Method for topology identification of active low-voltage distribution network based on voltage correlation and energy balance and computer medium

Abstract

The application discloses a kind of active low-voltage power distribution network topology identification method based on voltage correlation and energy balance, comprising the following steps: S1, obtaining power distribution transformer information and terminal user information;Power distribution transformer information includes the basic measurement data of power distribution transformer;Terminal user information includes the location information, voltage characteristics and power characteristics of user;S2, the first data set is obtained by carrying out preset processing to power distribution transformer information;The second data set is obtained by carrying out preset processing to terminal user information;S3, according to the first data set and the second data set, the voltage correlation principle is combined to divide user belonging relationship, obtain first result;According to the first data set and the second data set, the energy balance principle is combined to identify topological relationship, obtain second result;S4, first result is compared with second result, obtain first difference set;According to the first difference set, the topological relationship is modified, obtain third result, complete topological identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of active low-voltage power distribution network topology identification, and particularly relates to an active low-voltage power distribution network topology identification method based on voltage correlation and energy balance and a computer medium. BACKGROUND

[0002] The topology structure of a power distribution network refers to the topological connection relationship between terminal user electricity meters and distribution substation transformers, and is an important component of the power distribution network. Correctly identifying the topological relationship of an active low-voltage power distribution network is an important guarantee for carrying out short-circuit fault positioning, existing line expansion and reconstruction, and line loss reduction and other businesses. Therefore, accurate identification of the topology structure of an active low-voltage power distribution network plays a key role in realizing safe and stable operation of the power distribution network.

[0003] However, with the large number of distributed resources connected to the power distribution network at the present stage, the power flow of the power distribution network is reversed, the operating conditions are increasingly complex, the correlation of user voltage time series data is weakened, and the line loss is increased, which leads to an increase in errors of traditional power distribution network topology identification methods using voltage and power, and brings major challenges to actual power distribution network topology identification problems.

[0004] Therefore, there is an urgent need for a new technical solution to solve the technical problem of accurately identifying the topology structure of an active low-voltage power distribution network with a large number of distributed resources connected. SUMMARY

[0005] The present application provides an active low-voltage power distribution network topology identification method based on voltage correlation and energy balance and a computer medium to solve the technical problem of accurately identifying the topology structure of an active low-voltage power distribution network with a large number of distributed resources connected.

[0006] To achieve the above-mentioned purpose, the present application provides an active low-voltage power distribution network topology identification method based on voltage correlation and energy balance, comprising the following steps:

[0007] S1, obtaining power distribution transformer information and terminal user information; the power distribution transformer information includes basic measurement data of the power distribution transformer; the terminal user information includes location information, voltage characteristics and power characteristics of the user.

[0008] S2, performing a preset processing on the power distribution transformer information to obtain a first data set; performing a preset processing on the terminal user information to obtain a second data set.

[0009] S3, dividing the user belonging relationship according to the first data set and the second data set combined with the voltage correlation principle to obtain a first result; identifying the topological relationship according to the first data set and the second data set combined with the energy balance principle to obtain a second result.

[0010] S4. Compare the first result with the second result to obtain the first difference set; correct the topological relationship based on the first difference set to obtain the third result and complete the topological identification.

[0011] Preferably, S2 includes:

[0012] Let there be *a* distribution transformers and *b* end users in the area to be identified. Let the set of distribution transformers A and the set of end users B be:

[0013] A = {1, 2, ..., a}

[0014] B = {1, 2, ..., b}

[0015] The distribution transformer information is organized to obtain the first dataset, which contains the voltage time series data U of the α-th distribution transformer. Aα and power timing data P Aα for:

[0016] U Aα =[U Aα1 U Aα2 ,…,u Aαt ,…,u AαW ] T

[0017] P Aα =[p Aα1 ,p Aα2 ,…,p Aαt ,…,p AαW ] T

[0018] Among them, u Aαt Let p be the voltage of the α-th distribution transformer at time t; Aαt Let be the power of the α-th distribution transformer at time t; W be the data length; t and a be positive integers, t = 1, 2, ... W, a = 1, 2, ... a; T be the transpose operator.

[0019] The end-user information is organized to obtain the second dataset, which contains the voltage timing data U of the β-th end-user. Bβ and power timing data P Bβ for:

[0020] U Bβ =[u Bβ1 ,u Bβ2 ,…,u Bβt ,…,u BβW ] T

[0021] P Bβ =[p Bβ1 ,pBβ2 ..., p Bβt ..., p BβW ] T

[0022] wherein, u Bβt is the voltage of the βth end user at time t; p Bβt is the power of the βth end user at time t; β is a positive integer, β = 1, 2, … b.

