Method for quickly acquiring power failure information of power distribution network area

Abstract

The invention discloses a method for quickly acquiring power failure information of a power distribution network area. The method comprises the steps of power grid area system architecture construction, data processing, fault study and judgment and experiment and result analysis. The beneficial effects of the invention are that based on an original concentrator and an original electric energy meter of a transformer area, a scheme of combining a power-off and power-on event reporting function and a power-off and power-on fault research and judgment system of an acquisition master station is adopted, so that the defects of a traditional power monitoring system in the aspects of power-off and power-on event rapid reporting and fault research and judgment are effectively overcome; through optimization of a system architecture, improvement of a data processing flow and innovation of a fault studying and judging mechanism, rapid and accurate reporting and deep analysis of power-off and power-on events of a transformer area side and a user side are realized.

Description

Technical Field

[0001] The present invention relates to a method for quickly acquiring power outage information in a distribution network substation, specifically a method based on the original concentrator and electric energy meter in the substation, combining a power outage event reporting function with a power outage fault analysis system of a collection master station, and belongs to the technical field of power monitoring. Background Art

[0002] In the operation and management of modern power systems, the power supply stability of the substation, as the basic unit of power distribution, is directly related to the power consumption experience of the majority of users. Timely monitoring and rapid processing of power outages are key links in ensuring the reliability of power supply. Traditional power monitoring methods often have many limitations when facing power outages on the substation side and the user side, such as delayed reporting, inaccurate fault analysis, and the need for additional large-scale hardware investment.

[0003] In the prior art, 1) the patent with publication number CN112821553A discloses a smart IoT sensing and control system and method based on a low-voltage distribution network substation, which uses energy controllers, branch box monitoring devices, meter box monitoring devices, new energy monitoring devices, etc. as IoT sensing nodes, which are installed at the main switch outlets, branch box outlets, meter box outlets, distributed photovoltaics, and charging piles on the low-voltage side of the distribution transformer, respectively, to realize the low-voltage switch status monitoring of each level of the low-voltage distribution network, and the synchronous collection of periodic voltage, current, power, indication and other electric energy data of segmented lines, users, distributed photovoltaics, and charging piles, as well as the edge computing functions such as quasi-real-time line loss calculation and abnormal alarm in the substation, so as to realize the functions of active reporting of power outages for low-voltage users, automatic identification of substation topology, meter box access control status monitoring, distributed photovoltaic monitoring, and orderly charging of charging piles, etc. Application; 2) A comprehensive evaluation method and system for distribution network substations disclosed in publication number CN114462848A, based on the historical power consumption data of the distribution network substation to be evaluated, obtains the evaluation index of the distribution network substation to be evaluated, and calculates the variance contribution coefficient of each evaluation index according to the evaluation index data; obtains the expert scoring data on the importance of each evaluation index, calculates the weight value of each evaluation index based on the scoring data, calculates the variance contribution coefficient and the weight value of each evaluation index based on the mean method to obtain the comprehensive evaluation weight value of each evaluation index, and calculates the comprehensive evaluation score according to the comprehensive evaluation weight value and the evaluation index data. On the basis of the evaluation based on the variance contribution calculation, the expert evaluation method is also superimposed to conduct an accurate, clear, convenient and scientific and reasonable evaluation of the distribution network substation.

[0004] However, existing power monitoring systems mostly rely on regular inspections or relatively simple fault alarm mechanisms. In terms of power outage event monitoring, the communication protocol between the energy meter and the concentrator may not be optimized for rapid event reporting, resulting in delayed information transmission. Moreover, the main acquisition station lacks intelligent fault analysis capabilities and can often only obtain simple power outage status information, making it difficult to accurately determine the type, scope and cause of the fault. This has brought great difficulties to the troubleshooting and repair work of power operation and maintenance personnel, prolonged the power outage time, increased power operation costs and reduced user satisfaction. The substation is usually equipped with a concentrator and an energy meter. The concentrator is responsible for collecting data from multiple energy meters in the substation and uploading it to the main acquisition station, while the energy meter is mainly responsible for energy metering tasks. However, these devices themselves have certain hardware resources and data processing capabilities. If their potential can be fully tapped, through software function upgrades and communication protocol optimization, they can play a key role in the monitoring and reporting of power outages and power-on events, avoiding the cost and installation complexity brought about by large-scale new hardware equipment. Therefore, it is of great practical significance to develop a system based on existing substation equipment resources that can efficiently realize rapid reporting and in-depth analysis of power outages and power-on events. Summary of the invention

[0005] The purpose of the present invention is to provide a method for quickly acquiring power outage information in a distribution network substation in order to solve at least one of the above technical problems.

