Low-voltage dynamic topology identification method based on GIS coordinate reduction calculation range

Abstract

The application discloses a low-voltage dynamic topology identification method based on GIS coordinate reduced calculation range, and the method is centered on a total table and diverges outward, and the total table and sub-table distance of a corresponding small range is obtained through total and sub-table similarity threshold judgment; thus, topology identification can be determined in a small range, and the calculation amount and calculation complexity are greatly reduced; through covering of a total and sub-table refinement module, the method has strong repetition operation copy property, and a small range can be continuously superposed, so that the purpose of large range coverage is achieved.

Description

Technical Field

[0001] This invention belongs to the fields of smart grid and smart electricity technology, and specifically relates to a low-voltage dynamic topology identification method based on GIS coordinates to narrow the calculation range. Background Technology

[0002] Since the beginning of the 21st century, advancements in science and technology and rising consumption levels have propelled China's real estate industry into a period of rapid growth. The increase in residential housing and electricity demand has led to problems such as disorganized record-keeping in both new and old communities, omissions in record-keeping, and incorrect construction. In the past, due to relatively underdeveloped technology, the topology diagrams for many low-voltage distribution areas were not yet mature, some even still in their rudimentary stages. When abnormalities, damage, faults, or even fires occurred in the lines, it was difficult to accurately pinpoint the problem, resulting in low efficiency and excessively long investigation times; this undoubtedly caused a significant waste of human, material, and financial resources.

[0003] Currently, methods such as signal characteristics, discrete subtopology sequences, fast topology identification, and clustering algorithms are used to solve problems in small residential areas and commercial land. However, the computation of algorithms for large-area residential areas results in a huge amount of Cartesian product computation, leading to the problem of computing power expansion, which undoubtedly brings great difficulties and challenges to the promotion of the technology.

[0004] Therefore, a low-voltage dynamic topology identification method with a narrower computational scope is needed to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a low-pressure dynamic topology identification method based on GIS coordinates to narrow the calculation range, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a low-pressure dynamic topology identification method based on GIS coordinates to reduce the calculation range, characterized by comprising the following steps:

[0007] 1. Obtain the master table GIS information and user table GIS information of the user data collection area, and calculate the distance between tables using Euclidean spatial distance:

[0008] r GD =R*arccos(cos(latitude) Tran )*cos(latitude user )*cos(longitude Tran -lonngitude user )+sin(longitude Tran )*sinn(lonngitude user ))

[0009] In the formula, r GD The distance between tables, longitude Tran latitude Tran This table contains latitude and longitude coordinate data for the master table, where the user table is a sub-table. user latitude user This contains the latitude and longitude coordinate data for the separate tables, where R is the Earth's radius;

[0010] II. Based on the Euclidean distance calculation formula, calculate the distance between each sub-table and each master table to obtain the master-sub-table distance matrix:

[0011]

[0012] In the formula, A m B represents the m-th sub-table. n Represents the nth summary table, |AB| mn Represents the m-th sub-table A m With the nth summary table B n The distance between them;

[0013] 3. Draw a main circle with the main table as the center and R as the radius. Then draw concentric circles with different radii outside the main circle. Record the sub-tables in different ranges in the set. The concentric circles form annulus. Count the number of sub-tables in different annulus.

[0014] IV. Using a topological recognition weak feature matching algorithm, obtain the similarity matrix between the main table and the sub-tables:

[0015]

[0016] In the formula, A m B represents the m-th sub-table. n S|AB| represents the nth summary table. mn Represents the m-th sub-table A m With the nth summary table B n The similarity score;

[0017] 5. Based on the similarity matrix between the main table and the sub-tables obtained in step 4, when the similarity is lower than the set threshold and is closest to the set threshold, the distance between the main table and the sub-table is the maximum distance between the main table and the sub-tables, Lr.

[0018] 6. Based on the maximum total table-to-sub-table distance Lr obtained in step 5, select the total table and sub-table sets within the range of the maximum total table-to-sub-table distance Lr from the total table-to-sub-table distance matrix obtained in step 2 for subsequent topology calculations to obtain the topology relationship.

[0019] Preferably, the weak feature matching algorithm for topology identification in step four includes the following steps:

[0020] 41. Calculate the similarity between the voltage curve data of the sub-meters and the master meter, and take the substation area and phase with the highest similarity as the actual affiliation of the user topology;

[0021] 42. Subsequently, the similarity values ​​between the main table and the user table are obtained by performing topological identification and matching between the sub-tables with different radii obtained in step three and the main table.

