Method and device for identifying relationship between power distribution lines

Abstract

The application discloses a method and device for identifying relationship between distribution network lines and transformers. The method for identifying relationship between distribution network lines and transformers comprises the following steps: obtaining voltage data and power data of n users of L feeder lines and numbering in a preset order; calculating an n*n correlation coefficient matrix y according to the voltage data of the n users; grouping backbone users of the n*n correlation coefficient matrix to obtain L backbone groups; wherein one backbone group corresponds to one feeder line; if there are remaining users in addition to the users in the L backbone groups, determining the attribution of the remaining users according to the correlation degree of the users and the L backbone groups to obtain the result of identifying relationship between distribution network lines and transformers; and verifying the result by line loss rate according to the power data. The embodiment of the application realizes the identification of relationship between distribution network lines and transformers, which is beneficial to the fine management and line loss control of the distribution network.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method and apparatus for identifying the relationship between distribution network line transformers. Background Technology

[0002] As power system construction progresses, new construction and relocation projects are undertaken in distribution network lines, leading to changes in the relationships between distribution network lines and transformers. However, due to inadequate management, the correspondence between feeders and users remains unclear, i.e., the distribution network line-transformer relationship is ambiguous, which significantly impacts the refined management of the distribution network and the control of line losses. Summary of the Invention

[0003] This invention provides a method and apparatus for identifying the relationship between distribution network line transformers, thereby facilitating the refined management and line loss control of the distribution network.

[0004] According to one aspect of the present invention, a method for identifying the relationship between distribution network line transformers is provided, comprising:

[0005] Acquire voltage and power data of n users from L feeders and number them in a preset order; where L > 0 and n > 0.

[0006] Calculate the n×n correlation coefficient matrix y based on the voltage data of the n users;

[0007] The n×n correlation coefficient matrix is ​​divided into backbone user groups to obtain L backbone groups; where one backbone group corresponds to one feeder.

[0008] If there are remaining users besides those in the L backbone groups, the affiliation of the remaining users is determined based on the correlation between the users and the L backbone groups, so as to obtain the result of distribution network line transformer relationship identification.

[0009] The identification results are verified based on the power consumption data and the line loss rate.

[0010] Optionally, the method for calculating the n×n correlation coefficient matrix y includes:

[0011] Let y[i,i]=0, Where i ≤ n;

[0012] Let y[i,j]=y[j,i]=corr(i,j); where j≤n, and corr(i,j) represents the voltage correlation between user i and user j.

[0013] Optionally, the method for calculating the voltage correlation coefficient corr(i,j) between user i and user j includes:

[0014] corr(i,j)=max(correl(i_p,j_q)); where correl(s,t) represents the Wilson correlation coefficient between sequences s and t, and i_p represents the p-phase voltage data sequence of user i, p∈{A,B,C}.

[0015] Optionally, the calculation method for the L backbone groups includes:

[0016] Based on the n×n correlation coefficient matrix, calculate whether any two users are strongly correlated, and identify users who have at least one strongly correlated user as core users; wherein, the set of core users is the core user set setU';

[0017] The backbone user set setU' is divided into multiple backbone groups; wherein each backbone group consists of at least two backbone users; any user in a backbone group has another user in the same group that is strongly correlated with it; any two users in a backbone group are directly strongly correlated, or any two users in the same group form a user sequence with these two users, the user sequence being called a strong correlation chain, and any two adjacent users in the user sequence being strongly correlated; a backbone group can be defined as the union of all strong correlation chains containing one backbone user;

[0018] The set of the backbone groups is denoted as the backbone group set ListGroups, and the number of the backbone groups is denoted as the first group number nGroup;

[0019] The backbone group is adjusted according to the relationship between the first group number and the number of feeders L until the first group number nGroup is equal to the number of feeders L.

[0020] Optionally, the method for adjusting the backbone group based on the relationship between the first group number nGroup and the number of feeders L includes:

[0021] If the number of the first group nGroup is greater than the number of feeders L, then the backbone groups are merged.

[0022] If the number of the first group nGroup is less than the number of feeders L, then the backbone group is regrouped or split.

[0023] Optionally, the method for confirming the core user includes:

[0024] If y[i,j]≥pCorrBtwUsers, then users i and j are strongly correlated, and at least one of the strongly correlated users is a core user;

[0025] The backbone user set setU' is determined by setU' = {i|max(y[i])≥pCorrBtwUsers}; where y[i] represents the i-th row of the matrix y, and pCorrBtwUsers is a parameter representing the strong correlation standard;

[0026] The rule for dividing the backbone user set setU' into multiple backbone groups Group includes:

[0027] ; where pCorrBtwUsers is a parameter representing strong correlation.