[0006] The present invention achieves the above-mentioned purpose through the following technical scheme: a method for quickly obtaining power outage information of a distribution network station area, the method comprising the following steps: S1. Build the system architecture of the power grid area, optimize and upgrade the hardware and software systems of the original concentrators in the area: expand the functions of the original electric energy meters in the area, install the power outage event reporting function module in the original concentrators in the area, collect the power outage event data uploaded by the concentrator through the power outage fault judgment system of the main station, and analyze and judge it; S2, data processing, processing the data of the electric energy meter, the concentrator, and the main station; S3, fault analysis, judging the fault type based on big data analysis, combining the fault location of the substation topology structure, and inferring the fault cause based on multiple factors; S4. Experiment and result analysis: select several representative substations for experiments, and conduct comparative analysis of event reporting time and fault diagnosis accuracy on the experimental results.

[0007] As a further solution of the present invention: In S1, optimizing and upgrading the original concentrator hardware and software system in the substation area specifically includes: Add data cache space to the concentrator hardware to cope with the burst data transmission requirements that may occur when a power outage occurs; The upgraded concentrator software system has a built-in power outage event judgment logic module. The power outage event judgment logic module monitors the data flow and data characteristics received from the electric energy meter in real time. When data interruption or data changes that meet the characteristics of a power outage event are found, it is preliminarily judged as a possible power outage event and the subsequent data processing process is started.

[0008] As a further solution of the present invention: the function expansion of the original electric energy meter in the substation area specifically includes: Implant a power-off event monitoring chip in the energy meter or upgrade the function of the existing chip. The power-off event monitoring chip monitors the key power parameters of the energy meter in real time and uses a high-precision sampling algorithm to quickly and accurately capture abnormal changes in power parameters. Once a power outage event is determined to have occurred, the energy meter immediately generates an event report containing detailed event information and sends the report to the concentrator according to the optimized communication protocol.

[0009] As a further solution of the present invention: the power outage event reporting function module is responsible for receiving the power outage event report reported by the electric energy meter, and during the receiving process, performing data integrity check to check whether there is data loss or error; The received power outage event reports are sorted and packaged, and the power outage event reports of multiple electricity meters are classified and integrated according to the chronological order and the substation topology structure. The substation identification and concentrator number information are added. At the same time, an accurate timestamp is marked for each power outage event report with a time accuracy of milliseconds. An efficient communication transmission strategy is used to upload the packaged event data to the collection master station.

[0010] As a further solution of the present invention: the power failure analysis system of the acquisition master station includes a data receiving and caching module, a big data analysis module and an intelligent fault analysis module; The data receiving and caching module is responsible for receiving the power-off and power-on event data uploaded by the concentrator and storing it in a high-speed cache database. The cache database adopts a distributed storage architecture and can quickly write and read a large amount of data to meet the needs of real-time data processing. Big data analysis module: Use big data technology to conduct in-depth analysis of event data in the cache database; first, mine historical power outage event data and establish a power outage event feature model for the substation area. The power outage event feature model includes the occurrence patterns of power outage events in different time periods, different seasons, and different load conditions. Then, match and compare the newly received event data with the feature model, and preliminarily determine the type of event through algorithms such as similarity calculation; Intelligent fault analysis module: Based on big data analysis, it further combines the topological structure information of the substation, equipment operating parameter information and real-time meteorological data to perform intelligent fault analysis; the intelligent fault analysis module generates a detailed fault analysis report, which includes the possible cause of the fault, the scope of the affected substation, the estimated number of users involved, and the priority recommendations for fault repair.

