CN114089262A - Intelligent error analysis model construction method for small-electric-quantity intelligent electric energy meter - Google Patents
Intelligent error analysis model construction method for small-electric-quantity intelligent electric energy meter Download PDFInfo
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Abstract
The invention discloses a method for constructing an intelligent error analysis model of a small-electric-quantity intelligent electric energy meter, which relates to the technical field of intelligent error analysis and solves the technical problem that the model cannot monitor a circuit transmission line and a household meter in real time aiming at the corresponding household meter, so that the corresponding fault point cannot be quickly found, and the final result is misjudged, the collected detection metering value is subjected to linear discrete processing, different node information is evaluated by adopting a discrete equation, the obtained value is compared with a preset value, the detection metering value of a circuit is determined by analyzing the comparison result, the circuit node is tracked, the corresponding node is found at a fixed point, professional maintenance personnel are dispatched, the circuit in a node area is detected and maintained, an abnormal node can be quickly and effectively found, and the maintenance work can be quickly and effectively carried out by external personnel, the maintenance efficiency is accelerated, and meanwhile, the rapid circulation of the lines is guaranteed.
Description
Technical Field
The invention belongs to the technical field of intelligent error analysis of electric power, and particularly relates to a method for constructing an intelligent error analysis model of a small-electric-quantity intelligent electric energy meter.
Background
The intelligent electric energy meter is composed of a measuring unit, a data processing unit, a communication unit and the like, and has the functions of electric energy metering, data processing, real-time monitoring, automatic control, information interaction and the like.
The method aims at the problem that errors generally exist between a power numerical value monitored by an intelligent electric energy meter and a preset numerical value, but the original reasons of the error numerical value are difficult to find, the out-of-tolerance value of the out-of-tolerance meter is generally solved by a least square method, parameter estimation is carried out on a regression model from the angle of error fitting, if abnormal points exist in data, the regression result is greatly influenced, misjudgment or missed judgment can be caused, meanwhile, the model cannot monitor a circuit transmission line and a user meter in real time aiming at the corresponding user meter, so that corresponding fault points cannot be found quickly, and the final result judgment is wrong.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a method for constructing an intelligent error analysis model of a small-electric-quantity intelligent electric energy meter, which solves the problem that a circuit transmission line and a user meter cannot be monitored in real time aiming at the corresponding user meter, so that a corresponding fault point cannot be found quickly, and the final result judgment is wrong.
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides a method for constructing an intelligent error analysis model of a small-electric-quantity intelligent electric energy meter, including the following steps:
s1, collecting power supply quantity of a station area total meter, fitting power consumption of a household meter and real power consumption of the household meter, performing difference processing on the power supply quantity of the station area total meter and the fitting power consumption of the household meter to obtain a fitting residual Ni, and performing difference processing on the power supply quantity of the station area total meter and the real power consumption of the household meter to obtain an actual residual Si;
s2, performing root mean square residual formula processing on the fitting residual Ni and the actual residual Si again, wherein the expression form is
Obtaining a root mean square residual value RNi of the fitting residual Ni, and then obtaining a root mean square residual value RSi of the actual residual Si by adopting the same formula, wherein n is the number of numerical measurement;
s3, carrying out evaluation value processing on the calculated root mean square residual value RNi and RSi again, and comparing the corresponding evaluation value with a preset error value interval to obtain corresponding comparison results which are respectively a normal error and an over-value error;
s4, processing the excessive value error in the comparison result, carrying out fixed point marking on the collected household meter, generating a power transmission route between the distribution area and the household meter, dividing the power transmission route into K detection metering points, and carrying out detection calculation on the K detection metering points to obtain corresponding detection metering values;
and S5, carrying out linear discrete processing according to the detection metering value obtained by the detection calculation, and transmitting the processing result to an external terminal.
Preferably, the evaluation value processing in step S3 is performed by: by passing
Obtaining a corresponding evaluation value PJi, comparing the evaluation value PJi with an error interval value, when PJi belongs to the error interval value, judging the collected and monitored numerical value to be a normal error, not performing the next work, ending the direct training, when PJi does not belong to the error interval value, judging the collected and detected numerical value to be an over-value error, and performing abnormal metering point in the error-generating lineAnd (5) checking and detecting.
