CN103744047A - Method for locating out-of-tolerance electric-energy meters in operation - Google Patents

Method for locating out-of-tolerance electric-energy meters in operation Download PDF

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CN103744047A
CN103744047A CN201310712853.3A CN201310712853A CN103744047A CN 103744047 A CN103744047 A CN 103744047A CN 201310712853 A CN201310712853 A CN 201310712853A CN 103744047 A CN103744047 A CN 103744047A
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april
user
power consumption
line loss
day
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王涓
吴旭鸣
孙杰
薛林政
王爱凤
张季军
戴振华
杨阳
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Taizhou Power Supply Co of Jiangsu Electric Power Co
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Taizhou Power Supply Co of Jiangsu Electric Power Co
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Abstract

The invention provides a method for locating out-of-tolerance electric-energy meters in operation. The method includes: 1. acquiring line loss electric quantity of a specific stage area at each day for a period of time and power consumption of each user at each day under the stage area; 2. according to a Pearson correlation coefficient calculation formula (as is described in the specification), using the acquired line loss electric quantity of the state area at each day as X and the power consumption of each user at each day or power consumption after combination of users as Y, the number of acquired samples as n, calculating the Pearson correlation coefficient of the power consumption of each user at each day and the line loss electric quantity of the stage area at each day, or calculating the Pearson correlation coefficient of the power consumption after combination of the different users and the line loss electric quantity of the state area; 3. comparing the correlation coefficient of the power consumption of each user and the line loss electric quantity of the stage area or the coefficient of the power consumption after the combination of the users and the line loss electric quantity of the stage area so that user electric-energy meters with correlation coefficient >=0.8 is suspicious electric-energy meters; 4. locating rapidly the suspicious out-of-tolerance electric-energy meters according to the above-mentioned steps.

Description

A kind of method of overproof electric energy meter in positioning trip
Technical field
The present invention relates to the method for overproof electric energy meter in a kind of positioning trip.
Background technology
At present table meter is generally adopted the modes such as sampling observation, periodic calibration, cycle rotation to carry out management and control.
Periodic calibration is only carried out field measurement for large user's electric energy meter, and just carries out once every year, cannot find in time overproof table meter, because low-voltage customer quantity is very large, cannot carry out periodic calibration, can only adopt the mode of sampling observation.Low-voltage customer electric energy meter monthly arranges sampling observation once, and each tens, be difficult to find targetedly overproof electric energy meter, there is blindness.Cycle rotation once cannot be found overproof table meter in 10 years in time.Be not specifically designed in the prior art at present the overproof method of operation electric energy meter of finding, only when analysis station district line loss is abnormal, could find has abnormal electric energy meter under platform district, and generally adopt the power consumption broken line graph He Tai district loss electric weight broken line graph of each user under the method Jiang Tai district of manual observation to compare, find out comparatively similar one group and analyze investigation, but the shortcoming of this way has: 1. workload is large, need to be by according to the power consumption Yu Tai district line loss electric weight generating folding line chart of each household, under Ru Tai district, user is more, and workload is well imagined; 2. lookup result out of true, because the method is to adopt the mode of manually checking broken line graph to carry out, belongs to qualitative analysis, and the degree of correlation of user's electric weight Yu Tai district line loss electric weight is lacked to quantitative test.
Summary of the invention
The invention provides the method for overproof electric energy meter in a kind of positioning trip, it can adopt Pearson correlation coefficient algorithm, quick and precisely finds out operating overproof table meter.
The present invention has adopted following technical scheme: a kind of method of overproof electric energy meter in positioning trip, and it comprises the following steps: step 1, gathers under the line loss electric weight He Gaitai district of every day, certain district for a period of time each user power consumption of every day;
Figure 888978DEST_PATH_IMAGE001
using the platform every day district line loss electric weight collecting as X, power consumption after the every daily power consumption of each user or each user combination is as Y, the quantity of the sample gathering is as n, calculate like this Pearson correlation coefficient of the power consumption Yu Tai district line loss every day electric weight of each user every day, or calculate the Pearson correlation coefficient between the power consumption Yu Tai district line loss electric weight after each user's combination; [U1] step 3, the related coefficient between more each user's electric weight Yu Tai district line loss electric weight, or the related coefficient between the power consumption Yu Tai district line loss electric weight after user's combination, user's electric energy meter of related coefficient>=0.8 is suspicious electric energy meter; Step 4, according to the suspicious overproof electric energy meter in the suspicious quick location of above step
In described step 1, gather under the line loss electric weight He Gaitai district of every day, certain district time period of power consumption of each user every day for being at least 30 days.The sample gathering in step 2 must occur in pairs, i.e. a corresponding Y of X, and sample number is greater than or equals 30 pairs.
