracting leakage point characteristic indexes through leakage point data; performing cluster analysis on the leakage point data indexes to form cluster category characteristics; and finally extracting the leakage point characteristics of the user. The leakage point result data and the acquisition process behavior data are analyzed through load acquisition, so that the leakage point condition of a user can be deeply mastered, the leakage point characteristics of equipment can be timely found, and the acquisition success rate is improved.
Description
Technical Field
The invention relates to the field of power information acquisition, in particular to a clustering algorithm-based power acquisition load leakage point feature mining method.
Background
In the power collection business, the collection of the user load data is a basic work in the power data collection, and has great significance in the collection of the user electric quantity load and other data. At present, in the process of collecting power load data, the condition that data collection is unsuccessful often occurs, and user load data is lost at certain moments to form a leakage point phenomenon. The leakage acquisition accuracy in the existing system is not high, the acquisition success rate is relatively low, and the information of the leakage point situation is not perfect.
Disclosure of Invention
The invention aims to solve the technical problems and the technical task of improving the prior art, and provides a clustering algorithm-based power acquisition load leakage point feature mining method, aiming at deeply grasping leakage point conditions and improving the success rate of leakage point acquisition. For this purpose, the present invention adopts the following technical scheme.
A clustering algorithm-based power acquisition load leakage point feature mining method comprises the following steps:
1) Collecting load data from an acquisition system;
2) Extracting corresponding metering point leakage point data;
3) Extracting leakage point characteristic indexes through leakage point data;
4) Analyzing the leakage point data index through a clustering algorithm to form clustering type characteristics of metering points;
5) And extracting the leakage point characteristic of each user through the clustering category characteristic of the metering points.
The leakage point result data and the acquisition process behavior data are analyzed through the load acquisition, the leakage point condition of a user can be deeply mastered, the leakage point characteristics of equipment can be timely found, the acquisition success rate is improved, references are provided for acquisition personnel, the acquisition work is convenient to develop, the auxiliary acquisition rate is improved, and the timely normal operation of the equipment is ensured.
As a preferable technical means: in the step 3), counting each index of each metering point of the leakage point data, wherein index content comprises the total number of the leakage points, the leakage point continuity characteristic, the leakage point week characteristic and the leakage point moment characteristic, the total number of the leakage points is used for counting the whole leakage point condition of the metering point, the leakage point continuity characteristic is used for describing the number of times of 2 or more continuous leakage points of the metering point and reflecting the serious condition of the leakage point, the leakage point week characteristic is used for describing the leakage point time law of the metering point in a week period, and the leakage point moment characteristic is used for describing the leakage point moment law of the metering point of the leakage point in 24 hours a day. The extraction of the characteristic index of the leakage point is effectively realized.
As a preferable technical means: in step 4), by a distance function
Wherein
Calculating the distance between each metering point by representing each calculation index of any two metering points, and gathering the points with similar distances together to form each characteristic category. And the clustering category characteristics of the metering points are effectively realized.
The beneficial effects are that: the leakage point result data and the acquisition process behavior data are analyzed through the load acquisition, the leakage point condition of a user can be deeply mastered, the leakage point characteristics of equipment can be timely found, the acquisition success rate is improved, references are provided for acquisition personnel, the acquisition work is convenient to develop, the auxiliary acquisition rate is improved, and the timely normal operation of the equipment is ensured.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the attached drawings.
As shown in fig. 1, the power collection load leakage point feature mining method based on the clustering algorithm comprises the following steps:
1) Acquisition load data of a user of the spot transformer is acquired from an acquisition system, wherein the acquisition load data of the user of the spot transformer is acquired from the current date for one month.
2) And matching the acquired metering point, time and related load data with the complete metering point and time to extract corresponding metering point leakage point data.
