CN115079081A - Intelligent electric meter metering abnormity identification method and device based on electricity consumption data - Google Patents
Intelligent electric meter metering abnormity identification method and device based on electricity consumption data Download PDFInfo
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Abstract
The invention discloses a method and a device for identifying metering abnormality of an intelligent electric meter based on power consumption data, wherein the method comprises the following steps: acquiring original data of power consumption, wherein the original data comprises user file data, power supply quantity data and intelligent electric meter power consumption detail data; analyzing and processing the obtained original data to obtain effective data; constructing an intelligent electric meter metering abnormity identification model, inputting effective data into the intelligent electric meter metering abnormity identification model for calculation, and obtaining the optimal estimated value of the metering error of each user meter under the transformer area; sorting the absolute values of the optimal estimation values of the metering errors of the user meters in the transformer area in a descending order, and recommending the meters corresponding to the sorted number which is alpha% of the total number P of the user meters in the transformer area and is rounded upwards as suspected abnormal smart meters for field verification. According to the method, the error of the intelligent electric meter is solved by optimizing the Gihonov parameter through a generalized cross verification method, and a new means is provided for error calculation of the intelligent electric meter.
Description
Technical Field
The invention belongs to the technical field of electric power metering, and particularly relates to a method and a device for identifying metering abnormality of an intelligent electric meter based on power consumption data.
Background
The intelligent electric meter is an important tool for power utilization and energy-saving management, plays an important role in aspects of power utilization metering, trade settlement, power supply control and the like, and has wide social attention on accuracy. The stable operation of the intelligent electric meter, particularly the metering accuracy, is related to the vital interests of power grid companies and thousands of households, and the metering error of the intelligent electric meter of a user is required to be within 2% in China. The intelligent electric meters are purchased in batches at the early stage of a power grid company in China and installed on terminal users, and the batch of intelligent electric meters reach the verification period year, namely the rotation time, so that the following problems occur: firstly, the intelligent electric energy meter in operation may have a metering misalignment phenomenon before rotation due to temperature, humidity, dust and other reasons; before the rotation, all on-site verification and laboratory detection cannot be regularly carried out on the running intelligent electric energy meters, and the false detection rate of all running electric energy meters cannot be guaranteed to be zero; thirdly, users lack professional metering knowledge and equipment, and cannot find out the intelligent electric energy meter with inaccurate metering in time. The expiration rotation of the intelligent electric meter in the mode of one-cutting is obviously unreasonable. Meanwhile, the work of disassembling and processing old tables, purchasing and installing new tables and the like caused by table replacement generates huge manpower and material waste, and the large-scale table meter disassembly and replacement wastes resources, does not meet the national strategy of carbon peak reaching and carbon neutralization, and needs power failure processing, thereby bringing inconvenience to users.
Disclosure of Invention
The invention mainly aims to overcome the defects and shortcomings in the prior art, and provides a method and a device for identifying metering abnormality of an intelligent electric meter based on power consumption data.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a method for identifying metering abnormality of an intelligent electric meter based on power consumption data, which comprises the following steps:
acquiring original data of power consumption, wherein the original data comprises user file data, power supply quantity data and intelligent electric meter power consumption detail data;
analyzing and processing the obtained original data to obtain effective data;
constructing an intelligent electric meter metering abnormity identification model, inputting effective data into the intelligent electric meter metering abnormity identification model for calculation, and obtaining the optimal estimated value of the metering error of each user meter under the transformer area; the intelligent ammeter metering abnormity identification model is as follows:
||Ax-b|| 2 +||Γx|| 2 ＝min
wherein A is an electricity utilization matrix; b is the measured line loss electric quantity, and x is a variable to be solved;
wherein the content of the first and second substances,
gamma is called as Gihonov matrix, lambda is a regular parameter, | | · | | | represents the 2 norm of Ou;
sorting absolute values of the optimal estimated values of the metering errors of the user meters in the transformer area in a descending order, recommending the meters corresponding to the sorted quantity which is integrated upwards by alpha% of the total quantity P of the user meters in the transformer area as suspected abnormal smart meters for field verification, wherein alpha is a set threshold value, and determining the scale and range of the suspected field verification meter through the alpha value.