[0028] Optionally, the step of adjusting the backbone group according to the relationship between the first number nGroup and the number of feeders L includes:

[0029] When nGroup < L, in the step of confirming the backbone users, increase the parameter pCorrBtwUsers representing the strong correlation standard, re-group the backbone user set until nGroup≥L;

[0030] Alternatively, when nGroup < L, take the backbone group Group corresponding to min{min{max{y[i,j]|j∈Group}|i∈Group}|Group∈ListGroups};

[0031] For the backbone group Group, take the users i and j corresponding to min{max{y[i,j]|j∈Group}|i∈Group};

[0032] Centered on i and j, divide the backbone group Group into two backbone groups, namely: [Group]_i = {i}∪{k|y[i,k]>y[j,k],k∈Group} and [Group]_j = {j}∪{k|y[j,k]>y[i,k],k∈Group}; and update the first number nGroup and the backbone group set ListGroups;

[0033] Repeat the steps of updating the first number nGroup and the backbone group set ListGroups until nGroup≥L.

[0034] Optionally, the step of adjusting the backbone group according to the relationship between the first number nGroup and the number of feeders L includes:

[0035] When nGroup > L, define the backbone group correlation function corrBtwGroups(A, B) = ∑(i ∈ A, j ∈ B, y[i, j] ≥ pCorrBtwGroups) y[i, j]; where A, B ∈ ListGroups, and pCorrBtwGroups is a parameter representing the strong correlation criterion;

[0036] Merge the two largest backbone groups in the backbone group correlation function corrBtwGroups(), and update the first number nGroup and the backbone group set ListGroups;

[0037] Repeat the step of updating the first number nGroup and the backbone group set ListGroups until nGroup = L.

[0038] Optionally, in the step of adjusting the backbone groups according to the relationship between the first number nGroup and the number of feeders L, it further includes:

[0039] If the backbone group correlation function corrBtwGroups(A, B) = 0, , B ∈ ListGroups and nGroup > L; then lower the parameter pCorrBtwGroups until , B ∈ ListGroups, .

[0040] Optionally, the method for determining the attribution of the remaining users according to the correlation between the user and the L backbone groups includes:

[0041] For and A ∈ ListGroups, define the user-backbone group correlation function corrBtwUserAndGroup(i, A) = ∑(j ∈ A, y[i, j] ≥ pCorrBtwUserAndGroup) y[i, j], where A ∈ ListGroups, and pCorrBtwUserAndGroup is a parameter representing the correlation criterion;

[0042] Calculate the correlation with each backbone group, and assign i to the affiliated group corresponding to the backbone group with the largest correlation; where the affiliated users in the affiliated group are the users that can be assigned among the remaining users;

[0043] If max(y[i]) < pCorrBtwUserAndGroup, then this user is an isolated user; where the isolated user set is denoted as GroupIslated, and the number of isolated users is denoted as nIslated.

[0044] Optionally, the method for verifying the identification result based on the power consumption data using the line loss rate includes:

[0045] Calculate the line loss rate of L feeders based on the power data;

[0046] If the line loss rate of the L feed lines is higher than the first threshold, then the identification result is the final result;

[0047] Otherwise, if LnIslated > pComplexity, where pComplexity is an auxiliary analysis calculation used to control the line loss rate, then adjust the parameters and re-analyze or end the analysis.

[0048] Optionally, if the line loss rate of the L feeders is less than or equal to a first threshold, the method further includes:

[0049] If L nIslated If ≤pComplexity, then the identification result using the power data is analyzed as follows:

[0050] By matching L of the aforementioned backbone groups with L of the aforementioned feeders, there are a total of L! possible feeder matching results;

[0051] For each feeder matching result, the nIslated isolated users are assigned to L backbone groups, for a total of L nIslated The result of the seed allocation;

[0052] For each of the aforementioned allocation results, the line loss rate of the L feeders is calculated, and the final grouping result is determined based on the calculated line loss rate.

[0053] Optionally, when calculating L! types of feeder matching results, the following is also included:

[0054] Obtain the relationship data between the feeder and the users; the relationship data includes the attribution relationship between the feeder and at least some users;

[0055] If L backbone groups and L feeders form a natural match; and the natural match is that the majority of users of each backbone group belong to the same feeder in the relational data, and the majority of users of different backbone groups do not belong to the same feeder in the relational data; then the line loss rate auxiliary analysis is performed on the natural match.

[0056] According to another aspect of the present invention, an apparatus for identifying the relationship between distribution network line transformers is provided, comprising:

[0057] The data acquisition module is used to acquire voltage and power data of n users from L feeders and number them in a preset order; where L>0 and n>0.

[0058] The correlation coefficient matrix calculation module is used to calculate an n×n correlation coefficient matrix y based on the voltage data of the n users;

[0059] The backbone user grouping module is used to group the n×n correlation coefficient matrix into backbone user groups, resulting in L backbone groups; where one backbone group corresponds to one feeder.