[0022] The application of voltage similarity to topological relationships has been validated.

[0023] Preferably, in step 42, the Pearson similarity algorithm is used to perform topological identification and matching between the sub-table and the main table. Using the Pearson similarity algorithm for topological identification and matching between the sub-table and the main table is a similarity algorithm that has been experimentally verified to be very effective.

[0024] Preferably, in step three, the radius R is the closest distance between the master table and the sub-table. Using the closest distance between the master table and the sub-table as the initial radius R avoids the influence of selecting a radius that is too large or too small.

[0025] Preferably, in step three, the different radii are R+100i, where i is a positive integer.

[0026] Preferably, in step one, the power supply station is used as the unit to obtain the master table GIS information and user table GIS information of the user collection archive for the substation area.

[0027] Invention Principle: This invention introduces Geographic Information System (GIS) technology, first identifying topological features in a small area, and then achieving large-area topological identification through small-area overlay; dynamically dividing a large area into multiple small areas reduces the computational load of the Cartesian product, thereby greatly reducing the time complexity of the program and increasing the likelihood of large-scale engineering projects being implemented.

[0028] Beneficial effects: The low-pressure dynamic topology identification method based on GIS coordinates to narrow the calculation range of the present invention radiates outward from the master table as the center, and obtains the distance between the master table and the sub-tables in the corresponding small range by judging the similarity threshold between the master table and the sub-tables; thus, the topology identification can be determined in a small range first, which greatly reduces the amount of calculation and computational complexity; by covering the master table and sub-table fine-grained modules, it has a strong repeatability of repeated operations, and can achieve the purpose of large-scale coverage by continuously superimposing small ranges. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating a low-pressure dynamic topology identification method based on GIS coordinates to narrow the calculation range.

[0030] Figure 2This is a schematic diagram of the main circle and concentric circles with the master table as the center;

[0031] Figure 3 This is a diagram showing the calculation relationship between the master table and the sub-tables. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0033] Please see Figure 1 The dynamic topology identification method for low-voltage distribution areas proposed in this invention, which uses GIS latitude and longitude conversion to reduce the calculation range, was implemented and its effectiveness was verified in a distribution area of ​​a power supply station in Jiangning, Nanjing. For details of the implementation and the results, please refer to the following steps.

[0034] Based on the proposed dynamic topology identification method for low-voltage distribution areas that uses GIS latitude and longitude conversion to reduce the calculation range, the method was implemented and its effectiveness verified in a distribution area of ​​a power supply station in Jiangning, Nanjing. The specific implementation and results are shown in the following steps.

[0035] Step 1: Taking the power supply station as the unit, obtain the GIS information of the transformer master table (Master1, Master2, Master3, ..., MasterN) and the GIS information of the user tables (Sub1, Sub2, Sub3, ..., SubN) within the user collection archive of that power supply station. Calculate the distance between the tables using Euclidean spatial distance.

[0036] The calculation formula is as follows:

[0037] r GD =R*arccos(cos(latitude) Tran )*cos(laatitude user )*cos(longitude Tran longitude user )+sin(longitude tran )*sin(longitude user ))

[0038] in:

[0039] r GD Distance between tables, in meters

[0040] longitude Tran latitudeTran Latitude and longitude coordinate data in the summary table

[0041] longitude user latitude user Latitude and longitude coordinate data in separate tables

[0042] R is the Earth's radius, taken as 6371 kilometers.

[0043] Step 2: Based on the Euclidean distance calculation formula, calculate the distance between each sub-table and each master table to obtain the master-sub-table distance matrix;

[0044]

[0045] Where Am represents the sub-table (Sub) and Bn represents the master table (Master).

[0046] |AB|mn represents the distance between the sub-table Am (Sub) and the master table Bn (Master).

[0047] Step 3: Based on the calculation results of Step 2, with the master table MasterN as the center, the radius R is the nearest distance between sub-tables in the master table; subsequently, the radius expands outward in the form of an arithmetic sequence to form concentric circles, with a common difference of 100 meters, and the radius set is [0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, ...]. Sub-tables within different ranges are recorded in a set, where the circular jump method is based on the common difference of 100 meters. The number of sub-tables in [R1, R2-R1, R3-R2, ...] within different circles is counted, as shown in the diagram. Figure 2 As shown.