Preferably, the step of detecting and calculating the K detected measuring points in step S4 to obtain corresponding detected measuring values includes:
s41, dividing a line transmission route between the distribution room general table and the user table into K detection metering points, wherein K is 1, 2, 3, … … and K, and each unit is spaced by 500 meters;
s42, S322, the detection equation for K detection measurement points is
Wherein ekThe root mean square residual value representing the Kth metering point is evaluated by the formula in step S2, where LLtT，KLine loss value LL of K node in T daytTWherein Y istTTo detect the metric.
Preferably, the line loss value LL in step S42tTThe calculation method is as follows:
s421, using E astlThe random calibration error value of the equipment on the tl day is represented, wherein i belongs to T, the calibration error value is acquired by adopting precision equipment to acquire line noise to obtain a calibration error value, and the calibration error value is processed in the following processing mode:
f is the monitored noise value;
s422, then passing
Obtaining the line loss value LLtlWhere K, tl are not constrained by ∈tlAnd eK。
Preferably, the step of detecting the measured value and performing linear discrete processing in step S5 includes:
s51, adopting a discrete formula
Obtain corresponding discrete value LStTWherein
Is YtTAnd j is greater than 1;
s52, LSt2When the value is less than X1, wherein X1 is a preset value, the discrete value LS is determinedt2Belongs to the normal range;
and S53, adding one to the j value, repeating the steps until the discrete value is more than or equal to X1, wherein X1 is set by an operator, if the discrete value is judged to belong to an abnormal range, marking the cable metering points in the interval range, and transmitting the corresponding metering point information to the corresponding external terminal.
Preferably, in step S51, the discrete value between 1 and 2 is calculated by making j equal to 2, so as to obtain the designated discrete value LSt2。
Preferably, in step S1, the data collection is performed by using a tie switch, where the real power consumption of the household meter is the input power of the electric energy + the input power of the tie switch + the power of the private grid + the power of the public grid.
Preferably, in step S2, the calculation formula is previously set up by an external operator in the model operation step, and the error interval value in step S3 is set up, the error interval value is set in the error interval unit inside the model, and the difference value comparison unit inside the model performs the comparison processing on the numerical value.
Compared with the prior art, the invention has the beneficial effects that: when an intelligent error analysis model of the electric meter is constructed, the corresponding evaluation value of the household meter data is obtained, the obtained evaluation value is compared with a preset error interval, data processing operation is finished by comparing error-free data, further processing is carried out by comparing error data, fixed-point marking is carried out on the corresponding household meter, a power transmission route between a distribution area and the household meter is generated at the same time, data of a plurality of detection metering points are collected and processed, corresponding detection metering values are obtained, the data of the detection metering points also comprise calculation of line loss values, the detection metering value test can be more accurate by considering the line loss values, and external personnel can conveniently know the state of line loss;
meanwhile, the circuit loss value is internally evaluated by a precise noise value generated in the process of transmitting power by the circuit, the generated total circuit loss value is directly transmitted to a master station, an operator of the master station analyzes data so as to optimize the transmitted circuit, the acquired detection metering value is subjected to linear discrete processing, different node information is evaluated by adopting a discrete equation, the obtained value is compared with a preset value, the detection metering value of the circuit is determined by analyzing the comparison result, and then the corresponding node is found at a fixed point by tracking the node of the circuit, a professional maintainer is dispatched, the circuit in a node area is detected and maintained, an abnormal node can be quickly and effectively found, and an external person can quickly and effectively carry out maintenance work, thereby improving the maintenance efficiency, meanwhile, the rapid circulation of the line is also ensured.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
As shown in fig. 1, a method for constructing an intelligent error analysis model of a small-electric-quantity intelligent electric energy meter includes the following steps:
s1, collecting power supply quantity of a station area total meter, fitting power consumption of a household meter and real power consumption of the household meter, inputting various data for difference calculation, wherein the power supply quantity of the station area total meter and the fitting power consumption of the household meter are subjected to difference processing to obtain fitting residual Ni, and then the power supply quantity of the station area total meter and the real power consumption of the household meter are subjected to difference processing to obtain actual residual Si, wherein i represents different household meters;
