CN112288339A - Transformer area household variation relation identification method based on electric quantity and voltage data analysis - Google Patents
Transformer area household variation relation identification method based on electric quantity and voltage data analysis Download PDFInfo
- Publication number
- CN112288339A CN112288339A CN202011543253.5A CN202011543253A CN112288339A CN 112288339 A CN112288339 A CN 112288339A CN 202011543253 A CN202011543253 A CN 202011543253A CN 112288339 A CN112288339 A CN 112288339A
- Authority
- CN
- China
- Prior art keywords
- user
- area
- station
- abnormal
- voltage
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention provides a station area household variation relation identification method based on electric quantity and voltage data analysis, which is characterized by calculating the line loss rate of a station area to be analyzed by combining electric quantity data, determining a line loss abnormal station area according to the line loss rate of the station area, and dividing the abnormal station area into I-type abnormal station areas and II-type abnormal station areas; identifying abnormal users of the user variation relationship in the I-type abnormal distribution area by using a local abnormal factor algorithm, and finishing the adjustment of the user variation relationship in the I-type abnormal distribution area; and calculating the voltage similarity, judging the station area attribution condition of the abnormal user, and finishing the adjustment of the station change relationship of the II-type abnormal station area. According to the method, the line loss rate abnormal area is locked by using the electricity data, and the abnormal users in the abnormal area are identified by combining the voltage data, so that the identification of the station area user change relationship is completed, the user change relationship is clear and accurate, and the occurrence of the line loss rate abnormal situation of the low-voltage area is effectively reduced.
Description
Technical Field
The invention relates to the field of power systems, in particular to a transformer area household variation relation identification method based on electric quantity and voltage data analysis.
Background
The line loss rate is an important technical and economic index of a power supply enterprise, and the line loss treatment of the low-voltage transformer area is important daily management work of the power supply enterprise. The error of the household variable relationship is an important reason for the abnormal line loss rate of the low-voltage transformer area, and the clear and accurate household variable relationship is a premise for developing the line loss control of the low-voltage transformer area. However, due to the problem that account management modes and histories are left, the low-voltage transformer area user change relationship is incorrect, and lean management of the low-voltage transformer area is severely restricted. Therefore, it is necessary to provide an effective method for identifying station-to-station relationship.
Aiming at the problem of abnormal household-to-variable relationships, at present, power personnel often search users with abnormal household-to-variable relationships in a line patrol mode, and carry out adjustment of the household-to-variable relationships, or carry out identification of the household-to-variable relationships by using a platform area identification instrument. However, a large amount of manpower is consumed in a line patrol mode, and the line patrol mode is difficult to be continuously developed in daily life; the identification mode based on the platform district identification instrument needs extra equipment investment, and has safety risk in the instrument use process. With the popularization and application of smart electric meters, the identification of the station area household variable relationship based on metering data becomes the current research trend, and documents propose a method for identifying the station area household variable relationship based on voltage correlation, and utilize the characteristics of similar voltage fluctuation trends of the same station area and different voltage fluctuation trends of different station areas to identify the household variable relationship. A large amount of manpower is consumed in a line patrol mode, and daily continuous development is difficult. The identification mode based on the platform district identification instrument needs additional equipment investment, and has safety risk in the use process. The voltage correlation analysis method only carries out the identification of the user variation relation from the voltage data perspective, and the electricity consumption data is not fully utilized.
Disclosure of Invention
The invention provides a transformer area household change relation identification method based on electric quantity and voltage data analysis.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
a transformer area user variation relation identification method based on electric quantity and voltage data analysis comprises the following steps:
s1: calculating the line loss rate of the distribution area to be analyzed by combining the electric quantity data, and determining a line loss abnormal distribution area according to the distribution area line loss rate, wherein the distribution area is divided into I-type abnormal distribution areas and II-type abnormal distribution areas;
s2: identifying abnormal users of the user change relationship in the I-type abnormal distribution area by using an abnormal user detection algorithm, and finishing the adjustment of the user change relationship in the I-type abnormal distribution area;
s3: and calculating voltage similarity, integrating the voltage similarity, the line loss rate of the station area and the fluctuation index of the line loss rate of the station area, judging the station area attribution condition of the abnormal user, and finishing the user variation relation adjustment of the II-type abnormal station area.
