CN113033897A - Method for identifying station area subscriber variation relation based on electric quantity correlation of subscriber branch - Google Patents
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Abstract
The invention belongs to the field of power topology big data analysis, and particularly relates to a method for identifying a station area user variation relation based on the electric quantity correlation of user branches. The method comprises the following steps: s1: acquiring station user branch data, power consumption data and adjacent station data; s2: establishing an electric quantity correlation analysis model of a user branch; s3: diagnosing the affiliation relationship between the user branch and the area by using a power consumption correlation analysis model; s4: and outputting the station area user change relation result. The invention provides positive correlation characteristics by using Pearson correlation coefficients based on user classification, user branch data and adjacent station area data, and diagnoses the affiliation relationship between a user branch and a plurality of station areas by using the quantitative trend change of the positive and negative correlation coefficient values of the user branch and 2 or more station areas, thereby realizing the identification of the station-to-user relationship. The point selection strategy for the users with different electricity utilization characteristics to participate in the correlation calculation is optimized, positive and negative correlation characteristics are enhanced, the recognizable user range is further expanded, and the accuracy rate of the identification of the relationship between the users is improved.
Description
Technical Field
The invention belongs to the field of power topology big data analysis, and particularly relates to a method for identifying a station area user variation relation based on the electric quantity correlation of user branches.
Background
With the comprehensive coverage of the electricity information acquisition system, the electricity sales data of the examination table at the transformer side of the transformer area and the user electric energy meter can be acquired every day as the most important operation data. According to the law of conservation of energy, the power supply and sale of the platform area have the following relationship: and the power supply amount of the test meter in the transformer area is the sum of the power selling amount of each sub-meter of each user and the line loss of the transformer area.
When the user table does not belong to the station area, the power consumption is increased by how much kWh, the line loss of the station area is reduced by how much at the same time, and the Pearson correlation coefficient of the user table and the line loss presents a negative correlation characteristic.
The Pearson correlation coefficient is used as an index reflecting the degree of linear correlation between the 2 sequence data X and Y, and the value of the Pearson correlation coefficient is between [ -1 and 1], and can be used for judging the level of the correlation between X and Y. When Pearson's coefficient is used as a sample, it is denoted as R (X, Y).
Wherein: n is the number of samples, Xi, Yi are the i-point observed values corresponding to the variables X, Y,
is the average number of X samples and,
is the average number of Y samples.
When the user change identification is carried out by using a power consumption correlation analysis method, a Pearson correlation coefficient of the power consumption of a user table and the line loss of a station area is generally calculated within a certain time period, and if the numerical value is smaller than a threshold value of-0.8, the user and the station area are considered to have no attribution relationship.
Because the station area needing to be subjected to the station change identification is generally a public station area, when the station change identification is carried out by utilizing the correlation analysis result of the power consumption and the electric quantity, because the power consumption of a single user is lower in the station area, the negative correlation characteristic is not obvious, the user can not be accurately judged to belong to a certain station area, meanwhile, the existing correlation technology can only judge that the user does not belong to the current station area, all the station areas need to be searched for correlation analysis, N-1 station areas are judged to be negatively correlated with the user from the N station areas, the target station area can be accurately positioned, the consumption of computing resources is too large, and the number of the station areas is large, and the accuracy is extremely low.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for identifying the station area user variation relation based on the electric quantity correlation of the user branch, which has the advantages of less calculation resource occupation and high accuracy.
The invention is realized as the following, and the method for identifying the station area user variation relation based on the electric quantity correlation of the user branch is characterized in that: the method comprises the following steps:
s1: acquiring station user branch data, power consumption data and adjacent station data;
s2: establishing an electric quantity correlation analysis model based on user load electrical design according to the station area user branch data, the power consumption data and the adjacent station area data;
s3: diagnosing the affiliation relationship between the user branch and the area by using a power consumption correlation analysis model;
s4: and analyzing the result and outputting the result according to a given standard output format.
