In-depth analysis method and system based on low-voltage data collection from data center

Abstract

The present invention provides a deep analysis method and system based on low-voltage data collection from a data center, and relates to the technical field of distribution network automation. The method includes: obtaining power consumption data of multiple electric meters in each substation in the i-th and i-1-th detection cycles; obtaining corrected power consumption data of multiple electric meters in each substation in the i-th detection cycle according to the power consumption data of multiple electric meters; obtaining the total power consumption data of transformers in each substation in the i-th detection cycle; determining the line loss rate according to the total power consumption data of transformers in each substation in the i-th detection cycle and the corrected power consumption data of multiple electric meters in each substation; determining the proportion of high-loss substations and the proportion of negative-loss substations according to the line loss rate; determining the loss anomaly score according to the proportion of high-loss substations and the proportion of negative-loss substations; determining the quality status of power equipment according to the loss anomaly score. According to the present invention, the working efficiency of power system data analysis can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network automation, and in particular to a method and system for analyzing low-voltage data collected by a data center. Background Art

[0002] In the relevant technologies, the data collected by the power system currently requires manual intervention every day, and data statistics take up a lot of time, resulting in poor timeliness and passiveness in the management of some businesses. There is a lack of effective statistical caliber for users with frequent failures, and a lack of effective monitoring of the equipment operation quality. Therefore, it is necessary to carry out relevant data analysis applications to improve work efficiency and work quality.

[0003] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0004] The present invention provides a deep analysis method and system based on low-voltage data collected by a data center. By analyzing the power consumption data of abnormal meters and non-abnormal meters in the substation, the common indicators of line loss in the substation are dynamically updated, the statistical workload is reduced, the timeliness of indicator updates is improved, professional management and control and grassroots business promotion are facilitated, and the technical problem that the equipment operation quality is difficult to effectively monitor is solved.

[0005] According to a first aspect of the present invention, a method for in-depth analysis of low-voltage data collected by a data center is provided, comprising:

[0006] Obtaining power consumption data of multiple electric meters in each area in the i-th detection cycle, and power consumption data of multiple electric meters in each area in the i-1-th detection cycle, where i is a positive integer;

[0007] According to the power consumption data of multiple electric meters in each area in the i-th detection cycle and the power consumption data of multiple electric meters in each area in the i-1-th detection cycle, the corrected power consumption data of multiple electric meters in each area in the i-th detection cycle are obtained;

[0008] Obtain the total power consumption data of each transformer in the i-th detection cycle;

[0009] Determine the line loss rate of each substation according to the total power consumption data of the transformers in each substation in the i-th detection cycle and the corrected power consumption data of multiple electric meters in each substation;

[0010] According to the line loss rate, determine the proportion of high-loss areas and the proportion of negative-loss areas;

[0011] Determine a loss anomaly score according to the proportion of the high-loss area and the proportion of the negative-loss area;

[0012] The quality status of the power equipment is determined based on the loss anomaly score.

[0013] According to a second aspect of the present invention, a deep analysis system based on low-voltage data collected by a data center is provided, comprising:

[0014] The power consumption data module obtains the power consumption data of multiple power meters in each substation in the i-th detection cycle, and the power consumption data of multiple power meters in each substation in the i-1-th detection cycle, where i is a positive integer;

[0015] The power consumption data correction module obtains the corrected power consumption data of the multiple power meters in each substation in the i-th detection cycle according to the power consumption data of the multiple power meters in each substation in the i-th detection cycle and the power consumption data of the multiple power meters in each substation in the i-1-th detection cycle;

[0016] The total power consumption data module obtains the total power consumption data of each transformer in the i-th detection cycle;

[0017] The line loss rate module determines the line loss rate of each substation according to the total power consumption data of the transformers in each substation in the i-th detection cycle and the corrected power consumption data of multiple electric meters in each substation;

[0018] A proportion module determines the proportion of high-loss areas and the proportion of negative-loss areas according to the line loss rate;

[0019] A loss anomaly module, which determines a loss anomaly score according to the proportion of the high-loss area and the proportion of the negative-loss area;

[0020] The power equipment quality module determines the quality status of the power equipment according to the loss anomaly score.