As a preferred technical scheme, the data is acquired from a metering automation system, a sampling system or a centralized meter reading system.
As a preferred technical scheme, the user profile data is used for positioning a suspected abnormal electric meter during field verification, and comprises a user number, a name and an electric meter address;
the power supply quantity data comprises information of acquisition date, power supply quantity and whether the acquisition of the electric meter data is successful or not;
and the electricity consumption detail data of the intelligent ammeter is the daily electricity consumption of the historical date of each user ammeter in the transformer area.
As a preferred technical solution, in the step of analyzing and processing the acquired original data and deleting invalid data,
deleting the data missing from the part of historical dates during data processing;
for data with a part of historical dates of which the acquisition success rate is not 100%, indicating that the data of the electric meter is lost, and deleting the data corresponding to the historical dates with the acquisition success rate not 100%;
and deleting the electric meter for the data of which the electricity consumption on all historical dates is blank values.
As a preferred technical solution, in the step of analyzing and processing the acquired original data and deleting invalid data,
the reading of the electric meter on certain historical dates is 0 value, the reading is carried out on other dates, the non-blank value, the non-0 value data quantity and the total quantity of the historical data are compared through a formula (1), if the proportion is less than 10%, all data of the table area total table and each user table corresponding to the historical dates of the non-blank value and the non-0 value of the table are deleted, and then the data of the table are deleted;
where T is the total number of history dates, C j And representing a calculation function, counting the number of non-blank values and non-0 values in the historical date.
As a preferred technical scheme, the effective data is input into the smart meter metering anomaly identification model for calculation, so as to obtain the optimal estimated value of the metering error of each user meter under the distribution room, specifically:
(x) f (Ax-b) T (Ax-b)+(Γx) T (Γx) (2)
Let f (x) have a derivative of 0 for x as follows:
solving the estimated value of x as:
solving the optimal canonical parameters:
the method adopts a regularized parameter optimization method based on generalized cross validation, and the basic principle is that any phase b in the measured value b of the equation (2) i When the removal is carried out, the selected regular parameters can predict the change caused by the removal item, and a generalized cross GCV function is constructed according to an error model and a regularization solution, wherein the expression of the generalized cross GCV function is as follows:
in formula (5), trace is the trace of the matrix, representing the summation of diagonal elements of the matrix, and I is the identity matrix;
obtaining the optimal Gihonov regularization parameter lambda by solving the minimum value of the formula (5) opt ；
V. will lambda opt Bring into the Gihono matrix, get
Sixthly, the Gamma is further treated opt Substitution formula (4) calculates the estimated value of the unknown quantity
Seventhly, handle
Reduction of writing to
Wherein
Namely the optimal estimated value of the metering error of each user table under the transformer area.
The invention provides a system for identifying metering abnormality of an intelligent electric meter based on power consumption data, which is applied to the method for identifying metering abnormality of the intelligent electric meter based on the power consumption data and comprises a data collection module, a data processing module, an error estimation module and an out-of-tolerance recommendation module;
the data collection module is used for acquiring original data of power consumption, wherein the original data comprises user file data, power supply quantity data and intelligent electric meter power consumption detail data;
the data processing module is used for analyzing and processing the acquired original data to obtain effective data;
the error estimation module is used for constructing an intelligent electric meter metering abnormity identification model, inputting effective data into the intelligent electric meter metering abnormity identification model for calculation, and obtaining the optimal estimation value of the metering error of each user meter under the transformer area; the intelligent ammeter metering abnormity identification model is as follows:
||Ax-b|| 2 +||Γx|| 2 ＝min
wherein A is an electricity utilization matrix; b is the measured line loss electric quantity, and x is a variable to be solved;
wherein the content of the first and second substances,
gamma is called as Gihonov matrix, lambda is a regular parameter, | | · | | | represents the 2 norm of Ou;
and the out-of-tolerance recommending module is used for sequencing the absolute values of the optimal estimated values of the metering errors of the user meters in the distribution area from large to small, recommending the meters corresponding to the sorted quantity which is obtained by rounding up alpha% of the total quantity P of the user meters in the distribution area as suspected abnormal smart meters for field verification, wherein alpha is a set threshold value, and determining the scale and the range of the suspected table for field verification through the alpha value.
