CN112462133A - Electricity stealing judgment method for private mutual inductor of high-voltage user - Google Patents
Electricity stealing judgment method for private mutual inductor of high-voltage user Download PDFInfo
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Abstract
A method for judging electricity stealing of a private mutual inductor of a high-voltage user comprises the following steps: step 1, selecting an abnormal line in a power grid based on line loss rate and loss electric quantity of the line; step 2, determining the time dimension of the daily electric quantity of the user, traversing the abnormal route according to the dimension, and calculating a correlation coefficient between the high-voltage special variable user in the abnormal route and the lost electric quantity of the route; and 3, determining the electricity stealing users based on the correlation coefficient and the daily electricity utilization ratio. Based on the method of the invention, the electricity stealing users can be accurately screened out.
Description
Technical Field
The invention relates to the field of power grid electricity stealing identification, in particular to a method for judging electricity stealing of a private mutual inductor of a high-voltage user.
Background
At present, with the direction development of diversified economy, the range of industrial power utilization and residential power utilization is gradually expanded, the power consumption is continuously increased, and power resources fall into a shortage state. For the benefit of the enterprises and individuals, illegal benefits are obtained by adopting illegal electricity utilization or electricity stealing modes, and the stable operation of the power grid is seriously influenced. The electricity stealing modes of power consumers are various and are developed towards intellectualization, specialization and concealment. In order to solve the electricity stealing phenomenon in time, the power supply unit also provides a plurality of electricity stealing identification methods. For example, the obvious electricity stealing users are judged through abnormal electricity utilization characteristic quantities, such as voltage loss, undervoltage, current loss, current imbalance, phase variation and other abnormal characteristics.
However, there is a way for electricity stealing users to replace current transformers privately, which can show no obvious abnormal features in terms of current, voltage and phase, and thus, the discovery of such electricity stealing users is difficult. Such concealed electricity theft is often present in high voltage private subscribers. Because the transformation ratio of the current transformer which is replaced privately is several times higher than the system internal parameters before replacement, the difference between the electric quantity of a metering user and the electric quantity of actual electricity stealing is huge, and therefore, the huge loss is caused to a power grid company.
For such a power stealing manner, a method for finding power utilization abnormality or meter fault according to the correlation between the power fluctuation of the user and the line loss change has been provided in the prior art, but a more practical and accurate correlation calculation method and a power stealing user determination manner are still lacking.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a method for judging electricity stealing of a private transformer of a high-voltage user. The method takes the line loss rate of a medium-voltage line as an analysis object, and calculates the relation between daily loss electric quantity and user daily electric quantity by using a Pearson correlation coefficient method, so as to screen out suspected electricity stealing users.
The invention adopts the following technical scheme. A method for judging electricity stealing of a private mutual inductor of a high-voltage user is characterized by comprising the following steps: step 1, selecting an abnormal line in a power grid based on a line loss rate and line loss electric quantity; step 2, determining the time dimension of the daily electric quantity of the user, traversing the abnormal route according to the dimension, and calculating a correlation coefficient between the high-voltage special transformer user in the abnormal route and the lost electric quantity of the route; and 3, determining the electricity stealing users based on the correlation coefficient and the daily electricity utilization ratio.
Preferably, step 1 specifically comprises: when the line loss rate of the line is always higher than a first preset threshold value within a first preset time period, and the line loss electric quantity is larger than a second preset threshold value, determining that the line is an abnormal line; or when the line loss rate change value of the line loss rate increased in a second preset time period is higher than a third preset threshold, the absolute value of the difference value between the line loss rate after sudden increase and the line loss rate on the day of sudden increase is lower than a fourth preset threshold, and meanwhile, the line loss electric quantity is larger than the second preset threshold, the line is determined to be an abnormal line.
Preferably, step 1 specifically comprises: a first predetermined threshold of 10%, said second predetermined threshold of 800kWh, a third predetermined threshold of 5%, a fourth predetermined threshold of 3%; the first preset time period starting time is at least 6 days before the calculation and analysis day, and the second preset time period is at least 6 days after the sudden increase day.
