Distribution automation terminal abnormity monitoring and early warning order sending management method

Abstract

The invention discloses a distribution automation terminal abnormity monitoring and early warning order sending management method, and relates to the technical field of distribution automation, and the method comprises the steps: obtaining terminal operation data from a distribution network master station system; performing analysis processing and statistics on the terminal operation data to obtain terminal abnormal data; and calculating a data portrait score of each automatic switch according to the terminal abnormal data, and generating an abnormal data list. According to the method, the technical scheme of operation data acquisition, analysis and scoring is applied, automatic data acquisition is realized through a python program, a standardized exception analysis rule system is established, and a data portrait scoring mechanism and a periodic exception list pushing mechanism are introduced. According to the method, the manual workload is remarkably reduced, the anomaly identification accuracy is improved, the preventative maintenance of the equipment is realized, effective support is provided for the intelligent operation and maintenance of the power distribution automation terminal, and the operation reliability and the maintenance efficiency of the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution automation, and in particular to a method for managing abnormality monitoring and early warning dispatching of power distribution automation terminals. Background Art

[0002] In recent years, efforts have been made to promote the construction of distribution automation, the effective coverage rate of distribution automation has increased rapidly, and the application scale of distribution automation terminals has gradually increased. During the operation of distribution network automation switches, due to the long service life of the equipment, damage to the core controller, damage to the communication module, etc., the distribution terminal has abnormal defects such as long-term communication interruption, frequent remote signaling, and remote control failure. At the same time, when the distribution network line is relocated, it will also cause equipment changes in the running distribution automation terminal, resulting in the loss of information association between the automation switch point table and the unavailability of the three remote functions. At present, the distribution network master station automation operation and maintenance personnel manually export alarm information from the distribution network master station system every week, sort out and analyze the abnormal problems of the distribution terminal from the massive data, and form a terminal abnormality list to output to the distribution operation and maintenance unit. The process is time-consuming and laborious, and cannot meet the real-time requirements of abnormal detection. If there are abnormal problems in the terminal, it cannot be notified to the distribution operation and maintenance unit in time for abnormal problem investigation, resulting in the long-term existence of terminal abnormal problems that affect the operation quality of the three remote functions of the terminal, and cannot effectively play the role of improving power supply reliability. Currently, there is a lack of effective technical methods and means to effectively monitor and analyze distribution automation operation data. This technology studies a distribution automation terminal abnormality monitoring and early warning dispatch management method to achieve real-time monitoring, accurate analysis and early warning issuance of distribution automation terminal operation abnormalities. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the present invention provides a distribution automation terminal abnormality monitoring and early warning dispatch management method, which can solve the problems mentioned in the background technology.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a distribution automation terminal abnormality monitoring and early warning dispatch management method, comprising: obtaining terminal operation data from the distribution network master station system; analyzing, processing and counting the terminal operation data to obtain terminal abnormality data; calculating the data portrait score of each automation switch based on the terminal abnormal data, and generating an abnormal data list.

[0006] As a preferred solution of the distribution automation terminal abnormal monitoring and early warning dispatch management method described in the present invention, the terminal operation data includes remote signal monitoring data, telemetry monitoring data, self-healing remote control monitoring data, morning exercise monitoring data, scheduling remote control monitoring data, online rate monitoring data, and master station information monitoring data.

[0007] As a preferred solution of the distribution automation terminal abnormal monitoring and early warning dispatch management method described in the present invention, wherein: the terminal abnormal data includes remote signal monitoring abnormal data, telemetering monitoring abnormal data, remote control monitoring abnormal data, online rate abnormal data, and master station information abnormal data; the remote signal monitoring abnormal data includes the remote local state of the switch, the reclosing state, the protection function state, the switch no energy storage state, the switch opening pressure loss state and the frequent switch remote signal; the telemetering monitoring abnormal data includes line voltage abnormality, phase voltage abnormality and current abnormality; the remote control monitoring abnormal data includes self-healing remote control failure, morning exercise remote control failure and scheduling remote control failure; the online rate abnormal data includes data with an online rate of 0% and data with a real-time operating status of shutdown and an online rate of 100%; the master station information abnormal data includes remote control association loss, automatic switch abnormality, automatic switch manual setting, stop alarm and update and remote control defect card.

[0008] As a preferred scheme of the distribution automation terminal abnormal monitoring and early warning dispatch management method described in the present invention, wherein: the data portrait score is calculated by scoring items and deduction items, the scoring items include weekly online rate and historical remote control success rate, and the deduction items include the number of remote control preset failures, the number of line voltage collection abnormalities, the number of current collection abnormalities, the number of phase voltage collection abnormalities, the number of switch failures to store energy, the number of switch opening loss of pressure problems, and the number of frequent remote signaling.

[0009] As a preferred solution of the abnormal monitoring and early warning dispatching management method for distribution automation terminals described in the present invention, wherein: obtaining terminal operation data from the distribution network master station system includes the following steps: constructing a python program to simulate the manual operation process; obtaining remote signal monitoring data from the distribution network master station system; the remote signal monitoring data includes the terminal remote local status, reclosing switch on / off status, protection function on / off status, switch no energy storage status, switch opening loss voltage signal and intelligent alarm frequent signal; obtaining telemetry monitoring data from the distribution network master station system; the telemetry monitoring data includes Uab on the power supply side and Uab on the load side of the distribution terminal, Ua, Ub, Uc on the power supply side of the distribution terminal and Ua, Ub, Uc on the load side and Substation outgoing switch Ia and distribution terminals Ia, Ib, Ic; obtain remote control monitoring data from the distribution network master station system; the remote control monitoring data includes self-healing remote control alarm data, network-wide remote control preset failure results, county-level remote control accuracy statistics and remote control accuracy statistics reports; obtain online rate monitoring data from the distribution network master station system; the online rate monitoring data includes county-level historical online rate statistics and historical online rate statistics reports; obtain master station information monitoring data from the distribution network master station system; the master station information monitoring data includes remote control association loss data, automatic switch abnormality data, automatic switch manual setting data, stop alarm and update data, and remote control defect card, test card, and unprotected card data.

