A low-voltage power grid topology big data identification method based on load mutation

Abstract

The application discloses a low-voltage power grid topology big data identification method based on load mutation, and aims at the problem that existing power grid data cannot analyze and judge local power grid use condition in detail, low-voltage power grid topology is easily caused, and a transformer working state cannot be automatically adjusted. The following scheme is provided, which comprises the following steps: S1: inputting a local area power grid use distribution map, recording a peak value and a minimum value of a local area power grid use peak period of a day, marking the peak value as M, and marking the minimum value as N; S2: using an average value formula (M+N) / 2 to obtain an intermediate value B, and recording the power grid use intermediate value; and S3: selecting a transformer for distribution work. The application can meet power grid power supply use condition by calculating a local power grid use peak value and a low value, and then equipping several transformers for conversion use, and can divide local areas one by one.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage power grid data identification, and in particular to a low-voltage power grid topology big data identification method based on load mutation. Background Art

[0002] For a long time, due to the continuous growth of electricity load and new users, the topology of the power supply network in the distribution area has been chaotic, and the topological relationship between distribution transformers, primary branch boxes, secondary branch boxes, and electricity meter boxes has often changed. Situations such as buried lines, line crossings, and lost construction drawings also occur from time to time. Problems such as burning due to severe overload of the distribution transformer load rate and damage to the distribution transformer due to severe imbalance of three-phase loads also occur. The patent document with publication (announcement) number: CN109034666B relates to a low-voltage power grid topology big data identification system and method based on load mutation, including a distribution transformer, M primary branch boxes connected in parallel to the output end of the distribution transformer, N secondary branch boxes connected in parallel to the output end of the primary branch box, and K electricity meter boxes connected in parallel to the output end of the secondary branch box; it also includes a distribution transformer monitoring terminal installed at the output end of the distribution transformer, a primary monitoring terminal installed at the input end of the primary branch box, a secondary monitoring terminal installed at the input end of the secondary branch box, and a meter box monitoring terminal installed at the input end of the electricity meter box.

[0003] The existing power grid data cannot be used to conduct detailed analysis and judgment of local power grid usage, which can easily cause confusion in the low-voltage power grid topology and fail to automatically adjust the working status of the transformer. Summary of the Invention

[0004] The purpose of the present invention is to provide a low-voltage power grid topology big data identification method based on load mutation, so as to solve the shortcomings of existing power grid data that cannot be analyzed in detail to judge the local power grid usage, easily cause low-voltage power grid topology chaos, and cannot automatically adjust the working status of the transformer.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for identifying low-voltage power grid topology based on load mutation big data includes the following steps:

[0007] Step S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N;

[0008] Step S2: Use the average value formula (M+N) / 2 to calculate the median value B, and record the median value of grid usage;

[0009] Step S3: Select a transformer for distribution, which specifically includes the following steps:

[0010] First, select a transformer with a rated power ≥ B;

[0011] Second, compare the relationship between B and M, calculate M / B and mark it as D. If D is a positive number, select D transformers to work;

[0012] Third, connect D+1 transformers in parallel through electromagnetic switches;

[0013] Step S4: Divide the local area into X areas, and record the grid usage of each area, recording the peak grid usage, low grid usage, and users in the local area;

[0014] Step S5: retrieve the peak value, low value and users of the power grid in X areas on a daily basis, each area having 10-15 households, and compare them with historical data;

[0015] Step S6: Eliminate areas with no changes and mark areas where any of the grid peak value, grid low value, and user usage has changed;

[0016] Step S7: manually retrieve local area data, and analyze and judge the local area's power grid peak value, power grid low value, and users;

[0017] Step S8: Control the operation of D+1 transformers to increase or decrease.

[0018] Wherein, in the step S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

[0019] Wherein, in the step S1, a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

[0020] In step S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

[0021] Wherein, in the step S3, the working power P of the intermediate value B is required to be less than or equal to the working power of the transformer.

[0022] Wherein, in step S3, the electromagnetic switch is used to remotely control the working state of the transformer.

