CN113281697B - Operation error online analysis method and system - Google Patents
Operation error online analysis method and system Download PDFInfo
- Publication number
- CN113281697B CN113281697B CN202110551763.5A CN202110551763A CN113281697B CN 113281697 B CN113281697 B CN 113281697B CN 202110551763 A CN202110551763 A CN 202110551763A CN 113281697 B CN113281697 B CN 113281697B
- Authority
- CN
- China
- Prior art keywords
- error
- error model
- weight
- model
- meter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
- G01R35/04—Testing or calibrating of apparatus covered by the other groups of this subclass of instruments for measuring time integral of power or current
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
An online analysis method and system for operation errors are disclosed, the method comprises the following steps: step 1, collecting historical data of electric meters of the same type to form a data set, and training through a neural network to obtain a first error model; step 2, constructing a second error model by using the platform area circuit structure; step 3, constructing an operation error model according to the weight by using the first error model in the step 1 and the second error model in the step 2; step 4, configuring a first error model weight and a second error model weight to obtain an operation error model; and 5, analyzing the running error of the electric meter on line by using the running error model obtained in the step 4. According to the method, the factors such as the change of internal parameters along with time, external fixed loss and line loss are considered comprehensively, and the method for obtaining the configuration weight in a data scanning mode can dynamically adjust weight distribution, so that the problem that two error models have different contribution degrees in calculating error accuracy rate along with the time is solved.
Description
Technical Field
The invention belongs to the field of electric meter data mining, and particularly relates to an operation error online analysis method and system.
Background
The traditional method for calibrating the metering error of the electric energy meter is to compare the electric energy meter to be calibrated with a standard device with a higher accuracy grade to obtain the error of the electric energy meter. Comparing a typical high accuracy grade electric energy meter, such as in a laboratory, to calibrate a low accuracy grade electric energy meter; and in the running process of the electric energy meter, comparing the metering difference between the electric energy meter and the on-site calibration instrument in the same period of time by adopting the on-site calibration instrument with higher accuracy to obtain the running error of the electric energy meter.
Based on the traditional method, the intelligent electric energy meter installed in transport needs to be subjected to on-site verification or be dismantled to be subjected to laboratory verification, the efficiency is low, the workload is large, various problems of the intelligent electric energy meter cannot be found in time, and the intelligent electric energy meter cannot be transported in full.
Through exploration, the applicant finds that under a certain transformer area, physical quantities such as power supply quantity of a general meter, power consumption quantity of a user meter, line loss and other fixed loss and the like follow the basic physical law of energy conservation, so that necessary internal relation exists; further, the electricity consumption of the user meter has a definite relationship with the electric energy metering of the user meter and the error of the user meter. The above findings mean that it is feasible to perform online analysis of the operation error of the intelligent electric energy meter by modeling in units of transformer areas.
Within a distribution area, metering loss or deviation can be caused by various reasons, including metering error of the electric energy meter, line loss, power consumption of the electric energy meter and other fixed losses. The purpose of this application is to be able to "find" out the metering error of the electric energy meter, then must solve a problem, that is: how to distinguish the energy metering loss caused by the error of the electric energy meter, the line loss and other fixed loss.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide an online operation error analysis method.
The invention adopts the following technical scheme. An online analysis method for operation errors comprises the following steps:
step 1, collecting historical data of electric meters of the same type to form a data set, and training through a neural network to obtain a first error model;
step 2, constructing a second error model by using the platform area circuit structure;
step 3, constructing an operation error model according to the weight by using the first error model in the step 1 and the second error model in the step 2;
step 4, configuring a first error model weight and a second error model weight to obtain an operation error model;
and 5, analyzing the running error of the electric meter on line by using the running error model obtained in the step 4.
Preferably, in step 1, the data set is represented by the following formula,
in the formula:
x represents a neural network input vector;
d i representing the number of days of operation of the ith electricity meter;
n i representing the bottom number in the ith ammeter;
U i indicating the battery voltage of the ith meter；
f i Representing the number of times of the ith ammeter fault;
y represents a neural network output;
e i error of the ith meter is represented;
the first error model is expressed in the following formula,
e i ＝e 1st (d i ,n i ，U i ,f i )
e 1st and expressing a first error calculation expression obtained through neural network training.
