CN114200386B - Online analysis method and system for operation errors of intelligent ammeter - Google Patents
Online analysis method and system for operation errors of intelligent ammeter Download PDFInfo
- Publication number
- CN114200386B CN114200386B CN202111572746.6A CN202111572746A CN114200386B CN 114200386 B CN114200386 B CN 114200386B CN 202111572746 A CN202111572746 A CN 202111572746A CN 114200386 B CN114200386 B CN 114200386B
- Authority
- CN
- China
- Prior art keywords
- analysis
- point
- data
- data acquisition
- module
- 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
- 238000004458 analytical method Methods 0.000 title claims abstract description 86
- 238000004891 communication Methods 0.000 claims abstract description 17
- 238000007405 data analysis Methods 0.000 claims abstract description 17
- 238000013500 data storage Methods 0.000 claims abstract description 15
- 230000005611 electricity Effects 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 7
- 238000000034 method Methods 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 5
- 230000001419 dependent effect Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 230000007613 environmental effect Effects 0.000 description 3
- 238000007726 management method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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
Abstract
The invention discloses an intelligent ammeter operation error online analysis method and system, wherein the intelligent ammeter operation error online analysis system comprises a data acquisition module, a data storage module, a data analysis module and an analysis result display module, wherein the data acquisition module is in communication connection with the data storage module, the data storage module is in communication connection with the data analysis module, and the data analysis module is in communication connection with the analysis result display module. According to the intelligent ammeter operation error analysis method, the intelligent ammeter operation error with the time length t before the analysis point is introduced into intelligent ammeter operation error online analysis, and the intelligent ammeter operation error change and trend thereof are obtained, so that the intelligent ammeter with larger error change amplitude can be positioned conveniently.
Description
Technical Field
The invention relates to the technical field of power grids. In particular to an online analysis method and an online analysis system for operation errors of a smart meter.
Background
Along with the popularization of intelligent electric meters, power supply enterprises can utilize an online monitoring technology to remotely monitor power consumption data of a power grid. Whether the intelligent ammeter running error meets the requirements of design specifications and practical application is a key of the basis of monitoring the user power supply quality and the informatization management of power supply enterprises, not only influences the accuracy of power utilization data acquisition, but also has great influence on the monitoring effect of the power utilization condition of the transformer area, and has great guiding effect on the intelligent power supply management of the transformer area, so that the analysis of the intelligent ammeter running error has great significance.
The existing method for calculating the operation errors of the intelligent electric meters in the transformer area lacks of effectively analyzing the operation error changes and trends of the intelligent electric meters.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to provide the intelligent ammeter operation error online analysis method and system, which introduce the intelligent ammeter operation error with the time length t before the analysis point into intelligent ammeter operation error online analysis to acquire the intelligent ammeter operation error change and trend thereof, thereby being convenient for positioning the intelligent ammeter with larger error change range.
In order to solve the technical problems, the invention provides the following technical scheme:
the intelligent ammeter operation error online analysis method comprises the following steps:
s1) determination of the analysis Point F m And anchor point F 0 Locating point F 0 For being located temporally at the analysis point F m Previous data points, analysis Point F m And anchor point F 0 The time length between the two is t; wherein m is a natural number greater than or equal to 1;
s2) at the locating point F 0 And analysis point F m Setting n data acquisition points f in between n Equally dividing the time length T into (n+1) sections, wherein each section has the time length delta T; wherein n is a natural number greater than or equal to 1;
s3) determining a data acquisition point f n Running error estimate ζ of (2) n Calculating an arithmetic average of running error estimates of n data acquisition points within a time period t
S4) determining the analysis Point F m Running error estimate ζ of (2) m And anchor point F 0 Running error estimate ζ of (2) 0 According to ζ 0 Zeta as determined in step S3) n Anduse of mean absolute percentage error and root mean square error to ζ m Evaluation, ζ m Relative to ζ n Andthe smaller the difference is, the stable operation condition of the intelligent electric meter is indicated, otherwise, the operation of the intelligent electric meter is indicated to be problematic.
