Power distribution network medium and low voltage model connection auxiliary method based on regulation and control cloud

Abstract

The invention relates to a regulation and control cloud-based power distribution network medium and low voltage model run-through auxiliary method, which comprises the following steps of: 1, generating a feeder line relation comparison table according to scheduling and marketing information; 2, according to the feeder relation comparison table generated in the step 1, carrying out accurate matching between a special transformer and a user and between a public transformer and a transformer area according to each transformer substation and each feeder; 3, generating a visual chart according to an accurate matching result; 4, performing fuzzy matching on the distribution transformer which fails in accurate matching in the step 2; 5, according to historical power failure and recovery information of feeder lines where fuzzy matching common and special transformers are located, comparing the historical power failure and recovery information with voltage values of the ammeter before and after power failure and recovery, if the voltage value of the ammeter in a power failure time period is just reduced to zero, determining that a surface fuzzy matching result accords with reality, and realizing verification of a fuzzy matching relationship; and 6, carrying out manual verification on the distribution transformers which are not successfully subjected to fuzzy matching in the step 4 and the distribution transformers which are wrongly subjected to fuzzy matching verification in the step 5. According to the invention, the number of medium and low pressure models needing manual checking is greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power dispatching, and particularly relates to a method for assisting in the penetration of medium and low voltage models in a distribution network based on a regulation cloud. Background Art

[0002] With the continuous advancement of the construction of a new power system, the scale of the power grid is constantly expanding, and the loads on the user side are becoming more diverse, which undoubtedly places higher requirements on power dispatching. Against this background, the regulation cloud emerges as the times require. Through the construction and optimization of the regulation cloud, we can effectively integrate and efficiently utilize the software and hardware resources in the field of power grid dispatching, thereby enhancing the coordination of power grid dispatching operations. At the same time, relying on its powerful data processing and analysis capabilities, the regulation cloud can provide comprehensive and accurate auxiliary decision-making support for dispatching agencies at all levels. This not only helps dispatching agencies at all levels make more scientific and reasonable dispatching decisions, but also further improves the intelligent level of power grid regulation, promoting the development of the power grid towards a more intelligent and automated direction.

[0003] Currently, the main network equipment above 35kV has been connected to the regulation cloud, and the real-time forwarding of data from each substation and user station can be achieved. However, the work of connecting the 10kV and below distribution network to the cloud is still in progress, and the difficulties are mainly concentrated on the penetration of medium and low voltage distribution transformer models and data into the cloud. According to the business division, the marketing specialty is mainly connected to users. The account information of public and special transformers is the most comprehensive in the marketing system. At the same time, the corresponding meter data has all been connected to the power consumption collection system of the marketing department. The dispatching automation system has no collection means, and the two systems are independent of each other, resulting in poor data penetration. In this mode, the path for the medium and low voltage models in the distribution network to penetrate into the cloud is as Figure 1 shown.

[0004] The graphics and models of the regulation cloud platform are sent to the main dispatching automation system in the formats of G files and E files respectively, which also include the medium and low voltage models below 10kV. However, due to the inability to collect the data scheduling on the variable side of the distribution, it can only be sent through the power consumption collection system. However, this measurement information corresponds to the account information in the marketing system, and the account information of public and special transformers on the dispatching side is not consistent with that on the marketing side. Therefore, there is an urgent need for a method for assisting in the penetration of medium and low voltage models in the distribution network based on the regulation cloud to efficiently obtain the penetration situation of marketing and dispatching data through technical means, provide a basis for subsequent data governance, and provide strong support for the penetration of medium and low voltage data into the cloud. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention proposes a method for assisting in the penetration of medium and low voltage models in a distribution network based on a regulation cloud.

[0006] The above object of the present invention is achieved by the following technical solutions:

[0007] A method for assisting in the penetration of medium and low voltage models in a distribution network based on a control cloud. This method is based on the medium voltage model of the distribution network from the dispatching system and the low voltage model data obtained from the data center on the control cloud, and compares and visually displays the matching degree of the medium and low voltage models of the distribution network in real time. It uses the characteristics of the measured changes in electric energy meters during line outages to verify the matching results, so as to assist system operation and maintenance personnel in carrying out model penetration management work; it includes the following steps:

[0008] Step 1: According to dispatching and marketing information, taking each substation as a subunit, conduct feeder comparison based on the dispatching model, and generate a feeder relationship comparison table;