[0048] Step 4: Employ a weak feature matching algorithm for low-voltage distribution area topology identification. The core of this algorithm is to calculate the similarity between the voltage curve data of individual meters and the main meter, and then take the distribution area and phase with the highest similarity as the actual topology affiliation for that user. Subsequently, topology identification matching (Pearson similarity algorithm) is performed between the sub-meters and the main meter within radii R1, R2-R1, ... to obtain the similarity values ​​between the main meter and individual meters. The voltage data matrix of the main meter and individual meters is as follows:

[0049]

[0050] The similarity matrix between the master table and the household table is as follows:

[0051]

[0052] Where Am represents the sub-table (Sub) and Bn represents the master table (Master).

[0053] S(|AB|mn) represents the similarity score between the sub-table Am (Sub) and the master table Bn (Master).

[0054] Step 5: Based on the similarity calculation results obtained in Step 4, a similarity threshold is determined. When the similarity is lower than the corresponding set threshold, a more reasonable maximum total table and sub-table distance Lr can be obtained.

[0055] Distance and similarity threshold settings:

[0056] [0-100 meters, 75%]

[0057] [100-200 meters, 80%]

[0058] [200-300 meters, 85%]

[0059] [300-400 meters, 90%]

[0060] [400-500 meters, 95%]

[0061] [500-600 meters, 90%]

[0062] [600-700 meters, 85%]

[0063] [700-800 meters, 80%]

[0064] [800-900 meters, 75%]

[0065] [900-1000 meters, 70%]

[0066] [Above 1000 meters, 70%]

[0067] Step 6: Based on the optimal distance Lr obtained in Step 5, select the set of total and sub-tables within a specific range of the optimal distance Lr from the set of total and sub-table distance matrices obtained in Step 2 for subsequent topology calculations, and finally confirm the topology relationship;

[0068] Summary: During the implementation and verification process, a distance of 400-500 meters was the optimal distance, meeting the set similarity threshold. Furthermore, in actual surveys, the identified total score table achieved a matching rate of over 95% with the actual files. This method effectively improves both computational complexity and identification performance compared to traditional blind selection-based range identification.

Claims

1. A low-pressure dynamic topology identification method based on GIS coordinates to narrow the calculation range, characterized in that, Includes the following steps:

1. Obtain the master table GIS information and user table GIS information of the user data collection area, and calculate the distance between tables using Euclidean spatial distance: , In the formula, The distance between tables. , This contains the latitude and longitude coordinate data for the master table, where the user table is the sub-table. , For the latitude and longitude coordinate data in the separate table, The radius of the Earth; II. Based on the Euclidean distance calculation formula, calculate the distance between each sub-table and each master table to obtain the master-sub-table distance matrix: , In the formula, This represents the m-th sub-table. This represents the nth summary table. This represents the m-th sub-table. With the nth summary table The distance between them; 3. Draw a main circle with the main table as the center and R' as the radius. Then draw concentric circles with different radii outside the main circle. Record the sub-tables in different ranges in the set. The concentric circles form annulus. Count the number of sub-tables in different annulus. In step three, the radius R' is the closest distance between the master table and the sub-table; In step three, the different radii are R'+100i, where i is a positive integer; IV. Using a topological recognition weak feature matching algorithm, obtain the similarity matrix between the main table and the sub-tables: , In the formula, This represents the m-th sub-table. This represents the nth summary table. This represents the m-th sub-table. With the nth summary table The similarity score; The topology identification weak feature matching algorithm described in step four includes the following steps:

41. Calculate the similarity between the voltage curve data of the sub-meter and the master meter, and take the sub-meter area and phase with the highest similarity as the actual belonging relationship of the sub-meter topology.

42. Subsequently, the similarity values ​​between the main table and the sub-tables are obtained by performing topological recognition matching between the sub-tables within concentric circles of different radii obtained in step three and the main table; In step 42, the Pearson similarity algorithm is used to perform topological identification and matching between the sub-table and the main table; 5. Based on the similarity matrix between the main table and the sub-tables obtained in step 4, when the similarity is lower than the set threshold and is closest to the set threshold, the distance between the main table and the sub-table is the maximum distance between the main table and the sub-tables, Lr.

6. Based on the maximum total table-to-sub-table distance Lr obtained in step 5, select the total table and sub-table sets within the range of the maximum total table-to-sub-table distance Lr from the total table-to-sub-table distance matrix obtained in step 2 for subsequent topology calculations to obtain the topology relationship.

2. The low-pressure dynamic topology identification method based on GIS coordinates to narrow the calculation range according to claim 1, characterized in that, In step one, the GIS information of the master table and the user table of the user collection files are obtained from the power supply station as a unit.

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