Further, in step S1, the station jth
Antenna loss rate
The calculation expression is:
in the formula, NjThe number of users under the station zone j,
for users ith under zone j
Electricity consumption per day;
is a station area jth
Daily power supply.
Further, the abnormal user detection algorithm flow in step S2 is as follows:
a. each user corresponds to 1 voltage data sequence, the voltage data of all the users form a data set D, and the distance D (e, f) between the user e and the user f is calculated, wherein e belongs to D, and f belongs to D;
b. calculating the kth distance d of the user e by combining the distances dk(e) Wherein, if the distance d (e, f) between the users e and f is dk(e) Then, it needs to satisfy:
(ii) there are a minimum of k users
Satisfy the requirement of
；
② there are at most k-1 users
Satisfy the requirement of
；
c. Combining d and dkAnalyzing k neighbor N of user ek(e) Wherein N isk(e) Including a distance from user e not exceeding dk(e) All users of (1);
Nk(e)={X∈D|d(e,X)≤dk(e), X≠e}
d. calculating the k-th reachable distance from user f to user e
；
e. Binding to NkAnd
local reachable density lrd for user e is calculatedk(e)；
f. Computing the local outlier LOF of user ek(e)；
g. If LOFk(e)>2, the user e is an abnormal user, otherwise, the user e is a normal user;
and calculating local outlier factors of all users, and determining abnormal users of the platform area to be analyzed.
Further, in step S3, the voltage similarity R between user i and station zone jijThe calculation expression is:
in the formula, NjThe number of users in the distribution area j is;
the voltage similarity of the user i and the station area j is shown;
the voltage similarity between the user i and the e-th user in the station area j is obtained; e =1, …, Nj；
Wherein, the voltage similarity of the user i and the station area j
The calculation expression is:
wherein D is the voltage time sequence length;
and
respectively obtaining the voltage data of the user i at the moment d and the average value of the voltage data of the user i;
and
respectively obtaining voltage data of the transformer area j at the moment d and the average value of the voltage data of the transformer area j;
voltage similarity between user i and the e-th user in zone j
The calculation expression is:
in the formula (I), the compound is shown in the specification,
and
the voltage data of the user e in the station zone j at the time d and the average value of the voltage data of the user e are respectively.
Further, the station area line loss rate fluctuation index B in step S3xComprises the following steps:
in the formula, T is the total days of the data to be analyzed, and x is the station number with the highest voltage similarity with the user i.
Further, the specific process of step S1 is:
s101, counting the number of the areas to be analyzed to be N, counting the total days of the data to be analyzed to be T, and enabling j = 1;
s102, if j is less than or equal to N, calculating the loss rate A of the first antenna of the station area jjlAnd step S103 is executed, otherwise, step S2 is executed;
s103, order Ajmax=max{Aj1,…,Ajl,…,AjT}，Ajmin=min{Aj1,…,Ajl,…,AjT}，amaxIs the upper limit value of the line loss rate of the transformer area, aminThe lower limit value of the line loss rate of the transformer area is set;
s104, determining a line loss abnormal area by combining the line loss rate index A of the area;
s104a if Ajmin≥aminAnd A isjmax≤amaxIf j = j +1, executing step S102, otherwise, executing step S104 b;
s104b, if Ajmin＜aminMarking the station zone j as an I-type abnormal station zone, and bringing the station zone j into an I-type station zone set CILet j = j +1, perform step S102, otherwise, perform step S104 c;
s104c, marking the station zone j as a type II abnormal station zone, and bringing the station zone j into a type II station zone set CIILet j = j +1, step S102 is executed.
Further, the specific process of step S2 is:
s201. statistics setCIThe number of the middle station area is NILet m = 1;
s202, if m is less than or equal to NIThen to set CIPerforming abnormal user detection on the user in the mth station area, identifying the abnormal user in the station area, and executing the step S203, otherwise, executing the step S3;
and S203, removing the abnormal users from the station area m, bringing the abnormal users into an abnormal user set U, and executing the step S202 by enabling m = m + 1.
