Line loss monitoring method and system based on load anomaly analysis

Abstract

The invention discloses a line loss monitoring method and system based on load anomaly analysis, and relates to the technical field of power grid monitoring. According to the monitoring method, a monitoring platform assigns field inspection equipment through a field inspection work order for an abnormal user whose electricity utilization is abnormal through analysis, the field inspection equipment comprises data receiving equipment and line load detection equipment, the data receiving equipment receives load data of an abnormal user load metering device in real time, and the line load detection equipment detects the load data of the abnormal user load metering device; and the line load detection equipment receives a data synchronization instruction issued by the monitoring platform, and the line load detection equipment uploads a field load detection result to the monitoring platform in real time under the data synchronization instruction, so that the problem that the monitoring result is influenced by personal behaviors in the monitoring process is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and in particular to a line loss monitoring method and system based on load anomaly analysis. Background Art

[0002] In recent years, line loss management technology has been vigorously developed, and the phenomenon of continuous high loss has been basically eliminated, but intermittent high loss and uneconomical equipment operation still exist, and the "hidden" and "intermittent" abnormal line loss problems still need to be managed. Taking load metering devices as an example, marketing system configuration errors, installation errors and other methods of stealing electricity will cause abnormal line losses. At the same time, these problems are usually intentional and subjective, which brings new challenges to line loss management.

[0003] For load anomalies caused by incorrect installation of load metering devices and other electricity thefts, the monitoring methods that can be used include: by monitoring the loads at different levels of the line and combining the load statistics on the branch side, the suspicious load branches are determined, and then on-site detection is used. The detection personnel use on-site load detection instruments to find the specific reasons for abnormal power consumption and line loss. In practice, there are still situations where abnormal load metering devices and the places where electricity thefts occur cannot be effectively located. Summary of the invention

[0004] In response to installation errors of load metering devices and other means of monitoring electricity theft, the present invention provides a line loss monitoring method and system based on load anomaly analysis.

[0005] The technical solution of the present invention is: a line loss monitoring method based on load abnormality analysis, comprising the following steps: S1. Obtain the operating load of different levels of the line through the load monitoring device, and determine whether the line loss on the line exceeds the theoretical line loss value; S2. When it is determined that the actual line loss on the line exceeds the theoretical line loss value, the load measurement data at different levels on the line and the historical load statistics of the lower-level substations are used to locate the specific substations in the lower-level substations where the abnormal line loss occurs; S3, collecting the current power consumption data of each user in the specific area, and combining the historical power consumption data of each user to analyze whether there are abnormal users with abnormal power consumption at present; S4. The monitoring platform generates an on-site inspection work order for abnormal users. The on-site inspection work order has an assigned on-site inspection device, which includes a data receiving device and a line load detection device. The data receiving device receives the load data of the abnormal user load metering device in real time. The line load detection device receives the data synchronization instruction issued by the monitoring platform. Under the data synchronization instruction, the line load detection device uploads the on-site load detection result to the monitoring platform in real time. S5. The monitoring platform determines whether the abnormal user has management line loss based on the current load monitoring results and the current load data of the abnormal user's load metering device.

[0006] In the prior art, management line loss generally refers to line loss caused by improper metering of metering equipment and other management reasons related to metering, such as improper installation and wiring of metering equipment, which causes the metering result to be too small, and the statistical metering result to be too small due to the parallel connection of lines at the front end of the metering equipment. The above results are generally caused by theft of electricity by people. According to the currently known statistical results, the line loss rate of abnormal line sections caused by these reasons can reach more than 20%, and even more than 50% in the highest case. The line loss rate of effectively managed lines is generally below 5%. Effective management of line losses is an important means to reduce the losses of power grid companies. At the same time, with the improvement of the intelligence level of smart grids and the development of line loss monitoring software and hardware technology, abnormal line losses can usually be effectively monitored. However, the load monitoring units and load monitoring terminals deployed on site are only installed at intervals along the line. When the relevant supervision system communicates with the load monitoring units and load monitoring terminals installed at different levels of the line, and determines the existence of abnormal line losses and the specific abnormal line sections where abnormal line losses occur through load collection data, it is usually necessary to coordinate the operation and maintenance personnel to carry portable line loss detection equipment based on the determined location of the abnormal line section, and conduct on-site inspections of the abnormal line section to confirm and troubleshoot abnormal line losses and accurately locate abnormal users.

[0007] In order to avoid errors in on-site investigation results due to human factors during the process of abnormal line loss investigation and accurate positioning of abnormal users, and to improve the efficiency of identifying abnormal users, the line loss monitoring method as described above is provided.

