CN114089257B - Electric energy meter burning on-line monitoring method, system and medium - Google Patents
Electric energy meter burning on-line monitoring method, system and medium Download PDFInfo
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- CN114089257B CN114089257B CN202111258366.5A CN202111258366A CN114089257B CN 114089257 B CN114089257 B CN 114089257B CN 202111258366 A CN202111258366 A CN 202111258366A CN 114089257 B CN114089257 B CN 114089257B
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- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The application discloses an on-line monitoring method, a system and a medium for a ammeter burning meter, which comprise the following steps: 1) Acquiring characteristic data of a monitored electric energy meter, wherein the characteristic data comprises electric characteristic data, product characteristic data and environment characteristic data; 2) And inputting the characteristic data into a pre-trained machine learning model to obtain a probability evaluation result of the future occurrence of the burn-in of the monitored electric energy meter, which is output by the machine learning model. The application is based on the mapping relation between the characteristic data including the electrical characteristic data, the product characteristic data, the environmental characteristic data and the probability evaluation result of the occurrence of the burning meter, can monitor the probability of the occurrence of the burning meter in the future through the history data of the electric energy meter on line, solves the difficult problem that the burning meter is difficult to monitor, can change the passive rush repair into the active rush repair, improves the electricity utilization experience of customers, improves the working efficiency of basic-level power supply service staff, and solves the problem that the rush repair of the burning meter is not timely.
Description
Technical Field
The application relates to an electric energy meter online monitoring technology, in particular to an electric energy meter burning meter online monitoring method, system and medium, which can be used for online monitoring the probability of the electric energy meter burning meter in the future through historical data of the electric energy meter.
Background
The electric energy meter is an important electric power device for carrying out electric power transaction and settlement. The electric energy meter is generally installed behind the user inlet wire switch and before the consumer, so that once the electric energy meter fails, the user power failure can be caused, the power consumption experience of the user is affected slightly, and economic and property losses are brought to the user. And a burning meter is a common fault of an electric energy meter. The reasons for the faults can be various, common reasons include that a wiring terminal is not screwed, poor contact is caused, the load is too large, and the like, and because of the lack of an on-line monitoring means, the power supply company generally performs work such as on-site investigation and meter replacement after receiving a contact call of a user, the processing period is long, the power utilization experience of the user is seriously affected, and meanwhile, a lot of temporary work is brought to basic-level power supply staff.
Disclosure of Invention
The application aims to solve the technical problems: aiming at the problems in the prior art, the application provides the on-line monitoring method, the system and the medium for the ammeter burning, which can monitor the probability of the ammeter burning condition in the future on line through the history data of the ammeter, solve the difficult problem that the ammeter burning phenomenon is difficult to monitor, change the passive rush repair into the active rush repair, improve the electricity utilization experience of customers, improve the work efficiency of staff of basic power supply service and solve the problem that the rush repair of the ammeter is not timely.
In order to solve the technical problems, the application adopts the following technical scheme:
an electric energy meter burning meter on-line monitoring method comprises the following steps:
1) Acquiring characteristic data of a monitored electric energy meter, wherein the characteristic data comprises electric characteristic data, product characteristic data and environment characteristic data;
2) And inputting the characteristic data into a pre-trained machine learning model to obtain a probability evaluation result of the future occurrence of the burn-in of the monitored electric energy meter, which is output by the machine learning model.
Optionally, the electrical characteristic data includes some or all of a daily power usage Q, a daily maximum voltage U, a daily maximum current I, and a meter reading terminal a of the monitored power meter.
Optionally, the product characteristic data includes part or all of manufacturer B of the monitored electric energy meter, city state C, running time D, installation year E, number F of burning meters in the district for two years, and line loss G of the district.
Optionally, the environmental characteristic data includes part or all of an environmental temperature T and an environmental humidity H of the monitored electric energy meter.
Optionally, the daily electricity consumption Q, the daily maximum voltage U, the daily maximum current I, the number F of burning tables in the station for two years, the line loss G of the station, the ambient temperature T and the ambient humidity H are original values based on a set unit, and the table walk final code a and the running time D are results obtained by adopting the following standardized processing:
x′＝log 10 (x)
in the above formula, x' is a result obtained by the normalization processing, and x is input before the normalization processing; the manufacturer B, the city C and the installation year E are results obtained by adopting One-hot coding processing.
Optionally, the machine learning model is an XGBoost-based machine learning model.
Optionally, step 1) is preceded by the step of training a machine learning model:
s1) obtaining characteristic data of a sample of the electric energy meter in a period of time before burning out, and attaching a label of whether the sample is burned out to form a sample data set, and dividing the sample data set into a training set and a testing set;
s2) training a XGBoost-based machine learning model based on a training set;
s3) testing the XGBoost-based machine learning model which completes the round of training based on the test set, and calculating an evaluation index of a test result by adopting a comprehensive evaluation index formed by the accuracy rate and the recall rate as an evaluation index;
s4) judging whether an evaluation index of the test result meets the requirement, if so, ending and exiting by taking the parameters of the current XGBoost-based machine learning model as the trained XGBoost-based machine learning model; otherwise, the jump performs step S2) to continue training the XGBoost-based machine learning model.
Optionally, the functional expression of the comprehensive evaluation index formed by the precision rate and the recall rate in the step S3) is:
in the above formula, F1 is a comprehensive evaluation index composed of an accuracy rate and a recall rate, P is the accuracy rate, and R is the recall rate.
In addition, the application also provides an on-line monitoring system for the electric energy meter burning meter, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the on-line monitoring method for the electric energy meter burning meter.
In addition, the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program programmed or configured to execute the electric energy meter burning meter online monitoring method.
Compared with the prior art, the application has the following advantages:
1. according to the application, the electric data of the electric energy meter is utilized, the product characteristic data and the environment characteristic data are combined, the electric energy meter burning meter on-line monitoring model is constructed, the state evaluation is carried out by predicting the future burning meter probability, the remote on-line evaluation can be carried out under the condition of not relying on manual on-site survey, and the problem of untimely burning meter repair is solved.
2. According to the application, the electric data of the electric energy meter is utilized, the product characteristic data and the environment characteristic data are combined to construct an on-line monitoring model of the electric energy meter, and based on the electric data and the data of the product characteristic data and the environment characteristic data, the comprehensive mapping of factors of the electric energy meter in the future occurrence of the burning situation and the electric energy meter in the future occurrence of the burning expression situation can be realized, and the accuracy of the electric energy meter in the future occurrence of the burning expression situation can be effectively improved.
3. The application can change passive rush repair into active rush repair, improve the electricity utilization experience of clients and improve the working efficiency of basic-level power supply service staff.
Drawings
Fig. 1 is a schematic diagram of the basic principle of the method according to the embodiment of the application.
Fig. 2 is a schematic diagram of a training flow of a machine learning model according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a confusion matrix (confusing matrix) of actual values (true) and predicted values (predicted) in a test set according to an embodiment of the present application.
Detailed Description
As shown in fig. 1, the method for monitoring the burning of the electric energy meter on line in this embodiment includes:
1) Acquiring characteristic data of a monitored electric energy meter, wherein the characteristic data comprises electric characteristic data, product characteristic data and environment characteristic data;
2) And inputting the characteristic data into a pre-trained machine learning model to obtain a probability evaluation result of the future occurrence of the burn-in of the monitored electric energy meter, which is output by the machine learning model.
According to the electric energy meter burning meter on-line monitoring method, the electric energy meter electric data are utilized, the product characteristic data and the environment characteristic data are combined, the electric energy meter burning meter on-line monitoring model is built, state evaluation is carried out by predicting future burning meter probability, remote on-line evaluation can be carried out under the condition that manual on-site investigation is not relied on, and the problem that burning meter repair is not timely is solved. According to the embodiment, the electric data of the electric energy meter is utilized, the product characteristic data and the environment characteristic data are combined, the model of the electric energy meter burning meter on-line monitoring is built, based on the electric data, the data of the product characteristic data and the environment characteristic data are combined, the comprehensive mapping of factors of the electric energy meter burning condition in the future and the electric energy meter burning expression condition in the future can be realized, and the accuracy of the electric energy meter burning expression condition prediction in the future can be effectively improved. According to the embodiment, passive rush-repair can be changed into active rush-repair, the electricity utilization experience of a client is improved, and the working efficiency of basic-level power supply service staff is improved.
In this embodiment, the electrical characteristic data includes the daily power consumption Q, the daily maximum voltage U, the daily maximum current I, and the meter running code a of the monitored power meter. Through research, the causal relationship between each characteristic of the electrical characteristic data and the burn-in table is found as follows: the daily electricity consumption Q has certain relevance with the meter burning of the electric energy meter, and the load can be increased and the daily electricity consumption is increased in a period of time before the meter burning occurs. And the load and daily electricity changes of the normal running users are relatively stable. The maximum daily voltage U and the maximum daily current A have certain relevance with the burning table, and the voltage and the current of a user of the burning table tend to be larger before the burning table occurs. The meter is walked to form a final code A, the larger the A is, the longer the meter running time or the larger the load is, the longer the running time is, the more serious the aging of the components in the meter is, and the more easily burnt out is; if the load is large, the meter is in a heating state for a long time, and is more easily burnt out.
In this embodiment, the product characteristic data includes manufacturer B of the monitored electric energy meter, city C, running time D (days in this embodiment), installation year E, number of burning meters F of two years near the station area, and line loss G of the station area. Through research, the causal relationship between each feature of the product feature data and the burn schedule is found as follows: and the manufacturing factories B have large differences in the meter burning rate among different manufacturers due to the differences in the process, the components and the technology adopted by each manufacturing manufacturer, and the meter produced by some manufacturers is easier to burn out. The state C of the city represents the place where the electric energy meter operates, and the electric energy meter operating in some states is easier to burn out due to the fact that the technical level and the environmental conditions of installation operation staff in different states are different. And the running time D is longer, so that the components in the meter are more seriously aged and are more easily burnt out. Mounting year E, because of being influenced by different batches of products of different manufacturers, batch product components of certain manufacturers in certain years are easier to burn out; the larger the F value of the number of burn-in tables in the area for two years is, the easier the burn-in tables are caused by the local climate environment or the technical level of local installation operation staff. The line loss G of the station area, which shows a certain fluctuation in a short period due to the meter measurement errors of the meter of the individual users in the station area, is generally shown as line loss reduction and even becomes negative line loss.
In this embodiment, the environmental characteristic data includes an environmental temperature T and an environmental humidity H of the monitored electric energy meter. The environmental temperature T and the environmental humidity H represent the environmental conditions of the operation place of the meter, and the burn-in rates of different seasons and different months at the same place have larger difference according to the statistical condition, and the burn-in rates under certain environmental conditions are higher.
The above-described electrical characteristic data, product characteristic data, and environmental characteristic data may be all or part of them as needed.
In this embodiment, the daily electricity consumption Q, the daily maximum voltage U, the daily maximum current I, the number of burning watches F in the area for two years, the ambient temperature T and the ambient humidity H are original values based on the set units, wherein the daily electricity consumption Q uses the set units of kWh, the daily maximum voltage U uses the set units of volts, the daily maximum current I uses the set units of amperes, the number of burning watches F in the area for two years uses the set units of only, the ambient temperature T uses the set units of celsius degrees, and the ambient humidity H uses the set units of% RH.
In this embodiment, the table-based stop code a and the running time D are the results obtained by the following normalization processing:
x′＝log 10 (x)
in the above formula, x' is a result obtained by the normalization process, and x is an input before the normalization process.
In this example, manufacturer B, state C and year of installation E are the results obtained using One-hot encoding. One-hot encoding method is a process that converts class variables into a form that is readily available to machine learning algorithms. Assuming that feature x has n categories, x= [ x ] 1 ,x 2 ,…,x n ]A certain set of features x is:
| sequence number | Category(s) |
| 1 | x 1 |
| 2 | x 2 |
| … | … |
| n | x n |
After the One-hot (One-hot) encoding process is adopted, the aggregate form of the feature x becomes:
as can be seen from the foregoing, in this embodiment, different standardized processing methods are adopted for different feature data: for the character-moving final code A and the running time D (days) of the meter, the numerical span range of A and D of different meters is too large, so that the model calculation amount is reduced to the greatest extent, and log function standardization processing is carried out on the model calculation amount. For the manufacturer B, the city C and the installation year E, because the three characteristics are category variables, in order to directly apply the three characteristics to a model classifier, a one-hot coding mode is adopted. For other features, the numerical range of the features is centralized, and in order to maintain the original features of the data as much as possible, the operation amount of data preprocessing is reduced, and the original values are adopted for operation.
In this embodiment, the machine learning model is an XGBoost-based machine learning model, and other machine learning models may be used as needed.
As shown in fig. 2, step 1) in this embodiment further includes a step of training a machine learning model:
s1) obtaining characteristic data of a sample of the electric energy meter in a period of time before burning out, and attaching a label of whether the sample is burned out to form a sample data set, and dividing the sample data set into a training set and a testing set; for example, in this embodiment, the cross-validation function built in the program is used to divide 80% into training sets and 20% into validation sets.
S2) training a XGBoost-based machine learning model based on a training set;
s3) testing the XGBoost-based machine learning model which completes the round of training based on the test set, and calculating an evaluation index of a test result by adopting a comprehensive evaluation index formed by the accuracy rate and the recall rate as an evaluation index;
s4) judging whether an evaluation index of the test result meets the requirement, if so, ending and exiting by taking the parameters of the current XGBoost-based machine learning model as the trained XGBoost-based machine learning model; otherwise, the jump performs step S2) to continue training the XGBoost-based machine learning model.
Parameters of XGBoost-based machine learning models can be classified into three types, general parameters, enhancement parameters, and learning target parameters. The problem of on-line monitoring of the electric energy meter in the embodiment belongs to the classification problem, and a softmax function is selected for the objective function of the constructed XGBoost model. In this embodiment, parameters of the XGBoost-based machine learning model include maximum depth (max_depth), iteration number (n_evasions), contraction step length (eta), gamma parameter, lambda regular coefficient, learning rate (learning_rate) of the XGBoost-based machine learning model, and other parameters adopt default values for the parameters to be subjected to parameter adjustment in the training process.
In this embodiment, the input feature variable data set is input into the trained model to obtain the probability of the electric energy meter burning, the probability is recorded as the burning, the label is 1, otherwise, the label is 0 when the electric energy meter is in normal operation. Compared with the real tag data, the accuracy rate, the recall rate and the comprehensive evaluation index (F1-Measure) are adopted as the evaluation indexes, and in the embodiment, the function expression of the comprehensive evaluation index formed by the accuracy rate and the recall rate in the step S3) is as follows:
in the above formula, F1 is a comprehensive evaluation index composed of an accuracy rate and a recall rate, P is the accuracy rate, and R is the recall rate.
Wherein, the function expression of the Precision P is:
wherein, the function expression of the Recall rate R (Recall) is:
wherein true cases (TP) represent the number of positive cases (1 is marked as 1), false positive cases (FP) represent the number of false cases (0 is marked as 1), true negative cases (TN) represent the number of false cases (0 is marked as 0), and false negative cases (FN) represent the number of positive cases (1 is marked as 0). Fig. 3 is a schematic diagram of a confusion matrix (configuration matrix) of actual values (true) and predicted values (predicted) in a test set according to an embodiment of the present application, wherein the number of samples in the test set in the embodiment is 29464, the figures are the test set burn table and the deviation situation of the actual values and the predicted values of the unfired tables, the upper left corner number of the matrix indicates the actual unfired tables, the predicted number of the unfired tables is 26338, the upper right corner number indicates the actual unfired tables, the predicted number of the burn tables is 258, the lower left corner number indicates the actual burn tables, the predicted number of the unfired tables is 927, the lower right corner number indicates the actual burn tables, and the predicted number of the burn tables is 1941. The corresponding evaluation index p=88.3%, r=67.7%, f1=76.6%. Therefore, the on-line monitoring method for the electric energy meter burning meter can accurately evaluate the probability of the electric energy meter burning meter running on site. After training of the machine learning model based on XGBoost is completed, characteristic data of the on-line electric energy meter is input to the machine learning model based on XGBoost, and the probability of burning of the on-line electric energy meter can be obtained through the machine learning model based on XGBoost, so that basic staff is guided to actively repair and eliminate defects.
In addition, the embodiment also provides an on-line monitoring system for the electric energy meter burning meter, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the on-line monitoring method for the electric energy meter burning meter.
In addition, the embodiment also provides a computer readable storage medium, and a computer program programmed or configured to execute the electric energy meter burning meter online monitoring method is stored in the computer readable storage medium.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present application, and the protection scope of the present application is not limited to the above examples, and all technical solutions belonging to the concept of the present application belong to the protection scope of the present application. It should be noted that modifications and adaptations to the present application may occur to one skilled in the art without departing from the principles of the present application and are intended to be within the scope of the present application.
Claims (7)
1. An on-line monitoring method for a burning meter of an electric energy meter is characterized by comprising the following steps:
1) Acquiring characteristic data of a monitored electric energy meter, wherein the characteristic data comprises electric characteristic data, product characteristic data and environment characteristic data; the electrical characteristic data comprise the daily electricity consumption Q, the daily maximum voltage U, the daily maximum current I and a meter reading final code A of the monitored electric energy meter; the product characteristic data comprise a manufacturer B of the monitored electric energy meter, a state C of the monitored electric energy meter, running time D, installation year E, the number F of burning meters in the area for two years and the line loss G of the area; the environmental characteristic data comprise the environmental temperature T and the environmental humidity H of the monitored electric energy meter;
2) And inputting the characteristic data into a pre-trained machine learning model to obtain a probability evaluation result of the future occurrence of the burn-in of the monitored electric energy meter, which is output by the machine learning model.
2. The method for on-line monitoring of electric energy meter burning meter according to claim 1, wherein the daily electricity consumption Q, the daily maximum voltage U, the daily maximum current I, the number F of burning meters in the area for nearly two years, the area line loss G, the ambient temperature T and the ambient humidity H are original values based on a set unit, and the meter walking end code a and the running time D are results obtained by adopting the following standardized processing:
x′=log 10 (x)
in the above-mentioned method, the step of,x' is the result of the normalization process,xis an input before normalization; the manufacturer B, the city C and the installation year E are results obtained by adopting One-hot coding processing.
3. The method for on-line monitoring of a watt-hour meter burn-in of claim 2, wherein the machine learning model is an XGBoost-based machine learning model.
4. The method for on-line monitoring of a watt-hour meter burn-in as claimed in claim 3, wherein the step 1) further comprises the step of training a machine learning model:
s1) obtaining characteristic data of a sample of the electric energy meter in a period of time before burning out, and attaching a label of whether the sample is burned out to form a sample data set, and dividing the sample data set into a training set and a testing set;
s2) training a XGBoost-based machine learning model based on a training set;
s3) testing the XGBoost-based machine learning model which completes the round of training based on the test set, and calculating an evaluation index of a test result by adopting a comprehensive evaluation index formed by the accuracy rate and the recall rate as an evaluation index;
s4) judging whether an evaluation index of the test result meets the requirement, if so, ending and exiting by taking the parameters of the current XGBoost-based machine learning model as the trained XGBoost-based machine learning model; otherwise, the jump performs step S2) to continue training the XGBoost-based machine learning model.
5. The method for on-line monitoring of the ammeter burning in accordance with claim 4, wherein the functional expression of the comprehensive evaluation index composed of the accuracy rate and the recall rate in step S3) is:
in the above-mentioned method, the step of,F1 is a comprehensive evaluation index composed of accuracy rate and recall rate,Pin order for the accuracy to be high,Ris the recall rate.
6. An on-line monitoring system for a watt-hour meter burn-in, comprising a microprocessor and a memory connected to each other, characterized in that the microprocessor is programmed or configured to perform the steps of the on-line monitoring method for a watt-hour meter burn-in according to any one of claims 1 to 5.