[0021] Technical effect: According to the present invention, when analyzing the meter data in the substation, first, the abnormal coefficient of the meter is obtained based on the ratio of the difference in the electricity consumption data of the meter in two adjacent detection cycles to the electricity consumption data in the previous detection cycle, thereby objectively reflecting whether the meter is abnormal; then, based on the electricity consumption data of the abnormal meter in the i-1th detection cycle and the electricity consumption data of the reference meter in the i-1 and i-th detection cycles, the first corrected electricity consumption data of the abnormal meter in the i-1 detection cycle is determined, thereby improving the accuracy and objectivity of the first corrected electricity consumption data; then, based on the electricity consumption data of the abnormal meter in the i-1th detection cycle and the electricity consumption data of the normal meter in the i-1 and i-th detection cycles, the second corrected electricity consumption data of the abnormal meter in the i-1 detection cycle is determined, thereby improving the accuracy and objectivity of the second corrected electricity consumption data; finally, based on the first corrected electricity consumption data, the second corrected electricity consumption data, the electricity consumption data of the non-abnormal meter, and the total electricity consumption data of the substation transformer, the corrected electricity consumption data is determined, thereby improving the accuracy and comprehensiveness of the corrected electricity consumption data. The present invention analyzes the electricity consumption data of abnormal meters and non-abnormal meters in the substation, realizes dynamic update of common indicators of line loss in the substation, reduces statistical workload, improves the timeliness of indicator update, and facilitates professional management and control and grassroots business promotion.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic flow chart of a method for deep analysis of low-voltage data collected by a data center is exemplarily shown according to an embodiment of the present invention;

[0025] Figure 2 A block diagram of a device for deep analysis of data collected at a low voltage on a data center is exemplarily shown according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0028] Figure 1 A flow chart of a method for deep analysis of low-voltage data collected by a data center is exemplarily shown according to an embodiment of the present invention. The method includes:

[0029] Step S101, obtaining power consumption data of multiple electric meters in each substation in the i-th detection cycle, and power consumption data of multiple electric meters in each substation in the i-1-th detection cycle, where i is a positive integer;

[0030] Step S102, obtaining corrected power consumption data of multiple electric meters in each substation in the i-th detection cycle according to the power consumption data of multiple electric meters in each substation in the i-th detection cycle and the power consumption data of multiple electric meters in each substation in the i-1-th detection cycle;

[0031] Step S103, obtaining the total power consumption data of each transformer in the i-th detection cycle;

[0032] Step S104, determining the line loss rate of each substation according to the total power consumption data of the transformers in each substation in the i-th detection cycle and the corrected power consumption data of multiple electric meters in each substation;

[0033] Step S105, determining the proportion of high-loss areas and the proportion of negative-loss areas according to the line loss rate;

[0034] Step S106, determining a loss anomaly score according to the proportion of the high-loss area and the proportion of the negative-loss area;

[0035] Step S107, determining the quality status of the power equipment according to the loss anomaly score.

[0036] According to the in-depth analysis method based on low-voltage data collected by the data center in an embodiment of the present invention, by analyzing the electricity consumption data of abnormal meters and non-abnormal meters in the substation, it is possible to dynamically update the commonly used indicators of line loss in the substation, reduce the statistical workload, improve the timeliness of indicator updates, and facilitate professional management and control and grassroots business promotion.

[0037] According to an embodiment of the present invention, in step S101, power consumption data of multiple electric meters in each substation in the i-th detection cycle and power consumption data of multiple electric meters in each substation in the i-1-th detection cycle are obtained, where i is a positive integer.

[0038] For example, in the power system, a substation refers to the power supply range or area of ​​a transformer. There may be multiple electricity meters in the substation. The detection cycle can be set to one week, and the electricity consumption data of multiple electricity meters in the substation in the first week and the second week can be obtained.

[0039] According to one embodiment of the present invention, in step S102, based on the power consumption data of multiple electricity meters in each substation in the i-th detection cycle and the power consumption data of multiple electricity meters in each substation in the i-1-th detection cycle, the corrected power consumption data of multiple electricity meters in each substation in the i-th detection cycle are obtained.