The present invention has following beneficial effect: adopted after above technical scheme, it is high that the present invention has efficiency, speed is fast, the advantage of accurate positioning, criterion changes quantitative test into by original qualitative analysis, the present invention not only can improve location efficiency, according to above method, only need be by Pearson correlation coefficient Formula mathematical model, or the Pearson correlation coefficient formula that utilizes excel to carry, by power information acquisition system Zhong Tai district loss electric weight, the information such as user's day electric weight import Pearson came mathematical model or the excel form set up, can calculate soon the related coefficient of each user power utilization amount or combination user power utilization Liang Yutai district loss electric weight, and by result according to related coefficient sort descending, the abnormal user of table meter can be listed at a glance, accurately locate fast, efficiency is very high, and calculate accurately, Pearson correlation coefficient algorithm belongs to quantitative test, correlativity between each user's power consumption Yu Tai district loss is quantitatively calculated, blindness and the uncertainty of qualitative analysis have been avoided, practical in addition.Due to larger in table count number, especially low-voltage customer, can not regularly carry out error actual measurement to all table meters, by the accurately abnormal electric energy meter of positioning error of Pearson correlation coefficient algorithm, increase the management and control dynamics to low-voltage customer operation electric energy meter, there is stronger practical value.
Accompanying drawing explanation
Fig. 1 is Mou Tai in April district loss electric weight and 5xxxxxx289 user power utilization quantitative change broken line graph in the embodiment of the present invention.
Embodiment
From the classification of line loss electric weight: practical line loss (statistical line losses)=theory wire loss (technical loss) ﹢ management line loss (operating loss)=can the not clear loss of loss on transformers and power transmission lines ﹢ fixed loss ﹢, wherein: not clear loss=user stealing ﹢ electrical network original paper electric leakage ﹢ copies core mistake ﹢ electric energy meter error
Suppose that under eliminating Liao Moutai district, other cause the reason that line loss is abnormal, Dang Moutai district line loss is abnormal, and just need to search is inaccurate the causing of electric energy meter metering by which user.
According to: electric energy meter error in dipping rate=(electric energy meter is copied and seen the actual electric weight of electric weight-user) actual electric weight × 100% of ÷ user;
Known, electric energy meter error in dipping=electric energy meter is copied and is seen electric weight × (1-1 ÷ (electric energy meter error rate ﹢ 1))
Therefore visible, user's electric energy meter error in dipping and the user electric weight linear dependence of checking meter.
From the definition of line loss electric weight: line loss electric weight=delivery-electricity sales amount
And user's electric weight sum of checking meter under electricity sales amount Ji Weitai district, therefore, line loss electric weight and the user electric weight linear dependence of checking meter, with electric energy meter error in dipping also linear dependence.
Pearson correlation coefficient claims again " Pearson product-moment correlation coefficient ", simple correlation coefficient, and it has described the tightness degree contacting between two spacing variablees.
The present invention carrys out overproof electric energy meter in positioning trip by following steps: a kind of method of overproof electric energy meter in positioning trip, it comprises the following steps: step 1, gather under the line loss electric weight He Gaitai district of every day, certain district for a period of time each user power consumption of every day, gather under the line loss electric weight He Gaitai district of every day, certain district time period of power consumption of each user every day for being at least 30 days; Step 2, according to Pearson correlation coefficient computing formula ,
Using the platform every day district line loss electric weight collecting as X, power consumption after the every daily power consumption of each user or each user combination is as Y, the quantity of the sample gathering is as n, the sample gathering must occur in pairs, an i.e. corresponding Y of X, and sample number is greater than or equals 30 pairs, calculate like this Pearson correlation coefficient of the power consumption Yu Tai district line loss every day electric weight of each user every day, or calculate the Pearson correlation coefficient between the power consumption Yu Tai district line loss electric weight after each user's combination; Step 3, the related coefficient between more each user's electric weight Yu Tai district line loss electric weight, or the related coefficient between the power consumption Yu Tai district line loss electric weight after user's combination, user's electric energy meter of related coefficient >=0.8 is suspicious electric energy meter; Step 4, according to the suspicious overproof electric energy meter in the suspicious quick location of above step
Below by embodiment, further illustrate technical scheme of the present invention: be the real case that Taizhou electric company uses Pearson correlation coefficient to search customer problem table meter below.