[0010] As a preferred solution of the distribution automation terminal abnormal monitoring and early warning dispatch management method described in the present invention, wherein: the terminal operation data is analyzed, processed and counted to obtain terminal abnormal data, including the following steps: analyzing and processing the remote signal monitoring data to obtain switch abnormal data; when the switch remote / local state is 0, it is determined that the local state is abnormal; when the reclosing switch is in the state of 0, it is determined that the reclosing switch is not abnormal; when the three-stage overcurrent protection working mode is started to 0, it is determined that the protection is not abnormal; when the switch does not store energy signal is 1, it is determined that the switch does not store energy abnormally; when the same switch sends out opening and AC pressure loss signals at the same time within 30 seconds, it is determined that the switch is opening and pressure loss abnormal; when the same switch signal occurs more than 10 times within 1 hour, it is determined that the switch remote signal is frequently abnormal.

[0011] The telemetry monitoring data is analyzed and processed to obtain abnormal electrical quantity data; for line voltage abnormality: match the Uab data on the load side and the Uab data on the power supply side of the same switch, when the Uab data on the power supply side and the Uab data on the load side are both 0, it is determined that the line voltage is not collected; when the Uab on the power supply side is 0 and the Uab on the load side is not 0, it is determined that the power supply side is not collected; when the Uab on the power supply side is not 0 and the Uab on the load side is 0, it is determined that the load side is not collected; when the Uab on the power supply side is less than 7 or greater than 12, it is determined that the line voltage data is abnormal; for phase voltage abnormality: match the three-phase voltage data on the power supply side and the load side of the same switch, and analyze the power supply side and load side phase voltage collection abnormalities according to the rule criterion table; for current abnormality: match the three-phase current data of the same switch and the current data of the substation outgoing switch, when the three-phase current difference is greater than 50, it is determined as three-phase imbalance; when the A phase current When the A-phase current is 0 and the C-phase current is not 0, it is determined that the A-phase current has not been collected; when the A-phase current is not 0 and the C-phase current is 0, it is determined that the C-phase current has not been collected; when the substation outgoing line switch current is less than any phase current, it is determined that the current data is unreasonable; the remote control monitoring data is analyzed and processed to obtain remote control abnormal data; the online rate monitoring data is analyzed and processed to obtain online rate abnormal data, when the online rate is 0.0% or the real-time operating status is out of service and the online rate is 100.0%, it is determined that the online rate is abnormal; the master station information monitoring data is analyzed and processed to obtain master station information abnormal data; among them, when the OID of the controlled object is blank, it is determined that the remote control association is lost abnormally; statistics on automatic switch abnormal data; statistics on automatic switch manual setting data; statistics on stop alarm and update data; statistics on remote control defective cards, test cards, and unprotected cards.

[0012] As a preferred solution of the distribution automation terminal abnormal monitoring and early warning dispatch management method described in the present invention, wherein: the data portrait score of each automation switch is calculated according to the terminal abnormal data, and an abnormal data list is generated, including the following steps:

[0013] Through the analysis and statistical results of the key operating information of the automation switch, the scoring rules for the data portrait of the distribution automation switch are established: the data portrait score is composed of "scoring items + deduction items"; the scoring items total 35 points, which are composed of the weekly online rate and the historical remote control success rate, and the score = weekly online rate * 20% + historical remote control success rate * 15%; the total score of the deduction items is 65 points, mainly including the number of remote control preset failures, the number of line voltage collection abnormalities, the number of current collection abnormalities, the number of phase voltage collection abnormalities, the number of switch energy storage failures, the number of switch opening loss of voltage problems, and the number of remote signaling currents, 7 indicators, each indicator deduction value = the number of abnormal occurrences in the week * allocated score / 7, score = 65-number of remote control preset failures * proportion value / 7-number of line voltage collection abnormalities * proportion value / 7-current collection abnormalities * ratio Example value / 7-Phase voltage collection abnormality*Proportional value / 7-Switch opening loss of pressure problem*Proportional value / 7-Number of switch opening loss of pressure problem*Proportional value / 7-Number of frequent remote signal abnormalities*Proportional value / 7; Obtain the interface data of "Entire network operational terminal ledger" and "Remote control association loss" to obtain the details of the entire network operational terminal ledger and the details of the automation switch; Count the number and operation status of the power distribution terminals in the county bureaus in the network area on a daily basis, and display the information fields including: county bureau, total number of accesses, total number of DTU accesses, total number of FTU accesses, total number of connections, total number of interruptions, online proportion, number of covered lines, and automation coverage rate; For each automation switch on a weekly basis, use the switch name as the output result in the unique identifier match, and calculate the data portrait score of each automation switch according to the automation switch data portrait scoring rules.

[0014] To further solve the above technical problems, the present invention provides the following technical solutions: a distribution automation terminal abnormal monitoring and early warning dispatch management system, comprising: a collection module, used to obtain terminal operation data from the distribution network master station system; an abnormal analysis module, used to analyze, process and count the terminal operation data to obtain terminal abnormal data; a statistical module, used to calculate the data portrait score of each automation switch based on the terminal abnormal data, and generate an abnormal data list.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the distribution automation terminal abnormality monitoring and early warning dispatch management method as described above are implemented.