Yet another aspect of the present invention provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the method for identifying smart meter metering anomalies based on electricity usage data.
In still another aspect, the present invention provides a computer-readable storage medium, which stores a program, and when the program is executed by a processor, the method for identifying metering abnormality of a smart meter based on power consumption data is implemented.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. according to the method, the low-voltage distribution area is taken as a unit, the daily electric quantity information of the intelligent electric meters of the distribution area and the intelligent electric meters of all users in the distribution area is only collected, the list of the intelligent electric meters with the abnormal suspect metering is ranked and recommended according to the data processing method, the model and the generalized cross validation optimization Gihonnov regular parameter solving method, and the power grid company can set the value of alpha according to the actual condition of the power grid company to determine the scale and the range of the on-site suspect meter. The method used by the invention has less information consumption, can process the ill-conditioned problem presented in the existing method, and has more practical recommendation mechanism.
2. According to the method, the electricity consumption data are preprocessed in a missing mode, an abnormal mode and the like, the errors of the intelligent electric meter are solved by optimizing the Gihono parameter through a generalized cross verification method, and a new means is provided for error calculation of the intelligent electric meter.
3. According to the method, the intelligent electric meters with the suspected metering abnormality are recommended through a sorting method, a new strategy is provided for the intelligent electric meters alternately, the inspection and identification efficiency is improved, the operation and maintenance workload of a power grid company is reduced, and the carbon emission is reduced.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for identifying metering abnormality of an intelligent electric meter based on electricity consumption data according to an embodiment of the invention;
FIG. 2 is a flow chart of data preprocessing according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating the result of generalized cross-validation optimization of regularization parameters according to an embodiment of the present invention;
FIG. 4 is a block diagram of an identification system for abnormal metering of an intelligent electric meter based on data of power consumption according to an embodiment of the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. 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 application.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention provides an intelligent electric meter metering abnormity identification method only adopting daily electric quantity data of a total electric energy meter of a distribution area and intelligent electric meters of each user for a distribution network distribution area, and on one hand, a calculation method for generalized cross validation optimization Gihonov regularization is provided aiming at a power consumption coefficient matrix ill-condition problem; and on the other hand, an abnormal table recommendation mechanism which is sorted according to the magnitude of the absolute value of the error is designed. Compared with the common least square method and the Gihonov method, the intelligent ammeter metering abnormity identification method has the advantages of good effect and strong practicability.
Referring to fig. 1, the method for identifying metering abnormality of an intelligent electric meter based on electricity consumption data in the embodiment includes the following steps:
s1, acquiring original data of power consumption;
in this embodiment, the required raw data may be obtained from a metering automation system, a sampling system, or a centralized meter reading system, and mainly includes:
1) the user file comprises a user number, a name and an ammeter address, and the information is used for positioning the suspected abnormal ammeter during field verification;
2) the power supply quantity comprises information such as acquisition date, power supply quantity, whether the data acquisition of the electric meter is successful or not and the like;
3) and the electricity consumption detail meter of each user intelligent electric meter records the daily electricity consumption of the historical date of each user electric meter in the distribution area.