[0009] Step 2: According to the feeder relationship comparison table generated in Step 1, conduct precise matching of special transformers and users, and public transformers and substations according to each substation and each feeder; when performing matching, it is necessary to simultaneously meet the condition that "the affiliated feeder + household number / substation number" is consistent;

[0010] Step 3: Generate a visualization chart according to the precise matching results;

[0011] Step 4: For the distribution transformers that failed in the precise matching in Step 2, conduct fuzzy matching; the conditions for fuzzy matching are: as long as any one of the household number, substation number, station number, and name can be satisfied, the fuzzy matching result is recorded, and the distribution transformers with successful matching wait to enter the next round of power outage verification;

[0012] Step 5: According to the historical power outage and restoration information of the feeders where the fuzzy matching public and special transformers are located, compare it with the voltage values of the electric energy meters before and after the power outage and restoration. If the voltage value of the electric energy meter exactly drops to zero during the power outage period, it means that the fuzzy matching result conforms to the actual situation, and the verification of the fuzzy matching relationship is realized;

[0013] Step 6: Conduct manual verification for the distribution transformers that failed in the fuzzy matching in Step 4 and the distribution transformers that failed in the verification of the fuzzy matching in Step 5.

[0014] Moreover, Step 1 includes:

[0015] 1.3. Define the data sources. Among them, the dispatching side data comes from the E file regularly uploaded by the distribution automation master station to the control cloud. This file records all the primary equipment models of the dispatching, which also includes the distribution transformer name, distribution transformer type, household number / substation number, and capacity information; the user and substation information on the marketing side comes from the model transmitted by the data center to the control cloud, including the distribution transformer name, distribution transformer type, household number / substation number, and capacity information;

[0016] 1.4. Extract the respective feeder details from the dispatching - side data and the marketing - side data, compare and match the feeder details of the two through common fields to find the corresponding relationship. If there are still feeders on the dispatching side that cannot find a matching relationship after the comparison, it means that there are no users of this feeder on the marketing side. Feedback to the marketing side for rectification, and finally generate a feeder comparison table between dispatching and marketing.

[0017] Moreover, step 2 includes:

[0018] 2.1 Read the public and special transformer information in the marketing system

[0019] 2.1.1 Read the special transformer information in the marketing system, and extract the key columns including user number, affiliated feeder, user name, metering method, electricity meter asset number, electricity consumption category, etc. Uniformly process the user number, removing the first three digits '120'. Extract the rows where the electricity meter number is not empty to obtain the filtered special transformer data. According to the input line name st_linename, obtain all the data rows on this line where the special transformer electricity meter number is not empty, and calculate the number of special transformers yx_zb_no on this line.

[0020] 2.1.2 Read the public transformer information in the marketing system, and extract the key columns including sub - station area number, affiliated feeder, sub - station area name, marketing sub - station area identifier, etc. Extract the rows where the sub - station area meter is not empty to obtain the filtered public transformer data. According to the input line name st_linename, obtain all the data rows on this line where the public transformer electricity meter number is not empty, and calculate the number of public transformers yx_gb_no on this line.

[0021] 2.2 Sequentially obtain all the special transformers, public transformers, and other information of each line in the distribution automation system, and perform matching with the marketing electricity meter system. Specifically:

[0022] 2.2.1 First, read the distribution transformer information and the currently matched electricity meter information in the distribution automation system, including the distribution transformer number, line name, user number, sub - station area number, etc.

[0023] 2.2.2 Then, extract the information of public transformers, special transformers, and other types of distribution transformers, and screen out the corresponding distribution transformer information according to the line name for subsequent use.

[0024] 2.2.3 Then, perform matching according to the user number and sub - station area number, and associate the distribution transformers and electricity meters with the same user number and sub - station area number.

[0025] 2.2.4 Finally, write the matching results into an Excel file to generate tables of distribution transformer information for different categories of to-be-checked, to-be-confirmed, and mis-matched ones; these tables are for users to view and check for subsequent manual processing; among them, the distribution transformers with successful matching await the next step of graphical visualization, and the distribution transformers with failed matching enter the dispatching non-matching pool and marketing non-matching pool respectively; the distribution transformers lacking household numbers and substation numbers and those that cannot be matched due to unknown types directly enter the dispatching non-matching pool.