[0023] Wherein, in the step S4, the local area is divided, and the power grid data is collected for each area, with the collection time interval being 1-4 days and the collection time being 30-60 minutes.

[0024] Wherein, in the step S5, a curve image is produced from the collected data and compared with historical data.

[0025] In the steps S6 and S7, the areas without changes are eliminated, and the data of the areas with data changes are retained, and then manually checked to determine the reasons for the changes in grid usage.

[0026] Wherein, in the step S8, if the grid usage decreases, the number of transformers in operation is reduced; if the grid usage increases, the number of transformers in operation is increased.

[0027] Compared with the existing technology, the beneficial effect of the present invention is that this solution calculates the peak and low values ​​of local power grid usage, and then equips several transformers for conversion, which can meet the power supply usage of the power grid. The local areas are divided one by one, and the power grid usage of each area is retrieved on a daily basis. It is then compared with historical data, and the changing patterns and reasons for the use of the local power grid are determined by automatic elimination and manual verification. The power grid data can be identified in detail, and the working status of the transformer can be easily switched, which is convenient for promotion and use in the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a low-voltage power grid topology big data identification method based on load mutation proposed by the present invention. DETAILED DESCRIPTION

[0029] It should be noted that, unless there is any conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0030] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0031] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when "comprises" and / or "includes" are used in this specification, it indicates the presence of features, steps, operations, parts or modules, components and / or combinations thereof.

[0032] Example 1

[0033] Reference Figure 1 This embodiment provides a method for identifying low-voltage power grid topology big data based on load mutation, comprising the following steps:

[0034] S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N;

[0035] S2: Use the average value formula (M+N) / 2 to calculate the median value B and record the median value of grid usage;

[0036] S3: Select a transformer for distribution work, which includes the following steps:

[0037] First, select a transformer with a rated power ≥ B;

[0038] Second, compare the relationship between B and M, calculate M / B and mark it as D. If D is a positive number, select D transformers to work;

[0039] Third, D+1 transformers are connected in parallel through electromagnetic switches.

[0040] S4: Divide the local area into X areas and record the grid usage of each area, record the peak value, low value and users of the local area grid,

[0041] S5: retrieve the peak value, minimum value, and number of users of the power grid in X areas (10 households in each area) on a daily basis and compare them with historical data;

[0042] S6: Eliminate areas with no changes and mark areas where any of the grid peak value, grid low value, and user usage has changed;

[0043] S7: Manually retrieve local area data and analyze and judge the local area's power grid peak value, power grid low value, and users;

[0044] S8: And control the operation of D+1 transformers to increase or decrease.

[0045] In this embodiment, in S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

[0046] In this embodiment, in S1 , a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

[0047] In this embodiment, in S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

[0048] In this embodiment, in S3 , the operating power P of the intermediate value B is required to be less than or equal to the operating power of the transformer.

[0049] In this embodiment, in S3, the electromagnetic switch is used to remotely control the working state of the transformer.

[0050] In this embodiment, in S4, the local area is divided, and power grid data is collected for each area. The collection time interval is 1 day, and the collection time is 30 minutes.

[0051] In this embodiment, in S5 , a curve image is generated from the collected data and compared with historical data.

[0052] In this embodiment, in S6 and S7, the areas without changes are eliminated, and the data of the areas with data changes are retained, and then manually checked to determine the reasons for the changes in grid usage.

[0053] In this embodiment, in S8, if the grid usage decreases, the number of transformers operating is reduced; if the grid usage increases, the number of transformers operating is increased.

[0054] Example 2

[0055] Reference Figure 1 A method for identifying low-voltage power grid topology based on load mutation big data includes the following steps:

[0056] S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N;

[0057] S2: Use the average value formula (M+N) / 2 to calculate the median value B and record the median value of grid usage;

[0058] S3: Select a transformer for distribution work, which includes the following steps:

[0059] First, select a transformer with a rated power ≥ B;

[0060] Second, compare the relationship between B and M, calculate M / B and mark it as D. If D is a positive number, select D transformers to work;

[0061] Third, D+1 transformers are connected in parallel through electromagnetic switches.