[0016] 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 automation terminal abnormality monitoring and early warning dispatch management method as described above are implemented.

[0017] The beneficial effects of the present invention are as follows: 1. Automatically obtain key operating information of distribution automation terminals from the distribution network master station system, and clarify 7 types of key information content during the terminal operation process; 2. Integrate data into the database according to the terminal abnormal problem category to form a complete abnormal problem pool, which can realize automatic update and statistical output of terminal abnormal data; 3. Real-time detection of terminal abnormal problems forms a problem library list and regularly pushes it to the distribution operation and maintenance unit, greatly improving the efficiency of operation and maintenance work; 4. It has scalability and can be promoted to the whole of Guizhou, and even to the professional application of Southern Power Grid distribution automation. After promotion, it can realize abnormal monitoring, early warning and unified management of distribution automation terminals, and can greatly improve the efficiency and level of distribution automation terminal operation and maintenance management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] Figure 1 This is a schematic diagram of the overall process of a distribution automation terminal abnormality monitoring and early warning dispatch management method proposed by the present invention;

[0020] Figure 2 It is a remote local status monitoring diagram of the automation switch in the abnormal monitoring and early warning dispatching management method of the distribution automation terminal proposed by the present invention;

[0021] Figure 3 It is a terminal reclosing state monitoring diagram in a distribution automation terminal abnormality monitoring and early warning dispatch management method proposed by the present invention;

[0022] Figure 4 It is a terminal protection switching state diagram in a distribution automation terminal abnormality monitoring and early warning dispatching management method proposed by the present invention;

[0023] Figure 5 It is a terminal switch non-energy storage diagram in a distribution automation terminal abnormality monitoring and early warning dispatch management method proposed by the present invention;

[0024] Figure 6 This is an alarm data diagram in a distribution automation terminal abnormality monitoring and early warning dispatch management method proposed by the present invention;

[0025] Figure 7 It is a schematic representation of the alarm data of frequent switch telesignaling in a distribution automation terminal abnormality monitoring and early warning dispatching management method proposed by the present invention;

[0026] Figure 8 This is a schematic diagram of the unreasonable line voltage inspection of distribution network terminals in a distribution automation terminal abnormal monitoring and early warning dispatch management method proposed by the present invention;

[0027] Fig. 9 This is a schematic diagram of monitoring unreasonable phase voltage of distribution network terminals in a distribution automation terminal abnormal monitoring and early warning dispatch management method proposed by the present invention;

[0028] Fig.10 This is a schematic representation of unreasonable current monitoring of distribution terminals in a distribution automation terminal abnormality monitoring and early warning dispatch management method proposed by the present invention. DETAILED DESCRIPTION

[0029] 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.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Example 1, reference Figures 1 to 10 , which is an embodiment of the present invention, provides a distribution automation terminal abnormality monitoring and early warning dispatch management method.

[0032] The present application provides an effective solution to the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to implement the distribution automation terminal abnormal monitoring and early warning dispatch management method.

[0033] Figure 1 The overall flow chart of a distribution automation terminal abnormality monitoring and early warning dispatch management method is shown, including:

[0034] S1: Obtain terminal operation data from the distribution network master station system.

[0035] Specifically, the terminal operation data includes remote signaling monitoring data, telemetry monitoring data, self-healing remote control monitoring data, morning exercise monitoring data, dispatching remote control monitoring data, online rate monitoring data, and master station information monitoring data.

[0036] Among them, the abnormal data of telesignal monitoring include the remote local status of the switch, the reclosing status, the protection function status, the switch no energy storage status, the switch opening pressure loss status and the frequent switch telesignaling; the abnormal data of telemetering monitoring include abnormal line voltage, abnormal phase voltage and abnormal current; the abnormal data of remote control monitoring include self-healing remote control failure, morning operation remote control failure and dispatching remote control failure; the abnormal data of online rate include the data with online rate of 0% and the data with real-time operation status of shutdown and online rate of 100%; the abnormal data of master station information include the loss of remote control association, abnormal movement of automatic switch, manual setting of automatic switch, stop alarm and update and remote control defect sign.

[0037] S101: Build a python program to simulate the manual operation process;

[0038] S102: Acquire telesignal monitoring data from the distribution network master station system; the telesignal monitoring data includes the remote local status of the terminal, the reclosing switch on / off status, the protection function on / off status, the switch non-energy storage status, the switch opening loss-of-voltage signal, and the intelligent alarm frequent sending signal;

[0039] S103: Acquire telemetry monitoring data from the distribution network master station system; the telemetry monitoring data includes the power supply side Uab and the load side Uab of the distribution terminal, the power supply side Ua, Ub, Uc and the load side Ua, Ub, Uc of the distribution terminal, and the substation outlet switch Ia and the distribution terminals Ia, Ib, Ic;

[0040] S104: Acquire remote control monitoring data from the distribution network master station system; the remote control monitoring data includes self-healing remote control alarm data, network-wide remote control preset failure results, remote control accuracy statistics of sub-county bureaus, and remote control accuracy statistics reports;

[0041] S105: Acquire online rate monitoring data from the distribution network master station system; the online rate monitoring data includes historical online rate statistics of the county bureau and historical online rate statistical reports;

[0042] S106: Obtain the master station information monitoring data from the distribution network master station system; the master station information monitoring data includes remote control association loss data, automatic switch change data, automatic switch manual setting data, stop alarm and update data, and remote control defective card, test card, and unprotected card data.