S2, analyzing and processing the acquired original data to obtain effective data, which is as follows:
s21, regarding the missing data of partial historical dates, the data corresponding to the historical dates are invalid in model calculation, and the data are deleted during data processing; the acquisition success rate of part of historical dates is not 100%, indicating that the electric meter data is lost, and deleting the data corresponding to the historical dates with the acquisition success rate of not 100%; and deleting the type of the electric meters when the electricity consumption of all the historical dates of the partial electric meters are blank values.
S22, if the readings of the electricity meter on some historical dates are 0 values, but the readings on other dates are, comparing the non-blank value, the non-0 value data quantity and the total quantity of the historical data by formula (1), if the ratio is less than 10%, deleting all data of the table area total table and each user table corresponding to the non-blank value and the non-0 value of the table on the historical date, and then deleting the data of the table, as shown in fig. 2.
Where T is the total number of history dates, C j And representing a calculation function, counting the number of non-blank values and non-0 values in the historical date.
S3, constructing an intelligent electric meter metering abnormity identification model, inputting effective data into the intelligent electric meter metering abnormity identification model for calculation, and obtaining the optimal estimation value of the metering error of each user meter under the transformer area.
Further, the smart meter metering anomaly identification model is as follows:
||Ax-b|| 2 +||Γx|| 2 ＝min
wherein A is an electricity utilization matrix; b is the line loss electricity quantity to be measured, and x is a variable to be solved;
wherein the content of the first and second substances,
gamma is called as Gihonov matrix, lambda is a regular parameter, | | · | | | represents the 2 norm of Ou;
sorting absolute values of the optimal estimated values of the metering errors of the user meters in the transformer area in a descending order, recommending the meters corresponding to the sorted quantity which is integrated upwards by alpha% of the total quantity P of the user meters in the transformer area as suspected abnormal smart meters for field verification, wherein alpha is a set threshold value, and determining the scale and range of the suspected field verification meter through the alpha value.
Furthermore, the intelligent electric meter metering abnormity identification model is adopted for calculation, and specifically the method comprises the following steps:
s31, let f (x) ═ Ax-b T (Ax-b)+(Γx) T (Γx) (2)
S32, let f (x) have a derivative of x of 0, as follows:
s33, obtaining the estimated value of x as:
s34, solving the optimal regular parameters:
adopting a Generalized Cross Validation (GCV) based regularized parameter optimization method, wherein the basic principle is that any phase b in the measured value b of the equation (2) i When the removal is carried out, the selected regularization parameter can predict the change caused by the removal term, and a generalized cross GCV function is constructed according to an error model and a regularization solution, wherein the expression of the generalized cross GCV function is as follows:
in formula (5), trace is the trace of the matrix, representing the summation of diagonal elements of the matrix, and I is the identity matrix;
obtaining the optimal Gihonov regularization parameter lambda by solving the minimum value of the formula (5) opt ；
S35, mixing lambda opt Bring into the Gihono matrix, get
S36, and mixing gamma opt Substitution formula (4) calculation of estimation value of unknown quantity
S37, handle
Reduction of writing to
Wherein
Namely the optimal estimation value of the metering error of each user table under the transformer area.
S4, recommending a metering abnormal smart electric meter;
s41, sorting the absolute values of the calculation error results of the user tables under the platform area by g (z), specifically as follows:
g(z)＝sort(z) (6)
wherein abs () represents the absolute value function, and sort () is the function of sorting functions from large to small.
And S42, when the electric meters which are suspected to be abnormal and are checked on site are recommended, sorting the calculation results according to the formula (6) and the formula (7), and then rounding up alpha% of the total number P of the user meters in the distribution area, namely, the electric meters corresponding to the formula (8) are recommended to be the intelligent electric meters which are suspected to be abnormal and checked on site.
According to the method, the low-voltage distribution area is taken as a unit, the daily electric quantity information of the intelligent electric meters of the distribution area and the intelligent electric meters of all users in the distribution area is only collected, the list of the intelligent electric meters with the abnormal suspect metering is ranked and recommended according to the data processing method, the model and the generalized cross validation optimization Gihonnov regular parameter solving method, and the power grid company can set the value of alpha according to the actual condition of the power grid company to determine the scale and the range of the on-site suspect meter.