[0011] As a further solution of the present invention: in S2, the data processing at the electric energy meter end specifically includes: The electric energy meter samples the electric power parameters at a high frequency, and the sampled data is input into the power-off event monitoring chip in real time; The monitoring chip processes the sampled data according to the preset power-off judgment algorithm. When it is determined that a power-off event has occurred, the event information is encoded in a specific data format to generate an event report; the event report includes but is not limited to the following information: the unique identification code of the energy meter, the time of the event, the type of event, the voltage and current values ​​of the last group of samples before the event, and the current operating status of the energy meter; The electric energy meter sends the event report to the concentrator through the power line carrier communication module. Before sending, the communication link is checked to ensure smooth communication. If the communication link fails, local caching is attempted and the event report is automatically resent after communication is restored.

[0012] As a further solution of the present invention: the data processing at the concentrator end specifically includes: The communication receiving module of the concentrator continuously monitors the communication signal from the electric energy meter, and once an event report is received, it is immediately transmitted to the power failure event reporting function module; The power-off event reporting function module first performs a data integrity check on the event report to check whether the data length meets the standard and whether there are any missing key data fields; if the data is complete, it proceeds to the next step; if the data is incomplete, it sends a data retransmission request to the electric energy meter; Organize and package complete event reports and sort them according to the grouping of electric energy meters in the substation area and the chronological order of event occurrence; Add the concentrator's own identification information, the station number and the current timestamp and other metadata information to the packaged event data, and then transmit the data to the communication sending module; The communication sending module selects the appropriate communication method according to the preset communication strategy to upload the event data to the acquisition main station; if the power carrier communication is normal, the power carrier communication is used first; if the power carrier communication fails or the signal quality is poor, it switches to wireless communication for data transmission; during the data transmission process, data encryption technology is used to encrypt the event data.

[0013] As a further solution of the present invention: the data collection and processing at the master station specifically includes: After the data receiving module of the acquisition master station receives the encrypted event data uploaded by the concentrator, it first performs a decryption operation and then stores the data in the cache database; The big data analysis module reads event data from the cache database and performs correlation analysis with historical data. At the same time, it combines the topological structure data of the substation area to determine the location and scope of the event. Based on the results of big data analysis, the intelligent fault analysis module conducts in-depth fault analysis by comprehensively considering equipment operating parameters and meteorological information factors. The intelligent fault analysis module generates a detailed fault analysis report, which includes fault cause analysis, impact scope assessment, repair suggestions and estimated repair time information, and stores the report in the database. At the same time, it is displayed to power operation and maintenance personnel through a visual interface so that they can quickly develop a fault repair plan.

[0014] As a further solution of the present invention: In S3, judging the fault type based on big data analysis specifically includes: The big data analysis module of the acquisition master station establishes a fault type classification model by mining a large amount of historical power outage event data; the fault types are divided into transient faults, permanent faults, intermittent faults and planned power outages; When new power-off event data is received, the features of the event data are input into the fault type classification model, and the probability distribution of the fault type is calculated by the model; The fault location based on the topological structure of the substation includes: The main acquisition station stores detailed topological information of the substation, including the connection relationship between transformers, distribution boxes, line branches and electric energy meters; After a power outage occurs, the location information of the energy meter in the event report is combined with the topology structure and a topology search algorithm is used to determine the area where the fault may occur. In addition to big data analysis and topological information, the main acquisition station also integrates other relevant information, including equipment operating parameters and meteorological data. Once the fault area is determined, these factors are comprehensively considered to infer the cause of the fault.

[0015] As a further solution of the present invention: in S4, when multiple representative substations are selected for experiments, concentrators and electric energy meters with upgraded functions are deployed in each substation, and a power outage fault analysis system for collecting the main station is built; during the experiment, various types of power outage events are simulated, including transient faults, permanent faults, intermittent faults and planned power outages; at the same time, the monitoring results of the traditional power monitoring system under the same experimental conditions are recorded for comparative analysis.