[0043] It should be noted that the current operation and maintenance monitoring of distribution automation terminals mainly relies on manual operation, and there are the following technical problems: First, the distribution network master station automation operation and maintenance personnel need to manually log in to the system regularly, switch different functional modules, set query conditions and export data. This repetitive manual operation is not only time-consuming and labor-intensive, but also prone to errors; second, due to the limitations of manual operation, the frequency of data acquisition is usually once a week, which cannot reflect the real-time status changes of terminal equipment in a timely manner; third, the system interface and operation steps need to be frequently switched during manual operation, which is easy to miss some key data; fourth, the operating habits and data acquisition methods of different operation and maintenance personnel may differ, resulting in the acquired data being not unified and standardized; fifth, manual operation methods are difficult to cope with the surge in data volume caused by the increase in the number of terminal devices. Step S1 achieves the following beneficial effects by building a python program to simulate the manual operation process: First, the data acquisition process that originally required repeated manual operations is automated, which greatly reduces manual workload and improves work efficiency; second, the program can flexibly configure the frequency of data acquisition, from the original once a week to daily or even more frequent monitoring, which significantly improves the real-time nature of data acquisition; third, the program automatically completes all data acquisition operations according to the preset process, avoiding omissions that may be caused by manual operation; fourth, unified procedural operations ensure the standardization and consistency of data acquisition; fifth, the program has batch processing capabilities, which can easily cope with the increase in data volume caused by the increase in the number of terminal devices; finally, this step provides a complete, timely and standardized data basis for subsequent abnormal monitoring and analysis, and is an important prerequisite for realizing intelligent monitoring of distribution automation terminals.

[0044] S2: Analyze, process and count the terminal operation data to obtain terminal abnormal data.

[0045] Specifically, the terminal abnormal data includes remote signaling monitoring abnormal data, telemetry monitoring abnormal data, remote control monitoring abnormal data, online rate abnormal data, and master station information abnormal data.

[0046] S201: Analyze and process the remote signal monitoring data to obtain switch abnormality data.

[0047] Among them, when the remote / local state of the switch is 0, it is determined that the local state is abnormal;

[0048] When the reclosing switch input state is 0, it is judged as abnormal that the reclosing switch is not input;

[0049] When the three-stage overcurrent protection working mode is started to 0, it is judged as protection failure abnormality;

[0050] When the switch does not store energy signal is 1, it is determined that the switch does not store energy abnormally;

[0051] When the same switch sends out the opening and AC voltage loss signals simultaneously within 30 seconds, it is judged as the switch opening voltage loss abnormality;

[0052] When the same switch signal occurs more than 10 times within 1 hour, it is judged as frequent abnormality of switch remote signal.

[0053] S202: Analyze and process the telemetry monitoring data to obtain abnormal electrical quantity data.

[0054] Among them, for abnormal line voltage: match the Uab data on the load side and the Uab data on the power side of the same switch. When the Uab on the power side and the Uab on the load side are both 0, it is determined that the line voltage is not collected; when the Uab on the power side is 0 and the Uab on the load side is not 0, it is determined that the power side is not collected; when the Uab on the power side is not 0 and the Uab on the load side is 0, it is determined that the load side is not collected; when the Uab on the power side is less than 7 or greater than 12, it is determined that the line voltage data is abnormal;

[0055] For phase voltage anomalies: match the three-phase voltage data on the power supply side and the load side of the same switch, and analyze the power supply side and load side phase voltage acquisition anomalies according to the rule criterion table;

[0056] For current anomalies: match the three-phase current data of the same switch and the current data of the substation outgoing line switch. When the three-phase current difference is greater than 50, it is determined to be three-phase unbalanced; when the A phase current is 0 and the C phase current is not 0, it is determined that the A phase current is not collected; when the A phase current is not 0 and the C phase current is 0, it is determined that the C phase current is not collected; when the substation outgoing line switch current is less than any phase current, it is determined that the current data is unreasonable.

[0057] S203: Analyze and process the remote control monitoring data to obtain remote control abnormality data.

[0058] Among them, the self-healing remote control alarm data with action descriptions of "automatic control selection failure", "automatic control selection failure", "self-healing control failure", "self-healing control failure" and the operator "da" are filtered;

[0059] Statistics of remote control preset failure results for the entire network;

[0060] Add three marking fields to the remote control accuracy statistical report: "Whether to include defects", "Control separation failure defects", and "Control combination failure defects", and calculate the "Number of control separation failures", "Number of control combination failures", and "Total number of failures".

[0061] S204: Analyze and process the online rate monitoring data to obtain online rate abnormal data. When the online rate is 0.0% or the real-time operating state is shutdown and the online rate is 100.0%, it is determined that the online rate is abnormal.

[0062] S205: Analyze and process the master station information monitoring data to obtain master station information abnormality data.

[0063] Among them, when the OID of the controlled object is blank, it is judged as a remote control association loss exception; statistics on automation switch changes; statistics on automation switch manual setting data; statistics on stop alarm and update data; statistics on remote control defective cards, test cards, and unprotected cards.

[0064] It should be noted that in the prior art, abnormal analysis of distribution terminals mainly relies on manual experience and judgment, and there are the following technical problems: first, faced with massive terminal operation data, manual analysis is inefficient and it is difficult to detect and handle abnormal situations in a timely manner; second, the experience and judgment standards of different analysts are inconsistent, resulting in subjective differences in abnormal judgment results; third, it is difficult to achieve comprehensive analysis of data in multiple dimensions by manual labor alone, and it is easy to miss some complex abnormal situations; fourth, manual analysis methods make it difficult to establish a systematic abnormal classification system, which is not conducive to the tracking and statistics of abnormal problems; fifth, the lack of standardized analysis processes and judgment rules affects the reliability of the analysis results. Step S2 achieves the following beneficial effects by establishing a complete abnormal analysis rule system: First, the abnormal judgment that originally relied on manual experience is transformed into a procedural analysis based on clear rules, which significantly improves the analysis efficiency and accuracy; second, the unified judgment rules ensure the consistency and comparability of the abnormal analysis results and eliminate human subjective differences; third, through the comprehensive analysis of multi-dimensional data such as telesignaling, telemetering, and remote control, a complete abnormality recognition model is established, which can detect complex abnormalities such as "switch opening loss of pressure" and "three-phase imbalance"; fourth, the systematic abnormality classification method facilitates the establishment of an abnormal problem database, which is helpful for statistical analysis and historical tracing; fifth, the standardized analysis process improves the reliability and repeatability of abnormal monitoring; finally, this step realizes the comprehensive, timely and accurate identification of terminal abnormalities, providing strong support for subsequent early warning dispatch and equipment maintenance.