Preferably, in step 2, the second error model is represented by the following formula,
in the formula:
c represents the electric meter measuring value of the transformer area;
m represents the number of district electric meters, j =1,2, \ 8230;
e θj representing the estimation error of the jth electric meter;
c j represents the jth electric meter measurement;
L 0 representing the fixed loss of the platform area;
L line indicating the line loss in the cell.
Preferably, in step 2, L is expressed as follows line Which represents the line loss of the station area,
in the formula:
Δ c represents the metering value of the station electric meter in the metering period T;
t represents a metering period;
Δc j represents the jth electric meter measurement value in the measurement period T;
K j the line loss proportionality constant is indicated.
Preferably, step 3, a running error model is constructed according to weight by expressing the first error model in step 1 and the second error model in step 2 in the following formula;
in the formula:
e 0j represents the operation error of jth ammeter in the district, j =1,2, \ 8230;, M;
α represents a weight of the first error model;
β represents the weight of the second error model.
Preferably, step 4, averagely configuring the first error model weight and the second error model weight, namely both 0.5, and obtaining the operation error model.
Preferably, step 4 comprises:
step 4.1, setting the weight of the first error model to be 0 and the weight of the second error model to be 1;
step 4.2, increasing the first error model weight by a set step length, simultaneously decreasing the second error model weight by the same step length, and calculating an operation error to form a configuration data set;
4.3, sampling the electric meters in the area to measure real running errors on site at set time intervals;
and 4.4, measuring the real running error in the step 4.3, and selecting the optimal weight configuration in the configuration data set formed in the step 4.2.
Preferably, step 5 comprises:
step 5.1, collecting the current operation days, the bottom number in the meter, the battery voltage and the failure times of the jth electricity meter in the distribution room, and calculating e by using a first error model j ；
Step 5.2, collecting historical data of the district electric meters, establishing an equation set by using a second error model, and calculating to obtain the estimation error of the jth electric meter;
and 5.3, substituting the calculation results of the step 5.1 and the step 5.2 into an operation error model, and calculating to obtain the operation error of the jth ammeter in the transformer area.
The second aspect of the present invention further provides an online operation error analysis system for operating the online operation error analysis method, including:
the data acquisition module is used for acquiring historical data and current operation data of the electric meter;
the data processing module is used for forming a data set by using the data acquired by the data acquisition module, acquiring a first error model through neural network training and constructing a second error model by using a platform area circuit structure;
the error analysis module is used for configuring a first error model weight and a second error model weight to obtain an operation error model; analyzing the running error of the ammeter on line by using a running error model;
and the display output module is used for displaying and outputting the ammeter operation error obtained by the calculation of the error analysis module.
Preferably, the system further comprises: and the data storage module is used for storing historical data of the electric meter and the historical result of online error calculation.
Compared with the prior art, the method has the advantages that the problem of insufficient precision caused by the fact that only an external topological structure is considered and errors are calculated by using a metering value per se in the prior art is solved, the consideration of internal parameters on precision influence factors is creatively introduced, and the correlation of time factors on error change is obtained by training a neural network; meanwhile, the incidence relation among fixed loss, line loss and errors in the topological relation in the transformer area is considered; the applicant finds that, as time goes by, the accuracy of the error results of the electric meters with different use times in a distribution area is different, and after a plurality of algorithms are fused, the problem of insufficient accuracy caused by the single algorithm can be solved only at a specific time point according to the proportion of the set weight. And the problem that the contribution degrees of the two error models in calculating the error accuracy are different along with the time is solved by dynamically adjusting the weight. And the weight distribution data set is established, so that the burden of real-time calculation is reduced, and the weight can be adjusted quickly and accurately.
Drawings
Fig. 1 is a flowchart of an online operation error analysis method provided by the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the present invention provides an online analysis method for operation errors, comprising the following steps:
step 1, collecting historical data of electric meters of the same type to form a data set, and training through a neural network to obtain a first error model.