Further, the specific process of step S3 is:
s301, making i =1, and making the number of users in the statistical set U be MUStatistical set CIIThe number of the middle station area is NII；
S302, if i is less than or equal to MUThen calculate user i in set U and set CIIVoltage integrated similarity R of middle transformer area jij（j=1, …, NII) Executing step S303, otherwise, ending the identification process;
s303. order Rix=max{Ri1,…,Rij,…,RiNIIX is the station area number with the highest voltage similarity with the user i, and the line loss rate A of the user i in the front and back station areas x of the station area x is calculatedxAnd line loss rate fluctuation index Bx；
S304. if the user i is brought into the station area x, the line loss rate A is increasedxAnd line loss rate fluctuation index BxDecreasing, the user i belongs to the station zone x, let i = i +1, and step S302 is executed, otherwise, let i = i +1, and step S302 is executed.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the method of the invention combines the electric quantity data to calculate the line loss rate of the station area to be analyzed, determines the abnormal station area of the line loss according to the line loss rate of the station area, and divides the abnormal station area of the line loss into I type abnormal station area and II type abnormal station area; identifying abnormal users of the user variation relationship in the I-type abnormal distribution area by using a local abnormal factor algorithm, and finishing the adjustment of the user variation relationship in the I-type abnormal distribution area; and calculating the voltage similarity, judging the station area attribution condition of the abnormal user, and finishing the adjustment of the station change relationship of the II-type abnormal station area. The method of the invention utilizes the electricity data to lock the abnormal area of the line loss rate, and combines the voltage data to identify the abnormal users in the abnormal area, thereby completing the identification of the station area user change relationship, ensuring the user change relationship to be clear and accurate, and effectively reducing the occurrence of the abnormal condition of the line loss rate in the low-voltage area.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, a transformer area subscriber relationship identification method based on electric quantity and voltage data analysis includes the following steps:
s1: calculating the line loss rate of the to-be-analyzed distribution room by combining the electric quantity data, determining a line loss abnormal distribution room according to the line loss rate of the distribution room, and dividing the abnormal distribution room into I-type and II-type abnormal distribution rooms:
s101, counting the number of the areas to be analyzed to be N, counting the total days of the data to be analyzed to be T, and enabling j = 1;
s102, if j is less than or equal to N, calculating the j-th antenna loss rate of the cell and executing the step S103, otherwise, executing the step S2;
s103, order Ajmax=max{Aj1,…,Ajl,…,AjT}，Ajmin=min{Aj1,…,Ajl,…,AjT}，amaxIs the upper limit value of the line loss rate of the transformer area, aminThe lower limit value of the line loss rate of the transformer area is set;
s104, determining a line loss abnormal area by combining the area line loss rate index A:
s104a if Ajmin≥aminAnd A isjmax≤amaxIf j = j +1, executing step S102, otherwise, executing step S104 b;
s104b, if Ajmin＜aminMarking the station zone j as an I-type abnormal station zone, and bringing the station zone j into an I-type station zone set CILet j = j +1, perform step S102, otherwise, perform step S104 c;
s104c, marking the station zone j as a type II abnormal station zone, and bringing the station zone j into a type II station zone set CIILet j = j +1, execute step S102;
s2: and (3) identifying the user with abnormal user change relationship in the I-type abnormal distribution area by using an abnormal user detection algorithm, and finishing the user change relationship adjustment of the I-type abnormal distribution area:
s201. statistic set CIThe number of the middle station area is NILet m = 1;
s202, if m is less than or equal to NIThen to set CIPerforming abnormal user detection on the user in the mth station area, identifying the abnormal user in the station area, and executing the step S203, otherwise, executing the step S3;
s203, removing abnormal users from the platform area m, bringing the abnormal users into an abnormal user set U, enabling m = m +1, and executing the step S202;
s3: calculating voltage similarity, integrating the voltage similarity, the line loss rate of the station area and the line loss rate fluctuation index of the station area, judging the station area attribution condition of the abnormal user, and finishing the user variation relation adjustment of the II-type abnormal station area:
s301, making i =1, and making the number of users in the statistical set U be MUStatistical set CIIThe number of the middle station area is NII；
S302, if i is less than or equal to MUThen calculate user i in set U and set CIIVoltage integrated similarity R of middle transformer area jij（j=1, …, NII) Executing step S303, otherwise, ending the identification process;
s303. order Rix=max{Ri1,…,Rij,…,RiNIIX is the station area number with the highest voltage similarity with the user i, and the line loss rate A of the user i in the front and back station areas x of the station area x is calculatedxAnd line loss rate fluctuation index Bx；
S304. if the user i is brought into the station area x, the line loss rate A is increasedxAnd line loss rate fluctuation index BxDecrease, then user i belongs to zone x, orderi = i +1, step S302 is performed, otherwise, let i = i +1, step S302 is performed.
