Distribution network self-healing line operation state detection and action analysis method and system

Abstract

The invention discloses a distribution network self-healing line operation state detection and action analysis method and system, and relates to the technical field of power system intellectualization, and the method comprises the steps: automatically obtaining the operation data of a self-healing line, and monitoring the abnormal operation mode of the self-healing line; monitoring the input and quit states of the self-healing line, and pushing the abnormal state of the self-healing line; and analyzing the self-healing action related information, and outputting a self-healing action analysis result. According to the method, the abnormal state of the line can be quickly captured by timely acquiring and monitoring the data, reliable basic data support is provided for subsequent decision making, the flexibility of line operation and the reliability of power supply are effectively improved, and the abnormal state pushing system is used for pushing the abnormal state of the line by integrating data of different analysis tables. And the abnormal information of the self-healing line is gathered and pushed to related personnel, so that the information transmission efficiency is improved, the completeness of a distribution network self-healing mechanism is improved, the standardization and automation of a fault processing flow are promoted, and a specific reference basis is provided for subsequent optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power system, and in particular to a method and system for detecting the operating status of a distribution network self-healing line and analyzing its motion. Background Art

[0002] With the rapid development of modern power systems, the intelligence level of power grids has been continuously improved, and the automation level of distribution networks has also been significantly improved. As an important part of smart grids, self-healing lines have received extensive attention and research in recent years. Self-healing line technology can automatically detect and quickly restore operating status when a fault occurs, greatly improving the reliability and stability of the power grid. Traditional power grid monitoring methods mostly rely on manual intervention, and the development of modern self-healing line technology is committed to achieving more flexible and intelligent automatic control, which not only reduces the possibility of human error, but also can quickly switch when a fault occurs to reduce power outage time, thereby improving the quality of power services.

[0003] Although the self-healing line technology has improved the automation level of the distribution network to a certain extent, the existing technology still has several shortcomings. Most existing systems rely on discrete monitoring equipment and decentralized analysis methods, resulting in poor comprehensiveness and real-time data, which in turn affects the response speed and accuracy of the self-healing system. The monitoring methods of traditional self-healing line systems are relatively simple, and usually rely on preset thresholds and rules for abnormal judgment. However, the power system is extremely complex, and the operating status is affected by many factors such as the environment, load fluctuations, and equipment aging, resulting in blind spots in threshold setting operations, which cannot adapt to the variability of fault types and complex situations, and are prone to false alarms and missed alarms. The analysis and feedback mechanism after the self-healing action is also not perfect. Usually, after the fault is handled, there is a lack of systematic evaluation of the effect of the self-healing action, and it is impossible to provide sufficient data support for subsequent optimization and improvement. The existing self-healing technology is still insufficient in pushing abnormal states, and the timeliness and accuracy of information transmission are not enough, making it difficult to notify maintenance personnel in time for on-site disposal. Although the existing computing and communication capabilities provide a foundation for smart grids, if information analysis and abnormal push are not in place, it is difficult to achieve optimal team collaboration, thereby reducing the efficiency and effectiveness of self-healing operations. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing self-healing line technology has the problems of poor real-time monitoring data, frequent false alarms and missed alarms, lack of effect evaluation after self-healing action, and how to achieve comprehensive and rapid data acquisition and intelligent analysis.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for detecting the operating status of a distribution network self-healing line and analyzing its action, comprising automatically acquiring the operating data of the self-healing line and monitoring the abnormal operating mode of the self-healing line; monitoring the input and output status of the self-healing line and pushing the abnormal status of the self-healing line; analyzing the information related to the self-healing action and outputting the self-healing action analysis results.

[0007] As a preferred solution of the distribution network self-healing line operation status detection and action analysis method described in the present invention, wherein: the automatic acquisition of self-healing line operation data includes setting a scheduled task through a python program, automatically logging into the three-zone Web distribution and dispatching intensive system every day to obtain relevant data tables including a self-healing ring locking table, an abnormal operating mode locking feeder table, a self-healing line automatic switch manual locking table, a switch position invalid table, a switch unprotected signal table, a remote control associated loss table, a boundary library associated loss table, a self-healing input and output alarm monitoring table, a feeder list table, a self-healing line list table, and a contact switch statistics table.

[0008] As a preferred solution of the method for detecting the operating status and analyzing the action of the self-healing line of the distribution network described in the present invention, wherein: monitoring the abnormal operating mode of the self-healing line includes determining the content of each table.