Preferably, step 2 specifically comprises: the time dimension of the daily electric quantity of the user is any number of days in the time dimension selection range of 7-15 days; and the time dimension selection range is obtained on the basis of eliminating the measurement and collection abnormal factors which influence the line loss rate fluctuation of the line.
Preferably, in step 2, the correlation between the abnormal line high-voltage specific variable user and the line loss electric quantity is calculated by using a pearson correlation coefficient, and the calculation formula is as follows:
wherein, X is the daily electric quantity vector of the high-voltage special transformer user in the abnormal circuit, and X is { X ═ X1，X2，…，Xi，…，XnY is daily power loss in the abnormal line, and Y ═ Y1，Y2，…，Yi，…，YnI is the ith day, n is the determined daily electric quantity time dimension of the user,
and
are the average values of X and Y over n days, respectively.
Preferably, step 3 further comprises: and when r is greater than 0.9, the user is confirmed as a suspected electricity stealing user.
Preferably, step 3 further comprises: determining suspected electricity stealing users based on the magnitude of the Pearson correlation coefficient and the change rate of the daily electricity utilization ratio; the daily power utilization ratio is the ratio of daily loss power in the abnormal circuit to daily power of the high-voltage special transformer user.
Preferably, step 3 further comprises: if the change rate of the daily power utilization ratio is lower than a fifth preset threshold value, determining a power stealing user of the high-voltage special transformer user; the fifth predetermined threshold is 50%.
Compared with the prior art, the method for judging electricity stealing of the private mutual inductor of the high-voltage user has the advantages that compared with the traditional current and voltage abnormality characteristic judgment, the hidden electricity stealing user is judged by means of quantitatively calculating the electric quantity fluctuation correlation under the condition of no current and voltage abnormality, and the practicability and the accuracy of judgment on the electricity stealing user are effectively improved.
Drawings
FIG. 1 is a schematic flow chart of a method for judging whether a private transformer of a high-voltage user steals electricity according to the invention;
FIG. 2 is a flow chart of an electricity stealing judgment method for a private transformer of a high-voltage user according to the invention;
FIG. 3 is a schematic diagram of abnormal fluctuation of line loss of a 10kV line in the method for judging electricity stealing of the private transformer of the high-voltage user;
FIG. 4 is a schematic diagram of the power loss of the abnormal line and the power consumption fluctuation of the high-voltage user in the method for judging the electricity stealing of the private mutual inductor of the high-voltage user according to the present invention;
FIG. 5 is a schematic diagram showing the variation of the correlation coefficient of the high voltage subscriber determined based on different time dimensions in the method for judging the electricity stealing of the private transformer of the high voltage subscriber of the present invention;
fig. 6 is a schematic diagram of daily power consumption ratio (i.e. ratio of power loss of line to daily power consumption of suspected power stealing users) fluctuation in the method for judging power stealing of the private transformer for the high-voltage users.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
Fig. 1 is a schematic flow chart of a method for judging electricity stealing of a private transformer of a high-voltage user according to the invention. As shown in fig. 1, a method for determining electricity stealing of a private transformer of a high-voltage user includes steps 1 to 3.
Generally, there is a clear definition for high voltage users in an electrical power system. Specifically, the high-voltage users are users directly supplied with power at a high voltage of 10kv or more.
Generally, an abnormal line in a power grid should be determined based on line loss rate data of the line in the power grid. Fig. 2 is a flow chart of an electricity stealing judgment method for the private transformer of the high-voltage user. As shown in fig. 2, a medium-voltage line can be selected, and the line loss rate and the loss capacity of the line can be calculated.
Preferably, when the line loss rate of the line is always higher than a first preset threshold value within a first preset time period, and the line loss electric quantity is greater than a second preset threshold value, the line is determined to be an abnormal line; or when the line loss rate change value of the line loss rate increased in a second preset time period is higher than a third preset threshold, the absolute value of the difference value between the line loss rate after sudden increase and the line loss rate on the day of sudden increase is lower than a fourth preset threshold, and meanwhile, the line loss electric quantity is larger than the second preset threshold, the line is determined to be an abnormal line.