[0026] Moreover, step 3 includes:

[0027] 3.1 First, preprocess the data, including removing the percentage sign and converting it into numerical data;

[0028] 3.2 Then, sort the substations according to the total number of mis-matches, draw a bar chart of the number of mis-matched distribution transformers for each substation, and save it as a picture file;

[0029] 3.3 Then, classify the data according to the error types of whether there are household numbers / substation numbers, draw a bar chart classified by whether there are numbers, and save it as a picture file;

[0030] 3.4 Then, analyze and process the mis-matching categories and proportions at the feeder level, draw a pie chart of the mis-matching proportions of distribution transformers for each line, and save it as a picture file;

[0031] 3.5 Finally, output the statistical results to different worksheets in the Excel file "Current Situation of Matching between Distribution Transformers and Marketing Electric Meters in the Distribution and Dispatching System.xlsx" and save it.

[0032] Moreover, in step 5, verify the fuzzy matching relationship by combining historical power outage and restoration information. Six time nodes are set for the selection of the power outage and restoration discrimination moment: namely, the opening moment t1, the first whole-hour moment before t1 - T, the earlier moment before t1 - 2T, the closing moment t2, the first whole-hour moment after t2 + T, and the later moment after t2 + 2T; it includes the following steps:

[0033] 5.1 First, screen all NULL or 0 values in the "UA" column in the historical measurement section file for fuzzy matching, and perform clustering analysis according to the content in the "meter_id" column, that is, classify them into as many categories as there are independent ids, and each category includes all row data with the same id, and output the meter data after clustering analysis;

[0034] 5.2 Then, sequentially read the data of "10kV line abbreviation", "opening time", and "closing time" from each row in the "Power Outage Information Sorting Table";

[0035] 5.3 Then, traverse the electricity meter data generated in 5.1 to determine the corresponding data information for the six set sampling time points and the switching-on and -off time periods; if the data for all six sampling time points are not missing, prompt "The electricity meter is damaged and cannot be judged"; if the voltages at points P1 or P2 are not all 0 / NULL and there are continuous 0 values or NULL values between the times t1 and t2, and the voltages at points Q1 and Q2 are not all 0 / NULL, it indicates that the feeder to which the electricity meter belongs is the same as the currently power-outage feeder; at this time, compare whether the feeder where the distribution transformer matched by the electricity meter is located is the same as the currently power-outage feeder. If they are the same, it indicates that the fuzzy matching is correct; if not, it indicates that the fuzzy matching is incorrect.

[0036] The advantages and positive effects of the present invention are as follows:

[0037] This patent proposes an auxiliary method for the penetration of medium- and low-voltage models in a distribution network based on the regulation cloud. Through this method, it is possible to achieve the matching calculation and visual display of medium- and low-voltage models in a distribution network at three levels of "region - substation - feeder", as well as the analysis and visual display based on different types of matching errors. Through fuzzy matching and power outage verification procedures, the automatic verification of suspicious medium- and low-voltage model penetration relationships is realized. It greatly reduces the number of medium- and low-voltage models that need to be manually checked, facilitates the operation and maintenance personnel to promptly master the penetration effect of medium- and low-voltage models in the distribution network, and assists in carrying out the governance work of medium- and low-voltage model penetration in the regulation cloud distribution network. Brief Description of the Drawings

[0038] Figure 1 is the cloud path diagram of the existing medium- and low-voltage models in the distribution network;

[0039] Figure 2 is the flow chart of generating the feeder relationship comparison table of the present invention;

[0040] Figure 3 is the matching logic diagram based on a single feeder of the present invention;

[0041] Figure 4 is the power outage and restoration verification example diagram of the present invention;

[0042] Figure 5 is the power outage and restoration verification principle diagram of the present invention;

[0043] Figure 6 is the matching error ratio (at the regional level) between the distribution transformer and the marketing electricity meter in the distribution and regulation system of the embodiment of the present invention;

[0044] Figure 7 is the bar chart of the wrong matching situation (at the substation level) in the embodiment of the present invention;

[0045] Figure 8 is the table chart of the wrong matching situation (at the line level) in the embodiment of the present invention;

[0046] Figure 9It is a diagram showing the matching situation of distribution transformers for 21 outgoing lines of XX substation in the embodiment of the present invention (orange indicates non - matching);

[0047] Figure 10 It is a bar chart classified by whether there is a number in the embodiment of the present invention. Detailed implementation manners

[0048] The structure of the present invention will be further described below in conjunction with the accompanying drawings and through embodiments. It should be noted that this embodiment is narrative rather than restrictive.