In one implementation of the present application, a certain region is taken as an example to specifically describe:
1. raw data information
The user table file data, the power supply table of the station area and the list of the user electricity consumption list are shown in table 1, table 2 and table 3. The cell has an electricity meter with 86 x 203 for abnormal measurements. The metering system collects the 1 st to the 11 th 09 th 2020 in 2018, and T is 1044 pieces of historical data. After the data preprocessing is performed according to the method of the present invention, the remaining electric meters 70 have 109 valid historical data n.
TABLE 1 Utility meter profile
TABLE 2 Power data of district (electric unit: kWh)
TABLE 3 electric quantity detail for each user intelligent meter (Unit: kWh)
2. Calculating results under different regularization parameters;
the calculation is performed by adopting a common least square method and a regularization method based on Gihonov, and the calculation results under different regularization parameters are shown in Table 4.
TABLE 4 common least squares and the results of the Gihonov regularization under different regularization parameters (where k is the regularization parameter, i.e. λ as mentioned above)
Therefore, the calculation result of the common least square method does not accord with objective logic, and the label table is not the one with the largest absolute value of error, and the result is unreliable because the original data electricity matrix A is ill-conditioned. By changing different regularization parameters lambda to respectively take 1,10,20,100 and 200, the result is obviously changed after Gihono regularization, but when the regularization parameter is smaller (lambda is 1), the electric meter with the largest absolute value is not a label meter, the electric energy meter with the second largest absolute value is a label meter, and when the lambda is 10 and 20, the electric meter with the largest absolute value is a label meter; and when the regular parameters are 100 and 200, the electric meters with the maximum absolute value errors after sorting according to the absolute value of the recommended mechanism errors are not label tables. In summary, by changing the size of the regular parameter, the calculation result changes accordingly, and the appropriate regular parameter can recommend the label table, that is, the electric meter with abnormal field verification metering.
Calculation result of GCV optimization regularization parameter
The generalized cross-validation method is adopted to obtain the optimal regularization parameters, the result is shown in fig. 3, when λ is 4.3021, the generalized cross function G (λ) obtains the minimum value, the corresponding error calculation result is shown in table 5, it can be known from table 5 that the meter number 86 × 203 with the largest absolute value of error has the error of-79.71%, the meter is the electric energy meter for field calibration, and the error of the field calibration is-51.48% because of electricity stealing by users. The method can directly calculate the optimal regular parameters and recommend the intelligent electric meter with abnormal metering.
TABLE 5 calculation results after optimization of regularized parameters by generalized cross-validation
It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present invention.
Based on the same idea as the method for identifying the metering abnormality of the smart meter based on the electricity consumption data in the embodiment, the invention also provides a system for identifying the metering abnormality of the smart meter based on the electricity consumption data, and the system can be used for executing the method for identifying the metering abnormality of the smart meter based on the electricity consumption data. For convenience of explanation, in the schematic structural diagram of the embodiment of the system for identifying metering abnormality of a smart meter based on electricity consumption data, only the part related to the embodiment of the present invention is shown, and those skilled in the art will understand that the illustrated structure does not constitute a limitation to the apparatus, and may include more or less components than those illustrated, or combine some components, or arrange different components.