In the above online analysis method for operation errors of the smart meter, in step S2), the number of the set data acquisition point groups is greater than or equal to 2 groups, and the number of the data acquisition points in each data acquisition point group is different from the number of the data acquisition points in other data acquisition point groups.
In the above online analysis method for operation errors of smart meter, in step S2), the number of data acquisition point groups is less than or equal to 7 groups.
In the above online analysis method for the operation error of the smart meter, in step S4), ζ is determined according to 0 Zeta as determined in step S3) n Anduse of mean absolute percentage error and root mean square error to ζ m In the evaluation, a plurality of data acquisition point groups are +.>And using the mean absolute percentage error and root mean square error to ζ in dependence upon Δζ m An evaluation is performed.
In the online analysis method for the operation error of the intelligent ammeter, in step S3), ζ n Calculated by the following formula:
ζ n ＝(P real world -U n I n Δtψcosφ)/P Real world ；
Wherein P is Real world The time length is the actual consumed electric energy value in delta t; u (U) n Is root mean square voltage within a duration Δt; i n Is root mean square current with the duration of delta t; cos phi is the power factor; psi is a correction coefficient;is the temperature coefficient of resistivity; t is the average daytime temperature; t (T) Often times Is at the normal temperature of 20 ℃; w (W) Real world Is the average humidity during the day; w (W) Often times 30% of daily humidity; and | is absolute value.
In the above-mentioned on-line analysis method for operation errors of smart meters, in step S4), when an operation error of a smart meter is analyzed, a historical operation error of an adjacent smart meter is introduced to evaluate the operation error of the smart meter.
According to the intelligent ammeter operation error online analysis method, the number of the introduced adjacent intelligent ammeters is 3-7.
The system for carrying out intelligent ammeter operation error online analysis by utilizing the intelligent ammeter operation error online analysis comprises:
the data acquisition module is used for acquiring an analysis point F m And anchor point F 0 Electricity consumption data, average daily gas temperature and average daily humidity; wherein the analysis point F m And anchor point F 0 The electricity consumption data comprises actual consumed electric energy value, voltage value and current value;
the data storage module is used for storing the data acquired by the data acquisition module;
the data analysis module is used for analyzing the intelligent ammeter at an analysis point F m Is evaluated;
the analysis result display module is used for displaying the operation error analysis result;
the data acquisition module is in communication connection with the data storage module, the data storage module is in communication connection with the data analysis module, and the data analysis module is in communication connection with the analysis result display module.
The system is characterized in that the data analysis module is in communication connection with the server.
The technical scheme of the invention has the following beneficial technical effects:
1. according to the intelligent ammeter operation error analysis method, the intelligent ammeter operation error with the time length t before the analysis point is introduced into intelligent ammeter operation error online analysis, so that the intelligent ammeter operation error change under similar environmental conditions and under different environmental conditions can be conveniently obtained, and the influence of the environmental conditions on the intelligent ammeter operation error can be conveniently analyzed.
2. According to the intelligent ammeter fault analysis method, the influence of the ambient temperature and the ambient humidity on the operation error of the intelligent ammeter is introduced into the intelligent ammeter operation error analysis, so that false alarm faults of the intelligent ammeter caused by the ambient factors can be avoided, and meanwhile, the electricity stealing behavior by the ambient factors can be avoided.
Drawings
FIG. 1 is a schematic diagram of the working principle of the intelligent ammeter operation error online analysis system in the invention;
FIG. 2 is a flow chart of the method for online analysis of the operation errors of the intelligent ammeter in the present invention.
Detailed Description
As shown in FIG. 1, the intelligent ammeter operation error online analysis system comprises a data acquisition module, a data storage module, a data analysis module and an analysis result display module, wherein the data acquisition module is in communication connection with the data storage module, the data storage module is in communication connection with the data analysis module, and the data analysis module is in communication connection with the analysis result display module.
