CN115524658B - Method and device for determining running error of electric energy meter - Google Patents
Method and device for determining running error of electric energy meter Download PDFInfo
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Abstract
The invention relates to the technical field of electric power data analysis, and provides a method and a device for determining an operation error of an electric energy meter. The method comprises the following steps: determining power consumption data of the electric energy meter in the transformer area; eliminating the correlation of quantization noise of different metering points in the power consumption data of the district electric energy meter to obtain stable power consumption data of the district electric energy meter; and constructing an electric energy meter error model and solving the electric energy meter error model based on the stable power consumption data of the district electric energy meter, and determining the operation error of the district electric energy meter. According to the method, the influence of quantization noise on the error coefficient when the electric energy meter error model is solved is relieved, and the stability and the accuracy of the intelligent electric meter operation error estimation are improved.
Description
Technical Field
The invention relates to the technical field of electric power data analysis, in particular to a method and a device for determining running errors of an electric energy meter.
Background
With the automatic collection mode of the electricity information collection system replacing the traditional manual meter reading mode, the online analysis method of the intelligent electric energy meter becomes a new means for evaluating and monitoring the operation quality of the electric energy meter. The existing online analysis method usually researches and calculates the operation errors of each intelligent electric energy meter under a platform area through a big data technology, and an operation error model of each intelligent electric energy meter under the platform area needs to be constructed to realize error estimation.
The existing operation error solving method generally adopts a regression method to directly solve on the basis of an operation error model of the electric energy meter to obtain an error coefficient. However, the metering accuracy inside the electric energy meter is often much higher than the displayed value, and the high-accuracy accumulated indication generates quantization noise when the displayed value is obtained with limited accuracy. If the quantization noise is not reasonably processed, the error coefficient of the electric energy meter solved by the operation error model has larger fluctuation, and the accuracy of solving the operation error of the electric energy meter is directly influenced.
Disclosure of Invention
The invention provides a method and a device for determining an operation error of an electric energy meter, which are used for solving the defect of low error estimation accuracy caused by quantization noise in the prior art and improving the accuracy and the stability of error estimation.
The invention provides a method for determining an operation error of an electric energy meter, which comprises the following steps:
determining power consumption data of the electric energy meter of the distribution room;
eliminating the correlation of quantization noise of different metering points in the power consumption data of the district electric energy meter to obtain stable power consumption data of the district electric energy meter;
and constructing an electric energy meter error model and solving the electric energy meter error model based on the stable power consumption data of the platform area electric energy meter, and determining the operation error of the platform area electric energy meter.
According to the method for determining the running error of the electric energy meter, the correlation of quantization noise of different metering points in the power consumption data of the electric energy meter in the distribution room is eliminated by adopting a generalized least square method.
According to the method for determining the operation error of the electric energy meter, provided by the invention, the method for eliminating the correlation of the quantization noise of different metering points in the power consumption data of the electric energy meter in the transformer area by adopting the generalized least square method comprises the following steps:
determining a power consumption matrix of the district electric energy meter based on the power consumption data of the district electric energy meter;
and eliminating the correlation of the quantization noise of different metering points by line-by-line conversion on the electricity consumption matrix of the electric energy meter in the transformer area.
According to the method for determining the running error of the electric energy meter, the correlation of the quantization noise of different metering points is eliminated for the electric energy meter electricity consumption matrix of the transformer area through line-by-line conversion, and the method comprises the following steps:
determining quantized noise data of different metering points based on the electricity consumption data of the electric energy meter of the transformer area;
determining a noise covariance matrix based on the different metering point quantized noise data;
determining an update weight of the line-by-line transformation based on the noise covariance matrix;
and carrying out line-by-line conversion on the electric energy matrix of the electric energy meter in the distribution area based on the updating weight of the line-by-line conversion.
According to the method for determining the running error of the electric energy meter, provided by the invention, the noise covariance matrix is determined based on the quantized noise data of different metering points, and the method comprises the following steps:
quantizing the noise data based on the different metering points to obtain a noise diagonal element estimation matrix and a noise adjacent diagonal element estimation matrix;
determining the noise covariance matrix based on the noise diagonal element estimation matrix and the noise pre-diagonal element estimation matrix.