[0023] Preferably, the step S3 of dividing the user relationship according to the voltage correlation principle based on the first data set and the second data set to obtain the first result comprises:

[0024] S31, performing voltage characteristic analysis on the low-voltage distribution network according to the first data set and the second data set to obtain a first voltage characteristic.

[0025] S32, calculating the voltage correlation between all distribution transformers and all end users according to the first voltage characteristic.

[0026] S33, obtaining the first result.

[0027] Preferably, the step S31 comprises:

[0028] Analyzing the variable relationship between voltage fluctuation and power, according to the voltage drop formula, the voltage relationship between the end user and the distribution transformer is obtained, which can be expressed by the following relationship:

[0029]

[0030] wherein, U0 is the outgoing voltage of the low-voltage side of the distribution transformer; U i is the voltage of the end user i; R j and X j are the resistance and reactance of the distribution branch j; P Lj and Q Lj are the active power and reactive power of the distribution branch j; i = 1, 2, … n, n is the number of end users.

[0031] In the transformer area, the reactive power transmitted by the line is small, and the reactive power loss of the low-voltage line is small, which can be ignored, so the above formula can be simplified as:

[0032]

[0033] wherein, P k is the active power injected into the end user k; P kloss is the line loss of the distribution branch k.

[0034] That is, the end users on the same line have similar voltage change trends, and the similarity increases as the electrical distance decreases.

[0035] Preferably, S32 includes:

[0036] S321. Voltage correlation is calculated using the Pearson correlation coefficient. The correlation coefficient r between the distribution transformer α and the end user β is... α,β This can be expressed by the following relation:

[0037]

[0038] Among them, u Aαt Let u be the voltage of the α-th distribution transformer at time t; Bβt Let be the voltage of the βth terminal user at time t.

[0039] Calculate the voltage correlation between the β-th end user and each element in the distribution transformer set A to obtain the voltage correlation set R of the β-th end user. β :

[0040] R β ={r 1,β ,r 2,β ,…,r a,β}

[0041] Let R β The largest element in is r α',β ,Right now:

[0042] r α',β =maxR β

[0043] Where α' is a positive integer between 1 and a.

[0044] Then it is assumed that the βth end user belongs to the α'th distribution transformer.

[0045] S322. Calculate the voltage correlation for each terminal user in the terminal user set B, and obtain the affiliation of each terminal user.

[0046] Preferably, in S3, the topological relationships are identified based on the first and second datasets combined with the energy balance principle, and the second result obtained includes:

[0047] Define a two-dimensional variable x α,β , used to indicate whether the β-th end user belongs to the α-th distribution transformer; when x α,β =1, indicating that the β-th end user belongs to the α-th distribution transformer; when x α,β =0, indicating that the β-th end user does not belong to the α-th distribution transformer; define vector X α Let X represent the topological relationship of the α-th distribution transformer, and define matrix X to represent the topological relationship of all distribution transformers:

[0048] X α =[x α,1 ,x α,2 ,…,x α,b ] T

[0049] X = [X1, X2, ..., X a ] T

[0050] Where a represents the number of distribution transformers in the area to be identified; b represents the number of end users in the area to be identified; α = 1, 2, ... a; β = 1, 2, ... b.

[0051] Define Q as a matrix formed by arranging a submatrices q along the diagonal:

[0052]

[0053] The Q matrix has a × W rows and a × b columns, where W is the length of the time series data.

[0054] Define ξ as the error vector, then considering the error, the power relationship P in the distribution network is:

[0055] P = QX T +ξ

[0056] Among them, X T Let X be the transpose of matrix X.

[0057] Therefore, the problem of solving the subordinate relationship between distribution transformers and end users is transformed into a quadratic programming problem, resulting in a quadratic programming model; solving the quadratic programming model yields the second result.

[0058] Preferred quadratic programming models include:

[0059] The objective function can be expressed by the following relation:

[0060] min||P-QX T || 2

[0061] Constraints can be expressed by the following relation:

[0062]

[0063] Preferably, S4 includes:

[0064] S41. Define the first result as N1 and the second result as N2. Compare the topological relationships of N1 and N2 to obtain the different parts as the first difference set N and the same parts as N3.

