CN115577993A - Transformer area household change identification method based on time sequence matching - Google Patents
Transformer area household change identification method based on time sequence matching Download PDFInfo
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Abstract
The invention relates to a station area subscriber identity module (FAM) identification method based on time sequence matching, which comprises the following steps of: 1. acquiring current step time sequence data and voltage step time sequence data of each meter box within time T according to electricity consumption data acquired by data acquisition equipment in each meter box; 2. performing transverse matching on the current step time sequence data of one meter box obtained in the step one and the voltage step time sequence data of the other meter box to obtain a transverse matching result; 3. and C, grouping the meter boxes according to the transverse matching result obtained in the step II, and combining the station area filing information to obtain an identification result of the household change. According to the invention, intelligent terminals are not required to be added to the transformer and the branch box, the user variable relation can be accurately identified only by depending on the power utilization data collected by the terminal at the side of the meter box, the user variable file management capability of the power distribution network is improved, and a basis is provided for topology analysis, line loss analysis and fault troubleshooting of the low-voltage power distribution network.
Description
Technical Field
The invention relates to a station area indoor variation identification method based on time sequence matching, and belongs to the technical field of intelligent power grids.
Background
The household transformer relationship refers to the subordination relationship between the electricity consumers and the transformer in the transformer area. Usually, each transformer has a fixed user, and the power supply station draws the corresponding relationship and stores the corresponding relationship as a user-to-user relationship. In recent years, due to changes (such as newly increased distribution transformers, re-planning and capacity expansion) caused by the construction and development of a power distribution network, increased number of power consumers, changed subscriber addresses, subscriber sales and the like, actual station-to-subscriber relationship and filing information are not consistent any more, and due to condition limitations such as line crossing, poor management and the like, the changed station-to-subscriber relationship cannot be updated to the filing information in time. Even some old cells lack the archives of the user-change relationship due to historical reasons. When the actual on-site user variation relationship is inconsistent with the filing information, a series of problems and hazards are caused, such as difficult charging, difficult line fault positioning, inaccurate line loss calculation, unreasonable arrangement of newly added loads, influence on load balance and the like. Therefore, it becomes important to accurately recognize the user-variable relationship.
The traditional method of determining the household variation relationship mainly depends on manual line patrol or short-time power failure, but the two methods are limited in application scene due to time and labor consumption or influence on power supply reliability, and cannot be popularized in a large range. In the existing automatic identification scheme of the household transformer, monitoring terminals are required to be installed on a meter box side and a transformer side, more detailed characteristic values such as the amplitude of current or voltage, the variation in a time window and the like can be used in most schemes, the data volume of a single load event is large, the characteristic values of all load events cannot be sent up under the limitation of the terminal data transmission bandwidth, and the accuracy of household transformer identification can be influenced to a certain extent.
Therefore, a new station change identification method is needed to solve the above problems.
Disclosure of Invention
In order to solve the problems in the prior art, the invention discloses a station area subscriber identity module (FAM) identification method based on time sequence matching, which has the following specific technical scheme:
a station area subscriber identity module (VAD) identification method based on time sequence matching comprises the following steps:
1. acquiring current step time sequence data and voltage step time sequence data of each meter box within time T according to electricity consumption data acquired by data acquisition equipment in each meter box;
2. performing transverse matching on the current step time sequence data of one meter box obtained in the step one and the voltage step time sequence data of the other meter box to obtain a transverse matching result;
3. and C, grouping the meter boxes according to the transverse matching result obtained in the step II, and combining the station area filing information to obtain an identification result of the household change.
Furthermore, a current step timing sequence data cleaning step is further included between the first step and the second step. Because a plurality of interference items exist in the current step time sequence data, the interference data in the current step time sequence data can be effectively removed by utilizing the current step time sequence data cleaning step, so that the user variation identification result is more accurate.
Furthermore, the specific steps of acquiring the current step time sequence data and the voltage step time sequence of each meter box within the time T according to the electricity consumption data acquired by the data acquisition equipment in each meter box in the step one are as follows:
1.1, capturing current transient change and voltage transient change of each meter box within time T by data acquisition equipment in each meter box, wherein each current transient change is a current step, and each voltage transient change is a voltage step;
1.2, recording the time of each current step in current step time sequence data, and recording the current step time sequence data every t1 time; recording the time of each voltage step in voltage step time sequence data, and recording the voltage step time sequence data once every t2 time;
1.3, dividing t1 time into t1/t minimum time units, recording whether a current step occurs in each minimum time unit by using a bit, if the current step occurs, setting the bit position of the time unit to be 1, and otherwise, setting the bit position to be 0; dividing t2 time into t2/t minimum time units, recording whether a voltage step occurs in each minimum time unit by using a bit, if the voltage step occurs, setting the bit position of the time unit to be 1, and otherwise, setting the bit position to be 0; where t is the minimum time unit.