Referring to fig. 4, in another embodiment of the present application, a system 100 for identifying metering anomalies of a smart meter based on power consumption data is provided, which includes a data collection module 101, a data processing module 102, an error estimation module 103, and an out-of-tolerance recommendation module 104;
the data collection module 101 is configured to obtain original data of power consumption, where the original data includes user profile data, power supply meter data, and electricity consumption detail data of an intelligent ammeter;
the data processing module 102 is configured to analyze and process the acquired original data to obtain valid data;
the error estimation module 103 is configured to construct an intelligent electric meter metering anomaly identification model, and input effective data into the intelligent electric meter metering anomaly identification model for calculation to obtain an optimal estimation value of metering error of each user meter in the distribution area; the intelligent ammeter metering abnormity identification model is as follows:
||Ax-b|| 2 +||Γx|| 2 ＝min
wherein A is an electricity utilization matrix; b is the measured line loss electric quantity, and x is a variable to be solved;
wherein the content of the first and second substances,
gamma is called as Gihonov matrix, lambda is a regular parameter, | | · | | | represents the 2 norm of Ou;
The out-of-tolerance recommending module 104 is configured to sort the absolute values of the optimal estimated values of the metering errors of the user meters in the distribution area in descending order, recommend the meters corresponding to the sorted number of the user meters P in the distribution area, which is integrated upwards by alpha%, as suspected abnormal smart meters for field verification, where alpha is a set threshold, and determine the scale and range of the suspected on-site verification meter according to the alpha value.
It should be noted that, the system for identifying metering abnormality of an intelligent electric meter based on electricity consumption data of the present invention corresponds to the method for identifying metering abnormality of an intelligent electric meter based on electricity consumption data of the present invention one to one, and the technical features and the beneficial effects thereof described in the above embodiment of the method for identifying metering abnormality of an intelligent electric meter based on electricity consumption data are all applicable to the embodiment of identifying metering abnormality of an intelligent electric meter based on electricity consumption data, and specific contents may refer to the description in the embodiment of the method of the present invention, and are not described herein again, and thus, the present invention is declared.
In addition, in the implementation of the system for identifying metering abnormality of a smart meter based on power consumption data according to the foregoing embodiment, the logical division of the program modules is only an example, and in practical applications, the foregoing function distribution may be performed by different program modules according to needs, for example, due to the configuration requirements of corresponding hardware or due to the convenience of implementation of software, that is, the internal structure of the system for identifying metering abnormality of a smart meter based on power consumption data is divided into different program modules to perform all or part of the functions described above.
Referring to fig. 5, in an embodiment, an electronic device 200 for implementing a method for identifying a smart meter metering anomaly based on power consumption data is provided, where the electronic device 200 may include a first processor 201, a first memory 202, and a bus, and may further include a computer program, such as a smart meter metering anomaly identification program 203, stored in the first memory 202 and operable on the first processor 201.
The first memory 202 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The first memory 202 may in some embodiments be an internal storage unit of the electronic device 200, such as a removable hard disk of the electronic device 200. The first memory 202 may also be an external storage device of the electronic device 200 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 200. Further, the first memory 202 may also include both an internal storage unit and an external storage device of the electronic device 200. The first memory 202 may be used to store not only application software installed in the electronic device 200 and various types of data, such as codes of the smart meter metering anomaly recognition program 203, but also temporarily store data that has been output or will be output.
The first processor 201 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The first processor 201 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 200 by running or executing programs or modules stored in the first memory 202 and calling data stored in the first memory 202.
Fig. 5 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 5 does not constitute a limitation of the electronic device 200, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
The smart meter metering exception identifying program 203 stored in the first memory 202 of the electronic device 200 is a combination of a plurality of instructions, and when running in the first processor 201, can realize that:
acquiring original data of power consumption, wherein the original data comprises user file data, power supply meter data and electricity consumption detail data of an intelligent electric meter;
analyzing and processing the obtained original data to obtain effective data;
constructing an intelligent electric meter metering abnormity identification model, inputting effective data into the intelligent electric meter metering abnormity identification model for calculation, and obtaining the optimal estimated value of the metering error of each user meter under the transformer area;
sorting absolute values of the optimal estimated values of the metering errors of the user meters in the transformer area in a descending order, recommending the meters corresponding to the sorted quantity which is integrated upwards by alpha% of the total quantity P of the user meters in the transformer area as suspected abnormal smart meters for field verification, wherein alpha is a set threshold value, and determining the scale and range of the suspected field verification meter through the alpha value.
