CN116008898A - Electric energy meter misalignment analysis method and system based on ridge regression model - Google Patents
Electric energy meter misalignment analysis method and system based on ridge regression model Download PDFInfo
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Abstract
The invention discloses an electric energy meter misalignment analysis method and system based on a ridge regression model. Wherein the method comprises the following steps: collecting daily frozen electric quantity data of all electric energy meters in a platform area as sub-table data, and collecting daily frozen electric quantity data of the total table of the platform area as total table data; subtracting the sum of all sub-table data from the total table data, calculating a line loss value curve of the platform area, and initializing a misalignment analysis point t=1, wherein t is more than or equal to 1 and less than or equal to n; calculating a t-th misalignment analysis point kernel function matrix; determining an objective function of a line loss value of a t-th misalignment analysis point and all sub-table data of the platform area, and obtaining an initial solution of the t-th misalignment analysis point; calculating an adaptive weighting matrix; obtaining a final solution weighting solution of the t point; if t+1 is less than or equal to n, updating t, namely t=t+1, recalculating a kernel function matrix, an initial solution and a weighted solution to perform misalignment analysis of the next point, otherwise, determining an out-of-tolerance table list according to out-of-tolerance specific gravity, and determining a judging result; and uploading the judging result.
Description
Technical Field
The invention relates to the technical field of electric energy meter detection, in particular to an electric energy meter misalignment analysis method and system based on a ridge regression model.
Background
The electric energy meter misalignment analysis scheme based on big data generally collects electricity utilization information of all user electric energy meters and total tables of the areas in a certain number of areas through intelligent measurement terminals of low-voltage areas, mainly uses daily frozen electric quantity data as a main part, establishes a linear equation set of the total tables of the areas and the user meters according to 'energy conservation', and writes the linear equation set into a matrix form as follows:
Y＝X(1+β)
wherein Y εR n Representing total electric quantity data of a platform region, X epsilon R n×m Representing electric quantity of electric energy meter of users in transformer area, beta epsilon R m And the vector is formed by the metering error value of each electric energy meter, n represents the number of data points, and m represents the number of the electric energy meters. Defining L=Y-sum (X) to represent the total data minus the line loss value added by the sub-table data, and the above formula is L=Xbeta, and the solution process is the least two problems, which are solved as follows
Wherein the method comprises the steps of
And the least square estimation of the beta coefficient is used for representing the calculation value of the metering error of the electric energy meter. Taking a single-phase table as an example, +.>
And if the absolute value of the coefficient corresponding to a certain electric energy meter is larger than 0.02, the electric energy meter is considered as an out-of-tolerance meter.
And the number of the electric quantity data acquisition points is not less than the number of the electric energy meters in the platform area for the electric meter misalignment analysis model.
The existing technical scheme based on big data means mainly adopts a linear regression method represented by least square. The method has the following defects: three problems are typically encountered in the application process by least squares: the least square belongs to a static model, the state of an object to be modeled is kept stable, and the metering error of the electric energy meter generally has an offset phenomenon in a long period; secondly, the least square is required to calculate the number of points to be larger than the number of the electric energy meters, but the condition that some users in the existing station areas exceed two hundred or more exists, the requirement on the acquisition period is larger, and when the number of data points is insufficient, the traditional least square cannot be solved, so that the detection of the metering state of the electric energy meters cannot be realized; thirdly, the least square solution is easy to generate a fitting phenomenon, more false alarm phenomena are easy to generate corresponding to the problem of the misalignment analysis of the ammeter, and the number of out-of-tolerance ammeter in practice is generally small, so that a clear misalignment analysis result cannot be given.
Disclosure of Invention
According to the invention, an electric energy meter misalignment analysis method and system based on a ridge regression model are provided, so that the defects existing in the prior art are overcome.