[0062] S4: Divide the local area into X areas and record the grid usage of each area, record the peak value, low value and users of the local area grid,

[0063] S5: retrieve the peak value, minimum value, and number of users of the power grid in X areas (each area has 11 households) on a daily basis and compare them with historical data;

[0064] S6: Eliminate areas with no changes and mark areas where any of the grid peak value, grid low value, and user usage has changed;

[0065] S7: Manually retrieve local area data and analyze and judge the local area's power grid peak value, power grid low value, and users;

[0066] S8: And control the operation of D+1 transformers to increase or decrease.

[0067] In this embodiment, in S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

[0068] In this embodiment, in S1 , a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

[0069] In this embodiment, in S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

[0070] In this embodiment, in S3 , the operating power P of the intermediate value B is required to be less than or equal to the operating power of the transformer.

[0071] In this embodiment, in S3, the electromagnetic switch is used to remotely control the working state of the transformer.

[0072] In this embodiment, in S4, the local area is divided, and power grid data is collected for each area. The collection time interval is 2 days, and the collection time is 40 minutes.

[0073] In this embodiment, in S5 , a curve image is generated from the collected data and compared with historical data.

[0074] In this embodiment, in S6 and S7, the areas without changes are eliminated, and the data of the areas with data changes are retained, and then manually checked to determine the reasons for the changes in grid usage.

[0075] In this embodiment, in S8, if the grid usage decreases, the number of transformers operating is reduced; if the grid usage increases, the number of transformers operating is increased.

[0076] Example 3

[0077] Reference Figure 1 A method for identifying low-voltage power grid topology based on load mutation big data includes the following steps:

[0078] S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N;

[0079] S2: Use the average value formula (M+N) / 2 to calculate the median value B and record the median value of grid usage;

[0080] S3: Select a transformer for distribution work, which includes the following steps:

[0081] First, select a transformer with a rated power ≥ B;

[0082] Second, compare the relationship between B and M, calculate M / B and mark it as D. If D is a positive number, select D transformers to work;

[0083] Third, D+1 transformers are connected in parallel through electromagnetic switches.

[0084] S4: Divide the local area into X areas and record the grid usage of each area, record the peak value, low value and users of the local area grid,

[0085] S5: retrieve the peak value, minimum value, and number of users of the power grid in X areas (12 households in each area) on a daily basis and compare them with historical data;

[0086] S6: Eliminate areas with no changes and mark areas where any of the grid peak value, grid low value, and user usage has changed;

[0087] S7: Manually retrieve local area data and analyze and judge the local area's power grid peak value, power grid low value, and users;

[0088] S8: And control the operation of D+1 transformers to increase or decrease.

[0089] In this embodiment, in S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

[0090] In this embodiment, in S1 , a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

[0091] In this embodiment, in S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

[0092] In this embodiment, in S3 , the operating power P of the intermediate value B is required to be less than or equal to the operating power of the transformer.

[0093] In this embodiment, in S3, the electromagnetic switch is used to remotely control the working state of the transformer.

[0094] In this embodiment, in S4, the local area is divided, and power grid data is collected for each area. The collection time interval is 3 days and the collection time is 50 minutes.

[0095] In this embodiment, in S5 , a curve image is generated from the collected data and compared with historical data.

[0096] In this embodiment, in S6 and S7, the areas without changes are eliminated, and the data of the areas with data changes are retained, and then manually checked to determine the reasons for the changes in grid usage.

[0097] In this embodiment, in S8, if the grid usage decreases, the number of transformers operating is reduced; if the grid usage increases, the number of transformers operating is increased.

[0098] Example 4

[0099] Reference Figure 1 A method for identifying low-voltage power grid topology based on load mutation big data includes the following steps:

[0100] S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N;

[0101] S2: Use the average value formula (M+N) / 2 to calculate the median value B and record the median value of grid usage;

[0102] S3: Select a transformer for distribution work, which includes the following steps:

[0103] First, select a transformer with a rated power ≥ B;

[0104] Second, compare the relationship between B and M, calculate M / B and mark it as D. If D is a positive number, select D transformers to work;

[0105] Third, D+1 transformers are connected in parallel through electromagnetic switches.

