CN112288339B - 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 PDF

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CN112288339B
CN112288339B CN202011543253.5A CN202011543253A CN112288339B CN 112288339 B CN112288339 B CN 112288339B CN 202011543253 A CN202011543253 A CN 202011543253A CN 112288339 B CN112288339 B CN 112288339B
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张殷
武利会
李新
王俊波
董镝
宋安琪
范心明
李国伟
唐琪
黎小龙
黄静
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Foshan Power Supply Bureau of Guangdong Power Grid Corp
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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

Transformer area household variation relation identification method based on electric quantity and voltage data analysis
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
Figure 354168DEST_PATH_IMAGE001
Antenna loss rate
Figure 68046DEST_PATH_IMAGE002
The calculation expression is:
Figure 880407DEST_PATH_IMAGE003
in the formula, NjThe number of users under the station zone j,
Figure 72354DEST_PATH_IMAGE004
for users ith under zone j
Figure 417884DEST_PATH_IMAGE001
Electricity consumption per day;
Figure 771505DEST_PATH_IMAGE005
is a station area jth
Figure 304118DEST_PATH_IMAGE001
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, all 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 d (e, f)k(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
Figure 34176DEST_PATH_IMAGE006
Satisfy the requirement of
Figure 968634DEST_PATH_IMAGE007
；
② there are at most k-1 users
Figure 227577DEST_PATH_IMAGE006
Satisfy the requirement of
Figure 513065DEST_PATH_IMAGE007
；
c. Combining d (e, f) and dk(e) Analyzing k neighbor N of user ek(e) Wherein N isk(e) Including a distance from user e not exceeding dk(e) All users X of (2);
Nk(e)={X∈D|d(e,X)≤dk(e), X≠e}
d. calculating the k-th reachable distance from user f to user e
Figure 303208DEST_PATH_IMAGE008
；
Figure 92173DEST_PATH_IMAGE009
e. Binding to Nk(e) And
Figure 787596DEST_PATH_IMAGE008
local reachable density lrd for user e is calculatedk(e)；
Figure 294801DEST_PATH_IMAGE010
f. Computing the local outlier LOF of user ek(e)；
Figure 101083DEST_PATH_IMAGE011
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:
Figure 10133DEST_PATH_IMAGE012
in the formula, NjThe number of users in the distribution area j is;
Figure 876458DEST_PATH_IMAGE013
the voltage similarity of the user i and the station area j is shown;
Figure 870959DEST_PATH_IMAGE014
for user i and under district j
Figure 480932DEST_PATH_IMAGE015
Voltage similarity of individual users;
Figure 244488DEST_PATH_IMAGE015
=1,…,Nj；
wherein, the voltage similarity of the user i and the station area j
Figure 281714DEST_PATH_IMAGE013
The calculation expression is:
Figure 264976DEST_PATH_IMAGE016
in the formula (I), the compound is shown in the specification,
Figure 413061DEST_PATH_IMAGE017
is the voltage timing length;
Figure 296703DEST_PATH_IMAGE018
and
Figure 504830DEST_PATH_IMAGE019
respectively at the time of user i
Figure 473924DEST_PATH_IMAGE020
And the average value of the voltage data of user i;
Figure 160120DEST_PATH_IMAGE021
and
Figure 163848DEST_PATH_IMAGE022
respectively, station j at time
Figure 542877DEST_PATH_IMAGE020
And the average value of the voltage data of the station zone j;
user i and zone j
Figure 733687DEST_PATH_IMAGE015
Voltage similarity of individual users
Figure 489153DEST_PATH_IMAGE014
The calculation expression is:
Figure 816229DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 130273DEST_PATH_IMAGE024
and
Figure 808379DEST_PATH_IMAGE025
respectively, under the station zone j
Figure 367537DEST_PATH_IMAGE015
The individual user is at the moment
Figure 80278DEST_PATH_IMAGE020
Voltage data and
Figure 535530DEST_PATH_IMAGE015
average voltage data for individual users.
Further, the station area line loss rate fluctuation index B in step S3xComprises the following steps:
Figure 700932DEST_PATH_IMAGE026
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 jth celllAntenna loss rate A ljAnd step S103 is executed, otherwise, step S2 is executed;
s103, order Ajmax=max{Aj1,…, A lj,…,AjT}，Ajmin=min{Aj1,…,A lj,…,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. 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.