Further, t is 1 second.
Furthermore, in step 1.3, if a current step occurs in a certain minimum time unit within the time t1, the bit position corresponding to the current step is set to 1 regardless of how many current steps occur in the minimum time unit, otherwise, the bit position is set to 0; if a voltage step occurs in a certain minimum time unit within the time t2, the bit position 1 corresponding to the minimum time unit is set no matter how many voltage steps occur in the minimum time unit, and otherwise, the bit position is set to 0. By adopting the scheme, whether each minimum unit time has a step or not only needs 1 bit to represent, so that all current step events and voltage step events in t1 and t2 time can be recorded by using the minimum data size.
Furthermore, the transient change of current in step 1.1 means that the current changes by more than 5A in 0.02 seconds, and the transient change of voltage means that the voltage changes by more than 0.2V in 0.02 seconds.
Further, the current step time sequence data is cleaned by adopting the following method:
if the bit positions corresponding to n continuous minimum time units in the current step timing sequence data are all 1, the bit position 1 of the first minimum time unit and the bit positions corresponding to the following n-1 minimum time units are all 0, wherein n is greater than 1.
If continuous bit is 1 in the current step time sequence data of one meter box, continuous bit is 1 in the voltage step time sequence data in other meter boxes matched with the meter box, and in a period of time range, transverse matching is carried out on the current step time sequence data and the voltage step time sequence data, so that a plurality of bit values are the same, calculation of a real offset value of the voltage step time sequence data is not facilitated, therefore, the interference item is removed, and the accuracy of transverse matching can be effectively improved.
Further, the current step time sequence data is cleaned by adopting the following method: and if the voltage step number of the voltage step time sequence data in the time window Tw in one meter box is N1, the number of the minimum time units in the time window Tw is N2, and when the value of N1/N2 reaches a threshold value or more, and the bit position of the current step time sequence data of the other meter box at the middle moment of the time window Tw is 1, the other meter box is arranged at the bit position 0 of the current step time sequence data at the middle moment of the time window Tw.
If the denser bit is 1 in the time window Tw in the voltage step time sequence data of one meter box, the denser bit is 1 in the current step time sequence data of other meter boxes matched with the meter box, and in a period of time range, transverse matching is carried out on the current step time sequence data and the voltage step time sequence data, so that the values of a plurality of bits are the same, which is not beneficial to calculating a real offset value, therefore, the interference item is removed, and the accuracy of transverse matching can be effectively improved.
Furthermore, the time window Tw is 100-400 minimum time units. The range of the time window Tw is reasonably set, and the current step time sequence data can be effectively cleaned in the time window.
Furthermore, the step two of transversely matching the current step time sequence data of one meter box obtained in the step one with the voltage step time sequence data of the other meter box comprises the following steps of:
the current step time sequence data of the meter box 1 in the time T is set as T I The voltage step time sequence data of the meter box 2 in the time T is T U Then:
2.1: by T I With respect to the time of (T) U From T U -L T changes to T U + L × T, where L is a positive integer, the step length is T, and the new voltage time sequence data after each change is T U ', comparison T I And T U ', and record T I And T U ' number cnt corresponding to bit value identity offset And T U The offset value offset of';
2.2: if cnt offset If the offset corresponding to the maximum value of (1) is unique, it is determined that the meter box 1 and the meter box 2 are successfully matched within the time T, the time offset is the offset, and the matching rate MATCH _ P = cnt offset /cnt total， Wherein, cnt total Is shown as I 1x Number of medium current steps.