[0016] The beneficial effects of the present invention are: 1) The solution proposed in the present invention, which is based on the original concentrator and electric energy meter in the substation, combines the power outage event reporting function with the power outage fault analysis system of the main acquisition station, effectively solves the shortcomings of the traditional power monitoring system in terms of rapid reporting of power outage events and fault analysis; through the optimization of the system architecture, the improvement of the data processing flow and the innovation of the fault analysis mechanism, the rapid and accurate reporting and in-depth analysis of power outage events on the substation side and the user side are realized. The present invention has achieved remarkable results in terms of event reporting time, fault analysis accuracy and overall operation and maintenance efficiency improvement. It has high practical value and broad application prospects, and provides strong technical support for the intelligent operation and management of the power system. In future research, it can be further explored how to integrate the system with other power intelligent devices (such as intelligent switches, intelligent sensors, etc.) to achieve more comprehensive and intelligent power substation monitoring and management; 2) The system of the present invention can quickly report events and accurately identify faults. The power operation and maintenance personnel can quickly formulate and implement repair plans based on the fault analysis report provided by the acquisition master station. Compared with the traditional power monitoring system, it greatly shortens the average fault repair time, greatly improves the power supply reliability and operation and maintenance efficiency of the substation, and reduces the inconvenience and economic losses caused to users by power outages. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Obtain a flow chart for the present invention; Figure 2 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1, as Figure 1 to Figure 2 As shown, this embodiment provides a method for quickly obtaining power outage information of a distribution network area, and the method includes the following steps: S1. Build the system architecture of the power grid area, optimize and upgrade the hardware and software systems of the original concentrators in the area: expand the functions of the original electric energy meters in the area, install the power outage event reporting function module in the original concentrators in the area, collect the power outage event data uploaded by the concentrator through the power outage fault judgment system of the main station, and analyze and judge it; S2, data processing, processing the data of the electric energy meter, the concentrator, and the main station; S3, fault analysis, judging the fault type based on big data analysis, combining the fault location of the substation topology structure, and inferring the fault cause based on multiple factors; S4. Experiment and result analysis: select several representative substations for experiments, and conduct comparative analysis of event reporting time and fault diagnosis accuracy on the experimental results.

[0020] Embodiment 2: In addition to all the technical features of Embodiment 1, this embodiment also includes: The optimization and upgrade of the original concentrator hardware and software systems in the station area specifically include: Add data cache space to the concentrator hardware to cope with the burst data transmission requirements that may occur when a power outage occurs; The upgraded concentrator software system has a built-in power outage event judgment logic module. The power outage event judgment logic module monitors the data flow and data characteristics received from the electric energy meter in real time. When data interruption or data changes that meet the characteristics of a power outage event are found (such as the electric energy meter data flow suddenly drops to zero or recovers), it is preliminarily judged as a possible power outage event and the subsequent data processing process is started.

[0021] The function expansion of the original electric energy meter in the substation area specifically includes: A special power outage and power-on event monitoring chip is implanted in the electric energy meter or the function of the existing chip is upgraded. The power outage and power-on event monitoring chip monitors the key power parameters of the electric energy meter in real time, such as voltage and current, and adopts a high-precision sampling algorithm to quickly and accurately capture abnormal changes in power parameters. For example, when the voltage value drops from the normal power supply range to near zero or recovers from zero to the normal range in an extremely short time (such as within 10 milliseconds), it is determined that a power outage or power-on event has occurred.

[0022] Once a power outage event is determined to have occurred, the energy meter immediately generates an event report containing detailed event information (such as the energy meter number, the exact time of the event, the event type identifier, the current power parameter value, etc.), and sends the report to the concentrator according to the optimized communication protocol.

[0023] The power outage event reporting function module is responsible for receiving the power outage event report reported by the electric energy meter; during the receiving process, data integrity check is performed to check whether there is data loss or error; for example, the data in the event report is calculated through the checksum algorithm and compared with the checksum of the sending end.

[0024] The received power outage event reports are sorted and packaged, and the power outage event reports of multiple electricity meters are classified and integrated according to the time sequence and the substation topology structure, and the substation identification and concentrator number information are added so that the main acquisition station can quickly locate the area where the event occurred; at the same time, an accurate timestamp is marked for each power outage event report, and the time accuracy reaches the millisecond level; this is crucial for the time series analysis in the subsequent fault diagnosis. An efficient communication transmission strategy is used to upload the packaged event data to the acquisition main station. For example, the power carrier communication method is preferred. If the power carrier communication is interfered or fails, it will automatically switch to the backup wireless communication channel (such as 4G or 5G network) to ensure that the event data can be transmitted in a timely and stable manner.