[0009] The columns of the self-healing loop blocking table include feeder name, feeder group number, rated current, opposite feeder when the loop is running, loop operation status, self-healing function activation and deactivation status, self-healing function blocking status and graph model abnormal status. The self-healing loop blocking table counts the lines with loop operation listed as yes on a daily basis and outputs them to the self-healing line abnormal operation mode monitoring.xlsx.

[0010] The abnormal operating mode interlocking feeder table includes feeder name, feeder group, opposite feeder when loop is running, loop operation status and abnormal operating mode interlocking. According to the line self-healing function on and off status, the abnormal operating mode interlocking feeder information when the self-healing function is in the on and off states is counted separately, and the statistical results are output to the self-healing line abnormal operating mode monitoring.xlsx, with a cycle of once a day.

[0011] The manual blocking table of the automatic switch of the self-healing line is used to count all the automatic switches whose self-healing functions are manually blocked. According to the statistical needs, the automatic switches are divided into two categories: contact switches and non-contact switches. The table header includes the feeder group number, feeder name, switch name, whether it is an outgoing switch, and whether it is manually blocked.

[0012] The invalid switch position table includes situations where the current automatic switch opening and closing position signals are lost, suspicious or erroneous due to collection problems. The automatic switch information with invalid positions listed as yes is collected and presented in the invalid switch position table, including the plant name, feeder name, and switch name.

[0013] The switch without protection signal table includes the automatic switch without protection signal on the self-healing line, which will affect the self-healing function, including the automatic switch body has protection function and supporting signals but the main site table is not correctly associated with it, and also includes the on-site automatic switch without protection function. The switch without protection signal table provides a channel for the main station and distribution operation and maintenance personnel to browse the switches without protection signals by counting the automatic switches with no protection signals, and urges relevant staff to make rectifications.

[0014] The remote control association loss table counts the remote control association loss of automation switches in the entire network area. The statistical results are summarized in remote control association loss.xlsx and sent regularly every day through elink.

[0015] The boundary library association loss table counts the boundary library association loss situation, and summarizes the statistical results into boundary library association loss.xlsx, which is sent regularly every day through elink.

[0016] In the self-healing activation and deactivation alarm monitoring table, the activation and deactivation of the self-healing line function depends on whether the on-site distribution network line is in normal operation. When the planned work of power outage, power supply, and power transfer involves the self-healing line, the dispatcher manually activates or deactivates the self-healing function of the self-healing line according to the relevant professional approval opinions. The scheduled task is set through the Python program to simulate manual operation to query the historical operation records and extract the information fields of the relevant operation alarms, and the extracted results are output to the specified document.

[0017] The query results of the self-healing operation records include the node name, which indicates the network node where the self-healing operation is performed; the action time indicates the specific time when the operation is performed; the alarm content indicates the self-healing function activation and deactivation change operation of the specific line; the operation description record is manual setting, which indicates human operation rather than system or program operation; when the original status record is false and the new status record is true, it means that the self-healing function of the line has changed from the original deactivated state to the activated state; when the original status record is true and the new status record is false, it means that the self-healing function of the line has changed from the original activated state to the deactivated state; the specific operation steps and the name of the staff member are recorded by the operator.

[0018] The feeder list is a system basic information form that records the information of all 10kV lines, including self-healing lines and non-self-healing lines. The unique identifiers include the name, rdfID and asset management identifier. The line name and the region to which it belongs are extracted to match the relevant two-dimensional table.

[0019] The self-healing line list is a system basic information form that records all 10kV lines with self-healing functions. The feeder group indicates that the same lines are the same group of self-healing lines. It also includes rated current, current load and current margin as the basis for line transfer. The self-healing line list includes the source table of the self-healing ring locking table and the abnormal operating mode locking feeder table.

[0020] The tie switch statistics table is a system basic information form, which includes all automated switches and non-automated switches of the tie switch type on all distribution network lines. The tie switch statistics table provides tie switch information and assists in analyzing whether the self-healing action is correct when a line fails.

[0021] As a preferred solution of the distribution network self-healing line operation status detection and action analysis method described in the present invention, wherein: the monitoring of the self-healing line input and output status includes setting a daily timing task to filter out the lines with self-healing input and output status from the self-healing line list, and combining the self-healing input and output alarm monitoring to calculate the daily input time and daily output time of the line, and obtain the daily input rate, to assist the main station and on-site personnel in monitoring the input and output status of the line self-healing function.