[0008] Specifically, the present invention utilizes the characteristics of the prior art that different levels of power supply lines generally have gateway meters, feeder terminal equipment, integrated switch power equipment, load monitoring units / monitoring instruments / monitoring terminals, etc. In step S1, by setting the theoretical line loss value, combined with the currently monitored operating loads at different levels, it is determined whether there is abnormal line loss. For example, the line loss on the line branch can be the gateway meter data minus all feeder terminal equipment data, can be the primary and secondary integrated switch data minus all subsequent load monitoring unit data, can be the front-stage load monitoring instrument data minus the substation load monitoring data, etc. When it is determined through step S1 that there is abnormal line loss on the line, the specific substation with abnormal line loss is determined through step S2 to find the specific location where the abnormal line loss occurs on the line, and for the abnormal line loss The specific substation area, in step S3, uses the current electricity consumption data of each user to determine the abnormal users with abnormal electricity consumption among these users. After completing step S3, the abnormal line section suspected of having abnormal line loss can be located. In step S4, the operation and maintenance personnel carry a portable data receiving device and a line load detection device or a line loss tester according to the inspection work order information, and conduct an on-site inspection of the abnormal line section suspected of having abnormal line, measure the actual load of the line by on-site collection, and based on the load data provided by the load metering device used by the abnormal user provided by the data receiving device, the operation and maintenance personnel identify on-site whether there is abnormal line loss on the line section used by the abnormal user, and upload the detection result to the monitoring platform in real time, and use the data processing function provided by the monitoring platform to determine whether the abnormal user has a management line loss.

[0009] The differences between the present invention and the prior art include: in step S4, the on-site inspection work order issued by the monitoring platform has assigned on-site inspection equipment, and these on-site inspection equipment synchronizes load data with the monitoring platform and uploads on-site load detection results in real time, which can avoid the problem of erroneous on-site load detection results obtained by the monitoring platform due to human reasons such as data tampering and data entry errors, and avoid the situation where personal behavior during the monitoring process affects the monitoring results; by using on-site inspection equipment including data receiving equipment and line load detection equipment, operation and maintenance personnel can easily compare the data on the data receiving equipment and the line load detection equipment, and efficiently find the abnormal point location where line loss occurs on the line, which is particularly suitable for finding the location of stolen wires and leakage connected to the front end of the load metering device.

[0010] In a specific embodiment, the method for determining the specific station area is: In step S2, the actual line loss is calculated using load measurement data at different levels on the line; Using the historical load statistical data of each subordinate substation and the current load data of each subordinate substation, the difference between the average value of the historical load statistical data of each subordinate substation and the current load data is calculated. When the average value is greater than the current load data and the deviation of the difference from the line loss amount is less than the set threshold, the subordinate substation is determined as a specific substation.

[0011] In the above implementation, the characteristics of having multiple lower-level substations on the line and multiple users in each lower-level substation, under normal power consumption, multiple users can maintain a relatively stable total load in the substation, and provide a method for determining the difference between the metering result of the lower-level substation and the historical load statistical data according to the average value of the historical load statistical data and the current load data of each lower-level substation after judging the line loss, and comparing the difference with the line loss, and judging that the deviation corresponding to the difference is less than the threshold according to the set threshold, that is, the difference is close to the line loss, and the difference is the load gap data that is not involved in the load statistics, and the lower-level substation is determined as a specific substation where abnormal line loss occurs. The present invention is particularly suitable for the theft of electricity in a specific substation where generally only a small number of users subjectively do not include the used electric energy in the current load data statistics, that is, a technical solution is provided by obtaining the current load data used for load statistics of the lower-level substation, and comparing the data with the average value obtained by the historical load statistical data of the lower-level substation as a simulated actual load, to determine whether there is actual electricity consumption that is not included in the load statistics category under normal circumstances.

[0012] Since users have obvious time-divided electricity consumption characteristics, in order to improve the accuracy of specific area confirmation, in a specific embodiment, the historical load statistical data is time-divided load data divided according to the electricity consumption period, wherein the electricity consumption period is divided into time periods in each day based on days; When performing the difference calculation, the acquisition time of the current load data is collected, and the corresponding time-sharing load data is matched according to the acquisition time. The collection period of the matched time-sharing load data is in the same period as the acquisition time, and multiple time-sharing load data are matched in the historical load statistical data as a statistical data sample set. Different time-sharing load data in the statistical data sample set have different collection days; The average value is the average value of each time-sharing load data in the statistical data sample set.

[0013] In the above scheme, the natural day is used as the division unit, and the time of each day is divided into different data statistical periods according to the time of each day, and the load data of each data statistical period is used as the time-sharing load data. Furthermore, the time period in which the current load data is located is collected according to the load anomaly analysis, and the time-sharing load data with the same data collection time as the above time period is obtained from the historical load statistical data. The interference of accidental factors on the average value is reduced by means of statistical data sample sets, so as to achieve the purpose of further ensuring the accuracy of confirmation of specific substations.