[0060] The secondary grouping module is used to determine the affiliation of the remaining users based on the correlation between the users and the L backbone groups if there are remaining users in addition to the users in the L backbone groups, so as to obtain the result of distribution network line transformer relationship identification.

[0061] The verification module is used to verify the identification results based on the power consumption data and the line loss rate.

[0062] This invention implements a two-level grouping of users based on voltage data and verifies the grouping based on power consumption data, thus realizing a method for identifying distribution network line-transformer relationships based on voltage and power consumption data. Specifically, users are grouped into two levels based on the characteristics of the voltage correlation coefficient matrix. The first-level grouping (e.g., using the concept of strong correlation chains) obtains highly reliable topology identification results for some users. The second-level grouping determines the affiliation of the remaining users based on the correlation between the users and the backbone user group. Then, power consumption data is used to verify the identification results through line loss rate-assisted analysis. Therefore, this invention realizes the identification of distribution network line-transformer relationships, which is beneficial for achieving refined management of the distribution network and line loss control.

[0063] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 A flowchart illustrating a method for identifying the relationship between distribution network line transformers according to an embodiment of the present invention;

[0066] Figure 2 A flowchart illustrating a method for calculating a backbone group according to an embodiment of the present invention;

[0067] Figure 3This is a flowchart illustrating a method for adjusting the backbone group based on the relationship between the number of the first group and the number of feeders, provided in an embodiment of the present invention.

[0068] Figure 4 A flowchart illustrating another method for adjusting the backbone group based on the relationship between the number of the first group and the number of feeders, provided in an embodiment of the present invention;

[0069] Figure 5 This is a flowchart illustrating a method for determining the affiliation of remaining users based on the correlation between a user and L backbone groups, as provided in an embodiment of the present invention.

[0070] Figure 6 This is a flowchart illustrating a method for verifying identification results based on power consumption data using line loss rate, as provided in an embodiment of the present invention.

[0071] Figure 7 This is a schematic diagram of a method for identifying the relationship between distribution network line transformers according to an embodiment of the present invention;

[0072] Figure 8 This is a schematic diagram of a device for identifying the relationship between distribution network lines, provided in an embodiment of the present invention. Detailed Implementation

[0073] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0074] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0075] This invention provides a method for identifying distribution network line-transformer relationships. This method can be executed by a device for identifying distribution network line-transformer relationships, which can be implemented in software and / or hardware. This method can be applied to the identification of distribution network topology relationships, i.e., the identification of the subordinate relationship (line-transformer relationship) between distribution network users and feeder lines; it can also be applied to the identification of low-voltage distribution network topology relationships, i.e., the identification of the subordinate relationship (transformer-user relationship) between low-voltage users and low-voltage distribution areas.

[0076] Figure 1 This is a flowchart illustrating a method for identifying the relationship between distribution network line transformers, provided in an embodiment of the present invention. See also... Figure 1 The method for identifying the relationship between distribution network line transformers includes the following steps:

[0077] S110. Obtain the voltage and power data of n users on L feeders and number them in a preset order; where L > 0 and n > 0.

[0078] Among them, voltage data can be collected from users (distribution network users) at a high density and over a long period of time. For example, a high density can be one data point every 15 minutes, and a long period of time can be 7 days, or 672 data points. Electricity data can be collected from long-term daily electricity consumption data. For example, a long period of time can be more than one month.

[0079] S120. Calculate the n×n correlation coefficient matrix y based on the voltage data of n users.

[0080] S130. Group the n×n correlation coefficient matrix into backbone user groups to obtain L backbone groups; where one backbone group corresponds to one feeder.

[0081] Among these, core users are grouped as a first-level group. Specifically, the concept of strong correlation chains is used to group core users based on the n×n correlation coefficient matrix.

[0082] S140. If there are remaining users besides those in the L backbone groups, the affiliation of the remaining users is determined based on the correlation between the users and the L backbone groups, so as to obtain the result of the distribution network line-transformer relationship identification.

[0083] The attribution of the remaining users is determined as a secondary group.

[0084] S150. Verify the identification results based on the power consumption data and the line loss rate.

[0085] This invention implements a two-level grouping of users based on voltage data and verifies the grouping based on power consumption data, thus realizing a method for identifying distribution network line-transformer relationships based on voltage and power consumption data. Specifically, users are grouped into two levels based on the characteristics of the voltage correlation coefficient matrix. The first-level grouping (e.g., using the concept of strong correlation chains) obtains highly reliable topology identification results for some users. The second-level grouping determines the affiliation of the remaining users based on the correlation between the users and the backbone user group. Then, power consumption data is used to verify the identification results through line loss rate-assisted analysis. Therefore, this invention realizes the identification of distribution network line-transformer relationships, which is beneficial for achieving refined management of the distribution network and line loss control.

