CN115542236B - Electric energy meter operation error estimation method and device - Google Patents
Electric energy meter operation error estimation method and device Download PDFInfo
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- CN115542236B CN115542236B CN202211479374.7A CN202211479374A CN115542236B CN 115542236 B CN115542236 B CN 115542236B CN 202211479374 A CN202211479374 A CN 202211479374A CN 115542236 B CN115542236 B CN 115542236B
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Abstract
The invention relates to the technical field of data analysis, and provides a method and a device for estimating running errors of an electric energy meter, wherein the method comprises the following steps: establishing an error model of the electric energy meter; punishment and solving are carried out on error coefficients of each electric energy meter in the electric energy meter error model based on the regular coefficients, so that first error coefficients of each electric energy meter in the electric energy meter error model are obtained; determining a candidate out-of-tolerance meter of the platform region based on the first error coefficient of each electric energy meter in the platform region; punishment and solving are carried out on error coefficients of other electric energy meters except the candidate out-of-tolerance meter in the electric energy meter error model based on the regular coefficient, so that second error coefficients of all the electric energy meters in the electric energy meter are obtained; and determining the operation error of the electric energy meter of the platform region based on the second error coefficient of each electric energy meter in the platform region. The method and the device can screen out potential out-of-tolerance tables under the transformer area and reasonably distribute the regular coefficients to the potential out-of-tolerance tables under the transformer area so as to improve the calculation quality of the error coefficients of the ammeter under the transformer area and improve the accuracy of error estimation.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a method and a device for estimating running errors of an electric energy meter.
Background
With the automatic acquisition mode of the electricity consumption information acquisition system replacing the traditional manual meter reading mode, the online analysis method of the intelligent electric energy meter has become a new means for evaluating and monitoring the operation quality of the intelligent electric energy meter. In the existing online analysis method, the running errors of all intelligent electric energy meters under the transformer area are researched and calculated by using a large data technology by taking the transformer area as a unit, and the line loss items of the transformer area are calculated by constructing the running error models of all intelligent electric energy meters under the transformer area, so that error estimation is realized.
However, in an actual scene, the difference between error coefficients of the abnormal meter and the normal meter is often not considered, so that the error estimation of the electric energy meter of the transformer area is inaccurate, and the result that the accurate detection of the abnormal meter in the transformer area cannot be realized is caused.
Disclosure of Invention
The invention provides a method and a device for estimating running errors of an electric energy meter, which are used for solving the defect that in the prior art, error estimation is inaccurate due to the fact that the difference between an out-of-tolerance meter and a normal meter is not considered, carrying out fine expression on line loss items of a transformer area, and realizing accurate estimation on the running errors of the electric energy meter.
The invention provides an electric energy meter operation error estimation method, which comprises the following steps:
establishing an error model of the electric energy meter;
punishment and solving are carried out on error coefficients of each electric energy meter in the electric energy meter error model based on the regular coefficients, so that first error coefficients of each electric energy meter in the electric energy meter error model are obtained;
determining a candidate out-of-tolerance table of the platform area based on first error coefficients of all electric energy meters in the platform area;
punishment and solving are carried out on error coefficients of other electric energy meters except the candidate out-of-tolerance meter in the electric energy meter error model based on the regular coefficient, so that second error coefficients of all the electric energy meters in the electric energy meter are obtained;
and determining the operation error of the electric energy meter of the platform area based on the second error coefficient of each electric energy meter in the platform area.
According to the method for estimating the running error of the electric energy meter, which is provided by the invention, the candidate out-of-tolerance meter of the platform area is determined based on the first error coefficient of each electric energy meter in the platform area, and the method comprises the following steps:
determining differences of error distribution of each electric energy meter in the platform area and the platform area based on first error coefficients of each electric energy meter in the platform area;
and determining the electric energy meters with the differences meeting the first preset conditions as the candidate out-of-tolerance meters based on the differences of the error distribution of each electric energy meter and the station in the station.
According to the electric energy meter operation error estimation method provided by the invention, the first preset condition comprises any one of the following three conditions or any combination of the three conditions:
the absolute value of a first error coefficient of the electric energy meter meets a first threshold;
the statistical test result of the first error coefficient of the electric energy meter meets a second threshold;
the number of the candidate out-of-tolerance meters is L, L is a positive integer, and L is smaller than the total number of the electric energy meters in the station area.