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 jth celllAntenna loss rate, and executing step S103, otherwise, executing step S2;
s103, order Ajmax=max{Aj1,…,A lj,…,AjT}，Ajmin=min{Aj1,…, A lj,…,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 voltage with the user iThe station area number with the highest similarity is calculated, and the line loss rate A of the user i in the front and the 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.
Block jth of step S1lThe antenna loss rate calculation expression is as follows:
Figure 798201DEST_PATH_IMAGE003
in the formula, NjThe number of users under the station zone j,
Figure 365449DEST_PATH_IMAGE004
for users ith under zone j
Figure 257181DEST_PATH_IMAGE001
Electricity consumption per day;
Figure 909880DEST_PATH_IMAGE005
is a station area jth
Figure 810839DEST_PATH_IMAGE001
Daily power supply.
The abnormal user detection algorithm flow in step S2 is as follows:
a. each user corresponds to 1 voltage data sequence, all 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 d (e, f)k(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
Figure 468479DEST_PATH_IMAGE006
Satisfy the requirement of
Figure 796692DEST_PATH_IMAGE007
；
② there are at most k-1 users
Figure 671107DEST_PATH_IMAGE006
Satisfy the requirement of
Figure 375758DEST_PATH_IMAGE007
；
c. Combining d (e, f) and dk(e) Analyzing k neighbor N of user ek(e) Wherein N isk(e) Including a distance from user e not exceeding dk(e) All users X of (2);
Nk(e)={X∈D|d(e,X)≤dk(e), X≠e}
d. calculating the k-th reachable distance from user f to user e
Figure 652019DEST_PATH_IMAGE008
；
Figure 151133DEST_PATH_IMAGE009
e. Binding to Nk(e) And
Figure 512844DEST_PATH_IMAGE008
local reachable density lrd for user e is calculatedk(e)；
Figure 755607DEST_PATH_IMAGE010
f. Computing the local outlier LOF of user ek(e)；
Figure 886374DEST_PATH_IMAGE011
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:
Figure 556390DEST_PATH_IMAGE012
in the formula, NjThe number of users in the distribution area j is;
Figure 169511DEST_PATH_IMAGE013
the voltage similarity of the user i and the station area j is shown;
Figure 950386DEST_PATH_IMAGE014
for user i and under district j
Figure 201238DEST_PATH_IMAGE015
Voltage similarity of individual users;
Figure 307735DEST_PATH_IMAGE015
=1,…,Nj；
wherein, the voltage similarity of the user i and the station area j
Figure 644038DEST_PATH_IMAGE013
The calculation expression is:
Figure 228603DEST_PATH_IMAGE016
in the formula (I), the compound is shown in the specification,
Figure 333962DEST_PATH_IMAGE017
is the voltage timing length;
Figure 345781DEST_PATH_IMAGE018
and
Figure 903801DEST_PATH_IMAGE019
respectively at the time of user i
Figure 292057DEST_PATH_IMAGE020
And the average value of the voltage data of user i;
Figure 753388DEST_PATH_IMAGE021
and
Figure 670528DEST_PATH_IMAGE022
respectively, station j at time
Figure 247003DEST_PATH_IMAGE020
And the average value of the voltage data of the station zone j;
user i and zone j
Figure 907791DEST_PATH_IMAGE015
Voltage similarity of individual users
Figure 722164DEST_PATH_IMAGE014
The calculation expression is:
Figure 341364DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 873976DEST_PATH_IMAGE024
and
Figure 604035DEST_PATH_IMAGE025
respectively, under the station zone j
Figure 272914DEST_PATH_IMAGE015
The individual user is at the moment
Figure 63015DEST_PATH_IMAGE020
Voltage data and
Figure 82924DEST_PATH_IMAGE015
average voltage data for individual users.