According to the method for determining the running error of the electric energy meter, the step of performing line-by-line conversion on the electric energy meter power utilization matrix of the district based on the updating weight of the line-by-line conversion comprises the following steps:
initializing a first row weight of the electricity consumption matrix of the electric energy meter of the transformer area;
and updating the weights of other rows except the first row line by line based on the updating weights of the line-by-line conversion, and determining the power consumption matrix of the electric energy meter of the transformer area after the last row of weights are updated as stable power consumption data of the electric energy meter of the transformer area.
According to the method for determining the operation error of the electric energy meter, which is provided by the invention, an electric energy meter error model is constructed and solved based on the stable power consumption data of the district electric energy meter, and the operation error of the district electric energy meter is determined, and the method comprises the following steps:
constructing an electric energy meter error model based on the stable power consumption data of the electric energy meter of the distribution room;
solving the electric energy meter error model by adopting a linear regression method, and determining the error coefficient of the station area electric energy meter;
and determining the operation error of the district electric energy meter based on the error coefficient of the district electric energy meter.
The invention also provides a device for determining the running error of the electric energy meter, which comprises:
the input module is used for determining the electricity consumption data of the electric energy meter in the transformer area;
the correlation elimination module is used for eliminating the correlation of quantization noise of different metering points in the power consumption data of the district electric energy meter to obtain stable power consumption data of the district electric energy meter;
and the error estimation module is used for constructing an electric energy meter error model and solving the electric energy meter error model based on the stable power consumption data of the district electric energy meter, and determining the operation error of the district electric energy meter.
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 executes the program to realize the method for determining the running error of the electric energy meter.
The present 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 determining an operating error of an electric energy meter as in any one of the above.
The invention also provides a computer program product, which comprises a computer program, and when the computer program is executed by a processor, the method for determining the operation error of the electric energy meter is realized.
According to the method and the device for determining the running error of the electric energy meter, provided by the invention, the problem that the error coefficient of the electric energy meter is not accurately estimated due to large fluctuation generated in the process of solving the error coefficient by an electric energy meter error model caused by quantization noise is found by researching the special properties of the quantization noise of the electric energy meter electricity consumption data in a station area. By removing the correlation among different metering points of quantization noise, the noise in the stable power consumption data of the updated transformer area electric energy meter meets the good property given in the Gaussian-Markov determination, and the accuracy and the stability of error estimation are improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for determining an operating error of an electric energy meter according to the present invention;
FIG. 2 is a second schematic flow chart of the method for determining the operating error of the electric energy meter according to the present invention;
FIG. 3 is a schematic structural diagram of an apparatus for determining an operating error of an electric energy meter according to the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In an online monitoring model (namely an electric energy meter error model) for the misalignment error of the intelligent electric meter, the quantization error caused by limited metering precision of the electric meter is one of the sources of noise during model solving, particularly for high-frequency data (acquisition of 15 minutes or even lower high frequency), the quantization noise is a main source of noise because the metering electric quantity is lower in a short time and the amplitude of the quantization noise is unchanged, and the invention aims to relieve the influence of the quantization noise on coefficients during model solving and improve the stability and accuracy of the calculation of the misalignment error coefficients of the intelligent electric meter.
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 in a quadratic function relationship, and the finally established error model of the electric energy meter is as shown in formula 1:
wherein,
an electricity matrix for a summary table representing the power supply area is in the shape of
，
The number of the time points is measured and corresponds to the total number of the equation, and each point is the electricity consumption in the measurement interval.
The electricity utilization matrix for all users (sub-tables) in the distribution area is in the shape of
Each row measures the electricity consumption of each user in the interval,
is the number of the user table.
The error coefficient of each sub-table under the table area is represented in the shape of
。
Expressing a line loss term matrix constructed in an energy conservation equation in the shape of
，
The number of line loss terms added to construct the equation.
The line loss coefficient corresponding to each line loss term is expressed in the shape of
The physical meaning is equivalent resistance.
And the solid loss constant and the sum of the solid losses under the transformer area are expressed, and the solid losses generally come from the loss of the intelligent electric meter.