[0106] S4: Divide the local area into X areas and record the grid usage of each area, record the peak value, low value and users of the local area grid,

[0107] S5: retrieve the peak value, minimum value, and number of users of the power grid in X areas (each area has 14 households) on a daily basis and compare them with historical data;

[0108] S6: Eliminate areas with no changes and mark areas where any of the grid peak value, grid low value, and user usage has changed;

[0109] S7: Manually retrieve local area data and analyze and judge the local area's power grid peak value, power grid low value, and users;

[0110] S8: And control the operation of D+1 transformers to increase or decrease.

[0111] In this embodiment, in S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

[0112] In this embodiment, in S1 , a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

[0113] In this embodiment, in S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

[0114] In this embodiment, in S3 , the operating power P of the intermediate value B is required to be less than or equal to the operating power of the transformer.

[0115] In this embodiment, in S3, the electromagnetic switch is used to remotely control the working state of the transformer.

[0116] In this embodiment, in S4, the local area is divided, and power grid data is collected for each area. The collection time interval is 4 days and the collection time is 60 minutes.

[0117] In this embodiment, in S5 , a curve image is generated from the collected data and compared with historical data.

[0118] In this embodiment, in S6 and S7, the areas without changes are eliminated, and the data of the areas with data changes are retained, and then manually checked to determine the reasons for the changes in grid usage.

[0119] In this embodiment, in S8, if the grid usage decreases, the number of transformers operating is reduced; if the grid usage increases, the number of transformers operating is increased.

[0120] Example 5

[0121] Reference Figure 1 A method for identifying low-voltage power grid topology based on load mutation big data includes the following steps:

[0122] S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N;

[0123] S2: Use the average value formula (M+N) / 2 to calculate the median value B and record the median value of grid usage;

[0124] S3: Select a transformer for distribution work, which includes the following steps:

[0125] First, select a transformer with a rated power ≥ B;

[0126] Second, compare the relationship between B and M, calculate M / B and mark it as D. If D is a positive number, select D transformers to work;

[0127] Third, D+1 transformers are connected in parallel through electromagnetic switches.

[0128] S4: Divide the local area into X areas and record the grid usage of each area, record the peak value, low value and users of the local area grid,

[0129] S5: retrieve the peak value, minimum value, and number of users of the power grid in X areas (15 households in each area) on a daily basis and compare them with historical data;

[0130] S6: Eliminate areas with no changes and mark areas where any of the grid peak value, grid low value, and user usage has changed;

[0131] S7: Manually retrieve local area data and analyze and judge the local area's power grid peak value, power grid low value, and users;

[0132] S8: And control the operation of D+1 transformers to increase or decrease.

[0133] In this embodiment, in S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

[0134] In this embodiment, in S1 , a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

[0135] In this embodiment, in S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

[0136] In this embodiment, in S3 , the operating power P of the intermediate value B is required to be less than or equal to the operating power of the transformer.

[0137] In this embodiment, in S3, the electromagnetic switch is used to remotely control the working state of the transformer.

[0138] In this embodiment, in S4, the local area is divided, and power grid data is collected for each area. The collection time interval is 4 days and the collection time is 60 minutes.

[0139] In this embodiment, in S5 , a curve image is generated from the collected data and compared with historical data.

[0140] In this embodiment, in S6 and S7, the areas without changes are eliminated, and the data of the areas with data changes are retained, and then manually checked to determine the reasons for the changes in grid usage.

[0141] In this embodiment, in S8, if the grid usage decreases, the number of transformers operating is reduced; if the grid usage increases, the number of transformers operating is increased.

[0142] Comparative Example 1

[0143] A method for identifying low-voltage power grid topology based on load mutation big data includes the following steps:

[0144] S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N;

[0145] S2: Use the average value formula (M+N) / 2 to calculate the median value B and record the median value of grid usage;

[0146] S3: Select a transformer for distribution work, which includes the following steps:

[0147] First, select a transformer with a rated power ≥ B;

[0148] Second, compare the relationship between B and M, calculate M / B and mark it as D. If D is a positive number, select D transformers to work;

[0149] Third, D+1 transformers are connected in parallel through electromagnetic switches.

