CN112180316B - Electric energy meter metering error analysis method based on adaptive shrinkage ridge regression - Google Patents

Electric energy meter metering error analysis method based on adaptive shrinkage ridge regression Download PDF

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CN112180316B
CN112180316B CN202011032730.1A CN202011032730A CN112180316B CN 112180316 B CN112180316 B CN 112180316B CN 202011032730 A CN202011032730 A CN 202011032730A CN 112180316 B CN112180316 B CN 112180316B
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范建华
曹乾磊
黄晓娅
王磊
梁浩
徐体润
彭绍文
张长帅
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Qingdao Topscomm Communication Co Ltd
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Abstract

The invention discloses an electric energy meter metering error analysis method based on adaptive shrinkage ridge regression, which comprises the following steps of: the multi-meter-position meter box monitoring unit acquires sub-meter granularity active power data in a meter box and meter box end granularity active power data; subtracting the sum of all sub-meter power data from the total meter power data to obtain a meter box power deviation curve; defining a target function of the power deviation value and the power of the in-box sub-table, and calculating an estimation coefficient initial solution according to a ridge regression model; contracting the initial solution through a self-adaptive iterative process to obtain a final solution of the estimation coefficient; calculating an evaluation index, namely a fitting mean square error value and a fitting cosine similarity, which reflects the fitting degree of the regression fitting deviation curve and the power deviation value curve; and judging the out-of-tolerance user according to the fitting mean square error value, the fitting cosine similarity and the value of the estimation coefficient. According to the invention, metering error analysis can be carried out by only adding LTU equipment and acquiring the power consumption data of all sub-meters and general meters in the meter box, so that the method is easy to realize, and the time and the economic cost are saved.

Description

Electric energy meter metering error analysis method based on adaptive shrinkage ridge regression
Technical Field
The invention relates to the technical field of distribution network automation, in particular to an electric energy meter metering error analysis method based on self-adaptive shrinkage ridge regression.
Background
With the increasing of the overall electricity consumption of society, more and more attention is paid to an electric energy meter for measuring the electricity consumption. Whether the electric energy meter can accurately measure the trust relationship between the user and the electric power enterprise or not, the electric energy meter in real life is possibly inaccurate in measurement due to the quality problem of the electric energy meter, the influence of the use environment and the use time, and if the electric energy meter is inaccurate in measurement, economic loss is inevitably brought to one of the power supply and utilization parties. At present, electric power companies generally adopt a method of regularly replacing electric meters or manually using instruments for spot check to avoid the problem of inaccurate electric energy meter metering, but the method has high time and economic cost and low efficiency. Therefore, the research on a more effective electric energy meter metering error analysis technology has very practical significance for the development of the society.
Disclosure of Invention
Aiming at the problems, the invention overcomes the defects of the prior art and provides an electric energy meter metering error analysis method based on adaptive shrinkage ridge regression. According to the method, metering error analysis can be performed by only adding LTU equipment and acquiring power consumption data of all sub-meters and a general meter in a meter box.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
an electric energy meter metering error analysis method based on adaptive shrinkage ridge regression comprises the following steps:
step 1, a multi-meter-position meter box monitoring unit (LTU) collects 15-minute granularity active power data (hereinafter referred to as sub-meter power data) of sub-meters in a meter box and 15-minute granularity active power data (hereinafter referred to as total meter power data) of an inlet wire of the meter box, and the collection time is 24 hours;
step 2, subtracting the sum of all sub-meter power data from the total meter power data of the meter box end to obtain a meter box power deviation value curve;
step 3, defining a power deviation value and a target function of all sub-table powers in the meter box, and calculating an initial solution of an estimation coefficient 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 two evaluation indexes, namely a fitting mean square error value and a fitting cosine similarity, which reflect the fitting degree of the regression fitting deviation curve and the power deviation value curve;
and 6, judging according to the fitting mean square error value, the fitting cosine similarity and the value of the estimation coefficient, and outputting the out-of-tolerance user.
Further, the formula for calculating the meter box end power deviation value in the step 2 is as follows:
Figure GDA0003630994880000011
wherein ljRepresents the power deviation value, y, of the j-th data acquisition pointjTotal power reading, x, representing the jth data acquisition pointjiThe power reading of the ith sub-table of the jth data acquisition point is shown, and n represents the number of the sub-tables in the meter box.