[0014] In the above scheme, the closeness between the difference and the line loss is determined by the set threshold. Generally, when the average value associated with the difference is larger, the corresponding line has a larger normal line loss. When there is electricity theft that affects the current load data measurement, the current load data cannot be used as a reference for the current actual load data. In a specific embodiment, considering that the larger the historical load statistical data, the larger the normal line loss, in order to use the characteristics of the historical load statistical data as the general load characteristics of the current line, the general load characteristics are taken into consideration when considering the degree of closeness, so as to improve the versatility and accuracy of the specific substation confirmation method. The set threshold is a dynamic value associated with the average value, wherein the set threshold is proportional to the average value.

[0015] What is obviously different from the load characteristics of the substation area is that in terms of electricity consumption time, living users and most production users have obviously different power demands at different times. In order to ensure the reliability of identifying abnormal users and improve the efficiency of on-site investigation and line loss monitoring, in a specific embodiment, in step S3, the method for obtaining abnormal users is: S3-1. Obtain the electricity consumption pattern of users in the specific area. Specifically, collect historical electricity consumption data of each user in the specific area, and use the date as the statistical period, summarize the historical electricity consumption data by day, obtain the total daily electricity consumption based on the date as the statistical period, and obtain the statistical curve of the daily electricity consumption of users in the area changing with the date according to the change pattern of the daily total electricity consumption; S3-2, predicting the recent electricity consumption data of each user, specifically, predicting the recent electricity consumption data of each user based on the historical electricity consumption data of each user and the statistical curve; S3-3. Analyze whether each user is an abnormal user. Specifically, determine whether each user is an abnormal user based on the difference between the collected recent actual power consumption data of the user and the predicted recent power consumption data of the user.

[0016] In the above scheme, step S3-1 is to obtain the overall electricity consumption law of users in a specific area. The electricity consumption law can be used to reflect seasonal electricity consumption changes and production-related electricity consumption changes. The law is subsequently used to predict the user's recent electricity consumption data. As a technician in this field, the historical electricity consumption data of each user is obtained through the electricity metering equipment for the user, and the natural day is used as the statistical period. For example, after summarizing all the historical electricity consumption data of a certain day, the total electricity consumption of the day is obtained. After the daily total electricity consumption statistics for all statistical periods are performed, the change law can be obtained in the form of a statistical curve. In step S3-2, the change law is used as a variable factor for predicting the change of electricity consumption data, and the recent electricity consumption data of each user is used as the basic factor in the prediction process to predict the recent electricity consumption data of each user. In step S3-3, the difference between the collected recent actual electricity consumption data of the user and the predicted recent electricity consumption data of the user is determined. When the difference is greater than the set difference threshold, it is concluded that the user is an abnormal user or a suspected abnormal user. For example, when the difference between the predicted recent electricity consumption data of a certain user and the actual electricity consumption data detected by the metering equipment is greater than the set difference threshold, it is concluded that the recent actual electricity consumption data is significantly less than the recent predicted electricity consumption data, and there is a possibility that the actual electricity consumption data is incorrectly counted.

[0017] For production users who have stopped or reduced production, and life users who have been away for a long time, in order to reduce the impact of these factors on the accuracy of abnormal user identification, in a specific embodiment, in S3-3, when the user's recent actual electricity consumption data is less than the predicted recent electricity consumption data of the user, the user is marked as a suspected abnormal user; For suspected abnormal users, the differences between their recent electricity usage data and their historical electricity usage data are collected, and the differences include the concentrated electricity usage time periods in natural days. When the concentrated electricity usage time periods in the collected recent electricity usage data are different from the concentrated electricity usage time periods in the historical electricity usage data, or when there are no concentrated electricity usage time periods in the collected recent electricity usage data, the suspected abnormal user calibration is cancelled.

[0018] In the above scheme, when the user's recent actual electricity consumption data is less than the predicted recent electricity consumption data of the user, it is determined that the user has the possibility of detecting load abnormality and having actual electricity consumption that has not been included in the statistics. In this case, the user is marked as a suspected abnormal user, but among these users, there may be incorrect calibration due to production suspension, production reduction, and long-term absence. Furthermore, the historical concentrated electricity consumption time periods of these users are compared with the recent concentrated electricity consumption time periods. When the comparison results are different, it means that these users have different concentrated electricity consumption time periods, and it is concluded that these users are incorrectly calibrated abnormal users. Through this method, the accuracy of abnormal user calibration can be effectively improved and the number of abnormal users can be reduced.

[0019] In specific applications, according to the characteristics of the load and electricity consumption characteristics of each user, when the electricity consumption statistics at different times of a natural day do not have obvious differences, the load data can be used as a basis for identifying abnormal users. As a parallel scheme for analyzing abnormal users, in a specific embodiment, in step S3, the load data of the load metering devices of each user in the specific substation is collected, and combined with the historical load data of each user, it is analyzed whether there are abnormal users with abnormal load at the moment.

[0020] In a specific embodiment, in order to guide the operation and maintenance personnel to perform on-site audits and reflect the operation and maintenance personnel's on-site audits, in step S4, the data receiving device and the line load detection device both synchronize the location data of their locations to the monitoring platform in real time to provide a specific load data receiving location and an on-site load detection result upload location.