When a meter box produces a current that flows through the topologically upper wire, a voltage drop will occur due to the impedance, and then other meter boxes in the same area will detect the voltage drop at that moment. Ideally, all current events occur at the same time that a voltage drop is detected in the other meter boxes in the block, which is called lateral transfer. Therefore, the current step time sequence of one meter box is compared with the voltage step time sequence of the other meter box, and if the number of bit 1 in the current step time sequence data and the number of bit 1 in the voltage step time sequence data are more in the same time period, the two meter boxes can be judged to belong to the same platform area. The voltage step time sequence of the meter box in other areas and the current step time sequence of the meter box in the area are completely independent and have no correlation, and the probability that the number of bit in the two sequences is very large and the number of bit in the two sequences is just 1 is almost 0. The accuracy rate of two meter boxes which are successfully matched transversely by using the method and belong to the same distribution area is more than 99%.
Furthermore, the value range of L in the step 2.1 is 50-300.
Furthermore, according to the second step, the data of transverse matching between the current step time sequence data of one meter box and the voltage step time sequence data of the other meter box in M times T is calculated, and the corresponding offset in each time T is obtained i ，offset i Offset value offset, adjacent offset, of voltage step timing data in ith time T i And if the absolute value of the difference between the two meter boxes is less than or equal to 2 and the average matching rate is not less than MIN _ MATCH _ P, judging that the two meter boxes are transversely matched successfully. In order to further correct the transverse matching result, in steps 2.1 and 2.2, transverse matching is performed by using data within a time T, but sometimes the matching rate of successful matching is not high, in order to verify whether the transverse matching is a wrong matching, the data within M times T are used for matching, and if the M deviation values are the same or have small changes and change rules, it can be determined that the transverse matching is not a wrong matching. The daily offset value of the mismatch should be irregular.
Further, MIN _ MATCH _ P is 0.06-0.1.
Furthermore, grouping the meter boxes according to the horizontal matching result obtained in the step two in the step three comprises the following steps:
3.1: setting a designated meter box as A, searching all associated meter boxes of the meter box A and adding the associated meter boxes into a set groupTmp, wherein the associated meter boxes of A are meter boxes which can be successfully matched with A in the transverse direction, the meter boxes comprise meter boxes of which voltage step time sequence data can be successfully matched with current step time sequence data of A and meter boxes of which current step time sequence data can be successfully matched with the voltage step time sequence data of A, and all the meter boxes in the set groupTmp are assigned with the same group number groupID;
3.2: and traversing all the meter boxes by taking the cell as a unit, and if the meter boxes are not grouped, performing the operation of the step 3.1 on the meter boxes.
Furthermore, in the third step, by combining the station area profiling information, the user change identification result is specifically obtained as follows: when the number of the meter boxes with the same grouping number is more than or equal to 3 and accounts for more than 10% of the total number of the meter boxes in the cell; and meanwhile, compared with the file building information, the meter boxes with the same grouping number, wherein more than 70% of the meter boxes belong to a distribution area, and the group of meter boxes is assigned to the distribution area to obtain a user variation identification result.
The result of grouping the meter boxes according to the methods of steps 3.1 and 3.2 may have some groups with few meter boxes, and the meter boxes of these groups may not be in the area, so that the user-variable relationship cannot be determined, because a certain number of meter boxes are generally in the area; the meter boxes in a group do not belong to the same station area in the filing information, the station area to which the group belongs is determined according to the station area to which most of the meter boxes in the group belong, and a minority obeying majority principle is adopted in the judgment.
Further, the third step is specifically as follows:
every T time takes an account change identification result obtained by data calculation of the first N T times of the T time as a current-time calculation result, all current-time calculation results are arranged according to the time sequence obtained by the calculation results to form a historical result table, and the account change identification result calculated according to an account change identification strategy is recorded as a final result table, wherein the account change identification strategy is to compare the current-time calculation result with the result in the final result table, if the current-time calculation result is different from the result in the final result table, the account change identification results of nearly N3T times in the historical result table are inquired, if the result is stable, the final result table is modified, and the account change identification result of the T time is taken as the account change identification result of the meter table.
In the present invention, N3 is preferably 5 and T is preferably 24 hours. At this moment, the user change recognition result is better through testing.
The relationship between the variables is generally stable and will not change frequently. Therefore, when the current calculation result of a certain calculation is not consistent with the final result table, the topology may be actually changed, or the data may be abnormal to cause an identification error. At this time, the final result table is not changed, and the current-time calculation results are calculated for several times, if the topology changes really, the current-time calculation results in the following each time should be consistent, and at this time, the final result table can be modified. If a data exception causes a recognition error, it is usually recalculated to recover the result before the exception.