According to the method for estimating the running error of the electric energy meter, when the first preset condition includes the three conditions, the electric energy meter with the determined difference meeting the first preset condition is the candidate out-of-tolerance meter, and the method comprises the following steps:
determining that the electric energy meter of which the absolute value of the first error coefficient in the platform area meets a first threshold value is a first preselected electric energy meter;
determining that the electric energy meter with the statistical test result of the first error coefficient meeting a second threshold value in the first preselected electric energy meter is a second preselected electric energy meter;
and sequencing the second preselected electric energy meters according to the significance of the statistical test result, and determining the second preselected electric energy meters with the significance sequenced into the first L as the candidate out-of-tolerance meters.
According to the method for estimating the running error of the electric energy meter, provided by the invention, a ridge regression method is adopted for the solving method of the error model of the electric energy meter.
According to the method for estimating the running error of the electric energy meter, which is provided by the invention, the method for establishing the error model of the electric energy meter comprises the following steps:
and establishing an electric energy meter error model based on the electricity consumption of the electric energy meter of the area and line loss data of the electric energy meter of the area and combining with an energy conservation law.
According to the method for estimating the running error of the electric energy meter provided by the invention, the running error of the electric energy meter in the platform area is determined based on the second error coefficient of each electric energy meter in the platform area, and the method comprises the following steps:
and determining the operation error of the electric energy meter of the platform area based on the second error coefficient of each electric energy meter in the platform area and the line loss data of the electric energy meter of the platform area.
The invention also provides an electric energy meter operation error estimation device, which comprises:
the input module is used for establishing an error model of the electric energy meter;
the first solving module is used for punishing and solving error coefficients of each electric energy meter in the electric energy meter error model based on the regular coefficient to obtain first error coefficients of each electric energy meter in the electric energy meter error model;
the screening module is used for determining a candidate out-of-tolerance table of the platform area based on the first error coefficient of each electric energy meter in the platform area;
the second solving module is used for punishing and solving error coefficients of other electric energy meters except the candidate out-of-tolerance meter in the electric energy meter error model based on the regular coefficient to obtain second error coefficients of all the electric energy meters in the electric energy meter error model;
and the output module is used for determining the operation error of the electric energy meter in the platform area based on the second error coefficient of each electric energy meter in the platform area.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for estimating the running error of the electric energy meter according to any one of the above when executing the program.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of estimating an operating error of an electric energy meter as described in any of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements a method of estimating an operating error of a power meter as described in any of the above.
According to the electric energy meter operation error estimation method and device, the first error coefficient of each electric energy meter is calculated through conventional regression, then the candidate out-of-tolerance meter suspected of out-of-tolerance in the platform area is determined through the first loss coefficient, when the error coefficient is calculated for the second time, punishment is not conducted on the candidate out-of-tolerance meter coefficient based on the regular coefficient, then the improved second error coefficient is obtained through solving, and further accurate operation error is obtained. The method ensures the calculation accuracy of the out-of-tolerance meter, and can control the overall over-fitting degree of other electric energy meters by applying moderate regularization, so that the method is suitable for the characteristic that the error coefficients have composite distribution in the error model calculation, and improves the accuracy of error coefficient estimation.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an electric energy meter operation error estimation method provided by the invention;
FIG. 2 is a second flow chart of the method for estimating the operation error of the electric energy meter according to the present invention;
FIG. 3 is a schematic diagram of the structure of the operation error estimation device of the electric energy meter;
fig. 4 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The following describes the operation error estimation method of the electric energy meter with reference to fig. 1-2, and as shown in fig. 1, the operation error estimation method of the electric energy meter according to the embodiment of the invention at least includes the following steps:
For step 101, it should be noted that the electric energy meter error model is a model for researching and calculating the operation errors of each intelligent electric energy meter in the transformer area by taking the transformer area as a unit, and the model includes a user electricity consumption term and a line loss term, wherein the line loss term is the operation error of target solution. When the system is constructed, a relation model between line loss and the electricity consumption of a user is often constructed through an energy conservation law.
For step 102, it should be noted that the error coefficient is a key loop for solving the line loss term, and the error coefficients of different electric energy meters are often used to control the overfitting degree of the model by adding the same regular coefficient to solve for obtaining a more accurate coefficient. In this embodiment, the electric energy meter error model is punished and solved by a conventional method to obtain the first error coefficient, where the error coefficient is not required to be accurate, but only is required to be used for primarily screening out a possibly out-of-tolerance meter, so as to pave for a subsequent further optimization coefficient.
