Monitoring method, monitor, system and electronic equipment for abnormal electric energy metering

Abstract

The present application provides a monitoring method, monitor, system and electronic device for abnormal electric energy metering, which belongs to the field of electric power. The method includes: obtaining user metering parameters collected by an electric energy metering device; comparing the user metering parameters with the metering standard, screening out suspected abnormal users whose user metering parameters do not meet the metering standard; analyzing the user metering parameters of the suspected abnormal users based on a pre-established metering anomaly analysis optimization algorithm, and determining the abnormal users and abnormal information of the abnormal users based on the analysis results. Thus, based on the metering anomaly analysis optimization algorithm, an in-depth analysis is performed on the suspected abnormal users initially screened out to determine the abnormal users, thereby achieving automatic and accurate screening of abnormal users, reducing labor costs, and improving work efficiency.

Description

Technical Field

[0001] The present application relates to the field of electric power, and in particular to a method, a monitor, a system and an electronic device for monitoring abnormalities in electric energy metering. Background Art

[0002] The electricity metering system screens out a large amount of abnormal metering data daily, including data from dedicated utility transformers, low-voltage residential electricity users, and non-residential users. Currently, this abnormal data requires manual verification and analysis to identify false metering anomalies caused by on-site load imbalances, such as current loss and current imbalance, and to identify the true metering anomalies. While this method can accurately identify metering anomalies, it is labor-intensive and time-consuming. Summary of the Invention

[0003] In view of this, the present application provides a monitoring method, monitor, system and electronic equipment for abnormal electric energy metering, which solves the technical problem in the prior art that it takes a lot of manpower to find abnormal metering data.

[0004] According to one aspect of the present application, a method for monitoring electric energy metering anomalies includes: obtaining user metering parameters collected by an electric energy metering device; comparing the user metering parameters with metering standards, and screening out suspected abnormal users whose user metering parameters do not meet the metering standards; analyzing the user metering parameters of the suspected abnormal users based on a pre-established metering anomaly analysis optimization algorithm, and determining abnormal users and abnormal information of the abnormal users based on the analysis results, wherein the abnormal users are one or more of the suspected abnormal users.

[0005] In a possible embodiment, after analyzing the user metering parameters of the suspected abnormal user based on the pre-established metering anomaly analysis optimization algorithm and determining the abnormal user and the abnormal information of the abnormal user based on the analysis result, it also includes: generating an abnormal line graph based on the actual geographical location of the abnormal user, and displaying a metering anomaly management and control interface including the abnormal line graph through an application mobile handheld terminal or a web client.

[0006] In a possible embodiment, after analyzing the user metering parameters of the suspected abnormal user based on the pre-established metering anomaly analysis optimization algorithm and determining the abnormal user and the abnormal information of the abnormal user based on the analysis result, it also includes: sending the abnormal information to the application mobile handheld terminal, and receiving the abnormal user management result returned by the application mobile handheld terminal.

[0007] In a possible embodiment, after analyzing the user metering parameters of the suspected abnormal user based on the pre-established metering anomaly analysis optimization algorithm and determining the abnormal user and abnormal information of the abnormal user based on the analysis result, it also includes: obtaining positioning information collected by the electric energy metering device of the abnormal user, and determining a navigation route based on the positioning information, wherein the navigation route is used to accurately locate the electric energy metering device corresponding to the abnormal user.

[0008] In a possible embodiment, the user metering parameters of the suspected abnormal user are analyzed based on a pre-established metering anomaly analysis optimization algorithm, and the abnormal user and the abnormal information of the abnormal user are determined based on the analysis results, including: determining the abnormal name to which the user metering parameters of the suspected abnormal user belong based on preset judgment rules and the user type of the suspected abnormal user; counting the frequency of occurrence of the user metering parameters of the suspected abnormal user; if the frequency meets the preset frequency requirement, determining that the suspected abnormal user is an abnormal user, and marking the abnormal information of the abnormal user, the abnormal information including the abnormal name, the frequency and the abnormal level.

[0009] In a possible embodiment, the abnormal name includes current loss, phase sequence abnormality, reverse power abnormality and voltage loss, and the abnormal name of the user metering parameter of the suspected abnormal user is determined based on the preset judgment rule and the user type of the suspected abnormal user, including: for the suspected abnormal user whose user type is a dedicated transformer user, if the user metering parameter of the suspected abnormal user meets the first high supply high metering rule or the first high supply low metering rule, then the abnormal name of the user metering parameter of the suspected abnormal user is determined to be current loss; for the suspected abnormal user whose user type is a dedicated transformer user, if the difference between the sum of the absolute values of the three-phase power and the absolute value of the total active power is greater than the preset power value, and the current value meets the preset requirements, then it is determined The abnormal name of the user metering parameter of the suspected abnormal user is determined to be phase sequence abnormality; for the suspected abnormal user whose user type is non-photovoltaic user, if the total indication of the low-voltage three-phase phase active power is greater than the first preset indication, the total reverse active power indication of the dedicated public transformer is greater than the second preset indication, the total indication of the low-voltage single-phase reverse active power is greater than the third preset indication, and the forward active power is 0, then the abnormal name of the user metering parameter of the suspected abnormal user is determined to be reverse power abnormality; for the suspected abnormal user whose user type is a dedicated public transformer user, if the user metering parameter of the suspected abnormal user meets the second high supply high metering rule or the second high supply low metering rule, then the abnormal name of the user metering parameter of the suspected abnormal user is determined to be voltage loss.