Wherein, the data acquisition module is used for acquiring an analysis point F m And anchor point F 0 Electricity consumption data, average daily gas temperature and average daily humidity; wherein the analysis point F m And anchor point F 0 The electricity consumption data comprises actual consumed electric energy value, voltage value and current value; the data storage module is used for storing the data acquired by the data acquisition module; data analysisA module for analyzing the intelligent ammeter at an analysis point F m Is evaluated; the analysis result display module is used for displaying the operation error analysis result; the data acquisition module is in communication connection with the data storage module, the data storage module is in communication connection with the data analysis module, and the data analysis module is in communication connection with the analysis result display module. In this embodiment, the data analysis module is in communication connection with the server, so as to perform operation error results on the smart meter.
The intelligent ammeter operation error online analysis system is utilized to carry out online analysis on the intelligent ammeter operation error, and the method comprises the following steps:
s1) determination of the analysis Point F m And anchor point F 0 Locating point F 0 For being located temporally at the analysis point F m Previous data points, analysis Point F m And anchor point F 0 The time length between the two is t; wherein m is a natural number greater than or equal to 1;
s2) at the locating point F 0 And analysis point F m Setting n data acquisition points f in between n Equally dividing the time length T into (n+1) sections, wherein each section has the time length delta T; wherein n is a natural number greater than or equal to 1;
s3) determining a data acquisition point f n Running error estimate ζ of (2) n Calculating an arithmetic average of running error estimates of n data acquisition points within a time period t
S4) determining the analysis Point F m Running error estimate ζ of (2) m And anchor point F 0 Running error estimate ζ of (2) 0 According to ζ 0 Zeta as determined in step S3) n Anduse of mean absolute percentage error and root mean square error to ζ m Evaluation, ζ m Relative to ζ n Andthe smaller the differenceAnd (3) indicating that the operation condition of the intelligent electric meter is stable, otherwise, indicating that the operation of the intelligent electric meter is problematic.
In order to avoid that a single data set cannot represent possible running error variation of the smart meter, in step S2), the number of data acquisition point sets is set to be 5, and the number of data acquisition points in each data acquisition point set is different from the number of data acquisition points in other data acquisition point sets. And in step S4), according to ζ 0 Zeta as determined in step S3) n Anduse of mean absolute percentage error and root mean square error to ζ m In the evaluation, a plurality of data acquisition point groups are +.>And using the mean absolute percentage error and root mean square error to ζ in dependence upon Δζ m An evaluation is performed.
The territory of China spans a plurality of temperature zones from north to south, and has a relatively complex climate environment which can have a certain influence on the operation of electrical equipment, and a power supply circuit is not exceptional. Since the electrical equipment has a temperature-dependent electrical resistivity, one can consider the temperature-dependent electrical resistivity of the circuit when calculating the circuit loss, while in some areas of our country the temperature and humidity-dependent electrical resistivity of the electrical equipment (including the power supply circuit and the smart meter) are complex effects, considering only that the temperature-dependent electrical resistivity is insufficient to make a more accurate determination of the circuit loss, so that it is necessary to introduce a relative humidity-dependent electrical resistivity effect, in this embodiment, ζ is the step S3) n Calculated by the following formula:
ζ n ＝(P real world -U n I n Δtψcosφ)/P Real world ；
Wherein the method comprises the steps of，P Real world The time length is the actual consumed electric energy value in delta t; u (U) n Is root mean square voltage within a duration Δt; i n Is root mean square current with the duration of delta t; cos phi is the power factor; psi is a correction coefficient;is the temperature coefficient of resistivity; t is the average daytime temperature; t (T) Often times Is at the normal temperature of 20 ℃; w (W) Real world Is the average humidity during the day; w (W) Often times 30% of daily humidity; and | is absolute value.
The correction coefficient ψ in the embodiment is calculated by integrating the fact that the specific heat capacity of water is large and the fact that when the electric equipment is heated to normal temperature, a heat-preservation air layer with a certain temperature is formed around the electric equipment is only needed, so that the correction coefficient ψ is introduced in the relationship of the base number of temperature and humidity and the index.
And in step S4), when analyzing the operation error of a certain smart meter, introducing the historical operation errors of 7 adjacent smart meters to evaluate the operation error of the smart meter.