Generally, the line loss rate can be suddenly increased and maintained in a high loss state in a short time according to a long-term high loss state of the line loss rate or the line loss rate, and the line loss electric quantity is high, the line can be determined to be an abnormal line, and the abnormal line can be further analyzed.
Specifically, when the line loss rate of the line is greater than 0, whether the line loss rate of the line meets the condition is continuously judged. When the line loss rate of the line is higher than a first predetermined threshold value for a long time (for example, the first threshold value may be set to be a line loss value of 10%), and the line loss capacity is higher than a second threshold value (the second threshold value may be set to be 800kWh), the line is determined to be an abnormal line. In addition, since the line should be kept in a state higher than the first predetermined threshold value for a long period of time, a first preset time period may be set as the determination of whether or not it is long (at least 7 days or more). Or, when the variation value of the line loss rate is higher than a third predetermined threshold (which can be set to 5%), and the absolute value of the difference between the line loss rate after the increase and the line loss rate at the day of the increase is lower than a fourth predetermined threshold (which can be set to 3%), and the line loss electric quantity is higher than a second threshold (which can be set to 800kWh), the line is confirmed to be an abnormal line. The increase should be an abrupt increase, and whether the change of the line loss rate on the day of the abrupt increase is higher than a third threshold value or not is considered, and a second preset time period (at least equal to or more than 7 days) may be set for judging whether the absolute value of the difference between the line loss rate on the day of the increase and the line loss rate on the day of the increase is lower than a fourth predetermined threshold value or not as a judgment of whether the increase is an abrupt increase or not. The first predetermined threshold, the second predetermined threshold, the first preset time period and the second preset time period may be empirically determined with reference to an actual condition of the power grid. Specifically, the first predetermined threshold may be preset to be 10%, the second predetermined threshold may be 800kWh, the third predetermined threshold may be 5%, and the first predetermined time period and the second predetermined time period are at least greater than or equal to 7 days. Of course, different data can be selected as the judgment mode of the line condition according to the actual situation of the power grid.
The first preset time period starting time is at least 6 days before the calculation and analysis day, and the second preset time period is at least 6 days after the sudden increase day.
In one embodiment of the invention, 13 household power users are hung under a 10kV line of a certain power company. Wherein 6 users are high-voltage users, and 7 users are platform area assessment users. Fig. 3 is a schematic diagram of abnormal fluctuation of line loss of a 10kV line in the method for judging electricity stealing of the private transformer of the high-voltage user. As shown in fig. 3, the line loss fluctuates abnormally between day 1 and day 16 and day 3 and day 12. Before 1 month and 21 days, the line loss is about 20%, and during 1 month and 22 days to 2 months and 21 days, the line loss rate is greatly reduced, but the power consumption is reduced during the spring festival, and the line load is light due to shutdown and production stoppage of high-voltage users under the line caused by the influence of new crown pneumonia epidemic situation and the like, and the line loss rate greatly fluctuates, but the normal situation is met. After 22 days 2 months, however, the line loss suddenly increased greatly and remained in a high loss state. For example, the line loss is maintained at about 20% and is greater than 5% of the second predetermined threshold set in the present invention, and after the line loss suddenly increases in 2 months and 22 days, the line loss is kept in a high loss state for a long time, that is, the line loss is greater than 10% of the first predetermined threshold in all of the line loss between 2 months and 22 days and 3 months and 12 days, and the line is determined to be an abnormal line.
And 2, determining the time dimension of the daily electric quantity of the user, traversing the abnormal line determined in the step 1 according to the dimension, and calculating a Pearson correlation coefficient of the high-voltage special variable user in the abnormal line.
Preferably, the time dimension of the daily electricity quantity of the user is any number of days in the time dimension selection range of 7 to 15 days; and the time dimension selection range is obtained by eliminating the metering and collecting abnormal factors influenced by the line loss rate fluctuation of the line as much as possible.