Has the advantages that: the station area indoor variation identification method based on time sequence matching realizes relevance analysis of all load events with the least data volume, does not need to install a terminal at the side of a transformer, does not need to be powered off, does not need artificial manufacturing load, reduces cost, reduces implementation difficulty, improves identification accuracy, and has popularization and universality.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is an analysis object association diagram according to an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
Example 1
Referring to fig. 1 and 2, the method for identifying station subscriber variation based on time sequence matching according to the present invention includes the following steps:
the method comprises the following steps: all current step time sequences and all voltage step time sequence data of the three phases of the meter box every day are obtained from the electricity consumption data collected by the intelligent terminal arranged on the meter box. And calculating the user variation relation every time, wherein all three-phase current time sequence data and all three-phase voltage time sequence data of the last 7 days are taken, and each piece of data comprises a meter box ID, a phase, a date and a bit. The position of bit represents the time, and the value of bit represents the change data of current or voltage, i.e. 86400 seconds in 1 day, i.e. 86400 bits of current and voltage sequence in 1 day per meter box.
Step two: and cleaning the current step time sequence data. The cleansing rules for data cleansing are as follows:
in the first case: if the current step has continuous time values, namely the current step (n > 1) exists in each second within continuous n seconds, the current step time sequence only keeps the time value of the first second, the corresponding bit position 1 is removed from the following n-1 time values, and otherwise, the current step time sequence is set to 0.
And sorting the current time sequence data of a certain phase of a certain day of the meter box according to ascending time values, and if continuous time values exist, only keeping the first time and removing the subsequent time. For example, if the C-phase current step of the meter box 1 has 4 continuous pieces of data in 8/1/2022, and the time values are 20000, 20001, 20002, and 20003, respectively, the following 3 pieces of data are removed.
In the second case: and if the ratio of the number of the voltage step sequence steps in a certain time window to the window length reaches more than 0.4, rejecting the current step corresponding to the middle moment of the time window.
Step three: and calculating the transverse matching relation between any two meter boxes. The specific steps of calculating the transverse matching relationship are as follows:
current time sequence data I of phase x of meter box 1 on day D 1x Voltage time sequence data U of phase y of meter box 2 at D day 2y Then:
(1) with I 1x With reference to the time of (U) 2y Slave U 2y Change from-moveLen to U 2y + moveLen, step size 1, new voltage time series after each change is set to U 2y ', comparative example I 1x And U 2y ' and the number cnt of values at which the two time series coincide is recorded offset And corresponding U 2y Offset value offset, wherein the value range of moveLen is 50 to 300;
(2) if cnt offset If the maximum value of the time difference is unique, it is determined that the phase a of the meter box 1 and the phase B of the meter box 2 are successfully matched in 1/8 th day, and the time offset value is recorded as the offset i Match rate matchP i =cnt i /(I 1A Number of elements) where i ∈ [1,7 ]]；
(3) Calculating the matching condition of the daily A-phase current time sequence of the meter box 1 from 8 months and 2 days to 8 months and 7 days and the daily B-phase voltage time sequence of the meter box 2, if the matching condition is more than 3 continuous days, calculating the offset i If the standard deviation is not greater than 2 and the average matching rate is not less than MIN _ MATCH _ P (according to different station areas, MIN _ MATCH _ P is not equal to 0.06-0.1), the current time sequence of the meter box 1 and the voltage time sequence of the meter box 2 are judged to be successfully matched transversely.
Step four: and grouping the meter boxes according to the transverse matching relationship, and obtaining an initial result of the household change identification by combining the filing information of the power supply company. The concrete steps of transversely grouping the meter boxes are as follows:
(1) as shown in fig. 2, a total of 10 meter boxes in a cell are provided, and belong to 1 distribution area, the current time sequence of the meter box 1 can match the voltage time sequences of the upper meter boxes 2, 3, 4 and 5, the current time sequence of the meter box 6 can match the voltage time sequences of the upper meter boxes 2, 5, 7 and 8, and the current time sequence of the meter box 8 can match the voltage time sequences of the upper meter boxes 7 and 9. Then, the meter boxes 2, 3, 4 and 5 can be found by searching the associated meter boxes from the meter box 1, the meter boxes 6, 7 and 8 can be found according to the meter box 2, and the meter box 9 can be found according to the meter box 8. The meter boxes 1-9 can eventually be grouped by a horizontal matching relationship.