Step S1, where the calculation expression of the station zone j-th antenna loss ratio is:
in the formula, NjThe number of users under the station zone j,
for users ith under zone j
Electricity consumption per day;
is a station area jth
Daily power supply.
The abnormal user detection algorithm flow in step S2 is as follows:
a. each user corresponds to 1 voltage data sequence, the voltage data of all the users form a data set D, and the distance D (e, f) between the user e and the user f is calculated, wherein e belongs to D, and f belongs to D;
b. calculating the kth distance d of the user e by combining the distances dk(e) Wherein, if the distance d (e, f) between the users e and f is dk(e) Then, it needs to satisfy:
(ii) there are a minimum of k users
Satisfy the requirement of
；
② there are at most k-1 users
Satisfy the requirement of
；
c. Combining d and dkAnalyzing k neighbor N of user ek(e) Wherein N isk(e) Including a distance from user e not exceeding dk(e) All users of (1);
Nk(e)={X∈D|d(e,X)≤dk(e), X≠e}
d. calculating the k-th reachable distance from user f to user e
；
e. Binding to NkAnd
local reachable density lrd for user e is calculatedk(e)；
f. Computing the local outlier LOF of user ek(e)；
g. If LOFk(e)>2, the user e is an abnormal user, otherwise, the user e is a normal user;
and calculating local outlier factors of all users, and determining abnormal users of the platform area to be analyzed.
Voltage similarity R of user i and station j in step S3ijThe calculation expression is:
in the formula, NjThe number of users in the distribution area j is;
the voltage similarity of the user i and the station area j is shown;
the voltage similarity between the user i and the e-th user in the station area j is obtained; e =1, …, Nj；
Wherein, the voltage similarity of the user i and the station area j
The calculation expression is:
wherein D is the voltage time sequence length;
and
respectively obtaining the voltage data of the user i at the moment d and the average value of the voltage data of the user i;
and
respectively obtaining voltage data of the transformer area j at the moment d and the average value of the voltage data of the transformer area j;
voltage similarity between user i and the e-th user in zone j
The calculation expression is:
in the formula (I), the compound is shown in the specification,
and
the voltage data of the user e in the station zone j at the time d and the average value of the voltage data of the user e are respectively.
Station area line loss rate fluctuation index B in step S3xComprises the following steps:
in the formula, T is the total days of the data to be analyzed, and x is the station number with the highest voltage similarity with the user i.
The method calculates the line loss rate of the distribution area to be analyzed by combining the electric quantity data, determines the abnormal distribution area of the line loss according to the line loss rate of the distribution area, and divides the abnormal distribution area into I-type abnormal distribution areas and II-type abnormal distribution areas; identifying abnormal users of the user variation relationship in the I-type abnormal distribution area by using a local abnormal factor algorithm, and finishing the adjustment of the user variation relationship in the I-type abnormal distribution area; and calculating the voltage similarity, judging the station area attribution condition of the abnormal user, and finishing the adjustment of the station change relationship of the II-type abnormal station area.
The same or similar reference numerals correspond to the same or similar parts;
the positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (8)
1. A transformer area user variation relation identification method based on electric quantity and voltage data analysis is characterized by comprising the following steps:
s1: calculating the line loss rate of the distribution area to be analyzed by combining the electric quantity data, and determining a line loss abnormal distribution area according to the distribution area line loss rate, wherein the distribution area is divided into I-type abnormal distribution areas and II-type abnormal distribution areas;
s2: identifying abnormal users of the user change relationship in the I-type abnormal distribution area by using an abnormal user detection algorithm, and finishing the adjustment of the user change relationship in the I-type abnormal distribution area;
s3: and calculating voltage similarity, integrating the voltage similarity, the line loss rate of the station area and the fluctuation index of the line loss rate of the station area, judging the station area attribution condition of the abnormal user, and finishing the user variation relation adjustment of the II-type abnormal station area.
2. The method for identifying station area subscriber relationship based on power and voltage data analysis of claim 1, wherein step S1 is executed in the jth station area
Antenna loss rate
The calculation expression is:
in the formula, NjThe number of users under the station zone j,
for users ith under zone j
Electricity consumption per day;
is a station area jth
Daily power supply.