Specifically, in step 1, a data set is represented by the following formula,
in the formula:
x represents a neural network input vector;
d i representing the number of days of operation of the ith electricity meter;
n i representing the bottom number in the ith electric meter;
U i representing the ith meter battery voltage;
f i representing the number of times of the ith ammeter faults;
y represents a neural network output;
e i error of the ith meter is represented;
the first error model is expressed in the following formula,
e i ＝e 1st (d i ,n i ,U i ,f i )
e 1st and representing a first error calculation expression obtained by training the neural network.
It is noted that one skilled in the art may select any neural network model for training, including, but not limited to, KNN, DNN, SVM, DL, BP, DBN, RBF, CNN, RNN, ANN. Experiments prove that the single-layer sensing BP neural network can meet the requirements, and is a preferred embodiment of the invention. Although the neural network structure adopted by the invention is the prior art, the application of the neural network structure in error analysis is a contribution of the applicant of the invention to the prior art.
The method is characterized in that the reference value of historical data to the online running electric meter is fully utilized, the running days, the number of bottoms in the electric meter, the battery voltage and the failure times are used as input layers of a neural network, errors are used as output layers, the errors can be found through training of the neural network, the change rule of the errors of the electric meter after running along with the self structural parameters is introduced into online analysis and judgment through the weight distribution of subsequent steps. The problem of prior art only from the inaccurate that interior structural factor arouses of external topological structure analysis error does not consider is solved.
And 2, constructing a second error model by using the platform area circuit structure.
A simple case is set, namely a station area with only 1 general meter and 1 user meter, and if the actual power supply capacity of a certain day is 100 degrees, the power metering of the user meter is 97 degrees in three cases. The first case is that there is no line loss and fixed loss, but the electric energy meter has an error of-3%; the second condition is that the electric energy meter has no error and no fixed loss, but has 3-degree line loss; the third case is that the electric energy meter has no error and no line loss, but has a fixed loss of 3 degrees.
If the user has the same electricity consumption and the same electricity consumption characteristics every day, the user cannot distinguish whether the metering loss of 3 degrees of electricity is caused by the error of an electric energy meter, the line loss or the fixed loss. However, if the electricity consumption of the user changes the next day, for example, to 200 degrees, but the characteristics such as the electricity consumption time interval are still unchanged, the metering loss caused by the difference of the physical characteristics of the three factors will be greatly different, so that how much metering loss is caused by that reason can be distinguished.
When the power supply is 200 degrees, the metering loss caused by the error of the electric energy meter is linearly increased to 6 degrees, the metering loss caused by the line loss is nonlinearly increased to 12 degrees, the line loss is proportional to the square of the current on the line, when the power supply is doubled and the power utilization characteristic is unchanged, the current on the line is doubled, the line loss is changed to 4 times of the previous line loss, and the fixed loss is still maintained to be 3 degrees.
In actual work, the rules need to be embodied on a mathematical relation by taking the platform area as a unit, namely mathematical modeling is carried out. The specific method is based on the mathematical relationship of energy conservation and total score, the equations are formed by utilizing the relevant electric energy values in the same time period, the equations obtained in different time periods are combined to form an equation set, and the equation set is solved to obtain the characteristics of each electric energy meter hidden in the relationships. Therefore, the characteristics of all the electric energy meters in the transformer area are automatically reflected through a mathematical means.
Of course, there is still no point departing from the most core of the conventional verification means, that is, a standard is required to be used as a baseline, and other electric energy meters are compared with the baseline to obtain respective characteristics. Obviously, in a cell, the most suitable for the standard is the cell summary table, and the corresponding mathematical operation is to use the metering value of the cell summary table as the real power supply value of the cell, i.e. the cell summary table is considered as "accurate".