For step 103, it should be noted that, based on the first error coefficient of each electric energy meter in the platform, the embodiment of the invention screens and obtains a suspected list of out-of-tolerance meters according to the difference significance of the error value and the error distribution. Because in a practical scene, the small abnormal table coefficients of out-of-tolerance and misalignment are often far from the distribution interval of normal electric meters, it is not reasonable to use the same regular coefficient for all electric meters in the area where the out-of-tolerance table exists.
It should be noted that, in step 104, when the same regular coefficient is used, if the value of the regular coefficient is too large, the value is equivalent to that the assumed coefficient is from a priori distribution with smaller variance, the calculated error of the out-of-tolerance table may be smaller due to over-compression, and meanwhile, the error signal of the out-of-tolerance table which is not completely fit may affect the accurate measurement of the errors of other electric meters under the platform region, and if the regular coefficient is too small, the error calculation quality of the whole platform region may be affected due to over-fitting. Therefore, the invention tries to improve the calculation quality of the error coefficient of the ammeter under the district by identifying and judging the potential suspected out-of-tolerance table under the district and reasonably distributing the regular coefficient to the suspected out-of-tolerance table.
For step 105, it should be noted that, after determining the operation error of the electric energy meter of the platform area, the electric energy meter with the operation error reaching the standard of the out-of-tolerance meter is used as the out-of-tolerance meter screened out finally based on the preset out-of-tolerance meter standard, and the out-of-tolerance meter at this time is closer to the actual situation of the platform area than the candidate out-of-tolerance meter obtained by calculating according to the first error coefficient in step 103, that is, the identification accuracy of the out-of-tolerance meter is improved.
According to the electric energy meter operation error estimation method, the first error coefficient of each electric energy meter is calculated through conventional regression, then the candidate out-of-tolerance table suspected of out-of-tolerance in the platform area is determined through the first loss coefficient, when the error coefficient is calculated for the second time, punishment is not conducted on the candidate out-of-tolerance table coefficient based on the regular coefficient, then the improved second error coefficient is obtained through solving, and further accurate operation error is obtained. The method ensures the calculation accuracy of the out-of-tolerance meter, and can control the overall over-fitting degree of other electric energy meters by applying moderate regularization, so that the error coefficient is suitable for the characteristic of complex distribution in error model calculation, and the accuracy of error coefficient estimation is improved.
It can be appreciated that establishing the error model of the electric energy meter includes:
and establishing an electric energy meter error model based on the electricity consumption of the electric energy meter of the area and line loss data of the electric energy meter of the area and combining with an energy conservation law.
It should be noted that, in the embodiment of the present invention, based on the law of conservation of energy, the line loss obtained by using kirchhoff's law and the power consumption of the user are quadratic functions, and the model equation finally established is shown in formula 1:
wherein,,
electric matrix for showing total surface of power supply station area and its shape
，
The number of the metering time points corresponds to the total number of equations, and each point is the electricity consumption in the metering interval.
Electric matrix for representing all users (sub-meters) in a station area and is in the shape of
The electricity consumption of each user in each behavior measurement interval,
is the number of subscriber tables.
Error coefficients representing sub-tables under a region having the shape of
。
Representing a line loss term matrix constructed in an energy conservation equation in the shape of
，
Lines added to build equationsNumber of impairment terms.
The line loss coefficient corresponding to each line loss item is shaped as
The physical meaning is equivalent resistance.
The fixed loss constant is represented, and the fixed loss sum under the transformer area is generally derived from the loss of the intelligent ammeter.
It can be appreciated that the solution method for the error model of the electric energy meter adopts a ridge regression method.
It should be noted that, in the current smart meter misalignment error online monitoring model, the energy conservation equation is usually solved by a ridge regression algorithm, and the optimization objective is shown in formula 2:
wherein,,
representing regular coefficients, wherein error coefficients are subjected to the same regular coefficients
And the method is used for controlling the overfitting degree of the model to solve for more accurate coefficients.
In addition, it should be noted that, besides the ridge regression algorithm, other linear regression algorithms may be used to solve the error model of the electric energy meter, such as the Lasso regression algorithm.
It may be appreciated that determining a candidate out-of-tolerance table for a zone based on a first error coefficient for each power meter within the zone includes:
determining differences of error distribution of each electric energy meter in the platform area and the platform area based on first error coefficients of each electric energy meter in the platform area;
and determining the electric energy meters with the differences meeting the first preset conditions as candidate out-of-tolerance meters based on the differences of the error distribution of each electric energy meter in the station area and the station area.