[0150] S4: retrieve the peak value, low value and user data of the local area power grid and compare them with historical data;

[0151] S5: Eliminate areas with no changes and mark areas where any of the grid peak value, grid low value, and user usage has changed;

[0152] S6: Manually retrieve local area data and analyze and judge the local area's power grid peak value, power grid low value, and users;

[0153] S7: And control the operation of D+1 transformers to increase or decrease.

[0154] In this embodiment, in S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

[0155] In this embodiment, in S1 , a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

[0156] In this embodiment, in S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

[0157] In this embodiment, in S3 , the operating power P of the intermediate value B is required to be less than or equal to the operating power of the transformer.

[0158] In this embodiment, in S3, the electromagnetic switch is used to remotely control the working state of the transformer.

[0159] In this embodiment, in S5 , a curve image is generated from the collected data and compared with historical data.

[0160] In this embodiment, in S6 and S7, the areas without changes are eliminated, and the data of the areas with data changes are retained, and then manually checked to determine the reasons for the changes in grid usage.

[0161] In this embodiment, in S8, if the grid usage decreases, the number of transformers operating is reduced; if the grid usage increases, the number of transformers operating is increased.

[0162] Comparative Example 2

[0163] A method for identifying low-voltage power grid topology based on load mutation big data includes the following steps:

[0164] S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N;

[0165] S2: Use the average value formula (M+N) / 2 to calculate the median value B and record the median value of grid usage;

[0166] S3: Select a transformer for distribution work, which includes the following steps:

[0167] First, select a transformer with a rated power ≥ B;

[0168] Second, compare the relationship between B and M, calculate M / B and mark it as D. If D is a positive number, select D transformers to work;

[0169] Third, D+1 transformers are connected in parallel through electromagnetic switches.

[0170] S4: Divide the local area into X areas and record the grid usage of each area, record the peak value, low value and users of the local area grid,

[0171] S5: retrieve the peak value, minimum value, and number of users of the power grid in X areas (10 households in each area) on a daily basis and compare them with historical data;

[0172] S6: Eliminate areas with no changes and mark areas where any of the grid peak value, grid low value, and user usage has changed;

[0173] S7: Manually retrieve local area data, and analyze and judge the local area's power grid peak value, power grid low value and users.

[0174] In this embodiment, in S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

[0175] In this embodiment, in S1 , a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

[0176] In this embodiment, in S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

[0177] In this embodiment, in S3 , the operating power P of the intermediate value B is required to be less than or equal to the operating power of the transformer.

[0178] In this embodiment, in S3, the electromagnetic switch is used to remotely control the working state of the transformer.

[0179] In this embodiment, in S4, the local area is divided, and power grid data is collected for each area. The collection time interval is 1 day, and the collection time is 30 minutes.

[0180] In this embodiment, in S5 , a curve image is generated from the collected data and compared with historical data.

[0181] In this embodiment, in S6 and S7, the areas without changes are eliminated, and the data of the areas with data changes are retained, and then manually checked to determine the reasons for the changes in grid usage.

[0182] Comparative Example 3

[0183] A method for identifying low-voltage power grid topology based on load mutation big data includes the following steps:

[0184] S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N;

[0185] S2: Use the average value formula (M+N) / 2 to calculate the median value B and record the median value of grid usage;

[0186] S3: Select a transformer for distribution work, which includes the following steps:

[0187] First, select a transformer with a rated power ≥ B;

[0188] Second, compare the relationship between B and M, calculate M / B and mark it as D. If D is a positive number, select D transformers to work;

[0189] Third, D+1 transformers are connected in parallel through electromagnetic switches.