Suppose a station has a summary table, two user tables a and B, and suppose the station supplies 100 degrees each day. The relative metering error of the user table A is +3%, and the relative metering error of the user table B is-3%. The power usage of the user tables a and B varies every day. Under the real platform area, there are line loss and fixed loss. Set 4 sets of data as the following table, where the relative error of the metering of user tables a, B is still +3%, -3%, respectively. We solve the set of equations by building up 4 sets of data points in the table below.
| Data points | Data 1 | Data 2 | Data 3 | Data 4 |
| Line loss rate | 0.0125 | 0.0135 | 0.0125 | 0.0128 |
| Electric power of general meter | 100 | 100 | 100 | 100 |
| Fixed loss | 0.05 | 0.05 | 0.05 | 0.05 |
| Line loss | 1.25 | 1.35 | 1.25 | 1.28 |
| True value of electric energy meter A | 49 | 48 | 48.5 | 47.8 |
| True value of electric energy meter B | 49.7 | 50.6 | 50.2 | 50.87 |
| A measured value | 50.47 | 49.44 | 49.955 | 49.234 |
| B measurement value | 48.209 | 49.082 | 48.694 | 49.3439 |
In order to accurately solve the metering error of the electric energy meter, line loss and fixed loss must be considered. Consider by introducing ye in the equation y To estimate line loss (where y is the total table supply, e) y Is the line loss rate (unknown)), and introduces e 0 (unknowns) to estimate the fixed loss, based on the 4 sets of data in the previous data table, the following set of equations is established and solved:
from the above process and results, when the line loss and the fixed loss are considered in the above method, two equations need to be added to the equation set to support the solution of the unknown quantity, since the unknown quantity is added by two. The above process is further expressed as a general formula, i.e., the following steps.
Specifically, in step 2, the second error model is expressed by the following equation,
in the formula:
c represents the metering value of the electric meter of the transformer area;
m represents the number of district electric meters, j =1,2, \ 8230;
e θj representing the estimation error of the jth electric meter;
c j represents the jth electric meter measurement;
L 0 representing the fixed loss of the platform area;
L line indicating the line loss in the cell.
In step 2, L can also be expressed by the following formula line Which represents the line loss of the station area,
in the formula:
Δ c represents the metering value of the station electric meter in the metering period T;
t represents a metering period;
Δc j represents the jth electric meter measurement value in the measurement period T;
K j representing the line loss proportionality constant.
And 3, constructing an operation error model according to the weight by using the first error model in the step 1 and the second error model in the step 2.
Specifically, step 3, a first error model in step 1 and a second error model in step 2 are expressed by the following formulas to construct an operation error model according to weight;
in the formula:
e 0j represents the operation error of jth ammeter in the district, j =1,2, \ 8230;, M;
α represents a weight of the first error model;
β represents the weight of the second error model.
And 4, configuring a first error model weight and a second error model weight to obtain an operation error model. It is noted that the first error model weight and the second error model weight may be arbitrarily configured by those skilled in the art. In a preferred but non-limiting embodiment, the first error model weight and the second error model weight are configured on average, i.e. both 0.5, to obtain the running error model.
More preferably, step 4 comprises:
step 4.1, setting the weight of the first error model to be 0 and the weight of the second error model to be 1;
step 4.2, increasing the first error model weight by a set step length, simultaneously decreasing the second error model weight by the same step length, and calculating the operation error to form a configuration data set;
4.3, sampling the electric meters in the area to measure real running errors on site at set time intervals;
and 4.4, measuring the real running error in the step 4.3, and selecting the optimal weight configuration in the configuration data set formed in the step 4.2.
The applicant has found that the accuracy of the error results is different for meters with different times of use in a distribution area over time, and that the problem of insufficient accuracy of individual algorithms can be solved only at a specific time point according to a predetermined weight ratio after a plurality of algorithms are fused. And the problem that the contribution degrees of the two error models in calculating the error accuracy are different along with the time is solved by dynamically adjusting the weight. And the weight distribution data set is established, so that the burden of real-time calculation is reduced, and the weight can be adjusted quickly and accurately.
And 5, analyzing the running error of the electric meter on line by using the running error model obtained in the step 4. The step 5 comprises the following steps:
step 5.1, collecting the current operation days, the bottom number in the meter, the battery voltage and the failure times of the jth electricity meter in the distribution room, and calculating e by using a first error model j ；
Step 5.2, collecting historical data of the district electric meters, establishing an equation set by using a second error model, and calculating to obtain the estimation error of the jth electric meter;
and 5.3, substituting the calculation results of the step 5.1 and the step 5.2 into an operation error model, and calculating to obtain the operation error of the jth ammeter in the transformer area.