[0086] Based on the above embodiments, optionally, the calculation method of S120 and the n×n correlation coefficient matrix y specifically includes the following steps:

[0087] Let y[i,i]=0, Where i ≤ n;

[0088] Let y[i,j]=y[j,i]=corr(i,j); where j≤n, and corr(i,j) represents the voltage correlation between user i and user j.

[0089] Optionally, corr(i,j) = max(correl(i_p,j_q)); where correl(s,t) represents the Wilson correlation coefficient between sequences s and t, i_p represents the p-phase voltage data sequence of user i, p∈{A,B,C}, and j_q represents the q-phase voltage data sequence of user j, q∈{A,B,C}. Specifically, corr(i,j) can be expressed as calculating the average value of the Wilson correlation coefficients of the three-phase voltage sequences for each of the six phase matches (ABC-ABC, ABC-BCA, ABC-CAB, ABC-CBA, ABC-BAC, ABC-ACB) of i and j, and taking the largest value as corr(i,j).

[0090] It should be noted that if there are too many errors or missing user data, making it impossible to calculate the accurate value of corr(i,j), corr(i,j) can be set to 0.

[0091] Figure 2 This is a flowchart illustrating a method for calculating a backbone group according to an embodiment of the present invention. See also... Figure 2 Based on the above embodiments, optionally, the calculation method for S130 and L backbone groups specifically includes the following steps:

[0092] S131. Calculate whether any two users are strongly correlated based on the n×n correlation coefficient matrix, and identify users with at least one strongly correlated user as backbone users; where the set of backbone users is the backbone user set setU'.

[0093] Optionally, if y[i,j]≥pCorrBtwUsers, then user i and j are strongly correlated, and users with at least one strongly correlated user are backbone users; the backbone user set setU' is determined by setU'={i|max(y[i])≥pCorrBtwUsers}; where y[i] represents the i-th row of matrix y, and pCorrBtwUsers is a parameter representing the strong correlation degree standard, generally taking more than 80%.

[0094] S132. Divide the backbone user set setU' into multiple backbone groups Group; where each backbone group consists of at least two backbone users; for any user in a backbone group, there is another user in the same group who is strongly correlated with it; any two users in a backbone group are directly strongly correlated, or for any two users in the same group, there is a user sequence formed by these two users, and the user sequence is called a strong correlation chain, and any two adjacent users in the user sequence are strongly correlated; a backbone group can be defined as the union of all strong correlation chains containing a backbone user.

[0095] Optionally, in this step, the following relationship can be used for limitation ; where pCorrBtwUsers is a parameter representing the strong correlation degree standard, which can be set as needed in practical applications, and generally pCorrBtwUsers≥80%.

[0096] S133. Denote the set of backbone groups as the backbone group set ListGroups, and denote the number of backbone groups as the first number nGroup.

[0097] S134. Adjust the backbone groups according to the relationship between the first number and the number of feeders L until the first number nGroup is equal to the number of feeders L.

[0098] Optionally, if the first number nGroup is greater than the number of feeders L, then merge the backbone groups; if the first number nGroup is less than the number of feeders L, then regroup the backbone groups, or split the backbone groups.

[0099] Generally speaking, nGroup≥L, however, there is also the case of nGroup<L. Based on the above embodiments, optionally, Figure 3 This is a flowchart of a method for adjusting backbone groups according to the relationship between the first number and the number of feeders provided by an embodiment of the present invention. Refer to Figure 3 Specifically, it includes the following steps:

[0100] S210. Determine whether nGroup = L; if so, end the adjustment; otherwise, execute S220.

[0101] S220. Determine whether nGroup < L; if so, execute S230; otherwise, execute S240.

[0102] S230. Re-execute S131 - S133, increase the parameter pCorrBtwUsers representing the strong correlation standard, re-group the backbone user set, and return to S210.

[0103] S240. Define the backbone group correlation function corrBtwGroups(A, B) = ∑(i ∈ A, j ∈ B, y[i, j] ≥ pCorrBtwGroups) y[i, j]; where A, B ∈ ListGroups, and pCorrBtwGroups is the parameter representing the strong correlation standard.

[0104] Among them, corrBtwGroups(A, B) can be defined using the Manhattan distance or the Euclidean distance, etc.

[0105] S250. Merge the two largest backbone groups in the backbone group correlation function corrBtwGroups(), and update the first number nGroup and the backbone group set ListGroups.

[0106] Optionally, in this step, if the backbone group correlation function corrBtwGroups(A, B) = 0, , B ∈ ListGroups and nGroup > L; then lower the parameter pCorrBtwGroups until , B ∈ ListGroups, ∋ corrBtwGroups(A, B) > 0.