[0025] The power failure analysis system of the acquisition master station includes a data receiving and caching module, a big data analysis module, and an intelligent fault analysis module, among which: Data receiving and caching module: responsible for receiving the power-off and power-on event data uploaded by the concentrator and storing it in a high-speed cache database. The database adopts a distributed storage architecture and can quickly write and read large amounts of data to meet the needs of real-time data processing; Big data analysis module: Use big data technology to conduct in-depth analysis of event data in the cache database; first, mine historical power outage event data to establish a power outage event feature model for the substation; the model includes the occurrence patterns of power outage events in different time periods, seasons, and load conditions, such as event frequency, duration, and association with meteorological factors. Then, match and compare the newly received event data with the feature model, and use algorithms such as similarity calculation to preliminarily determine the type of event (such as instantaneous fault, permanent fault, planned power outage, etc.); Intelligent fault analysis module: Based on big data analysis, it further combines the topological structure information of the substation, equipment operating parameter information (such as transformer capacity, line impedance, etc.) and real-time meteorological data (obtained from the interface of the meteorological department) to conduct intelligent fault analysis; for example, if multiple adjacent electric energy meters have power outages at the same time, and combined with the topological structure, they are judged to be branches of the same power supply line, and the meteorological data shows that there are strong winds or heavy rains in the area, it is speculated that the line may be damaged by bad weather and cause the failure. This module generates a detailed fault analysis report, which includes the possible cause of the fault, the scope of the substation affected, the estimated number of users involved, and the priority recommendations for fault repair.

[0026] Embodiment 3, in addition to all the technical features of Embodiment 1, this embodiment also includes: the data processing at the electric energy meter end specifically includes: the electric energy meter samples the power parameters such as voltage and current at a high frequency (such as 1000 times per second), and the sampled data is input into the power-off event monitoring chip in real time; The monitoring chip processes the sampled data according to the preset power-off judgment algorithm. When it is determined that a power-off event has occurred, the event information is encoded in a specific data format (such as JSON format) to generate an event report; the event report includes but is not limited to the following information: the unique identification code of the energy meter (adopting the international standard equipment coding system), the time of the event (UTC time format accurate to milliseconds), the event type (power outage or power-on), the voltage and current values ​​of the last group of samples before the event occurs, and the current operating status of the energy meter (such as normal measurement, fault alarm, etc.); The electric energy meter sends the event report to the concentrator through the power line carrier communication module. Before sending, the communication link is checked to ensure smooth communication. If the communication link fails (such as the power line carrier signal strength is lower than the threshold), local caching is attempted and the event report is automatically resent after communication is restored.

[0027] Data processing at the concentrator end specifically includes: The communication receiving module of the concentrator continuously monitors the communication signal from the electric energy meter, and once an event report is received, it is immediately transmitted to the power failure event reporting function module; The power-off event reporting function module first performs a data integrity check on the event report to check whether the data length meets the standard and whether there are any missing key data fields; if the data is complete, it proceeds to the next step; if the data is incomplete, it sends a data retransmission request to the electric energy meter; Complete event reports are sorted and packaged, and sorted according to the grouping of electric energy meters in the substation and the chronological order of event occurrence; for example, event reports of electric energy meters on different branch lines in the same substation are classified separately to facilitate regional fault analysis by the main collection station.

[0028] Add the concentrator's own identification information, the station number and the current timestamp and other metadata information to the packaged event data, and then transmit the data to the communication sending module; The communication sending module selects the appropriate communication method according to the preset communication strategy to upload the event data to the acquisition main station; if the power carrier communication is normal, the power carrier communication is used first; if the power carrier communication fails or the signal quality is poor, it switches to the wireless communication method (such as 4G network) for data transmission; during the data transmission process, data encryption technology (such as AES encryption algorithm) is used to encrypt the event data to ensure the security of data transmission and prevent the data from being stolen or tampered with.