s2, the fitting residual Ni and the actual residual Si are processed by the root mean square residual formula again,which is expressed in the form of
Obtaining a root mean square residual value RNi of the fitting residual Ni, and then obtaining a root mean square residual value RSi of the actual residual Si by adopting the same formula, wherein n is the number of numerical measurement;
s3, the calculated root mean square residual value RNi and RSi are again subjected to evaluation value processing, which has the following processing formula:
obtaining corresponding evaluation values PJi, wherein C1 and C2 are both preset fixed coefficient factors, and comparing the calculated evaluation value PJi with a preset error interval value, which is set by an external operator, in the following way:
s31, when PJi belongs to the error interval value, the acquired and monitored numerical value is judged to be a normal error, the next work is not carried out, and the direct training is finished;
s32, when PJi does not belong to the error interval value, the acquired and detected value is judged to be an over-value error, and the abnormal metering point in the error-generating line is checked and detected, wherein the checking and detecting mode is as follows:
s321, dividing a line transmission route between the distribution area summary table and the subscriber table into K detection measurement points, where K is 1, 2, 3, … …, and K, and each unit is spaced by 500 meters;
s322, the detection equation of the K detection metering points is
Wherein ekThe root mean square residual value representing the Kth metering point is evaluated by the formula in step S2, where LLtT，KRepresents the line loss value of the Kth node in the T day, wherein YtTTo detect the metric, the line loss value is calculated as follows:
s3221, using ∈tlAnd (3) representing a random calibration error value of the equipment on the tl th day, wherein i belongs to T, and the calibration error value adopts precision equipment to lineCollecting noise to obtain a calibration error value, and processing the calibration error value in the following processing mode:
f is the monitored noise value;
s3222, and further pass
Obtaining the line loss value LLtlWhere K, tl are not constrained by ∈tlAnd eK。
In step S322, the K detected measured values are all processed by linear discrete processing, and the discrete value processing mode is:
wherein
Is YtTJ is greater than 1, calculating the discrete value between j equal to 2 and 1 to obtain the designated discrete value LSt2When LS ist2When the value is less than X1, wherein X1 is a preset value, the discrete value LS is determinedt2Belongs to the normal range;
adding one to the j value, repeating the steps until the discrete value is more than or equal to X1, wherein X1 is set by an operator, if the discrete value is judged to belong to an abnormal range, marking the cable metering points in the range, transmitting the corresponding metering point information to a corresponding external terminal, checking by the external operator, and detecting the specified line range;
in the step S1, data collection is performed by using an interconnection switch, wherein the collected data is directly sent to an intelligent error analysis model for calculation, wherein the real power consumption of the household meter is the electric energy input power, the interconnection switch input power, the special transformer internet power and the public transformer internet power, and the circuit interconnection switch generally plays a role in circuit maintenance and circuit faults, so that the influence of file information caused by untimely update can be eliminated;
in the step S2, an external operator plans a calculation formula into the model operation step in advance, and plans the error interval value in the step S3, wherein the error interval value is set in an error interval unit in the model, and a difference value comparison unit in the model compares the values;
after the training in the step S31 is finished, performing the second acquisition training on the data of the user table of another node, and repeating the steps S1, S2 and S3;
when the method is used for detecting the measuring points, the line loss of the measuring points is considered, so that measured data and results can be more accurate, meanwhile, calculation is carried out through an algorithm in a model, the statistical line loss value of a corresponding station area can be effectively obtained through the line loss values of part of the measuring points, the station area processes the statistical line loss value and solves the problem through linear programming, some constraint conditions can be introduced into branch loss items to enable the model to accurately fit the branch loss, in the specific solving process, the dual-shape method can be used for solving, the linear programming is used for solving the abnormal data more friendly, singular variance users are optimized through a method for constructing a linear programming model, all singular variance user problems are solved, and the accuracy is improved.