[0021] In order to deal with the possible situation where users know about the on-site audit work in advance and restore orderly power consumption on their own before the operation and maintenance personnel perform the on-site audit work, thereby rendering the subsequent on-site audit work invalid, in a specific embodiment, the monitoring platform continuously obtains the load data of abnormal users during the period after the on-site audit work order is found under the monitoring platform and before the monitoring platform obtains the uploaded on-site load detection results, and when the load data suddenly changes and the load data mutation amount is not monitored by the upstream load monitoring device of the line, the monitoring platform issues an on-site investigation instruction to the data receiving device.

[0022] In the above scheme, when the load data obtained by the user metering device suddenly changes, and the mutation is not detected by the superior load detection device, it means that the mutation is included in the load statistics of the superior load detection device and is not included in the load statistics for the specific user. In this case, the user who generates the mutation can be marked as a suspected abnormal user. After the operation and maintenance personnel receive the on-site investigation instructions issued by the monitoring platform through the data receiving device, the operation and maintenance personnel can be prompted to focus on the suspected abnormal user or implement a long-term line loss monitoring plan for the user.

[0023] Line loss monitoring system based on load anomaly analysis, including: An acquisition module is used to obtain the operating loads of different levels of the line through a load monitoring device, and determine whether the line loss on the line exceeds the theoretical line loss value; The positioning module is used to locate the specific substation where abnormal line loss occurs in the lower substation by using the load measurement data of different levels on the line and the historical load statistics of the lower substation when it is determined that the actual line loss on the line exceeds the theoretical line loss value; An analysis module is used to collect the current power consumption data of each user in the specific area, and analyze whether there are abnormal users with abnormal power consumption at present in combination with the historical power consumption data of each user; The upload module is used for the monitoring platform to generate an on-site inspection work order for abnormal users. The on-site inspection work order has an assigned on-site inspection device, which includes a data receiving device and a line load detection device. The data receiving device receives the load data of the abnormal user load metering device in real time, and the line load detection device receives the data synchronization instruction issued by the monitoring platform. Under the data synchronization instruction, the line load detection device uploads the on-site load detection result to the monitoring platform in real time; The judgment module is used for the monitoring platform to judge whether the abnormal user has management line loss based on the current load monitoring results and the current load data of the abnormal user's load metering device.

[0024] The present invention can avoid the problem of erroneous on-site load detection results obtained by the monitoring platform due to human factors such as data tampering and data entry errors during operation, and avoid the situation where personal behavior during the monitoring process affects the monitoring results; Operation and maintenance personnel can easily compare the data on the data receiving equipment and the line load detection equipment, and efficiently find the abnormal points where line loss occurs on the line. It is particularly suitable for finding the location of stolen wires and leakage connected to the front end of the load metering device. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a flowchart of a specific embodiment of the line loss monitoring method based on load anomaly analysis. DETAILED DESCRIPTION

[0026] The present invention is further described in detail below in conjunction with embodiments, but the present invention is not limited to the following embodiments: Embodiment 1: Combination Figure 1 , a line loss monitoring method based on load anomaly analysis includes the following steps: S1. Obtain the operating load of different levels of the line through the load monitoring device, and determine whether the line loss on the line exceeds the theoretical line loss value; S2. When it is determined that the actual line loss on the line exceeds the theoretical line loss value, the load measurement data at different levels on the line and the historical load statistics of the lower-level substations are used to locate the specific substations in the lower-level substations where the abnormal line loss occurs; S3, collecting the current power consumption data of each user in the specific area, and combining the historical power consumption data of each user to analyze whether there are abnormal users with abnormal power consumption at present; S4. The monitoring platform generates an on-site inspection work order for abnormal users. The on-site inspection work order has an assigned on-site inspection device, which includes a data receiving device and a line load detection device. The data receiving device receives the load data of the abnormal user load metering device in real time. The line load detection device receives the data synchronization instruction issued by the monitoring platform. Under the data synchronization instruction, the line load detection device uploads the on-site load detection result to the monitoring platform in real time. S5. The monitoring platform determines whether the abnormal user has management line loss based on the current load monitoring results and the current load data of the abnormal user's load metering device.