[0010] In a possible embodiment, after comparing the user metering parameters with the metering standards and screening out suspected abnormal users whose user metering parameters do not meet the metering standards, the method further includes: determining whether the suspected abnormal user is a suspected electricity theft user through a line loss and electricity load model library; if the suspected abnormal user is a suspected electricity theft user, sending the suspected electricity theft user information of the suspected electricity theft user to the APP mobile handheld terminal, so that the APP mobile handheld terminal can feedback the suspected electricity theft user confirmation result.

[0011] As another aspect of the present application, a monitor for electric energy metering anomalies is proposed, comprising an acquisition module for acquiring user metering parameters collected by an electric energy metering device; a suspected abnormal user screening module for comparing the user metering parameters with metering standards to screen out suspected abnormal users whose user metering parameters do not meet the metering standards; and an abnormal user determination module for analyzing the user metering parameters of the suspected abnormal users based on a pre-established metering anomaly analysis optimization algorithm, and determining the abnormal users and abnormal information of the abnormal users based on the analysis results, wherein the abnormal users are one or more of the suspected abnormal users.

[0012] As a third aspect of the present application, a monitoring system for abnormal electric energy metering is proposed, comprising: an electric energy metering device for collecting user metering parameters; and the monitor as described above.

[0013] As a fourth aspect of the present application, an electronic device is provided, comprising: a processor; and a memory for storing information executable by the processor; wherein the processor is configured to execute the method for monitoring abnormalities in electric energy metering as described above.

[0014] The present application provides a monitoring method, monitor, system and electronic device for abnormal electric energy metering, the method comprising: obtaining user metering parameters collected by an electric energy metering device; comparing the user metering parameters with the metering standard, screening out suspected abnormal users whose user metering parameters do not meet the metering standard; analyzing the user metering parameters of the suspected abnormal users based on a pre-established metering anomaly analysis optimization algorithm, and determining the abnormal users and abnormal information of the abnormal users based on the analysis results. Thus, based on the metering anomaly analysis optimization algorithm, an in-depth analysis is performed on the initially screened suspected abnormal users to determine the abnormal users, thereby achieving automatic and accurate screening of abnormal users, reducing labor costs, and improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 FIG2 is a flow chart of a method for monitoring abnormal electric energy metering provided by an embodiment of the present application;

[0017] Figure 2 FIG2 is a flow chart of a method for monitoring abnormal electric energy metering provided by another embodiment of the present application;

[0018] Figure 3 FIG2 is a flow chart of a method for monitoring abnormal electric energy metering provided by an embodiment of the present application;

[0019] Figure 4 FIG2 is a schematic diagram of closed-loop management of an APP mobile handheld terminal provided by an embodiment of the present application;

[0020] Figure 5 FIG2 is a flow chart of a method for monitoring abnormal electric energy metering provided by another embodiment of the present application;

[0021] Figure 6 FIG2 is a flow chart of a method for monitoring abnormal electric energy metering provided by another embodiment of the present application;

[0022] Figure 7 FIG2 is a flow chart of a method for monitoring abnormal electric energy metering provided by another embodiment of the present application;

[0023] Figure 8 Shown is a structural diagram of a monitor for abnormal electric energy metering provided by the present application;

[0024] Figure 9 The figure shows a schematic diagram of the working principle of a monitoring system for abnormal electric energy metering provided by the present application;

[0025] Figure 10 The following is a schematic diagram of a monitoring system for abnormal power metering provided by this application.

[0026] Figure 11 Shown is a structural schematic diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] In the description of the application, the meaning of "multiple" is at least two, for example two, three, etc., unless otherwise clearly and specifically limited. In the embodiments of the present application, all directional indications (such as up, down, left, right, front, back, top, bottom ...) are only used to explain the relative position relationship, motion situation, etc. between each component under a certain specific posture (as shown in the drawings). If this specific posture changes, this directional indication also changes accordingly. In addition, the terms "comprise" and "have" and any deformation thereof are intended to cover non-exclusive inclusion. For example, the process, method, system, product or equipment comprising a series of steps or units is not limited to the steps or units listed, but optionally also includes the steps or units not listed, or optionally also includes other steps or units inherent to these processes, methods, products or equipment.

