CN111930802A - Anti-electricity-stealing analysis method based on Lasso analysis - Google Patents
Anti-electricity-stealing analysis method based on Lasso analysis Download PDFInfo
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Abstract
The invention discloses an anti-electricity-stealing analysis method based on Lasso analysis, which comprises the following steps of firstly, obtaining all user electricity quantity freezing data of a whole station area and station area total meter electricity quantity freezing data from an acquisition system; step two, performing data preprocessing on all data, and performing interpolation processing on missing data; step three, subtracting the sum of all the user electricity consumption data from the station area general table data to obtain line loss values of each time period of the station area; step four, calculating the line loss of the transformer area and the regression coefficients of all the electric meters according to the Lasso regression model; step five, calculating the electricity stealing probability of each ammeter according to the line loss of the transformer area and the Lasso coefficient; and step six, positioning suspected electricity stealing users according to the electricity stealing probability. The method is simple to realize, only needs to acquire all user power consumption data and table area general table data of the whole table area, overcomes the influence of high interference of required data dimensionality, does not need artificial definition characteristics, and does not need to add excessive extra equipment.
Description
Technical Field
The invention relates to the field of distribution network automation systems, in particular to an anti-electricity-stealing analysis method based on Lasso analysis.
Background
Electric energy has become a necessity in social production and life, however, loss often occurs in the processes of power generation, power transmission and power distribution, and especially, increasing electricity stealing phenomena bring economic loss which is difficult to estimate. The consequences of power theft include a surge in power supply, overloading of the power system, a huge loss of revenue to the utility, and threats to public safety (e.g., fire and electric shock). Therefore, the research on the effective electricity stealing prevention detection technology has very practical significance for the development of the economic society.
Currently, data-driven-based electricity stealing detection methods attempt to find an abnormal pattern in the electricity usage information of all users. A large number of labeled sample sets are typically required to train the classifier, or sample data with both normal and power stealing behavior is necessary. However, it is very difficult and time consuming to establish a complete tagged normal and electricity stealing data set, especially for the latter, electricity stealing behavior is difficult to verify effectively so that the collected data tag cannot be judged accurately. Therefore, limited by the merits of the data set, the application of traditional pattern recognition modeling or cluster analysis methods is also subject to the elbow.
Disclosure of Invention
The invention aims at the problems, overcomes the defects of the prior art, provides an anti-electricity-stealing analysis method based on Lasso analysis,
in order to achieve the purpose, the invention adopts the following technical scheme:
an anti-electricity-stealing analysis method based on Lasso analysis comprises the following steps,
acquiring all user electric quantity freezing data and total table electric quantity freezing data of a whole station area from an acquisition system;
step two, performing data preprocessing on all data;
step three, subtracting the sum of all the user electricity consumption data from the station area general table data to obtain line loss values of each time period of the station area;
step four, calculating the line loss of the transformer area and the regression coefficients of all the electric meters according to the Lasso regression model;
step five, calculating the electricity stealing probability of each ammeter according to the line loss of the transformer area and the Lasso coefficient;
and step six, positioning suspected electricity stealing users according to the electricity stealing probability.
Further, in the fourth step, the line loss of the transformer area and the regression coefficients of all the electricity meters are calculated according to a Lasso regression model, wherein the optimization goal of the Lasso regression model is,
wherein y is a distribution area line loss vector, X is a power consumption matrix of all the electric meters, and lambda is a regular coefficient,
is a matrix of coefficients.
Furthermore, the regression calculation formula of the elastic net in the step six is as follows,
wherein y is the active power matrix of the branch, X is the active power matrix of the ammeter, lambda, gamma are regular coefficients,
is a matrix of coefficients.
Further, in the fifth step, the electricity stealing probability of each electric meter is calculated according to the line loss of the transformer area and the Lasso coefficient, wherein the calculation formula of the electricity stealing probability is as follows,
wherein beta represents a Lasso regression coefficient, alpha and gamma are coefficients of 0-1, the line loss of the v table region is more than 7% of the number of data points, and m represents the total number of data points.
Further, in the second step, data preprocessing is performed on all the data, and the preprocessing mode includes: carrying out interpolation processing on the missing data; remove > 50% of user data that is missing.
Furthermore, in the first step, the acquisition frequency of the frozen data is 30 min/time, the total acquisition success rate of the electric quantity of the transformer area is greater than 95%, and the acquisition success rate of the electric quantity of each meter is greater than 85%.
The invention has the beneficial effects that: the method solves the regression coefficient through the Lasso regression model, then calculates the electricity stealing probability of each ammeter according to the line loss of the transformer area and the Lasso coefficient, and finally positions suspected electricity stealing users according to the electricity stealing probability. The method is simple to realize, only needs to acquire all the power utilization data of the users in the whole cell and the data of the table area summary table, overcomes the influence of high interference of required data dimensionality, does not need artificial definition characteristics, and does not need to add excessive extra equipment.
Drawings
FIG. 1 is a general flow chart of the anti-electricity-stealing analysis method based on the Lasso analysis of the present invention.
Fig. 2 is a line loss diagram of the distribution room of the present invention at different time intervals.
FIG. 3 is a comparison graph of the total electricity quantity and the sum of the electricity quantities of the meters.
FIG. 4 is a plot of the Lasso regression coefficients for each table of the present invention.
Fig. 5 is a line loss fitting graph of each time period of the distribution room according to the present invention.