[0022] As a preferred scheme of the distribution network self-healing line operation status detection and action analysis method described in the present invention, wherein: the pushing of the abnormal state of the self-healing line includes integrating the self-healing loop locking table and the abnormal operation mode locking feeder table from the form information and outputting the self-healing line abnormal operation mode monitoring.xlsx, reflecting that the current branch county bureau has self-healing lines with abnormal self-healing states, and analyzing the causes of the line abnormalities according to the opposite side lines, loop operation, abnormal mode locking and the position of the connecting switch during loop operation.

[0023] As a preferred solution of the distribution network self-healing line operation status detection and action analysis method described in the present invention, the analysis of self-healing action related information includes setting a scheduled task through a python program, automatically logging into the distribution network OMS every day, locating and navigating to the automated workbench, simulating manual operation to obtain the self-healing action records to be analyzed, and obtaining key field information including municipal bureaus, district and county bureaus, line names, switch names, protection information, reclosing conditions, and power outage time.

[0024] After obtaining the self-healing action records to be analyzed in the distribution network OMS, open the Guizhou distribution network self-healing action visualization platform, use the line name and power outage time fields as matching conditions to query the corresponding self-healing events, and open the details page to perform automatic screenshot operations with the crawler and rpa robot.

[0025] As a preferred solution of the distribution network self-healing line operation status detection and action analysis method described in the present invention, wherein: the output of the self-healing action analysis results includes pushing the distribution network OMS system and the Guizhou distribution network self-healing action visualization platform to collect the action status of the self-healing line after a failure, organizing the self-healing action logic, and publishing it in the form of a briefing in the elink work group through the rpa robot.

[0026] According to the self-healing management work guidelines, one action is analyzed, and the self-healing action analysis report of the relevant lines is automatically generated and output as a word document.

[0027] Another object of the present invention is to provide a distribution network self-healing line operation status detection and action analysis system, which can monitor the self-healing line input and output status and push the self-healing line abnormal status, thereby solving the problem of inaccurate abnormal status detection in current self-healing line technology.

[0028] As a preferred solution of the distribution network self-healing line operation status detection and action analysis system described in the present invention, it includes: a data acquisition module, a status push module, and a self-healing action analysis module.

[0029] The data acquisition module is used to automatically acquire the self-healing line operation data and monitor the abnormal operation mode of the self-healing line; the status push module is used to monitor the self-healing line entry and exit status and push the self-healing line abnormal status; the self-healing action analysis module is used to analyze the self-healing action related information and output the self-healing action analysis result.

[0030] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for detecting the operating status of a distribution network self-healing line and analyzing its actions.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for detecting the operating status of a distribution network self-healing line and analyzing its actions.

[0032] Beneficial effects of the present invention: the distribution network self-healing line operation status detection and action analysis method provided by the present invention automatically obtains the self-healing line operation data, monitors the abnormal operation mode of the self-healing line, and can quickly capture the abnormal state of the line by timely acquiring and monitoring data, and provide reliable basic data support for subsequent decision-making, monitor the self-healing line input and output status, and push the abnormal state of the self-healing line, which effectively improves the flexibility of line operation and power supply reliability. The abnormal state push system integrates the data of different analysis tables, summarizes the abnormal information of the self-healing line and pushes it to relevant personnel, improves the efficiency of information transmission, analyzes the information related to the self-healing action, outputs the self-healing action analysis results, improves the perfection of the distribution network self-healing mechanism, promotes the standardization and automation of the fault handling process, and provides a specific reference basis for subsequent optimization. The present invention has achieved better results in terms of real-time data acquisition and monitoring capabilities, the accuracy of abnormal state push, and the detail of self-healing action analysis and feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 The present invention provides an overall flow chart of a method for detecting the operating status of a distribution network self-healing line and analyzing its actions in accordance with the first embodiment of the present invention.

[0035] Figure 2 A self-healing line input and output status monitoring table of a distribution network self-healing line operation status detection and action analysis method provided by the first embodiment of the present invention.

[0036] Figure 3 FIG2 is a second diagram of a self-healing line input / output status monitoring table of a distribution network self-healing line operation status detection and action analysis method provided by the first embodiment of the present invention.