[0028] In addition, references to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of such phrases in various places in the specification does not necessarily refer to the same embodiment, nor does it necessarily refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0029] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0030] Metering anomalies refer to suspected failures in electricity metering devices, potentially impacting metering accuracy and fair transactions between electricity suppliers and users. After a metering anomaly occurs, the metering team must search the electricity collection system for anomaly details, analyze parameters such as capacity, power, voltage, current, and power factor on a household-by-household basis, and conduct manual on-site investigations. Without the availability of relevant instrumentation for diagnostic assistance, collection anomalies require a manual household-by-household investigation into the causes of missed data collections and missing data. Manual dispatch and on-site processing are required, with no intelligent dispatching, remote diagnosis, or repair capabilities. This makes it difficult to quickly and accurately identify customers with metering anomalies and electricity theft, and impossible to navigate to the electricity metering device. Furthermore, manual on-site investigations and handling, as well as heightened work, have high error rates and pose personal safety risks such as electric shock and falls.

[0031] Based on big data technology and digital twin technology, and through the research and development ideas of Internet+, this application proposes a monitoring method for abnormal electricity metering.

[0032] Figure 1FIG. 1 is a flow chart of a method for monitoring abnormal energy metering according to an embodiment of the present application. The method for monitoring abnormal energy metering includes:

[0033] Step S101: Acquire user metering parameters collected by the electric energy metering device;

[0034] An electric energy metering device refers to a device used to measure electric energy, consisting of various types of electric energy meters or devices connected to voltage and current transformers (or dedicated secondary windings) and their secondary circuits. This includes electric energy metering cabinets (boxes, panels). Generally, electric energy metering devices can be smart meters. Electric energy metering devices can collect user metering parameters such as (positive and negative) active energy, voltage, current, and power, and send these collected user metering parameters to a monitor via a preset connection protocol. The monitor then verifies the user metering parameters, calculates energy consumption, and settles electricity bills.

[0035] Step S102: Compare the user metering parameters with the metering standards to screen out suspected abnormal users whose user metering parameters do not meet the metering standards;

[0036] Metering standards for various user types are pre-set, and the metering standards include at least the normal ranges of current, voltage, and active power for the three-phase current of various user types. User information for various user types is imported, and suspected abnormal users are automatically screened according to the entered metering standards on a daily basis. Specifically, when a user metering parameter is obtained, the metering standard corresponding to the user type to which the user metering parameter belongs is further determined, and the user metering parameter is compared with the metering standard. The user metering parameter that does not meet the metering standard is marked as a suspected abnormal metering parameter, and the user corresponding to the suspected abnormal metering parameter is marked as a suspected abnormal user.

[0037] Furthermore, according to the reasons why the user metering parameters of each suspected abnormal user are abnormal, an abnormal label of each suspected abnormal user is marked, and the abnormal label includes: abnormal power, abnormal voltage and current, abnormal power consumption, abnormal clock, and abnormal wiring.

[0038] Step S103: Analyze the user metering parameters of the suspected abnormal users based on a pre-established metering anomaly analysis optimization algorithm, and determine abnormal users and abnormal information of the abnormal users based on the analysis results, wherein the abnormal users are one or more of the suspected abnormal users.

[0039] During the work, it was found that among the suspected abnormal users of electricity metering, there are a large number of "false abnormal" users. For example, for abnormal users such as current loss and current imbalance, about 90% are false abnormalities caused by unbalanced on-site loads. Therefore, it is necessary to further screen the suspected abnormal users to accurately find the real abnormal users.

[0040] Based on a pre-established metering anomaly analysis optimization algorithm, the user metering parameters of suspected abnormal users are analyzed and compared to remove false anomalies, thereby identifying abnormal users from suspected abnormal users and marking abnormal information of abnormal users, where the abnormal information includes abnormality type, abnormality level, user address, collection point and other information.

[0041] Among them, the metering anomaly analysis optimization algorithm sets very precise anomaly judgment rules and judgment conditions based on user type and anomaly name. Based on the metering anomaly analysis optimization algorithm, it can realize accurate screening of user metering parameters of suspected abnormal users and screen out abnormal users.

[0042] Through the above steps, this embodiment performs an in-depth analysis on the suspected abnormal users initially screened out based on the metering anomaly analysis optimization algorithm to identify abnormal users, thereby achieving automatic and accurate screening of abnormal users, reducing labor costs, and improving work efficiency.

[0043] Figure 2 FIG. 1 is a flow chart of a method for monitoring abnormal energy metering provided by another embodiment of the present application. After step S103, the method further includes:

[0044] Step S1031: Generate an abnormal line graph based on the actual geographical location of the abnormal user, and display a metering abnormality management and control interface including the abnormal line graph through an application mobile handheld terminal or a web client.

[0045] Based on the actual geographic location of the abnormal users, the location of each abnormal user is marked on a map to generate an abnormal line map. In this embodiment, the location of the abnormal user's electric energy metering device is determined as the actual geographic location.