(2) If the station area household transformation filing information is 1 station area, the 10 meter boxes are contained, and then according to the result of the step (1), the following conditions are met: the group of meter boxes in the group have the number more than or equal to 3, account for more than 10% of the total number of the meter boxes in the cell and exceed 70% of the group of meter boxes, belong to a documenting area, so the meter boxes 1-9 all belong to the documenting area, and the result is the identification result of the user change.
Step five: and obtaining a final result of the user variable identification through a multi-day comprehensive optimization method. The method comprises the following specific steps:
(1) traversing the meter boxes 1-9 according to the preliminary results of the outdoor change identification obtained in the fourth step, comparing the preliminary results with the final results of the outdoor change identification in the database, if the preliminary results are different, inquiring the outdoor change identification result of the latest 5 days in the history list, and if the latest 5 days are the same outdoor change identification results, updating the final results of the outdoor change identification by the meter boxes; otherwise, the initial result of the user change identification is not stable yet, and the final result of the user change identification is not required to be updated temporarily.
(2) Assuming that there are 2 cells in a cell, a meter box is identified as belonging to cell 1 before, is identified as belonging to cell 2 now, and the identification results of the last 5 days are the same, all the change identification results related to cell 1 and cell 2 are updated.
The invention provides a station area household change identification method based on time sequence matching, which is characterized in that the electric characteristic change is monitored only at a meter box, the transient change of the current and the voltage at the side of the meter box is captured, the step change of the current and the voltage at the side of the meter box is recorded, the matching efficiency is high through characteristic management, the data volume is greatly reduced, all load events can be sent to a concentrator, a fusion terminal and other equipment at the secondary side of a main station or a transformer for calculation, and the accuracy is improved.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (16)
1. A station area subscriber identity module (IGI) identification method based on time sequence matching is characterized by comprising the following steps:
1. acquiring current step time sequence data and voltage step time sequence data of each meter box within time T according to electricity consumption data acquired by data acquisition equipment in each meter box;
2. performing transverse matching on the current step time sequence data of one meter box obtained in the step one and the voltage step time sequence data of the other meter box to obtain a transverse matching result;
3. and C, grouping the meter boxes according to the transverse matching result obtained in the step II, and combining the station area filing information to obtain an identification result of the household change.
2. The station area subscriber identity module based on time sequence matching according to claim 1, wherein: and a current step timing sequence data cleaning step is further included between the first step and the second step.
3. The station area subscriber identity module based on time sequence matching according to claim 1, wherein:
the specific steps of acquiring the current step time sequence data and the voltage step time sequence of each meter box in the time T according to the electricity consumption data acquired by the data acquisition equipment in each meter box in the first step are as follows:
the data acquisition equipment in each meter box captures the current transient change and the voltage transient change of the meter box within the time T, wherein each current transient change is a current step, and each voltage transient change is a voltage step;
recording the time of each current step in current step time sequence data, and recording the current step time sequence data once every t1 time; recording the time of each voltage step in voltage step time sequence data, and recording the voltage step time sequence data once every t2 time;
dividing t1 time into t1/t minimum time units, recording whether a current step occurs in each minimum time unit by using a bit, if the current step occurs, setting the bit position of the time unit to be 1, and otherwise, setting the bit position to be 0; dividing t2 time into t2/t minimum time units, recording whether a voltage step occurs in each minimum time unit by using a bit, if the voltage step occurs, setting the bit position of the time unit to be 1, and otherwise, setting the bit position to be 0; wherein t is the minimum time unit.
4. The station area subscriber identity module based on time sequence matching according to claim 3, wherein: t is 1 second.
5. The station area subscriber identity module based on timing matching according to claim 3, wherein: in step 1.3, if a current step occurs in a certain minimum time unit within t1 time, the bit position corresponding to the minimum time unit is set to be 1 no matter how many current steps occur in the minimum time unit, otherwise, the bit position is set to be 0; if a voltage step occurs in a certain minimum time unit within the time t2, the bit position corresponding to the minimum time unit is 1 no matter how many voltage steps occur in the minimum time unit, and otherwise, the bit position is 0.
6. The station area subscriber identity module based on timing matching according to claim 3, wherein: in step 1.1, the transient change of the current means that the change of the current exceeds 5A within 0.02 seconds, and the transient change of the voltage means that the change of the voltage exceeds 0.2V within 0.02 seconds.