The S1 performs the previous diagnosis of the neighboring station area through the analysis of the archival data.
The adjacent station area data comprises the name adjacent of the station area, the collected adjacent and/or the user branch data adjacent to power supply and distribution and the power consumption data.
And the electric quantity correlation analysis model in the S2 is used for dividing different electricity utilization characteristics of different types of users into a stable electricity utilization type, a non-stable electricity utilization type, a sawtooth electricity utilization type, a common standby electricity utilization type, a pulse electricity utilization type and an intermittent electricity utilization type.
And S3, diagnosing the affiliation relationship between the user branch and the distribution room by using the electric quantity correlation analysis model, wherein the affiliation relationship comprises point location selection, coefficient calculation and branch distribution room identification.
The point location selection is carried out according to different electricity utilization characteristic types:
a. for stable power utilization users (the power is in a section [ R1, R2 ], the power fluctuation rate is less than or equal to P1), calculating power data corresponding to a point location with large line loss power change (the line loss power is outside the section [ R3, R4 ], the fluctuation rate is greater than P2) to participate in correlation calculation; (ii) a
b. Introducing a daily electric quantity variation slope to a non-stationary electricity user, recording the slope as K, generating time series data of the daily electric quantity variation slope, wherein K is (the current day electric quantity-the previous day electric quantity)/1, selecting points outside a section [ K1, K2 ] as points with large fluctuation for the series data K, and selecting corresponding electric quantity data according to the points to participate in correlation calculation;
c. for the electricity users of the common standby power supply, reserving electricity quantity data of 0 point in each day before and after the electricity consumption is not 0 point to participate in correlation calculation;
d. selecting electricity consumption data of which the electricity consumption is not 0 point and the electricity consumption is 0 point in each 1 day before and after the electricity consumption is not 0 point for the sawtooth-shaped electricity consumption user to participate in correlation calculation;
e. for pulse type electricity users, selecting points with electricity consumption not being 0 points, if the point positions are not enough for correlation calculation, selecting point positions with large line loss electricity quantity change, and complementing the point positions required by calculation to participate in correlation calculation;
f. for intermittent electricity users, selecting electricity consumption of 0 point in each day before and after the electricity consumption of 0 point, selecting electricity consumption of 0 point in each day after the electricity consumption of 0 point, if the point is not enough for correlation calculation, selecting point positions with line loss electricity quantity outside the sections (R3 and R4) and fluctuation rate larger than P2 to complement calculation required point positions, and selecting electricity quantity data corresponding to the point positions to participate in correlation calculation.
The coefficient calculation and branch platform area identification means that 2 correlation coefficients are calculated between the branch electric quantity of a user and the line loss of the platform area, and the platform-to-user relationship is diagnosed according to the coefficient value trend change; one is a negative correlation coefficient, namely, a Pearson correlation coefficient of 2 time sequence data among the power quantity and the line loss of the transformer area after the merging of the users under the branch is calculated, and the user is diagnosed not to belong to a certain transformer area; and the other is a positive correlation coefficient, namely a Pearson correlation coefficient of the power consumption after merging of the users under the calculation branch and the power consumption of the power station area line loss time sequence data after the power consumption of the users under the calculation branch is eliminated.
The invention has the advantages and positive effects that: the invention provides positive correlation characteristics by using the Pearson correlation coefficient based on user classification, user branch data and adjacent station area data and compared with common negative correlation characteristics to identify that a user does not belong to the station area, and realizes the station-to-user relationship identification by using the quantitative trend change of the positive and negative correlation coefficient values of the user branch and 2 or more station areas.
The invention realizes the identification of the district relationship of the resident users with the largest district user occupation ratio by merging the branch electric quantity of the users; the point selection strategy for the users with different electricity utilization characteristics to participate in the correlation calculation is optimized, positive and negative correlation characteristics are enhanced, the user range capable of carrying out the station-to-user relationship identification is further expanded, and the accuracy rate of the station-to-user relationship identification is improved.