Further, the objective function defined in step 3 is as follows:
Figure GDA0003630994880000012
writing in a matrix form is:
Figure GDA0003630994880000013
where λ represents the ridge regression penalty coefficient, and the vector L ∈ RmExpressing the power deviation value, m is the number of data acquisition points, and the matrix X belongs to Rn×mRepresenting electrical power data for sub-meters within the meter box,
Figure GDA0003630994880000021
representing the estimated coefficient of the metering error of the electric meter, respectively representing the kth iteration and the kth +1 iteration by superscripts (k) and (k +1), wherein the subscript i represents the ith user, rhoi (k+1)An estimate, ρ, representing the metering error coefficient of the ith meter for the (k +1) th iterationi (k)An estimate, p, representing the metering error coefficient of the ith meter for the kth iteration(k+1)Vectors of estimated values representing error coefficients of measurement of the (k +1) th iteration meter, i.e.
Figure GDA0003630994880000022
Φ(k)The adaptive shrinkage coefficient matrix representing the kth iteration, namely:
Figure GDA0003630994880000023
the degradation in the initial solution is a general ridge regression model, namely:
Figure GDA0003630994880000024
where ρ is(0)Representing an initial estimation value vector of the electric meter metering error coefficient;
the initial solution of the estimated coefficients can now be obtained as:
Figure GDA0003630994880000025
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 according to the solution of the kth iteration
Figure GDA0003630994880000026
Step S2: computing new solutions
Figure GDA0003630994880000027
Step S3: increasing the iteration number, making k equal to k +1, repeating the steps S1 and S2, and if the 2-norm (namely the Euclidean distance) between the solutions obtained by two adjacent calculations is less than 10-3And outputting the final solution at the moment and recording as rho ═ rho1,ρ2,…,ρn]T；
Where ρ is1Final solution of the estimated coefficient for the 1 st meter, p2The final solution of the estimated coefficient of the 2 nd ammeter, and so on, rhonFor the nth electricity meterThe final solution of the estimated coefficients.
Further, the fitting mean square error calculation formula of the regression fitting deviation curve and the power deviation value curve in the step 5 is as follows:
Figure GDA0003630994880000028
the fitted cosine similarity calculation formula of the regression fitted deviation curve and the power deviation value curve is as follows:
Figure GDA0003630994880000031
wherein
Figure GDA0003630994880000032
And the fitting value of the j-th data acquisition point is obtained.
Further, the process of determining the suspected out-of-tolerance user in step 6 is: firstly, evaluating the reliability of the calculation result according to the fitting mean square error and the fitting cosine similarity value calculated in the step 5, and when mu is measured<0.05 and η>When 0.6 hour, the comprehensive fit is considered to reach the standard, and the solution rho is finally solved according to the estimated coefficient1,ρ2,…,ρn]TGiving out an out-of-tolerance table list by each corresponding sub-table, and positioning the corresponding sub-table with the absolute value of the coefficient larger than 0.02 as the out-of-tolerance table; otherwise, the data is considered to have possible exception, and the calculation is stopped.
The invention has the beneficial effects that: the method solves the regression coefficient through a self-adaptive shrinkage ridge regression model, and finally positions the super-difference table according to the similarity between a user regression fitting deviation value curve and an actual power deviation value curve and the size of an estimated regression coefficient. According to the invention, metering error analysis can be carried out by only adding LTU equipment and acquiring the power consumption data of all sub-meters and general meters in the meter box, so that the method is easy to realize, and the time and the 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 error coefficients of the sub-tables in the table box according to the embodiment of the present invention.
FIG. 3 is a comparison graph of a fitted deviation curve at a meter box end and an actual power deviation value curve in an embodiment of the 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 fig. 1, a method for analyzing metering errors of an electric energy meter based on adaptive shrinkage ridge regression includes the following steps:
step 1, a multi-meter-position meter box monitoring unit (LTU) collects 15-minute granularity active power data (hereinafter referred to as sub-meter power data) of 15 sub-meters in a meter box and 15-minute granularity active power data (hereinafter referred to as total meter power data) at an inlet wire of the meter box, the collection time is 24 hours, and 96 data points are counted;
step 2, subtracting the sum of all sub-meter power data from the meter box end total meter power data to obtain a meter box power deviation value curve; the formula for calculating the power deviation value of the meter box end is as follows:
Figure GDA0003630994880000033
wherein ljRepresents the power deviation value, y, of the j-th data acquisition pointjTotal power reading, x, representing the jth data acquisition pointjiThe power reading of the ith sub-table of the jth data acquisition point is shown, and n represents the number of the sub-tables in the meter box.