[0040] According to one embodiment of the present invention, based on the power consumption data of multiple meters in each substation in the i-th detection cycle and the power consumption data of multiple meters in each substation in the i-1 detection cycle, the corrected power consumption data of multiple meters in each substation in the i-th detection cycle is obtained, including: determining the abnormal meter according to the power consumption data of multiple meters in each substation in the i-th detection cycle and the power consumption data of multiple meters in each substation in the i-1 detection cycle; obtaining multiple reference meters in each substation whose type information of the measurement object is the target type information according to the target type information of the measurement object of the abnormal meter; obtaining the corrected power consumption data of multiple meters in each substation in the i-1 detection cycle according to the power consumption data of the abnormal meter in the i-1 detection cycle, the power consumption data of the reference meter in the i-1 detection cycle, and the power consumption data of the reference meter in the i-1 detection cycle; determining the corrected power consumption data of multiple meters in each substation in the i-1 detection cycle according to the power consumption data of the abnormal meter in the i-1 detection cycle, the power consumption data of the reference meter in the i-1 detection cycle, and the power consumption data of the reference meter in the i-1 detection cycle. The first corrected electricity consumption data of the abnormal meter in the i-th detection cycle is determined based on the electricity consumption data in the i-th detection cycle; the normal meter in the target area that is normal in the i-th detection cycle and the i-1-th detection cycle is determined according to the target area to which the abnormal meter belongs; the second corrected electricity consumption data of the abnormal meter in the i-th detection cycle is determined based on the electricity consumption data of the normal meter in the i-th detection cycle, the electricity consumption data of the normal meter in the i-1 detection cycle, and the electricity consumption data of the abnormal meter in the i-1 detection cycle; the corrected electricity consumption data is determined based on the first corrected electricity consumption data, the second corrected electricity consumption data, the electricity consumption data of multiple meters in the target area to which the abnormal meter belongs in the i-th detection cycle, and the total electricity consumption data of the area transformer of the target area in the i-th detection cycle.

[0041] For example, an abnormal meter refers to a meter whose electricity consumption data differs too much in adjacent detection cycles; based on the target type information of the measurement object of the abnormal meter, such as type information of school, home or factory, a non-abnormal meter whose type information of the measurement object is the target type information is determined as the reference meter; based on the relationship between the electricity consumption data of the reference meter and the abnormal meter in the previous detection cycle, the electricity consumption data of the abnormal meter in the current cycle is corrected to obtain the first corrected electricity consumption data; based on the substation to which the abnormal meter belongs, normal meters with normal electricity consumption data relationship in adjacent detection cycles are determined, such as in the same community, one meter is detected as an abnormal meter, and the rest are normal meters; based on the relationship between the electricity consumption data of the abnormal meter and the normal meter in the previous detection cycle, combined with the electricity consumption data of the normal meter in the current detection cycle, the second corrected electricity consumption data is determined; based on the corrected electricity consumption data determined in two ways, the corrected electricity consumption data is determined.

[0042] According to one embodiment of the present invention, based on the power consumption data of multiple electric meters in each area in the i-th detection cycle, and the power consumption data of multiple electric meters in each area in the i-1-th detection cycle, determining the abnormal electric meter includes: determining the abnormal coefficient A of the k-th electric meter in the j-th area according to formula (1): j，k ,

[0043]

[0044] Among them, W j，k，i is the power consumption data of the kth meter in the jth area during the i-th detection cycle, W j，k，i-1 The kth electric meter in the jth substation is the electricity consumption data in the i-1th detection cycle; when the abnormal coefficient is greater than or equal to the abnormal threshold, the kth electric meter in the jth substation is determined as an abnormal electric meter.

[0045] According to one embodiment of the present invention, in formula (1), W j，k，i and W j，k，i-1 are the power consumption data of the kth meter in the jth area in the i-th and i-1-th detection cycles, respectively. j，k，i -W j，k，i-1 | is the difference in power consumption data between the i-th and i-1-th detection cycles of the k-th meter in the j-th area, The abnormal coefficient is the ratio of the difference between the power consumption data of the kth meter in the jth area in the i-th and i-1th detection cycles to the power consumption data in the i-1th detection cycle. When the ratio is larger, the relative difference between the power consumption data in two adjacent cycles is larger, and the meter is more likely to be abnormal. When the value of is greater than or equal to the abnormal threshold, the meter is abnormal and is determined to be an abnormal meter. Of course, the above method of determining an abnormal meter is only an example, and other methods of determining an abnormal meter in the related art can also be used, and the present invention does not limit this.