7. The station area subscriber identity module based on time sequence matching according to claim 2, wherein: cleaning the current step time sequence data by adopting the following method:
if the bit positions corresponding to n continuous minimum time units in the current step timing sequence data are all 1, the bit position 1 of the first minimum time unit and the bit positions corresponding to the following n-1 minimum time units are all 0, wherein n is greater than 1.
8. The station area subscriber identity module based on time sequence matching according to claim 2, wherein: cleaning the current step time sequence data by adopting the following method: if the voltage step quantity of the voltage step time sequence data in the time window Tw in one meter box is N1, the quantity of the minimum time units in the time window Tw is N2, when the value of N1/N2 reaches a threshold value, and the bit position of the current step time sequence data at the middle moment of the time window Tw in the other meter box is 1, the other meter box is arranged at the bit position 0 of the current step time sequence data at the middle moment of the time window Tw.
9. The station area subscriber identity module based on timing matching according to claim 8, wherein: the time window Tw is 100-400 minimum time units.
10. The station area subscriber identity module based on timing matching according to claim 1, wherein:
step two, transversely matching the current step time sequence data of one meter box with the voltage step time sequence data of the other meter box obtained in the step one, and comprises the following steps:
the current step time sequence data of the phase x of the meter box 1 in the time T is set as T Ix And the voltage step time sequence data of the phase y of the meter box 2 in the time T is T Uy And then:
2.1: by T Ix With respect to the time of (T) Uy From T Uy -L x T changes to T Uy + L × T, where L is a positive integer, step size is T, and new voltage time sequence data after each change is T Uy ', comparison T Ix And T Uy ', and record T Ix And T Uy ' number cnt corresponding to bit value identity offset And T Uy The offset value offset of';
2.2: if cnt offset If the offset corresponding to the maximum value of (1) is unique, it is determined that the meter box 1 and the meter box 2 are successfully matched within the time T, the time deviation is the offset, and the matching rate MATCH _ P = cnt offset /cnt total， Wherein, cnt total Is T Ix The number of intermediate current steps.
11. The station area subscriber identity module based on timing matching according to claim 10, wherein: in step 2.1, the value range of L is 50-300.
12. According to claimThe station area subscriber identity module identification method based on time sequence matching, according to claim 10, further comprising: according to the second step, the data of transverse matching between the current step time sequence data of one meter box and the voltage step time sequence data of the other meter box in M time T is calculated, and the corresponding offset in each time T is obtained i ，offset i For the offset value offset of the voltage step timing data in the ith time T, the offset of M times T is calculated i Arranged from large to small or small to large, adjacent offsets i And if the absolute value of the difference between the two meter boxes is less than or equal to 2 and the average matching rate is not less than MIN _ MATCH _ P, judging that the two meter boxes are transversely matched successfully.
13. The station area subscriber identity module based on time sequence matching according to claim 12, wherein: MIN _ MATCH _ P is 0.06-0.1.
14. The station area subscriber identity module based on timing matching according to claim 1, wherein:
in the third step, grouping the meter boxes according to the transverse matching result obtained in the second step comprises the following steps:
3.1: setting a designated meter box as A, searching all associated meter boxes of the meter box A and adding the associated meter boxes into a set groupTmp, wherein the associated meter boxes of A are meter boxes which can be successfully matched with A in the transverse direction, the meter boxes comprise meter boxes of which voltage step time sequence data can be successfully matched with current step time sequence data of A and meter boxes of which current step time sequence data can be successfully matched with the voltage step time sequence data of A, and all the meter boxes in the set groupTmp are assigned with the same group number groupID;
3.2: and traversing all the meter boxes by taking the cell as a unit, and if the meter boxes are not grouped, performing the operation of the step 3.1 on the meter boxes.
15. The station area subscriber identity module based on timing matching according to claim 14, wherein: combining the station area profiling information in the third step, the obtained identification result of the user variation is specifically as follows: when the number of the meter boxes with the same grouping number is more than or equal to 3 and accounts for more than 10% of the total number of the meter boxes in the cell; and meanwhile, compared with the filing information, the meter boxes with the same group number groupID belong to a transformer area if more than 70% of the meter boxes belong to the transformer area, and the group of the meter boxes is assigned to the transformer area to obtain a user change identification result.