The second aspect of the present invention further provides an online operation error analysis system for operating the online operation error analysis method, including:
the data acquisition module is used for acquiring historical data and current operation data of the electric meter;
the data processing module is used for forming a data set by using the data acquired by the data acquisition module, acquiring a first error model through neural network training and constructing a second error model by using a platform area circuit structure;
the error analysis module is used for configuring a first error model weight and a second error model weight to obtain an operation error model; analyzing the running error of the electric meter on line by using a running error model;
and the display output module is used for displaying and outputting the electric meter operation error obtained by the calculation of the error analysis module.
Further preferably, the data storage module is used for storing historical data of the electric meter and historical results of online error calculation.
Compared with the prior art, the method has the advantages that the problem of insufficient precision caused by calculation errors of the metering value by only considering an external topological structure is solved, the consideration of internal parameters on precision influence factors is creatively introduced, and the correlation of time factors on error change is obtained by training a neural network; meanwhile, the incidence relation among fixed loss, line loss and errors in the topological relation in the transformer area is considered; the applicant finds that, as time goes by, the accuracy of the error results of the electric meters with different use times in a distribution area is different, and after a plurality of algorithms are fused, the problem of insufficient accuracy caused by the single algorithm can be solved only at a specific time point according to the proportion of the set weight. And the problem that the contribution degrees of the two error models in calculating the error accuracy are different along with the time is solved by dynamically adjusting the weight. And the weight distribution data set is established, so that the burden of real-time calculation is reduced, and the weight can be adjusted quickly and accurately.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (9)
1. An online operation error analysis method is characterized by comprising the following steps:
step 1, collecting historical data of electric meters of the same type to form a data set, and training through a neural network to obtain a first error model;
the data set is represented by the following formula,
in the formula:
x represents a neural network input vector;
d i representing the number of days of operation of the ith electricity meter;
n i representing the bottom number in the ith ammeter;
U i representing the ith meter battery voltage;
f i representing the number of times of the ith ammeter fault;
n represents the number of the collected electric meters of the same type;
y represents a neural network output;
e i error of the ith meter is represented;
step 2, constructing a second error model by using the platform area circuit structure;
step 3, constructing an operation error model according to the weight by using the first error model in the step 1 and the second error model in the step 2;
step 4, configuring a first error model weight and a second error model weight to obtain an operation error model; the step 4 specifically comprises the following steps:
step 4.1, setting the weight of the first error model to be 0 and the weight of the second error model to be 1;
step 4.2, increasing the first error model weight by a set step length, simultaneously decreasing the second error model weight by the same step length, and calculating the operation error to form a configuration data set;
step 4.3, sampling the electric meters in the distribution area on site at set time intervals to measure real operation errors;
step 4.4, measuring the real operation error in the step 4.3, and selecting the optimal weight configuration in the configuration data set formed in the step 4.2;
and 5, analyzing the running error of the electric meter on line by using the running error model obtained in the step 4.
2. The running error online analysis method according to claim 1, characterized in that:
in step 1, a first error model is expressed by the following formula,
e i ＝e 1st (d i ,n i ,U i ,f i )
e 1st and expressing a first error calculation expression obtained through neural network training.
3. The running error online analysis method according to claim 2, characterized in that:
in step 2, a second error model is expressed by the following formula,
in the formula:
c represents the electric meter measuring value of the transformer area;
m represents the number of electric meters in the transformer area, j =1,2, ...
e θj representing the estimation error of the jth electric meter;
c j represents the jth electric meter measurement;
L 0 representing the fixed loss of the platform area;
L line indicating the line loss in the cell.
4. The running error online analysis method according to claim 3, characterized in that:
in step 2, L is expressed by the following formula line Which represents the line loss of the station area,
in the formula:
Δ c represents the metering value of the station electric meter in the metering period T;
t represents a metering period;
Δc j represents the jth electric meter measurement value in the measurement period T;
K j line loss ratio of j-th meterA constant.
5. The running error online analysis method according to any one of claims 1 to 4, characterized in that:
step 3, expressing the first error model in the step 1 and the second error model in the step 2 according to the following formula, and constructing an operation error model according to weight;
in the formula:
e 0j represents the operation error of jth ammeter in the district, j =1,2, \ 8230;, M;
e j error of j-th meter;
e θj representing the estimation error of the jth electric meter;
α represents a weight of the first error model;
β represents the weight of the second error model.