[0046] In this way, the abnormal coefficient of the meter is obtained by the ratio of the difference in the electricity consumption data of the meter in two adjacent detection cycles to the electricity consumption data in the previous detection cycle, and then the abnormal meter is screened based on the abnormal coefficient, thereby objectively and accurately reflecting whether the meter is abnormal.

[0047] According to one embodiment of the present invention, according to the power consumption data of the abnormal meter in the i-1th detection cycle, the power consumption data of the reference meter in the i-1th detection cycle, and the power consumption data of the reference meter in the i-th detection cycle, determining the first corrected power consumption data of the abnormal meter in the i-th detection cycle, including: determining a target reference meter that is normal in both the i-th detection cycle and the i-1th detection cycle in the reference meter; obtaining the first corrected power consumption data W according to formula (2) 1，c ,

[0048]

[0049] Among them, W a，i-1 is the power consumption data of the abnormal meter in the i-1th detection cycle, W ca，s，i-1 is the power consumption data of the sth target reference meter in the i-1th detection cycle, W ca，s，i is the electricity consumption data of the sth target reference meter in the i-th detection cycle, n is the number of target reference meters, s≤n, and s and n are both positive integers.

[0050] According to one embodiment of the present invention, in formula (2), The power consumption data of all n target reference meters in the i-th detection cycle, The power consumption data of all n target reference meters in the i-1th detection cycle, is the ratio of the power consumption of the abnormal meter to the power consumption of all target reference meters in the i-1th detection cycle (i.e., the ratio of the power consumption of the abnormal meter to the power consumption of all similar and normal meters in multiple areas in the i-1th detection cycle). It can reflect the proportional relationship between the power consumption of the abnormal meter and that of similar meters in the previous detection cycle when the power consumption was normal. The first corrected electricity consumption data is obtained by multiplying the proportional relationship between the abnormal electricity consumption of the abnormal meter in the i-1th detection cycle and the electricity consumption of all target reference meters by all target reference electricity consumption data in the i-th detection cycle, that is, the theoretical electricity consumption data of the abnormal meter is inferred by using the above proportional relationship and the electricity consumption of similar meters in the current detection cycle.

[0051] In this way, based on the power consumption data of the abnormal meter in the i-1th detection cycle and the power consumption data of the target reference meter in the i-1th detection cycle, the proportional relationship between the power consumption of the abnormal meter and similar meters under normal power consumption can be determined, thereby determining the first corrected power consumption data of the abnormal meter in the i-th detection cycle based on the proportional relationship and the power consumption data in the i-th detection cycle, thereby improving the accuracy and objectivity of the first corrected power consumption data.

[0052] According to one embodiment of the present invention, according to the power consumption data of the normal power meter in the i-th detection cycle, the power consumption data of the normal power meter in the i-1th detection cycle and the power consumption data of the abnormal power meter in the i-1th detection cycle, determining the second corrected power consumption data of the abnormal power meter in the i-th detection cycle includes: obtaining the second corrected power consumption data W according to formula (3) 2，c ,

[0053]

[0054] Among them, W a，i-1 is the power consumption data of the abnormal meter in the i-1th detection cycle, W nor，x，i is the power consumption data of the xth normal meter in the ith detection cycle, W nor，x，i-1 is the electricity consumption data of the xth normal electricity meter in the i-1th detection cycle, m is the number of normal electricity meters, x≤m, and x and m are both positive integers.

[0055] According to one embodiment of the present invention, in formula (3), is the power consumption data of all x normal meters in the i-th detection cycle, is the power consumption data of all x normal meters in the i-1th detection cycle, is the ratio of the power consumption of the abnormal meter to the power consumption of all normal meters in the i-1th detection cycle (i.e., the ratio of the power consumption of the abnormal meter to the power consumption of the normal meters in the same area in the i-1th detection cycle). It can reflect the proportional relationship between the power consumption of the abnormal meter and the normal meters in the same area in the previous detection cycle when no abnormality occurred and the power consumption was normal. The second corrected electricity consumption data is obtained by multiplying the proportional relationship between the abnormal electricity consumption of the abnormal meter in the i-1th detection cycle and the electricity consumption of all normal electricity meters by the electricity consumption data of all normal electricity meters in the i-th detection cycle. That is, the theoretical electricity consumption data of the abnormal meter is inferred by using the above-mentioned proportional relationship and the electricity consumption of the normal meters in the same area in the current detection cycle.