According to a first aspect of the present invention, there is provided an electric energy meter misalignment analysis method based on a ridge regression model, comprising:
collecting daily frozen electric quantity data of all electric energy meters in a platform area as sub-table data, and collecting daily frozen electric quantity data of the total table of the platform area as total table data;
subtracting the sum of all sub-table data from the total table data, calculating a line loss value curve of the platform area, and initializing a misalignment analysis point t=1, wherein t is more than or equal to 1 and less than or equal to n;
calculating a t-th misalignment analysis point kernel function matrix;
determining an objective function of a line loss value of a t-th misalignment analysis point and all sub-table data of the platform area, and obtaining an initial solution of the t-th misalignment analysis point;
calculating an adaptive weighting matrix according to the initial solution of the t-th misalignment analysis point, the scale of the area and the number of points;
determining a weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area, and obtaining a final solution weighted solution of the t-th point according to the weighted objective function;
if t+1 is less than or equal to n, updating t, namely t=t+1, recalculating a kernel function matrix, an initial solution and a weighted solution to perform misalignment analysis of the next point, otherwise, determining an out-of-tolerance table list according to out-of-tolerance specific gravity, and determining a judging result;
and uploading the judging result.
Optionally, the calculation formula of the line loss value of the platform area includes:
calculating the line loss value of the platform area according to the following formula:
wherein l j A station area line loss value representing the jth data acquisition point, y j Total data representing jth data acquisition point, x ji And the data of the ith sub-table of the jth data acquisition point is represented, and m represents the number of electric energy meters in the platform region, namely the scale of the platform region.
Optionally, calculating the t-th misalignment analysis point kernel function matrix W (t) includes:
the t-th misalignment analysis point kernel function matrix W (t) is calculated according to the following formula:
wherein exp (g) represents an exponential operation, n represents a data point number, q represents a sequence number of the data point number, and h represents a kernel parameter.
Optionally, determining an objective function of the t-th misalignment analysis point line loss value and all sub-table data of the area includes:
determining the objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area according to the following formula:
wherein the vector L epsilon R n The representation is represented by l j The line loss value curve is formed, X epsilon R n×m Represented by x ji The sub-table data matrix is formed, lambda is the ridge parameter, and then the initial solution is that
Wherein the method comprises the steps of
For least squares estimation of β (t), I is an identity matrix in m dimensions.
Optionally, calculating an adaptive weighting matrix according to the initial solution of the t-th misalignment analysis point, the area scale and the point number, including:
the adaptive weighting matrix is calculated according to the following formula:
where ln (g) represents a logarithmic function based on a natural number e,
representation->
Is a component of the group.
Optionally, determining a weighted objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area includes:
determining a weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area according to the following formula:
the final solution of the t-th misalignment analysis point is
Wherein the method comprises the steps of
Is a least squares estimation of beta' (t).
Optionally, determining the out-of-tolerance list according to the out-of-tolerance specific gravity includes:
combining the final solutions of all n analysis points into coefficient matrix B, i.e
The ith row of the matrix B is formed, and the ith column (i is more than or equal to 1 and less than or equal to m) of the matrix B represents a metering error state change curve of the ith electric energy meter in the whole period;
counting the proportion of points with absolute value exceeding 0.02 of the metering error state change curve of the ith electric energy meter to the total point n, namely the out-of-tolerance proportion, and marking as r i If r i If the value is larger than the threshold value gamma, the table is regarded as an out-of-tolerance table, otherwise, the observation is continued.
According to another aspect of the present invention, there is also provided an electric energy meter misalignment analysis system based on a ridge regression model, including:
the table data acquisition module is used for acquiring daily frozen electric quantity data of all electric energy meters in the table area as sub-table data and acquiring daily frozen electric quantity data of the table area total table as total table data;
the calculating platform area line loss module is used for subtracting the sum of all sub-table data from the total table data, calculating a platform area line loss value curve and initializing a misalignment analysis point t=1, wherein t is more than or equal to 1 and less than or equal to n;
the calculating kernel function matrix module is used for calculating a t-th misalignment analysis point kernel function matrix;
the initial solution module is used for determining the objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area, and obtaining the initial solution of the t-th misalignment analysis point;
the calculation weighting matrix module is used for calculating an adaptive weighting matrix according to the initial solution of the t-th misalignment analysis point, the scale of the station area and the point number;
the weighted solution module is used for determining a weighted objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area, and obtaining a final solution weighted solution of the t-th point according to the weighted objective function;
the determining and judging result module is used for updating t if t+1 is less than or equal to n, namely t=t+1, recalculating a kernel function matrix, an initial solution and a weighted solution to perform misalignment analysis of the next point, otherwise, determining an out-of-tolerance list according to out-of-tolerance specific gravity, and determining a judging result;
and the uploading judgment result module is used for uploading the judgment result.
