CN112215479B - Electric meter anti-electricity-stealing analysis method based on self-adaptive shrinkage ridge regression - Google Patents

Electric meter anti-electricity-stealing analysis method based on self-adaptive shrinkage ridge regression Download PDF

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CN112215479B
CN112215479B CN202011032743.9A CN202011032743A CN112215479B CN 112215479 B CN112215479 B CN 112215479B CN 202011032743 A CN202011032743 A CN 202011032743A CN 112215479 B CN112215479 B CN 112215479B
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范建华
曹乾磊
王磊
梁浩
徐体润
彭绍文
张长帅
张乐群
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Abstract

The invention discloses an electric meter anti-electricity-stealing analysis method based on self-adaptive shrinkage ridge regression, which comprises the following steps of: acquiring daily freezing data of the electric quantity of users in the whole transformer area and daily freezing data of the electric quantity of an assessment total table in the transformer area from an electric information acquisition system; subtracting the sum of the daily freezing data of each user electric meter in all user areas from the daily freezing data of the examination total table of the areas to obtain an area line loss value curve; defining an objective function of the line loss value and daily freezing data of all sub-tables in the transformer area, and calculating an initial solution of an estimation coefficient according to a ridge regression model; contracting the initial solution of the estimation coefficient through a self-adaptive iteration process to obtain a final solution of the estimation coefficient; calculating a Pearson correlation coefficient of a user regression fitting curve and a line loss value curve; and judging suspected electricity stealing users according to the values of the Pearson correlation coefficient and the estimation coefficient. According to the invention, no additional equipment is needed, and the anti-electricity-stealing analysis can be carried out by only acquiring the data of the user ammeter and the distribution room general table, so that the method is easy to realize, and the time and the economic cost are saved.