[0190] S4: Divide the local area into X areas and record the grid usage of each area, record the peak value, low value and users of the local area grid,

[0191] S5: retrieve the peak value, minimum value, and number of users of the power grid in X areas (15 households in each area) on a daily basis and compare them with historical data;

[0192] S6: Manually retrieve local area data and analyze and judge the local area's power grid peak value, power grid low value, and users;

[0193] S7: And control the operation of D+1 transformers to increase or decrease.

[0194] In this embodiment, in S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

[0195] In this embodiment, in S1 , a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

[0196] In this embodiment, in S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

[0197] In this embodiment, in S3 , the operating power P of the intermediate value B is required to be less than or equal to the operating power of the transformer.

[0198] In this embodiment, in S3, the electromagnetic switch is used to remotely control the working state of the transformer.

[0199] In this embodiment, in S4, the local area is divided, and power grid data is collected for each area. The collection time interval is 4 days and the collection time is 60 minutes.

[0200] In this embodiment, in S5 , a curve image is generated from the collected data and compared with historical data.

[0201] In S7 of this embodiment, manual verification is performed to determine the reason for the change in grid usage.

[0202] In this embodiment, in S8, if the grid usage decreases, the number of transformers operating is reduced; if the grid usage increases, the number of transformers operating is increased.

[0203] With respect to the above-mentioned Examples 1-5 and Comparative Examples 1-3, the usage of the power grid data is analyzed to obtain the following data table:

[0204]

[0205] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for identifying low-voltage power grid topology big data based on load mutation, characterized in that: The following steps are involved: Step S1: Input the local area power grid usage distribution map, enter the peak value and minimum value of the local area power grid usage during the peak period of the day, mark the peak value as M, and mark the minimum value as N; Step S2: Use the average value formula (M+N) / 2 to calculate the median value B, and record the median value of grid usage; Step S3: Select a transformer for distribution, which specifically includes the following steps: First, select a transformer with a rated power ≥ B; Second, compare the relationship between B and M, calculate M / B and mark it as D, where D is an integer and D transformers are selected for operation; Third, connect D+1 transformers in parallel through electromagnetic switches; Step S4: Divide the local area into X areas, and record the grid usage of each area, recording the peak grid usage, low grid usage, and users in the local area; Step S5: retrieve the peak value, low value and users of the power grid in X areas on a daily basis, each area having 10-15 households, and compare them with historical data; Step S6: Eliminate the areas without changes and mark the areas where any of the grid peak value, grid low value and user usage has changed; Step S7: manually retrieve local area data, and analyze and judge the peak value, low value and users of the local area power grid; Step S8: Control the operation of D+1 transformers to increase or decrease; In step S8, if the grid usage decreases, the number of transformers working is reduced; if the grid usage increases, the number of transformers working is increased.

2. The method for identifying low-voltage power grid topology big data based on load mutation according to claim 1 is characterized in that: In step S1, the usage of the local regional power grid is planned in the form of an image, and the number of local users is noted.

3. The method for identifying low-voltage power grid topology big data based on load mutation according to claim 1 is characterized in that: In step S1, a graph of the usage change of the local power grid is drawn in the form of a curve table: the peak value is marked as M, the lowest value is marked as N, and the usage change time is noted.

4. The method for identifying low-voltage power grid topology big data based on load mutation according to claim 1 is characterized in that: In step S2, after determining the grid usage change graph of a region, the median value B of the grid usage is calculated.

5. The method for identifying low-voltage power grid topology big data based on load mutation according to claim 1 is characterized in that: In step S3, the electromagnetic switch is used to remotely control the working state of the transformer.

6. The method for identifying low-voltage power grid topology big data based on load mutation according to claim 1 is characterized in that: In step S4, the local area is divided, and power grid data is collected for each area. The collection time interval is 1-4 days, and the collection time is 30-60 minutes.

7. The method for identifying low-voltage power grid topology big data based on load mutation according to claim 1 is characterized in that: In step S5, a curve image is produced from the collected data and compared with historical data.

8. The method for identifying low-voltage power grid topology big data based on load mutation according to claim 1 is characterized in that: In the steps S6 and S7, the areas without changes are eliminated, and the data of the areas with changed data are retained, and then manually checked to determine the reasons for the changes in grid usage.

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