Optionally, the calculating platform area line loss module includes:
the calculating station area line loss submodule is used for calculating a station area line loss value according to the following formula:
wherein l j A station area line loss value representing the jth data acquisition point, y j Total data representing jth data acquisition point, x ji And the data of the ith sub-table of the jth data acquisition point is represented, and m represents the number of electric energy meters in the platform region, namely the scale of the platform region.
Optionally, the computing kernel function matrix module includes:
the calculation kernel function matrix submodule calculates a t-th misalignment analysis point kernel function matrix W (t) according to the following formula:
wherein exp (g) represents an exponential operation, n represents a data point number, q represents a sequence number of the data point number, and h represents a kernel parameter.
Optionally, obtaining an initial solution module includes:
the determining objective function sub-module is used for determining the objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area according to the following formula:
wherein the vector L epsilon R n The representation is represented by l j The line loss value curve is formed, X epsilon R n×m Represented by x ji The sub-table data matrix is formed, lambda is the ridge parameter, and then the initial solution is that
Wherein the method comprises the steps of
For least squares estimation of β (t), I is an identity matrix in m dimensions.
Optionally, the calculating the weight matrix module includes:
the calculation weighting matrix submodule is used for calculating an adaptive weighting matrix according to the following formula:
where ln (g) represents a logarithmic function based on a natural number e,
representation->
Is a component of the group.
Optionally, obtaining the weighted solution module includes:
the determining weighted objective function sub-module is used for determining the weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area according to the following formula:
the final solution of the t-th misalignment analysis point is
Wherein the method comprises the steps of
Is a least squares estimation of beta' (t).
Optionally, the determining a judgment result module includes:
a combined coefficient matrix sub-module for combining the final solutions of all n analysis points into a coefficient matrix B, i.e
The ith row of the matrix B is formed, and the ith column (i is more than or equal to 1 and less than or equal to m) of the matrix B represents a metering error state change curve of the ith electric energy meter in the whole period;
the statistical out-of-tolerance specific gravity sub-module is used for counting the specific gravity of the point number of which the absolute value of the metering error state change curve of the ith electric energy meter exceeds 0.02 to the total point number n, and is called out-of-tolerance specific gravity and marked as r i If r i If the value is larger than the threshold value gamma, the table is regarded as an out-of-tolerance table, otherwise, the observation is continued.
Therefore, the state change of the metering error of the electric energy meter can be obtained by introducing a kernel function matrix to convert the traditional least square into a dynamic solving mode; the method has the advantages that the ridge regression model is introduced, so that the problem that the traditional least square method cannot solve when the number of data points is less than the number of the electric energy meters is effectively solved; the adaptive weighting mechanism is designed, the out-of-tolerance table and the normal table are effectively separated while the different condition areas are automatically matched, and the universality is strong. Meanwhile, the invention is easy to realize, and can complete analysis by only acquiring all sub-table and total-table electric quantity data in the platform area, thereby saving time and economic cost and ensuring working efficiency.
Drawings
Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
fig. 1 is a schematic flow chart of an electric energy meter misalignment analysis method based on a ridge regression model according to the present embodiment;
FIG. 2 is a schematic diagram of an ammeter misalignment analysis flow based on an adaptive ridge regression model according to the present embodiment;
fig. 3 is a schematic diagram of out-of-tolerance specific gravity of different sub-tables according to the present embodiment;
fig. 4 is a schematic diagram showing the state change of the metering error curves of two out-of-tolerance tables according to the present embodiment;
fig. 5 is a schematic diagram of an electric energy meter misalignment analysis system based on a ridge regression model according to the present embodiment.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the examples described herein, which are provided to fully and completely disclose the present invention and fully convey the scope of the invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, like elements/components are referred to by like reference numerals.