Drawings
FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a schematic block diagram of the model diagnostics of the present invention;
FIG. 3 is a schematic view of the user classification of the present invention;
FIG. 4 is a schematic diagram of the acquisition proximity and power supply and distribution design proximity of the present invention;
FIG. 5 is a schematic diagram of various types of electrical features of the present invention.
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 with reference to the following embodiments.
Example 1:
as shown in fig. 1, the method for identifying station area subscriber variation relationship based on the power correlation of subscriber branch includes the following steps:
s1: acquiring station user branch data, power consumption data and adjacent station data;
s2: establishing an electric quantity correlation analysis model based on user load electrical design according to the station area user branch data, the power consumption data and the adjacent station area data;
s3: diagnosing the affiliation relationship between the user branch and the area by using a power consumption correlation analysis model;
s4: and analyzing the result and outputting the result according to a given standard output format.
The S1 performs the previous diagnosis of the neighboring station area through the analysis of the archival data. The adjacent station area data comprises the name adjacent of the station area, the user branch data of the collection adjacent and/or the power supply and distribution adjacent, and the power consumption data.
Since the household error generally occurs in the adjacent station area, when data analysis is carried out, the concept of the adjacent station area is introduced, including the name adjacency of the station area, the acquisition adjacency and the power supply and distribution adjacency.
In the power supply design of the transformer area, the power supply transformer of the transformer area is used as 10 kilovolt power distribution equipment, the naming rules thereof need to be complied with, such as 10kVABC-1 power distribution transformer and 10kVABC-2 transformer, ABC is used as a character string shared by 2 transformer areas, and the character string indicates that the power supply transformer is close to the distance or is in the same cell. The adjacent areas in this relationship are the areas with adjacent names.
When the acquisition system is built, due to the technical characteristics of the acquisition mode, for example, the micro-power wireless mode can acquire user electric energy meters of a plurality of nearby areas without blocking of the terminal accessories. The neighboring station areas of this relationship are acquisition neighbors.
The electric load is divided into a first-level load, a second-level load and a third-level load according to the reliability of power supply and the degree of political and economic loss or influence caused by power supply interruption. For the first-level and second-level users, dual power supplies can be provided during the electrical design of user loads, and 2 power areas for providing the dual power supplies are adjacent geographically due to the limitation of the power supply radius of the power areas. The adjacent stations in this relationship are designed to be adjacent for power supply and distribution. For example, three-phase important users may be supplied by 2 zones with completely different names, and elevator users of high-rise buildings may be supplied by 2 zones with adjacent names.
Fig. 4 shows a schematic diagram of acquisition proximity and supply and distribution design proximity, and the stations a/B/C may or may not be nominally adjacent. The user change error generally occurs in the adjacent station area, and the analysis range of the user attributive station area can be narrowed and the identification accuracy can be improved by analyzing and diagnosing the file data to obtain the adjacent station area.
In the power supply design of the transformer area, a power supply path generally supplies power from a transformer- > power supply line (overhead line or cable) - > power distribution equipment (electric pole or branch box or distribution room power distribution cabinet) - > power supply line- > meter box (centralized or single meter box) - > electric energy meter- > user.
The method is characterized in that the power utilization address characteristics, the acquisition characteristics and the user power utilization characteristics of the power utilization area users are summarized, the user branch concept is introduced, the power utilization area users are divided into 2 categories, namely six categories, and the user variable relation identification is conveniently performed by selecting a proper analysis method for each category of users.
Dividing users under the transformer area into residential building type (building, multi-storey and high-rise residence for short, and regular floor-room characteristics of electricity utilization addresses) users and non-residential building type (independent house for short), constructing a user branch data model, and dividing each major user branch into three subclasses according to the acquisition characteristics and the electricity utilization characteristics of the users. As shown in table 1.