[0049] An auxiliary method for the connection of medium - and low - voltage models in a distribution network based on the regulation cloud, please refer to Figure 2 - the figure. The inventive point is: based on the medium - voltage models (public transformers, special transformers) of the distribution network from the dispatching system and the low - voltage models (users, substations, metering points, electric energy meters, etc.) obtained from the data center on the regulation cloud, the method compares in real - time and visually displays the matching degree of the medium - and low - voltage models of the distribution network, and uses the characteristics of the change in the measurement of the electric energy meter during line power outage to verify the matching result, assisting the system operation and maintenance personnel to carry out the work of model connection governance. The present invention specifically includes the following steps:

[0050] Step 1: Generate a feeder relationship comparison table according to dispatching and marketing information

[0051] Since the feeder naming rules in the dispatching system and the marketing system are inconsistent, it is necessary to find the corresponding relationship between the two before carrying out the matching. The present invention takes each substation as a sub - unit and conducts feeder comparison based on the dispatching model. After traversing all substations, a final feeder relationship comparison table is generated, and the error reasons for the comparison failure can be given, which is convenient for subsequent governance. The specific process is as follows:

[0052] First, clarify the data sources. The data on the dispatching side comes from the E files regularly uploaded by the distribution automation master station to the control cloud. This file records all the primary equipment models of the dispatching, including information such as distribution transformer names, types, household numbers / unit numbers, and capacities. The user and substation area information on the marketing side comes from the models transmitted by the data center to the control cloud, which also includes the above information. Therefore, only the feeder details from the two methods need to be extracted and matched through common fields to find the corresponding relationships. Take a certain substation as an example: The line name on the dispatching side at Substation X is "Line Double Number + X11 Line", and the name on the marketing side is "X11". By extracting the numerical parts above, they are both 11, and thus can be matched. However, there is a problem in this link. If the marketing side fills in Y11 Line at Substation X, it can also pass the numerical comparison. So, a second judgment condition needs to be added, that is, when the numbers are the same, there must also be the same keywords to be considered a successful match. Otherwise, it will prompt that the substation affiliated to the marketing side is filled in incorrectly and feedback for rectification. If, after comparison, there are still feeders on the dispatching side that cannot find matching relationships, it means that there are no users of this feeder on the marketing side, and it should also feedback to the marketing side for rectification. Finally, a feeder comparison table between the dispatching and the marketing can be generated for the next step.

[0053] Step 2: Carry out precise matching of special transformers with users and public transformers with substations

[0054] According to the feeder relationship comparison table generated in Step 1, start precise matching for each substation and each feeder. When matching, it is necessary to simultaneously meet the condition that "the affiliated feeder + household number / unit number" is the same to ensure the accuracy of the matching.

[0055] The steps are as follows:

[0056] 2.1 Read the special and public transformer information in the marketing system

[0057] 2.1.1 Read the special transformer information in the marketing system, and extract the key columns including user number, affiliated feeder, user name, metering method, electricity meter asset number, electricity consumption category, etc.; uniformly process the user number, removing the first three digits '120'; extract the rows where the electricity meter number is not empty to obtain the filtered special transformer data; according to the input line name st_linename, obtain all the data rows on this line where the special transformer electricity meter number is not empty, and calculate the number of special transformers yx_zb_no on this line.

[0058] 2.1.2 Read the public transformer information in the marketing system, and extract the key columns including substation area number, affiliated feeder, substation area name, marketing substation area identifier, etc.; extract the rows where the substation area meter is not empty to obtain the filtered public transformer data; according to the input line name st_linename, obtain all the data rows on this line where the public transformer electricity meter number is not empty, and calculate the number of public transformers yx_gb_no on this line.

[0059] 2.2 Obtain all the special transformers, public transformers and other information of this line in the distribution automation system, and perform matching with the marketing meter system. Specifically:

[0060] 2.2.1 First, read the transformer information and the currently matched meter information in the distribution automation system, including the transformer number, line name, user number, substation area number, etc.;

[0061] 2.2.2 Then, extract the information of public transformers, special transformers and other types of transformers, and filter out the corresponding transformer information according to the line name for subsequent use.