6. The running error online analysis method according to claim 5, characterized in that:
and 4, averagely configuring the first error model weight and the second error model weight, namely both the first error model weight and the second error model weight are 0.5, and obtaining an operation error model.
7. The running error online analysis method according to any one of claims 1 to 6, characterized in that:
the step 5 comprises the following steps:
step 5.1, collecting the current operation days, the bottom number in the meter, the battery voltage and the failure times of the jth ammeter in the transformer area, and calculating the error e of the jth ammeter by using a first error model j ；
Step 5.2, collecting historical data of the district electric meters, establishing an equation set by using a second error model, and calculating to obtain the estimation error of the jth electric meter;
and 5.3, substituting the calculation results of the step 5.1 and the step 5.2 into an operation error model, and calculating to obtain the operation error of the jth ammeter in the transformer area.
8. An operation error online analysis system that operates the operation error online analysis method according to any one of claims 1 to 7, comprising:
the data acquisition module is used for acquiring historical data and current operation data of the electric meter;
the data processing module is used for forming a data set by using the data acquired by the data acquisition module, obtaining a first error model through neural network training and constructing a second error model by using a platform area circuit structure;
the error analysis module is used for configuring a first error model weight and a second error model weight to obtain an operation error model; analyzing the running error of the electric meter on line by using a running error model;
and the display output module is used for displaying and outputting the electric meter operation error obtained by the calculation of the error analysis module.
9. The running error online analysis system of claim 8, wherein:
the system further comprises: and the data storage module is used for storing historical data of the electric meter and the historical result of online error calculation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110551763.5A CN113281697B (en) | 2021-05-20 | 2021-05-20 | Operation error online analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110551763.5A CN113281697B (en) | 2021-05-20 | 2021-05-20 | Operation error online analysis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113281697A CN113281697A (en) | 2021-08-20 |
| CN113281697B true CN113281697B (en) | 2023-04-18 |
Family
ID=77280321
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110551763.5A Active CN113281697B (en) | 2021-05-20 | 2021-05-20 | Operation error online analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113281697B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113762470A (en) * | 2021-08-23 | 2021-12-07 | 迟源 | Prediction model construction method, prediction method, device, equipment and medium |
| CN114528929B (en) * | 2022-02-14 | 2023-06-20 | 国网山东省电力公司营销服务中心(计量中心) | Multi-source data platform region measuring system and method |
| CN114339477B (en) * | 2022-03-14 | 2022-07-12 | 浙江万胜智能科技股份有限公司 | Data acquisition management method and system based on multi-table integration |
| CN114355274B (en) * | 2022-03-14 | 2022-07-12 | 浙江万胜智能科技股份有限公司 | Regular calibration method and system for electricity consumption information data |
| CN115567899B (en) * | 2022-08-23 | 2023-10-03 | 浙江晨泰科技股份有限公司 | Error analysis method and device for intelligent ammeter |
| CN115600414B (en) * | 2022-10-24 | 2023-04-18 | 中国电力科学研究院有限公司 | Electric energy meter operation error solving method and device based on AI algorithm |
| CN115840185B (en) * | 2023-02-20 | 2023-07-04 | 威胜集团有限公司 | Voltage transformer error online monitoring and analyzing method, medium and terminal |
| CN116718979B (en) * | 2023-08-08 | 2023-10-24 | 北京京仪北方仪器仪表有限公司 | Smart electric meter operation error measurement method and system |
| CN117131926A (en) * | 2023-10-24 | 2023-11-28 | 山东计保电气有限公司 | Electric energy metering error analysis method based on data storage |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0252172U (en) * | 1988-10-11 | 1990-04-13 | ||