TABLE 1 subscriber Branch Classification under zone
Note that: in the scheme, in the private users of the residents of the buildings, the users of the single meter box are not regarded as a final-stage user branch, and the users of the centralized meter box and the users below the centralized meter box can be regarded as a final-stage user branch.
Note two: the user address clustering branch refers to the user addresses of a plurality of users, wherein the user addresses have the same keywords, such as the country of the Lincang road-friendly 12 teams.
Third, note: the aggregated address user branch may be composed of a plurality of last level user branches.
As shown in fig. 5, when the pearson coefficient is used to calculate the power consumption and the power correlation, the power consumption of the user in the area ratio, the power consumption/area line loss ratio, and the fluctuation of the power consumption of the user all affect the calculation result. After the classification of the users in the distribution area is obtained, different electricity utilization characteristics of the users in different classes are extracted, and electricity quantity data of a time point with large electricity consumption fluctuation is selected to participate in calculation, so that correlation characteristics are enhanced.
The electric quantity correlation analysis model divides different electricity utilization characteristics of different types of users into a stable electricity utilization type, a non-stable electricity utilization type, a sawtooth electricity utilization type, a common standby electricity utilization type, a pulse electricity utilization type and an intermittent electricity utilization type.
For users with different electricity utilization characteristics, point location strategies participating in correlation calculation are selected as follows:
a. for stable power utilization users (the power is in a section [ R1, R2 ], the power fluctuation rate is less than or equal to P1), calculating power data corresponding to a point location with large line loss power change (the line loss power is outside the section [ R3, R4 ], the fluctuation rate is greater than P2) to participate in correlation calculation;
b. introducing a daily electric quantity change slope into a non-stationary electric user (the electric quantity is in an interval [ R1, R2 ], the electric quantity fluctuation rate is greater than P1), recording as K, and generating time series data of the daily electric quantity change slope;
k ═ 1 (current day electric quantity-previous day electric quantity)
For the sequence data K, selecting points outside the intervals (K1, K2) as points with large fluctuation change, and selecting corresponding electric quantity data according to the points to participate in correlation calculation;
c. for the electricity users of the common standby power supply, reserving electricity quantity data of 0 point in each day before and after the electricity consumption is not 0 point to participate in correlation calculation;
d. selecting electricity consumption data of which the electricity consumption is not 0 point and the electricity consumption is 0 point in each 1 day before and after the electricity consumption is not 0 point for the sawtooth-shaped electricity consumption user to participate in correlation calculation;
e. for pulse type electricity users, selecting a point with electricity consumption not being 0 point, if the point is not enough to perform correlation calculation, selecting a point with large line loss electricity quantity change (the line loss electricity quantity is out of a section (R3, R4), the fluctuation rate is more than P2), complementing the point needed by calculation to participate in correlation calculation;
f. for intermittent electricity users, selecting electricity consumption of 0 point in each day before and after the electricity consumption of 0 point, selecting electricity consumption of 0 point in each day after the electricity consumption of 0 point, if the point is not enough for correlation calculation, selecting point positions with line loss electricity quantity outside the sections (R3 and R4) and fluctuation rate larger than P2 to complement calculation required point positions, and selecting electricity quantity data corresponding to the point positions to participate in correlation calculation.