Under Mou Tai district, have 21 users, the line loss per unit in Gai Tai in April district is always high at 10%-20%, and platform in April district loss electric weight is as table 1, and each user's electric weight is as table 2
Table 1 Mou Tai loses electric weight statistical form April in district
Date Line loss electric weight Date Line loss electric weight
April 1 19.61 April 16 12.04
April 2 14.66 April 17 16.93
April 3 20.8 April 18 18.69
April 4 17.65 April 19 11.88
April 5 21.44 April 20 13.57
April 6 19.44 April 21 18.7
April 7 NULL April 22 16.12
April 8 NULL April 23 19.95
April 9 NULL April 24 10.01
April 10 NULL April 25 15.69
April 11 NULL April 26 23.22
April 12 NULL April 27 11.7
April 13 NULL April 28 15.78
April 14 NULL April 29 13.48
April 15 14.92 April 30 14.81
Table 2 Mou Tai district April each user power utilization amount statistical form
Date 5xxxxxx664 5xxxxxx808 5xxxxxx289 5xxxxxx286 5xxxxxx635
April 1 0 3.12 0.99 1.28 1.47
April 2 0.04 2.37 0.6 1.44 1.86
April 3 0.08 2.55 0.95 1.67 1.33
April 4 0 2.32 0.71 1.66 1.32
April 5 0 2.61 1.18 1.9 1.95
April 6 0 3.13 0.92 1.38 1.38
April 7 0 2.26 0.87 1.58 1.38
April 8 0 2.6 0.93 1.44 1.32
April 9 0 3.22 0.62 2.02 1.41
April 10 0 3.3 0.52 1.06 1.42
April 11 0 2.79 0.77 1.67 1.47
April 12 0.04 2.27 0.68 1.27 1.14
April 13 0.07 3.12 0.42 1.7 1.19
April 14 0 1.94 0.48 1.22 1.29
April 15 0 3.68 0.55 2.63 1.64
April 16 0 3 0.43 1.77 1.25
April 17 0 4.19 0.64 1.4 1.63
April 18 0 1.96 0.61 1.13 1.28
April 19 0 2.42 0.57 1.4 1.54
April 20 0 2.96 0.39 1.13 0.73
April 21 0 2.54 0.75 1.35 1.45
April 22 0.03 1.86 0.56 1.57 1.64
April 23 0.07 2.52 0.81 1.32 1.54
April 24 0 3.05 0.28 1.18 1.08
April 25 0 3.26 0.39 1.48 1.32
April 26 0 2.14 1.37 1.28 1.52
April 27 0 2.64 0.38 1.51 1.35
April 28 0 3.62 0.54 1.15 1.46
April 29 0 2.48 0.49 1.8 1.98
April 30 0 1.92 0.6 1.4 1.87
Date 5xxxxxx198 5xxxxxx083 5xxxxxx287 5xxxxxx813 5xxxxxx756
April 1 1.85 3.56 1.94 3.42 1.69
April 2 1.55 4.37 1.62 3.05 2.02
April 3 2.22 3.91 2.44 1.99 2.8
April 4 2.38 4.4 2.61 3.13 2.34
April 5 1.97 4.21 1.93 3.95 1.34
April 6 5.05 3.96 2.05 2.59 1.3
April 7 6.22 2.13 1.68 2.93 1.07
April 8 5.09 2.39 1.49 3.25 3.93
April 9 3.03 2.46 1.26 3.34 5.88
April 10 5.2 2.37 1.4 4.05 5.27
April 11 4.09 2.25 1.47 2.66 5.17
April 12 1.51 2.1 2.82 2 4.51
April 13 1.53 2.73 2.12 5.39 4.04
April 14 2.57 2.59 1.52 2.88 3.68
April 15 2.16 2.42 1.5 2 4.42
April 16 2.86 1.92 1.52 2.02 5.05
April 17 3.05 2.17 1.87 2.61 3.91
April 18 4.25 2.42 1.98 3.01 4.27
April 19 4.96 2.84 2.28 2.81 5.49
April 20 3.32 1.27 2 3.53 3.98
April 21 3.45 1.52 1.35 4.9 3.35
April 22 3.56 0.57 1.6 3.63 4.67
April 23 2.03 0.58 1.51 4.21 4.84
April 24 1.81 0.65 1.87 4.28 4.92
April 25 3.65 0.74 2.13 3.3 5.17
April 26 3.77 0.71 1.51 5.61 4.69
April 27 4.43 0.78 1.61 4.19 4.61
April 28 3.16 1 2.35 3.59 3.9
April 29 2.93 1 1.69 3.69 4.84
April 30 1.76 2.27 1.78 5.2 2.83
Date 5xxxxxx328 5xxxxxx285 5xxxxxx006 5xxxxxx218 5xxxxxx868
