CN112541020A - Low-voltage distribution network topology refinement identification method based on electric energy meter voltage matching - Google Patents
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Abstract
The invention relates to a low-voltage distribution network topology refining identification method based on electric energy meter voltage matching, which belongs to the technical field of intelligent power grids and comprises the following steps: (1) the method comprises the steps of obtaining a voltage data sequence of an electric energy meter to be analyzed from electricity utilization information acquisition data, (2) conducting similarity analysis to obtain the most relevant electric energy meter of the electric energy meter to be analyzed, (3) conducting correlation analysis to obtain a relevant electric meter box of the electric meter box to be analyzed, (4) drawing a correlation diagram, and (5) identifying the branch dependency relationship of the electric meter box. According to the invention, topology refinement of the low-voltage distribution network can be realized only by analyzing the voltage data of the electric energy meter, and the management capability of the power grid is improved.
Description
Technical Field
The invention relates to a low-voltage distribution network topology refined identification method based on electric energy meter voltage matching, and belongs to the technical field of intelligent power grids. In particular to a topology identification method for realizing low-voltage distribution network topology branch refinement by analyzing data sequence similarity and performing association matching based on an electric energy meter voltage data sequence acquired by an electricity consumption information acquisition system.
Background
The power distribution network is an important public infrastructure for national economy and social development. In recent years, the construction investment of power distribution networks in China is continuously increased, the development of the power distribution networks has remarkable effect, but the power utilization level is still different from the international advanced level, the development of urban and rural areas is unbalanced, and the power supply quality needs to be improved. The method has the advantages of building urban and rural overall, safe and reliable, economic and efficient, advanced in technology and environment-friendly distribution network facilities and service systems, not only ensuring the residents and promoting the investment, but also driving the level of the manufacturing industry to be improved, providing powerful support for adapting to energy interconnection and promoting the development of the Internet +, and having important significance for stable growth, reform promotion, structure adjustment and people improvement. The power distribution network is composed of overhead lines, cables, towers, distribution transformers, isolating switches, reactive power compensators, a plurality of accessory facilities and the like, plays a role in distributing electric energy in the power network, and is an important public infrastructure for national economy and social development. With the continuous development of power distribution networks, the types of power supply equipment of the power grids are more and more, and data and information data related to the power grids are more and more complex. The statistics of the distribution network needs to be performed and analyzed by planners who converge to a local city in each county, and due to the large data volume and the large number of models of the distribution network, the workload and the period of the operation and maintenance personnel performing the statistics and analysis are large, the problems of the data are difficult to find, the problem data are possibly generated to participate in the calculation by taking the problem data as the original quantity, the distribution network planning result is not accurate enough or problems occur, and the operation and maintenance are difficult.
The low-voltage distribution transformer area is the minimum unit and the data source of the power distribution network, and has the outstanding problems of disordered connection files of a transformer substation, low active sensing level of power failure events, low automation degree of abnormal line loss positioning, low real-time monitoring level of equipment states and the like for a long time, so that a series of consequences of difficult line loss management, long rush repair time, high equipment failure rate and the like are caused. The accuracy of topology identification is the basis of other functions, but the prior art cannot realize the detailed identification of the low-voltage distribution network topology according to branches on the premise of lacking of branch monitoring, so that the accurate positioning of faults, the lean analysis of line loss and the sectional calculation of impedance are limited. Meanwhile, the current intelligent electric meter is only limited to remote automatic meter reading, the non-metering application of the operation and maintenance of the support area is still weak, the value of power utilization information acquisition data is fully mined, and the support topology identification capability is improved.