It should be noted that if the time dimension of the daily electricity quantity is too small, the fluctuation of the correlation coefficient is relatively large, and further, the degree of distinction between the correlation coefficients of different users is not obvious. If the time dimension of the daily electricity quantity is selected to be too large, metering abnormality occurs in the long time of the user meter, or signal abnormality occurs in the collected signals in the long time, the probability that metering data are missing in the long time is greatly increased, for example, metering data of a certain few days are missing in the monitoring master station. When data is missing, the calculation of the Pearson correlation coefficient is greatly influenced. Therefore, in consideration of minimizing the influence of the line loss rate due to the abnormal factors of measurement and collection, the selection range of the time dimension is defined to be 7 days to 15 days.
In the process of traversing all high-voltage special transformer users in abnormal lines, only the high-voltage special transformer users hung under the medium-voltage lines of the power grid can be considered under the condition that the evaluation user measurement in the low-voltage public transformer area is excluded without abnormality. Because the gateway table is usually installed and operated at the positions of power generation enterprises such as internet surfing, cross-regional connecting lines, provincial network connecting lines, intra-provincial network off-line and the like, the problem that a user privately exchanges a mutual inductor does not exist, and the line route loss can be affected when the metering is abnormal.
Preferably, the calculation formula of the pearson correlation coefficient of the abnormal line in step 2 is:
wherein, X is the daily electric quantity vector of the high-voltage special transformer user in the abnormal circuit, and X is { X ═ X1，X2，…，Xi，…，XnY is daily power loss in the abnormal line, and Y ═ Y1，Y2，…，Yi，…，YnI is the ith day, n is the determined daily electric quantity time dimension of the user,
and
are the average values of X and Y over n days, respectively.
In an embodiment of the present invention, when the line loss abnormally increases suddenly, the line loss of the abnormal line and the power consumption of each high-voltage user in the line in the current time dimension can be determined. Fig. 4 is a schematic diagram of the power loss of the abnormal line and the power consumption fluctuation of the high-voltage user in the method for judging the electricity stealing of the private transformer of the high-voltage user. As shown in fig. 4, in the time period of the month from 2 months 22 days to 3 months 21 days in the time dimension, the high-voltage users 5 have long-term power shortage, the high-voltage users 6 have full capacity reduction, and the daily power is all empty. The power consumption of the high-voltage users 4 is highly synchronized with the fluctuation of the line loss.
At this time, the pearson correlation coefficient in the abnormal line of each high-voltage subscriber can be calculated. Table 1 shows the pearson correlation coefficient of each high voltage subscriber in the abnormal line. As shown in table 1, the correlation coefficient between the high-voltage subscriber 4 and the line loss is 0.98, which is a very strong positive correlation. The value of the correlation coefficient is more than 0.9, and the suspicion of electricity stealing is high. On-site inspection shows that the name plate of the household current transformer is suspected to be reinstalled, and a transformation ratio test is carried out on the transformer, wherein the test results comprise that two transformation ratios are 600/5A, and one transformation ratio is 500/5A. And the transformation ratio file information of three current transformers in the marketing system is 400/5A.
Table 1 pearson correlation coefficient of high voltage subscriber in abnormal line
In addition, the calculated pearson correlation coefficients are different for different time dimensions. Fig. 5 is a schematic diagram of a change of a correlation coefficient of a high-voltage subscriber determined based on different time dimensions in the method for judging whether the high-voltage subscriber private mutual inductor steals electricity. As shown in fig. 5, pearson correlation coefficient calculation was performed for 4 high-voltage users under the line at different time intervals from 2 months and 22 days. When the time dimension is set within 7 days, the 1-3 correlation coefficient fluctuation of the high-voltage users is large. When the time dimension is within 5 days, the relative coefficient ratio of the high-voltage user 2 is larger, and the judgment and analysis are influenced. In different time dimensions, the correlation coefficient of the high-voltage user 4 is kept above 0.97, and the high-voltage user 4 has strong correlation. Therefore, in order to screen all users with high voltage from suspected electricity stealing users, a suitable time dimension, such as 7 to 14 days in the present invention, can be set.
And 3, determining suspected electricity stealing users based on the magnitude of the Pearson correlation coefficient and the change rate of the daily electricity utilization ratio.
Generally, a larger value of r indicates a higher level of plausibility for the user. Preferably, when r >0.9, the daily lost power change and the user daily power change show extremely strong correlation. Thus, the user can be confirmed as a suspected electricity stealing user.