[0065] S42. Construct a target terminal user address information database based on existing terminal user data; divide the user location information in the target terminal user address information database into a preset number of levels according to a preset location level plan.

[0066] S43. Compare the first difference set N with the elements in the target terminal user address information database, retain the part that matches the topology connection relationship as the first part, and verify the part that does not match the topology connection relationship in the field to obtain the second part. Concatenate the first part, the second part and N3 to obtain the third result, and complete the topology identification.

[0067] The present invention also provides a computer medium, including a processor, a memory, and a computer program for implementing the method of the present invention.

[0068] The present invention has the following beneficial effects:

[0069] The present invention provides an active low-voltage distribution network topology identification method based on voltage correlation and energy balance. It analyzes user affiliation and distribution network topology based on the principles of voltage correlation and energy balance, respectively. The analysis results of the two schemes are then compared, analyzed, and corrected. This invention integrates the advantages of both schemes, overcoming the shortcomings of failing to determine affiliation due to overly similar voltage characteristics or power flow reversals. It optimizes the identification process and further improves identification accuracy. Through mutual verification of the two schemes, the method of the present invention can achieve accurate topology identification of active low-voltage distribution networks with a large number of distributed resources.

[0070] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0071] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0072] Figure 1 This is a flowchart illustrating a preferred embodiment of the present invention. Detailed Implementation

[0073] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0074] See Figure 1 In a preferred embodiment of the present invention, an active low-voltage distribution network topology identification method based on voltage correlation and energy balance is provided, comprising the following steps:

[0075] S1. Obtain information on distribution transformers and end users; information on distribution transformers includes basic measurement data of the distribution transformers; information on end users includes user location information, voltage characteristics, and power characteristics.

[0076] S2. The distribution transformer information is pre-processed to obtain the first dataset; the end-user information is pre-processed to obtain the second dataset. S2 specifically includes:

[0077] Let there be *a* distribution transformers and *b* end users in the area to be identified. Let the set of distribution transformers A and the set of end users B be:

[0078] A = {1, 2, ..., a}

[0079] B = {1, 2, ..., b}

[0080] The distribution transformer information is organized to obtain the first dataset, which contains the voltage time series data U of the α-th distribution transformer. Aα and power timing data P Aα for:

[0081] U Aα =[u Aα1 ,u Aα2 ,…,u Aαt ,…,u AαW ] T

[0082] P Aα =[p Aα1 ,p Aα2 ,…,p Aαt ,…,p AαW ] T

[0083] Among them, u Aαt Let p be the voltage of the α-th distribution transformer at time t; Aαt Let be the power of the α-th distribution transformer at time t; W is the data length; t and a are positive integers, t = 1, 2, ... W, a = 1, 2, ... a; T is the transpose operator;

[0084] The end-user information is organized to obtain the second dataset, which contains the voltage timing data U of the β-th end-user. Bβ and power timing data P Bβ for:

[0085] U Bβ =[u Bβ1 ,u Bβ2 ,…,u Bβt ,…,uBβW ] T

[0086] P Bβ =[p Bβ1 ,p Bβ2 ,…,p Bβt ,…,p BβW ] T

[0087] Among them, u Bβt p is the voltage of the β-th terminal user at time t; Bβt Let be the power of the β-th terminal user at time t; β is a positive integer, β = 1, 2, ..., b.

[0088] S3. Based on the first and second datasets and the principle of voltage correlation, classify the user affiliation to obtain the first result; based on the first and second datasets and the principle of energy balance, identify the topological relationship to obtain the second result.

[0089] (1) In S3, the user affiliation is divided based on the first and second datasets combined with the voltage correlation principle, resulting in the first result, which specifically includes:

[0090] S31. Analyze the voltage characteristics of the low-voltage distribution network based on the first and second datasets to obtain the first voltage characteristic. S31 specifically includes:

[0091] Analyzing the relationship between voltage fluctuations and power variations, and based on the voltage drop formula, the voltage relationship between end users and distribution transformers can be derived, expressed by the following formula:

[0092]

[0093] Where U0 is the low-voltage side output voltage of the distribution transformer; U i R is the voltage of end user i; j and X j P represents the resistance and reactance of distribution branch j; Lj and Q Lj Let i be the active power and reactive power of distribution branch j; i = 1, 2, ..., n, where n is the number of end users;

[0094] Within the transformer substation area, the reactive power transmitted by the lines is relatively small, and the reactive power loss of the low-voltage lines is also relatively small and can be ignored. Therefore, the above formula can be simplified to:

[0095]

[0096] Among them, P k To inject active power into end user k; P kloss For the line loss of distribution branch k;

[0097] In low-voltage distribution networks, the voltage of end users gradually decreases along the line, and end users with similar electrical distances exhibit similar voltage change trends. That is, end users on the same line have similar voltage change trends, and the similarity increases as the electrical distance decreases.