When the intelligent ammeter of the user A0 is subjected to online analysis of operation errors, the operation errors of the intelligent ammeter of the user A1, the user A2, the user A3, the user A4, the user A5, the user A6 and the user A7, which are in the same area and are positioned on the same power supply line and are adjacent to the intelligent ammeter, are introduced at the same moment. Wherein t=120 mi n, Δt 1 ＝60s，Δt 2 ＝90s，Δt 3 ＝150s，Δt 3 ＝160，Δt 5 =450 s; the test site is nanning, and intelligent ammeter operation error online analysis is carried out on each selected day in four seasons, and the average daytime temperature and average daytime humidity of the four days are shown in table 1.
Table 1 ambient temperature and humidity during on-line analysis of Smart electric meter running errors
After the intelligent ammeter operation error online analysis method and the existing intelligent ammeter operation error online analysis method are utilized to analyze the intelligent ammeter operation error of the user A, the larger the relative humidity is, the larger the difference between the intelligent ammeter operation error analysis results obtained by the two methods is, and particularly in a low-temperature high-humidity environment, the difference between the intelligent ammeter operation error result analyzed by the existing intelligent ammeter operation error online analysis method and the intelligent ammeter operation error result analyzed by the intelligent ammeter operation error online analysis method is larger. According to the intelligent ammeter operation error result analyzed by the existing intelligent ammeter operation error online analysis method, the condition that electricity is stolen by using low-power-consumption equipment in the family of the user A is judged, the condition that no electricity is stolen is found after the on-site monitoring is carried out, and according to the intelligent ammeter operation error result analyzed by the intelligent ammeter operation error online analysis method, the intelligent ammeter operation error of the user A is judged to be in a normal range. The temperature monitoring of the wires before and after the power-on shows that the wire loss generated by the power-on wires is increased in the environment with higher humidity.
In the invention, when the intelligent ammeter operation error is analyzed on line, besides introducing temperature as an influence factor of line loss change, humidity is also introduced as another influence factor of line loss change, and the reason is that: at the same temperature, the humidity is different, and when the wire reaches the temperature in the normal power on process, more electric energy needs to be converted into heat to reduce the influence of cooling on the conductive capacity of the wire, especially for the wire running in the region with higher humidity in winter. And the historical operation errors of the adjacent intelligent electric meters are introduced to serve as reference contrast of the operation errors of the intelligent electric meters, so that certain users can be prevented from stealing electricity by using low-power-consumption equipment for a long time.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While the obvious variations or modifications which are extended therefrom remain within the scope of the claims of this patent application.
Claims (8)
1. The intelligent ammeter operation error online analysis method is characterized by comprising the following steps of:
s1) determination of the analysis Point F m And anchor point F 0 Locating point F 0 For being located temporally at the analysis point F m Previous data points, analysis Point F m And anchor point F 0 The time length between the two is t; wherein m is a natural number greater than or equal to 1;
s2) at the locating point F 0 And analysis point F m Setting n data acquisition points f in between n Equally dividing the time length T into (n+1) sections, wherein each section has the time length delta T; wherein n is a natural number greater than or equal to 1;
s3) determining a data acquisition point f n Running error estimate ζ of (2) n Calculating an arithmetic average of running error estimates of n data acquisition points within a time period t
S4) determining the analysis Point F m Running error estimate ζ of (2) m And anchor point F 0 Running error estimate ζ of (2) 0 According to ζ 0 Zeta as determined in step S3) n Anduse of mean absolute percentage error and root mean square error to ζ m Evaluation, ζ m Relative to ζ n And->The smaller the difference is, the stable operation condition of the intelligent electric meter is indicated, otherwise, the operation of the intelligent electric meter is indicated to be problematic.
2. The online analysis method of operation errors of a smart meter according to claim 1, wherein in step S2), the number of data acquisition point groups set is greater than or equal to 2, and the number of data acquisition points in each data acquisition point group is different from the number of data acquisition points in other data acquisition point groups.
3. The online analysis method of operation errors of a smart meter according to claim 2, wherein in step S2), the number of set data acquisition point groups is less than or equal to 7 groups.