[0062] 2.2.3 Then, perform matching according to the user number and substation area number, and associate the transformers and meters with the same user number and substation area number;

[0063] 2.2.4 Finally, write the matching results into an Excel file to generate transformer information tables of different categories such as to be checked, to be confirmed, and mis-matched. These tables can be viewed and checked by users for subsequent manual processing; among them, the successfully matched transformers await the next step of graphical visualization display, and the mis-matched transformers enter the dispatching mis-matching pool and the marketing mis-matching pool respectively. Here it is noted that other types of transformers refer to those without user numbers and substation area numbers, and those that cannot be matched due to unknown types will directly enter the dispatching mis-matching pool.

[0064] Step 3: Generate a visualization chart based on the accurate matching results

[0065] To facilitate tracing the source of mis-matching results, the matching results at the substation level and the feeder level are provided, including tables, pie charts, and Pareto charts. Through the visual display by substation and by feeder, it can be intuitively understood which areas have relatively concentrated mis-matching, so as to prioritize governance.

[0066] The implementation steps are as follows:

[0067] 3.1 First, preprocess the data, including removing the percentage sign and converting it into numerical data.

[0068] 3.2 Then, sort the substations according to the total number of mis-matched, draw a bar chart of the number of mis-matched transformers in each substation, and save it as a picture file.

[0069] 3.3 Then, classify the data according to the error types of whether there is a user number / substation area number, draw a bar chart classified by whether there is a number, and save it as a picture file.

[0070] 3.4 Then, analyze and process the mis-matching categories and proportions at the feeder level, draw a pie chart of the mis-matching proportion of transformers in each line, and save it as a picture file.

[0071] 3.5 Finally, output the statistical results to different worksheets in the Excel file "Status of Matching between Distribution Transformers and Marketing Electric Meters in the Dispatching and Coordination System.xlsx" and save it.

[0072] Step 4: Conduct fuzzy matching for the distribution transformers that failed in the first round of matching

[0073] To reduce the workload of manual verification, a second round of fuzzy matching is carried out in this link. That is, it is not necessary to simultaneously meet "subordinate feeder + household number / transformer number". The matching conditions are relaxed. As long as any one of the household number, transformer number, station number, or even the name can be satisfied, the fuzzy matching result is recorded. The distribution transformers with successful matching wait to enter the next round of power outage verification.

[0074] Step 5: Verify the fuzzy matching relationship in combination with historical power outage and restoration information

[0075] According to the historical power outage and restoration information of the public and special transformers in the fuzzy matching feeder, compare it with the voltage values of the electric meters before and after the power outage and restoration. If the voltage value of the electric meter exactly drops to zero during the power outage period, it indicates that the fuzzy matching result conforms to the actual situation.

[0076] To verify the fuzzy matching relationship in combination with historical power outage and restoration information, to reduce contingency, 6 time nodes are selected for the selection of the power outage and restoration discrimination moment: namely, the opening moment t1, the first whole hour moment before t1 - T, the earlier moment before t1 - 2T, the closing moment t2, the first whole hour moment after t2 + T, and the later moment after t2 + 2T; including the following steps:

[0077] 5.1 First, screen all NULL or 0 values in the "UA" column in the historical measurement section file of the fuzzy matching, and conduct clustering analysis according to the content of the "meter_id" column, that is, classify it into as many categories as there are independent ids. Each category includes all row data of the same id (the data that is NULL or 0 for one electric meter in a day), and output the electric meter data after clustering analysis.

[0078] 5.2 Then, traverse the data of "10kV line abbreviation", "opening time", and "closing time" in each row from the "Power Outage Information Sorting Table".

[0079] 5.3 Then, traverse the electricity meter data generated in 5.1 and judge the data information corresponding to the set six sampling time points and the switching-on and switching-off time periods. If the data of all six sampling time points are not missing (i.e., all are null / 0), then prompt "The electricity meter is damaged and cannot be judged". If the voltages at points P1 or P2 are not all 0 / NULL and there are continuous 0 values or NULL values between the time t1 and t2, and the voltages at points Q1 and Q2 are not all 0 / NULL, it indicates that the feeder to which the electricity meter belongs is the same as the currently power-off feeder. At this time, compare whether the feeder where the distribution transformer matched by the electricity meter fuzzily is the same as the currently power-off feeder. If they are the same, it means that the fuzzy matching is correct; if not, it means that the fuzzy matching is incorrect.