[0098] S32. Calculate the voltage correlation between all distribution transformers and all end users based on the first voltage characteristic. S32 specifically includes:

[0099] S321. Use the Pearson correlation coefficient to calculate voltage correlation. The Pearson correlation coefficient is a method to measure the similarity between two variables. Its value is between -1 and 1, where 0 represents no correlation between the two variables, 1 represents a perfect positive correlation between the two variables, and -1 represents a perfect negative correlation between the two variables. The correlation between the two variables can be judged by the magnitude of the absolute value of the Pearson correlation coefficient. The larger the absolute value, the stronger the correlation.

[0100] The correlation coefficient r between distribution transformer α and end user β α,β This can be expressed by the following relation:

[0101]

[0102] Among them, u Aαt Let u be the voltage of the α-th distribution transformer at time t; Bβt Let be the voltage of the β-th terminal user at time t;

[0103] Calculate the voltage correlation between the β-th end user and each element in the distribution transformer set A to obtain the voltage correlation set R of the β-th end user. β :

[0104] R β ={r 1,β ,r 2,β ,…,r a,β}

[0105] Let R β The largest element in is r α',β ,Right now:

[0106] r α',β =maxR β

[0107] Where α' is a positive integer between 1 and a;

[0108] Then it is assumed that the βth end user belongs to the α'th distribution transformer;

[0109] S322. Calculate the voltage correlation for each terminal user in the terminal user set B, and obtain the affiliation of each terminal user.

[0110] S33, the first result is obtained.

[0111] In a preferred embodiment of the present invention, the user affiliation is divided by the principle of voltage correlation, which makes the calculation of the corresponding part of the method simple and convenient.

[0112] (2) In S3, the topological relationships are identified based on the first and second datasets combined with the energy balance principle, and the second result is obtained, specifically including:

[0113] Define a two-dimensional variable x α,β , used to indicate whether the β-th end user belongs to the α-th distribution transformer; when x α,β =1, indicating that the β-th end user belongs to the α-th distribution transformer; when x α,β =0, indicating that the β-th end user does not belong to the α-th distribution transformer; define vector X α Let X represent the topological relationship of the α-th distribution transformer, and define matrix X to represent the topological relationship of all distribution transformers:

[0114] X α =[x α,1 ,x α,2 ,…,x α,b ] T

[0115] X = [X1, X2, ..., X a ] T

[0116] Where a represents the number of distribution transformers in the area to be identified; b represents the number of end users in the area to be identified; α = 1, 2, ... a; β = 1, 2, ... b;

[0117] Define Q as a matrix formed by arranging a submatrices q along the diagonal:

[0118]

[0119] The Q matrix has a × W rows and a × b columns, where W is the length of the time series data.

[0120] Define ξ as the error vector, then considering the error, the power relationship P in the distribution network is:

[0121] P = QX T +ξ

[0122] Among them, X T Let X be the transpose of matrix X;

[0123] Therefore, the problem of solving the subordinate relationship between distribution transformers and end users is transformed into a quadratic programming problem, resulting in a quadratic programming model; solving the quadratic programming model yields the second result.

[0124] In a preferred embodiment of the present invention, the quadratic programming model includes:

[0125] The objective function can be expressed by the following relation:

[0126] min||P-QX T || 2

[0127] Constraints can be expressed by the following relation:

[0128]

[0129] S4. Compare the first result with the second result to obtain the first difference set; based on the first difference set and the fuzzy identification method, correct the topological relationship to obtain the third result, thus completing the topological identification. S4 specifically includes:

[0130] S41. Define the first result as N1 and the second result as N2. Compare the topological relationships of N1 and N2 to obtain the different parts as the first difference set N and the same parts as N3.

[0131] S42. Construct a target terminal user address information database based on existing terminal user data; divide the user location information in the target terminal user address information database into a preset number of levels according to a preset location level plan.