[0046] After identifying the abnormal user, the operating parameters, marketing identity document (ID), metering method, wiring method, and collection point information of the abnormal user are further obtained, and the number of abnormal users with the same abnormal tag is counted based on the marked abnormal tag. In addition, based on the abnormal level classification standard, the abnormal level of each abnormal user is determined. The scope of responsibility and affiliation between each operation and maintenance management department and each dedicated public transformer are set to quickly determine the operation and maintenance management part of the abnormal user, so as to avoid the incident of abnormal users being unmanaged or multiple people managing the same abnormal user, thereby improving the efficiency of abnormal user processing.

[0047] In this embodiment, different icons are used to respectively display the abnormality type, abnormality level, abnormality quantity, operating parameters, marketing ID, metering method, wiring method, user address, and collection point information. In this way, an abnormal line diagram including different information represented by different icons can be obtained.

[0048] Furthermore, a metering anomaly management and control interface including the abnormal line graphic is generated and displayed through an application (APP) mobile handheld terminal or a web client, so that operation and maintenance personnel can clearly understand the metering anomaly situation in the area under their jurisdiction from the metering anomaly management and control interface. The APP mobile handheld terminal can be a smart device with network connection function, such as a mobile phone or iPad, and the web client can be a computer, portable computer, etc.

[0049] This embodiment displays abnormal line graphics including abnormal user-related information on an APP mobile handheld terminal or a web client for operation and maintenance personnel to view, thereby realizing visual management of abnormal users and improving abnormal user management efficiency.

[0050] Figure 3 FIG. 1 is a flow chart of a method for monitoring abnormal energy metering provided by another embodiment of the present application. After step S103, the method further includes:

[0051] Step S1032: Send the abnormal information to the application mobile handheld terminal, and receive the abnormal user management result returned by the application mobile handheld terminal.

[0052] Assigning orders to the abnormal users: sending the abnormal information to the APP mobile handheld terminal, wherein the APP mobile handheld terminal is also connected to the metering abnormality troubleshooting instrument for communication, and the APP mobile handheld terminal performs abnormal user management after receiving the verification data uploaded by the metering abnormality troubleshooting instrument, and feeds back the abnormal user management results to the monitor.

[0053] In this embodiment, the APP mobile handheld terminal communicates with field equipment such as power metering devices and concentrators through infrared, power line carrier, and USB interfaces, obtains data stored in the power metering devices, and completes tasks according to the APP mobile handheld terminal functions. The information data and work completion status are then uploaded to the monitor through the interface, forming a seamless closed-loop management. Figure 4 , Figure 4 The figure shows a closed-loop management diagram of an APP mobile handheld terminal provided by an embodiment of the present application. Figure 4As shown, the APP mobile handheld terminal is connected to the password machine to implement password management for the APP mobile handheld terminal. The APP mobile handheld terminal is connected to the GIS (Geographic Information System) to use the GIS map to display the fault type, number, operation mark, and operator of abnormal users in each region, based on power supply areas at all levels and substations as dimensions. After integration, work orders are dispatched, indicating the last operation time, and regularly updating the progress and scope of abnormal processing.

[0054] The mobile app features include: remote automated order processing, SIM card fault detection, meter calibration, automatic access to meter reading schedules, on-site supplemental meter readings, cost-controlled power outage restoration, meter installation and replacement, on-site emergency repairs, automated workflow guidance, automatic log upload, and on-site audits. Using the app, maintenance personnel can conduct on-site operations and communicate with the monitoring system in real time.

[0055] When the monitor identifies an abnormal user, it will dispatch the abnormal user to the APP mobile handheld terminal in the corresponding area according to the abnormal user's geographical location, so that the operation and maintenance personnel can handle it in time.

[0056] In addition, the APP mobile handheld terminal is also connected to the metering anomaly troubleshooting instrument. The metering anomaly troubleshooting instrument uses a combination of 485 communication lines and Bluetooth to communicate with the electric energy metering device. Under the operation of on-site operation and maintenance personnel, it can obtain on-site user metering parameters collected by the electric energy metering device. After obtaining the on-site user metering parameters, the metering anomaly troubleshooting instrument verifies the error data in the electric energy metering device based on a preset error verification technology to obtain accurate user metering parameters, and uploads the accurate user metering parameters to the controller. With the assistance of the metering anomaly troubleshooting instrument, it can realize metering cross-household inspection, electric energy metering device fault detection, electricity theft investigation, online detection of harmonic content and operating conditions, online metering error detection, on-site photography and other functions.

[0057] The APP mobile handheld terminal receives the verification data uploaded by the metering anomaly troubleshooting instrument, performs abnormal user management, and feeds back abnormal user management results to the monitor, such as uploading supplementary copy data, uploading time calibration results, and feeding back on-site images.

[0058] This embodiment combines the online management of abnormal users with offline operations by assigning the abnormal users to the APP mobile handheld terminal and performing on-site inspections and abnormal management with the help of a metering abnormality troubleshooting instrument, thereby improving the efficiency of handling abnormal users.

[0059] Figure 5 FIG. 1 is a flow chart of a method for monitoring abnormal energy metering provided by another embodiment of the present application. After step S103, the method further includes:

[0060] Step S1033: obtaining positioning information collected by the electric energy metering device of the abnormal user, and determining a navigation route based on the positioning information, wherein the navigation route is used to accurately locate the electric energy metering device corresponding to the abnormal user.