The method for determining the operation error of the electric energy meter according to the embodiment of the present invention is described below with reference to fig. 1-2, and as shown in fig. 1, the method at least includes the following steps:
102, eliminating the correlation of quantization noise of different metering points in the power consumption data of the district electric energy meter to obtain stable power consumption data of the district electric energy meter;
and 103, constructing an electric energy meter error model and solving the electric energy meter error model based on the stable power consumption data of the district electric energy meter, and determining the operation error of the district electric energy meter.
In step 101, it should be noted that the power consumption data of the electric energy meters in the distribution room includes the power consumption of each electric energy meter in the distribution room within a certain time, and the minimum metering unit of the data is the power consumption in the unit metering interval.
With reference to step 102, it should be noted that, in general, the equation set of the error model of the electric energy meter is directly solved on the basis of the original equation by using methods such as least square and ridge regression to obtain an error coefficient, and at this time, each line in the equation set corresponds to an energy conservation relation in a unit measurement interval (since the application range of the embodiment of the present invention is mainly high-frequency data, it is replaced by 15min in the following). Because the electricity quantity recorded by the intelligent electricity quantity is the accumulated electricity consumption of the user, the electricity consumption in one metering point in the embodiment of the invention is obtained by subtracting the electric energy representation values of two adjacent points which are originally recorded, and the electricity quantity representation value truncation processes of two adjacent points can be regarded as independent when the average electricity consumption in the metering interval is obviously greater than the metering precision.
In the embodiment of the invention, the first step
The quantization noise above the individual coulometric point is expressed as
The quantization noise contained in the 15min electric quantity data obtained by subtracting the two adjacent indicating values is
Next 15min electric quantity data adjacent to itContaining quantization noise of
See the common part therein
This results in quantization noise being correlated among different data points in the current misalignment model. However, this violates the assumption that the noise of each data point should be uncorrelated in the gaussian-markov decision, and the quality of the parameter estimation obtained by using the error model of the electric energy meter will no longer be optimal, which may cause the error coefficient of the electric energy meter obtained by the solution to fluctuate greatly. Therefore, the estimation accuracy and stability of the error coefficient of the electric energy meter can be improved by eliminating the correlation of noise in different data points.
For step 103, it should be noted that, because the noise at each point in the stable power consumption data of the electric energy meter in the distribution room satisfies the assumption in the gaussian-markov decision, the electric energy meter error coefficient after the influence of the quantization noise is alleviated can be obtained by solving using a linear regression method based on the transformed stable power consumption data, so as to implement accurate error estimation.
According to the method for determining the running error of the electric energy meter, provided by the invention, the problem that the error coefficient of the electric energy meter is not accurately estimated due to large fluctuation caused by quantization noise in the process of solving the error coefficient by an electric energy meter error model is found by researching the special properties of the quantization noise of the electric energy meter electricity consumption data in a station area. By removing the correlation among different metering points of quantization noise, the noise in the stable power consumption data of the updated transformer area electric energy meter meets the good property given in the Gaussian-Markov determination, and the accuracy and the stability of error estimation are improved.
It can be understood that the correlation of the quantization noise of different metering points in the power consumption data of the electric energy meter in the area is eliminated by adopting a generalized least square method.
It should be noted that the key of the generalized least square method is to construct a linear transformation matrix
The amount of the solvent to be used is, for example,
is in the shape of
The transformed noise satisfies three properties of homovariance, 0 mean and irrelevance assumed in the Gaussian-Markov decision, and the irrelevance is a problem to be solved by the embodiment of the invention. Needs to be based on in a general solving process
Performing matrix decomposition on the covariance matrix to obtain a transformation matrix
。
It can be understood that, the method for eliminating the correlation of the quantization noise of different metering points in the power consumption data of the district electric energy meter by using the generalized least square method includes:
determining a power consumption matrix of the district electric energy meter based on the power consumption data of the district electric energy meter;
and eliminating the correlation of the quantization noise of different metering points by line-by-line conversion on the electricity consumption matrix of the electric energy meter in the transformer area.
It should be noted that, since the target scene in the embodiment of the present invention is the platform area high-frequency data, the target scene has the characteristics of low metering interval and many points. Thus, pair
The operation of decomposing and inverting the matrix of the size and the like can cause extremely high time complexity and often cannot meet the requirement of online calculation and real-time monitoring of an error model of the electric energy meter. To this end, the embodiments of the present invention provide a generalized least square method based on dynamic programming linear complexity based on the principle and property of quantization noise generation, whereOn the basis of the power consumption data of the electric energy meter in the original station area, the data after noise decorrelation is obtained by line-by-line conversion.