[0056] In this way, based on the power consumption data of the abnormal meter in the i-1th detection cycle and the power consumption data of the normal meter in the same area in the i-1th detection cycle, the proportional relationship between the power consumption of the abnormal meter and the normal meter in the same area under normal power consumption can be determined, thereby determining the second corrected power consumption data of the abnormal meter in the i-1 detection cycle based on the proportional relationship and the power consumption data in the i-th detection cycle, thereby improving the accuracy and objectivity of the second corrected power consumption data.

[0057] According to one embodiment of the present invention, the correction power consumption data is determined according to the first correction power consumption data, the second correction power consumption data, the power consumption data of multiple meters in the target substation to which the abnormal power meter belongs in the i-th detection cycle, and the total power consumption data of the substation transformer of the target substation in the i-th detection cycle, including: obtaining the minimum power error Err according to formula (4),

[0058]

[0059] Among them, W 1，c，y is the first corrected power consumption data of the yth abnormal meter in the target area, W i，z is the power consumption data of the zth non-abnormal meter in the target area during the i-th detection cycle, Y is the total number of abnormal meters in the target area, M is the total number of meters in the target area, and W 2，c，y is the second corrected power consumption data of the yth abnormal electric meter in the target substation, y≤Y, z≤MY, and y, Y, z and M are all positive integers; determine the data corresponding to the minimum power error in the first corrected power consumption data and the second corrected power consumption data as the corrected power consumption data.

[0060] According to one embodiment of the present invention, in formula (4), is the sum of the first correction power consumption data of all Y abnormal meters in the target area, is the sum of the second corrected power consumption data of all Y abnormal meters in the target area, is the sum of the power consumption data of all MY non-abnormal meters in the target area during the i-th detection cycle, W T The power consumption data of transformers in the substation area is It is the error between the total power consumption of each meter in the substation determined according to the first correction power consumption and the power consumption data of the transformer in the substation, that is, the total error of each meter in the target substation after correction using a normal meter of the same type as the abnormal meter. The error between the total power consumption of each meter in the area determined according to the second corrected power consumption and the power consumption data of the transformer in the area, that is, the total error of each meter in the target area after correction using a normal meter in the same area as the abnormal meter. The minimum value of the error after correction by the above two methods can be taken to obtain the minimum power error, so that after correction, the total error of each meter in the target area is minimized. Then, the data corresponding to the minimized error can be used as the corrected power consumption data. For example, if The first corrected power consumption data can be used as the corrected power consumption data if The second corrected power consumption data may be used as the corrected power consumption data.

[0061] In this way, the total error of each meter in the target area is determined based on the first corrected electricity consumption data, the second corrected electricity consumption data, the non-abnormal electricity consumption data, and the total electricity consumption data of the target area transformer, and the minimum value is selected from the total errors of each meter in the target area corresponding to the first corrected electricity consumption data and the second corrected electricity consumption data as the minimum electricity error, and then the one with the smaller error between the first corrected electricity consumption data and the second corrected electricity consumption data can be determined, and the one with the smaller error can be selected as the corrected electricity consumption data, thereby improving the accuracy and objectivity of the corrected electricity consumption data.

[0062] According to one embodiment of the present invention, in step S103, the total power consumption data of each transformer in each area in the i-th detection cycle is obtained. For example, the total power consumption data of each transformer in each area in the second week is obtained.

[0063] According to one embodiment of the present invention, in step S104, the line loss rate of each substation is determined based on the total power consumption data of the transformers in each substation in the i-th detection cycle and the corrected power consumption data of multiple electric meters in each substation.

[0064] For example, the line loss rate is the percentage of electric energy lost in the power network to the electric energy supplied by the power network. In this example, the line loss rate is equal to the sum of the corrected electricity consumption data of the abnormal meters in the substation in the second week and the normal electricity consumption data of the meters, and the error between the total electricity consumption data of the substation transformer in the second week and the total electricity consumption data of the substation transformer.