[0061] There are a large number of electricity metering devices, with distribution points spread across many areas and a complex on-site environment. Manual memory of locations is inaccurate and time-consuming. There are also problems such as the inconsistency between the locations of some electricity metering devices and power supply points, unstable electronic distribution network map URLs, and inconsistency between power line maps and actual roads. These problems have seriously increased unnecessary workload and reduced work efficiency.

[0062] The electric energy metering device used in this embodiment includes a positioning platform. The positioning platform has functions such as real-time location storage, route intelligent navigation, storage of text and pictures, and confidential account sharing. It automatically locates the exact location during on-site inspection and processing, and can display reference objects such as large and small streets on the site and surrounding well-known buildings. It displays detailed path descriptions of the metering device ledger information, shortens the distance, reduces energy consumption, avoids congested and bumpy roads, reduces the chance of traffic accidents and vehicle damage, improves work safety, and improves the accuracy of the positioning of the electric energy metering device.

[0063] The monitor obtains the positioning information collected by the abnormal user's electricity metering device through a preset communication connection, determines the navigation route based on the positioning information, and can accurately locate the electricity metering device corresponding to the abnormal user, thereby improving the accuracy of positioning, reducing the difficulty of positioning, and helping to improve on-site processing efficiency.

[0064] Figure 6 FIG. 1 is a flow chart of a method for monitoring abnormal energy metering provided by another embodiment of the present application, wherein step S103 includes:

[0065] Step S10301: Determine the abnormal name to which the user metering parameter of the suspected abnormal user belongs based on a preset judgment rule and the user type of the suspected abnormal user;

[0066] User types include dedicated transformer users, public transformer users, and public-dedicated transformer users. Abnormalities typically include current loss, abnormal phase sequence, abnormal reverse power flow, and voltage loss. In a three-phase power supply system, all three phase currents and power values must meet requirements for stable power supply. Errors in one or more phases can lead to power supply anomalies.

[0067] For the suspected abnormal user whose user type is a dedicated transformer user, if the user metering parameters of the suspected abnormal user meet the first high supply and high metering rule or the first high supply and low metering rule, the abnormal name to which the user metering parameters of the suspected abnormal user belong is determined to be current loss; in addition, the dedicated transformer users with current loss do not include agricultural irrigation and drainage, and agricultural irrigation and drainage electricity users in poor counties.

[0068] Among them, the first high supply and high measurement rule includes: phase A is within the preset range and phase C is greater than the first current value, or phase C is within the preset range and phase A is greater than the first current value; the first high supply and low measurement rule includes: any one of the three-phase currents is within the preset range, and the sum of the other two phases is greater than the first current threshold; and / or any two phases of the three-phase current are within the preset range, and the current value of the other phase is greater than the second current threshold; wherein the preset range is 0-0.05A, the first current value can be 0.5A, the first current threshold can be 2A, and the second current threshold can be 1A.

[0069] For the suspected abnormal user whose user type is a dedicated transformer user, if the difference between the sum of the absolute values of the three-phase power and the absolute value of the total active power is greater than the preset power value, and the current value meets the preset requirements, then it is determined that the abnormal name of the user metering parameter of the suspected abnormal user is phase sequence abnormality;

[0070] Among them, the preset power value can be 10% of the total power of the three-phase current, the preset requirement can be that the current values of phase A and phase C are greater than 0.1, and the preset requirement can also be that the three-phase current is greater than 5% of the rated current, that is, 0.25A.

[0071] For the suspected abnormal user whose user type is non-photovoltaic user, if the total indication value of the low-voltage three-phase reverse active power is greater than the first preset indication value, the total indication value of the reverse active power of the dedicated public transformer is greater than the second preset indication value, the total indication value of the low-voltage single-phase reverse active power is greater than the third preset indication value, and the forward active power is 0, then it is determined that the abnormal name of the user metering parameter of the suspected abnormal user is reverse power abnormality; among which the user type of reverse power abnormality is all users, but photovoltaic users are excluded.

[0072] Among them, the first preset indication value may be 1, the second preset indication value may be 10, and the third preset indication value may be 1.

[0073] For the suspected abnormal user whose user type is a dedicated public transformer user, if the user metering parameter of the suspected abnormal user meets the second high supply high metering rule or the second high supply low metering rule, it is determined that the abnormal name to which the user metering parameter of the suspected abnormal user belongs is voltage loss.

[0074] The second high-supply high-voltage measurement rule is: the voltage of phase A or phase C is less than or equal to 70V and the sum of the voltages of phases A and C is less than 160V; or the current of any phase A, B, or C is greater than or equal to 5% of the maximum current. However, the situation where phases A and C are all zero is not included in the second high-supply high-voltage measurement rule. The second high-supply low-voltage measurement rule is: the voltage of phase A is less than 150V; and / or the voltage of phase B is less than 150V; and / or the voltage of phase C is less than 150V; or the current of any phase A, B, or C is greater than or equal to 5% of the maximum current. The second high-supply low-voltage measurement rule excludes the situation where phases A, B, and C are all zero.