Specifically, the changing thinking is as follows: before assuming the electricity quantity matrix of the electric energy meter in the transformer area
The row-wise transform has satisfied the requirement and each row is simply a linear combination of itself and the previous rows, then when considering transform number one
When in use, the reason is that
The row does not contain the original first
The column components are therefore the same as
The lines are uncorrelated and simultaneously due to preceding
The rows have fulfilled their requirements and are therefore not related to each other, so that only the rows need to be connected
In-line and after-conversion
Subtracting the related components in the row, and reasonably adjusting the weight to ensure the variance and the front
The points are the same.
The method for determining the running error of the electric energy meter realizes generalized least square transformation through line-by-line transformation based on the quantization noise property, avoids decomposing a large matrix of the dot number square scale in high-frequency data, and can be applied to real-time online calculation.
It can be understood that, the eliminating the correlation of the quantization noise of the different metering points by the line-by-line transformation to the electricity consumption matrix of the district electric energy meter includes:
determining quantized noise data of different metering points based on power consumption data of the electric energy meter of the distribution room;
determining a noise covariance matrix based on the different metering point quantized noise data;
determining an update weight of the line-by-line transformation based on the noise covariance matrix;
and carrying out line-by-line conversion on the electric energy matrix of the electric energy meter in the distribution area based on the updating weight of the line-by-line conversion.
It should be noted that the second step is to model the quantization noise form
The quantization noise above the individual coulometric point is expressed as
The quantization noise contained in the 15min electric quantity data obtained by subtracting the two adjacent indicating values is
，
Is the first
Quantization noise data for each measurement point. After modeling, the quantization noise is clearer in nature, and the covariance matrix can be simplified and estimated on the basis of the quantization noise. The update weight is the weight of each element of each line after transformation.
It is to be understood that the determining a noise covariance matrix based on the quantization noise data of the different metrology points includes:
quantizing the noise data based on the different metering points to obtain a noise diagonal element estimation matrix and a noise adjacent diagonal element estimation matrix;
determining the noise covariance matrix based on the noise diagonal element estimation matrix and the noise pre-diagonal element estimation matrix.
It should be noted that, the first in the electricity consumption matrix of the electric energy meter in the original district
Quantization noise contained in point noise
Random noise independent of each point
，
The thermal noise generated on the circuit in a unit time can be generally considered as thermal noise, because the thermal noise at different points and the quantization noise at different stage points are independent of each other, that is, each variable between different subscripts is an independent random variable, when the covariance matrix of the noise between each measurement point is constructed, the covariance matrix is recorded as
In the shape of
Only the common variable is arranged between two points with the difference between the horizontal coordinate and the vertical coordinate less than or equal to 1, and the other points are 0 independently, so that only the diagonal element and the adjacent diagonal element adjacent to the diagonal element are considered. Meanwhile, each point noise generally assumes that the variance is the same, so that all diagonal elements are the same and adjacent diagonal elements are the same. The present embodiment therefore only requires two values to represent the covariance matrix here.
Furthermore, since the solution result is not affected by multiplying the two sides of the equation by a non-0 constant, the diagonal element can be appointed to be 1, and at this time, only the adjacent diagonal element is needed to represent the covariance matrix.
Specifically, the calculation method of the diagonal element estimation matrix and the noise adjacent diagonal element estimation matrix includes the following steps:
step a1, solving formula 1 according to the existing method to obtain a fitting residual error matrix
As a sample of the noise,
as shown in equation 2:
step a2, obtaining the noise diagonal element estimation matrix based on the assumption that the noise of each point has the same average value of 0
And said noise diagonal element estimation matrix
Wherein the subscript
Refers to a length taken from the second element of
The matrix of (a) is a matrix of (b),
denotes a length of the element cut to the penultimate
Of the matrix of (a).
Step a3, obtaining the estimation of the adjacent diagonal when the appointed diagonal is 1
Is marked as
，
The covariance matrix to be estimated in step a1 can be characterized according to the ratio of random noise to quantization noise
Is between 0 and-0.5.