[0029] The data processing at the main station includes: After the data receiving module of the acquisition master station receives the encrypted event data uploaded by the concentrator, it first performs a decryption operation and then stores the data in the cache database; The big data analysis module reads event data from the cache database and performs correlation analysis with historical data. For example, by querying the historical data for power outage and power-on events in the same substation in similar time periods or weather conditions, the similarities and differences between this event and previous events are analyzed; at the same time, the location and scope of the event are determined in combination with the topological structure data of the substation. For example, if the power meter on a branch line in a substation frequently experiences power outages and power-on events, while other branch lines are normal, it can be preliminarily determined that the fault point is located on this branch line; Based on the results of big data analysis, the intelligent fault analysis module further comprehensively considers equipment operating parameters and meteorological information factors to conduct in-depth fault analysis; for example, if big data analysis shows that a certain substation frequently experiences power outages in hot weather and the transformer load rate in this substation is high, the intelligent fault analysis module infers that the transformer may have failed due to overload in a high temperature environment. The intelligent fault analysis module generates a detailed fault analysis report, which includes fault cause analysis, impact range assessment, repair suggestions, and estimated repair time information, and stores the report in the database, and displays it to power operation and maintenance personnel through a visual interface so that they can quickly formulate a fault repair plan.

[0030] Embodiment 4, in addition to all the technical features of Embodiment 1, this embodiment also includes: judging the fault type based on big data analysis specifically includes: The big data analysis module of the acquisition master station establishes a fault type classification model by mining a large amount of historical power outage and power-on event data; fault types are divided into instantaneous faults (such as instantaneous power outages caused by lightning strikes), permanent faults (such as continuous power outages caused by line aging and breakage), intermittent faults (such as intermittent power outages caused by poor equipment contact) and planned power outages (such as power outages for equipment maintenance pre-arranged by the power company); When new power outage event data is received, the characteristics of the event data (such as the duration of the power outage, the change pattern of power parameters before and after the event, the frequency of similar events in the same area, etc.) are input into the fault type classification model, and the probability distribution of the fault type is calculated by the model; for example, if the power outage duration is extremely short (less than 1 second), and the voltage waveform before and after the event shows typical spike pulse characteristics, and at the same time, similar events in the area occur frequently under thunderstorm weather, then the probability of judging it as a transient fault is higher.

[0031] The fault location based on the topological structure of the substation includes: The main acquisition station stores detailed topological information of the substation, including the connection relationship between transformers, distribution boxes, line branches and electric energy meters; After a power outage occurs, a topological search algorithm is used to determine the area where the fault may have occurred based on the location information of the electric energy meter in the event report and the topological structure. For example, if multiple adjacent electric energy meters experience power outages at the same time, a backtracking search is performed along the power supply line toward the power supply side starting from these electric energy meters until the dividing point with the normal power supply area is found, thereby determining that the fault is located on the line or equipment between the dividing point and the power outage electric energy meter.

[0032] In addition to big data analysis and topological information, the main acquisition station also integrates other relevant information, including equipment operating parameters (obtained from the substation equipment management system), meteorological data (obtained from the meteorological department interface), etc. After the fault area is determined, these factors are comprehensively considered to infer the cause of the fault. For example, if a line in the fault area has a power outage in high temperature and high humidity weather, and the insulation resistance value of the line shows a downward trend in recent monitoring, and the electricity consumption in the area has increased significantly in the near future, it is speculated that the short circuit fault may be caused by the aging of the line insulation under the dual effects of bad weather and high load.

[0033] Embodiment 5, in addition to all the technical features of Embodiment 1, this embodiment also includes: when selecting multiple representative substations for experiments, in each substation, a concentrator and electric energy meter with upgraded functions are deployed, and a main station power failure analysis system is built; during the experiment, various types of power failure events are simulated, including instantaneous failures (simulating lightning strikes by applying short-term high-voltage pulses on specific lines), permanent failures (artificially disconnecting lines to simulate line breaks), intermittent failures (setting loose devices at equipment connection points to simulate poor contact) and planned power failures (power failures for maintenance of some equipment in the substation according to a predetermined plan); at the same time, the monitoring results of the traditional power monitoring system under the same experimental conditions are recorded for comparative analysis; When comparing and analyzing the event reporting time of the experimental results, after the power outage event occurs, the electric energy meter can determine the event and generate a report within an average of 10 milliseconds, and the concentrator completes data processing and uploads it to the main acquisition station within an average of 50 milliseconds after receiving the report; while in the traditional power monitoring system, the electric energy meter takes an average of 50 milliseconds to determine the event, and the concentrator takes an average of more than 500 milliseconds to process and upload data. This shows that the system of the present invention has a significant reduction in event reporting time, and can transmit power outage event information to the main acquisition station more quickly, which buys more time for fault analysis and processing.