[0075] In addition, for users with high supply and high metering and three-phase four-wire connection, if the rated voltage is 57.7V, when the voltage is lower than 70% and the current is greater than or equal to 5% of the maximum current, the missing items will be included in the analysis and judgment.

[0076] Step S10302: Counting the frequency of occurrence of the user metering parameters of the suspected abnormal user;

[0077] After determining the anomaly name to which the user metering parameters of suspected abnormal users belong, it is necessary to count the frequency of occurrence of the user metering parameters under this anomaly name on a daily basis. Understandably, if an anomaly only occurs once, it may be a coincidence and will not affect the collection of user metering parameters on that day or in subsequent periods. If an anomaly occurs multiple times, it is necessary to prioritize and conduct an anomaly investigation.

[0078] Step S10303: If the frequency meets the preset frequency requirement, the suspected abnormal user is determined to be an abnormal user, and abnormal information of the abnormal user is marked, where the abnormal information includes the abnormal name, the frequency, and the abnormal level.

[0079] In this embodiment, the preset frequency requirements corresponding to different abnormal names are different: the preset frequency requirement for current loss is three times or more per day; the preset frequency requirement for phase sequence abnormality is 24 times per day, and the reverse current abnormality meets the preset frequency requirement as long as it occurs once; the preset frequency requirement for voltage loss is more than three times per day.

[0080] If the frequency meets the preset frequency requirement, the suspected abnormal user is determined to be an abnormal user. The abnormal level of the abnormal user is recorded. Current loss, phase sequence abnormality, and voltage loss are all defaulted to level 1 events. For reverse power abnormality, the abnormal level is determined according to the different user types:

[0081] For low-voltage three-phase users: if the reverse active power is greater than or equal to 0.5, it is determined as a Level 1 event; if the reverse active power is greater than or equal to 0.5 and less than 1, it is determined as a Level 2 event; if the reverse active power is greater than or equal to 1 and less than 2, it is determined as a Level 3 event; other cases are Level 4 events.

[0082] For dedicated transformer users: if the reverse active power is greater than or equal to 0.2, it is determined as a Level 1 event; if the reverse active power is greater than or equal to 0.1 and less than 0.2, it is determined as a Level 2 event; if the reverse active power is greater than or equal to 0 and less than 0.1, it is determined as a Level 3 event; other cases are Level 4 events.

[0083] For low-voltage single-phase users: if the reverse active power is greater than or equal to 0.5, it is determined as a Level 1 event; if the reverse active power is greater than or equal to 0.1 and less than 0.5, it is determined as a Level 2 event; if the reverse active power is greater than or equal to 0 and less than 0.1, it is determined as a Level 3 event; other situations are Level 4 events.

[0084] In this embodiment, if the user metering parameters of a suspected abnormal user meet the judgment rules and the preset frequency requirements, it is determined to be an abnormal event, and if it responds continuously for 3 days during the abnormal time, it is converted into a historical abnormality.

[0085] Through the above steps, this embodiment determines abnormal events based on preset judgment rules and the frequency of occurrence of user metering parameters, implements secondary screening of suspected abnormal events, and improves the efficiency and accuracy of abnormal event identification.

[0086] Figure 7 FIG. 1 is a flow chart of a method for monitoring abnormal energy metering provided by another embodiment of the present application. After step S102, the method further includes:

[0087] Step S104: determining whether the suspected abnormal user is a suspected electricity theft user through the line loss and power load model library;

[0088] The electricity load model is mainly used to identify customers suspected of electricity theft with abnormal electricity metering.

[0089] Theoretical calculation values for 10kV line loss and 0.4kV substation line loss are set, and a library of power load models for different user types is established. Suspected abnormal users identified by the monitor are compared with the established power load models, 10kV line loss, and 0.4kV substation line loss, automatically identifying suspected electricity theft customers.

[0090] For the load model of a 10kV dedicated transformer municipal lighting customer, the system diagnosed a loss of current in phase B. By checking the power load model library and the marketing business system, it was found that this customer was a lighting user. During daytime hours, 6:00 AM to 7:00 PM, when all streetlights are turned off, a loss of current in phase B is normal. The load model for a 10kV dedicated transformer municipal lighting customer conforms to the following formula:

[0091] (1)

[0092] (2)

[0093] Among them, k is the coefficient; p is the instantaneous lighting power; P is the total lighting power of the time period.

[0094] Step S105: If the suspected abnormal user is a suspected electricity thief, the suspected electricity thief information of the suspected electricity thief is sent to the application mobile handheld terminal, so that the application mobile handheld terminal can feed back a suspected electricity thief confirmation result.

[0095] For suspected abnormal users, if the output result of the power load model library is a suspected electricity theft user, the suspected electricity theft user will be marked and a report will be sent to the APP handheld terminal for the operation and maintenance personnel of the APP mobile handheld terminal to verify the suspected electricity theft user and feed back the verification results to the monitor through the APP mobile handheld terminal.