Further, the modules/units integrated with the electronic device 200, if implemented in the form of software functional units and sold or used as independent products, may be stored in a non-volatile computer-readable storage medium. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (9)
1. The intelligent ammeter metering abnormity identification method based on electricity consumption data is characterized by comprising the following steps:
acquiring original data of power consumption, wherein the original data comprises user file data, power supply quantity data and intelligent electric meter power consumption detail data;
analyzing and processing the obtained original data to obtain effective data;
constructing an intelligent electric meter metering abnormity identification model, inputting effective data into the intelligent electric meter metering abnormity identification model for calculation, and obtaining the optimal estimated value of the metering error of each user meter under the transformer area; the intelligent ammeter metering abnormity identification model is as follows:
||Ax-b|| 2 +||Γx|| 2 ＝min
wherein A is an electricity utilization matrix; b is the measured line loss electric quantity, and x is a variable to be solved;
wherein the content of the first and second substances,
gamma is called as Gihonov matrix, lambda is a regular parameter, | | · | | | represents the 2 norm of Ou;
sorting absolute values of the optimal estimated values of the metering errors of the user meters in the transformer area in a descending order, recommending the meters corresponding to the sorted quantity which is integrated upwards by alpha% of the total quantity P of the user meters in the transformer area as suspected abnormal smart meters for field verification, wherein alpha is a set threshold value, and determining the scale and range of the suspected field verification meter through the alpha value.
2. The method for identifying metering abnormality of the smart meter based on the electricity consumption data as claimed in claim 1, wherein the data is obtained from a metering automation system, a utilization system or a centralized meter reading system.
3. The method for identifying the metering abnormality of the smart meter based on the electricity consumption data as claimed in claim 1, wherein the user profile data is used for positioning a suspected abnormal meter when carrying out field verification and comprises a user number, a name and a meter address;
the power supply quantity data comprises information of acquisition date, power supply quantity and whether the acquisition of the electric meter data is successful or not;
and the electricity consumption detail data of the intelligent ammeter is the daily electricity consumption of the historical date of each user ammeter in the transformer area.
4. The method for recognizing the metering abnormality of the smart meter based on the electricity consumption data as claimed in claim 1, wherein in the step of analyzing and processing the acquired original data and deleting invalid data,
deleting the data missing from the part of historical dates during data processing;
for data with a part of historical dates of which the acquisition success rate is not 100%, indicating that the data of the electric meter is lost, and deleting the data corresponding to the historical dates with the acquisition success rate not 100%;
and deleting the electric meter for the data of which the electricity consumption on all historical dates is blank values.
5. The method for identifying metering abnormality of smart meter based on electricity consumption data according to claim 1 or 4, characterized in that in the step of analyzing and processing the obtained raw data and deleting invalid data,
the reading of the electric meter on certain historical dates is 0 value, the reading is carried out on other dates, the non-blank value, the non-0 value data quantity and the total quantity of the historical data are compared through a formula (1), if the proportion is less than 10%, all data of the table area total table and each user table corresponding to the historical dates of the non-blank value and the non-0 value of the table are deleted, and then the data of the table are deleted;
where T is the total number of history dates, C j And representing a calculation function, counting the number of non-blank values and non-0 values in the historical date.