Description

Electric meter anti-electricity-stealing analysis method based on self-adaptive shrinkage ridge regression
Technical Field
The invention relates to the technical field of distribution network automation, in particular to an electric meter anti-electricity-stealing analysis method based on self-adaptive shrinkage ridge regression.
Background
Nowadays, electricity becomes a necessary energy source in social production life, however, electric energy loss often occurs in the processes of power generation, power transmission and power distribution, especially the phenomenon of increasing electricity stealing, which brings about economic loss which is difficult to estimate. The consequences of electricity theft include a surge in the supply of electricity, overloading of the power system, a significant loss to the utility, and frequent occurrences of public safety threats such as 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.
The conventional electricity stealing detection method includes checking installation or configuration of a suspicious electric meter, comparing readings of an abnormal electric meter with readings of a normal electric meter, checking a bypass power line, installing a specific detection device, and the like. However, these methods are very time consuming, expensive and inefficient and do not match the demands of today's large-scale electricity usage. In recent years, the construction and development of strong smart grids and ubiquitous power internet of things enable mass power utilization data to be collected and stored. Therefore, more intelligent electricity stealing detection methods are receiving increasing attention.
Disclosure of Invention
Aiming at the problems, the invention overcomes the defects of the prior art, provides an electric meter anti-electricity-stealing analysis method based on self-adaptive shrinkage ridge regression, solves and estimates a regression coefficient through a self-adaptive shrinkage ridge regression model, and then positions suspected electricity-stealing users according to the similarity and regression coefficient of a regression fitting line loss curve of the users and an actual line loss curve. According to the method, additional equipment is not needed, and the anti-electricity-stealing analysis can be carried out only by acquiring data of the district user electricity meter and the district general meter.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
an electric meter anti-electricity-stealing analysis method based on self-adaptive shrinkage ridge regression comprises the following steps:
step 1, acquiring daily freezing data of user electric quantity of a whole transformer area and daily freezing data of electric quantity of a transformer area assessment summary table from an electric quantity information acquisition system;
step 2, subtracting the sum of daily freezing data of each user electric meter in the distribution area from daily freezing data of a distribution area assessment total meter to obtain a distribution area line loss value curve;
step 3, defining a target function of the line loss value and all sub-table daily freezing data of the transformer area, and calculating an estimation coefficient initial solution according to a ridge regression model;
step 4, shrinking the initial solution of the estimation coefficient through a self-adaptive iteration process to obtain a final solution of the estimation coefficient;
step 5, calculating a Pearson correlation coefficient of a user regression fitting line loss curve and an actual line loss value curve;
and 6, judging suspected electricity stealing users according to the values of the Pearson correlation coefficient and the estimation coefficient.
Further, the number of data days acquired in the step 1 is more than or equal to two times of the number of users in the distribution area.
Further, the calculation formula of the platform area line loss value curve in the step 2 is as follows:
Figure BDA0002704236830000011
wherein ltDenotes the line loss value, y, of the t-th daytDenotes the assessment summary reading, x, on day ttiAnd (4) representing the meter reading of the ith user on the t day, wherein m is the number of the users in the region.
Further, the objective function defined in step 3 is as follows:
Figure BDA0002704236830000021
writing in a matrix form is:
Figure BDA0002704236830000022
wherein
Figure BDA0002704236830000023
Representing the estimated coefficients (the factor of electricity stealing), the superscripts (k +1) and (k) representing the (k +1) th and kth iterations, respectively, n representing the number of days of data acquisition, the subscript i representing the ith user, the vector L ∈ RnRepresenting the line loss value curve, the matrix X belongs to Rm×nData representing daily freeze of users in a distribution area, theta(k)The adaptive shrinkage coefficient matrix representing the kth iteration, namely:
Figure BDA0002704236830000024
the degradation in the initial solution is a general ridge regression model, namely:
Figure BDA0002704236830000025
the estimation coefficient initial solution is obtained at this time:
Figure BDA0002704236830000026
where E denotes an identity matrix.
Further, the process of adaptively contracting the initial solution in the step 4 is as follows:
step S1: calculating an adaptive shrinkage coefficient matrix from the solution of the kth iteration
Figure BDA0002704236830000027
Step S2: computing new solutions
Figure BDA0002704236830000028
Step S3: repeating the steps S1 and S2 when the iteration number k is k +1, and if the 2 norm between the two adjacent calculation solutions is less than 10-3Stopping iteration, and outputting the final solution at the moment, which is recorded as beta ═ beta1，β2，…，βm]T。
Further, in step 5, the pearson correlation coefficient calculation formula of the user regression fitting line loss curve and the actual line loss value curve is as follows:
Figure BDA0002704236830000029
wherein f istRegression for day t userThe fitting value is calculated by the formula:
Figure BDA00027042368300000210
Figure BDA00027042368300000211
and
Figure BDA00027042368300000212
the mean values of the fitted curve and the line loss curve, respectively.
Further, the process of determining the suspected electricity stealing user in step 6 is as follows: firstly, evaluating the reliability of a calculation result according to the magnitude of the Pearson correlation coefficient calculated in the step 5, and considering that the data is possibly abnormal when rho is less than or equal to 0.6, the calculation result is not reliable, and the calculation is stopped; when rho is more than 0.6, the estimation coefficient is finally solved to beta ═ beta1，β2，…，βm]TThe corresponding user with the median coefficient value larger than 1 is positioned as a suspected electricity stealing user, and the label of the suspected electricity stealing user is as follows:
Ind(i)＝arg{βi＞1}
the invention has the beneficial effects that: and solving the estimated regression coefficient through the self-adaptive shrinkage ridge regression model, and positioning the suspected electricity stealing users according to the similarity and the regression coefficient of the regression fitting line loss curve of the users and the actual line loss curve. According to the method, additional equipment is not needed, and electricity stealing prevention analysis can be carried out only by acquiring data of the district user ammeter and the district general table, so that the method is easy to implement, and time and economic cost are saved.
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FIG. 1 is a general flow diagram of the present invention.
Fig. 2 is a diagram of the estimated coefficients (power stealing multiples) of the cell users in the embodiment of the present invention.
Fig. 3 is a comparison graph of a table area user fitting line loss curve and an actual line loss curve in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
With reference to the attached drawing 1, an electric meter anti-electricity-stealing analysis method based on adaptive shrinkage ridge regression comprises the following steps:
step 1, acquiring 384-day freezing data of 152 user electric quantities of an exemplary distribution area and 384-day freezing data of an assessment total table electric quantity of the distribution area from an electric quantity information acquisition system.
Step 2, subtracting the sum of daily freezing data of each user electric meter in the distribution area from daily freezing data of a distribution area assessment total meter to obtain a distribution area line loss value curve; the calculation formula of the distribution room line loss value curve is as follows:
Figure BDA0002704236830000031
wherein ltDenotes the line loss value, y, of the t-th daytDenotes the assessment summary reading, x, on day ttiAnd (4) representing the meter reading of the ith user on the t day, wherein m is the number of the users in the region.
Step 3, defining a target function of the line loss value and all sub-table daily freezing data of the transformer area, and calculating an estimation coefficient initial solution according to a ridge regression model; the defined objective function is as follows:
Figure BDA0002704236830000032
writing in a matrix form is:
Figure BDA0002704236830000033
wherein
Figure BDA0002704236830000034
Representing the estimation coefficient (power stealing multiple), the superscripts (k +1) and (k) respectively represent the (k +1) th iteration and the kth iteration, and the n tableIndicating the number of days of data acquisition, the index i indicates the ith user, and the vector L ∈ RnRepresenting the line loss value curve, the matrix X belongs to Rm×nData representing daily freeze of users in a distribution area, theta(k)The adaptive shrinkage coefficient matrix representing the kth iteration, namely:
Figure BDA0002704236830000041
the degradation in the initial solution is a general ridge regression model, namely:
Figure BDA0002704236830000042
the estimation coefficient initial solution is obtained at this time:
Figure BDA0002704236830000043
where E denotes an identity matrix.
Step 4, shrinking the initial solution of the estimation coefficient through a self-adaptive iteration process to obtain a final solution of the estimation coefficient; the process of the adaptive shrinkage initial solution is as follows:
step S1: calculating an adaptive shrinkage coefficient matrix from the solution of the kth iteration
Figure BDA0002704236830000044
Step S2: computing new solutions
Figure BDA0002704236830000045
Step S3: repeating the steps S1 and S2 when the iteration number k is k +1, and if the 2 norm between the two adjacent calculation solutions is less than 10-3Stopping iteration, and outputting the final solution at the moment, which is recorded as beta ═ beta1，β2，…，βm]T。
Fig. 2 shows the estimated coefficients of all users in the background area after 34 iterations in the embodiment of the present invention, and it can be found that the coefficient of one user is very high, and the power stealing multiple is about 12.
Step 5, calculating a Pearson correlation coefficient of a user regression fitting line loss curve and an actual line loss value curve; the pearson correlation coefficient calculation formula of the user regression fitting line loss curve and the actual line loss value curve is as follows:
Figure BDA0002704236830000046
wherein f istAnd fitting a line loss value for the regression of the user on the t day by using a calculation formula as follows:
Figure BDA0002704236830000047
Figure BDA0002704236830000048
and
Figure BDA0002704236830000049
respectively, a fitted line loss curve and a mean value of the line loss curve.
Fig. 3 is a comparison graph of a table area user fitting line loss curve and an actual line loss curve in the embodiment of the present invention.
And 6, judging suspected electricity stealing users according to the values of the Pearson correlation coefficient and the estimation coefficient. The process for judging the suspected electricity stealing users is as follows: firstly, evaluating the reliability of a calculation result according to the magnitude of the Pearson correlation coefficient calculated in the step 5, and considering that the data is possibly abnormal when rho is less than or equal to 0.6, the calculation result is not reliable, and the calculation is stopped; when rho is more than 0.6, the estimation coefficient is finally solved to beta ═ beta1，β2，…，βm]TThe corresponding user with the median coefficient value larger than 1 is positioned as a suspected electricity stealing user, and the label of the suspected electricity stealing user is as follows:
Ind(i)＝arg{βi＞1}
in the embodiment, the calculated similarity is 0.8867, and the calculation result is credible. And (4) positioning a user stealing electricity through the graph 2, and judging that the result is consistent with the actual checking result.
The above-mentioned embodiments are illustrative of the specific embodiments of the present invention, and are not restrictive, and those skilled in the relevant art can make various changes and modifications to obtain corresponding equivalent technical solutions without departing from the spirit and scope of the present invention, so that all equivalent technical solutions should be included in the scope of the present invention.