Disclosure of Invention
In order to solve the technical problems, the invention provides a low-voltage distribution network topology refinement identification method based on electric energy meter voltage matching, which has the following specific technical scheme:
a low-voltage distribution network topology refining identification method based on electric energy meter voltage matching comprises the following steps:
step 1: acquiring voltage data sequences of all affiliated electric energy meters in the low-voltage power distribution network to be analyzed from the electricity utilization information acquisition data, setting the affiliated electric energy meters as electric energy meters to be analyzed, setting all the electric energy meters to be analyzed as an electric energy meter set to be analyzed, setting the affiliated electric energy meters of the electric energy meters to be analyzed as electric energy meter boxes to be analyzed, and setting all the electric energy meter boxes to be analyzed as electric energy meter box sets to be analyzed;
step 2: performing similarity analysis on the voltage data sequence obtained in the step one to obtain the most relevant electric energy meter of each electric energy meter to be analyzed;
and step 3: performing ammeter box correlation analysis on the most relevant ammeter of all the ammeter to be analyzed to obtain an ammeter box to which each most relevant ammeter belongs, setting the ammeter boxes as the relevant ammeter boxes of the ammeter box to be analyzed, wherein the relevant ammeter boxes of all the ammeter boxes to be analyzed are relevant ammeter box sets of the ammeter box to be analyzed;
and 4, step 4: drawing an ammeter box association diagram according to all association relations of the ammeter box set to be analyzed and the related ammeter box set of the ammeter box to be analyzed;
and 5: according to the ammeter box correlation diagram, identifying the branch dependency relationship of the ammeter box, wherein the branch dependency relationship is constructed in the following manner: the ammeter box group is constructed according to the connection relation of the ammeter box sets to be analyzed in the ammeter box association diagram, namely, each ammeter box in the ammeter box group is connected to other ammeter boxes in the ammeter box group through a directed association line, the ammeter boxes belonging to one ammeter box group are set to be in the same branch, and the branch subordination relation of all the ammeter boxes is established.
Further, the specific steps of step 2 are:
step 2.1: acquiring the file number of an electric meter box set to be analyzed;
step 2.2: and (2) carrying out voltage similarity calculation on the electric energy meter to be analyzed and the electric energy meters to be analyzed in other electric energy meter boxes to be analyzed, wherein the file numbers of the electric energy meters to be analyzed are different from those of the electric energy meter box to be analyzed, and the calculation formula is as follows:
whereinAFor a voltage data sequence of the electric energy meter to be analyzed,
the average value of the voltage data sequence of the electric energy meter to be analyzed is obtained;Bfor the voltage data series of other electric energy meters to be analyzed,
the average value of the voltage data sequences of other electric energy meters to be analyzed is obtained; taking other electric energy meters to be analyzed with the maximum voltage similarity as the most relevant electric energy meters of the electric energy meters to be analyzed;
step 2.3: and (4) repeating the step 2.2 for each electric energy meter to be analyzed in the electric energy meter to be analyzed in a centralized manner to obtain the most relevant electric energy meter of each electric energy meter to be analyzed.
Further, reading the file numbers of all the ammeter boxes in the relevant ammeter box set of the ammeter box to be analyzed in the step 3.
Further, the specific steps of step 4 are:
step 4.1: drawing all the electric meter boxes to be analyzed, and correspondingly numbering the files of the electric meter boxes to be analyzed;
step 4.2: reading the incidence relation of all the electric meter boxes to be analyzed, wherein the incidence relation refers to the incidence relation of the electric meter boxes to be analyzed and the relevant electric meter boxes of the electric meter boxes to be analyzed;
step 4.3: and drawing the directed correlation lines by taking the electric meter box to be analyzed as the starting point and taking the related electric meter box to be analyzed as the terminal point according to all the correlation relations and the electric meter box file numbers of the remarks to form an electric meter box correlation diagram.
The invention has the beneficial effects that: the topology refinement of the low-voltage distribution network can be realized only by using the similarity matching analysis of the voltage data of the electric energy meter without adding branch measuring equipment, and the management capability of the power grid is improved.
Drawings
Figure 1 is a flow chart of the method of the present invention,
figure 2 is a specific example analysis object association graph,
FIG. 3 is a specific example meter box correlation diagram.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
As shown in fig. 1, the method comprises the following specific steps:
step 1: the method comprises the steps that voltage data sequences of all affiliated electric energy meters in the low-voltage power distribution network to be analyzed are acquired from electricity utilization information acquisition data, the affiliated electric energy meters are set as the electric energy meters to be analyzed, all the electric energy meters to be analyzed are electric energy meter sets to be analyzed, the affiliated electric energy meters of the electric energy meters to be analyzed are electric energy meter boxes to be analyzed, and all the electric energy meter boxes to be analyzed are electric energy meter box sets to be analyzed.