[0107] Return to S210.

[0108] The process of adjusting the backbone group according to the relationship between the first number and the number of feeders is realized through S210 - S250.

[0109] Figure 4 It is a schematic flowchart of another method for adjusting the backbone group according to the relationship between the first number and the number of feeders provided by the embodiment of the present invention. Refer to Figure 4 , in another embodiment of the present invention, the adjustment method when nGroup < L, the method for adjusting the backbone group according to the relationship between the first number and the number of feeders specifically includes the following steps:

[0110] S310. Determine whether nGroup = L; if so, end the adjustment; otherwise, execute S320.

[0111] S320. Determine whether nGroup < L; if so, execute S330; otherwise, execute S360.

[0112] S330. Select the backbone group Group corresponding to min{min{max{y[i,j]|j∈Group}|i∈Group}|Group∈ListGroups}.

[0113] S340. For the backbone group Group, select the users i and j corresponding to min{max{y[i,j]|j∈Group}|i∈Group}.

[0114] S350. Centered on i and j, divide the backbone group Group into two backbone groups, namely: [Group]_i = {i} ∪ {k|y[i,k]>y[j,k],k∈Group} and [Group]_j = {j} ∪ {k|y[j,k]>y[i,k],k∈Group}; and update the first number nGroup and the backbone group set ListGroups.

[0115] Repeat the steps of updating the first number nGroup and the backbone group set ListGroups until nGroup = L; and return to S310.

[0116] S360. Define the backbone group correlation function corrBtwGroups(A,B)=∑(i∈A,j∈B,y[i,j]≥pCorrBtwGroups)y[i,j]; where A,B∈ListGroups and pCorrBtwGroups is a parameter representing the strong correlation standard.

[0117] Optionally, pCorrBtwGroups is generally taken as more than 40%.

[0118] S370. Merge the two largest backbone groups in the backbone group correlation function corrBtwGroups() and update the first number nGroup and the backbone group set ListGroups.

[0119] Optionally, in this step, if the backbone group correlation function corrBtwGroups(A,B)=0, , B∈ListGroups and nGroup > L; then lower the parameter pCorrBtwGroups until , B∈ListGroups, .

[0120] Return to S310.

[0121] The process of adjusting the backbone group according to the relationship between the first group of numbers and the number of feeder lines is implemented through S310 - S370.

[0122] Figure 5 It is a flowchart showing the method for determining the attribution of remaining users according to the relevance between users and L backbone groups provided by an embodiment of the present invention. Refer to Figure 5 Based on the above embodiments, optionally, S140 specifically includes the following steps:

[0123] S141. For and A ∈ ListGroups, define the relevance function between the user and the backbone group corrBtwUserAndGroup(i, A) = ∑(j ∈ A, y[i, j] ≥ pCorrBtwUserAndGroup)y[i, j], where A ∈ ListGroups, and pCorrBtwUserAndGroup is a parameter representing the relevance criterion.

[0124] Optionally, the relevance function between the user and the backbone group corrBtwUserAndGroup(i, A) is defined using the Manhattan distance, and the Euclidean distance or others can also be used. Generally, pCorrBtwUserAndGroup is taken to be more than 50%.

[0125] S142. Calculate the relevance between

[0126] and each backbone group, and assign i to the affiliated group corresponding to the backbone group with the maximum relevance; among them, the affiliated users in the affiliated group are the users that can be assigned among the remaining users.

[0127] Among them, the affiliated users are not in any strong relevance chain, but have a medium-strength (≥ pCorrBtwUserAndGroup) relevance with some users. Users with a relevance of 0 to all backbone groups cannot be assigned, and these users are weakly relevant to all other users.

[0127] S143. If max(y[i]) < pCorrBtwUserAndGroup, then this user is an isolated user; among them, the set of isolated users is denoted as GroupIslated, and the number of isolated users is denoted as nIslated.

[0128] The steps of determining the attribution of the remaining users according to the relevance between the user and L backbone groups are completed through S141 - S143.

[0129] Figure 6This is a flowchart illustrating a method for verifying identification results based on line loss rate using power consumption data, as provided in an embodiment of the present invention. (See also...) Figure 6 Based on the above embodiments, optionally, S150 specifically includes the following steps:

[0130] S151. Calculate the line loss rate of L feeders based on the power consumption data.

[0131] S152. Determine whether the line loss rate of L feeders is higher than the first threshold. If yes, execute S153; otherwise, execute S154.