| CN107462863A (en) * | 2017-09-05 | 2017-12-12 | 中国电力科学研究院 | A kind of intelligent electric energy meter kinematic error operational diagnostics analysis method and system |
| CN109597014A (en) * | 2018-11-30 | 2019-04-09 | 国网上海市电力公司 | A kind of electric energy meter error diagnostic method based on artificial intelligence technology |
| CN110490409A (en) * | 2019-07-05 | 2019-11-22 | 国网江苏省电力有限公司电力科学研究院 | A kind of low-voltage platform area line loss per unit mark post value setting method based on DNN |
| CN110531304A (en) * | 2019-08-07 | 2019-12-03 | 深圳供电局有限公司 | A kind of calculation method, equipment and medium for monitoring platform area misalignment rate on-line |
| CN111103565A (en) * | 2019-12-11 | 2020-05-05 | 国网天津市电力公司电力科学研究院 | Data transformation method and system based on intelligent electric energy meter metering error analysis |
| AU2020101900A4 (en) * | 2020-08-21 | 2020-11-05 | Qinghu Rising Sunshine Data Technology (Beijing) Co., Ltd. | A method, device and equipment for detecting abnormal electric meter |
| CN112346000A (en) * | 2020-10-30 | 2021-02-09 | 国网山东省电力公司营销服务中心(计量中心) | Intelligent electric energy meter operation error data statistical processing system and method |
| CN112417371A (en) * | 2020-12-01 | 2021-02-26 | 国网上海市电力公司 | Method for monitoring running state of intelligent electric energy meter in distribution network area |
| CN112611997A (en) * | 2020-12-01 | 2021-04-06 | 国网河南省电力公司电力科学研究院 | Online verification method and system for hitching relation of platform area gateway table |
| CN112684400A (en) * | 2020-11-25 | 2021-04-20 | 国网江苏省电力有限公司营销服务中心 | Method and system for monitoring electric energy meter operation error data of small electric quantity distribution area |
| CN112684398A (en) * | 2020-11-23 | 2021-04-20 | 国网江苏省电力有限公司营销服务中心 | Electric energy meter operation error monitoring method and system based on dynamic line loss |
| CN112698261A (en) * | 2020-11-25 | 2021-04-23 | 国网湖南省电力有限公司 | Fault early warning method and device for intelligent electric energy meter |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110532505B (en) * | 2019-08-07 | 2023-08-22 | 深圳供电局有限公司 | Method for calculating misalignment rate of ammeter |
| CN110907884A (en) * | 2019-12-06 | 2020-03-24 | 国网天津市电力公司电力科学研究院 | Electric energy meter error diagnosis and analysis method based on non-invasive measurement |
| CN112686493A (en) * | 2020-11-24 | 2021-04-20 | 国网新疆电力有限公司营销服务中心(资金集约中心、计量中心) | Method for evaluating running state and replacing of intelligent electric meter in real time by relying on big data |
| CN112379177A (en) * | 2021-01-18 | 2021-02-19 | 中国电力科学研究院有限公司 | Method and system for determining low-voltage line loss and electric energy meter operation error of transformer area |
| CN113126019B (en) * | 2021-04-19 | 2023-05-12 | 广东电网有限责任公司计量中心 | Remote estimation method, system, terminal and storage medium for error of intelligent ammeter |
-
2021
- 2021-05-20 CN CN202110551763.5A patent/CN113281697B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0252172U (en) * | 1988-10-11 | 1990-04-13 | ||
| CN107462863A (en) * | 2017-09-05 | 2017-12-12 | 中国电力科学研究院 | A kind of intelligent electric energy meter kinematic error operational diagnostics analysis method and system |
| CN109597014A (en) * | 2018-11-30 | 2019-04-09 | 国网上海市电力公司 | A kind of electric energy meter error diagnostic method based on artificial intelligence technology |
| CN110490409A (en) * | 2019-07-05 | 2019-11-22 | 国网江苏省电力有限公司电力科学研究院 | A kind of low-voltage platform area line loss per unit mark post value setting method based on DNN |
| CN110531304A (en) * | 2019-08-07 | 2019-12-03 | 深圳供电局有限公司 | A kind of calculation method, equipment and medium for monitoring platform area misalignment rate on-line |
| CN111103565A (en) * | 2019-12-11 | 2020-05-05 | 国网天津市电力公司电力科学研究院 | Data transformation method and system based on intelligent electric energy meter metering error analysis |
| AU2020101900A4 (en) * | 2020-08-21 | 2020-11-05 | Qinghu Rising Sunshine Data Technology (Beijing) Co., Ltd. | A method, device and equipment for detecting abnormal electric meter |
| CN112346000A (en) * | 2020-10-30 | 2021-02-09 | 国网山东省电力公司营销服务中心(计量中心) | Intelligent electric energy meter operation error data statistical processing system and method |