[0037] Figure 4 A feeder blocking table diagram in an abnormal operation mode of a distribution network self-healing line operation status detection and action analysis method provided in the first embodiment of the present invention.

[0038] Figure 5 A self-healing action record table diagram of a distribution network self-healing line operation status detection and action analysis method provided by the first embodiment of the present invention.

[0039] Figure 6 An overall flow chart of a distribution network self-healing line operation status detection and action analysis system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] Example 1, reference Figure 1-Figure 5 , is an embodiment of the present invention, and provides a distribution network self-healing line operation status detection and action analysis method, comprising:

[0042] S1: Automatically obtain the self-healing line operation data and monitor the abnormal operation mode of the self-healing line.

[0043] Furthermore, automatic acquisition of self-healing line operation data includes setting up scheduled tasks through Python programs, automatically logging into the three-zone Web distribution and dispatching intensive system every day to obtain relevant data tables including self-healing ring locking table, abnormal operation mode locking feeder table, self-healing line automatic switch manual locking table, switch position invalid table, switch unprotected signal table, remote control association loss table, boundary library association loss table, self-healing input and output alarm monitoring table, feeder list table, self-healing line list table, and interconnection switch statistics table.

[0044] It should be noted that monitoring the abnormal operating mode of the self-healing line includes determining the content of each table.

[0045] The columns of the self-healing loop blocking table include feeder name, feeder group number, rated current, opposite feeder when the loop is running, loop operation status, self-healing function activation and deactivation status, self-healing function blocking status and graph model abnormal status. The self-healing loop blocking table counts the lines with loop operation listed as yes on a daily basis and outputs them to the self-healing line abnormal operation mode monitoring.xlsx.

[0046] The abnormal operating mode interlocking feeder table includes feeder name, feeder group, opposite feeder when loop is running, loop operation status and abnormal operating mode interlocking. According to the line self-healing function on and off status, the abnormal operating mode interlocking feeder information when the self-healing function is in the on and off states is counted separately, and the statistical results are output to the self-healing line abnormal operating mode monitoring.xlsx, with a cycle of once a day.

[0047] The manual blocking table of the automatic switch of the self-healing line is used to count all the automatic switches whose self-healing functions are manually blocked. According to the statistical needs, the automatic switches are divided into two categories: contact switches and non-contact switches. The table header includes the feeder group number, feeder name, switch name, whether it is an outgoing switch, and whether it is manually blocked.

[0048] The invalid switch position table includes situations where the current automatic switch opening and closing position signals are lost, suspicious or erroneous due to collection problems. The automatic switch information with invalid positions listed as yes is collected and presented in the invalid switch position table, including the plant name, feeder name, and switch name.

[0049] The switch without protection signal table includes the automatic switch without protection signal on the self-healing line, which will affect the self-healing function, including the automatic switch body has protection function and supporting signals but the main site table is not correctly associated with it, and also includes the on-site automatic switch without protection function. The switch without protection signal table provides a channel for the main station and distribution operation and maintenance personnel to browse the switches without protection signals by counting the automatic switches with no protection signals, and urges relevant staff to make rectifications.

[0050] The remote control association loss table counts the remote control association loss of automation switches in the entire network area. The statistical results are summarized in remote control association loss.xlsx and sent regularly every day through elink.

[0051] The boundary library association loss table counts the boundary library association loss situation, and summarizes the statistical results into boundary library association loss.xlsx, which is sent regularly every day through elink.

[0052] In the self-healing activation and deactivation alarm monitoring table, the activation and deactivation of the self-healing line function depends on whether the on-site distribution network line is in normal operation. When the planned work of power outage, power supply, and power transfer involves the self-healing line, the dispatcher manually activates or deactivates the self-healing function of the self-healing line according to the relevant professional approval opinions. The scheduled task is set through the Python program to simulate manual operation to query the historical operation records and extract the information fields of the relevant operation alarms, and the extracted results are output to the specified document.

[0053] The query results of the self-healing operation records include the node name, which indicates the network node where the self-healing operation is performed; the action time indicates the specific time when the operation is performed; the alarm content indicates the self-healing function activation and deactivation change operation of the specific line; the operation description record is manual setting, which indicates human operation rather than system or program operation; when the original status record is false and the new status record is true, it means that the self-healing function of the line has changed from the original deactivated state to the activated state; when the original status record is true and the new status record is false, it means that the self-healing function of the line has changed from the original activated state to the deactivated state; the specific operation steps and the name of the staff member are recorded by the operator.