[0132] The first threshold indicates a high overall line loss rate for the L feeders. If the overall line loss rate of the L feeders is high, no auxiliary line loss rate analysis is performed. The reason is that the basic idea behind auxiliary line loss rate analysis is to calculate the line loss rate of each feeder based on the identification results when the overall line loss rate is within a reasonable range. If the calculated line loss rates of each feeder are within a reasonable range and relatively stable, the identification results are considered correct. A high overall line loss rate is generally caused by missing data, incorrect records (missing users), high theoretical line loss rate (high line resistance), electricity theft, etc. When the overall line loss rate is high, there must be at least one feeder with a high calculated line loss rate, rendering the auxiliary line loss rate analysis meaningless.

[0133] The recognition results of S153, S130, and S140 are the final results.

[0134] S154. Determine if L nIslated If pComplexity is found, then execute S155; otherwise, execute S156.

[0135] Here, pComplexity is an auxiliary analytical calculation used to control the line loss rate. As those skilled in the art will understand, it can be set as needed in practical applications. Generally, pComplexity ≥ 1024, with the upper limit determined by the computing power and tolerable runtime.

[0136] S155. Adjust the parameters to re-analyze or end the analysis.

[0137] Specifically, if L nIslated If `pComplexity` indicates an analysis failure, the analysis needs to be re-analyzed or terminated. The adjusted parameters can be any parameters from the preceding steps.

[0138] S156. Using L backbone groups to match L feeders, there are a total of L! kinds of feeder matching results.

[0139] The purpose of matching is to specify a master table (gateway table) to calculate the line loss rate. To reduce computational load, if L backbone groups form a natural match with L feeders, that is, most members of each backbone group belong to the same feeder in the file, and most members of different backbone groups do not belong to the same feeder in the file, then only this natural match needs to be analyzed for the line loss rate, without needing to perform L! attempts.

[0140] S157. For each feeder matching result, assign nIslated isolated users to L backbone groups, for a total of L... nIslated The allocation results.

[0141] As the analysis above shows, the backbone group also has a corresponding subsidiary group. Therefore, it can also be considered that the nIslated isolated users are assigned to L backbone groups + subsidiary groups.

[0142] S158. For each allocation result, calculate the line loss rate of L feeders, and determine the final grouping result based on the calculated line loss rate.

[0143] Steps S151-S158 complete the verification of the identification results based on the power consumption data and line loss rate, thus completing the matching-allocation results. To reduce computational load, the analysis can be terminated after obtaining a "matching-allocation" result that ensures low and stable line loss rates for all L feeders. When nIslated=0, no allocation is needed; in this case, the line loss rate auxiliary analysis is only used to verify the grouping results.

[0144] Figure 7 This is a schematic diagram illustrating the logic of a method for identifying the relationship between distribution network line transformers, provided in an embodiment of the present invention. See also... Figure 7 Based on the above embodiments, optionally, the method for identifying the relationship between distribution network line transformers includes the following steps:

[0145] Acquire power consumption data (L master meters + n users) and voltage data (L feeders with n users in total);

[0146] Based on the voltage data (L feeders with a total of n users), calculate the user relationship coefficient matrix to obtain an n×n correlation coefficient matrix y; the specific implementation steps are as described in S120 of the aforementioned embodiments.

[0147] User classification is performed based on max(y[i]), with specific implementation steps referring to S130 in the aforementioned embodiments. The backbone user set is the set of users where max(y[i]) ≥ pCorrBtwUsers. The backbone user set is grouped into primary groups, determined by the strong correlation chains in the n×n correlation coefficient matrix y, resulting in backbone group 1, backbone group 2, ..., backbone group L. The value of nGroup is determined; if nGroup < L, backbone group splitting or grouping iteration is performed; if nGroup > L, backbone group merging is performed.

[0148] The subordinate user set is the set of users whose corrBtwUserAndGroup ≤ max(y[i]) < pCorrBtwUsers. The subordinate user set is then divided into two subgroups, the specific implementation steps of which refer to S140 in the aforementioned embodiments. The subgrouping is determined by the relevance of the user to the backbone group, resulting in Subgroup 1, Subgroup 2, ..., Subgroup L.

[0149] The isolated user set is the set of users whose max(y[i]) < corrBtwUserAndGroup. The grouping results are verified based on the line loss rate, and the affiliation of isolated users is determined. The specific implementation steps are as described in S150 of the aforementioned embodiments.

[0150] This invention also provides a device for identifying the relationship between distribution network line transformers. This device can be implemented in hardware and / or software and is used to execute the method for identifying the relationship between distribution network line transformers provided in any embodiment of this invention. It has the corresponding functional modules and beneficial effects of executing the method. Figure 8 This is a schematic diagram of a device for identifying the relationship between distribution network line transformers, provided in an embodiment of the present invention. See also... Figure 8 The device for identifying the relationship between the distribution network lines includes:

[0151] The data acquisition module 810 is used to acquire voltage and power data of n users from L feeders and number them in a preset order; where L>0, n>0.