[0065] S3: Calculate the data profile score of each automation switch based on the terminal abnormal data and generate an abnormal data list.

[0066] Specifically, the data portrait score is calculated through scoring items and deduction items. The scoring items include the weekly online rate and the historical remote control success rate. The deduction items include the number of remote control preset failures, the number of line voltage collection abnormalities, the number of current collection abnormalities, the number of phase voltage collection abnormalities, the number of times the switch failed to store energy, the number of switch opening pressure loss problems, and the number of frequent remote signal transmissions.

[0067] Scoring rules for distribution automation switch data profile scoring:

[0068] 1. The data profile score consists of "scoring items + deduction items";

[0069] 2. The total score is 35 points, which is composed of weekly online rate (20 points) and historical remote control success rate (15 points). Score = weekly online rate * 20% + historical remote control success rate * 15%;

[0070] 3. The total score of deduction items is 65 points, mainly including 7 indicators: number of remote control preset failures (15 points), number of line voltage collection abnormalities (10 points), number of current collection abnormalities (15 points), number of phase voltage collection abnormalities (5 points), number of switch failures (10 points), number of switch opening loss of voltage problems (5 points), and number of telesignal frequent current (5 points). The deduction value of each indicator = number of abnormal occurrences in the week * allocated score / 7, score = 65-number of remote control preset failures * proportion value / 7-number of line voltage collection abnormalities * proportion value / 7-current collection abnormalities * proportion value / 7-phase voltage collection abnormalities * proportion value / 7-switch opening loss of voltage problems * proportion value / 7-number of switch opening loss of voltage problems * proportion value / 7-number of telesignal frequent abnormalities * proportion value / 7

[0071] Get data: Get the data from the "Entire Network Operated Terminal Ledger" and "Remote Control Association Lost" interface to get the details of the entire network's operated terminal ledger and automation switch details.

[0072] Output requirements: Statistics on the number and operation status of power distribution terminals in county-level bureaus in the network area on a daily basis, and display information fields including: county-level bureau, total number of accesses, total number of DTU accesses, total number of FTU accesses, total number of connections, total number of interruptions, online percentage, number of covered lines, and automation coverage rate. For each automation switch on a weekly basis, use the switch name as a unique identifier to match the output results in (II), and calculate the data profile score of each automation switch according to the automation switch data profile scoring rules.

[0073] In summary, the present invention automatically obtains key operating information of distribution automation terminals from the distribution network master station system, and clarifies 7 types of key information content during the terminal operation process; integrates data into the database according to the terminal abnormal problem category to form a complete abnormal problem pool, which can realize automatic update and statistical output of terminal abnormal data; real-time detected terminal abnormal problems form a problem library list and regularly push it to the distribution operation and maintenance unit, greatly improving the operation and maintenance work efficiency; the present invention is scalable and can be promoted to the whole of Guizhou, and even to the professional application of Southern Power Grid distribution automation. After promotion, it can realize abnormal monitoring, early warning and unified management of distribution automation terminals, and can greatly improve the efficiency and level of distribution automation terminal operation and maintenance management.

[0074] Embodiment 2 is an embodiment of the present invention, which provides a distribution automation terminal abnormal monitoring and early warning dispatch management system, including:

[0075] The collection module is used to obtain terminal operation data from the distribution network master station system; the abnormality analysis module is used to analyze, process and count the terminal operation data to obtain terminal abnormal data; the statistical module is used to calculate the data portrait score of each automation switch based on the terminal abnormal data and generate an abnormal data list.

[0076] Embodiment 3 is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software function 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in 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 and other media that can store program codes.

[0077] 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.

[0078] 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, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0079] 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, a plurality of 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.

[0080] Example 4 is an embodiment of the present invention, which provides a distribution automation terminal abnormality monitoring and early warning dispatch management method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0081] First, the terminal operation data is automatically obtained: through Python programming, the human operation process is simulated, and the machine is used to automatically obtain the distribution automation terminal operation information from the distribution network master station system on a regular basis, which mainly includes 7 types of key information: (1) remote signal monitoring; (2) telemetry monitoring; (3) self-healing remote control monitoring; (4) morning operation monitoring; (5) dispatching remote control monitoring; (6) online rate monitoring; (7) master station information monitoring.

[0082] The contents of telesignal monitoring include the local status of the terminal remotely, the status of the reclosing switch, the status of the protection function, the status of the switch without energy storage, the status of the switch opening without voltage, and the frequent telesignaling of the switch. The contents of telemetering monitoring include the monitoring of abnormal line voltage, abnormal phase voltage, and abnormal current. The contents of self-healing remote control monitoring are the daily self-healing remote control alarm data. The contents of morning exercise monitoring are the results of the failure of remote control preset during the daily automatic morning exercise of the system. The contents of dispatch remote control monitoring are the system's "county bureau remote control accuracy statistics" and "remote control accuracy statistics report" data. The contents of online rate monitoring are the system's "county bureau historical online rate statistics" and "historical online rate statistics report" data. The contents of master station information monitoring include "remote control association loss", "automatic switch abnormal movement", "automatic switch manual setting", "stop alarm and update", "remote control defect card", "test card", and "no protection card" data.