The least square method is a mathematical optimization method for measuring adjustment aiming at observation data, a function of an optimal result is found through the minimum sum of squares of errors, the least square method is used for carrying out parameter estimation on a regression model from the angle of error fitting, if abnormal points exist in data, the regression result is greatly influenced, and misjudgment or missing judgment can be caused.
When the intelligent error analysis model is constructed, a large amount of numerical calculation is needed, and a neural network model training mode is adopted, so that the database in the analysis model forms data memory, and the data is rapidly processed.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows: when an intelligent error analysis model of the electric meter is constructed, firstly, the electric meter power consumption and the station area power consumption of different household meters are collected, the collected data are subjected to difference processing to obtain different difference values, then, the different difference values are subjected to root mean square residual error value processing to obtain corresponding evaluation values of the household meter data, the obtained evaluation values are compared with preset error intervals to obtain data, the data processing operation is finished by comparing the error-free data, the data with errors are further processed, the corresponding household meters are marked at fixed points, electric power transmission routes between the station area and the household meters are generated at the same time, the corresponding routes are divided into nodes, a plurality of nodes are sequentially detected to form a plurality of detection metering points, the data of the plurality of detection metering points are collected to obtain corresponding detection metering values, wherein, the data of the detection metering point also comprises a line loss value, and the test of the detection metering value can be more accurate by considering the line loss value, meanwhile, the line loss value is internally evaluated by a precise noise value generated by the transmission of electric power of the line, and the generated total line loss value is directly transmitted to a central office, the operator of the central office analyzes the data, so as to optimize the conveying line, carry out linear discrete processing on the collected detection metering value, and the discrete equation is adopted to evaluate different node information, the obtained numerical value is compared with the preset numerical value, and through the analysis of the comparison result, the detection metering value of the line is determined, then the line node is tracked, the corresponding node is found at a fixed point, professional maintenance personnel are dispatched, and the line in the node area is detected and maintained.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. A method for constructing an intelligent error analysis model of a small-electric-quantity intelligent electric energy meter is characterized by comprising the following steps:
s1, collecting power supply quantity of a station area total meter, fitting power consumption of a household meter and real power consumption of the household meter, performing difference processing on the power supply quantity of the station area total meter and the fitting power consumption of the household meter to obtain a fitting residual Ni, and performing difference processing on the power supply quantity of the station area total meter and the real power consumption of the household meter to obtain an actual residual Si;
s2, carrying out root mean square residual formula processing on the fitting residual Ni and the actual residual Si, wherein the expression form is
Obtaining a root mean square residual value RNi of the fitting residual Ni, and then obtaining a root mean square residual value RSi of the actual residual Si by adopting the same formula, wherein n is the number of numerical measurement;
s3, carrying out evaluation value processing on the calculated root mean square residual value RNi and RSi again, and comparing the corresponding evaluation value with a preset error value interval to obtain corresponding comparison results which are respectively a normal error and an over-value error;
s4, processing the excessive value error in the comparison result, carrying out fixed point marking on the collected household meter, generating a power transmission route between the distribution area and the household meter, dividing the power transmission route into K detection metering points, and carrying out detection calculation on the K detection metering points to obtain corresponding detection metering values;
and S5, carrying out linear discrete processing on the detection metering value obtained by corresponding detection calculation, and transmitting the processing result to an external terminal.
2. The method for constructing the intelligent error analysis model of the small-electric-quantity intelligent electric energy meter according to claim 1, wherein the evaluation value processing in step S3 is performed in a manner that: by passing
And obtaining a corresponding evaluation value PJi, comparing the evaluation value PJi with the error interval value, when PJi belongs to the error interval value, judging that the acquired and monitored numerical value is a normal error, not performing the next work, ending the direct training, when PJi does not belong to the error interval value, judging that the acquired and detected numerical value is an over-value error, and checking and detecting an abnormal metering point in the error-generating line.