[0152] The correlation coefficient matrix calculation module 820 is used to calculate the n×n correlation coefficient matrix y based on the voltage data of n users;

[0153] The backbone user grouping module 830 is used to group the n×n correlation coefficient matrix into backbone users, resulting in L backbone groups; where one backbone group corresponds to one feeder.

[0154] The secondary grouping module 840 is used to determine the affiliation of the remaining users based on the correlation between the users and the L backbone groups if there are remaining users in addition to the users in the L backbone groups, so as to obtain the result of distribution network line transformer relationship identification.

[0155] The verification module 850 is used to verify the identification results based on the power consumption data and the line loss rate.

[0156] This invention implements a two-level grouping of users based on voltage data and verifies the grouping based on power consumption data, thus realizing a device for identifying distribution network line-transformer relationships based on voltage and power consumption data. Specifically, users are grouped in two levels according to the characteristics of the voltage correlation coefficient matrix. The first-level grouping (e.g., using the concept of strong correlation chains) obtains highly reliable topology identification results for some users. The second-level grouping determines the affiliation of the remaining users based on the correlation between the users and the backbone user group. Then, power consumption data is used to verify the identification results through line loss rate-assisted analysis. Therefore, this invention realizes the identification of distribution network line-transformer relationships, thereby facilitating the refined management of the distribution network and the control of line losses.

[0157] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0158] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for identifying the relationship between distribution network line transformers, characterized in that, include: Acquire voltage and power data of n users from L feeders and number them in a preset order; where L > 0 and n > 0. Calculate the n×n correlation coefficient matrix y based on the voltage data of the n users; The n×n correlation coefficient matrix is ​​divided into backbone user groups to obtain L backbone groups; where one backbone group corresponds to one feeder. If there are remaining users besides those in the L backbone groups, the affiliation of the remaining users is determined based on the correlation between the users and the L backbone groups, so as to obtain the result of distribution network line transformer relationship identification. The identification results are verified based on the power consumption data using the line loss rate. The calculation method for the L backbone groups includes: Based on the n×n correlation coefficient matrix, calculate whether any two users are strongly correlated, and identify users who have at least one strongly correlated user as core users; wherein, the set of core users is the core user set setU'; The backbone user set setU' is divided into multiple backbone groups; wherein each backbone group consists of at least two backbone users; any user in a backbone group has another user in the same group that is strongly correlated with it; any two users in a backbone group are directly strongly correlated, or any two users in the same group form a user sequence with these two users, the user sequence being called a strong correlation chain, and any two adjacent users in the user sequence being strongly correlated; a backbone group can be defined as the union of all strong correlation chains containing one backbone user; The set of the backbone groups is denoted as the backbone group set ListGroups, and the number of the backbone groups is denoted as the first group number nGroup; The backbone group is adjusted according to the relationship between the first group number and the number of feeders L until the first group number nGroup is equal to the number of feeders L.

2. The method for identifying the relationship between distribution network line transformers according to claim 1, characterized in that, The method for calculating the n×n correlation coefficient matrix y includes: Let y[i,i]=0, Where i ≤ n; Let y[i,j]=y[j,i]=corr(i,j); where j≤n, and corr(i,j) represents the voltage correlation between user i and user j.

3. The method for identifying the relationship between distribution network line transformers according to claim 2, characterized in that, The method for calculating the voltage correlation corr(i,j) between user i and user j includes: corr(i,j)=max(correl(i_p,j_q)); where correl(s,t) represents the Wilson correlation coefficient between sequences s and t, i_p represents the p-phase voltage data sequence of user i, p∈{A,B,C}, and j_q represents the q-phase voltage data sequence of user j, q∈{A,B,C}.

4. The method for identifying the relationship between distribution network line transformers according to claim 1, characterized in that, The method for adjusting the backbone group based on the relationship between the first group number nGroup and the number of feeders L includes: If the number of the first group nGroup is greater than the number of feeders L, then the backbone groups are merged. If the number of the first group nGroup is less than the number of feeders L, then the backbone group is regrouped or split.

5. The method for identifying the relationship between distribution network line transformers according to claim 2, characterized in that, The method for identifying the core users includes: If y[i, j] ≥ pCorrBtwUsers, then users i and j are strongly correlated, and a user with at least one strongly correlated user is a backbone user; The backbone user set setU' is determined by setU' = {i|max(y[i]) ≥ pCorrBtwUsers}; where y[i] represents the i-th row of matrix y, and pCorrBtwUsers is a parameter representing the strong correlation degree standard; The rule for dividing the backbone user set setU' into multiple backbone groups Group includes: ; where pCorrBtwUsers is a parameter indicating strong correlation.