[0054] The feeder list is a system basic information form that records the information of all 10kV lines, including self-healing lines and non-self-healing lines. The unique identifiers include the name, rdfID and asset management identifier. The line name and the region to which it belongs are extracted to match the relevant two-dimensional table.

[0055] The self-healing line list is a system basic information form that records all 10kV lines with self-healing functions. The feeder group indicates that the same lines are the same group of self-healing lines. It also includes rated current, current load and current margin as the basis for line transfer. The self-healing line list includes the source table of the self-healing ring locking table and the abnormal operating mode locking feeder table.

[0056] The tie switch statistics table is a system basic information form, which includes all automated switches and non-automated switches of the tie switch type on all distribution network lines. The tie switch statistics table provides tie switch information and assists in analyzing whether the self-healing action is correct when a line fails.

[0057] It should also be noted that automatically acquiring the operating data of self-healing lines is one of the core steps to achieve dynamic monitoring of smart grids. By realizing automatic data collection, the real-time nature of data acquisition and the comprehensiveness of overall grid monitoring are improved, key operating information is extracted from multiple data tables, the dependence on manpower is reduced, and operational efficiency is promoted. Diversified data sources build a comprehensive monitoring system, so that self-healing lines in different states can be accurately monitored, and the consistency and integrity of data are optimized. In complex power systems, any information asymmetry or delay may cause significant losses. The decision-making process is based on reliable data, thereby effectively avoiding these risks. Automatic data acquisition can also timely capture potential faults caused by environmental changes, equipment aging and other problems, which is helpful for preventive maintenance. In the early stages of a fault, through automatic analysis of data, distribution network managers can respond quickly and take preventive and corrective measures to ensure that any abnormal operating conditions can be recorded and processed in a timely manner, reducing the risk of business interruption due to fault delays and improving power supply stability and customer satisfaction.

[0058] S2: monitors the self-healing line entry and exit status, and pushes the self-healing line abnormal status.

[0059] Furthermore, monitoring the self-healing line input and output status includes setting a daily scheduled task to filter out the lines with self-healing input and output status from the self-healing line list, and combining the self-healing input and output alarm monitoring to calculate the daily input time and daily output time of the line, and obtain the daily input rate, to assist the main station and on-site personnel in monitoring the input and output status of the line self-healing function.

[0060] It should be noted that pushing the abnormal status of the self-healing line includes integrating the self-healing loop locking table and the abnormal operating mode locking feeder table from the form information and outputting the self-healing line abnormal operating mode monitoring.xlsx, which reflects the self-healing lines with abnormal self-healing status in the current sub-county bureau, and analyzes the causes of the line abnormality based on the opposite line, loop operation, abnormal locking and interconnection switch position during loop operation.

[0061] It should also be noted that, referring to Figure 2-Figure 3 , self-healing line input and output status monitoring.xlsx, displays the self-healing input and output status of the line on the day, and calculates the daily input rate of each line.

[0062] It should also be noted that, referring to Figure 4 When the loop operation is 1, and the opposite feeder during loop operation is the opposite line of the same feeder group, it can be judged that the feeder group is currently in a closed loop operation state with the contact switch in the closed position. When the loop operation is 0, the abnormal operation mode lockout is 1, and the contact switch is in the closed position, there may be a situation where the planned work is conducting the load.

[0063] It should also be noted that by monitoring the activation and deactivation status of the self-healing function through daily scheduled tasks and combining it with the abnormal status monitoring function, the automation of real-time dynamic monitoring is realized, and the collected operating data is fully utilized to calculate the input and output time of each line, and the daily input rate is obtained, which provides strong support for system planning and on-site maintenance, ensuring that the power network still has sufficient redundancy and reliability during peak loads. The automatic push of abnormal status information function improves the efficiency and accuracy of information transmission. Using the set monitoring algorithm, the dispatcher can not only understand the operating status of the current line, but also quickly determine the switch status and the cause of the abnormality. This mechanism of real-time push of abnormal information helps operation and management personnel at all levels to take action on the problem in the shortest time and restore the system to normal at the lowest cost, effectively reducing maintenance costs, and realizing the transparency and standardization of power grid management. By recording and transmitting accurate event data, it promotes management analysis and knowledge accumulation of fault handling, provides reliable data support for future operation optimization, deepens the understanding of the role of self-healing lines in complex power networks, and forms a reliable, repeatable and efficient distribution framework.