3. The method for constructing the intelligent error analysis model of the small electric quantity intelligent electric energy meter according to claim 1, wherein the step of detecting and calculating the K detection metering points in the step S4 to obtain the corresponding detection metering values comprises the following steps:
s41, dividing a line transmission route between the distribution room general table and the user table into K detection metering points, wherein K is 1, 2, 3, … … and K, and each unit is spaced by 500 meters;
s42, S322, the detection equation for K detection measurement points is
Wherein ekThe root mean square residual value representing the Kth metering point is evaluated by the formula in step S2, where LLtT，KLine loss value LL of K node in T daytTWherein Y istTTo detect the metric.
4. The method for constructing the intelligent error analysis model of the small-electric-quantity intelligent electric energy meter according to claim 3, wherein the line loss value LL in the step S42tTThe calculation method is as follows:
s421, using E astlThe random calibration error value of the equipment on the tl day is represented, wherein i belongs to T, the calibration error value is acquired by adopting precision equipment to acquire line noise to obtain a calibration error value, and the calibration error value is processed in the following processing mode:
f is the monitored noise value;
s422, then passing
Obtaining the line loss value LLtlWhere K, tl are not constrained by ∈tlAnd eK。
5. The method for constructing the intelligent error analysis model of the small-electric-quantity intelligent electric energy meter according to claim 4, wherein the step of detecting the metering value and performing linear discrete processing in the step S5 comprises the steps of:
s51, adopting a discrete formula
Obtain corresponding discrete value LStTWherein
Is YtTAnd j is greater than 1;
s52, LSt2When the value is less than X1, wherein X1 is a preset value, the discrete value LS is determinedt2Belongs to the normal range;
and S53, adding one to the j value, repeating the steps until the discrete value is more than or equal to X1, wherein X1 is set by an operator, if the discrete value is judged to belong to an abnormal range, marking the cable metering points in the interval range, and transmitting the corresponding metering point information to the corresponding external terminal.
6. The method for constructing the intelligent error analysis model of the small-electric-quantity intelligent electric energy meter according to claim 5, wherein in the step S51, if j is equal to 2, the discrete value from 1 to 2 is calculated to obtain the designated discrete value LSt2。
7. The method as claimed in claim 1, wherein the data collection in step S1 is collected by using a tie switch, and the real power consumption of the household meter is the input power of the electric energy + the input power of the tie switch + the power of the private network and the power of the public network.
8. The method for constructing the intelligent error analysis model of the small-electric-quantity intelligent electric energy meter according to claim 1, wherein in the step S2, the calculation formula is pre-set into the model operation step by an external operator, and meanwhile, the error interval value in the step S3 is set in the error interval unit inside the model, and the difference comparison unit inside the model compares the values.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115112948A (en) * | 2022-07-29 | 2022-09-27 | 苏州维众数据技术有限公司 | Multi-branch electric quantity calibration method and device, intelligent terminal and storage medium |
| CN115616473A (en) * | 2022-12-02 | 2023-01-17 | 北京志翔科技股份有限公司 | Identification method, device, equipment and storage medium of out-of-tolerance electric energy meter |
| CN115629354A (en) * | 2022-11-30 | 2023-01-20 | 北京志翔科技股份有限公司 | Method and device for identifying out-of-tolerance electric energy meter based on power consumption adjustment amplitude |
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2021
- 2021-11-18 CN CN202111371413.7A patent/CN114089262A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115112948A (en) * | 2022-07-29 | 2022-09-27 | 苏州维众数据技术有限公司 | Multi-branch electric quantity calibration method and device, intelligent terminal and storage medium |
| CN115629354A (en) * | 2022-11-30 | 2023-01-20 | 北京志翔科技股份有限公司 | Method and device for identifying out-of-tolerance electric energy meter based on power consumption adjustment amplitude |
| CN115616473A (en) * | 2022-12-02 | 2023-01-17 | 北京志翔科技股份有限公司 | Identification method, device, equipment and storage medium of out-of-tolerance electric energy meter |
| CN115616473B (en) * | 2022-12-02 | 2023-04-07 | 北京志翔科技股份有限公司 | Identification method, device, equipment and storage medium of out-of-tolerance electric energy meter |
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