[0080] As Figure 9 shown, taking the A line of XX Substation as an example, at 19:53 on October 3, 2023, the zero-sequence overcurrent protection of switch A tripped. At 21:15 on October 3, 2023, the faulty cable was isolated and switch A was closed to energize. By observing the historical measurements of the electricity meter of user A, it can be found that during the power outage of line A, the voltage value of this user was empty, that is, no voltage could be collected. With the restoration of power supply, the electricity meter values were also restored one after another. It was verified that the matching relationship of user A was correct, and subsequent historical sampling and ratio conversion could be carried out to support cloud applications.

[0081] Step 6: For the distribution transformers with unsuccessful fuzzy matching in Step 4 and the distribution transformers with incorrect fuzzy matching verified in Step 5, conduct manual verification. It is necessary to organize forces for on-site verification. Through on-site verification, the actual "household-line-transformer" relationship can be determined and corrected in the marketing system and the dispatching system, and finally the penetration of the medium-voltage model in the distribution network can be achieved.

[0082] The method of the present invention has been verified for feasibility in the regulation cloud platform of a certain city and can be popularized and applied to the regulation cloud platforms of other provincial companies. The visualization display interface is as Figures 6 - 10 shown

[0083] Although the embodiments and drawings of the present invention are disclosed for the purpose of illustration, those skilled in the art can understand that various substitutions, changes and modifications are possible without departing from the spirit of the present invention and the appended claims. Therefore, the scope of the present invention is not limited to the content disclosed in the embodiments and drawings.

Claims

1. A method for assisting the penetration of medium and low voltage models in a distribution network based on a regulation cloud, characterized in that: This method is based on the medium-voltage model of the distribution network from the dispatching system on the regulation cloud and the low-voltage model data obtained from the data center, which are compared in real time and the matching degree of the medium- and low-voltage models of the distribution network is visually displayed. By using the characteristics of the change in the electricity meter measurement during line power outage, the matching result is verified to assist system operation and maintenance personnel in carrying out model penetration management work; it includes the following steps: Step 1: According to the dispatching and marketing information, taking each substation as a subunit, conduct feeder comparison based on the dispatching model, and generate a feeder relationship comparison table; Step 2: According to the feeder relationship comparison table generated in Step 1, conduct precise matching of special transformers and users, and public transformers and distribution areas for each substation and each feeder; when performing the matching, it is necessary to meet the condition that "the affiliated feeder + customer number / area number" is consistent at the same time; Step 3: Generate a visualization chart according to the precise matching result; Step 4: For the distribution transformers that failed in the precise matching in Step 2, conduct fuzzy matching; the condition for fuzzy matching is: as long as any one of the customer number, area number, substation number, and name can be satisfied, the fuzzy matching result is recorded, and the distribution transformers with successful matching wait to enter the next round of power outage verification; Step 5: According to the historical power outage and restoration information of the feeders where the public and special transformers are located in the fuzzy matching, compare it with the voltage value of the electricity meter before and after the power outage and restoration. If the voltage value of the electricity meter exactly drops to zero during the power outage period, it indicates that the fuzzy matching result conforms to the actual situation, and the verification of the fuzzy matching relationship is realized; Step 6: Conduct manual verification for the distribution transformers that failed in the fuzzy matching in Step 4 and the distribution transformers that verified the fuzzy matching error in Step 5.

2. The method for assisting in the penetration of medium and low voltage models in a distribution network based on a regulation cloud according to claim 1, wherein: Step 1 includes: 1.

1. Define the data sources. Among them, the dispatching-side data comes from the E file regularly uploaded by the distribution automation main station to the regulation cloud. This file records all the primary equipment models of the dispatching, which also includes the distribution transformer name, distribution transformer type, customer number / area number, capacity information; the user and distribution area information on the marketing side comes from the model transmitted by the data center to the regulation cloud, including the distribution transformer name, distribution transformer type, customer number / area number, capacity information; 1.

2. Extract the respective feeder details from the dispatching-side data and the marketing side, compare and match the feeder details of the two through the common fields to find the corresponding relationship; if there are still feeders on the dispatching side that cannot find the matching relationship after the comparison, it indicates that there are no users of this feeder on the marketing side, and it is fed back to the marketing side for rectification, and finally a feeder comparison table between the dispatching and the marketing is generated.