On the basis of the user load classification enhanced correlation analysis result, the invention provides that 2 correlation coefficients are calculated between the branch electric quantity of the user and the line loss of the transformer area, and the transformer area correlation is diagnosed according to the coefficient value trend change. One is a negative correlation coefficient, namely a Pearson correlation coefficient of 2 time series data between the power after merging of the users under the branch and the line loss of the station area (the power of the branch to be calculated is counted in the station area) is calculated, and the users are diagnosed not to belong to a certain station area; the other is positive correlation coefficient, namely, the power of the merged users under the calculation branch, and the Pearson correlation coefficient of time series data among line loss of the distribution area (the power of the branch to be calculated is not counted in the distribution area) after the power of the users under the calculation branch is removed, so that the diagnosis user belongs to a certain distribution area. And judging the affiliation of the station area by quantizing the trend change of the 2 correlation coefficients. R0Negative poleAnd R0Is justFor the 2 correlation coefficients of the user with region 0, R1Negative poleAnd R1Is justFor 2 correlation coefficients of the user and the station area 1, there are:
station negative correlation difference K0-R0Negative pole-R1Negative pole
Station area positive correlation difference K1 ═ R0Is just-R1Is just
Difference product M-K0-K1
The sum of the differences S is K0+ K1
The diagnostic logic is shown in table 2.
TABLE 2
When the number of the station areas is more than 2, selecting one station area (the area to which the user under the user branch belongs) or the current station area to which the user branch belongs (the area to which the user under the user branch belongs) as the station area 0, respectively diagnosing with the plurality of station areas, and diagnosing the target station area as the one with a large difference value and a large absolute value.
A specific diagnostic flow diagram is shown in fig. 2. And diagnosing the affiliation relationship between the user branch and the distribution room by using the power consumption correlation analysis model, wherein the affiliation relationship comprises point location selection, coefficient calculation and distribution room identification, and finally outputting a result.
Experiments prove that
According to the invention, on the basis of the classification of the users in the distribution area and the identification of the user branches, the characteristics that all users under the same user branch are supplied with power by the same distribution area are utilized, the electric quantity of the user branches is combined to carry out electric quantity and electric quantity correlation analysis, and the problem that the electric quantity correlation of the users with the minimum electric quantity with the most occupation ratio in the distribution area is difficult to identify can be solved. Secondly, by acquiring the adjacent station areas, the scope of the user and the station area attribution diagnosis is narrowed, and the requirement on computing resources can be greatly reduced. Finally, the invention proposes rules for the home diagnosis of the target cell by quantifying the trend in a number of cells (typically 2, up to 4) by means of correlation values. Through the steps, the station abnormal identification accuracy and the user attribution target station area identification accuracy can be greatly improved. The accuracy can reach 99% through the verification of the actual station area.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. The method for identifying the station area user variation relation based on the electric quantity correlation of the user branch is characterized in that: the method comprises the following steps:
s1: acquiring station user branch data, power consumption data and adjacent station data;
s2: establishing an electric quantity correlation analysis model based on the user branch according to the station area user branch data, the power consumption data and the adjacent station area data;
s3: diagnosing the affiliation relationship between the user branch and the area by using a power consumption correlation analysis model;
s4: and analyzing the result and outputting the result according to a given standard output format.
2. The method for identifying station area subscriber variation relationships based on the power correlation of subscriber branches as claimed in claim 1, wherein said S1 is performed to diagnose the adjacent station areas through the analysis of the profile data.
3. The method for identifying the station-to-station variation relationship based on the power correlation of the subscriber branch as claimed in claim 1 or 2, wherein the adjacent station data comprises the subscriber branch data and the power consumption data of the adjacent station name, the adjacent collection and/or power supply and distribution.
4. The method for identifying station area subscriber variation relationships based on subscriber branch electricity quantity correlations as claimed in claim 1, wherein the electricity quantity correlation analysis model in S2 divides different electricity usage characteristics of different types of subscribers into a stationary electricity usage type, a non-stationary electricity usage type, a sawtooth electricity usage type, a common standby electricity usage type, a pulse electricity usage type and an intermittent electricity usage type.
5. The method according to claim 1, wherein the step S3 is implemented by using a power correlation analysis model to diagnose the affiliation between a subscriber branch and a distribution area, including point location selection, coefficient calculation, and distribution area identification.