[0027] In the prior art, management line loss generally refers to line loss caused by improper metering of metering equipment and other management reasons related to metering, such as improper installation and wiring of metering equipment, which causes the metering result to be too small, and the statistical metering result to be too small due to the parallel connection of lines at the front end of the metering equipment. The above results are generally caused by theft of electricity by people. According to the currently known statistical results, the line loss rate of abnormal line sections caused by these reasons can reach more than 20%, and even more than 50% in the highest case. The line loss rate of effectively managed lines is generally below 5%. Effective management of line losses is an important means to reduce the losses of power grid companies. At the same time, with the improvement of the intelligence level of smart grids and the development of line loss monitoring software and hardware technology, abnormal line losses can usually be effectively monitored. However, the load monitoring units and load monitoring terminals deployed on site are only installed at intervals along the line. When the relevant supervision system communicates with the load monitoring units and load monitoring terminals installed at different levels of the line, and determines the existence of abnormal line losses and the specific abnormal line sections where abnormal line losses occur through load collection data, it is usually necessary to coordinate the operation and maintenance personnel to carry portable line loss detection equipment based on the determined location of the abnormal line section, and conduct on-site inspections of the abnormal line section to confirm and troubleshoot abnormal line losses and accurately locate abnormal users.

[0028] In order to avoid errors in on-site investigation results due to human factors during the process of abnormal line loss investigation and accurate positioning of abnormal users, and to improve the efficiency of identifying abnormal users, the line loss monitoring method as described above is provided.

[0029] Specifically, the present invention utilizes the characteristics of the prior art that different levels of power supply lines generally have gateway meters, feeder terminal equipment, integrated switch power equipment, load monitoring units / monitoring instruments / monitoring terminals, etc. In step S1, by setting the theoretical line loss value, combined with the currently monitored operating loads at different levels, it is determined whether there is abnormal line loss. For example, the line loss on the line branch can be the gateway meter data minus all feeder terminal equipment data, can be the primary and secondary integrated switch data minus all subsequent load monitoring unit data, can be the front-stage load monitoring instrument data minus the substation load monitoring data, etc. When it is determined through step S1 that there is abnormal line loss on the line, the specific substation with abnormal line loss is determined through step S2 to find the specific location where the abnormal line loss occurs on the line, and for the abnormal line loss The specific substation area, in step S3, uses the current electricity consumption data of each user to determine the abnormal users with abnormal electricity consumption among these users. After completing step S3, the abnormal line section suspected of having abnormal line loss can be located. In step S4, the operation and maintenance personnel carry a portable data receiving device and a line load detection device or a line loss tester according to the inspection work order information, and conduct an on-site inspection of the abnormal line section suspected of having abnormal line, measure the actual load of the line by on-site collection, and based on the load data provided by the load metering device used by the abnormal user provided by the data receiving device, the operation and maintenance personnel identify on-site whether there is abnormal line loss on the line section used by the abnormal user, and upload the detection result to the monitoring platform in real time, and use the data processing function provided by the monitoring platform to determine whether the abnormal user has a management line loss.

[0030] The differences between the present invention and the prior art include: in step S4, the on-site inspection work order issued by the monitoring platform has assigned on-site inspection equipment, and these on-site inspection equipment synchronizes load data with the monitoring platform and uploads on-site load detection results in real time, which can avoid the problem of erroneous on-site load detection results obtained by the monitoring platform due to human reasons such as data tampering and data entry errors, and avoid the situation where personal behavior during the monitoring process affects the monitoring results; by using on-site inspection equipment including data receiving equipment and line load detection equipment, operation and maintenance personnel can easily compare the data on the data receiving equipment and the line load detection equipment, and efficiently find the abnormal point location where line loss occurs on the line, which is particularly suitable for finding the location of stolen wires and leakage connected to the front end of the load metering device.

[0031] Based on the above, in more detail, the method for determining the specific area is: In step S2, the actual line loss is calculated using load measurement data at different levels on the line; Using the historical load statistical data of each subordinate substation and the current load data of each subordinate substation, the difference between the average value of the historical load statistical data of each subordinate substation and the current load data is calculated. When the average value is greater than the current load data and the deviation of the difference from the line loss amount is less than the set threshold, the subordinate substation is determined as a specific substation.

[0032] In the above implementation, the characteristics of having multiple lower-level substations on the line and multiple users in each lower-level substation, under normal power consumption, multiple users can maintain a relatively stable total load in the substation, and provide a method for determining the difference between the metering result of the lower-level substation and the historical load statistical data according to the average value of the historical load statistical data and the current load data of each lower-level substation after judging the line loss, and comparing the difference with the line loss, and judging that the deviation corresponding to the difference is less than the threshold according to the set threshold, that is, the difference is close to the line loss, and the difference is the load gap data that is not involved in the load statistics, and the lower-level substation is determined as a specific substation where abnormal line loss occurs. The present invention is particularly suitable for the theft of electricity in a specific substation where generally only a small number of users subjectively do not include the used electric energy in the current load data statistics, that is, a technical solution is provided by obtaining the current load data used for load statistics of the lower-level substation, and comparing the data with the average value obtained by the historical load statistical data of the lower-level substation as a simulated actual load, to determine whether there is actual electricity consumption that is not included in the load statistics category under normal circumstances.