3. The method for identifying station area subscriber relationship based on power and voltage data analysis of claim 2, wherein the abnormal subscriber detection algorithm of step S2 comprises:
a. each user corresponds to 1 voltage data sequence, the voltage data of all the users form a data set D, and the distance D (e, f) between the user e and the user f is calculated, wherein e belongs to D, and f belongs to D;
b. calculating the kth distance d of the user e by combining the distances dk(e) Wherein, if the distance d (e, f) between the users e and f is dk(e) Then, it needs to satisfy:
(ii) there are a minimum of k users
Satisfy the requirement of
；
② there are at most k-1 users
Satisfy the requirement of
；
c. Combining d and dkAnalyzing k neighbor N of user ek(e) Wherein N isk(e) Including a distance from user e not exceeding dk(e) All users of (1);
Nk(e)={X∈D|d(e,X)≤dk(e), X≠e}
d. calculating the k-th reachable distance from user f to user e
；
e. Binding to NkAnd
local reachable density lrd for user e is calculatedk(e)；
f. Computing the local outlier LOF of user ek(e)；
g. If LOFk(e)>2, the user e is an abnormal user, otherwise, the user e is a normal user;
and calculating local outlier factors of all users, and determining abnormal users of the platform area to be analyzed.
4. The method for identifying station area subscriber relationship based on power and voltage data analysis of claim 3, wherein in step S3, the voltage similarity R between user i and station area j isijThe calculation expression is:
in the formula, NjThe number of users in the distribution area j is;
the voltage similarity of the user i and the station area j is shown;
the voltage similarity between the user i and the e-th user in the station area j is obtained; e =1, …, Nj；
Wherein, the voltage similarity of the user i and the station area j
The calculation expression is:
wherein D is the voltage time sequence length;
and
respectively obtaining the voltage data of the user i at the moment d and the average value of the voltage data of the user i;
and
respectively obtaining voltage data of the transformer area j at the moment d and the average value of the voltage data of the transformer area j;
voltage similarity between user i and the e-th user in zone j
The calculation expression is:
in the formula (I), the compound is shown in the specification,
and
the voltage data of the user e in the station zone j at the time d and the average value of the voltage data of the user e are respectively.
5. The transformer area household variation relation identification method based on electric quantity and voltage data analysis of claim 4, wherein the transformer area line loss rate fluctuation index B in step S3xComprises the following steps:
in the formula, T is the total days of the data to be analyzed, and x is the station number with the highest voltage similarity with the user i.
6. The method for identifying station area subscriber relationship based on power and voltage data analysis of claim 5, wherein the specific process of step S1 is:
s101, counting the number of the areas to be analyzed to be N, counting the total days of the data to be analyzed to be T, and enabling j = 1;
s102, if j is less than or equal to N, calculating the loss rate A of the first antenna of the station area jjlAnd step S103 is executed, otherwise, step S2 is executed;
s103, order Ajmax=max{Aj1,…,Ajl,…,AjT}，Ajmin=min{Aj1,…,Ajl,…,AjT}，amaxIs the upper limit value of the line loss rate of the transformer area, aminThe lower limit value of the line loss rate of the transformer area is set;
s104, determining a line loss abnormal area by combining the line loss rate index A of the area;
s104a if Ajmin≥aminAnd A isjmax≤amaxIf j = j +1, executing step S102, otherwise, executing step S104 b;
s104b, if Ajmin＜aminMarking the station zone j as an I-type abnormal station zone, and bringing the station zone j into an I-type station zone set CILet j = j +1, perform step S102, otherwise, perform step S104 c;
s104c, marking the station zone j as a type II abnormal station zone, and bringing the station zone j into a type II station zone set CIILet j = j +1, step S102 is executed.
7. The method for identifying station area subscriber relationship based on power and voltage data analysis of claim 6, wherein the specific process of step S2 is:
s201. statistic set CIThe number of the middle station area is NILet m = 1;
s202, if m is less than or equal to NIThen to set CIPerforming abnormal user detection on the user in the mth station area, identifying the abnormal user in the station area, and executing the step S203, otherwise, executing the step S3;
and S203, removing the abnormal users from the station area m, bringing the abnormal users into an abnormal user set U, and executing the step S202 by enabling m = m + 1.