Step 2: performing similarity analysis on the voltage data sequence obtained in the step one to obtain the most relevant electric energy meter of each electric energy meter to be analyzed;
the method comprises the following specific steps:
step 2.1: acquiring the file number of an electric meter box set to be analyzed;
step 2.2: and (2) carrying out voltage similarity calculation on the electric energy meter to be analyzed and the electric energy meters to be analyzed in other electric energy meter boxes to be analyzed, wherein the file numbers of the electric energy meters to be analyzed are different from those of the electric energy meter box to be analyzed, and the calculation formula is as follows:
whereinAFor a voltage data sequence of the electric energy meter to be analyzed,
is to be treatedAnalyzing the mean value of the voltage data sequence of the electric energy meter;Bfor the voltage data series of other electric energy meters to be analyzed,
the average value of the voltage data sequences of other electric energy meters to be analyzed is obtained; taking other electric energy meters to be analyzed with the maximum voltage similarity as the most relevant electric energy meters of the electric energy meters to be analyzed;
step 2.3: and (4) repeating the step 2.2 for each electric energy meter to be analyzed in the electric energy meter to be analyzed in a centralized manner to obtain the most relevant electric energy meter of each electric energy meter to be analyzed.
And step 3: performing ammeter box correlation analysis on the most relevant ammeter of all the ammeter to be analyzed to obtain an ammeter box to which each most relevant ammeter belongs, setting the ammeter boxes as the relevant ammeter boxes of the ammeter box to be analyzed, wherein the relevant ammeter boxes of all the ammeter boxes to be analyzed are relevant ammeter box sets of the ammeter box to be analyzed; and reading the file numbers of all the ammeter boxes in the relevant ammeter box set of the ammeter box to be analyzed.
And 4, step 4: drawing an ammeter box association diagram according to all association relations of the ammeter box set to be analyzed and the related ammeter box set of the ammeter box to be analyzed;
the method comprises the following specific steps:
step 4.1: drawing all the electric meter boxes to be analyzed, and correspondingly numbering the files of the electric meter boxes to be analyzed;
step 4.2: reading the incidence relation of all the electric meter boxes to be analyzed, wherein the incidence relation refers to the incidence relation of the electric meter boxes to be analyzed and the relevant electric meter boxes of the electric meter boxes to be analyzed;
step 4.3: and drawing the directed correlation lines by taking the electric meter box to be analyzed as the starting point and taking the related electric meter box to be analyzed as the terminal point according to all the correlation relations and the electric meter box file numbers of the remarks to form an electric meter box correlation diagram.
And 5: according to the ammeter box correlation diagram, identifying the branch dependency relationship of the ammeter box, wherein the branch dependency relationship is constructed in the following manner: the ammeter box group is constructed according to the connection relation of the ammeter box sets to be analyzed in the ammeter box association diagram, namely, each ammeter box in the ammeter box group is connected to other ammeter boxes in the ammeter box group through a directed association line, the ammeter boxes belonging to one ammeter box group are set to be in the same branch, and the branch subordination relation of all the ammeter boxes is established.
Next, an analysis is performed by taking a certain low-voltage platform area as an example, and as shown in fig. 2, a box transformer substation dependency graph of an analysis object of a specific example is shown.
The electric energy meter set to be analyzed is { electric energy meter 1, electric energy meter 2, electric energy meter 3, electric energy meter 4, electric energy meter 5, electric energy meter 6, electric energy meter 7, electric energy meter 8, electric energy meter 9, electric energy meter 10, electric energy meter 11, electric energy meter 12, electric energy meter 13, electric energy meter 14, electric energy meter 15, electric energy meter 16, electric energy meter 17 and electric energy meter 18 }. The ammeter box to be analyzed is integrated into { ammeter box 1, ammeter box 2, ammeter box 3, ammeter box 4, ammeter box 5, ammeter box 6 }.
Wherein:
{ electric energy meter 1, electric energy meter 2, and electric energy meter 3} belong to electric meter box 1;
{ electric energy meter 4, electric energy meter 5, and electric energy meter 6} belong to electric meter box 2;
{ electric energy meter 7, electric energy meter 8, and electric energy meter 9} belong to electric meter box 3;
{ electric energy meter 10, electric energy meter 11, and electric energy meter 12} belong to electric meter box 4;
{ electric energy meter 13, electric energy meter 14, and electric energy meter 15} belong to electric meter box 5;
{ electric energy meter 16, electric energy meter 17, electric energy meter 18} is subordinate to the electric meter box 6.
Step 1: and acquiring voltage data sequences of the electric energy meters belonging to the low-voltage distribution network to be analyzed from the electricity utilization information acquisition data.