It is understood that, the step of converting the power consumption matrix of the station area electric energy meter line by line based on the updated weights of the line by line includes:
initializing a first row weight of the electricity consumption matrix of the electric energy meter of the transformer area;
and updating the weights of other rows except the first row line by line based on the updating weights of the line-by-line conversion, and determining the power consumption matrix of the electric energy meter of the transformer area after the last row of weights are updated as stable power consumption data of the electric energy meter of the transformer area.
It should be noted that, compared to the general transformation algorithm in the generalized least square, the line-by-line iterative method used in the patent based on the quantization noise characteristic avoids decomposing the large matrix of the square scale of the dot number in the high frequency data. In addition, the embodiment realizes a transformation method for the electric energy consumption matrix of the electric energy meter in the transformer area, which is independent from a specific model solving method, so that the method can be mixed with other various algorithms without independent adaptation.
Specifically, the progressive conversion process includes the following steps:
step b1, under the initial condition, the first row of the matrix is processed in such a way that the first row is not transformed and variables are set
Setting an initial value to be 1;
wherein,
meaning the weight value of the corresponding line after removing the correlation component at the time of transformation.
And b2, performing line-by-line conversion on the weight of the next line according to the previous line until the conversion of the electricity utilization quantity matrix of the electric energy meter of the whole station area is completed.
Note that, for the first of the matrix
Line, after update first
Line of
Can be expressed as in equation 3:
wherein,
，
the meaning of (1) is that coefficients used when removing the previous line component, the weights of the updated line
。
The correlation between the noises contained in each point of the obtained equation set is 0, and simultaneously the homodyne property required in the Gaussian-Markov determination can be met through proper weight adjustment, so that the disturbance from quantization noise can be greatly relieved by solving the error coefficient of the electric energy meter through the transformed equation, and meanwhile, the components of random noise are considered in a balanced manner.
It can be understood that, the constructing and solving an electric energy meter error model based on the stable power consumption data of the platform area electric energy meter, and determining the operation error of the platform area electric energy meter includes:
constructing an electric energy meter error model based on the stable power consumption data of the electric energy meter of the distribution room;
solving the electric energy meter error model by adopting a linear regression method, and determining the error coefficient of the station area electric energy meter;
and determining the operation error of the district electric energy meter based on the error coefficient of the district electric energy meter.
It should be noted that, in the misalignment error online monitoring model of the current smart meter, the energy conservation equation is often solved by a linear regression algorithm such as a ridge regression algorithm or a Lasso regression algorithm. After the operation error of the electric energy meter in the transformer area is determined, the electric energy meter with the operation error reaching the standard of the over-tolerance meter is used as the finally screened over-tolerance meter based on the preset standard of the over-tolerance meter, and compared with the situation that the quantized noise is not processed, the screened over-tolerance meter is closer to the actual situation of the transformer area, and the identification accuracy of the over-tolerance meter is improved.
As shown in fig. 2, an embodiment of the present invention discloses a method for determining an operation error of an electric energy meter, which at least includes the following steps:
and step 205, constructing and solving an electric energy meter error model based on the stable power consumption data of the district electric energy meter, and determining the operation error of the district electric energy meter.
According to the method for determining the operation error of the electric energy meter, the noise in a new equation meets the good property given in the Gaussian-Markov decision by removing the correlation between different points of the quantization noise, and therefore the accuracy and the stability of calculation are improved. In addition, compared with a general transformation algorithm in generalized least squares, the line-by-line iteration method based on the quantization noise characteristic in the embodiment of the invention avoids decomposing a large matrix of the square scale of the number of points in high-frequency data, so that the algorithm can be applied to real-time online calculation.
The following describes the operation error determining apparatus of the electric energy meter provided by the present invention, and the operation error determining apparatus of the electric energy meter described below and the operation error determining method of the electric energy meter described above may be referred to correspondingly.
As shown in fig. 3, an apparatus for determining an operating error of an electric energy meter according to an embodiment of the present invention includes:
the input module 301 is used for determining power consumption data of the electric energy meter of the transformer area;
the correlation elimination module 302 is configured to eliminate correlation of quantization noise at different metering points in the power consumption data of the platform area electric energy meter, so as to obtain stable power consumption data of the platform area electric energy meter;
and the error estimation module 303 is configured to construct an electric energy meter error model and solve the electric energy meter error model based on the stable power consumption data of the platform area electric energy meter, and determine an operation error of the platform area electric energy meter.