[0033] Since users have obvious time-divided electricity consumption characteristics, in order to improve the accuracy of specific area confirmation, based on the above, in more detail, the historical load statistical data is the time-divided load data divided according to the electricity consumption period, wherein the electricity consumption period is divided into time periods in each day based on the day as the division unit; When performing the difference calculation, the acquisition time of the current load data is collected, and the corresponding time-sharing load data is matched according to the acquisition time. The collection period of the matched time-sharing load data is in the same period as the acquisition time, and multiple time-sharing load data are matched in the historical load statistical data as a statistical data sample set. Different time-sharing load data in the statistical data sample set have different collection days; The average value is the average value of each time-sharing load data in the statistical data sample set.

[0034] In the above scheme, the natural day is used as the division unit, and the time of each day is divided into different data statistical periods according to the time of each day, and the load data of each data statistical period is used as the time-sharing load data. Furthermore, the time period in which the current load data is located is collected according to the load anomaly analysis, and the time-sharing load data with the same data collection time as the above time period is obtained from the historical load statistical data. The interference of accidental factors on the average value is reduced by means of statistical data sample sets, so as to achieve the purpose of further ensuring the accuracy of confirmation of specific substations.

[0035] In the above scheme, the closeness between the difference and the line loss is determined by the set threshold. Generally, the larger the average value associated with the difference is, the larger the normal line loss is on the corresponding line. When there is electricity theft that affects the current load data measurement, the current load data cannot be used as a reference for the current actual load data. Based on the above, in more detail, considering that the larger the historical load statistical data is, the larger the normal line loss is, in order to take the characteristics of the historical load statistical data as the general load characteristics of the current line, the general load characteristics are taken into consideration when considering the degree of closeness, so as to improve the versatility and accuracy of the specific substation confirmation method. The set threshold is a dynamic value associated with the average value, wherein the set threshold is proportional to the average value.

[0036] What is obviously different from the load characteristics of the substation is that in terms of electricity consumption time, living users and most production users have significantly different power demands at different times. In order to ensure the reliability of identifying abnormal users and improve the efficiency of on-site investigation and line loss monitoring, based on the above, in more detail, in step S3, the method for obtaining abnormal users is: S3-1. Obtain the electricity consumption pattern of users in the specific area. Specifically, collect historical electricity consumption data of each user in the specific area, and use the date as the statistical period, summarize the historical electricity consumption data by day, obtain the total daily electricity consumption based on the date as the statistical period, and obtain the statistical curve of the daily electricity consumption of users in the area changing with the date according to the change pattern of the daily total electricity consumption; S3-2, predicting the recent electricity consumption data of each user, specifically, predicting the recent electricity consumption data of each user based on the historical electricity consumption data of each user and the statistical curve; S3-3. Analyze whether each user is an abnormal user. Specifically, determine whether each user is an abnormal user based on the difference between the collected recent actual power consumption data of the user and the predicted recent power consumption data of the user.

[0037] In the above scheme, step S3-1 is to obtain the overall electricity consumption law of users in a specific area. The electricity consumption law can be used to reflect seasonal electricity consumption changes and production-related electricity consumption changes. The law is subsequently used to predict the user's recent electricity consumption data. As a technician in this field, the historical electricity consumption data of each user is obtained through the electricity metering equipment for the user, and the natural day is used as the statistical period. For example, after summarizing all the historical electricity consumption data of a certain day, the total electricity consumption of the day is obtained. After the daily total electricity consumption statistics for all statistical periods are performed, the change law can be obtained in the form of a statistical curve. In step S3-2, the change law is used as a variable factor for predicting the change of electricity consumption data, and the recent electricity consumption data of each user is used as the basic factor in the prediction process to predict the recent electricity consumption data of each user. In step S3-3, the difference between the collected recent actual electricity consumption data of the user and the predicted recent electricity consumption data of the user is determined. When the difference is greater than the set difference threshold, it is concluded that the user is an abnormal user or a suspected abnormal user. For example, when the difference between the predicted recent electricity consumption data of a certain user and the actual electricity consumption data detected by the metering equipment is greater than the set difference threshold, it is concluded that the recent actual electricity consumption data is significantly less than the recent predicted electricity consumption data, and there is a possibility that the actual electricity consumption data is incorrectly counted.

[0038] For production users who have stopped or reduced production, and life users who have been away for a long time, in order to reduce the impact of these factors on the accuracy of abnormal user identification, based on the above, in more detail, in S3-3, when the user's recent actual electricity consumption data is less than the predicted recent electricity consumption data of the user, the user is marked as a suspected abnormal user; For suspected abnormal users, the differences between their recent electricity usage data and their historical electricity usage data are collected, and the differences include the concentrated electricity usage time periods in natural days. When the concentrated electricity usage time periods in the collected recent electricity usage data are different from the concentrated electricity usage time periods in the historical electricity usage data, or when there are no concentrated electricity usage time periods in the collected recent electricity usage data, the suspected abnormal user calibration is cancelled.