[0083] Secondly, the above 7 types of key monitoring information data are processed, analyzed and counted according to the corresponding rules and requirements.

[0084] 1. Remote signal monitoring.

[0085] like Figure 2 As shown, the remote local status:

[0086] Monitoring signal: remote / local status of switch;

[0087] Obtain data: remote on-site status monitoring of the terminal.

[0088] Data processing rules: The remote / local state of the switch is "0" (1 is remote state; 0 is local state)

[0089] Output requirements: Count the number of "on-site status" of each sub-county bureau on a daily basis (data statistics summary), output the Excel table with detailed details according to the "on-site status list", record the number of abnormalities on a weekly basis, and pay attention to converting the responsibility area into the sub-county bureau display. The display information fields include: sub-county bureau, plant station, feeder, terminal name, switch name, and number of times. The corresponding relationship between the sub-county bureau and the responsibility area number:

[0090] Table 1 Correspondence between county bureaus and responsible area codes

[0091]

[0092]

[0093] like Figure 3 As shown, the reclosing switch throw and retract status:

[0094] Monitoring signal: reclosing switch input status (automatic reclosing switch input, reclosing switch soft pressure plate, overcurrent protection starts reclosing switch, zero-sequence overcurrent protection starts reclosing switch, one-time reclosing switch input, total reclosing switch input, reclosing switch input soft pressure plate, reclosing switch function soft pressure plate input and output, one-time reclosing switch input and output

[0095] Get data: terminal reclosing status monitoring.

[0096] Data processing rules: filter the "actual value collection time" to the time range of the past two weeks calculated from the current time and the "actual value" of the reclosing state is 0).

[0097] Output requirements: Count the number of "failed reclosing switches" in each sub-county bureau on a daily basis (data statistics summary), and output the details in an Excel table according to the "reclosing switch exit list". Record the number of abnormalities on a weekly basis. Pay attention to converting the responsibility area into sub-county bureau display. The display information fields include: sub-county bureau, plant station, feeder, terminal name, switch name, and number of times.

[0098] like Figure 4 As shown, the protection function is in the state of activation and deactivation:

[0099] Monitoring signal: three-stage overcurrent protection working mode start signal;

[0100] Obtain data: terminal protection deployment and withdrawal status monitoring.

[0101] Data processing rules: The three-stage overcurrent protection working mode is started as "0".

[0102] Output requirements: Count the number of "unprotected" items in each county bureau on a daily basis (data statistics summary), output the Excel table with detailed information according to the "unprotected list", record the number of abnormalities on a weekly basis, and pay attention to converting the responsibility areas into county bureaus for display. The display information fields include: county bureau, plant station, feeder, terminal name, switch name, and number of times.

[0103] like Figure 5 As shown, the switch is not storing energy:

[0104] Monitoring signal: switch no energy storage signal;

[0105] Get data: Terminal switch not charged monitoring.

[0106] Data processing rules: switch is not storing energy, which is "1"

[0107] Output requirements: Count the number of "switches without energy storage" in each county bureau on a daily basis (data statistics summary), and output the Excel table with detailed information according to the "list of switches without energy storage", record the number of abnormalities on a weekly basis, and pay attention to converting the responsibility area into county bureau display. The display information fields include: county bureau, plant station, feeder, terminal name, switch name, and number of times.

[0108] like Figure 6 As shown, the switch is open and voltage-loss state:

[0109] Monitoring signal: switch opening and AC voltage loss signal;

[0110] Acquire data: alarm data within one day (switch action + abnormal signal).

[0111] Data processing rules: Find out the switches that have triggered opening (action) and AC voltage loss (action) within 30 seconds.

[0112] Output requirements: Count the number of "switch opening loss of pressure problems" in each sub-county bureau on a daily basis (data statistics summary), and output the excel table with detailed details according to the "switch opening loss of pressure problem list", record the number of abnormalities on a weekly basis, and match the corresponding terminal name, plant station, sub-county bureau and other information through the feeder + switch name. The displayed information fields include: sub-county bureau, plant station, feeder, terminal name, switch name, and number of times.

[0113] like Figure 7 As shown, the switch remote signal is frequent:

[0114] Monitoring signal: intelligent alarm frequent signal reasoning;

[0115] Obtain data: intelligent alarm within a day (signal frequency reasoning).

[0116] Data processing rules: filter switches whose switch signals occur more than 10 times in 1 hour within a day.

[0117] Output requirements: Count the number of "frequent switch remote signals" in each county bureau on a daily basis (data statistics summary), output the excel table with detailed details according to "frequent switch remote signals", record the number of abnormalities on a weekly basis, match the corresponding terminal name, feeder, county bureau and other information through the substation + switch name, and display the information fields including: county bureau, plant station, feeder, terminal name, switch name, and number of times.

[0118] 2. Telemetry monitoring.

[0119] (1) Line voltage abnormality (such as Figure 8 shown):

[0120] Monitoring content: Uab on the power supply side and Uab on the load side of the distribution terminal;

[0121] Acquisition of data: Monitoring of unreasonable line voltage at distribution terminals.

[0122] Data processing rules: Use the switch on the power supply side to match the Uab data on the load side, traverse the data, find the Uab data on the load side and the Uab data on the power supply side of the same switch, and output the results according to the rules in Table 2. When the rule criterion is true, the corresponding result is output.