It should be noted that, the first error coefficient is a basic error coefficient obtained after the first round of solving the error model of the electric energy meter by using a common linear regression method, the error distribution of the platform region specifically refers to the error coefficient distribution situation of the whole platform region, and the difference between each electric energy meter in the platform region and the error distribution situation of the platform region is the difference significance of comparing the first error coefficient of each electric energy meter with the error coefficient distribution situation of the whole platform region. The purpose of setting the first preset condition is to screen out the electric energy meter with larger overall error distribution phase difference of the transformer area, and because the small abnormal meter coefficients of the out-of-tolerance and the misalignment are often far from the distribution phase difference of the normal electric meter, the step can effectively screen out the possibly out-of-tolerance meter.
It is understood that the first preset condition includes any one of the following three conditions or any combination of the three conditions:
the absolute value of the first error coefficient of the electric energy meter meets a first threshold;
the statistical test result of the first error coefficient of the electric energy meter meets a second threshold;
the number of the candidate out-of-tolerance meters is L, L is a positive integer, and L is smaller than the total number of the electric energy meters in the platform area.
In the first condition, the first error coefficient is positive and negative, and thus the absolute value thereof needs to be taken for comparison. The first threshold is a standard for screening directly for the calculated first error coefficient, and therefore the magnitude of the first error coefficient is significantly different from the distribution of all meters under the district, i.e. the first threshold may be three times the standard deviation of the error calculated based on the first error coefficient.
For the second condition, it should be noted that, the statistical test result of the first error coefficient of the electric energy meter means that the distribution difference significance of the first error coefficient and all electric energy meters under the platform area is solved by adopting a statistical test method, and the statistical test method can be selected from t test, F test, chi-square test and the like. The second threshold value is thus also a test value threshold value which is set depending on the difference in the statistical test mode and the actual data and which satisfies the respective confidence.
For the third condition, it should be noted that, because the distribution of the out-of-tolerance tables is sparse and the number is small, the number L of the candidate out-of-tolerance tables does not need to be set to be large, for example, L is practically set to be 3, and 3 candidate numbers are enough for out-of-tolerance table screening.
It may be appreciated that, in the case where the first preset condition includes three conditions, determining that the electric energy meter whose difference satisfies the first preset condition is a candidate out-of-tolerance meter includes:
and determining that the electric energy meter of which the absolute value of the first error coefficient in the platform area meets a first threshold value is a first preselected electric energy meter.
It should be noted that, the first error value is significantly different from the overall distribution of the cell errors, i.e., the first condition is shown in equation 3:
wherein,,
a first error coefficient representing an i-th block table of the zone,
and the standard deviation of errors of all the ammeter under the platform area is shown.
And determining that the electric energy meter with the statistical test result of the first error coefficient meeting the second threshold value in the first preselected electric energy meter is a second preselected electric energy meter.
It should be noted that the embodiment of the present invention uses the t-test to calculate the statistical test result. Specifically, the optimization objective of t-test for embodiments of the present invention is shown in equation 4:
wherein,,
the representation is based on
The regular terms added by the matrix, the general extension form of the regular matrix,
is part of a feature matrix in a linear regression model,
。
the t-test value of the first error coefficient is calculated by using equation 5:
wherein,,
the t-test value of the first preselected electric energy meter for the ith block,
for the estimated coefficient noise covariance matrix,
，
，
as a trace of the matrix,
，
a mapping matrix from the fitted object to the fitted resulting object.
Specifically, the second threshold is a condition for judging that the statistical test result is significant, and in the embodiment of the present invention, the second threshold is 3, that is, each first pre-selected electric energy meter needs to be calculated
After that, if
It is considered to be a significant difference and is determined to be a second preselected electrical energy meter. 3 is a common threshold in statistical tests, corresponding to 99.7% confidence when assuming a normal/gaussian distribution.
And sorting the second preselected electric energy meters according to the significance of the statistical test result, and determining the second preselected electric energy meters with the significance sorted into the first L as candidate out-of-tolerance meters.
It should be noted that, after the saliency of the second pre-selected electric energy meter is ranked, the electric energy meter with the saliency ranked in the first three positions is selected. Specifically, a method for sorting the saliency of the three bits before t-test is selected, as shown in formula 6:
and the instruction is carried out to the device,
，
and
respectively the first
And (d)
Block secondThe t-test value of the electric energy meter is preselected.