Step 2: and (2) carrying out voltage similarity calculation on the electric energy meter to be analyzed and the electric energy meters to be analyzed in other electric energy meter boxes to be analyzed, wherein the file numbers of the electric energy meters to be analyzed are different from those of the electric energy meter box to be analyzed, and the calculation formula is as follows:
the following results were obtained:
this gives:
the most relevant electric energy meter of the electric energy meter 1 is an electric energy meter 10;
the most relevant electric energy meter of the electric energy meters 2 is an electric energy meter 5;
the most relevant electric energy meter of the electric energy meters 3 is an electric energy meter 6;
the most relevant electric energy meter of the electric energy meters 4 is an electric energy meter 7;
the most relevant electric energy meter of the electric energy meters 5 is the electric energy meter 11;
the most relevant electric energy meter of the electric energy meters 6 is an electric energy meter 9;
the most relevant electric energy meter of the electric energy meters 7 is the electric energy meter 1;
the most relevant electric energy meter of the electric energy meters 8 is the electric energy meter 11;
the most relevant electric energy meter of the electric energy meters 9 is the electric energy meter 6;
the most relevant electric energy meter of the electric energy meters 10 is the electric energy meter 1;
the most relevant electric energy meter of the electric energy meters 11 is the electric energy meter 8;
the most relevant electric energy meter of the electric energy meters 12 is the electric energy meter 6;
the most relevant electric energy meter of the electric energy meters 13 is the electric energy meter 16;
the most relevant electric energy meter of the electric energy meters 14 is the electric energy meter 17;
the most relevant electric energy meter of the electric energy meters 15 is the electric energy meter 18;
the most relevant electric energy meter of the electric energy meters 16 is the electric energy meter 13;
the most relevant electric energy meter of the electric energy meters 17 is the electric energy meter 14;
the most relevant electric energy meter of the electric energy meters 18 is the electric energy meter 15.
And step 3: performing ammeter box correlation analysis on the most relevant ammeter of all the ammeter to be analyzed to obtain an ammeter box to which each most relevant ammeter belongs, setting the ammeter boxes as the relevant ammeter boxes of the ammeter box to be analyzed, wherein the relevant ammeter boxes of all the ammeter boxes to be analyzed are relevant ammeter box sets of the ammeter box to be analyzed; and reading the file numbers of all the ammeter boxes in the relevant ammeter box set of the ammeter box to be analyzed.
Thereby obtaining the relevant electric meter box set of each electric meter box:
the most relevant ammeter boxes of the ammeter box 1 are collected as { ammeter box 2, ammeter box 4 };
the most relevant ammeter boxes of the ammeter boxes 2 are collected as { ammeter box 3, ammeter box 4 };
the most relevant ammeter boxes of the ammeter boxes 3 are integrated as { ammeter box 1, ammeter box 2 and ammeter box 4 };
the most relevant ammeter boxes of the ammeter boxes 4 are collected as { ammeter box 1, ammeter box 2, ammeter box 3 };
the most relevant ammeter boxes of the ammeter boxes 5 are collected as { ammeter box 6 };
the most relevant ammeter box set of the ammeter boxes 6 is { ammeter box 5 }.
And 4, step 4: and drawing an ammeter box association diagram according to all the association relations of the ammeter box set to be analyzed and the related ammeter box set of the ammeter box to be analyzed, as shown in fig. 3.
And 5: according to the ammeter box correlation diagram, identifying the branch dependency relationship of the ammeter box, wherein the branch dependency relationship is constructed in the following manner: the ammeter box group is constructed according to the connection relation of the ammeter box sets to be analyzed in the ammeter box association diagram, namely, each ammeter box in the ammeter box group is connected to other ammeter boxes in the ammeter box group through a directed association line, the ammeter boxes belonging to one ammeter box group are set to be in the same branch, and the branch subordination relation of all the ammeter boxes is established.
Thus, { ammeter case 1, ammeter case 2, ammeter case 3, ammeter case 4} belong to the same branch; { meter box 5, meter 6} belongs to the same branch.