[0065] According to an embodiment of the present invention, in step S105, the proportion of high-loss stations and the proportion of negative-loss stations are determined according to the line loss rate.

[0066] For example, a high-loss substation means that the total electricity consumption of multiple meters in the substation is much smaller than the total electricity consumption data of the transformer. For example, if the total electricity consumption data of the transformer is 150Kwh, and the total electricity consumption of the meters in the substation is 100Kwh, the line loss rate is 33%, and 33% of the electricity is lost in the wires or other power equipment, or there may be electricity theft by users; a negative-loss substation means that the total electricity consumption of multiple meters in the substation is greater than the total electricity consumption data of the transformer. For example, if the total electricity consumption data of the transformer is 80Kwh, and the electricity consumption of the meters in the substation is 100Kwh, it means that there are abnormalities or statistical errors in the meters and other equipment in the substation.

[0067] According to one embodiment of the present invention, in step S106, a loss anomaly score is determined based on the proportion of the high-loss stations and the proportion of the negative-loss stations.

[0068] According to one embodiment of the present invention, the loss anomaly score is determined based on the proportion of the high-loss stations and the proportion of the negative-loss stations, including: summing the proportion of the high-loss stations and the proportion of the negative-loss stations to obtain the loss anomaly score.

[0069] For example, a high-loss area is an area with a positive line loss rate that is higher than a preset line loss rate threshold. The proportion of high-loss areas in all areas can be counted, for example, the proportion of high-loss areas is 10%. Negative-loss areas are areas with a negative line loss rate. The proportion of negative-loss areas in all areas can be counted, for example, the proportion of negative-loss areas is 5%. Based on the sum of the proportion of high-loss areas and the proportion of negative-loss areas, the loss anomaly score can be determined to be 15%.

[0070] According to an embodiment of the present invention, in step S107, the quality status of the power equipment is determined according to the loss anomaly score.

[0071] For example, the proportion of high-loss areas and the proportion of negative-loss areas are summed to determine the loss anomaly score. Both high-loss and negative-loss situations can indicate that there are certain problems with the power equipment. Therefore, the larger the sum of the two proportions, that is, the larger the loss anomaly score, the worse the quality of the power equipment, and the smaller the loss anomaly score, the better the quality of the power equipment. If the loss anomaly score is large, a maintenance prompt message can be generated to prompt the staff to repair or replace the equipment.

[0072] According to the in-depth analysis method based on low-voltage data collected by the data center in the embodiment of the present invention, when analyzing the meter data in the substation, first, according to the ratio of the difference in the electricity consumption data of the meter in two adjacent detection cycles to the electricity consumption data in the previous detection cycle, the abnormal coefficient of the meter is obtained, thereby objectively reflecting whether the meter is abnormal; then, according to the electricity consumption data of the abnormal meter in the i-1th detection cycle and the electricity consumption data of the reference meter in the i-1th and i-th detection cycles, the first corrected electricity consumption data of the abnormal meter in the i-1th detection cycle is determined, thereby improving the accuracy and objectivity of the first corrected electricity consumption data; then, according to the electricity consumption data of the abnormal meter in the i-1th detection cycle and the electricity consumption data of the normal meter in the i-1th and i-th detection cycles, the second corrected electricity consumption data of the abnormal meter in the i-1th detection cycle is determined, thereby improving the accuracy and objectivity of the second corrected electricity consumption data; finally, according to the first corrected electricity consumption data, the second corrected electricity consumption data, the electricity consumption data of the non-abnormal meter, and the total electricity consumption data of the substation transformer, the corrected electricity consumption data is determined, thereby improving the accuracy and comprehensiveness of the corrected electricity consumption data. The present invention analyzes the electricity consumption data of abnormal meters and non-abnormal meters in the substation, realizes dynamic update of common indicators of line loss in the substation, reduces statistical workload, improves the timeliness of indicator update, and facilitates professional management and control and grassroots business promotion.