Step 3, defining a power deviation value and a target function of all sub-table powers in the meter box, and calculating an initial solution of an estimation coefficient according to a ridge regression model; the defined objective function is as follows:
Figure GDA0003630994880000034
writing in a matrix form is:
Figure GDA0003630994880000035
wherein the vector L ∈ RmExpressing the power deviation value, m is the number of data acquisition points, and the matrix X belongs to Rn×mRepresenting the electrical power data for the sub-meters within the meter box,
Figure GDA0003630994880000041
representing the metering error estimation coefficient of the electric meter, superscripts (k) and (k +1) respectively representing the kth iteration and the kth +1 iteration, subscript i representing the ith user, phi(k)The adaptive shrinkage coefficient matrix representing the kth iteration, namely:
Figure GDA0003630994880000042
the degradation in the initial solution is a general ridge regression model, namely:
Figure GDA0003630994880000043
the initial solution of the estimated coefficients can now be obtained as:
Figure GDA0003630994880000044
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 according to the solution of the kth iteration
Figure GDA0003630994880000045
Step S2: computingNovel solution
Figure GDA0003630994880000046
Step S3: increasing the iteration number, making k equal to k +1, repeating the steps S1 and S2, and if the 2-norm (i.e. the euclidean distance) between the two adjacent solutions is less than 10-3And outputting the final solution at this time, and recording as rho ═ rho1,ρ2,…,ρn]T。
As can be seen from FIG. 2, the sub-table 4 in the meter box has a metering error value of about-3.9%, the sub-table 7 has a metering error value of about + 3.2%, the sub-table 12 has a metering error value of about + 3.1%, and the sub-table 14 has a metering error value of about-3.5%.
Step 5, calculating two evaluation indexes, namely a fitting mean square error value and a fitting cosine similarity, which reflect the fitting degree of the regression fitting deviation curve and the power deviation value curve; the fitting mean square error calculation formula of the regression fitting deviation curve and the power deviation value curve is as follows:
Figure GDA0003630994880000047
the fitted cosine similarity calculation formula of the regression fitted deviation curve and the power deviation value curve is as follows:
Figure GDA0003630994880000048
wherein
Figure GDA0003630994880000049
And the fitting value of the j-th data acquisition point is obtained.
FIG. 3 is a comparison graph of a fitted deviation curve at a meter box end and an actual power deviation value curve in an embodiment of the invention.
And 6, judging according to the fitting mean square error value, the fitting cosine similarity and the value of the estimation coefficient, and outputting the out-of-tolerance user. The process of judging the suspected out-of-tolerance user comprises the following steps: first, the mean square error and the fitting calculated according to step 5Evaluating the reliability of the calculation result according to the magnitude of the cosine similarity value when fitting the mean square error mu<0.05 and fitted cosine similarity η>Considering the comprehensive fit to reach the standard at 0.6, and finally solving rho ═ rho according to the estimated coefficient1,ρ2,…,ρn]TGiving out an out-of-tolerance table list by each corresponding sub-table, and positioning the corresponding sub-table with the absolute value of the coefficient larger than 0.02 as the out-of-tolerance table; otherwise, the data is considered to have possible exception and the calculation is stopped.