According to the electric energy meter operation error determining device, the problem that the electric energy meter error coefficient estimation is inaccurate due to large fluctuation generated in the process of solving the error coefficient by the electric energy meter error model caused by quantization noise is found by researching the special properties of the quantization noise of the electric energy meter electricity consumption data in the station area. By removing the correlation among different metering points of quantization noise, the noise in the stable power consumption data of the updated transformer area electric energy meter meets the good property given in the Gaussian-Markov determination, and the accuracy and the stability of error estimation are improved.
It can be understood that the correlation of the quantization noise of different metering points in the power consumption data of the electric energy meter in the area is eliminated by adopting a generalized least square method.
It can be understood that, the method for eliminating the correlation of the quantization noise of different metering points in the power consumption data of the district electric energy meter by using the generalized least square method includes:
determining a power consumption matrix of the district electric energy meter based on the power consumption data of the district electric energy meter;
and eliminating the correlation of the quantization noise of the different metering points through line-by-line conversion on the electricity consumption matrix of the electric energy meter of the distribution area.
It can be understood that, the eliminating the correlation of the quantization noise of the different metering points by the line-by-line transformation to the electricity consumption matrix of the district electric energy meter includes:
determining quantized noise data of different metering points based on the electricity consumption data of the electric energy meter of the transformer area;
based on the different metering point quantization noise data, determining a noise covariance matrix;
determining an update weight of the line-by-line transformation based on the noise covariance matrix;
and carrying out line-by-line conversion on the electric energy matrix of the electric energy meter in the distribution area based on the updating weight of the line-by-line conversion.
It is to be understood that the determining a noise covariance matrix based on the quantization noise data of the different metering points includes:
quantizing the noise data based on the different metering points to obtain a noise diagonal element estimation matrix and a noise adjacent diagonal element estimation matrix;
determining the noise covariance matrix based on the noise diagonal element estimation matrix and the noise pre-diagonal element estimation matrix.
It is understood that, the step of converting the power consumption matrix of the station area electric energy meter line by line based on the updated weights of the line by line includes:
initializing a first row weight of the electricity consumption matrix of the electric energy meter of the transformer area;
and updating the weights of other rows except the first row line by line based on the updating weights of the line-by-line conversion, and determining the power consumption matrix of the electric energy meter of the transformer area after the last row of weights are updated as stable power consumption data of the electric energy meter of the transformer area.
It can be understood that, the constructing and solving an electric energy meter error model based on the stable power consumption data of the platform area electric energy meter, and determining the operation error of the platform area electric energy meter includes:
constructing an electric energy meter error model based on the stable power consumption data of the electric energy meter of the distribution room;
solving the error model of the electric energy meter by adopting a linear regression method, and determining the error coefficient of the district electric energy meter;
and determining the operation error of the district electric energy meter based on the error coefficient of the district electric energy meter.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor) 410, a communication Interface 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke logic instructions in the memory 430 to perform a method of determining an operating error of a power meter, the method comprising:
determining power consumption data of the electric energy meter in the transformer area;
eliminating the correlation of quantization noise of different metering points in the power consumption data of the district electric energy meter to obtain stable power consumption data of the district electric energy meter;
and constructing an electric energy meter error model and solving the electric energy meter error model based on the stable power consumption data of the district electric energy meter, and determining the operation error of the district electric energy meter.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to 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), a magnetic disk, or an optical disk, and various media capable of storing program codes.
In another aspect, the present invention further provides a computer program product, the computer program product including a computer program, the computer program being stored on a non-transitory computer readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing a method for determining an operation error of an electric energy meter, the method being provided by the above methods, and the method includes:
determining power consumption data of the electric energy meter of the distribution room;
eliminating the correlation of quantization noise of different metering points in the power consumption data of the district electric energy meter to obtain stable power consumption data of the district electric energy meter;
and constructing an electric energy meter error model and solving the electric energy meter error model based on the stable power consumption data of the district electric energy meter, and determining the operation error of the district electric energy meter.