[0034] When comparing and analyzing the accuracy of fault analysis of the experimental results, for various simulated fault types, the main station power failure analysis system of the present invention can accurately judge the fault type with an accuracy rate of more than 90%, and can accurately locate the fault area to the specific line branch or equipment with an accuracy rate of more than 85%. The fault analysis accuracy of the traditional power monitoring system is only about 60%, and the fault location is often vague, and can only determine the approximate range of the substation. This shows that the system of the present invention has greatly improved the accuracy of fault analysis, and can provide more accurate fault information for power operation and maintenance personnel, which helps to improve the efficiency of fault repair.

[0035] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0036] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for quickly obtaining power outage information in a distribution network area, characterized in that: The method comprises the following steps: S1. Build the system architecture of the power grid area, optimize and upgrade the hardware and software systems of the original concentrators in the area: expand the functions of the original electric energy meters in the area, install the power outage event reporting function module in the original concentrators in the area, collect the power outage event data uploaded by the concentrator through the power outage fault judgment system of the main station, and analyze and judge it; S2, data processing, processing the data of the electric energy meter, the concentrator, and the main station; S3, fault analysis, judging the fault type based on big data analysis, combining the fault location of the substation topology structure, and inferring the fault cause based on multiple factors; S4. Experiment and result analysis: select several representative substations for experiments, and conduct comparative analysis of event reporting time and fault diagnosis accuracy on the experimental results.

2. The method according to claim 1, characterized in that: In S1, the optimization and upgrading of the original concentrator hardware and software system in the substation area specifically includes: Add data cache space to the concentrator hardware to cope with the burst data transmission requirements that may occur when a power outage occurs; The upgraded concentrator software system has a built-in power outage event judgment logic module. The power outage event judgment logic module monitors the data flow and data characteristics received from the electric energy meter in real time. When data interruption or data changes that meet the characteristics of a power outage event are found, it is preliminarily judged as a possible power outage event and the subsequent data processing process is started.

3. The method according to claim 2, characterized in that: The function expansion of the original electric energy meter in the said area specifically includes: Implant a power-off event monitoring chip in the electric energy meter or upgrade the function of the existing chip. The power-off event monitoring chip monitors the key power parameters of the electric energy meter in real time and uses a sampling algorithm to quickly and accurately capture abnormal changes in power parameters. Once a power outage event is determined to have occurred, the energy meter immediately generates an event report containing detailed event information and sends the report to the concentrator according to the optimized communication protocol.

4. The method according to claim 3, characterized in that: The power outage event reporting function module is responsible for receiving the power outage event report reported by the electric energy meter, and during the receiving process, performs data integrity check to check whether there is data loss or error; The received power outage event reports are sorted and packaged, and the power outage event reports of multiple electricity meters are classified and integrated according to the chronological order and the substation topology structure. The substation identification and concentrator number information are added. At the same time, an accurate timestamp is marked for each power outage event report with a time accuracy of milliseconds. The packaged event data is uploaded to the collection master station using a communication transmission strategy.

5. The method according to claim 4, characterized in that: The power failure analysis system of the acquisition master station includes a data receiving and caching module, a big data analysis module and an intelligent fault analysis module; The data receiving and caching module is responsible for receiving the power-off and power-on event data uploaded by the concentrator and storing it in a high-speed cache database. The high-speed cache database adopts a distributed storage architecture and can quickly write and read a large amount of data to meet the needs of real-time data processing. Big data analysis module: Use big data technology to conduct in-depth analysis of event data in the cache database; first, mine historical power outage event data to establish a power outage event feature model for the substation area, where the power outage event feature model includes the occurrence patterns of power outage events in different time periods, different seasons, and different load conditions; then, match and compare the newly received event data with the feature model, and preliminarily determine the type of event through algorithms such as similarity calculation; Intelligent fault analysis module: Based on big data analysis, it further combines the topological structure information of the substation, equipment operation parameter information and real-time meteorological data to perform intelligent fault analysis; the intelligent fault analysis module generates a fault analysis report, which includes the possible cause of the fault, the scope of the substation affected, the estimated number of users involved, and the priority recommendations for fault repair.