It can be understood that punishment and solving are performed on error coefficients of other electric energy meters except the candidate out-of-tolerance meter in the electric energy meter error model based on the regular coefficient, namely performing second round calculation on the suspected out-of-tolerance meter relaxation regular coefficient to obtain second error coefficients of all electric energy meters in the improved platform region.
Specifically, the optimization target shown in the formula 2 is changed to the optimization target shown in the formula 7:
wherein,,
the error coefficient of the preselected over-tolerance table is not punished based on the regular coefficient, namely only the regular terms of the electric energy table except the preselected over-tolerance table included by L are calculated. And then solving to obtain the improved second error coefficient.
According to the electric energy meter operation error estimation method, the solving mode of matching the regular coefficient with the composite coefficient distribution is calculated and adjusted through two-cycle ridge regression, and the method is similar to the method that unbiased least square estimation is carried out between suspected out-of-tolerance electric meters, so that the calculation accuracy of the out-of-tolerance electric meters is ensured, and meanwhile, the overall degree of overfitting can be controlled by applying moderate regular to other electric meters.
It can be appreciated that determining the operation error of the electric energy meter of the station area based on the second error coefficients of the electric energy meters in the station area includes:
and determining the operation error of the electric energy meter of the station area based on the second error coefficient of each electric energy meter in the station area and the line loss data of the electric energy meter of the station area.
It should be noted that the number of the substrates,
it can be understood that, as shown in fig. 2, the embodiment of the invention discloses a method for estimating the running error of an electric energy meter, which at least comprises the following steps:
1) The magnitude of the error value of the first error coefficient is obviously different from the integral distribution of the errors of the platform region;
2) The t-test value of the first error coefficient is judged to be obvious through statistical test;
3) The significant electricity meter coefficients are arranged at the front, and the significance is arranged at the first three positions;
204, performing a second round of calculation on the pre-selected out-of-tolerance table relaxation regular coefficients by using ridge regression to obtain improved second error coefficients;
According to the electric energy meter operation error estimation method, the regular coefficient is adjusted in the second round of calculation, and the method is similar to the first unbiased least square estimation between suspected out-of-tolerance electric meters, so that the accuracy of out-of-tolerance electric meter calculation is guaranteed, meanwhile, the overall degree of overfitting can be controlled by applying moderate regular to other electric meters, the method is suitable for the characteristic that the error coefficient has composite distribution in the misalignment model calculation, and the accuracy of error coefficient estimation is improved. In addition, compared with a method of directly adding a composite prior into an optimization target, the method only aims at solving the problem of ridge regression with simple solution in two steps of separate solution, has an easy-to-calculate analytic solution, and avoids the problem of difficult convergence when optimizing a complex target.
The operation error estimating device of the electric energy meter provided by the invention is described below, and the operation error estimating device of the electric energy meter described below and the operation error estimating method of the electric energy meter described above can be correspondingly referred to each other.
As shown in fig. 3, the operation error estimation device for an electric energy meter according to an embodiment of the present invention includes:
an input module 301, configured to establish an error model of the electric energy meter;
the first solving module 302 is configured to penalize and solve error coefficients of each electric energy meter in the electric energy meter error model based on the regular coefficients, so as to obtain first error coefficients of each electric energy meter in the electric energy meter error model;
a screening module 303, configured to determine a candidate out-of-tolerance table of the area based on a first error coefficient of each electric energy meter in the area;
the second solving module 304 is configured to punish and solve error coefficients of other electric energy meters in the electric energy meter error model except the candidate out-of-tolerance meter based on the regular coefficient, so as to obtain second error coefficients of each electric energy meter in the electric energy meter error model;
and the output module 305 is configured to determine an operation error of the electric energy meter in the area based on the second error coefficients of the electric energy meters in the area.
According to the electric energy meter operation error estimation device, the first solving module 302 calculates the first error coefficient of each electric energy meter through conventional regression, the screening module 303 determines the candidate out-of-tolerance table suspected of out-of-tolerance in the platform region through the first loss coefficient, the second solving module 304 does not punish the candidate out-of-tolerance table coefficient based on the regular coefficient when calculating the error coefficient for the second time, and then the improved second error coefficient can be obtained through solving, so that the accurate operation error is obtained. The device ensures the calculation accuracy of the out-of-tolerance meter, and can apply moderate regularization to other electric energy meters to control the overall degree of over-fitting, so that the device is suitable for the characteristic that the error coefficient has composite distribution in the error model calculation, and improves the accuracy of error coefficient estimation.