[0039] In the above scheme, when the user's recent actual electricity consumption data is less than the predicted recent electricity consumption data of the user, it is determined that the user has the possibility of detecting load abnormality and having actual electricity consumption that has not been included in the statistics. In this case, the user is marked as a suspected abnormal user, but among these users, there may be incorrect calibration due to production suspension, production reduction, and long-term absence. Furthermore, the historical concentrated electricity consumption time periods of these users are compared with the recent concentrated electricity consumption time periods. When the comparison results are different, it means that these users have different concentrated electricity consumption time periods, and it is concluded that these users are incorrectly calibrated abnormal users. Through this method, the accuracy of abnormal user calibration can be effectively improved and the number of abnormal users can be reduced.

[0040] In specific applications, according to the characteristics of the load and electricity consumption characteristics of each user, when the electricity consumption statistics at different times of a natural day do not have obvious differences, the load data can be used as a basis for identifying abnormal users. As a parallel scheme for analyzing abnormal users, based on the above, in more detail, in step S3, the load data of the load metering devices of each user in the specific substation is collected, and combined with the historical load data of each user, it is analyzed whether there are abnormal users with abnormal loads at the moment.

[0041] Based on the above, in more detail, in order to guide the operation and maintenance personnel to perform on-site audits and reflect the operation and maintenance personnel's on-site audits, in step S4, the data receiving equipment and the line load detection equipment both synchronize the location data of their locations to the monitoring platform in real time to provide a specific load data receiving location and an on-site load detection result upload location.

[0042] For the possible situation that users know about the on-site inspection in advance and restore orderly power consumption on their own before the operation and maintenance personnel perform the on-site inspection, thus rendering the subsequent on-site inspection invalid, based on the above, in more detail, after the monitoring platform finds the on-site inspection work order and before the monitoring platform obtains the uploaded on-site load detection results, the monitoring platform continuously obtains the load data of abnormal users, and when the load data suddenly changes and the load data mutation amount is not monitored by the upstream load monitoring device of the line, the monitoring platform issues an on-site investigation instruction to the data receiving device.

[0043] In the above scheme, when the load data obtained by the user metering device suddenly changes, and the mutation is not detected by the superior load detection device, it means that the mutation is included in the load statistics of the superior load detection device and is not included in the load statistics for the specific user. In this case, the user who generates the mutation can be marked as a suspected abnormal user. After the operation and maintenance personnel receive the on-site investigation instructions issued by the monitoring platform through the data receiving device, the operation and maintenance personnel can be prompted to focus on the suspected abnormal user or implement a long-term line loss monitoring plan for the user.

[0044] The line loss monitoring system based on load abnormality analysis in the present invention comprises: An acquisition module is used to obtain the operating loads of different levels of the line through a load monitoring device, and determine whether the line loss on the line exceeds the theoretical line loss value; The positioning module is used to locate the specific substation where abnormal line loss occurs in the lower substation by using the load measurement data of different levels on the line and the historical load statistics of the lower substation when it is determined that the actual line loss on the line exceeds the theoretical line loss value; An analysis module is used to collect the current power consumption data of each user in the specific area, and analyze whether there are abnormal users with abnormal power consumption at present in combination with the historical power consumption data of each user; The upload module is used for the monitoring platform to generate an on-site inspection work order for abnormal users. The on-site inspection work order has an assigned on-site inspection device, which includes a data receiving device and a line load detection device. The data receiving device receives the load data of the abnormal user load metering device in real time, and the line load detection device receives the data synchronization instruction issued by the monitoring platform. Under the data synchronization instruction, the line load detection device uploads the on-site load detection result to the monitoring platform in real time; The judgment module is used for the monitoring platform to judge whether the abnormal user has management line loss based on the current load monitoring results and the current load data of the abnormal user's load metering device.

[0045] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific embodiments of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, other embodiments obtained without departing from the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. Line loss monitoring method based on load anomaly analysis, It is characterized in that The following steps are involved: S1. Obtain the operating load of different levels of the line through the load monitoring device, and determine whether the line loss on the line exceeds the theoretical line loss value; S2. When it is determined that the actual line loss on the line exceeds the theoretical line loss value, the load measurement data at different levels on the line and the historical load statistics of the lower-level substations are used to locate the specific substations in the lower-level substations where the abnormal line loss occurs; S3, collecting the current power consumption data of each user in the specific area, and combining the historical power consumption data of each user to analyze whether there are abnormal users with abnormal power consumption at present; S4. The monitoring platform generates an on-site inspection work order for abnormal users. The on-site inspection work order has an assigned on-site inspection device, which includes a data receiving device and a line load detection device. The data receiving device receives the load data of the abnormal user load metering device in real time. The line load detection device receives the data synchronization instruction issued by the monitoring platform. Under the data synchronization instruction, the line load detection device uploads the on-site load detection result to the monitoring platform in real time. S5. The monitoring platform determines whether the abnormal user has management line loss based on the current load monitoring results and the current load data of the abnormal user's load metering device.