[0096] In this way, suspected electricity thieves are identified based on the line loss and electricity load model library, which greatly saves manpower in tracking and screening suspected electricity thieves, improves the efficiency of identifying suspected electricity thieves, helps to recover power grid assets and eliminate electricity usage malpractices.

[0097] As another aspect of the present application, the present application provides a monitoring system for abnormal electric energy metering. Figure 8 FIG. 1 is a schematic diagram of the structure of a monitor for abnormal electric energy metering provided by the present application. The monitor 2 includes:

[0098] An acquisition module 21 is used to acquire user metering parameters collected by an electric energy metering device;

[0099] The suspected abnormal user screening module 22 is used to compare the user metering parameters with the metering standards and screen out suspected abnormal users whose user metering parameters do not meet the metering standards;

[0100] The abnormal user determination module 23 is used to analyze the user metering parameters of the suspected abnormal users based on a pre-established metering anomaly analysis optimization algorithm, and determine the abnormal users and abnormal information of the abnormal users based on the analysis results, wherein the abnormal users are one or more of the suspected abnormal users.

[0101] As a third aspect of the present application, the present application provides a monitoring system for abnormal electric energy metering. Figure 9 FIG2 is a schematic diagram showing the working principle of a monitoring system for abnormal electric energy metering provided by the present application, wherein the monitoring system for abnormal electric energy metering includes:

[0102] An electric energy metering device, which is used to collect user metering parameters;

[0103] and the monitor 2 as described in 8 above.

[0104] The monitoring system for abnormal energy metering provided by this application is also interconnected and communicated with the WEB client, database, troubleshooting equipment, etc. Figure 10 The figure shows a schematic diagram of the framework of a monitoring system for abnormal electric energy metering provided by this application. Figure 10 As shown, the web client is used for acquiring two-dimensional GIS maps, fault detection, organizational visualization, graphical association, statistical analysis, and system management. The database includes a business database and a spatial database. Troubleshooting equipment includes a metering anomaly smart terminal, an app-based mobile handheld terminal, a metering anomaly troubleshooter, and possibly a smart communication module batch detector. To address the challenges of low reuse and detection efficiency of dismantled modules, a new method based on the smart communication module batch detector enables unconditional and rapid sorting, reduces costs, and improves equipment reuse and efficiency.

[0105] Below, reference Figure 11 To describe the electronic device according to the embodiment of the present application. Figure 11 Shown is a structural schematic diagram of an electronic device provided in one embodiment of the present application.

[0106] like Figure 11 As shown, electronic device 600 includes one or more processors 601 and memory 602 .

[0107] The processor 601 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or information execution capabilities, and may control other components in the electronic device 600 to perform desired functions.

[0108] The memory 601 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program information may be stored on the computer-readable storage medium, and the processor 601 may execute the program information to implement the above-described methods for monitoring electric energy metering anomalies according to various embodiments of the present application or other desired functions.

[0109] In one example, the electronic device 600 may further include an input device 603 and an output device 604 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0110] The input device 603 may include, for example, a keyboard, a mouse, and the like.

[0111] The output device 604 can output various information to the outside. The output device 604 can include, for example, a display, a communication network and a remote output device connected thereto.

[0112] Of course, to simplify, Figure 11 Only some of the components related to the present application in the electronic device 600 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device 600 may further include any other appropriate components according to specific application scenarios.

[0113] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program information, and when the computer program information is executed by a processor, the processor executes the steps of the method for monitoring electric energy metering anomalies according to various embodiments of the present application described in this specification.

[0114] The computer program product may be written in any combination of one or more programming languages to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages such as Java, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0115] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program information is stored. When the computer program information is executed by a processor, the processor executes the steps of the method for monitoring electric energy metering anomalies according to various embodiments of the present application in this specification.

[0116] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0117] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0118] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0119] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0120] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be applied in the widest sense consistent with the principles and novel features of the present invention.

[0121] The above description is only a preferred embodiment of the invention of this application and is not intended to limit the invention of this application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the invention of this application should be included in the scope of protection of the invention of this application.