[0132] In a preferred embodiment of this invention, a terminal user address information database is constructed based on terminal user data obtained from power distribution systems, metering systems, and geographic information systems (GIS). According to the "Specifications for Place Name and Address Data," electricity addresses are segmented using syntax. The electricity addresses are divided into 10 levels (Level 1–Level 10), with the following meanings: Level 1 represents provinces, autonomous regions, etc.; Level 2 represents cities, etc.; Level 3 represents counties, districts, etc.; Level 4 represents towns, townships, etc.; Level 5 represents streets, roads, avenues, etc.; Level 6 represents residential areas, gardens, flower gardens, etc.; Level 7 represents buildings, etc.; Level 8 represents unit numbers, etc.; Level 9 represents floor numbers, etc.; and Level 10 represents numbers, rooms, etc. The jurisdiction of each address level gradually decreases from Level 1 to Level 10.

[0133] S43. Compare the first difference set N with the elements in the target terminal user address information database, retain the part that matches the topology connection relationship as the first part, and verify the part that does not match the topology connection relationship in the field to obtain the second part. Concatenate the first part, the second part and N3 to obtain the third result, and complete the topology identification.

[0134] In a preferred embodiment of the present invention, by comparing the first result and the second result, then verifying the different parts against a constructed database, and then conducting on-site verification of the parts that cannot be matched in the verification results, the identification accuracy of the method of the present invention is further improved.

[0135] In a preferred embodiment of the present invention, a computer medium is also provided, including a processor, a memory, and a computer program for implementing the method of the present invention.

[0136] In summary, the active low-voltage distribution network topology identification method of the present invention, based on voltage correlation and energy balance, analyzes user affiliation and distribution network topology based on the principles of voltage correlation and energy balance, respectively. The analysis results of the two schemes are then compared, analyzed, and corrected, allowing the present invention to integrate the advantages of both schemes. This overcomes the shortcomings of being unable to determine affiliation due to overly similar single voltage characteristics or power flow reversals, optimizes the identification process, and further improves identification accuracy. Through mutual verification of the two schemes, the method of the present invention can achieve accurate topology identification of active low-voltage distribution networks with a large number of distributed resources.

[0137] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for topology identification of active low-voltage distribution networks based on voltage correlation and energy balance, characterized in that, Includes the following steps: S1. Obtain distribution transformer information and end-user information; the distribution transformer information includes basic measurement data of the distribution transformer; the end-user information includes the user's location information, voltage characteristics, and power characteristics; S2. Perform preset processing on the distribution transformer information to obtain a first dataset; perform preset processing on the terminal user information to obtain a second dataset; S3. Based on the first dataset and the second dataset, combined with the voltage correlation principle, classify the user affiliation to obtain the first result; Based on the first and second datasets and the principle of energy balance, the topological relationships are identified to obtain the second result; S4. Compare the first result with the second result to obtain the first set of differences; Based on the first set of differences, the topological relationships are corrected to obtain the third result, thus completing the topological identification. S4 includes: S41. Define the first result as N1 and the second result as N2; compare the topological relationship between N1 and N2 to obtain the different parts as the first difference set N and the same parts as N3; S42. Construct a target terminal user address information database based on existing terminal user data; divide the user location information in the target terminal user address information database into a preset number of levels according to a preset location level plan; S43. Compare the first difference set N with the elements in the target terminal user address information database, retain the part that matches the topology connection relationship as the first part, and perform on-site verification on the part that does not match the topology connection relationship to obtain the second part. Concatenate the first part, the second part and N3 to obtain the third result, and complete the topology identification.

2. The active low-voltage distribution network topology identification method based on voltage correlation and energy balance according to claim 1, characterized in that, S2 includes: Let there be *a* distribution transformers and *b* end users in the area to be identified. Let the set of distribution transformers A and the set of end users B be: A = {1, 2, ..., a} B = {1, 2, ..., b} The information of the distribution transformers is organized to obtain the first dataset, and the voltage time series data U of the α-th distribution transformer in the first dataset is obtained. Aα and power timing data P Aα for: IN Aα =[in Aα1 ,in Aα2 ,…,in Aαt ,…,in AαtW ] T P Aα =[p Aα1 ,p Aα2 ,…,p Aαt ,…,p AαW ] T Among them, u Aαt Let p be the voltage of the α-th distribution transformer at time t; Aαt Let be the power of the α-th distribution transformer at time t; W is the data length; t and a are positive integers, t = 1, 2, ... W, a = 1, 2, ... a; T is the transpose operator; The terminal user information is organized to obtain a second dataset, in which the voltage timing data U of the β-th terminal user is obtained. Bβ and power timing data P Bβ for: IN Bβ =[in Bβ1 ,in Bβ2 ,…,in Bβt ,…,in BβW ] T P Bβ =[p Bβ1 ,p Bβ2 ,…,p Bβt ,…,p BβW ] T Among them, u Bβt p is the voltage of the β-th terminal user at time t; Bβt Let be the power of the β-th terminal user at time t; β is a positive integer, β = 1, 2, ..., b.