It may be appreciated that determining a candidate out-of-tolerance table for a zone based on a first error coefficient for each power meter within the zone includes:
determining differences of error distribution of each electric energy meter in the platform area and the platform area based on first error coefficients of each electric energy meter in the platform area;
and determining the electric energy meters with the differences meeting the first preset conditions as candidate out-of-tolerance meters based on the differences of the error distribution of each electric energy meter in the station area and the station area.
It is understood that the first preset condition includes any one of the following three conditions or any combination of the three conditions:
the absolute value of the first error coefficient of the electric energy meter meets a first threshold;
the statistical test result of the first error coefficient of the electric energy meter meets a second threshold;
the number of the candidate out-of-tolerance meters is L, L is a positive integer, and L is smaller than the total number of the electric energy meters in the platform area.
It may be appreciated that, in the case where the first preset condition includes three conditions, determining that the electric energy meter whose difference satisfies the first preset condition is a candidate out-of-tolerance meter includes:
determining that an electric energy meter with the absolute value of a first error coefficient in the platform area meeting a first threshold value is a first preselected electric energy meter;
determining that the electric energy meter with the statistical test result of the first error coefficient meeting the second threshold value in the first preselected electric energy meter is a second preselected electric energy meter;
and sorting the second preselected electric energy meters according to the significance of the statistical test result, and determining the second preselected electric energy meters with the significance sorted into the first L as candidate out-of-tolerance meters.
It can be appreciated that the solution method for the error model of the electric energy meter adopts a ridge regression method.
It can be appreciated that establishing the error model of the electric energy meter includes:
and establishing an electric energy meter error model based on the electricity consumption of the electric energy meter of the area and line loss data of the electric energy meter of the area and combining with an energy conservation law.
It can be appreciated that determining the operation error of the electric energy meter of the station area based on the second error coefficients of the electric energy meters in the station area includes:
and determining the operation error of the electric energy meter of the station area based on the second error coefficient of each electric energy meter in the station area and the line loss data of the electric energy meter of the station area.
Fig. 4 illustrates a physical schematic diagram of an electronic device, as shown in fig. 4, which may include: processor 410, communication interface (Communications Interface) 420, memory 430 and communication bus 440, wherein processor 410, communication interface 420 and memory 430 communicate with each other via communication bus 440. Processor 410 may invoke logic instructions in memory 430 to perform a method for power meter operation error estimation, the method comprising:
establishing an error model of the electric energy meter;
punishment and solving are carried out on error coefficients of each electric energy meter in the electric energy meter error model based on the regular coefficients, so that first error coefficients of each electric energy meter in the electric energy meter error model are obtained;
determining a candidate out-of-tolerance meter of the platform region based on the first error coefficient of each electric energy meter in the platform region;
punishment and solving are carried out on error coefficients of other electric energy meters except the candidate out-of-tolerance meter in the electric energy meter error model based on the regular coefficient, so that second error coefficients of all the electric energy meters in the electric energy meter are obtained;
and determining the operation error of the electric energy meter of the platform region based on the second error coefficient of each electric energy meter in the platform region.
Further, the logic instructions in the memory 430 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, where the computer program can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, the computer can execute the method for estimating an operation error of an electric energy meter provided by the above methods, where the method includes:
establishing an error model of the electric energy meter;
punishment and solving are carried out on error coefficients of each electric energy meter in the electric energy meter error model based on the regular coefficients, so that first error coefficients of each electric energy meter in the electric energy meter error model are obtained;
determining a candidate out-of-tolerance meter of the platform region based on the first error coefficient of each electric energy meter in the platform region;
punishment and solving are carried out on error coefficients of other electric energy meters except the candidate out-of-tolerance meter in the electric energy meter error model based on the regular coefficient, so that second error coefficients of all the electric energy meters in the electric energy meter are obtained;
and determining the operation error of the electric energy meter of the platform region based on the second error coefficient of each electric energy meter in the platform region.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method for estimating an operation error of an electric energy meter provided by the above methods, the method comprising:
establishing an error model of the electric energy meter;
punishment and solving are carried out on error coefficients of each electric energy meter in the electric energy meter error model based on the regular coefficients, so that first error coefficients of each electric energy meter in the electric energy meter error model are obtained;
determining a candidate out-of-tolerance meter of the platform region based on the first error coefficient of each electric energy meter in the platform region;
punishment and solving are carried out on error coefficients of other electric energy meters except the candidate out-of-tolerance meter in the electric energy meter error model based on the regular coefficient, so that second error coefficients of all the electric energy meters in the electric energy meter are obtained;
and determining the operation error of the electric energy meter of the platform region based on the second error coefficient of each electric energy meter in the platform region.