8. The method for identifying station area subscriber relationship based on power and voltage data analysis of claim 7, wherein the specific process of step S3 is:
s301, making i =1, and making the number of users in the statistical set U be MUStatistical set CIIThe number of the middle station area is NII；
S302, if i is less than or equal to MUThen calculate user i in set U and set CIIVoltage integrated similarity R of middle transformer area jij（j=1, …, NII) Executing step S303, otherwise, ending the identification process;
s303. order Rix=max{Ri1,…,Rij,…,RiNIIX is the station area number with the highest voltage similarity with the user i, and the line loss rate A of the user i in the front and back station areas x of the station area x is calculatedxAnd line loss rate fluctuation index Bx；
S304. if the user i is brought into the station area x, the line loss rate A is increasedxAnd line loss rate fluctuation index BxDecreasing, the user i belongs to the station zone x, let i = i +1, and step S302 is executed, otherwise, let i = i +1, and step S302 is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011543253.5A CN112288339B (en) | 2020-12-24 | 2020-12-24 | Transformer area household variation relation identification method based on electric quantity and voltage data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011543253.5A CN112288339B (en) | 2020-12-24 | 2020-12-24 | Transformer area household variation relation identification method based on electric quantity and voltage data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112288339A true CN112288339A (en) | 2021-01-29 |
| CN112288339B CN112288339B (en) | 2021-07-13 |
Family
ID=74425119
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011543253.5A Active CN112288339B (en) | 2020-12-24 | 2020-12-24 | Transformer area household variation relation identification method based on electric quantity and voltage data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112288339B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113033897A (en) * | 2021-03-26 | 2021-06-25 | 国网上海市电力公司 | Method for identifying station area subscriber variation relation based on electric quantity correlation of subscriber branch |
| CN113269397A (en) * | 2021-04-25 | 2021-08-17 | 云南电网有限责任公司信息中心 | Method for checking user variation relation of equipment association characteristics based on atlas technology |
| CN113744089A (en) * | 2021-11-08 | 2021-12-03 | 广东电网有限责任公司佛山供电局 | Transformer area household variable relation identification method and device |
| CN114091608A (en) * | 2021-11-24 | 2022-02-25 | 国网河南省电力公司许昌供电公司 | Data mining-based user variable relationship identification method |
| CN115423250A (en) * | 2022-07-28 | 2022-12-02 | 国网浙江省电力有限公司营销服务中心 | Transformer area household variation relation analysis method |
| CN115542062A (en) * | 2022-11-07 | 2022-12-30 | 北京志翔科技股份有限公司 | Identification method, device, equipment and storage medium for user variable relation abnormity |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102654544A (en) * | 2012-05-24 | 2012-09-05 | 江苏方天电力技术有限公司 | Automatic identification model and method for switchhouse user change relationship |
| US20170207818A1 (en) * | 2014-07-17 | 2017-07-20 | Jiangsu Linyang Energy Co., Ltd. | Method for differentiating power distribution areas and phases by using voltage characteristics |
| CN109034585A (en) * | 2018-07-18 | 2018-12-18 | 国网湖北省电力有限公司 | Become relationship distinguished number and system based on the family Tai Qu in power information and geographical location |
| CN111400371A (en) * | 2020-03-13 | 2020-07-10 | 上海电力大学 | Voltage correlation verification-based user variable relationship identification method |
| CN111505434A (en) * | 2020-04-10 | 2020-08-07 | 国网浙江余姚市供电有限公司 | Method for identifying fault hidden danger of low-voltage distribution meter box line and meter box |
-
2020
- 2020-12-24 CN CN202011543253.5A patent/CN112288339B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102654544A (en) * | 2012-05-24 | 2012-09-05 | 江苏方天电力技术有限公司 | Automatic identification model and method for switchhouse user change relationship |
| US20170207818A1 (en) * | 2014-07-17 | 2017-07-20 | Jiangsu Linyang Energy Co., Ltd. | Method for differentiating power distribution areas and phases by using voltage characteristics |
| CN109034585A (en) * | 2018-07-18 | 2018-12-18 | 国网湖北省电力有限公司 | Become relationship distinguished number and system based on the family Tai Qu in power information and geographical location |
| CN111400371A (en) * | 2020-03-13 | 2020-07-10 | 上海电力大学 | Voltage correlation verification-based user variable relationship identification method |