6. The method for identifying metering anomaly of the smart meter based on the power consumption data as claimed in claim 1, wherein the effective data is input into a smart meter metering anomaly identification model for calculation to obtain the optimal estimated value of the metering error of each user meter under a distribution area, specifically:
(x) f (Ax-b) T (Ax-b)+(Γx) T (Γx) (2)
Let f (x) have a derivative of 0 for x as follows:
thirdly, obtaining the estimated value of x as:
solving the optimal canonical parameters:
the method adopts a regular parameter optimization method based on generalized cross validation, and the basic principle is that any phase b in the measured value b of the equation (2) i When the removal is carried out, the selected regularization parameter can predict the change caused by the removal term, and a generalized cross GCV function is constructed according to an error model and a regularization solution, wherein the expression of the generalized cross GCV function is as follows:
in formula (5), trace is the trace of the matrix, representing the summation of diagonal elements of the matrix, and I is the identity matrix;
obtaining the optimal Gihonov regularization parameter lambda by solving the minimum value of the formula (5) opt ；
V. will lambda opt Bring into the Gihono matrix, get
Sixthly, the gamma is cut opt Substitution formula (4) calculation of estimation value of unknown quantity
Seventhly, handle
Reduction of writing to
Wherein
Namely the optimal estimated value of the metering error of each user table under the transformer area.
7. The system for identifying the metering abnormality of the smart electric meter based on the electricity consumption data is characterized by being applied to the method for identifying the metering abnormality of the smart electric meter based on the electricity consumption data in any one of claims 1 to 6, and comprising a data collection module, a data processing module, an error estimation module and an out-of-tolerance recommendation module;
the data collection module is used for acquiring original data of power consumption, wherein the original data comprises user file data, power supply quantity data and intelligent electric meter power consumption detail data;
the data processing module is used for analyzing and processing the acquired original data to obtain effective data;
the error estimation module is used for constructing an intelligent electric meter metering abnormity identification model, inputting effective data into the intelligent electric meter metering abnormity identification model for calculation, and obtaining the optimal estimation value of the metering error of each user meter under the transformer area; the intelligent ammeter metering abnormity identification model is as follows:
||Ax-b|| 2 +||Γx|| 2 ＝min
wherein A is an electricity utilization matrix; b is the measured line loss electric quantity, and x is a variable to be solved;
wherein, the first and the second end of the pipe are connected with each other,
gamma is called as Gihonov matrix, lambda is a regular parameter, | | · | | | represents the 2 norm of Ou;
and the out-of-tolerance recommending module is used for sequencing the absolute values of the optimal estimated values of the metering errors of the user meters in the distribution area from large to small, recommending the meters corresponding to the sorted quantity which is obtained by rounding up alpha% of the total quantity P of the user meters in the distribution area as suspected abnormal smart meters for field verification, wherein alpha is a set threshold value, and determining the scale and the range of the suspected table for field verification through the alpha value.
8. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the method for identifying anomalies in metering of a smart meter based on electricity usage data as claimed in any one of claims 1 to 6.
9. A computer-readable storage medium storing a program, wherein the program, when executed by a processor, implements the method for identifying metering anomalies in a smart meter based on electricity consumption data according to any one of claims 1 to 6.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115951295A (en) * | 2022-11-11 | 2023-04-11 | 国网山东省电力公司营销服务中心(计量中心) | Automatic identification method and system for daily clear power abnormity |
| CN116148753A (en) * | 2023-04-18 | 2023-05-23 | 北京京仪北方仪器仪表有限公司 | Intelligent electric energy meter operation error monitoring system |
| CN116699499A (en) * | 2023-05-29 | 2023-09-05 | 浙江东鸿电子股份有限公司 | Ammeter precision and reliability automatic detection system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115951295A (en) * | 2022-11-11 | 2023-04-11 | 国网山东省电力公司营销服务中心(计量中心) | Automatic identification method and system for daily clear power abnormity |
| CN116148753A (en) * | 2023-04-18 | 2023-05-23 | 北京京仪北方仪器仪表有限公司 | Intelligent electric energy meter operation error monitoring system |
| CN116699499A (en) * | 2023-05-29 | 2023-09-05 | 浙江东鸿电子股份有限公司 | Ammeter precision and reliability automatic detection system |
| CN116699499B (en) * | 2023-05-29 | 2024-04-12 | 浙江东鸿电子股份有限公司 | Ammeter precision and reliability automatic detection system |
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