The apparatus embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. An electric energy meter operation error estimation method is characterized by comprising the following steps:
establishing an error model of the electric energy meter;
punishment and solving are carried out on error coefficients of each electric energy meter in the electric energy meter error model based on the regular coefficients, so that first error coefficients of each electric energy meter in the electric energy meter error model are obtained;
determining differences of error distribution of each electric energy meter in the platform area and the platform area based on first error coefficients of each electric energy meter in the platform area;
determining electric energy meters with differences meeting first preset conditions as candidate out-of-tolerance meters based on differences of error distribution of each electric energy meter and a station in the station;
punishment and solving are carried out on error coefficients of other electric energy meters except the candidate out-of-tolerance meter in the electric energy meter error model based on the regular coefficient, so that second error coefficients of all the electric energy meters in the electric energy meter are obtained;
and determining the operation error of the electric energy meter of the platform area based on the second error coefficient of each electric energy meter in the platform area.
2. The method of claim 1, wherein the first preset condition comprises any one of or any combination of the following three conditions:
the absolute value of a first error coefficient of the electric energy meter meets a first threshold;
the statistical test result of the first error coefficient of the electric energy meter meets a second threshold;
the number of the candidate out-of-tolerance meters is L, L is a positive integer, and L is smaller than the total number of the electric energy meters in the station area.
3. The electric energy meter operation error estimation method according to claim 2, wherein, in the case where the first preset condition includes the three conditions, the electric energy meter for which the determined difference satisfies the first preset condition is the candidate out-of-tolerance meter, comprising:
determining that the electric energy meter of which the absolute value of the first error coefficient in the platform area meets a first threshold value is a first preselected electric energy meter;
determining that the electric energy meter with the statistical test result of the first error coefficient meeting a second threshold value in the first preselected electric energy meter is a second preselected electric energy meter;
and sequencing the second preselected electric energy meters according to the significance of the statistical test result, and determining the second preselected electric energy meters with the significance sequenced into the first L as the candidate out-of-tolerance meters.
4. A method for estimating an operation error of an electric energy meter according to any one of claims 1 to 3, wherein a ridge regression method is adopted for the solving method of the error model of the electric energy meter.
5. A method for estimating an operation error of an electric energy meter according to any one of claims 1 to 3, wherein the establishing an error model of the electric energy meter comprises:
and establishing an electric energy meter error model based on the electricity consumption of the electric energy meter of the area and line loss data of the electric energy meter of the area and combining with an energy conservation law.
6. The method for estimating an operation error of a power meter according to claim 5, wherein determining the operation error of the power meter in the area based on the second error coefficients of the power meters in the area comprises:
and determining the operation error of the electric energy meter of the platform area based on the second error coefficient of each electric energy meter in the platform area and the line loss data of the electric energy meter of the platform area.
7. An electric energy meter operation error estimation device, characterized by comprising:
the input module is used for establishing an error model of the electric energy meter;
the first solving module is used for punishing and solving error coefficients of each electric energy meter in the electric energy meter error model based on the regular coefficient to obtain first error coefficients of each electric energy meter in the electric energy meter error model;
the screening module is used for determining the difference between each electric energy meter in the platform area and the platform area error distribution based on the first error coefficient of each electric energy meter in the platform area; determining electric energy meters with differences meeting first preset conditions as candidate out-of-tolerance meters based on differences of error distribution of each electric energy meter in the transformer area and the transformer area;
the second solving module is used for punishing and solving error coefficients of other electric energy meters except the candidate out-of-tolerance meter in the electric energy meter error model based on the regular coefficient to obtain second error coefficients of all the electric energy meters in the electric energy meter error model;
and the output module is used for determining the operation error of the electric energy meter in the platform area based on the second error coefficient of each electric energy meter in the platform area.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of power meter operation error estimation of any one of claims 1 to 6 when the program is executed by the processor.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method of operation error estimation of an electric energy meter according to any of claims 1 to 6.
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