April 1 9.67 4.92 4.52 3.68 2.16
April 2 10.21 4.54 4.56 2.37 2.21
April 3 9.59 4.67 6.08 1.72 2.07
April 4 11.11 4.48 5.92 3.44 0.51
April 5 10.12 3.7 6.95 4.1 1.45
April 6 9.11 4.81 6.7 2.87 2.97
April 7 10.76 4.67 5.12 4.03 2.7
April 8 10.02 4.25 6.48 4.83 2.71
April 9 10.08 4.97 7 4.79 6.37
April 10 9.7 4.85 7.97 5.8 4.18
April 11 10.33 4.8 5.13 5.97 2.82
April 12 9.95 4.53 5.31 2.76 2.65
April 13 10.94 5.41 7.99 2.96 3.19
April 14 9.37 4.52 6.09 1.64 2.17
April 15 9.12 4.15 3.9 1.79 2.05
April 16 9.09 4.48 5.17 1.84 2.87
April 17 10.25 4.6 3.31 2.05 2.41
April 18 9.46 4.96 4.34 2.13 3.5
April 19 9.73 5.35 4.4 4.99 4.05
April 20 11.71 5.16 6.82 3.61 3.23
April 21 10.07 5.31 5.71 4.93 2.24
April 22 9.17 4.24 5.51 4.72 3.68
April 23 11.19 4.52 4.36 5.44 6.38
April 24 12.02 4.81 4.69 4.27 3.76
April 25 10.67 4.72 4.23 5.36 3.35
April 26 9.06 4.42 4.38 3.4 3.16
April 27 11.83 4.33 3.15 4.48 3.02
April 28 8.28 4.74 5.76 1.48 2.82
April 29 9.71 3.87 5.46 3.91 2.86
April 30 9.04 4.48 6.11 3.52 1.98
Date 5xxxxxx046 5xxxxxx951 5xxxxxx667 5xxxxxx045 5xxxxxx288
April 1 6.98 12.26 12.71 1.49 2.73
April 2 7.09 10.57 9.77 1.54 3.57
April 3 6.95 5.51 12.91 2.15 3.37
April 4 6.43 9.05 11.95 2.07 5.67
April 5 5.96 7.13 8.91 1.32 3.85
April 6 6.82 14.6 9.01 1.5 3.39
April 7 5.9 11.34 12.55 1.73 3.46
April 8 5.35 2.58 14.7 1.47 3.53
April 9 6.43 12.03 13.64 1.79 3.72
April 10 6.53 1.78 11.2 1.7 3.64
April 11 4.6 13.49 15.24 1.27 3.94
April 12 6.7 4.76 8.43 1.34 3.67
April 13 6.83 4.08 7.14 1.65 3.29
April 14 7.39 6.56 12.1 1.4 4.74
April 15 5.86 4.58 11.23 1.91 3.9
April 16 5.33 6.72 12.34 1.6 4.11
April 17 5.55 4.21 9.02 1.72 3.07
April 18 6.5 7.19 6.39 1.66 3.89
April 19 6.82 15.11 8.2 1.72 3.73
April 20 6.95 7.86 9.7 2.02 3.26
April 21 5.51 7.19 11.07 1.64 4.1
April 22 5.32 10.79 8.53 2.24 3.2
April 23 6.47 7.42 9.82 2.14 3.69
April 24 5.97 0.9 15.94 1.47 2.89
April 25 5.95 10.12 13.07 1.89 4.05
April 26 5.91 9.95 11.91 1.58 3.52
April 27 5.92 10.17 10.73 1.46 3.67
April 28 6.71 10.05 10.42 1.92 3.87
April 29 4.91 12.21 10.71 1.45 3.54
April 30 5.49 9.6 5.99 1.34 4.18
Before adopting Pearson came algorithm, want to find out the user of the abnormal electric energy meter in Gai Tai district, the method for use is that the power consumption curve Yu Tai district line loss electric quantity curve at each family is compared, and manually finds out user like comparing class, then investigates.The shortcoming of this way has:
1. workload is large, need to be by according to the power consumption Yu Tai district line loss electric weight generating folding line chart of each household, and under Ru Tai district, user is more, and workload is well imagined;
2. lookup result out of true, because the method is to adopt the mode of manually checking broken line graph to carry out, belongs to qualitative analysis, and the degree of correlation of user's electric weight Yu Tai district line loss electric weight is lacked to quantitative test.