Unless otherwise indicated, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, it will be understood that terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
According to a first aspect of the present invention, there is provided a method 100 for analyzing misalignment of an electric energy meter based on a ridge regression model, referring to fig. 1, the method 100 includes:
s101, collecting daily frozen electric quantity data of all electric energy meters in a station area as sub-table data, and collecting daily frozen electric quantity data of a total table of the station area as total table data;
s102, subtracting the sum of all sub-table data from the total table data, calculating a line loss value curve of a platform area, and initializing a misalignment analysis point t=1, wherein t is more than or equal to 1 and less than or equal to n;
s103, calculating a t-th misalignment analysis point kernel function matrix W (t);
s104, determining an objective function of a line loss value of a t-th misalignment analysis point and all sub-table data of the platform area, and obtaining an initial solution of the t-th misalignment analysis point;
s105, calculating an adaptive weighting matrix according to the initial solution of the t-th misalignment analysis point, the scale of the area and the number of points;
s106, determining a weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area, and obtaining a final solution weighted solution of the t-th point according to the weighted objective function;
s107, if t+1 is less than or equal to n, updating t, namely t=t+1, recalculating a kernel function matrix, an initial solution and a weighted solution to perform misalignment analysis of the next point, otherwise, determining an out-of-tolerance list according to out-of-tolerance specific gravity, and determining a judging result;
s108, uploading the judging result.
Specifically, referring to fig. 2, step S1, the intelligent measurement terminal continuously collects daily frozen electric quantity data of all electric energy meters in the station area through carrier communication, which is called sub-table data; collecting daily frozen electric quantity data of a table area summary table, which is called summary table data;
step S2, subtracting the sum of all sub-table data from the total table data to obtain a line loss value curve of the transformer area, and initializing a misalignment analysis point t=1, wherein t is more than or equal to 1 and less than or equal to n;
step S3, calculating a t-th misalignment analysis point kernel function matrix W (t), wherein the formula is
Wherein exp (g) represents an exponent operation n represents a data point number, q represents a sequence number of the data point number, and h represents a core parameter;
step S4, defining an objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area, and obtaining an initial solution of the t-th point;
step S5, calculating a corresponding self-adaptive weighting matrix through an initial solution of a t-th misalignment analysis point, a platform area scale and a point number;
step S6, defining a weighted objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area to obtain a final solution of the t-th point;
step S7, if t+1 is less than or equal to n, updating t, namely t=t+1, repeating the steps S3-S6 to perform misalignment analysis of the next point, otherwise, determining an out-of-tolerance list according to the out-of-tolerance specific gravity;
and S8, the intelligent measurement terminal uploads the judgment result.
Further, in step S2, the formula for calculating the line loss of the area is:
where lj represents the line loss value of the station area of the jth data acquisition point, yj represents the total data of the jth data acquisition point, xji represents the ith sub-table data of the jth data acquisition point, and m represents the number of electric energy meters in the station area, namely the scale of the station area.
Further, in step S4, the objective function of the line loss value and all sub-table data of the area is
Wherein vector L ε R n Represents a line loss value curve formed by lj, X ε R n×m Representing the sub-table data matrix formed by xji, wherein lambda is a ridge parameter, and the initial solution is
Wherein the method comprises the steps of
For least squares estimation of β (t), I is an identity matrix in m dimensions.
Further, in step S5, the adaptive weighting matrix is
Where ln (g) represents a logarithmic function based on a natural number e,
representation->
Is a component of the group.
Further, in step S6, the weighted objective function of the line loss value and all sub-table data of the area is
The final solution of the t-th misalignment analysis point is
Wherein the method comprises the steps of
A least squares estimate of β' (t);
further, the step of determining the out-of-tolerance table in step S7 is as follows:
a1: combining the final solutions of all n analysis points into coefficient matrix B, i.e
The ith row of the matrix B is formed, and the ith column (i is more than or equal to 1 and less than or equal to m) of the matrix B represents a metering error state change curve of the ith electric energy meter in the whole period;
a2: counting the proportion of points with absolute value exceeding 0.02 of the metering error state change curve of the ith electric energy meter to the total point n, namely the out-of-tolerance proportion, and marking as r i If r i If the value is larger than the threshold value gamma, the table is regarded as an out-of-tolerance table, otherwise, the observation is continued.