In still another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to execute the methods provided by the above methods to determine an operation error of a power meter, the method including:
determining power consumption data of the electric energy meter in the transformer area;
eliminating the correlation of quantization noise of different metering points in the power consumption data of the district electric energy meter to obtain stable power consumption data of the district electric energy meter;
and constructing an electric energy meter error model and solving the electric energy meter error model based on the stable power consumption data of the district electric energy meter, and determining the operation error of the district electric energy meter.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. An electric energy meter operation error determination method is characterized by comprising the following steps:
determining power consumption data of the electric energy meter in the transformer area;
eliminating the correlation of quantization noises of different metering points in the power consumption data of the district electric energy meter by adopting a generalized least square method to obtain stable power consumption data of the district electric energy meter;
constructing an electric energy meter error model and solving the electric energy meter error model based on the stable power consumption data of the district electric energy meter, and determining the operation error of the district electric energy meter;
the method for eliminating the correlation of the quantization noise of different metering points in the power consumption data of the electric energy meter in the transformer area by adopting the generalized least square method comprises the following steps:
determining a power consumption matrix of the district electric energy meter based on the power consumption data of the district electric energy meter;
determining quantized noise data of different metering points based on the electricity consumption data of the electric energy meter of the transformer area;
determining a noise covariance matrix based on the different metering point quantized noise data;
determining an update weight of the line-by-line transformation based on the noise covariance matrix;
and carrying out line-by-line conversion on the electric energy matrix of the electric energy meter in the distribution area based on the updating weight of the line-by-line conversion.
2. The method for determining the operating error of the electric energy meter according to claim 1, wherein the determining a noise covariance matrix based on the quantized noise data of the different metering points comprises:
quantizing the noise data based on the different metering points to obtain a noise diagonal element estimation matrix and a noise adjacent diagonal element estimation matrix;
determining the noise covariance matrix based on the noise diagonal element estimation matrix and the noise pre-diagonal element estimation matrix.
3. The method for determining the operation error of the electric energy meter according to claim 1, wherein the step of converting the electricity consumption matrix of the district electric energy meter line by line based on the update weight of the line by line comprises the following steps:
initializing a first row weight of the electricity consumption matrix of the electric energy meter of the transformer area;
and updating the weights of other rows except the first row line by line based on the updating weights of the line-by-line conversion, and determining the power consumption matrix of the electric energy meter of the transformer area after the last row of weights are updated as stable power consumption data of the electric energy meter of the transformer area.
4. The method for determining the operation error of the electric energy meter according to any one of claims 1 to 3, wherein the step of constructing and solving an electric energy meter error model based on the stable power consumption data of the district electric energy meter to determine the operation error of the district electric energy meter comprises the following steps:
constructing an electric energy meter error model based on the stable power consumption data of the electric energy meter of the distribution room;
solving the electric energy meter error model by adopting a linear regression method, and determining the error coefficient of the station area electric energy meter;
and determining the operation error of the district electric energy meter based on the error coefficient of the district electric energy meter.
5. An electric energy meter operation error determination apparatus, comprising:
the input module is used for determining the electricity consumption data of the electric energy meter in the transformer area;
the correlation elimination module is used for eliminating the correlation of quantization noise of different metering points in the power consumption data of the district electric energy meter by adopting a generalized least square method to obtain stable power consumption data of the district electric energy meter;
the error estimation module is used for constructing an electric energy meter error model and solving the electric energy meter error model based on the stable power consumption data of the district electric energy meter, and determining the operation error of the district electric energy meter;
the method for eliminating the correlation of the quantization noise of different metering points in the power consumption data of the electric energy meter in the transformer area by adopting the generalized least square method comprises the following steps:
determining a power consumption matrix of the district electric energy meter based on the power consumption data of the district electric energy meter;
determining quantized noise data of different metering points based on power consumption data of the electric energy meter of the distribution room;
determining a noise covariance matrix based on the different metering point quantized noise data;
determining an update weight of the line-by-line transformation based on the noise covariance matrix;
and carrying out line-by-line conversion on the electric energy matrix of the electric energy meter in the distribution area based on the updating weight of the line-by-line conversion.
6. 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 for determining an operating error of an electric energy meter according to any one of claims 1 to 4 when executing the program.
7. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for determining an operating error of a power meter according to any of claims 1 to 4.
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