6. The method according to claim 1, characterized in that: In S2, the data processing at the electric energy meter end specifically includes: The electric energy meter samples the electric power parameters at a high frequency, and the sampled data is input into the power-off event monitoring chip in real time; The monitoring chip processes the sampled data according to the preset power-off judgment algorithm. When it is determined that a power-off event has occurred, the event information is encoded in a specific data format to generate an event report; the event report includes but is not limited to the following information: the unique identification code of the energy meter, the time of the event, the type of event, the voltage and current values ​​of the last group of samples before the event, and the current operating status of the energy meter; The electric energy meter sends the event report to the concentrator through the power line carrier communication module. Before sending, the communication link is checked to ensure smooth communication. If the communication link fails, local caching is attempted and the event report is automatically resent after communication is restored.

7. The method according to claim 6, characterized in that: The concentrator-side data processing specifically includes: The communication receiving module of the concentrator continuously monitors the communication signal from the electric energy meter, and once an event report is received, it is immediately transmitted to the power failure event reporting function module; The power-off event reporting function module first performs a data integrity check on the event report to check whether the data length meets the standard and whether there are any missing key data fields; if the data is complete, it proceeds to the next step; if the data is incomplete, it sends a data retransmission request to the electric energy meter; Organize and package complete event reports and sort them according to the grouping of electric energy meters in the substation area and the chronological order of event occurrence; Add the concentrator's own identification information, the station number and the current timestamp metadata information to the packaged event data, and then transmit the data to the communication sending module; The communication sending module selects the appropriate communication method according to the preset communication strategy to upload the event data to the acquisition main station; if the power carrier communication is normal, the power carrier communication is used first; if the power carrier communication fails or the signal quality is poor, it switches to wireless communication for data transmission; during the data transmission process, data encryption technology is used to encrypt the event data.

8. The method according to claim 7, characterized in that: The data processing at the acquisition master station specifically includes: After the data receiving module of the acquisition master station receives the encrypted event data uploaded by the concentrator, it first performs a decryption operation and then stores the data in the cache database; The big data analysis module reads event data from the cache database and performs correlation analysis with historical data. At the same time, it combines the topological structure data of the substation area to determine the location and scope of the event. Based on the results of big data analysis, the intelligent fault analysis module conducts in-depth fault analysis by comprehensively considering equipment operating parameters and meteorological information factors. The intelligent fault analysis module generates a detailed fault analysis report, which includes fault cause analysis, impact scope assessment, repair suggestions and estimated repair time information, and stores the report in the database. At the same time, it is displayed to power operation and maintenance personnel through a visual interface so that they can quickly develop a fault repair plan.

9. The method according to claim 1, characterized in that: In S3, judging the fault type based on big data analysis specifically includes: The big data analysis module of the acquisition master station establishes a fault type classification model by mining a large amount of historical power outage event data; the fault types are divided into transient faults, permanent faults, intermittent faults and planned power outages; When new power-off event data is received, the features of the event data are input into the fault type classification model, and the probability distribution of the fault type is calculated by the model; The fault location based on the topological structure of the substation includes: The main acquisition station stores detailed topological information of the substation, including the connection relationship between transformers, distribution boxes, line branches and electric energy meters; After a power outage occurs, the location information of the energy meter in the event report is combined with the topology structure and a topology search algorithm is used to determine the area where the fault may occur. In addition to big data analysis and topological information, the main acquisition station also integrates other relevant information, including equipment operating parameters and meteorological data. Once the fault area is determined, these factors are comprehensively considered to infer the cause of the fault.

10. The method according to claim 1, characterized in that: In S4, when multiple representative substations are selected for the experiment, in each substation, a concentrator and electric energy meter with upgraded functions are deployed, and a main station power failure analysis system is built; During the experiment, various types of power outages and power-on events were simulated, including transient faults, permanent faults, intermittent faults, and planned power outages. At the same time, the monitoring results of the traditional power monitoring system under the same experimental conditions were recorded for comparative analysis.

Images