6. The method for identifying the relationship between distribution network line transformers according to claim 5, characterized in that, The step of adjusting the backbone group according to the relationship between the first number nGroup and the number of feeders L includes: When nGroup < L, in the step of confirming the backbone users, increase the parameter pCorrBtwUsers representing the strong correlation degree standard, and re-group the backbone user set until nGroup ≥ L; Alternatively, when nGroup < L, take the backbone group Group corresponding to min{min{max{y[i, j]|j ∈ Group}|i ∈ Group}|Group ∈ ListGroups}; For the backbone group Group, take the users i and j corresponding to min{max{y[i, j]|j ∈ Group}|i ∈ Group}; Centered on i and j, divide the backbone group Group into two backbone groups, namely: [Group]_i = {i} ∪ {k|y[i, k] > y[j, k], k ∈ Group} and [Group]_j = {j} ∪ {k|y[j, k] > y[i, k], k ∈ Group}; and update the first number nGroup and the backbone group set ListGroups; Repeat the steps of updating the first number nGroup and the backbone group set ListGroups until nGroup ≥ L.

7. The method for identifying the relationship between distribution network line transformers according to claim 5, characterized in that, The step of adjusting the backbone group according to the relationship between the first number nGroup and the number of feeders L includes: When nGroup > L, define the backbone group correlation function corrBtwGroups(A, B) = ∑(i ∈ A, j ∈ B, y[i, j] ≥ pCorrBtwGroups)y[i, j]; where A, B ∈ ListGroups, and pCorrBtwGroups is a parameter representing the strong correlation degree standard; Merge the two backbone groups with the largest correlation in the backbone group correlation function corrBtwGroups(), and update the first number nGroup and the backbone group set ListGroups; Repeat the steps of updating the first number nGroup and the backbone group set ListGroups until nGroup = L.

8. The method for identifying the relationship between distribution network line transformers according to claim 7, characterized in that, ​ If the correlation function of the backbone group, corrBtwGroups(A,B), is satisfied, If B ∈ ListGroups and nGroup > L, then decrease the parameter pCorrBtwGroups until... B∈ListGroups, .

9. The method for identifying the relationship between distribution network line transformers according to claim 5, characterized in that, ​ right And A∈ListGroups, define the relevance function between the user and the backbone group as corrBtwUserAndGroup(i,A)=∑(j∈A,y[i,j]≥pCorrBtwUserAndGroup)y[i,j], where A∈ListGroups and pCorrBtwUserAndGroup are parameters representing the relevance criteria; calculate The correlation between i and each of the core groups is determined, and i is assigned to the subordinate group corresponding to the core group with the highest correlation; wherein, the subordinate users in the subordinate group are the users that can be assigned from the remaining users; If max(y[i]) < pCorrBtwUserAndGroup, then this user is an isolated user; where the set of isolated users is denoted as GroupIslated, and the number of isolated users is denoted as nIslated.

10. The method for identifying the relationship between distribution network line transformers according to claim 9, characterized in that, The method for verifying the recognition result by the line loss rate according to the power consumption data includes: Calculating the line loss rates of L feeders according to the power consumption data; If the line loss rates of L feeders are higher than the first threshold, then the recognition result is the final result; Otherwise, if L nIslated >pComplexity, where pComplexity is an auxiliary analysis calculation used to control line loss rate. If the parameters are adjusted, the analysis is re-analyzed or terminated. Here, L is the number of feeders and nIslated is the number of isolated users.

11. The method for identifying the relationship between distribution network line transformers according to claim 10, characterized in that, In the case where the line loss rates of L feeders are less than or equal to the first threshold, it further includes: If L nIslated If ≤pComplexity, then the identification result using the power data is analyzed as follows: Using L backbone groups to match L feeders, there are L! possible feeder matching results; for each feeder matching result, nIslated isolated users are assigned to L backbone groups, resulting in L... nIslated The result of the seed allocation; For each allocation result, calculating the line loss rates of L feeders, and determining the final grouping result according to the calculation results of the line loss rates.

12. The method for identifying the relationship between distribution network line transformers according to claim 11, characterized in that, When calculating L! feeder matching results, it further includes: Obtaining the relationship data between the feeders and users; the relationship data includes the attribution relationship between the feeders and at least some users; If L backbone groups and L feeders form a natural match; the natural match means that the majority of users in each backbone group belong to the same feeder in the relationship data, and the majority of users in different backbone groups do not belong to the same feeder in the relationship data; then perform line loss rate assisted analysis on the natural match.

13. A device for identifying the relationship between distribution network lines, characterized in that, Execute the method for identifying the distribution network line-transformer relationship according to any one of claims 1-12. The device includes: A data acquisition module, configured to acquire voltage data and power consumption data of n users of L feeders, and number them in a preset order; where L > 0 and n > 0; A correlation coefficient matrix calculation module, configured to calculate an n×n correlation coefficient matrix y according to the voltage data of the n users; [[ID= ​ ​

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