| CN112684398A (en) * | 2020-11-23 | 2021-04-20 | 国网江苏省电力有限公司营销服务中心 | Electric energy meter operation error monitoring method and system based on dynamic line loss |
| CN112684400A (en) * | 2020-11-25 | 2021-04-20 | 国网江苏省电力有限公司营销服务中心 | Method and system for monitoring electric energy meter operation error data of small electric quantity distribution area |
| CN112698261A (en) * | 2020-11-25 | 2021-04-23 | 国网湖南省电力有限公司 | Fault early warning method and device for intelligent electric energy meter |
| CN112417371A (en) * | 2020-12-01 | 2021-02-26 | 国网上海市电力公司 | Method for monitoring running state of intelligent electric energy meter in distribution network area |
| CN112611997A (en) * | 2020-12-01 | 2021-04-06 | 国网河南省电力公司电力科学研究院 | Online verification method and system for hitching relation of platform area gateway table |
Non-Patent Citations (2)
| Title |
|---|
| Slobodan Skundric.Metrological Supervision of Electrical Energy Meters in Substations up to 35 kV.《2007 9th International Conference on Electrical Power Quality and Utilisation》.2007,全文. * |
| 宝慧青.电力计量误差产生原因分析及改进措施研究.《应用能源技术》.2020,(第undefined期),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113281697A (en) | 2021-08-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113281697B (en) | Operation error online analysis method and system | |
| WO2022110558A1 (en) | Smart electricity meter malfunction early warning method and device | |
| CN108593990B (en) | Electricity stealing detection method based on electricity consumption behavior mode of electric energy user and application | |
| FI119261B (en) | Method and system of meter calibration | |
| CN109242041A (en) | A kind of electric energy meter abnormal deviation data examination method, device, equipment and storage medium | |
| WO2022021726A1 (en) | Pmu-based power system state estimation performance evaluation method | |
| CN107133406A (en) | A kind of method for fast searching on power system steady state voltage stability domain border | |
| CN101793887A (en) | Construction method of fuzzy neural network expert system for water quality assessment in turbot culture | |
| CN107741578B (en) | Original meter reading data processing method for remote calibration of running error of intelligent electric energy meter | |
| CN113406558B (en) | Ammeter misalignment detection method and device based on linear regression and electronic equipment | |
| CN110070282A (en) | A kind of low-voltage platform area line loss analysis of Influential Factors method based on Synthesis Relational Grade | |
| CN103748755A (en) | Power system state estimation device and power system using same | |
| CN113126019A (en) | Intelligent ammeter error remote estimation method, system, terminal and storage medium | |
| CN111510500A (en) | Water quality monitoring data calibration method based on edge calculation | |
| CN113010998A (en) | Ammeter error estimation method based on following line loss and forgetting factor least square method | |
| CN112130109A (en) | Method for detecting metering performance abnormity of intelligent electric energy meter | |
| CN115267645B (en) | Error calculation method, monitoring system and equipment of low-power-factor electric energy meter | |
| Fangxing et al. | Estimation of smart meters errors using meter reading data | |
| CN115508770A (en) | KL-NB algorithm-based electric energy meter operation state online evaluation method | |
| Kong et al. | A remote estimation method of smart meter errors based on neural network filter and generalized damping recursive least square | |
| CN115201743A (en) | Method and device for determining error of metering point of low-voltage transformer area and storage medium | |
| CN103900666A (en) | Long-time running measuring precision control method of membrane type gas meter | |
| CN107655549A (en) | A kind of intelligent gas meter is in Line synthesis error calibration method | |
| CN114152806B (en) | Electric energy sensor with three-way array structure and measurement system and method formed by same | |
| CN114152811B (en) | Electric energy meter with three-way array structure and measuring system and measuring method formed by electric energy meter |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |