CN114609480A - Power grid loss abnormal data detection method, system, terminal and medium - Google Patents
Power grid loss abnormal data detection method, system, terminal and medium Download PDFInfo
- Publication number
- CN114609480A CN114609480A CN202210525568.XA CN202210525568A CN114609480A CN 114609480 A CN114609480 A CN 114609480A CN 202210525568 A CN202210525568 A CN 202210525568A CN 114609480 A CN114609480 A CN 114609480A
- Authority
- CN
- China
- Prior art keywords
- transformer
- data set
- transmission line
- regression model
- data
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Bioinformatics & Computational Biology (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Algebra (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a method, a system, a terminal and a medium for detecting abnormal data of power grid loss, relates to the technical field of power grid monitoring, and solves the problem that in the prior art, problem equipment cannot be efficiently found out only by means of line loss rate, and the technical scheme is as follows: establishing a corresponding transformer nonlinear regression model and a corresponding transmission line nonlinear regression model through a transformer data set and a transmission line data set in a power grid, then obtaining a corresponding first prediction result according to sample data extracted from the transformer data set through the transformer nonlinear regression model, obtaining a corresponding second prediction result according to the sample data extracted from the transmission line data set through the transmission line nonlinear regression model, comparing the first prediction result and the second prediction result with data actually recorded in the transformer data set and the transmission line data set, screening out final abnormal data, and visually displaying the abnormal data. The aim of reducing labor cost while improving detection efficiency is achieved.
Description
Technical Field
The invention relates to the technical field of power grid monitoring, in particular to a method, a system, a terminal and a medium for detecting abnormal power grid loss data.
Background
At present, line loss management and loss reduction management are important contents for cost reduction and efficiency improvement of companies. With the continuous deepening of the informatization and digitization of the power grid, the power grid accumulates a large amount of data, and a new method and thought are provided for loss reduction management. However, at present, on one hand, the quality of basic data collected by a power grid is still not high, and on the other hand, the loss cause is complex, and a traditional method which only depends on a line loss rate as a decision basis cannot efficiently find out a device with problems. Therefore, a scheme is needed to reduce the manual troubleshooting amount, and the labor cost is reduced while the detection efficiency is improved.
Disclosure of Invention
The invention aims to provide a method, a system, a terminal and a medium for detecting abnormal data of power grid loss, and the purpose of improving detection efficiency and reducing labor cost is achieved.
The technical purpose of the invention is realized by the following technical scheme:
a power grid loss abnormal data detection method comprises the following steps:
acquiring a transformer data set in a power grid and a power transmission line data set corresponding to a transformer;
Screening the transformer data set to obtain a transformer data set with normal copper-iron loss ratio and normal load rate;
constructing a transformer nonlinear regression model and a transmission line nonlinear regression model based on a transformer data set with normal copper-iron loss ratio and normal load rate;
predicting data in the transformer data set by using a transformer nonlinear regression model to obtain a transformer abnormal data set;
and predicting the data in the power transmission line data set by using a power transmission line nonlinear regression model to obtain a power transmission line abnormal data set.
Further, the obtaining process of the transformer data set with normal copper-iron loss ratio and normal load factor specifically comprises the following steps:
obtaining a corresponding transformer copper-iron loss ratio set and a transformer load rate set based on the transformer data set;
setting a transformer copper-iron loss ratio threshold and a transformer load rate threshold;
and screening the transformer data set based on the transformer copper-iron loss ratio threshold and the transformer load rate threshold to obtain the transformer data set with normal copper-iron loss ratio and normal load rate.
Further, the construction process of the transformer nonlinear regression model and the transmission line nonlinear regression model specifically comprises the following steps:
Acquiring a power transmission line data set corresponding to the transformer data set with the normal copper-iron loss ratio and the normal load rate on the basis of the transformer data set with the normal copper-iron loss ratio and the normal load rate;
constructing a transformer nonlinear regression model based on the transformer data set with normal copper-iron loss ratio and normal load rate;
and constructing a nonlinear regression model of the power transmission line based on the power transmission line data set corresponding to the transformer data set with normal copper-iron loss ratio and normal load rate.
Further, the transformer nonlinear regression model is:
wherein, the first and the second end of the pipe are connected with each other,
is numerically equal to the total loss of the transformerThe sum of the copper loss and the iron loss,
for the active power supply of the line on which the transformer is located,
is the rated capacity of the transformer and is,
、
、
are coefficients of the regression model.
Further, the nonlinear regression model of the power transmission line is as follows:
wherein the content of the first and second substances,
in order to be a loss of the transmission line,
is the equivalent resistance of the transmission line,
the active power supply capacity of the power transmission line,
the reactive power supply quantity of the power transmission line is provided,
、
、
are coefficients of the regression model.
Further, the specific process for obtaining the abnormal data set of the transformer is as follows:
calculating the distance from the transformer data set to a corresponding position on a transformer nonlinear regression model to obtain a corresponding first distance set;
And obtaining an abnormal data set of the transformer based on the first distance set.
Further, the specific process for obtaining the abnormal data set of the power transmission line is as follows:
calculating the distance from the data in the power transmission line data set to the corresponding position on the power transmission line nonlinear regression model to obtain a corresponding second distance set;
and acquiring an abnormal data set of the power transmission line based on the second distance set.
A power grid loss anomaly data detection system comprising:
the data acquisition module is used for acquiring a transformer data set in a power grid and a power transmission line data set corresponding to the transformer;
the data processing module is used for screening the transformer data set to obtain a transformer data set with normal copper-iron loss ratio and normal load rate;
the model training module is used for constructing a transformer nonlinear regression model and a transmission line nonlinear regression model based on a transformer data set with normal copper-iron loss ratio and normal load rate;
the first testing module is used for predicting data in the transformer data set by using a transformer nonlinear regression model to obtain a transformer abnormal data set;
and the second testing module is used for predicting the data in the power transmission line data set by using the power transmission line nonlinear regression model to obtain a power transmission line abnormal data set.
An electronic terminal, comprising:
a memory for storing a computer program;
and the processor is used for executing the computer program stored in the memory so as to enable the electronic terminal to execute the power grid loss abnormal data detection method.
A computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements a grid loss anomaly data detection method as described above.
Compared with the prior art, the invention has the following beneficial effects:
a power grid loss abnormal data detection method includes the steps of establishing a corresponding transformer nonlinear regression model and a corresponding power transmission line nonlinear regression model through a transformer data set and a power transmission line data set in a power grid, obtaining a corresponding first prediction result through the transformer nonlinear regression model according to sample data extracted from the transformer data set, obtaining a corresponding second prediction result through the power transmission line nonlinear regression model according to the sample data extracted from the power transmission line data set, comparing the first prediction result and the second prediction result with data actually recorded in the transformer data set and the power transmission line data set, screening out final abnormal data and carrying out visual display. The aim of reducing labor cost while improving detection efficiency is achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a schematic diagram of a detection flow of a method for detecting abnormal data of power grid loss according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a topology structure of a power grid loss abnormal data detection system according to an embodiment of the present invention.
Reference numbers and corresponding part names in the drawings:
100-a data acquisition module; 200-a data processing module; 300-a model training module; 400-a first test module; 500-second test module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly or indirectly connected to the other element.
It will be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like, as used herein, refer to an orientation or positional relationship indicated in the drawings that is solely for the purpose of facilitating the description and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and is therefore not to be construed as limiting the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Example (b): provided are a power grid loss abnormal data detection method, a system, a terminal and a medium.
As shown in fig. 1, a method for detecting abnormal data of power grid loss.
Acquiring a transformer data set in a power grid and a power transmission line data set corresponding to a transformer;
and acquiring historical record data of the power grid and data such as the line length and the line type number of the power transmission line.
Specifically, historical data of the power grid can be acquired from a monitoring server of the power grid; the acquired historical record data includes information such as copper loss, iron loss, actual operating power and rated capacity of the transformer, and line length and line model of each power transmission line. After the information is acquired, the information can be put in uniformlyExcelIn the list of the document, the data of the corresponding list is extracted for calculation during calculation. Wherein, the copper-iron loss ratio of the transformer is the ratio of the copper loss and the iron loss of the transformer. The load factor of a transformer is the ratio of the actual operating power and the rated capacity of the transformer. And calculating the equivalent resistance of the power transmission line according to the equivalent resistance of each kilometer of length corresponding to the line model and the line length.
Screening the transformer data set to obtain a transformer data set with normal copper-iron loss ratio and normal load rate, which specifically comprises the following steps:
calculating the copper-iron loss ratio and the load rate of each transformer according to historical data of the power grid; and meanwhile, calculating the equivalent resistance of each transformer corresponding to the power transmission line according to the line length and the line model. And removing the data of abnormal copper-iron loss ratio and the data of abnormal load rate in each transformer according to the set copper-iron loss ratio threshold and the set load rate threshold.
In one embodiment, the copper-iron loss ratio in the above threshold is2At a load rate of110%(ii) a If the copper-iron loss ratio of the transformer is larger than2Or the load factor of the transformer is greater than110%Then the corresponding data is removed. By preliminarily cleaning the data by using the threshold value, on one hand, the data with obvious errors can be found, and on the other hand, the influence of the data with overlarge deviation on the constructed regression model can be avoided.
And classifying the transformer and the transmission line according to the transmission voltage grade.
In one embodiment, can be as follows220kV、110kV、35kVAnd10kVthe four transmission voltage classes divide the transformer and the transmission line into four categories. In this way, the abnormal loss can be more specifically positioned.
In order to more accurately position, each transmission line can be numbered according to the actual distribution condition of the line.
When the transformer and the transmission line are classified, the classification may be performed according to the actual transmission voltage class, and is not limited to the classification by the above four transmission voltage classes.
Based on a transformer data set with normal copper-iron loss ratio and normal load rate, a transformer nonlinear regression model and a transmission line nonlinear regression model are constructed, and the method specifically comprises the following steps:
And constructing the nonlinear regression models corresponding to the transformers and the transmission lines according to the classified data.
In one embodiment, the non-linear regression model for each transformer is represented as:
further, the transformer nonlinear regression model is:
wherein, the first and the second end of the pipe are connected with each other,
is the total loss of the transformer, is numerically equal to the sum of the copper and iron losses of the transformer,
for the active power supply of the line on which the transformer is located,
is the rated capacity of the transformer and is,
、
、
are coefficients of the regression model.
Further, the nonlinear regression model of the power transmission line is as follows:
wherein the content of the first and second substances,
in order to be a loss of the transmission line,
is the equivalent resistance of the transmission line,
the active power supply capacity of the power transmission line,
the reactive power supply quantity of the power transmission line is provided,
、
、
are coefficients of the regression model.
It should be noted that the coefficients in the two types of regression models may be obtained by solving using data corresponding to the transformer and the power transmission line by a least square method.
Predicting data in the transformer data set by using a transformer nonlinear regression model to obtain a transformer abnormal data set; predicting the data in the power transmission line data set by using a power transmission line nonlinear regression model to obtain a power transmission line abnormal data set, which specifically comprises the following steps:
And predicting input data through the nonlinear regression model to obtain a corresponding prediction result, comparing the prediction result with a record value in the historical record data, screening out final abnormal data and carrying out visual display.
In one embodiment, this may be achieved by:
and calculating the spatial distance between each prediction result and the corresponding historical record data in the regression model and sequencing the prediction results from large to small.
Specifically, the result obtained by the nonlinear regression model of the transformer is a plane in the three-dimensional space, and at this time, points in the sample data need to be calculated (
，
，
) To a corresponding point in the plane (
，
，
) Of (2) isL1(ii) a The nonlinear regression model of the transmission line obtains a hyperplane in a four-dimensional space, and points in sample data need to be calculated (
，
，
，
) To a corresponding point in the hyperplane: (
，
，
，
) Is a distance ofL2. After the corresponding spatial distance is obtained through calculation, the abnormal data can be sorted from large to small in order to facilitate screening.
And screening out data corresponding to the spatial distance in the front of the sequence according to a preset proportion as final abnormal data and carrying out visual display.
In one embodiment, can be as follows 5%The data corresponding to the spatial distance in the front of the sequence is screened out as final abnormal data according to the proportion, and then the data can be displayed in a visualized mode through a display screen and the like.
As shown in fig. 2, a system for detecting abnormal data of power grid loss.
In an implementation manner, an embodiment of the present invention provides a power grid loss abnormal data detection system, including:
data acquisition module100The system is used for acquiring historical record data of a power grid and data such as line length and line type number of a power transmission line;
data processing module200The device is used for calculating the copper-iron loss ratio and the load rate of each transformer according to historical record data of a power grid; and meanwhile, calculating the equivalent resistance of each transformer corresponding to the power transmission line according to the line length and the line model. Removing data with abnormal copper-iron loss ratios and data with abnormal load rates in the transformers according to the set copper-iron loss ratio threshold and the set load rate threshold;
model (model)Training module300The nonlinear regression models are used for constructing the corresponding nonlinear regression models according to the classified data corresponding to the transformers and the transmission lines;
first test module400The transformer data set is used for predicting data in the transformer data set by using a transformer nonlinear regression model to obtain a transformer abnormal data set, comparing a prediction result with a record value in historical record data, screening out final abnormal data and carrying out visual display;
Second test module500And the device is used for predicting the data in the power transmission line data set by using a power transmission line nonlinear regression model to obtain a power transmission line abnormal data set, comparing a prediction result with a record value in historical record data, screening out final abnormal data and carrying out visual display.
In summary, the method, system, terminal and medium for detecting abnormal power grid loss data provided by the embodiments of the present invention, which are based on a regression method, include obtaining a transformer data set in a power grid and a power transmission line data set corresponding to the transformer; screening the transformer data set to obtain a transformer data set with normal copper-iron loss ratio and normal load rate; constructing a transformer nonlinear regression model and a transmission line nonlinear regression model based on a transformer data set with normal copper-iron loss ratio and normal load rate; predicting data in the transformer data set by using a transformer nonlinear regression model to obtain a transformer abnormal data set; and predicting the data in the power transmission line data set by using a power transmission line nonlinear regression model to obtain a power transmission line abnormal data set. Through the mode, the aim of reducing labor cost while improving detection efficiency is achieved.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A power grid loss abnormal data detection method is characterized by comprising the following steps:
acquiring a transformer data set in a power grid and a power transmission line data set corresponding to a transformer;
screening the transformer data set to obtain a transformer data set with normal copper-iron loss ratio and normal load rate;
constructing a transformer nonlinear regression model and a transmission line nonlinear regression model based on a transformer data set with normal copper-iron loss ratio and normal load rate;
predicting data in the transformer data set by using a transformer nonlinear regression model to obtain a transformer abnormal data set;
and predicting the data in the power transmission line data set by using a power transmission line nonlinear regression model to obtain a power transmission line abnormal data set.
2. The method for detecting abnormal power grid loss data according to claim 1, wherein the obtaining process of the transformer data set with normal copper-iron loss ratio and normal load rate specifically comprises the following steps:
obtaining a corresponding transformer copper-iron loss ratio set and a corresponding transformer load rate set based on the transformer data set;
setting a transformer copper-iron loss ratio threshold value and a transformer load rate threshold value;
and screening the transformer data set based on the transformer copper-iron loss ratio threshold and the transformer load rate threshold to obtain the transformer data set with normal copper-iron loss ratio and normal load rate.
3. The method for detecting abnormal data of power grid loss according to claim 1, wherein the transformer nonlinear regression model and the transmission line nonlinear regression model are specifically constructed by the following steps:
acquiring a power transmission line data set corresponding to the transformer data set with the normal copper-iron loss ratio and the normal load rate on the basis of the transformer data set with the normal copper-iron loss ratio and the normal load rate;
constructing a transformer nonlinear regression model based on the transformer data set with normal copper-iron loss ratio and normal load rate;
and constructing a nonlinear regression model of the power transmission line based on the power transmission line data set corresponding to the transformer data set with normal copper-iron loss ratio and normal load rate.
4. The method for detecting abnormal data of power grid loss according to claim 3, wherein the nonlinear regression model of the transformer is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the total loss of the transformer, is numerically equal to the sum of the copper and iron losses of the transformer,
for the active power supply of the line on which the transformer is located,
is the rated capacity of the transformer and is,
、
、
are the coefficients of the regression model.
5. The method for detecting abnormal data of power grid loss according to claim 3, wherein the nonlinear regression model of the power transmission line is as follows:
wherein, the first and the second end of the pipe are connected with each other,
in order to be a loss of the transmission line,
is the equivalent resistance of the transmission line,
the active power supply capacity of the power transmission line,
the reactive power supply quantity of the power transmission line is provided,
、
、
are coefficients of the regression model.
6. The method for detecting abnormal data of power grid loss according to claim 1, wherein the specific process for obtaining the abnormal data set of the transformer is as follows:
calculating the distance from the transformer data set to a corresponding position on a transformer nonlinear regression model to obtain a corresponding first distance set;
and obtaining an abnormal data set of the transformer based on the first distance set.
7. The method for detecting abnormal data of power grid loss according to claim 1, wherein the specific process for obtaining the abnormal data set of the power transmission line is as follows:
Calculating the distance from the data in the power transmission line data set to the corresponding position on the power transmission line nonlinear regression model to obtain a corresponding second distance set;
and acquiring an abnormal data set of the power transmission line based on the second distance set.
8. A power grid loss anomaly data detection system is characterized by comprising:
the data acquisition module is used for acquiring a transformer data set in a power grid and a power transmission line data set corresponding to the transformer;
the data processing module is used for screening the transformer data set to obtain a transformer data set with normal copper-iron loss ratio and normal load rate;
the model training module is used for constructing a transformer nonlinear regression model and a transmission line nonlinear regression model based on a transformer data set with normal copper-iron loss ratio and normal load rate;
the first testing module is used for predicting data in the transformer data set by using a transformer nonlinear regression model to obtain a transformer abnormal data set;
and the second testing module is used for predicting the data in the power transmission line data set by using the power transmission line nonlinear regression model to obtain a power transmission line abnormal data set.
9. An electronic terminal, comprising:
A memory for storing a computer program;
a processor for executing the computer program stored in the memory to cause an electronic terminal to execute a power grid loss anomaly data detection method according to any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, the computer program characterized in that: the program when executed by a processor implements a method of grid loss anomaly data detection as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210525568.XA CN114609480B (en) | 2022-05-16 | 2022-05-16 | Power grid loss abnormal data detection method, system, terminal and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210525568.XA CN114609480B (en) | 2022-05-16 | 2022-05-16 | Power grid loss abnormal data detection method, system, terminal and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114609480A true CN114609480A (en) | 2022-06-10 |
| CN114609480B CN114609480B (en) | 2022-08-16 |
Family
ID=81870696
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210525568.XA Active CN114609480B (en) | 2022-05-16 | 2022-05-16 | Power grid loss abnormal data detection method, system, terminal and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114609480B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106953322A (en) * | 2017-05-12 | 2017-07-14 | 国网上海市电力公司 | A kind of method that power distribution network reduces network loss |
| US20200293554A1 (en) * | 2018-03-15 | 2020-09-17 | Alibaba Group Holding Limited | Abnormal sample prediction |
| CN112085156A (en) * | 2020-07-15 | 2020-12-15 | 国网安徽省电力有限公司怀远县供电公司 | Method and system for predicting shunt line loss rate and evaluating loss reduction potential |
| CN112149873A (en) * | 2020-08-25 | 2020-12-29 | 北京合众伟奇科技有限公司 | Low-voltage transformer area line loss reasonable interval prediction method based on deep learning |
| CN112488395A (en) * | 2020-12-01 | 2021-03-12 | 湖南大学 | Power distribution network line loss prediction method and system |
| CN113224748A (en) * | 2021-04-26 | 2021-08-06 | 国网辽宁省电力有限公司沈阳供电公司 | Method for calculating line loss of low-voltage distribution station area |
| CN113255096A (en) * | 2021-03-31 | 2021-08-13 | 中国电力科学研究院有限公司 | High-loss line abnormal distribution area positioning method and system based on forward stepwise regression |
| CN113691174A (en) * | 2021-08-19 | 2021-11-23 | 云南电网有限责任公司电力科学研究院 | Distribution transformer gear identification method and system based on exponential nonlinear regression |
| CN113887916A (en) * | 2021-09-27 | 2022-01-04 | 国网山西省电力公司电力科学研究院 | Dynamic quantitative evaluation method and system for line loss of power distribution network |
-
2022
- 2022-05-16 CN CN202210525568.XA patent/CN114609480B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106953322A (en) * | 2017-05-12 | 2017-07-14 | 国网上海市电力公司 | A kind of method that power distribution network reduces network loss |
| US20200293554A1 (en) * | 2018-03-15 | 2020-09-17 | Alibaba Group Holding Limited | Abnormal sample prediction |
| CN112085156A (en) * | 2020-07-15 | 2020-12-15 | 国网安徽省电力有限公司怀远县供电公司 | Method and system for predicting shunt line loss rate and evaluating loss reduction potential |
| CN112149873A (en) * | 2020-08-25 | 2020-12-29 | 北京合众伟奇科技有限公司 | Low-voltage transformer area line loss reasonable interval prediction method based on deep learning |
| CN112488395A (en) * | 2020-12-01 | 2021-03-12 | 湖南大学 | Power distribution network line loss prediction method and system |
| CN113255096A (en) * | 2021-03-31 | 2021-08-13 | 中国电力科学研究院有限公司 | High-loss line abnormal distribution area positioning method and system based on forward stepwise regression |
| CN113224748A (en) * | 2021-04-26 | 2021-08-06 | 国网辽宁省电力有限公司沈阳供电公司 | Method for calculating line loss of low-voltage distribution station area |
| CN113691174A (en) * | 2021-08-19 | 2021-11-23 | 云南电网有限责任公司电力科学研究院 | Distribution transformer gear identification method and system based on exponential nonlinear regression |
| CN113887916A (en) * | 2021-09-27 | 2022-01-04 | 国网山西省电力公司电力科学研究院 | Dynamic quantitative evaluation method and system for line loss of power distribution network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114609480B (en) | 2022-08-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107832855B (en) | Line loss multi-source diagnosis method and system based on correlation analysis | |
| CN112307435B (en) | Method for judging and screening abnormal electricity consumption based on fuzzy clustering and trend | |
| CN103729557A (en) | Static N-X batch processing method on basis of PSD-BPA | |
| CN110988422A (en) | Electricity stealing identification method and device and electronic equipment | |
| CN111160791A (en) | Abnormal user identification method based on GBDT algorithm and factor fusion | |
| CN107271809A (en) | A kind of status of electric power amount dynamic threshold acquisition methods applied towards big data | |
| CN109450089B (en) | Transformer area low voltage identification method and device and terminal equipment | |
| CN106530139A (en) | Method for calculating the index parameter of grid investment analysis model | |
| CN117118808B (en) | Multi-source ammeter data acquisition and analysis method, system and storage medium based on Internet of things | |
| CN113902241A (en) | Power grid equipment maintenance strategy system and method based on comprehensive state evaluation | |
| CN115796708A (en) | Intelligent quality inspection method, system and medium for big data for engineering construction | |
| CN113033617A (en) | Deep mining analysis method based on line loss data of big data transformer area | |
| CN113554360A (en) | Power transmission line running state visual management method and system and storage medium | |
| CN109359847A (en) | A kind of quantitative analysis method of Line Loss of Distribution Network System influence factor | |
| CN115345466A (en) | Power distribution network online evaluation system based on network frame topology and multi-source data fusion | |
| CN114609480B (en) | Power grid loss abnormal data detection method, system, terminal and medium | |
| CN110738415A (en) | Electricity stealing user analysis method based on electricity utilization acquisition system and outlier algorithm | |
| CN107742883A (en) | A kind of power system topology island system for rapidly identifying and method based on Spark | |
| CN110555619A (en) | Power supply capacity evaluation method based on intelligent power distribution network | |
| CN107862459B (en) | Metering equipment state evaluation method and system based on big data | |
| CN116485159B (en) | Work order centralized management method of power supply system | |
| CN110310098B (en) | Automatic construction method and device for distribution network planning problem library | |
| CN105184523B (en) | Grid operation mode data digging method and system based on CART decision tree | |
| CN111582717A (en) | Active power distribution network planning method based on big data technology | |
| CN114298188A (en) | Intelligent analysis method and system for power equipment faults |
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 |