Now adopt Pearson correlation coefficient algorithm to carry out computational analysis as follows:
Using platform every day district line loss electric weight as X, the every daily power consumption of each user is as Y, calculate respectively the Pearson correlation coefficient of each user power utilization Liang Yutai district line loss statistics, because system failed and gathered computer board district loss electric weight April 7 to April 14, according to Pearson correlation coefficient computing method, the value of two ordered series of numbers is appearance in pairs, Gu two ordered series of numbers all should calculate Pearson correlation coefficient by April 7 after the numerical value on April 14 is cast out, result is as table 3.
Each user power utilization Liang Yutai in table 3 Mou Tai district March district line loss
Pearson correlation coefficient
Customs Assigned Number Pearson correlation coefficient
5xxxxxx289 0.9
5xxxxxx083 0.31
5xxxxxx664 0.3
5xxxxxx006 0.23
5xxxxxx046 0.21
5xxxxxx635 0.2
5xxxxxx813 0.16
5xxxxxx045 0.13
5xxxxxx951 0.06
5xxxxxx287 0.06
5xxxxxx288 0.03
5xxxxxx198 -0.05
5xxxxxx286 -0.05
5xxxxxx218 -0.08
5xxxxxx285 -0.11
5xxxxxx667 -0.12
5xxxxxx868 -0.13
5xxxxxx808 -0.16
5xxxxxx328 -0.31
5xxxxxx756 -0.49
By result of calculation, can be found out, the loss electric weight height correlation in the power consumption of user 5xxxxxx289 and platform district, place, its Pearson correlation coefficient is up to 0.9, and other users the highest also only have 0.3, we also can verify with broken line graph the correlation circumstance of this user Yu Tai district loss electric weight, as Fig. 1, by Fig. 1, can be experienced intuitively, the situation of change of the loss electric weight in Gai Tai district and 5xxxxxx289 user power utilization amount approaches and fits like a glove, the loss electric weight in Ji Tai district is followed the variation of 5xxxxxx289 user power utilization amount and is changed, also verified that the result of calculation of Pearson correlation coefficient is entirely true simultaneously.After finding out the issue table meter in Gai Tai district, Taizhou electric company site inspection of sending someone, and the electric energy meter that has gained this user carries out laboratory verification, finds that the error of this electric energy meter reaches-92.4%, and table meter existing serious negative error, and verification result is as table 4.
Table 4 is the report of 5xxxxxx289 user's electric energy meter calibration
Figure DEST_PATH_IMAGE003AAAA

Claims (3)

1. a method for overproof electric energy meter in positioning trip, it comprises the following steps:
Step 1, gathers under the line loss electric weight He Gaitai district of every day, certain district for a period of time each user power consumption of every day;
Step 2, according to Pearson correlation coefficient computing formula
Figure 838340DEST_PATH_IMAGE001
using the platform every day district line loss electric weight collecting as X, power consumption after the every daily power consumption of each user or each user combination is as Y, the quantity of the sample gathering is as n, calculate like this Pearson correlation coefficient of the power consumption Yu Tai district line loss every day electric weight of each user every day, or calculate the Pearson correlation coefficient between the power consumption Yu Tai district line loss electric weight after each user's combination;
Step 3, the related coefficient between more each user's electric weight Yu Tai district line loss electric weight, or the related coefficient between the power consumption Yu Tai district line loss electric weight after user's combination, user's electric energy meter of related coefficient >=0.8 is suspicious electric energy meter;
Step 4, according to the suspicious overproof electric energy meter in the suspicious quick location of above step.