FIG. 6 is a graph showing the probability of electricity stealing for each meter of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings 1-6 and examples to illustrate the technical solutions of the present invention. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
With reference to fig. 1, the anti-electricity-stealing analysis method based on Lasso analysis of the present invention includes the following steps,
the method comprises the steps that firstly, all user electric quantity freezing data of a whole station area and total table electric quantity freezing data of the station area are obtained from an acquisition system, the acquisition frequency of the freezing data is 30 min/time, the total acquisition success rate of the station area electric quantity is greater than 95%, and the acquisition success rate of each table meter electric quantity is greater than 85%.
Step two, carrying out data preprocessing pretreatment on all the data, wherein the method comprises the following steps: carrying out interpolation processing on the missing data; remove > 50% of user data that is missing.
And step three, subtracting the sum of all the user electricity consumption data from the station area general table data to obtain line loss values of each time period of the station area, wherein a line loss graph of each time period is shown in fig. 2, and a comparison graph of the total table electricity quantity and the electricity quantity sum of each meter is shown in fig. 3.
Step four, fitting the line loss data by using the power consumption data of each user according to a Lasso regression model, and calculating the regression coefficients of the line loss of the transformer area and all the electric meters, wherein the optimization target of the Lasso regression model is,
wherein y is a distribution area line loss vector, X is a power consumption matrix of all the electric meters, and lambda is a regular coefficient,
is a matrix of coefficients. The obtained Lasso regression coefficient is shown in fig. 4, and the line loss fitting graph of each time interval of the station area is shown in fig. 5.
Step five, calculating the electricity stealing probability of each ammeter according to the transformer area line loss and the Lasso coefficient, wherein the calculation formula of the electricity stealing probability is as follows,
wherein beta represents a Lasso regression coefficient, alpha and gamma are coefficients of 0-1, the line loss of the v table region is more than 7% of the number of data points, and m represents the total number of data points. The graph of the probability of stealing electricity by each user is shown in figure 6.
And step six, positioning suspected electricity stealing users according to the electricity stealing probability.
In this embodiment: the anti-electricity-stealing analysis method based on the Lasso analysis is tested and verified by using the actual field data of a certain region. And the number of the tables is 61, 138 metering points are counted, wherein regression coefficients and power stealing probabilities of 4 users are calculated according to Lasso analysis and are larger, the users are listed as suspected power stealing users, and the calculation result is consistent with the actual power stealing situation on site.
In conclusion, the regression coefficient is solved through the Lasso regression model, then the electricity stealing probability of each ammeter is calculated according to the line loss of the transformer area and the Lasso coefficient, and finally suspected electricity stealing users are positioned according to the electricity stealing probability. The method is simple to realize, only needs to acquire all the power utilization data of the users in the whole cell and the data of the table area summary table, overcomes the influence of high interference of required data dimensionality, does not need artificial definition characteristics, and does not need to add excessive extra equipment.
The above embodiments are illustrative of specific embodiments of the present invention, and are not restrictive of the present invention, and those skilled in the relevant art can make various changes and modifications without departing from the spirit and scope of the present invention to obtain corresponding equivalent technical solutions, and therefore all equivalent technical solutions should be included in the scope of the present invention.
Claims (5)
1. An anti-electricity-stealing analysis method based on Lasso analysis is characterized by comprising the following steps:
acquiring all user electric quantity freezing data and total table electric quantity freezing data of a whole station area from an acquisition system;
step two, performing data preprocessing on all data;
step three, subtracting the sum of all the user electricity consumption data from the station area general table data to obtain line loss values of each time period of the station area;
step four, calculating the line loss of the transformer area and the regression coefficients of all the electric meters according to the Lasso regression model;
step five, calculating the electricity stealing probability of each ammeter according to the line loss of the transformer area and the Lasso coefficient;
and step six, positioning suspected electricity stealing users according to the electricity stealing probability.
2. The method of claim 1, wherein the power stealing prevention analysis method based on the Lasso analysis comprises: in the fourth step, the line loss of the transformer area and the regression coefficients of all the electric meters are calculated according to a Lasso regression model, wherein the optimization target of the Lasso regression model is,
wherein y is a distribution area line loss vector, X is a power consumption matrix of all the electric meters, and lambda is a regular coefficient,
is a matrix of coefficients.
3. The Lasso resolution-based anti-theft analysis method according to claim 1 or 2, wherein: step five, calculating the electricity stealing probability of each ammeter according to the transformer area line loss and the Lasso coefficient, wherein the electricity stealing probability calculation formula is as follows,
wherein beta represents a Lasso regression coefficient, alpha and gamma are coefficients of 0-1, the line loss of the v table region is more than 7% of the number of data points, and m represents the total number of data points.
4. The Lasso resolution-based anti-theft analysis method according to claim 1 or 2, wherein: and in the second step, data preprocessing is carried out on all the data, and the preprocessing mode comprises the following steps: carrying out interpolation processing on the missing data; remove > 50% of user data that is missing.
5. The Lasso resolution-based anti-theft analysis method according to claim 1 or 2, wherein: in the first step, the acquisition frequency of the frozen data is 30 min/time.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114548845A (en) * | 2022-04-27 | 2022-05-27 | 北京智芯微电子科技有限公司 | Distribution network management method, device and system |
| CN115330202A (en) * | 2022-08-15 | 2022-11-11 | 烟台东方威思顿电气有限公司 | Data-driven low-voltage distribution station area electricity stealing analysis method |
| CN116008714A (en) * | 2023-03-23 | 2023-04-25 | 青岛鼎信通讯股份有限公司 | Anti-electricity-stealing analysis method based on intelligent measurement terminal |
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Cited By (4)
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