7. A computer readable storage medium having stored therein a computer program programmed or configured to perform the method for on-line monitoring of a watt-hour meter burn-in of any one of claims 1 to 5.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104880690A (en) * | 2015-06-15 | 2015-09-02 | 国网上海市电力公司 | Method for evaluating operation of electric energy meter |
| CN104991223A (en) * | 2015-07-27 | 2015-10-21 | 国网重庆市电力公司电力科学研究院 | Watt-hour meter state assessment index selection and quantization method |
| CN109492783A (en) * | 2018-11-14 | 2019-03-19 | 中国电力科学研究院有限公司 | A kind of Application of Power Metering Instruments failure risk prediction technique based on GBDT |
| CN109633518A (en) * | 2019-01-29 | 2019-04-16 | 国网湖南省电力有限公司 | A kind of intelligent electric energy meter comprehensive performance evaluation method, apparatus |
| CN109839535A (en) * | 2019-03-23 | 2019-06-04 | 深圳市江机实业有限公司 | Scene damage single-phase intelligent electric energy meter electricity data extraction element and its operating method |
| CN110222991A (en) * | 2019-06-10 | 2019-09-10 | 国网江苏省电力有限公司常州供电分公司 | Metering device method for diagnosing faults based on RF-GBDT |
| CN110794360A (en) * | 2019-10-22 | 2020-02-14 | 中国电力科学研究院有限公司 | Method and system for predicting fault of intelligent electric energy meter based on machine learning |
| CN111025081A (en) * | 2019-11-07 | 2020-04-17 | 广州南方电力集团科技发展有限公司 | Residential voltage monitoring method for power distribution area |
| CN112487046A (en) * | 2020-10-30 | 2021-03-12 | 深圳供电局有限公司 | Power utilization information analysis method, device, system, computer equipment and storage medium |
| WO2021098246A1 (en) * | 2019-11-19 | 2021-05-27 | 河南许继仪表有限公司 | Electric energy meter service life prediction method and apparatus, and storage medium |
| CN113409166A (en) * | 2021-08-19 | 2021-09-17 | 国网江西综合能源服务有限公司 | XGboost model-based method and device for detecting abnormal electricity consumption behavior of user |
-
2021
- 2021-10-27 CN CN202111258366.5A patent/CN114089257B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104880690A (en) * | 2015-06-15 | 2015-09-02 | 国网上海市电力公司 | Method for evaluating operation of electric energy meter |
| CN104991223A (en) * | 2015-07-27 | 2015-10-21 | 国网重庆市电力公司电力科学研究院 | Watt-hour meter state assessment index selection and quantization method |
| CN109492783A (en) * | 2018-11-14 | 2019-03-19 | 中国电力科学研究院有限公司 | A kind of Application of Power Metering Instruments failure risk prediction technique based on GBDT |
| CN109633518A (en) * | 2019-01-29 | 2019-04-16 | 国网湖南省电力有限公司 | A kind of intelligent electric energy meter comprehensive performance evaluation method, apparatus |
| CN109839535A (en) * | 2019-03-23 | 2019-06-04 | 深圳市江机实业有限公司 | Scene damage single-phase intelligent electric energy meter electricity data extraction element and its operating method |
| CN110222991A (en) * | 2019-06-10 | 2019-09-10 | 国网江苏省电力有限公司常州供电分公司 | Metering device method for diagnosing faults based on RF-GBDT |
| CN110794360A (en) * | 2019-10-22 | 2020-02-14 | 中国电力科学研究院有限公司 | Method and system for predicting fault of intelligent electric energy meter based on machine learning |
| CN111025081A (en) * | 2019-11-07 | 2020-04-17 | 广州南方电力集团科技发展有限公司 | Residential voltage monitoring method for power distribution area |
| WO2021098246A1 (en) * | 2019-11-19 | 2021-05-27 | 河南许继仪表有限公司 | Electric energy meter service life prediction method and apparatus, and storage medium |
| CN112487046A (en) * | 2020-10-30 | 2021-03-12 | 深圳供电局有限公司 | Power utilization information analysis method, device, system, computer equipment and storage medium |
| CN113409166A (en) * | 2021-08-19 | 2021-09-17 | 国网江西综合能源服务有限公司 | XGboost model-based method and device for detecting abnormal electricity consumption behavior of user |
Non-Patent Citations (1)
| Title |
|---|
| 薛阳 等.电能表故障与地域气候、行业负荷关系研究.《中国电力》.2017,(第08期),103-110. * |
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