Claims

1. A method for monitoring abnormal energy metering, characterized in that: include: Obtain user metering parameters collected by the electric energy metering device; Comparing the user's metering parameters with the metering standards to screen out suspected abnormal users whose user metering parameters do not meet the metering standards; Analyzing the user metering parameters of the suspected abnormal users based on a pre-established metering anomaly analysis optimization algorithm, and determining abnormal users and abnormal information of the abnormal users based on the analysis results, wherein the abnormal users are one or more of the suspected abnormal users; The method includes analyzing the user metering parameters of the suspected abnormal user based on a pre-established metering anomaly analysis optimization algorithm, and determining the abnormal user and abnormal information of the abnormal user based on the analysis result, including: determining the abnormal name to which the user metering parameters of the suspected abnormal user belong based on a preset judgment rule and the user type of the suspected abnormal user, the abnormal name including current loss, phase sequence abnormality, reverse power abnormality, and voltage loss; counting the frequency of occurrence of the user metering parameters of the suspected abnormal user; if the frequency meets the preset frequency requirement, determining the suspected abnormal user as an abnormal user, and marking the abnormal information of the abnormal user, the abnormal information including the abnormal name, the frequency, and the abnormality level; The abnormal name of the user metering parameter of the suspected abnormal user is determined based on the preset judgment rule and the user type of the suspected abnormal user, including: for the suspected abnormal user whose user type is a dedicated transformer user, if the user metering parameter of the suspected abnormal user meets the first high supply high metering rule or the first high supply low metering rule, then the abnormal name of the user metering parameter of the suspected abnormal user is determined to be current loss; for the suspected abnormal user whose user type is a dedicated transformer user, if the difference between the sum of the absolute values of the three-phase power and the absolute value of the total active power is greater than the preset power value, and the current value meets the preset requirements, then the user metering parameter of the suspected abnormal user is determined to belong to The abnormal name is phase sequence abnormality; for the suspected abnormal user whose user type is non-photovoltaic user, if the total indication of the low-voltage three-phase phase active power is greater than the first preset indication, the total reverse active power of the dedicated public transformer is greater than the second preset indication, the total indication of the low-voltage single-phase reverse active power is greater than the third preset indication, and the forward active power is 0, then it is determined that the abnormal name of the user metering parameter of the suspected abnormal user is reverse power abnormality; for the suspected abnormal user whose user type is a dedicated public transformer user, if the user metering parameter of the suspected abnormal user meets the second high supply high metering rule or the second high supply low metering rule, then it is determined that the abnormal name of the user metering parameter of the suspected abnormal user is voltage loss.

2. The method according to claim 1, characterized in that After analyzing the user metering parameters of the suspected abnormal user based on the pre-established metering anomaly analysis optimization algorithm and determining the abnormal user and abnormal information of the abnormal user based on the analysis result, the method further includes: An abnormal line graph is generated based on the actual geographical location of the abnormal user, and a metering abnormality management and control interface including the abnormal line graph is displayed through an application mobile handheld terminal or a web client.

3. The method according to claim 1, characterized in that After analyzing the user metering parameters of the suspected abnormal user based on the pre-established metering anomaly analysis optimization algorithm and determining the abnormal user and abnormal information of the abnormal user based on the analysis result, the method further includes: The abnormal information is sent to the application mobile handheld terminal, and the abnormal user management result returned by the application mobile handheld terminal is received.

4. The method according to claim 1, wherein After analyzing the user metering parameters of the suspected abnormal user based on the pre-established metering anomaly analysis optimization algorithm and determining the abnormal user and abnormal information of the abnormal user based on the analysis result, the method further includes: The positioning information collected by the electric energy metering device of the abnormal user is obtained, and a navigation route is determined based on the positioning information, where the navigation route is used to accurately locate the electric energy metering device corresponding to the abnormal user.

5. The method according to claim 1, wherein After comparing the user metering parameters with the metering standards and screening out suspected abnormal users whose user metering parameters do not meet the metering standards, the method further includes: Determine whether the suspected abnormal user is a suspected electricity theft user through the line loss and power load model library; If the suspected abnormal user is a suspected electricity thief, the suspected electricity thief information of the suspected electricity thief is sent to the application mobile handheld terminal, so that the application mobile handheld terminal can feed back a suspected electricity thief confirmation result.

6. A monitor for abnormal electric energy metering, characterized in that: include: An acquisition module is used to obtain user metering parameters collected by the electric energy metering device; A suspected abnormal user screening module is used to compare the user metering parameters with the metering standards and screen out suspected abnormal users whose user metering parameters do not meet the metering standards; an abnormal user determination module, configured to analyze the user metering parameters of the suspected abnormal users based on a pre-established metering anomaly analysis optimization algorithm, and determine abnormal users and abnormal information of the abnormal users based on the analysis results, wherein the abnormal users are one or more of the suspected abnormal users; The abnormal user determination module is specifically configured to determine the abnormal name of the user metering parameter of the suspected abnormal user based on preset judgment rules and the user type of the suspected abnormal user, wherein the abnormal name includes current loss, phase sequence abnormality, reverse power abnormality, and voltage loss; The frequency of occurrence of the user metering parameter of the suspected abnormal user is counted; if the frequency meets the preset frequency requirement, the suspected abnormal user is determined to be an abnormal user, and abnormal information of the abnormal user is marked, where the abnormal information includes the abnormal name, the frequency, and the abnormal level.

7. A monitoring system for abnormal electric energy metering, characterized in that: include: An electric energy metering device, which is used to collect user metering parameters; And the monitor according to claim 6.

8. An electronic device, characterized in that: The electronic device comprises: processor; and a memory for storing information executable by the processor; The processor is configured to execute the method for monitoring abnormal electric energy metering according to any one of claims 1 to 5.

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