16. The station area subscriber identity module based on time sequence matching according to claim 1, wherein:
the third step is specifically as follows:
every T time takes the user change identification result obtained by calculating the data of the first N T times of the T time as the current-period calculation result, all current-period calculation results are arranged according to the time sequence obtained by the calculation results to form a historical result table, the final user change identification result calculated according to the user change identification strategy is recorded as a final result table, the user change identification strategy is that the current-period calculation result is compared with the result in the final result table, if the current-period calculation result is different from the final result table, the user change identification results of nearly N3T times in the historical result table are inquired, if the result is stable, the final result table is modified, and the user change identification result of the T time is taken as the meter-case user change identification result.
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Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120022713A1 (en) * | 2010-01-14 | 2012-01-26 | Deaver Sr Brian J | Power Flow Simulation System, Method and Device |
| US20150280418A1 (en) * | 2013-06-11 | 2015-10-01 | Electrical Materials Company | Low fault current isolator system |
| US20170223807A1 (en) * | 2006-03-28 | 2017-08-03 | Wireless Environment, Llc. | Cloud connected lighting system |
| JP2019062656A (en) * | 2017-09-26 | 2019-04-18 | 株式会社三社電機製作所 | Inspection device |
| CN110646690A (en) * | 2019-09-10 | 2020-01-03 | 国网浙江省电力有限公司电力科学研究院 | Method and system for identifying user variation relationship based on characteristic current signal |
| CN111080105A (en) * | 2019-12-06 | 2020-04-28 | 国网辽宁省电力有限公司沈阳供电公司 | Transformer area user-to-user relationship identification method and system based on voltage time sequence data |
| CN111221778A (en) * | 2020-04-14 | 2020-06-02 | 广东电网有限责任公司佛山供电局 | Matching method for low-voltage user and distribution room membership |
| CN111505446A (en) * | 2020-05-25 | 2020-08-07 | 广州市奔流电力科技有限公司 | Method, device and equipment for identifying subscriber relationship of platform area vacant house subscriber |
| CN111600380A (en) * | 2020-03-28 | 2020-08-28 | 青岛鼎信通讯股份有限公司 | Variable load and constant current detection-based household variable relation identification method |
| CN111897888A (en) * | 2020-08-17 | 2020-11-06 | 国网辽宁省电力有限公司沈阳供电公司 | Household variable relation identification method based on Spark framework and coacervation hierarchical clustering algorithm |
| CN112366690A (en) * | 2020-10-28 | 2021-02-12 | 江苏智臻能源科技有限公司 | Low-voltage transformer area transverse node relation identification method based on strong synchronous voltage feature matching |
| CN112636335A (en) * | 2020-12-07 | 2021-04-09 | 云南电网有限责任公司昆明供电局 | Low-voltage distribution network user variation relation checking method and system based on voltage data decorrelation selection and correlation analysis |
| CN112819649A (en) * | 2020-12-30 | 2021-05-18 | 国网北京市电力公司 | Method and device for determining station area subscriber change relationship |
| CN113033666A (en) * | 2021-03-26 | 2021-06-25 | 国网上海市电力公司 | Transformer area household change identification method integrating acquisition service and load design rule |
| CN113176458A (en) * | 2021-03-08 | 2021-07-27 | 深圳职业技术学院 | Low-voltage transformer area household relation identification method aiming at incomplete data |
| CN113362076A (en) * | 2021-05-21 | 2021-09-07 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | Power distribution network 10kV dual-power-supply user variation relation analysis method |
| CN113746094A (en) * | 2021-09-06 | 2021-12-03 | 东南大学 | Low-voltage transformer area family change relation identification method based on high-density data sequence matching |
| CN114243712A (en) * | 2021-11-04 | 2022-03-25 | 中国电力科学研究院有限公司 | Low-voltage distribution area topology identification method and system |
| CN114709826A (en) * | 2022-06-07 | 2022-07-05 | 江苏瑞电智芯信息科技有限公司 | Low-voltage transformer area topology identification algorithm and system based on load event sequence diagram analysis |