2. the method for overproof electric energy meter in positioning trip according to claim 1, is characterized in that gathering in described step 1 under the line loss electric weight He Gaitai district of every day, certain district time period of power consumption of each user every day for being at least 30 days.
3. the method for overproof electric energy meter in positioning trip according to claim 1, is characterized in that the sample gathering in described step 2 must occur in pairs, i.e. a corresponding Y of X, and sample number is greater than or equals 30 pairs.
CN201310712853.3A 2013-12-23 2013-12-23 Method for locating out-of-tolerance electric-energy meters in operation Pending CN103744047A (en)

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CN104360225A (en) * 2014-10-17 2015-02-18 国家电网公司 Line loss positioning device and line loss positioning method for low-voltage overhead line
CN104280714B (en) * 2014-10-17 2015-12-30 国家电网公司 A kind of measuring apparatus operation exception detection method
CN104360225B (en) * 2014-10-17 2017-02-15 国家电网公司 Line loss positioning device and line loss positioning method for low-voltage overhead line
CN104280714A (en) * 2014-10-17 2015-01-14 国家电网公司 Running abnormality detecting method of metering device
CN105319438B (en) * 2015-11-13 2018-04-10 浙江创力电子股份有限公司 A kind of method and system that user power utilization is detected by gauge table
CN105319438A (en) * 2015-11-13 2016-02-10 浙江创力电子股份有限公司 A method and a system for detecting electricity use of users by using meters
CN106557992A (en) * 2016-11-25 2017-04-05 国网河南省电力公司周口供电公司 Operation control line loss analysis method based on multifactor impact
CN106682817A (en) * 2016-12-08 2017-05-17 国网浙江杭州市富阳区供电公司 Acquisition abnormity emergency degree judgment method
CN106803125A (en) * 2016-12-08 2017-06-06 国网浙江省电力公司 A kind of acquisition abnormity urgency level computational methods based on the conversion of standard electricity consumer
CN106780125A (en) * 2016-12-08 2017-05-31 国网浙江杭州市富阳区供电公司 A kind of acquisition abnormity urgency level computational methods based on monthly power consumption
CN106780125B (en) * 2016-12-08 2019-12-06 国网浙江杭州市富阳区供电公司 method for calculating abnormal collection emergency degree based on average monthly power consumption
CN106682817B (en) * 2016-12-08 2019-12-10 国网浙江杭州市富阳区供电公司 Judgment method for collecting abnormal emergency degree
CN107529652A (en) * 2017-09-11 2018-01-02 国网天津市电力公司电力科学研究院 A kind of phenomenon of the failure correlation analysis for tearing back electric energy meter open
CN110045196B (en) * 2018-01-15 2020-12-22 国网江苏省电力公司常州供电公司 High-voltage power supply line management line loss checking method
CN110045196A (en) * 2018-01-15 2019-07-23 国网江苏省电力公司常州供电公司 High voltage supply line management line loss checks method
CN109063930A (en) * 2018-08-30 2018-12-21 上海电力学院 A kind of dynamic wind power plant general power prediction technique based on clustering
CN109461096A (en) * 2018-11-05 2019-03-12 杭州致成电子科技有限公司 A kind of family change relationship anomalous discrimination method based on electricity relevance
CN109407039A (en) * 2018-11-13 2019-03-01 许继集团有限公司 Intelligent electric meter and its system, self-diagnosing method and fault detection method
CN111444241A (en) * 2020-03-26 2020-07-24 南京工程学院 Data mining-based accurate positioning method for line loss abnormity associated users of distribution room
CN112558000A (en) * 2020-12-05 2021-03-26 青岛鼎信通讯股份有限公司 Correlation screening-based electric energy meter metering error analysis method
CN113156358A (en) * 2021-03-19 2021-07-23 国网陕西省电力公司营销服务中心(计量中心) Overhead transmission line abnormal line loss analysis method and system
CN113156358B (en) * 2021-03-19 2023-09-22 国网陕西省电力公司营销服务中心(计量中心) Method and system for analyzing abnormal line loss of overhead transmission line
CN113888360A (en) * 2021-10-28 2022-01-04 广东电网有限责任公司 Method, device, equipment and storage medium for identifying electricity stealing users
CN115542238A (en) * 2022-11-30 2022-12-30 北京志翔科技股份有限公司 Super-differential meter detection method and device

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Application publication date: 20140423