4. The online analysis method for operation errors of a smart meter according to any one of claims 1 to 3, wherein ζ is in step S3) n Calculated by the following formula:
ζ n ＝(P real world -U n I n Δtψcosφ)/P Real world ；
Wherein P is Real world The time length is the actual consumed electric energy value in delta t; u (U) n Is root mean square voltage within a duration Δt; i n Is root mean square current with the duration of delta t; cos phi is the power factor; psi is a correction coefficient;is the temperature coefficient of resistivity; t is the average daytime temperature; t (T) Often times Is at the normal temperature of 20 ℃; w (W) Real world Is the average humidity during the day; w (W) Often times 30% of daily humidity; and | is absolute value.
5. The method according to claim 4, wherein in step S4), when analyzing the operation error of a certain smart meter, the operation error of a neighboring smart meter is estimated by introducing the historical operation error of the smart meter.
6. The on-line analysis method for operation errors of smart meters according to claim 5, wherein the number of the introduced adjacent smart meters is 3 to 7.
7. A system for performing online analysis of operation errors of a smart meter by using the online analysis method of operation errors of a smart meter according to any one of claims 1 to 6, comprising:
the data acquisition module is used for acquiring an analysis point F m And anchor point F 0 Electricity consumption data, average daily gas temperature and average daily humidity; wherein the analysis point F m And anchor point F 0 The electricity consumption data comprises actual consumed electric energy value, voltage value and current value;
the data storage module is used for storing the data acquired by the data acquisition module;
the data analysis module is used for analyzing the intelligent ammeter at an analysis point F m Is evaluated;
the analysis result display module is used for displaying the operation error analysis result;
the data acquisition module is in communication connection with the data storage module, the data storage module is in communication connection with the data analysis module, and the data analysis module is in communication connection with the analysis result display module.
8. The system of claim 7, wherein the data analysis module is communicatively coupled to the server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111572746.6A CN114200386B (en) | 2021-12-21 | 2021-12-21 | Online analysis method and system for operation errors of intelligent ammeter |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111572746.6A CN114200386B (en) | 2021-12-21 | 2021-12-21 | Online analysis method and system for operation errors of intelligent ammeter |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114200386A CN114200386A (en) | 2022-03-18 |
| CN114200386B true CN114200386B (en) | 2023-10-24 |
Family
ID=80655772
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111572746.6A Active CN114200386B (en) | 2021-12-21 | 2021-12-21 | Online analysis method and system for operation errors of intelligent ammeter |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114200386B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116754830B (en) * | 2023-06-13 | 2024-01-09 | 武汉阿迪克电子股份有限公司 | Metering method and device of electric energy meter and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4002940B1 (en) * | 2006-08-31 | 2007-11-07 | 株式会社アドバンテスト | Error factor measurement device, method, program, recording medium, output correction device including the device, and reflection coefficient measurement device |
| CN103869142A (en) * | 2012-12-18 | 2014-06-18 | 罗伯特·博世有限公司 | Method and Device for Monitoring Signal Levels |
| CN106054106A (en) * | 2016-05-25 | 2016-10-26 | 宁波三星医疗电气股份有限公司 | Watt-hour meter clock abnormality automatic detection method based on power line carrier |
| CN107621315A (en) * | 2017-09-05 | 2018-01-23 | 天津市电力科技发展有限公司 | Check method during a kind of on-line operation intelligent calorimeter |
| CN107703475A (en) * | 2017-09-05 | 2018-02-16 | 天津市电力科技发展有限公司 | Check method during a kind of on-line operation intelligent electric energy meter |
| CN109298379A (en) * | 2018-12-06 | 2019-02-01 | 国网四川省电力公司电力科学研究院 | A kind of recognition methods of the intelligent electric meter site error exception based on data monitoring |
| CN109376926A (en) * | 2018-10-23 | 2019-02-22 | 南方电网科学研究院有限责任公司 | Error prediction method, apparatus, equipment and the storage medium of electric energy metering device |
| 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 |