[0064] S3: Analyze the self-healing action related information and output the self-healing action analysis results.

[0065] Furthermore, the analysis of self-healing action related information includes setting up scheduled tasks through Python programs, automatically logging into the distribution network OMS every day, locating and navigating to the automated workbench, simulating manual operations to obtain the self-healing action records to be analyzed, and obtaining key field information including municipal bureaus, district and county bureaus, line names, switch names, protection information, reclosing conditions, and power outage time.

[0066] After obtaining the self-healing action records to be analyzed in the distribution network OMS, open the Guizhou distribution network self-healing action visualization platform, use the line name and power outage time fields as matching conditions to query the corresponding self-healing events, and open the details page to perform automatic screenshot operations with the crawler and rpa robot.

[0067] It should be noted that the output of self-healing action analysis results includes pushing the distribution network OMS system and Guizhou distribution network self-healing action visualization platform to collect the action status after the self-healing line fails, sorting out the self-healing action logic, and publishing it in the form of a briefing in the elink work group through the rpa robot.

[0068] According to the self-healing management work guidelines, one action is analyzed, and the self-healing action analysis report of the relevant lines is automatically generated and output as a word document.

[0069] It should also be noted that, referring to Figure 5, set up scheduled tasks through the Python program, automatically log in to the distribution network OMS every day, locate and navigate to the automated workbench, simulate manual operations to obtain the self-healing action records to be analyzed, and obtain key field information including municipal bureaus, district and county bureaus, line names, switch names, protection information, reclosing conditions, and power outage time.

[0070] It should also be noted that analyzing the information related to the self-healing action and outputting the analysis results are the key links in realizing the optimization of the self-healing action of the power grid. By automatically logging into the distribution network OMS system, obtaining and analyzing the detailed records of the self-healing action, it is possible to conduct a comprehensive and accurate diagnosis of the fault event. By using an automated analysis platform, the ability to understand the fault event is improved. The automated analysis information not only provides a basic description of the event, but also combines multiple data dimensions (such as local bureaus, line names, etc.) to achieve a multi-angle analysis of power events. The analysis process uses Python and RPA robot technology, so that the system can effectively simulate manual analysis operations, reduce manpower investment and improve efficiency. On this basis It outputs analysis reports of self-healing actions, provides detailed reference materials for the maintenance team, clarifies the logic and results of each action, not only promotes the transparency of on-site operations, but also provides solid experience guidance for future optimization, reduces unnecessary consumption of repeated inquiries and communications, and promotes the development of intelligent power grid management. The knowledge generated around the analysis results can help the distribution network make more accurate predictions and adjustments during the deployment and implementation of self-healing functions in the future. The system provides decision support for subsequent steps through a series of detailed action analysis and visualization results generation, which not only improves the power grid's ability to respond to immediate challenges, but also strengthens its safety and continuous improvement capabilities in the face of long-term challenges.

[0071] Embodiment 2 is an embodiment of the present invention, which provides a method for detecting the operating status and analyzing the action of a distribution network self-healing line. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0072] First, the system extracts the current feeder operation status from the self-healing loop blocking table to ensure that all loops operate normally. For any abnormally blocked or unsynchronized lines, the system generates detailed monitoring reports every day and marks them as objects for key inspection. The program collects the self-healing function input or output data under all abnormal blocking states from the abnormally blocked feeder table, and generates statistical reports every day for subsequent analysis and regulation. The experiment arranges automatic tasks to select the lines in the input and output states from the self-healing line list every day, and calculates the daily input and output time of the lines in combination with the self-healing input and exit alarm monitoring table to derive the daily input rate. Each change in the self-healing state (for example, the self-healing function changes from off to on) will trigger the abnormal alarm mechanism inside the system and promptly notify the relevant personnel, while recording the specific operators and The operation time is shortened to ensure that all status changes are manually verified in compliance with regulations; the system is coordinated with a set of refined fault location and analysis processes, and the newly acquired self-healing action records in the power grid OMS, including key data of the municipal bureau, block area, and line name, are further processed in the Guizhou distribution network self-healing action visualization platform through automated programs; in this process, crawler technology and RPA automatic crawling tools are applied to extract line fault details from the visualization platform, and more detailed fault handling is enabled in conjunction with manually verified alarm events; the final briefing and detailed analysis report are published to the elink work group and provided to all team members. From the experimental results, it can be seen that the present invention has advantages in line self-healing function, fault handling efficiency and abnormal status push, which helps operation and maintenance personnel to intervene in time and promotes the stable operation of the power system.