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 (4)
1. A low-voltage distribution network topology refining identification method based on electric energy meter voltage matching is characterized by comprising the following steps: the method comprises the following steps:
step 1: acquiring voltage data sequences of all affiliated electric energy meters in the low-voltage power distribution network to be analyzed from the electricity utilization information acquisition data, setting the affiliated electric energy meters as electric energy meters to be analyzed, setting all the electric energy meters to be analyzed as an electric energy meter set to be analyzed, setting the affiliated electric energy meters of the electric energy meters to be analyzed as electric energy meter boxes to be analyzed, and setting all the electric energy meter boxes to be analyzed as electric energy meter box sets to be analyzed;
step 2: performing similarity analysis on the voltage data sequence obtained in the step one to obtain the most relevant electric energy meter of each electric energy meter to be analyzed;
and step 3: performing ammeter box correlation analysis on the most relevant ammeter of all the ammeter to be analyzed to obtain an ammeter box to which each most relevant ammeter belongs, setting the ammeter boxes as the relevant ammeter boxes of the ammeter box to be analyzed, wherein the relevant ammeter boxes of all the ammeter boxes to be analyzed are relevant ammeter box sets of the ammeter box to be analyzed;
and 4, step 4: drawing an ammeter box association diagram according to all association relations of the ammeter box set to be analyzed and the related ammeter box set of the ammeter box to be analyzed;
and 5: according to the ammeter box correlation diagram, identifying the branch dependency relationship of the ammeter box, wherein the branch dependency relationship is constructed in the following manner: the ammeter box group is constructed according to the connection relation of the ammeter box sets to be analyzed in the ammeter box association diagram, namely, each ammeter box in the ammeter box group is connected to other ammeter boxes in the ammeter box group through a directed association line, the ammeter boxes belonging to one ammeter box group are set to be in the same branch, and the branch subordination relation of all the ammeter boxes is established.
2. The low-voltage distribution network topology refined identification method based on electric energy meter voltage matching is characterized in that: the specific steps of the step 2 are as follows:
step 2.1: acquiring the file number of an electric meter box set to be analyzed;
step 2.2: and (2) carrying out voltage similarity calculation on the electric energy meter to be analyzed and the electric energy meters to be analyzed in other electric energy meter boxes to be analyzed, wherein the file numbers of the electric energy meters to be analyzed are different from those of the electric energy meter box to be analyzed, and the calculation formula is as follows:
whereinAFor a voltage data sequence of the electric energy meter to be analyzed,
the average value of the voltage data sequence of the electric energy meter to be analyzed is obtained;Bfor the voltage data series of other electric energy meters to be analyzed,
the average value of the voltage data sequences of other electric energy meters to be analyzed is obtained; taking other electric energy meters to be analyzed with the maximum voltage similarity as the most relevant electric energy meters of the electric energy meters to be analyzed;
step 2.3: and (4) repeating the step 2.2 for each electric energy meter to be analyzed in the electric energy meter to be analyzed in a centralized manner to obtain the most relevant electric energy meter of each electric energy meter to be analyzed.
3. The low-voltage distribution network topology refined identification method based on electric energy meter voltage matching is characterized in that: and reading the file numbers of all the ammeter boxes in the relevant ammeter box set of the ammeter box to be analyzed in the step 3.
4. The low-voltage distribution network topology refined identification method based on electric energy meter voltage matching is characterized in that: the specific steps of the step 4 are as follows:
step 4.1: drawing all the electric meter boxes to be analyzed, and correspondingly numbering the files of the electric meter boxes to be analyzed;
step 4.2: reading the incidence relation of all the electric meter boxes to be analyzed, wherein the incidence relation refers to the incidence relation of the electric meter boxes to be analyzed and the relevant electric meter boxes of the electric meter boxes to be analyzed;
step 4.3: and drawing the directed correlation lines by taking the electric meter box to be analyzed as the starting point and taking the related electric meter box to be analyzed as the terminal point according to all the correlation relations and the electric meter box file numbers of the remarks to form an electric meter box correlation diagram.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180356449A1 (en) * | 2015-12-17 | 2018-12-13 | HYDRO-QUéBEC | Updating a topology of a distribution network by successive reallocation of the meters |
| CN110120668A (en) * | 2019-05-08 | 2019-08-13 | 许昌许继软件技术有限公司 | A kind of area's topology automatic identification method and system |
| CN111404157A (en) * | 2020-04-17 | 2020-07-10 | 国网湖南省电力有限公司 | Automatic verification method and system for topological structure of low-voltage distribution network platform area |
| CN112018767A (en) * | 2020-11-02 | 2020-12-01 | 江苏智臻能源科技有限公司 | Method for determining actual user dependent distribution and phase based on operation data |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180356449A1 (en) * | 2015-12-17 | 2018-12-13 | HYDRO-QUéBEC | Updating a topology of a distribution network by successive reallocation of the meters |
| CN110120668A (en) * | 2019-05-08 | 2019-08-13 | 许昌许继软件技术有限公司 | A kind of area's topology automatic identification method and system |
| CN111404157A (en) * | 2020-04-17 | 2020-07-10 | 国网湖南省电力有限公司 | Automatic verification method and system for topological structure of low-voltage distribution network platform area |
| CN112018767A (en) * | 2020-11-02 | 2020-12-01 | 江苏智臻能源科技有限公司 | Method for determining actual user dependent distribution and phase based on operation data |
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