| WO2022148074A1 (en) * | 2021-01-06 | 2022-07-14 | 华南理工大学 | Method for solving for converter valve states and valve currents based on valve-side current timing characteristics |
-
2022
- 2022-12-09 CN CN202211575645.9A patent/CN115577993B/en active Active
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170223807A1 (en) * | 2006-03-28 | 2017-08-03 | Wireless Environment, Llc. | Cloud connected lighting system |
| US20120022713A1 (en) * | 2010-01-14 | 2012-01-26 | Deaver Sr Brian J | Power Flow Simulation System, Method and Device |
| US20150280418A1 (en) * | 2013-06-11 | 2015-10-01 | Electrical Materials Company | Low fault current isolator system |
| JP2019062656A (en) * | 2017-09-26 | 2019-04-18 | 株式会社三社電機製作所 | Inspection device |
| CN110646690A (en) * | 2019-09-10 | 2020-01-03 | 国网浙江省电力有限公司电力科学研究院 | Method and system for identifying user variation relationship based on characteristic current signal |
| CN111080105A (en) * | 2019-12-06 | 2020-04-28 | 国网辽宁省电力有限公司沈阳供电公司 | Transformer area user-to-user relationship identification method and system based on voltage time sequence data |
| CN111600380A (en) * | 2020-03-28 | 2020-08-28 | 青岛鼎信通讯股份有限公司 | Variable load and constant current detection-based household variable relation identification method |
| CN111221778A (en) * | 2020-04-14 | 2020-06-02 | 广东电网有限责任公司佛山供电局 | Matching method for low-voltage user and distribution room membership |
| CN111505446A (en) * | 2020-05-25 | 2020-08-07 | 广州市奔流电力科技有限公司 | Method, device and equipment for identifying subscriber relationship of platform area vacant house subscriber |
| CN111897888A (en) * | 2020-08-17 | 2020-11-06 | 国网辽宁省电力有限公司沈阳供电公司 | Household variable relation identification method based on Spark framework and coacervation hierarchical clustering algorithm |
| CN112366690A (en) * | 2020-10-28 | 2021-02-12 | 江苏智臻能源科技有限公司 | Low-voltage transformer area transverse node relation identification method based on strong synchronous voltage feature matching |
| CN112636335A (en) * | 2020-12-07 | 2021-04-09 | 云南电网有限责任公司昆明供电局 | Low-voltage distribution network user variation relation checking method and system based on voltage data decorrelation selection and correlation analysis |
| CN112819649A (en) * | 2020-12-30 | 2021-05-18 | 国网北京市电力公司 | Method and device for determining station area subscriber change relationship |
| WO2022148074A1 (en) * | 2021-01-06 | 2022-07-14 | 华南理工大学 | Method for solving for converter valve states and valve currents based on valve-side current timing characteristics |
| CN113176458A (en) * | 2021-03-08 | 2021-07-27 | 深圳职业技术学院 | Low-voltage transformer area household relation identification method aiming at incomplete data |
| CN113033666A (en) * | 2021-03-26 | 2021-06-25 | 国网上海市电力公司 | Transformer area household change identification method integrating acquisition service and load design rule |
| CN113362076A (en) * | 2021-05-21 | 2021-09-07 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | Power distribution network 10kV dual-power-supply user variation relation analysis method |
| CN113746094A (en) * | 2021-09-06 | 2021-12-03 | 东南大学 | Low-voltage transformer area family change relation identification method based on high-density data sequence matching |
| CN114243712A (en) * | 2021-11-04 | 2022-03-25 | 中国电力科学研究院有限公司 | Low-voltage distribution area topology identification method and system |
| CN114709826A (en) * | 2022-06-07 | 2022-07-05 | 江苏瑞电智芯信息科技有限公司 | Low-voltage transformer area topology identification algorithm and system based on load event sequence diagram analysis |
Non-Patent Citations (5)
| Title |
|---|
| JIAWEI SONG 等: "User-transformer relation identification based on power balance model and adaptive AFSA", 《JOURNAL OF PHYSICS：CONFERENCE SERIES，2021 INTERNATIONAL CONFERENCE ON SMART TRANSPORTATION, ENERGY AND POWER》 * |
| LAI ZHOU 等: "Consumer phase identification in low-voltage distribution network considering vacant users", 《INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS》 * |
| 唐捷 等: "基于数据驱动的低压配电网线户关系识别方法", 《电力系统自动化》 * |
| 宋玮琼 等: "基于电压时序数据的配电台区户变关系智能识别", 《电力系统及其自动化学报》 * |
| 陆欣 等: "基于高频同步采集与边缘计算的低压配电网电气拓扑发现技术", 《电信科学》 * |
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