Station area line loss rate fluctuation index B in step S3xComprises the following steps:
Figure 85515DEST_PATH_IMAGE026
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 (3)

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: calculating voltage similarity, integrating the voltage similarity, the line loss rate of the transformer area and the line loss rate fluctuation index of the transformer area, judging the transformer area attribution condition of the abnormal user, and finishing the user variation relation adjustment of the II-type abnormal transformer area;
number j of cells in step S1
Figure 717535DEST_PATH_IMAGE001
Antenna loss rate
Figure 436092DEST_PATH_IMAGE002
The calculation expression is:
Figure 155786DEST_PATH_IMAGE003
in the formula, NjThe number of users under the station zone j,
Figure 199966DEST_PATH_IMAGE004
for users ith under zone j
Figure 5111DEST_PATH_IMAGE001
Electricity consumption per day;
Figure 527359DEST_PATH_IMAGE005
is a station area jth
Figure 101560DEST_PATH_IMAGE001
The amount of daily power supply;
the abnormal user detection algorithm flow in step S2 is as follows:
a. each user corresponds to 1 voltage data sequence, all 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 d (e, f)k(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
Figure 316640DEST_PATH_IMAGE006
Satisfy the requirement of
Figure 77923DEST_PATH_IMAGE007
；
② there are at most k-1 users
Figure 669441DEST_PATH_IMAGE006
Satisfy the requirement of
Figure 596684DEST_PATH_IMAGE007
；
c. Combining d (e, f) and dk(e) Analyzing k neighbor N of user ek(e) Wherein N isk(e) Including a distance from user e not exceeding dk(e) All users X of (2);
Nk(e)={X∈D|d(e,X)≤dk(e), X≠e}
d. calculating the k-th reachable distance from user f to user e
Figure 982666DEST_PATH_IMAGE008
；
Figure 496824DEST_PATH_IMAGE009
e. Binding to Nk(e) And
Figure 626454DEST_PATH_IMAGE008
local reachable density lrd for user e is calculatedk(e)；
Figure 644088DEST_PATH_IMAGE010
f. Computing the local outlier LOF of user ek(e)；
Figure 466551DEST_PATH_IMAGE011
g. If LOFk(e)>2, the user e is an abnormal user, otherwise, the user e is a normal user;
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:
Figure 202426DEST_PATH_IMAGE012
in the formula, NjThe number of users in the distribution area j is;
Figure 870167DEST_PATH_IMAGE013
the voltage similarity of the user i and the station area j is shown;
Figure 273467DEST_PATH_IMAGE014
for user i and under district j
Figure 266831DEST_PATH_IMAGE015
Voltage similarity of individual users;
Figure 224422DEST_PATH_IMAGE015
=1,…,Nj；
wherein, the voltage similarity of the user i and the station area j
Figure 961434DEST_PATH_IMAGE013
The calculation expression is:
Figure 717775DEST_PATH_IMAGE016
in the formula (I), the compound is shown in the specification,
Figure 350882DEST_PATH_IMAGE017
is the voltage timing length;
Figure 326928DEST_PATH_IMAGE018
and
Figure 602052DEST_PATH_IMAGE019
respectively at the time of user i
Figure 448785DEST_PATH_IMAGE020
And the average value of the voltage data of user i;
Figure 783951DEST_PATH_IMAGE021
and
Figure 981715DEST_PATH_IMAGE022
respectively, station j at time
Figure 60529DEST_PATH_IMAGE020
And the average value of the voltage data of the station zone j;
user i and zone j
Figure 27348DEST_PATH_IMAGE015
Voltage similarity of individual users
Figure 2257DEST_PATH_IMAGE014
The calculation expression is:
Figure 952896DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 569822DEST_PATH_IMAGE024
and
Figure 155262DEST_PATH_IMAGE025
respectively, under the station zone j
Figure 301072DEST_PATH_IMAGE015
The individual user is at the moment
Figure 473428DEST_PATH_IMAGE020
Voltage data and
Figure 894045DEST_PATH_IMAGE015
average voltage data for individual users;
station area line loss rate fluctuation index B in step S3xComprises the following steps:
Figure 569877DEST_PATH_IMAGE026
in the formula, T is the total days of data to be analyzed, and x is the number of the station area with the highest voltage similarity with the user i;
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 jth celllAntenna loss rate A ljAnd step S103 is executed, otherwise, step S2 is executed;
s103, order Ajmax=max{Aj1,…, A lj,…,AjT}，Ajmin=min{Aj1,…,A lj,…,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.
2. The method for identifying station area subscriber relationship based on power and voltage data analysis of claim 1, 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.
3. The method for identifying station area subscriber relationship based on power and voltage data analysis of claim 2, 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 increasedxLine of sumLoss 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.
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