| CN111505434A (en) * | 2020-04-10 | 2020-08-07 | 国网浙江余姚市供电有限公司 | Method for identifying fault hidden danger of low-voltage distribution meter box line and meter box |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113033897A (en) * | 2021-03-26 | 2021-06-25 | 国网上海市电力公司 | Method for identifying station area subscriber variation relation based on electric quantity correlation of subscriber branch |
| CN113269397A (en) * | 2021-04-25 | 2021-08-17 | 云南电网有限责任公司信息中心 | Method for checking user variation relation of equipment association characteristics based on atlas technology |
| CN113744089A (en) * | 2021-11-08 | 2021-12-03 | 广东电网有限责任公司佛山供电局 | Transformer area household variable relation identification method and device |
| CN113744089B (en) * | 2021-11-08 | 2022-02-15 | 广东电网有限责任公司佛山供电局 | Transformer area household variable relation identification method and device |
| CN114091608A (en) * | 2021-11-24 | 2022-02-25 | 国网河南省电力公司许昌供电公司 | Data mining-based user variable relationship identification method |
| CN114091608B (en) * | 2021-11-24 | 2024-02-20 | 国网河南省电力公司许昌供电公司 | Household variable relation identification method based on data mining |
| CN115423250A (en) * | 2022-07-28 | 2022-12-02 | 国网浙江省电力有限公司营销服务中心 | Transformer area household variation relation analysis method |
| CN115542062A (en) * | 2022-11-07 | 2022-12-30 | 北京志翔科技股份有限公司 | Identification method, device, equipment and storage medium for user variable relation abnormity |
| CN115542062B (en) * | 2022-11-07 | 2024-01-09 | 北京志翔科技股份有限公司 | Method, device, equipment and storage medium for identifying user change relation abnormality |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112288339B (en) | 2021-07-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112288339B (en) | Transformer area household variation relation identification method based on electric quantity and voltage data analysis | |
| Xu et al. | Scalable planning for energy storage in energy and reserve markets | |
| Wang et al. | Secondary forecasting based on deviation analysis for short-term load forecasting | |
| Fang et al. | Data-driven-based stochastic robust optimization for a virtual power plant with multiple uncertainties | |
| CN110389267A (en) | A kind of low-voltage platform area platform family relation recognition method based on intelligent electric energy meter acquisition data | |
| Wu et al. | Optimal battery sizing for frequency regulation and energy arbitrage | |
| CN110034561B (en) | Wind power plant energy storage capacity optimization method based on cloud energy storage lease service | |
| CN111200293A (en) | Battery loss and distributed power grid battery energy storage day-ahead random scheduling method | |
| Xu et al. | Adaptive DE algorithm for novel energy control framework based on edge computing in IIoT applications | |
| CN105447082A (en) | Distributed clustering method for mass load curves | |
| Dong | A data-driven long-term dynamic rating estimating method for power transformers | |
| CN112270510B (en) | Individual and group voltage similarity-based user variation relationship identification method | |
| CN108989092A (en) | A kind of wireless network predicting method, electronic equipment and storage medium | |
| CN110837965A (en) | Method for rapidly distinguishing station area house change relations | |
| CN112636335B (en) | Low-voltage distribution network user transformer relation verification method and system based on voltage data decorrelation selection and correlation analysis | |
| CN109711621A (en) | The industrial park load forecasting method combined based on discriminant analysis and support vector machines | |
| CN113887809A (en) | Power distribution network supply and demand balance method, system, medium and computing equipment under double-carbon target | |
| Yang et al. | An electricity data cluster analysis method based on SAGA-FCM algorithm | |
| CN106384307A (en) | Differentiated evaluation method for county-area power distribution network plan | |
| Hamon et al. | Applying stochastic optimal power flow to power systems with large amounts of wind power and detailed stability limits | |
| CN111651448B (en) | Low-voltage topology identification method based on noise reduction differential evolution | |
| CN114828042A (en) | Base station system control method, device, equipment, base station system and storage medium | |
| CN112712104A (en) | User and transformer topological connection relation verification method based on outlier detection | |
| Chang et al. | Research of power consumers behavior using fuzzy c-means algorithm | |
| CN110336337A (en) | Optimize the energy source indoor deployment and power regulating method of radio frequency charging service profit |
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 |