| CN111596255A (en) * | 2020-07-27 | 2020-08-28 | 国网江西省电力有限公司电力科学研究院 | Metering error remote online detection method of reinforcement learning algorithm |
| 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 |
-
2021
- 2021-12-21 CN CN202111572746.6A patent/CN114200386B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4002940B1 (en) * | 2006-08-31 | 2007-11-07 | 株式会社アドバンテスト | Error factor measurement device, method, program, recording medium, output correction device including the device, and reflection coefficient measurement device |
| CN103869142A (en) * | 2012-12-18 | 2014-06-18 | 罗伯特·博世有限公司 | Method and Device for Monitoring Signal Levels |
| CN106054106A (en) * | 2016-05-25 | 2016-10-26 | 宁波三星医疗电气股份有限公司 | Watt-hour meter clock abnormality automatic detection method based on power line carrier |
| CN107621315A (en) * | 2017-09-05 | 2018-01-23 | 天津市电力科技发展有限公司 | Check method during a kind of on-line operation intelligent calorimeter |
| CN107703475A (en) * | 2017-09-05 | 2018-02-16 | 天津市电力科技发展有限公司 | Check method during a kind of on-line operation intelligent electric energy meter |
| CN109376926A (en) * | 2018-10-23 | 2019-02-22 | 南方电网科学研究院有限责任公司 | Error prediction method, apparatus, equipment and the storage medium of electric energy metering device |
| CN109298379A (en) * | 2018-12-06 | 2019-02-01 | 国网四川省电力公司电力科学研究院 | A kind of recognition methods of the intelligent electric meter site error exception based on data monitoring |
| 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 |
| CN111596255A (en) * | 2020-07-27 | 2020-08-28 | 国网江西省电力有限公司电力科学研究院 | Metering error remote online detection method of reinforcement learning algorithm |
| 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 |
Non-Patent Citations (1)
| Title |
|---|
| 智能电表多应力可靠性综合试验系统;张智勇等;环境技术增刊;第9-14页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114200386A (en) | 2022-03-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110471024B (en) | Intelligent electric meter online remote calibration method based on measurement data analysis | |
| CN108593990B (en) | Electricity stealing detection method based on electricity consumption behavior mode of electric energy user and application | |
| CN106505557B (en) | Remote measurement error identification method and device | |
| Chen et al. | Determination of customer load characteristics by load survey system at Taipower | |
| CN102708306A (en) | Prediction method for q-precentile life of intelligent meter | |
| WO2021109517A1 (en) | Method for detecting reliability of results of load identification device | |
| CN110070282A (en) | A kind of low-voltage platform area line loss analysis of Influential Factors method based on Synthesis Relational Grade | |
| CN114200386B (en) | Online analysis method and system for operation errors of intelligent ammeter | |
| CN113267699B (en) | Power supply line electricity stealing judgment method and application thereof | |
| CN102072747A (en) | Thermal parameter field monitoring-based building group energy consumption monitoring system and method thereof | |
| CN112130109A (en) | Method for detecting metering performance abnormity of intelligent electric energy meter | |
| CN106248567B (en) | Typical environment detection method for low-pressure metering box | |
| CN110533247B (en) | Monthly power consumption prediction method adopting temperature data anomaly point compensation | |
| Fangxing et al. | Estimation of smart meters errors using meter reading data | |
| Mohan et al. | Solar energy disaggregation using whole-house consumption signals | |
| CN114152896A (en) | Misalignment replacing system based on acquisition platform | |
| CN116933157A (en) | Electricity larceny detection method | |
| CN102252709A (en) | Method for diagnosing faults of non-electricity measurement system | |
| JP5658597B2 (en) | Energy-saving behavior evaluation system | |
| CN111520871A (en) | Energy saving rate testing method and system for energy saving modification of central air conditioning system | |
| CN110780129A (en) | Electricity stealing and leakage positioning method based on current deviation analysis technology | |
| CN109389330A (en) | Power distribution network infrastructure project analysis of prices method, apparatus, computer equipment and storage medium | |
| CN202041228U (en) | Building group energy consumption monitoring device based on thermotechnical parameter field monitoring | |
| CN205909931U (en) | Soft meter of irradiance based on online vector of supporting | |
| CN112749820A (en) | Wind power prediction method and system |
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 |