3. The active low-voltage distribution network topology identification method based on voltage correlation and energy balance according to claim 2, characterized in that, In step S3, the user affiliation is determined based on the first and second datasets combined with the voltage correlation principle, resulting in the following first result: S31. Perform voltage characteristic analysis on the low-voltage distribution network based on the first dataset and the second dataset to obtain the first voltage characteristic; S32. Calculate the voltage correlation between all distribution transformers and all end users based on the first voltage characteristic; S33, The first result is obtained.

4. The active low-voltage distribution network topology identification method based on voltage correlation and energy balance according to claim 3, characterized in that, S31 includes: Analyzing the relationship between voltage fluctuations and power variations, and based on the voltage drop formula, the voltage relationship between end users and distribution transformers can be derived, expressed by the following formula: Where U0 is the low-voltage side output voltage of the distribution transformer; U i R is the voltage of end user i; j and X j P represents the resistance and reactance of distribution branch j; Lj and Q Lj Let i be the active power and reactive power of distribution branch j; i = 1, 2, ..., n, where n is the number of end users; Within the transformer substation area, the reactive power transmitted by the lines is relatively small, and the reactive power loss of the low-voltage lines is also relatively small and can be ignored. Therefore, the above formula can be simplified to: Among them, P k To inject active power into end user k; P kloss For the line loss of distribution branch k; That is, end users on the same line have similar voltage change trends, and the similarity increases as the electrical distance decreases.

5. The active low-voltage distribution network topology identification method based on voltage correlation and energy balance according to claim 4, characterized in that, S32 includes: S321. Voltage correlation is calculated using the Pearson correlation coefficient. The correlation coefficient r between the distribution transformer α and the end user β is... α,β This can be expressed by the following relation: Among them, u Aαt Let u be the voltage of the α-th distribution transformer at time t; Bβt Let be the voltage of the β-th terminal user at time t; Calculate the voltage correlation between the β-th end user and each element in the distribution transformer set A to obtain the voltage correlation set R of the β-th end user. β : R β ＝{r 1,β ,r 2,β ,…,r a,β} Let R β The largest element in is r α',β ,Right now: r α',β =maxR β Where α' is a positive integer between 1 and a; Then it is assumed that the βth end user belongs to the α'th distribution transformer; S322. Calculate the voltage correlation for each terminal user in the terminal user set B, and obtain the affiliation of each terminal user.

6. The active low-voltage distribution network topology identification method based on voltage correlation and energy balance according to claim 5, characterized in that, In step S3, the topological relationships are identified based on the first and second datasets combined with the energy balance principle, resulting in the second outcome, which includes: Define a two-dimensional variable x α,β , used to indicate whether the β-th end user belongs to the α-th distribution transformer; when x α,β =1, indicating that the β-th end user belongs to the α-th distribution transformer; when x α,β =0, indicating that the β-th end user does not belong to the α-th distribution transformer; define vector X α Let X represent the topological relationship of the α-th distribution transformer, and define matrix X to represent the topological relationship of all distribution transformers: X α =[x α,1 ,x α,2 ,…,x α,b ] T X=[X1,X2,…,X a ] T Where a represents the number of distribution transformers in the area to be identified; b represents the number of end users in the area to be identified; α = 1, 2, ... a; β = 1, 2, ... b; Define Q as a matrix formed by arranging a submatrices q along the diagonal: The Q matrix has a × W rows and a × b columns, where W is the length of the time series data. Define ξ as the error vector, then considering the error, the power relationship P in the distribution network is: P=QX T +ξ Among them, X T Let X be the transpose of matrix X; Therefore, the problem of solving the subordinate relationship between distribution transformers and end users is transformed into a quadratic programming problem, resulting in a quadratic programming model; solving the quadratic programming model yields the second result.

7. The active low-voltage distribution network topology identification method based on voltage correlation and energy balance according to claim 6, characterized in that, The quadratic programming model includes: The objective function can be expressed by the following relation: min||P-QX T || 2 Constraints can be expressed by the following relation:

8. A computer medium, characterized in that, It includes a processor, a memory, and a computer program that implements the method according to any one of claims 1 to 7.

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