2. The line loss monitoring method based on load abnormality analysis according to claim 1, It is characterized in that The method for determining the specific area is: In step S2, the actual line loss is calculated using load measurement data at different levels on the line; Using the historical load statistical data of each subordinate substation and the current load data of each subordinate substation, the difference between the average value of the historical load statistical data of each subordinate substation and the current load data is calculated. When the average value is greater than the current load data and the deviation of the difference from the line loss amount is less than the set threshold, the subordinate substation is determined as a specific substation.

3. The line loss monitoring method based on load abnormality analysis according to claim 2, It is characterized in that The historical load statistical data is time-sharing load data divided according to the power consumption period, wherein the power consumption period is a period of each day divided based on the day; When performing the difference calculation, the acquisition time of the current load data is collected, and the corresponding time-sharing load data is matched according to the acquisition time. The collection period of the matched time-sharing load data is in the same period as the acquisition time, and multiple time-sharing load data are matched in the historical load statistical data as a statistical data sample set. Different time-sharing load data in the statistical data sample set have different collection days; The average value is the average value of each time-sharing load data in the statistical data sample set.

4. The line loss monitoring method based on load abnormality analysis according to claim 2 or 3, It is characterized in that The set threshold is a dynamic value associated with the average value, wherein the set threshold is in direct proportion to the average value.

5. The line loss monitoring method based on load abnormality analysis according to claim 1, It is characterized in that In step S3, the method for obtaining abnormal users is: S3-1. Obtain the electricity consumption pattern of users in the specific area. Specifically, collect historical electricity consumption data of each user in the specific area, and use the date as the statistical period, summarize the historical electricity consumption data by day, obtain the total daily electricity consumption based on the date as the statistical period, and obtain the statistical curve of the daily electricity consumption of users in the area changing with the date according to the change pattern of the daily total electricity consumption; S3-2, predicting the recent electricity consumption data of each user, specifically, predicting the recent electricity consumption data of each user based on the historical electricity consumption data of each user and the statistical curve; S3-3. Analyze whether each user is an abnormal user. Specifically, determine whether each user is an abnormal user based on the difference between the collected recent actual power consumption data of the user and the predicted recent power consumption data of the user.

6. The line loss monitoring method based on load abnormality analysis according to claim 5, It is characterized in that In S3-3, when the user's recent actual electricity consumption data is less than the predicted recent electricity consumption data of the user, the user is marked as a suspected abnormal user; For suspected abnormal users, the differences between their recent electricity usage data and their historical electricity usage data are collected, and the differences include the concentrated electricity usage time periods in natural days. When the concentrated electricity usage time periods in the collected recent electricity usage data are different from the concentrated electricity usage time periods in the historical electricity usage data, or when there are no concentrated electricity usage time periods in the collected recent electricity usage data, the suspected abnormal user calibration is cancelled.

7. The line loss monitoring method based on load abnormality analysis according to claim 1, It is characterized in that In step S3, the load data of the load metering device of each user in the specific area is collected, and combined with the historical load data of each user, it is analyzed whether there are abnormal users with abnormal load at present.

8. The line loss monitoring method based on load abnormality analysis according to any one of claims 1 to 7, It is characterized in that In step S4, the data receiving device and the line load detection device both synchronize the location data of their locations to the monitoring platform in real time.

9. The line loss monitoring method based on load abnormality analysis according to any one of claims 1 to 7, It is characterized in that During the period after the monitoring platform issues an on-site inspection work order and before the monitoring platform obtains the uploaded on-site load detection results, the monitoring platform continuously obtains the load data of abnormal users, and when the load data suddenly changes and the load data mutation amount is not monitored by the upstream load monitoring device of the line, the monitoring platform issues an on-site investigation instruction to the data receiving device.

10. Line loss monitoring system based on load anomaly analysis, It is characterized in that include: An acquisition module is used to obtain the operating loads of different levels of the line through a load monitoring device, and determine whether the line loss on the line exceeds the theoretical line loss value; The positioning module is used to locate the specific substation where abnormal line loss occurs in the lower substation by using the load measurement data of different levels on the line and the historical load statistics of the lower substation when it is determined that the actual line loss on the line exceeds the theoretical line loss value; An analysis module is used to collect the current power consumption data of each user in the specific area, and analyze whether there are abnormal users with abnormal power consumption at present in combination with the historical power consumption data of each user; The upload module is used for the monitoring platform to generate an on-site inspection work order for abnormal users. The on-site inspection work order has an assigned on-site inspection device, which includes a data receiving device and a line load detection device. The data receiving device receives the load data of the abnormal user load metering device in real time, and the line load detection device receives the data synchronization instruction issued by the monitoring platform. Under the data synchronization instruction, the line load detection device uploads the on-site load detection result to the monitoring platform in real time; The judgment module is used for the monitoring platform to judge whether the abnormal user has management line loss based on the current load monitoring results and the current load data of the abnormal user's load metering device.

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