[0073] Example 3, reference Figure 6 , which is an embodiment of the present invention, provides a distribution network self-healing line operation status detection and action analysis system, including a data acquisition module, a status push module, and a self-healing action analysis module.

[0074] The data acquisition module is used to automatically acquire the operating data of the self-healing line and monitor the abnormal operating mode of the self-healing line; the status push module is used to monitor the entry and exit status of the self-healing line and push the abnormal status of the self-healing line; the self-healing action analysis module is used to analyze the self-healing action related information and output the self-healing action analysis results.

[0075] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0076] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0077] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0078] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for detecting the operating status of a distribution network self-healing line and analyzing its motion, characterized in that: include: Automatically obtain the operating data of the self-healing line and monitor the abnormal operating mode of the self-healing line; Monitor the self-healing line entry and exit status, and push the abnormal status of the self-healing line; Analyze the self-healing action related information and output the self-healing action analysis results.

2. The method for detecting the operating status of a distribution network self-healing line and analyzing its operation according to claim 1, characterized in that: The automatic acquisition of self-healing line operation data includes setting a scheduled task through a python program, and automatically logging into the three-zone Web distribution and intensive system every day to obtain relevant data tables including a self-healing ring locking table, an abnormal operating mode locking feeder table, a self-healing line automatic switch manual locking table, a switch position invalid table, a switch unprotected signal table, a remote control association loss table, a boundary library association loss table, a self-healing input and output alarm monitoring table, a feeder list table, a self-healing line list table, and a contact switch statistics table.

3. The method for detecting the operating status and analyzing the operation of the self-healing line of the distribution network according to claim 2, characterized in that: The monitoring of the abnormal operation mode of the self-healing line includes determining the content of each table; The columns of the self-healing loop blocking table include feeder name, feeder group number, rated current, opposite feeder during loop operation, loop operation status, self-healing function on / off status, self-healing function blocking status, and graph model abnormal status. The self-healing loop blocking table counts the lines with the loop operation column as yes on a daily basis and outputs them to the self-healing line abnormal operation mode monitoring.xlsx; The abnormal operation mode blocking feeder table includes feeder name, feeder group, opposite feeder during loop operation, loop operation status and abnormal operation mode blocking. According to the line self-healing function on / off status, the abnormal operation mode blocking feeder information of the self-healing function in the on / off state is counted respectively, and the statistical results are output to the self-healing line abnormal operation mode monitoring.xlsx, with a cycle of once a day; The manual blocking table of the automatic switch of the self-healing line is used to count all the automatic switches whose self-healing functions are manually blocked. According to the statistical needs, the automatic switches are divided into two categories: tie switches and non-tie switches. The table header includes the feeder group number, feeder name, switch name, whether it is an outgoing switch, and whether it is manually blocked; The invalid switch position table includes the current automatic switch position signal loss, suspicious or wrong situation caused by acquisition problems. The automatic switch information with invalid position listed as yes is collected and presented in the invalid switch position table, including plant station name, feeder name, switch name; The switch without protection signal table includes the situation where the automatic switch without protection signal on the self-healing line will affect the self-healing function, including the situation where the automatic switch itself has protection function and supporting signals but the main site table is not correctly associated with it, and also includes the situation where the on-site automatic switch has no protection function. The switch without protection signal table provides a channel for the main site and distribution operation and maintenance personnel to browse the switches without protection signals by counting the automatic switches with no protection signals, and urges the relevant staff to make rectifications; The remote control association loss table counts the remote control association loss of automation switches in the entire network area. The statistical results are summarized in remote control association loss.xlsx and sent regularly through elink every day; The boundary library association loss table counts the boundary library association loss, and the statistical results are summarized in boundary library association loss.xlsx, which is sent regularly every day through elink; In the self-healing activation and deactivation alarm monitoring table, the activation and deactivation of the self-healing line depends on whether the on-site distribution network line is in normal operation. When the planned work of power outage, power supply, and power transfer involves the self-healing line, the dispatcher manually activates or deactivates the self-healing function of the self-healing line according to the relevant professional approval opinions. The scheduled task is set through the Python program to simulate manual operation to query the historical operation records and extract the information fields of the relevant operation alarms, and the extraction results are output to the specified document; The query result of the self-healing operation record includes the node name, which indicates the network node where the self-healing operation is performed; the action time indicates the specific time of the operation; the alarm content indicates the self-healing function of the specific line is switched on and off; the operation description is recorded as manual setting, which indicates manual operation rather than system or program operation; when the original state record is false and the new state record is true, it means that the self-healing function of the line is changed from the original withdrawn state to the engaged state; when the original state record is true and the new state record is false, it means that the self-healing function of the line is changed from the original engaged state to the withdrawn state; the specific operation steps and the name of the staff are recorded by the operator; The feeder list is a system basic information form that records the information of all 10kV lines, including self-healing lines and non-self-healing lines. The unique identifier includes the name, rdfID and asset management identifier. The line name and the region to which it belongs are extracted to match the relevant two-dimensional table. The self-healing line list is a system basic information form, which records all 10kV lines with self-healing function. The feeder group indicates that the same lines are the same group of self-healing lines. It also includes rated current, current load and current margin as the basis for line transfer. The self-healing line list includes the self-healing loop blocking table and the source table of the feeder blocking table in abnormal operation mode; The tie switch statistics table is a system basic information form, which includes all automated switches and non-automated switches of the tie switch type on all distribution network lines. The tie switch statistics table provides tie switch information and assists in analyzing whether the self-healing action is correct when a line fails.