The invention is further described with reference to the examples of implementation of figures 2-4. In the example, the data come from a certain low-voltage station area, 60 electric energy meters are arranged in the station area, and 90-day frozen electric quantity data are collected. Through the above using steps of the invention, two out-of-tolerance meters (the 9 th and 15 th electric energy meters) in the platform area can be identified, the out-of-tolerance specific gravity is shown in figure 3, and the specific metering error change curve is shown in figure 4. In particular, in the implementation of the invention: in step S4, the ridge parameter lambda is set to 1; the threshold γ of step A2 in step S7 is set to 0.5.
Therefore, the state change of the metering error of the electric energy meter can be obtained by introducing a kernel function matrix to convert the traditional least square into a dynamic solving mode; the method has the advantages that the ridge regression model is introduced, so that the problem that the traditional least square method cannot solve when the number of data points is less than the number of the electric energy meters is effectively solved; the adaptive weighting mechanism is designed, the out-of-tolerance table and the normal table are effectively separated while the different condition areas are automatically matched, and the universality is strong. Meanwhile, the invention is easy to realize, and can complete analysis by only acquiring all sub-table and total-table electric quantity data in the platform area, thereby saving time and economic cost and ensuring working efficiency.
Optionally, the calculation formula of the line loss value of the platform area includes:
calculating the line loss value of the platform area according to the following formula:
wherein l j A station area line loss value representing the jth data acquisition point, y j Total data representing jth data acquisition point, x ji And the data of the ith sub-table of the jth data acquisition point is represented, and m represents the number of electric energy meters in the platform region, namely the scale of the platform region.
Optionally, calculating the t-th misalignment analysis point kernel function matrix W (t) includes:
the t-th misalignment analysis point kernel function matrix W (t) is calculated according to the following formula:
wherein exp (g) represents an exponential operation, n represents a data point number, q represents a sequence number of the data point number, and h represents a kernel parameter.
Optionally, determining an objective function of the t-th misalignment analysis point line loss value and all sub-table data of the area includes:
determining the objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area according to the following formula:
wherein the vector L epsilon R n The representation is represented by l j The line loss value curve is formed, X epsilon R n×m Represented by x ji The sub-table data matrix is formed, lambda is the ridge parameter, and then the initial solution is that
Wherein the method comprises the steps of
For least squares estimation of β (t), I is an identity matrix in m dimensions.
Optionally, calculating an adaptive weighting matrix according to the initial solution of the t-th misalignment analysis point, the area scale and the point number, including:
the adaptive weighting matrix is calculated according to the following formula:
where ln (g) represents a logarithmic function based on a natural number e,
representation->
Is a component of the group.
Optionally, determining a weighted objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area includes:
determining a weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area according to the following formula:
the final solution of the t-th misalignment analysis point is
Wherein the method comprises the steps of
Is a least squares estimation of beta' (t).
Optionally, determining the out-of-tolerance list according to the out-of-tolerance specific gravity includes:
combining the final solutions of all n analysis points into coefficient matrix B, i.e
The ith row of the matrix B is formed, and the ith column (i is more than or equal to 1 and less than or equal to m) of the matrix B represents a metering error state change curve of the ith electric energy meter in the whole period;
counting the proportion of points with absolute value exceeding 0.02 of the metering error state change curve of the ith electric energy meter to the total point n, namely the out-of-tolerance proportion, and marking as r i If r i If the value is larger than the threshold value gamma, the table is regarded as an out-of-tolerance table, otherwise, the observation is continued.
Therefore, the state change of the metering error of the electric energy meter can be obtained by introducing a kernel function matrix to convert the traditional least square into a dynamic solving mode; the method has the advantages that the ridge regression model is introduced, so that the problem that the traditional least square method cannot solve when the number of data points is less than the number of the electric energy meters is effectively solved; the adaptive weighting mechanism is designed, the out-of-tolerance table and the normal table are effectively separated while the different condition areas are automatically matched, and the universality is strong. Meanwhile, the invention is easy to realize, and can complete analysis by only acquiring all sub-table and total-table electric quantity data in the platform area, thereby saving time and economic cost and ensuring working efficiency. .