3) The method comprises the steps of summarizing data indexes of the leakage point data as input of analysis and mining, counting each index of each metering point in the past month, wherein index content comprises the total number of the leakage points, the continuity characteristic of the leakage points, the week characteristic of the leakage points and the time characteristic of the leakage points, the total number of the leakage points is used for counting the condition of the whole leakage point 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 point and reflecting the serious condition of the leakage point, the week characteristic of the leakage points is used for describing the time law of the leakage point of the metering point in a week period, the total number of the indexes is 7, the time characteristic of the leakage point is used for describing the time law of the leakage point of 24 hours in a day, and the total number of the leakage point is 24 leakage point indexes, and the following table is specific.
4) Analyzing the data indexes of the missing points through a clustering algorithm, carrying out clustering mining analysis on the indexes by using the clustering algorithm after counting each calculation index of the metering points to form clustering type characteristics of the metering points, and carrying out distance function
Wherein->
Calculating the distance between each metering point by representing each calculation index of any two metering points, and gathering the points with similar distances together to form each characteristic category. Each category is a specific embodiment of the calculation index, for example, a certain category feature may be continuous leak points in the past month, the proportion of the leak points in the Wednesday, and the proportion of the leak points in the 17 points.
5) And extracting the leakage point characteristics of each user through the clustering type characteristics of the metering points, wherein for example, the leakage point characteristics of the users under the category can be marked as the leakage point of the month is serious, the frequent leakage point is Tuesday and the frequent leakage point moment is 17 when one month is continuous leakage point, tuesday leakage point ratio is high and 17 leakage point ratio is high.
In this example, the clustering algorithm is an analysis process of grouping a collection of physical or abstract objects into multiple classes composed of similar objects, and by using a load leakage point acquisition result, through cluster analysis, the leakage point objects can be classified into different classes according to the similarity of leakage point features, leakage point information is extracted, and the load leakage point features are intuitively represented. The distribution rule of the load leakage points is studied currently, the number and the continuity of the leakage points of the object are analyzed from the existing leakage point data, the distribution rule is distributed along with time, and the leakage point condition is modeled by using a clustering algorithm according to different information.
The electric power collection load leakage point feature mining method based on the clustering algorithm shown in the figure 1 is a specific embodiment of the invention, has shown the outstanding essential characteristics and remarkable progress of the invention, can be subjected to equivalent modification in the aspects of shape, structure and the like according to actual use requirements, and is within the scope of protection of the scheme.
Claims (4)
1. The power acquisition load leakage point feature mining method based on the clustering algorithm is characterized by comprising the following steps of:
1) Collecting load data from an acquisition system;
2) Extracting corresponding metering point leakage point data;
3) Extracting leakage point characteristic indexes through leakage point data;
4) Analyzing the leakage point data index through a clustering algorithm to form clustering type characteristics of metering points;
5) Extracting the missing point characteristics of each user through the clustering category characteristics of the metering points;
in the step 3), counting each index of each metering point of the leakage point data, wherein index content comprises the total number of the leakage points, the leakage point continuity characteristic, the leakage point week characteristic and the leakage point moment characteristic, the total number of the leakage points is used for counting the whole leakage point condition of the metering point, the leakage point continuity characteristic is used for describing the number of times of 2 or more continuous leakage points of the metering point and reflecting the serious condition of the leakage point, the leakage point week characteristic is used for describing the leakage point time law of the metering point in a week period, and the leakage point moment characteristic is used for describing the leakage point moment law of the metering point of the leakage point in 24 hours a day.
2. The clustering algorithm-based power acquisition load leakage point feature mining method is characterized by comprising the following steps of: in the step 1), the period of acquiring the acquired load data from the acquisition system is that the current date is pushed back for at least one month.
3. The clustering algorithm-based power acquisition load leakage point feature mining method is characterized by comprising the following steps of: in the step 2), the acquired metering point, time and related load data are matched with the complete metering point and time, and corresponding metering point leakage point data are extracted from the metering point leakage point data.
4. The clustering algorithm-based power acquisition load leakage point feature mining method is characterized by comprising the following steps of: in step 4), by a distance function
To calculate the distance between each metering point, and to group together the points with similar distances to form each characteristic category, wherein (x i ，y i ) Representing the respective calculation index of any two metering points.
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