4. The method for detecting the operating status of a distribution network self-healing line and analyzing its operation according to claim 3, characterized in that: The monitoring of the self-healing line input and output status includes setting a daily scheduled task to filter out the lines with self-healing input and output status from the self-healing line list, combining the self-healing input and output alarm monitoring, calculating the daily input time and daily output time of the line, and obtaining the daily input rate, to assist the main station and on-site personnel in monitoring the input and output status of the line self-healing function.

5. The method for detecting the operating status of a distribution network self-healing line and analyzing its operation according to claim 4, characterized in that: The pushing of the abnormal status of the self-healing line includes integrating the self-healing loop locking table and the abnormal operating mode locking feeder table from the form information and outputting the self-healing line abnormal operating mode monitoring.xlsx, which reflects that the current sub-county bureau has self-healing lines with abnormal self-healing status, and analyzes the causes of the line abnormality based on the opposite line, loop operation, abnormal mode locking and contact switch position during loop operation.

6. The method for detecting the operating status of a distribution network self-healing line and analyzing its operation according to claim 5, characterized in that: The analysis of the self-healing action related information includes setting a scheduled task through a python program, automatically logging into the distribution network OMS every day, locating and navigating to the automated workbench, simulating manual operations to obtain the self-healing action records to be analyzed, and obtaining key field information including the municipal bureau, district and county bureau, line name, switch name, protection information, reclosing status, and power outage time; After obtaining the self-healing action records to be analyzed in the distribution network OMS, open the Guizhou distribution network self-healing action visualization platform, use the line name and power outage time fields as matching conditions to query the corresponding self-healing events, and open the details page to perform automatic screenshot operations with the crawler and rpa robot.

7. The method for detecting the operating status and analyzing the operation of the self-healing line of a distribution network according to claim 6, characterized in that: The output of the self-healing action analysis results includes pushing the self-healing action status after the self-healing line fails through the distribution network OMS system and the Guizhou distribution network self-healing action visualization platform, sorting out the self-healing action logic, and publishing it in the form of a briefing in the elink work group through the rpa robot; According to the self-healing management work guidelines, one action is analyzed, and the self-healing action analysis report of the relevant lines is automatically generated and output as a word document.

8. A system using the method for detecting the operating status of a distribution network self-healing line and analyzing its motion according to any one of claims 1 to 7, characterized in that: Including data acquisition module, status push module, and self-healing action analysis module; The data acquisition module is used to automatically acquire the self-healing line operation data and monitor the abnormal operation mode of the self-healing line; The status push module is used to monitor the self-healing line entry and exit status and push the self-healing line abnormal status; The self-healing action analysis module is used to analyze the self-healing action related information and output the self-healing action analysis result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distribution network self-healing line operation status detection and action analysis method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distribution network self-healing line operation status detection and action analysis method described in any one of claims 1 to 7 are implemented.

Images