6. The method according to claim 5, wherein the point location selection is performed according to different power utilization feature types:
a. for users with stable power consumption, calculating electric quantity data corresponding to the point position with large line loss electric quantity variation in point position selection to participate in correlation calculation;
b. introducing a daily electric quantity variation slope to a non-stationary electricity user, recording the slope as K, generating time series data of the daily electric quantity variation slope, wherein K is (the current day electric quantity-the previous day electric quantity)/1, selecting points outside a section [ K1, K2 ] as points with large fluctuation for the series data K, and selecting corresponding electric quantity data according to the points to participate in correlation calculation;
c. for the electricity users of the common standby power supply, reserving electricity quantity data of 0 point in each day before and after the electricity consumption is not 0 point to participate in correlation calculation;
d. selecting electricity consumption data of which the electricity consumption is not 0 point and the electricity consumption is 0 point in each 1 day before and after the electricity consumption is not 0 point for the sawtooth-shaped electricity consumption user to participate in correlation calculation;
e. for pulse type electricity users, selecting points with electricity consumption not being 0 points, if the point positions are not enough for correlation calculation, selecting point positions with large line loss electricity quantity change, and complementing the point positions required by calculation to participate in correlation calculation;
f. for intermittent electricity users, selecting electricity consumption of 0 point in each day before and after the electricity consumption of 0 point, selecting electricity consumption of 0 point in each day after the electricity consumption of 0 point, if the point is not enough for correlation calculation, selecting point positions with line loss electricity quantity outside the sections (R3 and R4) and fluctuation rate larger than P2 to complement calculation required point positions, and selecting electricity quantity data corresponding to the point positions to participate in correlation calculation.
7. The method according to claim 5, wherein the coefficient calculation and the branch area identification are performed by calculating 2 correlation coefficients between the branch power of the subscriber and the line loss of the branch area and diagnosing the subscriber relationship according to the coefficient value trend; one is a negative correlation coefficient, namely, a Pearson correlation coefficient of 2 time sequence data among the power quantity and the line loss of the transformer area after the merging of the users under the branch is calculated, and the user is diagnosed not to belong to a certain transformer area; and the other is a positive correlation coefficient, namely a Pearson correlation coefficient of the power consumption after merging of the users under the calculation branch and the power consumption of the power station area line loss time sequence data after the power consumption of the users under the calculation branch is eliminated.
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| CN116486590A (en) * | 2023-05-10 | 2023-07-25 | 漆燕 | Ammeter data analysis system based on remote meter reading mode |
| WO2024037393A1 (en) * | 2022-08-17 | 2024-02-22 | 北京智芯微电子科技有限公司 | Topology recognition model compression method and apparatus, electronic device and medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106250927A (en) * | 2016-07-29 | 2016-12-21 | 国网河南省电力公司电力科学研究院 | Power distribution network topological structure method of calibration based on k arest neighbors sorting algorithm |
| CN109461096A (en) * | 2018-11-05 | 2019-03-12 | 杭州致成电子科技有限公司 | A kind of family change relationship anomalous discrimination method based on electricity relevance |
| CN109919425A (en) * | 2019-01-23 | 2019-06-21 | 国网浙江省电力有限公司 | A kind of platform area customer relationship error correction method based on correlation analysis |