In accordance with another aspect of the present invention, there is also provided a system 500 for analyzing misalignment of an electric energy meter based on a ridge regression model, referring to fig. 5, the system 500 comprising:
the table data collection module 510 is configured to collect daily frozen power data of all electric energy meters in the platform area as sub-table data, and collect daily frozen power data of a total table of the platform area as total table data;
the calculating platform area line loss module 520 is configured to subtract the sum of all sub-table data from the total table data, calculate a platform area line loss value curve, and initialize a misalignment analysis point t=1, where t is greater than or equal to 1 and less than or equal to n;
a calculate kernel function matrix module 530 for calculating a t-th misalignment analysis point kernel function matrix;
the initial solution obtaining module 540 is configured to determine an objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the area, and obtain an initial solution of the t-th misalignment analysis point;
a module 550 for calculating a weight matrix according to the initial solution, the area scale and the number of points of the t-th misalignment analysis point;
the obtaining weighted solution module 560 is configured to determine a weighted objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area, and obtain a final solution weighted solution of the t-th point according to the weighted objective function;
a determining and judging result module 570, configured to update t if t+1 is less than or equal to n, i.e., t=t+1, recalculate the kernel function matrix, the initial solution, and the weighted solution to perform misalignment analysis of the next point, or determine an out-of-tolerance table list according to the out-of-tolerance specific gravity, and determine a judging result;
and an upload judgment result module 580 for uploading the judgment result.
Optionally, the calculating platform area line loss module includes:
the calculating station area line loss submodule is used for calculating a station area line loss value according to the following formula:
wherein l j A station area line loss value representing the jth data acquisition point, y j Total data representing jth data acquisition point, x ji And the data of the ith sub-table of the jth data acquisition point is represented, and m represents the number of electric energy meters in the platform region, namely the scale of the platform region.
Optionally, the computing kernel function matrix module includes:
the calculation kernel function matrix submodule calculates a t-th misalignment analysis point kernel function matrix W (t) according to the following formula:
wherein exp (g) represents an exponential operation, n represents a data point number, q represents a sequence number of the data point number, and h represents a kernel parameter.
Optionally, obtaining an initial solution module includes:
the determining objective function sub-module is used for determining the objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area according to the following formula:
wherein the vector L epsilon R n The representation is represented by l j Line loss value curve formed by X epsilon Rn×m Represented by x ji The sub-table data matrix is formed, lambda is the ridge parameter, and then the initial solution is that
Wherein the method comprises the steps of
For least squares estimation of β (t), I is an identity matrix in m dimensions.
Optionally, the calculating the weight matrix module includes:
the calculation weighting matrix submodule is used for calculating an adaptive weighting matrix according to the following formula:
where ln (g) represents a logarithmic function based on a natural number e,
representation->
Is a component of the group.
Optionally, obtaining the weighted solution module includes:
the determining weighted objective function sub-module is used for determining the weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area according to the following formula:
the final solution of the t-th misalignment analysis point is
Wherein the method comprises the steps of
Is a least squares estimation of beta' (t).
Optionally, the determining a judgment result module includes:
a combined coefficient matrix sub-module for combining the final solutions of all n analysis points into a coefficient matrix B, i.e
Form row t of matrix B, when the matrixThe ith column (i is more than or equal to 1 and less than or equal to m) of the B represents a metering error state change curve of the ith electric energy meter in the whole period;
the statistical out-of-tolerance specific gravity sub-module is used for counting the specific gravity of the point number of which the absolute value of the metering error state change curve of the ith electric energy meter exceeds 0.02 to the total point number n, and is called out-of-tolerance specific gravity and marked as r i If r i If the value is larger than the threshold value gamma, the table is regarded as an out-of-tolerance table, otherwise, the observation is continued.