As shown in table 1, the correlation coefficient between the high-voltage subscriber 4 and the line loss is 0.98, which is a very strong positive correlation. The value of the correlation coefficient is more than 0.9, and the suspicion of electricity stealing is high.
Preferably, a suspected electricity stealing user is determined based on the magnitude of the Pearson correlation coefficient and the change rate of the daily electricity utilization ratio; the daily power utilization ratio is the ratio of daily loss power in the abnormal circuit to daily power of the high-voltage special transformer user. And if the change rate of the daily power utilization ratio is lower than a fifth preset threshold value, determining that the high-voltage special transformer user is a power stealing user. The fifth predetermined threshold is 50%.
Typically, the line loss comprises a technical line loss Δ JExercise machineAnd manage line loss Δ JPipe. Line loss formula delta J-delta J can be obtained according to the definition and the constitution mode of the line lossExercise machine+ΔJPipe. Wherein, the technical line loss is also called theoretical line loss and is based on the load condition of the current time of the power gridAnd the fixed line loss value can be obtained through theoretical calculation, which is determined together with the parameters of the power supply equipment. For example, for a power saving company, the current line loss yield should be maintained between 0% and 10%.
The management line loss is determined by managing the line itself. For example, the operations of managers such as line change and connection relations, metering device faults, wrong wiring and the like all affect the management of line loss. In addition, when the situations of electricity collection, electricity stealing and the like occur, the line loss management is also influenced. If the management line loss is too high, the manager can detect the abnormal line and eliminate or reduce most of the management line loss through troubleshooting. And the electricity stealing phenomenon can be discovered accordingly.
For example, when an electricity stealing user privately replaces a current transformer with a large transformation ratio in a line, the site actual comprehensive multiplying power which can be obtained by an electricity stealing site test is N', and the comprehensive multiplying power of the current transformer of the electricity stealing user recorded in an electricity selling marketing system is N. According to the comprehensive multiplying power of the current transformer of the electricity stealing user recorded in the electricity selling marketing system, the electricity selling marketing system can deduce that the daily electricity consumption of the user is
The amount of electricity will be less than the user's actual electricity usage. The line loss of the abnormal circuit caused by the difference between the actual electricity consumption and the sold electricity can be calculated by a formula. The line loss of the abnormal circuit is
When the loads of other users in the line are stable, the technical line loss is delta JExercise machineThe fluctuation of (2) is not large and tends to be constant. Moreover, the electricity stealing users can not replace the electricity stealing mutual inductor for many times at any time, so the ratio value
Should also be a fixed value. At the moment, the line loss delta J of the line and the daily electricity consumption J of the suspected electricity stealing user tend to change linearlyAnd (4) transforming. Thus, for the formula
And (4) conversion is carried out, and according to the ratio of the line loss delta J of the line to the daily electricity quantity J of the suspected electricity stealing users, which suspected electricity stealing users are the electricity stealing users can be judged. And the transformation ratio of the mutual inductor replaced by the electricity stealing user can be remotely calculated.
In one embodiment of the invention, the proportional relation between the line losses of the abnormal lines of the suspected electricity stealing users is calculated and displayed in the form of a chart. Fig. 6 is a schematic diagram of daily power consumption ratio (i.e. ratio of power loss of line to daily power consumption of suspected power stealing users) fluctuation in the method for judging power stealing of the private transformer for the high-voltage users. As shown in fig. 6, in the time dimension, the proportional relationship fluctuates between 0.38 and 0.44, and at this time, the fluctuation range of the proportional relationship is less than 50%, which indicates that the fluctuation of the proportional relationship is small, and the technical line loss is smooth, so that the suspected electricity stealing user can be determined to be the electricity stealing user.