[0123] Table 2 Line voltage rule criteria

[0124]

[0125] Output requirements: Count the number of "line voltage collection anomalies" in each county bureau on a daily basis (data statistics summary), and output the excel table with detailed details according to "line voltage collection anomalies". Find the corresponding terminal name and county bureau information through the plant station + feeder + switch name. The displayed information fields include: county bureau, plant station, feeder, terminal name, switch name, and output problem.

[0126] (2) Abnormal phase voltage (such as Fig. 9 shown):

[0127] Monitoring content: Ua, Ub, Uc on the power supply side of the distribution terminal and Ua, Ub, Uc on the load side;

[0128] Acquisition of data: Monitoring of unreasonable phase voltage at distribution terminals.

[0129] Data processing rules: The same switch information matches the power supply side Ua, Ub, Uc and load side Ua, Ub, Uc information

[0130] Use the switch on the power supply side Ua to match the power supply side Ub and Uc data, traverse the data, find the Ua, Ub, and Uc data on the power supply side of the same switch, and output the results according to the rules in Table 3 below.

[0131] Table 3 Phase voltage rule criteria on the power supply side

[0132]

[0133]

[0134] Use the switch on the load side Ua to match the load side Ub and Uc data, traverse the data, find the load side Ua, Ub, and Uc data of the same switch, and output the results according to the rules in Table 4 below.

[0135] Table 4 Load side phase voltage rule criterion

[0136]

[0137] Output requirements: Count the number of "phase voltage collection anomalies" in each county bureau on a daily basis (data statistics summary), integrate the output results of the phase voltage data on the power supply side and the phase voltage data on the load side, and output the excel table in detail according to the "line voltage collection anomalies". Match the corresponding terminal name and county bureau information through the plant + feeder + switch name. The displayed information fields include: county bureau, plant, feeder, terminal name, switch name, output problem (power supply side), output problem (load side).

[0138] (3) If the current is abnormal (such as Fig.10 shown):

[0139] Monitoring content: substation outgoing line switch Ia and distribution terminals Ia, Ib, Ic;

[0140] Acquisition of data: unreasonable current monitoring at distribution terminals.

[0141] Data processing rules: match the Ib and Ic data with the switch of the distribution terminal Ia, traverse the data, and find the data of the distribution terminals Ia, Ib, and Ic with the same switch; find the substation outgoing line switch Ia data according to the feeder to which the switch of the distribution terminal Ia belongs, and output the results according to the rules in Table 5 below.

[0142] Table 5 Current rule criteria

[0143]

[0144]

[0145] Output requirements: Count the number of "current collection anomalies" in each county bureau on a daily basis (data statistics summary), and output the excel table with detailed details according to "line voltage collection anomalies". Match the corresponding terminal name and county bureau information through the plant + feeder + switch name. The displayed information fields include: county bureau, plant, feeder, terminal name, switch name, and output problem.

[0146] 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 distribution automation terminal abnormal monitoring and early warning dispatch management method, characterized in that: include: Obtain terminal operation data from the distribution network master station system; Analyze, process and count the terminal operation data to obtain terminal abnormal data; The data profile score of each automation switch is calculated based on the terminal abnormal data, and an abnormal data list is generated.

2. The distribution automation terminal abnormality monitoring and early warning dispatch management method according to claim 1, characterized in that: The terminal operation data includes remote signaling monitoring data, telemetry monitoring data, self-healing remote control monitoring data, morning exercise monitoring data, dispatch remote control monitoring data, online rate monitoring data, and master station information monitoring data.

3. The distribution automation terminal abnormality monitoring and early warning dispatch management method according to claim 2, characterized in that: The terminal abnormal data includes remote signaling monitoring abnormal data, telemetry monitoring abnormal data, remote control monitoring abnormal data, online rate abnormal data, and master station information abnormal data; The remote signal monitoring abnormal data includes the remote local state of the switch, the reclosing state, the protection function state, the switch no energy storage state, the switch opening pressure loss state and the frequent remote signal of the switch; The telemetry monitoring abnormal data includes line voltage abnormality, phase voltage abnormality and current abnormality; The remote control monitoring abnormal data includes self-healing remote control failure, morning exercise remote control failure and scheduling remote control failure; The abnormal online rate data includes data with an online rate of 0% and data with a real-time operating status of shutdown and an online rate of 100%; The master station information abnormal data includes remote control association loss, automation switch abnormality, manual setting of automation switch, stop alarm and update, and remote control defect sign.

4. The distribution automation terminal abnormality monitoring and early warning dispatch management method according to claim 3, characterized in that: The data portrait score is calculated through scoring items and deduction items. The scoring items include the weekly online rate and the historical remote control success rate. The deduction items include the number of remote control preset failures, the number of line voltage collection abnormalities, the number of current collection abnormalities, the number of phase voltage collection abnormalities, the number of times the switch failed to store energy, the number of switch opening pressure loss problems, and the number of frequent remote signal transmissions.

5. The distribution automation terminal abnormality monitoring and early warning dispatch management method according to claim 4, characterized in that: Obtaining terminal operation data from the distribution network master station system includes the following steps: Build a python program to simulate the manual operation process; Acquire remote signal monitoring data from the distribution network master station system; the remote signal monitoring data includes the remote local status of the terminal, the reclosing switch on / off status, the protection function on / off status, the switch no energy storage status, the switch opening loss of pressure signal and the intelligent alarm frequent signal; Acquire telemetry monitoring data from the distribution network master station system; the telemetry monitoring data includes the power supply side Uab and the load side Uab of the distribution terminal, the power supply side Ua, Ub, Uc and the load side Ua, Ub, Uc of the distribution terminal, and the substation outlet switch Ia and the distribution terminals Ia, Ib, Ic; Acquire remote control monitoring data from the distribution network master station system; the remote control monitoring data includes self-healing remote control alarm data, network-wide remote control preset failure results, county-level remote control accuracy statistics, and remote control accuracy statistics reports; Acquire online rate monitoring data from the distribution network master station system; the online rate monitoring data includes historical online rate statistical data of the county bureau and historical online rate statistical reports; Obtain master station information monitoring data from the distribution network master station system; The master station information monitoring data includes remote control association loss data, automation switch abnormal data, automation switch manual setting data, stop alarm and update data, and remote control defective card, test card, and unprotected card data.