The electric energy meter misalignment analysis system 500 based on the ridge regression model according to the embodiment of the present invention corresponds to the electric energy meter misalignment analysis method 100 based on the ridge regression model according to another embodiment of the present invention, and is not described herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (14)
1. The utility model provides an electric energy meter misalignment analysis method based on a ridge regression model, which is characterized by comprising the following steps:
collecting daily frozen electric quantity data of all electric energy meters in a platform area as sub-table data, and collecting daily frozen electric quantity data of the total table of the platform area as total table data;
subtracting the sum of all sub-table data from the total table data, calculating a line loss value curve of the platform area, and initializing a misalignment analysis point t=1, wherein t is more than or equal to 1 and less than or equal to n;
calculating a t-th misalignment analysis point kernel function matrix W (t);
determining an objective function of a line loss value of a t-th misalignment analysis point and all sub-table data of the platform area, and obtaining an initial solution of the t-th misalignment analysis point;
calculating an adaptive weighting matrix according to the initial solution of the t-th misalignment analysis point, the scale of the area and the number of points;
determining a weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area, and obtaining a final solution weighted solution of the t-th point according to the weighted objective function;
if t+1 is less than or equal to n, updating t, namely t=t+1, recalculating a kernel function matrix, an initial solution and a weighted solution to perform misalignment analysis of the next point, otherwise, determining an out-of-tolerance table list according to out-of-tolerance specific gravity, and determining a judging result;
and uploading the judging result.
2. The method of claim 1, wherein the calculation formula of the line loss value of the station area includes:
calculating the line loss value of the platform area according to the following formula:
wherein l j A station area line loss value representing the jth data acquisition point, y j Total data representing jth data acquisition point, x ji And the data of the ith sub-table of the jth data acquisition point is represented, and m represents the number of electric energy meters in the platform region, namely the scale of the platform region.
3. The method according to claim 1, wherein calculating a t-th misalignment analysis point kernel function matrix W (t) comprises:
the t-th misalignment analysis point kernel function matrix W (t) is calculated according to the following formula:
wherein exp (g) represents an exponential operation, n represents a data point number, q represents a sequence number of the data point number, and h represents a kernel parameter.
4. The method of claim 1, wherein determining an objective function of the t-th misalignment analysis point line loss value and all sub-table data for the region comprises:
determining the objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area according to the following formula:
wherein the vector L epsilon R n The representation is represented by l j The line loss value curve is formed, X epsilon R n×m Represented by x ji The sub-table data matrix is formed, lambda is the ridge parameter, and then the initial solution is that
Wherein the method comprises the steps of
For least squares estimation of β (t), I is an identity matrix in m dimensions.
5. The method of claim 1, wherein calculating an adaptive weighting matrix based on the initial solution of the t-th misalignment analysis point, the region size, and the number of points comprises:
the adaptive weighting matrix is calculated according to the following formula:
wherein ln (g) is expressed in natureThe number e is a logarithmic function of the base,
representation->
Is a component of the group.
6. The method of claim 1, wherein determining a weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data for the region comprises:
determining a weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area according to the following formula:
the final solution of the t-th misalignment analysis point is
Wherein the method comprises the steps of
Is a least squares estimation of beta' (t).
7. The method of claim 1, wherein determining the list of out-of-tolerance tables based on out-of-tolerance specific gravity comprises:
combining the final solutions of all n analysis points into coefficient matrix B, i.e
The ith row of the matrix B is formed, and the ith column (i is more than or equal to 1 and less than or equal to m) of the matrix B represents a metering error state change curve of the ith electric energy meter in the whole period;
counting the proportion of points with absolute value exceeding 0.02 of the metering error state change curve of the ith electric energy meter to the total point n, namely the out-of-tolerance proportion, and marking as r i If r i If the value is larger than the threshold value gamma, the table is regarded as an out-of-tolerance table, otherwise, the observation is continued.
8. An electric energy meter misalignment analysis system based on a ridge regression model, comprising:
the table data acquisition module is used for acquiring daily frozen electric quantity data of all electric energy meters in the table area as sub-table data and acquiring daily frozen electric quantity data of the table area total table as total table data;
the calculating platform area line loss module is used for subtracting the sum of all sub-table data from the total table data, calculating a platform area line loss value curve and initializing a misalignment analysis point t=1, wherein t is more than or equal to 1 and less than or equal to n;
the calculating kernel function matrix module is used for calculating a t-th misalignment analysis point kernel function matrix;
the initial solution module is used for determining the objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area, and obtaining the initial solution of the t-th misalignment analysis point;
the calculation weighting matrix module is used for calculating an adaptive weighting matrix according to the initial solution of the t-th misalignment analysis point, the scale of the station area and the point number;
the weighted solution module is used for determining a weighted objective function of the line loss value of the t-th misalignment analysis point and all sub-table data of the platform area, and obtaining a final solution weighted solution of the t-th point according to the weighted objective function;
the determining and judging result module is used for updating t if t+1 is less than or equal to n, namely t=t+1, recalculating a kernel function matrix, an initial solution and a weighted solution to perform misalignment analysis of the next point, otherwise, determining an out-of-tolerance list according to out-of-tolerance specific gravity, and determining a judging result;
and the uploading judgment result module is used for uploading the judgment result.