Compared with the prior art, the method for judging electricity stealing of the private mutual inductor of the high-voltage user has the advantages that compared with the traditional current and voltage abnormality characteristic judgment, the hidden electricity stealing user is judged by means of quantitatively calculating the electric quantity fluctuation correlation under the condition of no current and voltage abnormality, and the practicability and the accuracy of judgment on the electricity stealing user are effectively improved.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (8)
1. A method for judging electricity stealing of a private mutual inductor of a high-voltage user is characterized by comprising the following steps:
step 1, selecting an abnormal line in a power grid based on a line loss rate and line loss electric quantity;
step 2, determining the time dimension of the daily electric quantity of the user, traversing the abnormal route according to the dimension, and calculating a correlation coefficient between the high-voltage special variable user in the abnormal route and the lost electric quantity of the route;
and 3, determining the electricity stealing users based on the correlation coefficient and the daily electricity utilization ratio.
2. The method for determining the electricity stealing of the private transformer of the high-voltage user according to claim 1, wherein the step 1 specifically comprises:
when the line loss rate of the line is always higher than a first preset threshold value within a first preset time period, and the line loss electric quantity is larger than a second preset threshold value, determining that the line is an abnormal line; alternatively, the first and second electrodes may be,
and when the line loss rate change value of the line loss rate increased in a second preset time period is higher than a third preset threshold, the absolute value of the difference value between the line loss rate after sudden increase and the line loss rate on the day of sudden increase is lower than a fourth preset threshold, and the line loss electric quantity is larger than the second preset threshold, the line is determined to be an abnormal line.
3. The method for determining the electricity stealing of the private transformer of the high-voltage user according to claim 2, wherein the step 1 specifically comprises:
the first predetermined threshold is 10%, the second predetermined threshold is 800kWh, the third predetermined threshold is 5%, the fourth predetermined threshold is 3%;
the initial time of the first preset time period is at least 6 days before the calculation and analysis day, and the second preset time period is at least 6 days after the sudden increase day.
4. The method for determining the electricity stealing of the private transformer of the high-voltage user according to claim 1, wherein the step 2 specifically comprises:
the time dimension of the daily electric quantity of the user is any number of days in the time dimension selection range of 7-15 days; and the number of the first and second electrodes,
the time dimension selection range is obtained on the basis of eliminating the metering and collecting abnormal factors which influence the line loss rate fluctuation of the line.
5. The method for determining the stealing of electricity by the private transformer for the high-voltage user according to claim 4, wherein the correlation between the users with the abnormal line high-voltage private transformer and the line loss power in the step 2 is calculated by using a Pearson correlation coefficient, and the calculation formula is as follows:
wherein, X is the daily electric quantity vector of the high-voltage special transformer user in the abnormal circuit, and X is { X ═ X1，X2，…，Xi，…，XnY is daily power loss in the abnormal line, and Y ═ Y1，Y2，…，Yi，…，YnI is the ith day, n is the determined daily electric quantity time dimension of the user,
and
are the average values of X and Y over n days, respectively.
6. The method for determining the stealing of electricity by using the private transformer of the high-voltage user as claimed in claim 5, wherein the step 3 further comprises:
and when r is greater than 0.9, the user is confirmed as a suspected electricity stealing user.
7. The method for determining the stealing of electricity by using the private transformer of the high-voltage user as claimed in claim 1, wherein the step 3 further comprises:
determining suspected electricity stealing users based on the magnitude of the Pearson correlation coefficient and the change rate of the daily electricity utilization ratio; and the daily power utilization ratio is the ratio of daily loss electric quantity in the abnormal circuit to daily electric quantity of the high-voltage special transformer user.
8. The method for determining the electricity stealing of the private transformer of the high-voltage user as claimed in claim 7, wherein:
if the change rate of the daily power utilization ratio is lower than a fifth preset threshold value, determining the electricity stealing users of the high-voltage special transformer users;
the fifth predetermined threshold is 50%.
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Cited By (4)
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| 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 |
| CN114076841A (en) * | 2021-11-12 | 2022-02-22 | 国网安徽省电力有限公司旌德县供电公司 | Electricity stealing behavior identification method and system based on electricity utilization information data |
| CN114217160A (en) * | 2022-02-18 | 2022-03-22 | 青岛鼎信通讯股份有限公司 | Method for installing and positioning load monitoring unit of medium-voltage distribution line |
| CN114742405A (en) * | 2022-04-11 | 2022-07-12 | 国网江苏省电力有限公司营销服务中心 | Electricity stealing identification method and system based on line loss multi-dimensional correlation analysis |
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