The fitting mean square error calculated in the embodiment of the invention is 0.0003, the fitting similarity is 0.9505, and the calculation result is judged to be credible. The judgment result obtained according to fig. 2 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 (1)

1. A method for analyzing metering errors of an electric energy meter based on adaptive shrinkage ridge regression is characterized by comprising the following steps:
step 1, a multi-meter-position meter box monitoring unit LTU acquires 15-minute granularity active power data of sub-meters in a meter box and 15-minute granularity active power data of an inlet wire of the meter box, wherein the acquisition time is 24 hours; active power data of 15 minutes of granularity of sub-meters in the meter box, namely sub-meter power data, and active power data of 15 minutes of granularity of an inlet wire of the meter box, namely total meter power data;
step 2, subtracting the sum of all sub-meter power data from the meter box end total meter power data to obtain a meter box power deviation value curve; the formula for calculating the power deviation value of the meter box end is as follows:
Figure FDA0003646847260000011
wherein ljIndicating the jth data acquisitionPower deviation value of collection point, yjTotal power reading, x, representing the jth data acquisition pointjiThe power reading of the ith sub-table of the jth data acquisition point is represented, and n represents the number of the sub-tables in the meter box;
step 3, defining a power deviation value and a target function of all sub-table powers in the meter box, and calculating an initial solution of an estimation coefficient according to a ridge regression model; the defined objective function is as follows:
Figure FDA0003646847260000012
writing in a matrix form is:
Figure FDA0003646847260000013
where λ represents the ridge regression penalty coefficient, and the vector L ∈ RmExpressing the power deviation value, m is the number of data acquisition points, and the matrix X belongs to Rn ×mRepresenting electrical power data for sub-meters within the meter box,
Figure FDA0003646847260000014
representing the estimated coefficient of the metering error of the electric meter, respectively representing the kth iteration and the kth +1 iteration by superscripts (k) and (k +1), wherein the subscript i represents the ith user, rhoi (k+1)An estimate, ρ, representing the metering error coefficient of the ith meter for the (k +1) th iterationi (k)An estimated value, rho, representing the metering error coefficient of the ith ammeter in the kth iteration(k+1)Vectors of estimated values representing error coefficients of measurement of the (k +1) th iteration meter, i.e.
Figure FDA0003646847260000015
Φ(k)The adaptive shrinkage coefficient matrix representing the kth iteration, namely:
Figure FDA0003646847260000016
the degradation in the initial solution is a general ridge regression model, namely:
Figure FDA0003646847260000017
where ρ is(0)Representing an initial estimation value vector of the electric meter metering error coefficient;
the initial solution of the estimated coefficients can now be obtained as:
Figure FDA0003646847260000018
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 self-adaptive contraction initial solution is as follows:
step S1: calculating an adaptive shrinkage coefficient matrix from the solution of the kth iteration
Figure FDA0003646847260000021
Step S2: computing new solutions
Figure FDA0003646847260000022
Step S3: increasing the iteration number, making k equal to k +1, repeating the step S1 and the step S2, and if the 2-norm Euclidean distance between the two adjacent solutions is less than 10-3And outputting the final solution at this time, and recording as rho ═ rho1,ρ2,…,ρn]T；
Where ρ is1Final solution of the estimated coefficient for the 1 st meter, p2The final solution of the estimated coefficient of the 2 nd ammeter, and so on, rhonThe final solution of the estimated coefficient of the nth ammeter is obtained;
step 5, calculating two evaluation indexes, namely a fitting mean square error value and a fitting cosine similarity, which reflect the fitting degree of the regression fitting deviation curve and the power deviation value curve; the fitting mean square error calculation formula of the regression fitting deviation curve and the power deviation value curve is as follows:
Figure FDA0003646847260000023
the fitted cosine similarity calculation formula of the regression fitted deviation curve and the power deviation value curve is as follows:
Figure FDA0003646847260000024
wherein
Figure FDA0003646847260000025
Fitting values of j-th data acquisition points;
step 6, judging according to the fitting mean square error value, the fitting cosine similarity and the value of the estimation coefficient, and outputting out-of-tolerance users; the judgment process of the out-of-tolerance user is as follows: firstly, evaluating the reliability of a calculation result according to the fitting mean square error and the fitting cosine similarity value calculated in the step 5, considering that the comprehensive fitting reaches the standard when mu is less than 0.05 and eta is more than 0.6, and finally solving rho to [ rho ] according to an estimation coefficient1,ρ2,…,ρn]TGiving out an out-of-tolerance table list by each corresponding sub-table, and positioning the corresponding sub-table with the absolute value of the coefficient larger than 0.02 as the out-of-tolerance table; otherwise, the data is considered to have possible exception, and the calculation is stopped.
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