9. The system of claim 8, wherein the computing a cell line loss module comprises:
the calculating station area line loss submodule is used for calculating a station area line loss value according to the following formula:
wherein l j A station area line loss value representing the jth data acquisition point, y j Total data representing jth data acquisition point, x ji And the data of the ith sub-table of the jth data acquisition point is represented, and m represents the number of electric energy meters in the platform region, namely the scale of the platform region.
10. The system of claim 8, wherein the computing a kernel function matrix module comprises:
the calculation kernel function matrix submodule calculates a t-th misalignment analysis point kernel function matrix W (t) according to the following formula:
wherein exp (g) represents an exponential operation, n represents a data point number, q represents a sequence number of the data point number, and h represents a kernel parameter.
11. The system of claim 8, wherein obtaining an initial solution module comprises:
the determining objective function sub-module is used for determining the objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area according to the following formula:
wherein the vector L epsilon R n The representation is represented by l j The line loss value curve is formed, X epsilon R n×m Represented by x ji The data matrix of the sub-table is composed, lambda is the ridge parameterThen the initial solution is
Wherein the method comprises the steps of
For least squares estimation of β (t), I is an identity matrix in m dimensions.
12. The system of claim 8, wherein calculating the weight matrix module comprises:
the calculation weighting matrix submodule is used for calculating an adaptive weighting matrix according to the following formula:
where ln (g) represents a logarithmic function based on a natural number e,
representation->
Is a component of the group.
13. The system of claim 8, wherein obtaining a weighted solution module comprises:
the determining weighted objective function sub-module is used for determining the weighted objective function of the t-th misalignment analysis point line loss value and all sub-table data of the platform area according to the following formula:
the final solution of the t-th misalignment analysis point is
Wherein the method comprises the steps of
Is a least squares estimation of beta' (t).
14. The system of claim 8, wherein determining the decision result module comprises:
a combined coefficient matrix sub-module for combining the final solutions of all n analysis points into a coefficient matrix B, i.e
The ith row of the matrix B is formed, and the ith column (i is more than or equal to 1 and less than or equal to m) of the matrix B represents a metering error state change curve of the ith electric energy meter in the whole period;
the statistical out-of-tolerance specific gravity sub-module is used for counting the specific gravity of the point number of which the absolute value of the metering error state change curve of the ith electric energy meter exceeds 0.02 to the total point number n, and is called out-of-tolerance specific gravity and marked as r i If r i If the value is larger than the threshold value gamma, the table is regarded as an out-of-tolerance table, otherwise, the observation is continued.
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| CN116859322A (en) * | 2023-09-05 | 2023-10-10 | 青岛鼎信通讯股份有限公司 | Electric energy meter metering error monitoring method based on intelligent measurement terminal |
| CN116859321A (en) * | 2023-09-04 | 2023-10-10 | 青岛鼎信通讯科技有限公司 | Electric energy meter metering error monitoring method based on energy controller |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116859321A (en) * | 2023-09-04 | 2023-10-10 | 青岛鼎信通讯科技有限公司 | Electric energy meter metering error monitoring method based on energy controller |
| CN116859321B (en) * | 2023-09-04 | 2023-12-29 | 青岛鼎信通讯科技有限公司 | Electric energy meter metering error monitoring method based on energy controller |
| CN116859322A (en) * | 2023-09-05 | 2023-10-10 | 青岛鼎信通讯股份有限公司 | Electric energy meter metering error monitoring method based on intelligent measurement terminal |
| CN116859322B (en) * | 2023-09-05 | 2024-01-09 | 青岛鼎信通讯股份有限公司 | Electric energy meter metering error monitoring method based on intelligent measurement terminal |
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