3. The method for assisting in the penetration of medium and low voltage models in a distribution network based on a control cloud according to claim 1, wherein Step 2 includes: 2.1 Read the public and special transformer information in the marketing system 2.1.1 Read the special transformer information in the marketing system, and extract the key columns including user number, affiliated feeder, user name, metering method, electricity meter asset number, electricity consumption category, etc.; uniformly process the user number, removing the first three digits '120'; extract the rows where the electricity meter number is not empty to obtain the filtered special transformer data; according to the input line name st_linename, obtain all the data rows on this line where the special transformer electricity meter number is not empty, and calculate the number of special transformers yx_zb_no on this line; 2.1.2 Read the public transformer information in the marketing system and extract the key columns including the substation area number, the affiliated feeder, the substation area name, the marketing substation area identifier, etc.; extract the rows where the substation area table is not empty to obtain the filtered public transformer data; according to the input line name st_linename, obtain the data rows where the public transformer meter numbers on this line are not empty, and calculate the number of public transformers yx_gb_no on this line. 2.2 Sequentially obtain all the special transformers, public transformers and other information of each line in the distribution automation system and perform matching with the marketing meter system. Specifically: 2.2.1 First, read the distribution transformer information and the currently matched meter information in the distribution automation system, including the distribution transformer number, line name, user number, substation area number, etc. 2.2.2 Then, extract the information of public transformers, special transformers and other types of distribution transformers, and filter out the corresponding distribution transformer information according to the line name for subsequent use. 2.2.3 Then, perform matching according to the user number and substation area number, and associate the distribution transformers and meters with the same user number and substation area number. 2.2.4 Finally, write the matching results into an Excel file to generate tables of different categories of distribution transformer information for pending inspection, pending confirmation, and incorrect matching; these tables are provided for users to view and check for subsequent manual processing; among them, the successfully matched distribution transformers wait for the next step of graphical visualization display, and the incorrectly matched distribution transformers enter the dispatching mismatch pool and the marketing mismatch pool respectively; the distribution transformers lacking household numbers and substation numbers and the distribution transformers that cannot be matched due to unknown types directly enter the dispatching mismatch pool.

4. The method for assisting in the penetration of medium- and low-voltage models in a distribution network based on a regulation cloud according to claim 1, wherein Step 3 includes: 3.1 First, preprocess the data, including removing the percentage sign and converting it into numerical data. 3.2 Then, sort the substations according to the total number of incorrect matches, draw a bar chart of the number of incorrect matches of distribution transformers in each substation, and save it as a picture file. 3.3 Then, classify the data according to the error type of whether there is a household number / substation number, draw a bar chart classified by whether there is a number, and save it as a picture file. 3.4 Then, analyze and process the error matching categories and proportions at the feeder level, draw a pie chart of the error matching proportion of distribution transformers in each line, and save it as a picture file. 3.5 Finally, output the statistical results to different worksheets in the Excel file "Current Situation of Matching between Distribution Transformers and Marketing Meters in the Distribution and Regulation System.xlsx" and save.

5. The method for assisting in the penetration of medium- and low-voltage models in a distribution network based on a control cloud according to claim 1, wherein: In step 5, verify the fuzzy matching relationship in combination with historical power outage and restoration information. Six time nodes are adopted for the selection of the power outage and restoration discrimination moment: namely, the t1 opening moment, the first whole hour moment before t1 - T, the earlier moment before t1 - 2T, the t2 closing moment, the first whole hour moment after t2 + T, and the later moment after t2 + 2T. It includes the following steps: 5.1 First, filter all NULL or 0 values in the "UA" column in the fuzzy matching historical measurement section file, and perform clustering analysis according to the content of the "meter_id" column, that is, classify it into as many categories as there are independent ids, and each category includes all the row data of the same id, and output the meter data after clustering analysis. 5.2 Then, sequentially read the data of "10kV line abbreviation", "opening time", and "closing time" for each row from the "Power Outage Information Sorting Table". 5.3 Then, traverse the electricity meter data generated in 5.1, and judge the corresponding data information for the set six sampling time points and the opening and closing time periods; if there is no missing data for the six sampling time points, prompt "The electricity meter is damaged and cannot be judged"; if the voltages at points P1 or P2 are not all 0 / NULL and there are continuous 0 values or NULL values between the time points t1 and t2, and the voltages at points Q1 and Q2 are not all 0 / NULL, it indicates that the feeder to which the electricity meter belongs is the same as the current power outage feeder; at this time, compare whether the feeder where the distribution transformer matched by the electricity meter is located is the same as the current power outage feeder. If they are the same, it means that the fuzzy match is correct; if they are not the same, it means that the fuzzy match is incorrect.

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