[0073] Figure 2 A block diagram of a device for deep analysis of low-voltage data collected by a data center is exemplarily shown according to an embodiment of the present invention, the device comprising:

[0074] The power consumption data module obtains the power consumption data of multiple power meters in each substation in the i-th detection cycle, and the power consumption data of multiple power meters in each substation in the i-1-th detection cycle, where i is a positive integer;

[0075] The power consumption data correction module obtains the corrected power consumption data of the multiple power meters in each substation in the i-th detection cycle according to the power consumption data of the multiple power meters in each substation in the i-th detection cycle and the power consumption data of the multiple power meters in each substation in the i-1-th detection cycle;

[0076] The total power consumption data module obtains the total power consumption data of each transformer in the i-th detection cycle;

[0077] The line loss rate module determines the line loss rate of each substation according to the total power consumption data of the transformers in each substation in the i-th detection cycle and the corrected power consumption data of multiple electric meters in each substation;

[0078] A proportion module determines the proportion of high-loss areas and the proportion of negative-loss areas according to the line loss rate;

[0079] A loss anomaly module, which determines a loss anomaly score according to the proportion of the high-loss area and the proportion of the negative-loss area;

[0080] The power equipment quality module determines the quality status of the power equipment according to the loss anomaly score.

[0081] It should be understood by those skilled in the art that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments, and the embodiments of the present invention may be deformed or modified in any way without departing from the principles.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A deep analysis method based on low-voltage data collection from a data center. It is characterized in that include: Obtaining power consumption data of multiple electric meters in each area in the i-th detection cycle, and power consumption data of multiple electric meters in each area in the i-1-th detection cycle, where i is a positive integer; According to the power consumption data of multiple electric meters in each area in the i-th detection cycle and the power consumption data of multiple electric meters in each area in the i-1-th detection cycle, the corrected power consumption data of multiple electric meters in each area in the i-th detection cycle are obtained; Obtain the total power consumption data of each transformer in the i-th detection cycle; Determine the line loss rate of each substation according to the total power consumption data of the transformers in each substation in the i-th detection cycle and the corrected power consumption data of multiple electric meters in each substation; According to the line loss rate, determine the proportion of high-loss areas and the proportion of negative-loss areas; Determine a loss anomaly score according to the proportion of the high-loss area and the proportion of the negative-loss area; Determining the quality status of the power equipment according to the loss anomaly score; According to the power consumption data of multiple electric meters in each area in the i-th detection cycle and the power consumption data of multiple electric meters in each area in the i-1-th detection cycle, the corrected power consumption data of multiple electric meters in each area in the i-th detection cycle is obtained, including: Determine abnormal electric meters according to the electric power consumption data of multiple electric meters in each substation in the i-th detection cycle and the electric power consumption data of multiple electric meters in each substation in the i-1-th detection cycle; According to the target type information of the measurement object of the abnormal electric meter, a plurality of reference electric meters in each substation whose type information of the measurement object is the target type information are obtained; Determine first corrected power consumption data of the abnormal power meter in the i-th detection cycle according to the power consumption data of the abnormal power meter in the i-1th detection cycle, the power consumption data of the reference power meter in the i-1th detection cycle, and the power consumption data of the reference power meter in the i-th detection cycle; According to the target area to which the abnormal electric meter belongs, determining the normal electric meters in the target area that are normal in the i-th detection cycle and the i-1-th detection cycle; Determine the second corrected power consumption data of the abnormal power meter in the i-th detection cycle according to the power consumption data of the normal power meter in the i-th detection cycle, the power consumption data of the normal power meter in the i-1th detection cycle, and the power consumption data of the abnormal power meter in the i-1th detection cycle; Determine the correction power consumption data according to the first correction power consumption data, the second correction power consumption data, the power consumption data of multiple power meters in the target substation to which the abnormal power meter belongs in the i-th detection cycle, and the total power consumption data of the substation transformer in the target substation in the i-th detection cycle; Determining first corrected power consumption data of the abnormal power meter in the i-th detection cycle according to the power consumption data of the abnormal power meter in the i-1th detection cycle, the power consumption data of the reference power meter in the i-1th detection cycle, and the power consumption data of the reference power meter in the i-th detection cycle, comprising: Among the reference electric meters, determining a target reference electric meter that is normal in both the i-th detection cycle and the i-1-th detection cycle; According to the formula Obtain the first corrected power consumption data W 1，c , where W a，i-1 is the power consumption data of the abnormal meter in the i-1th detection cycle, W ca，s，i-1 is the power consumption data of the sth target reference meter in the i-1th detection cycle, W ca，s，i is the electricity consumption data of the sth target reference meter in the i-th detection cycle, n is the number of target reference meters, s≤n, and s and n are both positive integers; Determining second corrected power consumption data of the abnormal power meter in the i-th detection cycle according to the power consumption data of the normal power meter in the i-th detection cycle, the power consumption data of the normal power meter in the i-1th detection cycle, and the power consumption data of the abnormal power meter in the i-1th detection cycle, comprising: According to the formula Obtain the second corrected power consumption data W 2，c , where W a，i-1 is the power consumption data of the abnormal meter in the i-1th detection cycle, W nor，x，i is the power consumption data of the xth normal meter in the ith detection cycle, W nor，x，i-1 is the power consumption data of the xth normal meter in the i-1th detection cycle, m is the number of normal meters, x≤m, and x and m are both positive integers; Determining the corrected power consumption data according to the first corrected power consumption data, the second corrected power consumption data, power consumption data of multiple power meters in the target substation to which the abnormal power meter belongs in the i-th detection cycle, and the total power consumption data of the substation transformer of the target substation in the i-th detection cycle, includes: According to the formula Get the minimum power error Err, where W 1 ,c, y is the first corrected power consumption data of the yth abnormal meter in the target area, W i，z is the power consumption data of the zth non-abnormal meter in the target area during the i-th detection cycle, Y is the total number of abnormal meters in the target area, M is the total number of meters in the target area, and W 2 ,c, y is the second corrected power consumption data of the yth abnormal electric meter in the target area, y≤Y, z≤MY, and y, Y, z and M are all positive integers; W T It is the power consumption data of transformer in the substation area; The data corresponding to the minimum power error among the first corrected power consumption data and the second corrected power consumption data is determined as the corrected power consumption data.