6. The distribution automation terminal abnormality monitoring and early warning dispatch management method according to claim 5, characterized in that: Analyzing, processing and counting the terminal operation data to obtain terminal abnormal data includes the following steps: The remote signal monitoring data is analyzed and processed to obtain switch abnormality data; when the remote / local state of the switch is 0, it is determined that the local state is abnormal; when the reclosing switch input state is 0, it is determined that the reclosing switch is not abnormal; When the three-stage overcurrent protection working mode is started to 0, it is judged as protection failure abnormality; when the switch does not store energy signal is 1, it is judged as switch does not store energy abnormality; when the same switch sends out opening and AC voltage loss signals at the same time within 30 seconds, it is judged as switch opening voltage loss abnormality; when the same switch signal occurs more than 10 times within 1 hour, it is judged as switch remote signal frequent abnormality; The telemetry monitoring data is analyzed and processed to obtain abnormal electrical quantity data; for line voltage abnormality: match the Uab data on the load side and the Uab data on the power supply side of the same switch, when the Uab data on the power supply side and the Uab data on the load side are both 0, it is determined that the line voltage is not collected; when the Uab data on the power supply side is 0 and the Uab data on the load side is not 0, it is determined that the power supply side is not collected; when the Uab data on the power supply side is not 0 and the Uab data on the load side is 0, it is determined that the load side is not collected; when the Uab data on the power supply side is less than 7 or greater than 12, it is determined that the line voltage data is abnormal; for phase voltage abnormality: match the three-phase voltage data on the power supply side and the load side of the same switch, and analyze the abnormal phase voltage collection on the power supply side and the load side according to the rule criterion table; For current anomalies: match the three-phase current data of the same switch and the current data of the substation outgoing line switch. When the difference between the three-phase currents is greater than 50, it is determined that the three-phase is unbalanced; when the A-phase current is 0 and the C-phase current is not 0, it is determined that the A-phase current is not collected; when the A-phase current is not 0 and the C-phase current is 0, it is determined that the C-phase current is not collected; when the substation outgoing line switch current is less than any phase current, it is determined that the current data is unreasonable; Analyzing and processing the remote control monitoring data to obtain remote control abnormality data; Analyze and process the online rate monitoring data to obtain abnormal online rate data. When the online rate is 0.0% or the real-time operating state is shutdown and the online rate is 100.0%, it is determined that the online rate is abnormal. The master station information monitoring data is analyzed and processed to obtain the master station information abnormal data; wherein, when the OID of the controlled object is blank, it is determined to be a remote control association loss abnormality; statistics on automation switch abnormality data; statistics on automation switch manual setting data; statistics on stop alarm and update data; statistics on remote control defective cards, test cards, and unprotected cards.

7. The distribution automation terminal abnormality monitoring and early warning dispatch management method according to claim 6, characterized in that: Calculating the data profile score of each automation switch according to the terminal abnormal data and generating an abnormal data list includes the following steps: Through the analysis and statistical results of the key operation information of the automation switch, the scoring rules for the data portrait of the distribution automation switch are established: the data portrait score is composed of "scoring items + deduction items"; the scoring items total 35 points, which are composed of weekly online rate and historical remote control success rate, score = weekly online rate * 20% + historical remote control success rate * 15%; the total score of the deduction items is 65 points, mainly including the number of remote control preset failures, the number of line voltage acquisition abnormalities, the number of current acquisition abnormalities, the number of phase voltage acquisition abnormalities, the number of switch energy storage failures, the number of switch opening loss of voltage problems, and the number of frequent remote signal currents, 7 indicators, each indicator deduction value = the number of abnormal occurrences in the week * allocated score / 7, score = 65-number of remote control preset failures * proportion value / 7-number of line voltage acquisition abnormalities * proportion value / 7-current acquisition abnormalities * proportion value / 7-phase voltage acquisition abnormalities * proportion value / 7-switch opening loss of voltage problems * proportion value / 7-number of switch opening loss of voltage problems * proportion value / 7-number of frequent remote signal abnormalities * proportion value / 7; Obtain the data from the "Entire Network Operated Terminal Ledger" and "Remote Control Association Lost" interfaces, and obtain the details of the entire network's operated terminal ledger and automation switch details; Statistics on the number and operation status of power distribution terminals in county bureaus in the network area on a daily basis, with information fields including: county bureau, total number of accesses, total number of DTU accesses, total number of FTU accesses, total number of connections, total number of interruptions, online percentage, number of covered lines, and automation coverage rate; For each automation switch on a weekly basis, the switch name is used as the output result in the unique identification matching, and the data profile score of each automation switch is calculated according to the automation switch data profile scoring rules.

8. A system using the distribution automation terminal abnormality monitoring and early warning dispatch management method as claimed in any one of claims 1 to 7, characterized in that: include: The acquisition module is used to obtain terminal operation data from the distribution network master station system; The abnormality analysis module is used to analyze, process and count the terminal operation data to obtain terminal abnormality data; The statistical module is used to calculate the data portrait score of each automation switch according to the terminal abnormal data and generate an abnormal data list.

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 automation terminal abnormality monitoring and early warning dispatch management 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 automation terminal abnormality monitoring and early warning dispatch management method described in any one of claims 1 to 7 are implemented.

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