| CN110231528A (en) * | 2019-06-17 | 2019-09-13 | 国网重庆市电力公司电力科学研究院 | Transformer family based on load characteristic model library becomes anomalous identification method and device |
| CN110503570A (en) * | 2019-07-16 | 2019-11-26 | 国网江苏省电力有限公司滨海县供电分公司 | A kind of exception electricity consumption data detection method, system, equipment, storage medium |
| CN110516912A (en) * | 2019-07-24 | 2019-11-29 | 长沙恒电聚能电子科技有限公司 | A kind of power distribution station family becomes the recognition methods of relationship |
| CN110837965A (en) * | 2019-11-07 | 2020-02-25 | 国网山东省电力公司威海供电公司 | Method for rapidly distinguishing station area house change relations |
| CN111092487A (en) * | 2019-12-06 | 2020-05-01 | 国网河北省电力有限公司电力科学研究院 | Application of broadband carrier to non-metering function of intelligent electric energy meter |
| CN111951125A (en) * | 2020-08-12 | 2020-11-17 | 积成电子股份有限公司 | Transformer area abnormal user variation relation identification method based on big data analysis |
| CN112131783A (en) * | 2020-09-04 | 2020-12-25 | 国电南瑞科技股份有限公司 | Power distribution station area big data-based household transformer topology relation identification method |
| CN112288339A (en) * | 2020-12-24 | 2021-01-29 | 广东电网有限责任公司佛山供电局 | Transformer area household variation relation identification method based on electric quantity and voltage data analysis |
-
2021
- 2021-03-26 CN CN202110327658.3A patent/CN113033897A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106250927A (en) * | 2016-07-29 | 2016-12-21 | 国网河南省电力公司电力科学研究院 | Power distribution network topological structure method of calibration based on k arest neighbors sorting algorithm |
| CN109461096A (en) * | 2018-11-05 | 2019-03-12 | 杭州致成电子科技有限公司 | A kind of family change relationship anomalous discrimination method based on electricity relevance |
| CN109919425A (en) * | 2019-01-23 | 2019-06-21 | 国网浙江省电力有限公司 | A kind of platform area customer relationship error correction method based on correlation analysis |
| CN110231528A (en) * | 2019-06-17 | 2019-09-13 | 国网重庆市电力公司电力科学研究院 | Transformer family based on load characteristic model library becomes anomalous identification method and device |
| CN110503570A (en) * | 2019-07-16 | 2019-11-26 | 国网江苏省电力有限公司滨海县供电分公司 | A kind of exception electricity consumption data detection method, system, equipment, storage medium |
| CN110516912A (en) * | 2019-07-24 | 2019-11-29 | 长沙恒电聚能电子科技有限公司 | A kind of power distribution station family becomes the recognition methods of relationship |
| CN110837965A (en) * | 2019-11-07 | 2020-02-25 | 国网山东省电力公司威海供电公司 | Method for rapidly distinguishing station area house change relations |
| CN111092487A (en) * | 2019-12-06 | 2020-05-01 | 国网河北省电力有限公司电力科学研究院 | Application of broadband carrier to non-metering function of intelligent electric energy meter |
| CN111951125A (en) * | 2020-08-12 | 2020-11-17 | 积成电子股份有限公司 | Transformer area abnormal user variation relation identification method based on big data analysis |
| CN112131783A (en) * | 2020-09-04 | 2020-12-25 | 国电南瑞科技股份有限公司 | Power distribution station area big data-based household transformer topology relation identification method |
| CN112288339A (en) * | 2020-12-24 | 2021-01-29 | 广东电网有限责任公司佛山供电局 | Transformer area household variation relation identification method based on electric quantity and voltage data analysis |
Non-Patent Citations (1)
| Title |
|---|
| 王涓;吴旭鸣;王爱凤;: "应用皮尔逊相关系数算法查找异常电能表用户", 电力需求侧管理, no. 02, 20 March 2014 (2014-03-20) * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024037393A1 (en) * | 2022-08-17 | 2024-02-22 | 北京智芯微电子科技有限公司 | Topology recognition model compression method and apparatus, electronic device and medium |
| CN115684918A (en) * | 2023-01-04 | 2023-02-03 | 北京志翔科技股份有限公司 | Switch state identification method and device |
| CN115684918B (en) * | 2023-01-04 | 2023-04-07 | 北京志翔科技股份有限公司 | Switch state identification method and device |
| CN116486590A (en) * | 2023-05-10 | 2023-07-25 | 漆燕 | Ammeter data analysis system based on remote meter reading mode |
| CN116486590B (en) * | 2023-05-10 | 2024-03-22 | 江阴众和电力仪表有限公司 | Ammeter data analysis system based on remote meter reading mode |
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