2. According to claim 1, the in-depth analysis method based on low-voltage data collection from the data center, It is characterized in that According to the power consumption data of multiple power meters in each area in the i-th detection cycle, and the power consumption data of multiple power meters in each area in the i-1-th detection cycle, the abnormal power meter is determined, including: According to the formula Determine the abnormal coefficient A of the kth meter in the jth substation j，k , where W j，k，i is the power consumption data of the kth meter in the jth area in the i-th detection cycle, W j，k，i-1 The power consumption data of the kth meter in the jth substation in the i-1th detection cycle; When the abnormal coefficient is greater than or equal to the abnormal threshold, the kth electric meter in the jth substation is determined as an abnormal electric meter.

3. According to claim 1, the in-depth analysis method based on low-voltage data collection from the data center, It is characterized in that According to the proportion of high-loss areas and the proportion of negative-loss areas, the loss anomaly score is determined, including: The loss anomaly score is obtained by summing the proportion of the high-loss areas and the proportion of the negative-loss areas.

4. A deep analysis system for implementing the deep analysis method based on low-voltage data collected by a data center as described in any one of claims 1 to 3, It is characterized in that include: The power consumption data module obtains the power consumption data of multiple power meters in each substation in the i-th detection cycle, and the power consumption data of multiple power meters in each substation in the i-1-th detection cycle, where i is a positive integer; The power consumption data correction module obtains the corrected power consumption data of the multiple power meters in each substation in the i-th detection cycle according to the power consumption data of the multiple power meters in each substation in the i-th detection cycle and the power consumption data of the multiple power meters in each substation in the i-1-th detection cycle; The total power consumption data module obtains the total power consumption data of each transformer in the i-th detection cycle; The line loss rate module determines the line loss rate of each substation according to the total power consumption data of the transformers in each substation in the i-th detection cycle and the corrected power consumption data of multiple electric meters in each substation; A proportion module determines the proportion of high-loss areas and the proportion of negative-loss areas according to the line loss rate; A loss anomaly module, which determines a loss anomaly score according to the proportion of the high-loss area and the proportion of the negative-loss area; The power equipment quality module determines the quality status of the power equipment according to the loss anomaly score.

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