Claims (2)

1. An electric meter anti-electricity-stealing analysis method based on self-adaptive shrinkage ridge regression is characterized by comprising the following steps:
step 1, acquiring daily freezing data of user electric quantity of a whole transformer area and daily freezing data of electric quantity of a transformer area assessment summary table from an electric quantity information acquisition system;
step 2, subtracting the sum of daily freezing data of each user electric meter in the distribution area from daily freezing data of a distribution area assessment total meter to obtain a distribution area line loss value curve; the calculation formula of the distribution room line loss value curve is as follows:
Figure FDA0003499167980000011
wherein ltDenotes the line loss value, y, of the t-th daytDenotes the assessment summary reading, x, on day ttiThe electricity meter reading of the ith user in the t day is shown, and m is the number of the users in the transformer area;
step 3, defining a target function of the line loss value and all sub-table daily freezing data of the transformer area, and calculating an estimation coefficient initial solution according to a ridge regression model; the objective function is as follows:
Figure FDA0003499167980000012
writing in a matrix form is:
Figure FDA0003499167980000013
wherein
Figure FDA0003499167980000014
Representing the estimated coefficients, superscripts (k +1) and (k) representing the (k +1) th and kth iterations, respectively, n representing the number of days of data acquisition, subscript i representing the ith user, and vector L ∈ RnRepresenting the line loss value curve, the matrix X belongs to Rm×nData representing daily freeze of users in a distribution area, theta(k)The adaptive shrinkage coefficient matrix representing the kth iteration, namely:
Figure FDA0003499167980000015
the degradation in the initial solution is a ridge regression model, namely:
Figure FDA0003499167980000016
the estimation coefficient initial solution is obtained at this time:
Figure FDA0003499167980000017
wherein E represents an identity matrix;
step 4, shrinking the initial solution of the estimation coefficient through a self-adaptive iteration process to obtain a final solution of the estimation coefficient; the process of the adaptive shrinkage initial solution is as follows:
step S1: calculating an adaptive shrinkage coefficient matrix from the solution of the kth iteration
Figure FDA0003499167980000018
Step S2: computing new solutions
Figure FDA0003499167980000019
Step S3: the number of iterations k is k +1,repeating the steps S1 and S2, if the 2 norm between the two adjacent solutions is less than 10-3Stopping iteration, and outputting the final solution at the moment, which is recorded as beta ═ beta1,β2,…,βm]T；
Step 5, calculating a Pearson correlation coefficient of a user regression fitting line loss curve and a line loss value curve; the user regression fits the pearson correlation coefficient calculation formula of the line loss curve and the line loss value curve as follows:
Figure FDA0003499167980000021
wherein f istThe regression fitting value of the user on the t day is calculated by the following formula:
Figure FDA0003499167980000022
Figure FDA0003499167980000023
and
Figure FDA0003499167980000024
respectively fitting a line loss curve and the mean value of the line loss curve;
step 6, judging suspected electricity stealing users according to the values of the Pearson correlation coefficient and the estimation coefficient, wherein the judging process of the suspected electricity stealing users is as follows: firstly, evaluating the reliability of a calculation result according to the magnitude of the Pearson correlation coefficient calculated in the step 5, and considering that data is abnormal when rho is less than or equal to 0.6, the calculation result is not reliable, and the calculation is stopped; when rho is more than 0.6, the estimation coefficient is finally solved to beta ═ beta1,β2,…,βm]TThe corresponding user with the median coefficient value larger than 1 is positioned as a suspected electricity stealing user, and the label of the suspected electricity stealing user is as follows:
Ind(i)＝arg{βi>1}。
2. the method as claimed in claim 1, wherein the number of days of data collected in step 1 is greater than or equal to two times the number of users in the region.
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