CN108683180B - Three-phase low-voltage power distribution network topology reconstruction method - Google Patents
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Abstract
A method for reconstructing topology of a three-phase low-voltage power distribution network. The method comprises the steps of acquiring time sequence voltage from an intelligent ammeter at a user side based on time sequence voltage data of a low-voltage distribution network, firstly solving a correlation coefficient between the time sequence voltage and reference voltage for each bus voltage data by using a correlation analysis method, identifying the phase with the maximum correlation coefficient, completing a phase identification process, and acquiring phase connection information of each bus. Dividing all buses into three groups of A phase, B phase and C phase according to different phases, and respectively obtaining the cross-correlation information among the three groups of buses by using Chow-Liu algorithm
And finally, integrating the three groups of reconstructed undirected networks to obtain a complete topological network structure.
Description
Technical Field
The invention belongs to the technical field of power distribution of a power system, and particularly relates to a technology for reconstructing topology of a three-phase low-voltage power distribution network terminal user by utilizing time sequence voltage data of a power distribution network.
Background
With the development of the technology, the intelligent monitoring equipment can accurately monitor the information of the high-voltage and medium-voltage power grids, but the intelligent equipment on the low-voltage side is not completely deployed, and the problems of frequent line change, difficulty in checking underground wiring, manual private line change and the like exist on the low-voltage user side, so that the phase information on the user side of the three-phase low-voltage power distribution network is often incomplete or wrong. However, network topology information of the three-phase low-voltage distribution network is essential for safe and stable operation of the network, and accurate user-level phase information is also of great significance for improving the operation performance of the three-phase low-voltage distribution network, for example, system loss can be reduced by adjusting three-phase balance.
In the current topology research of the power system, most of the topology error identification and the topology structure change of the high-voltage or medium-voltage power grid are researched, the network reconstruction is mainly carried out by utilizing a disconnecting link switch remote signaling signal matrix, but the method has low result accuracy rate due to the error of a false measurement device, and the high-voltage and medium-voltage power grid topology research method cannot be utilized because a monitoring device is not completely installed on a low-voltage power distribution network. For a method for identifying a phase of a power grid, the phase of the power distribution network is monitored by transmitting phase data through a power line based on a mu PMU, but the power line transmission signal has the defects of high error rate and the like. And the existing technology can not synchronously process the identification of the phase of the power distribution network and the identification of the topology. With the access of a large number of intelligent electric meters on the user side of the low-voltage distribution network, the voltage and the power consumption data of the three-phase low-voltage distribution network are monitored. The generation of large amounts of data provides the possibility of data-driven topology network structure reconstruction.
It can be seen that in the prior art, the following problems exist for the reconstruction of the power distribution network topology:
1. at present, phases are not distinguished in the problem of reconstruction of a topological network based on time sequence voltage data of a power distribution network;
2. most researches on the topology of the power system are limited to identification of topology errors of a power transmission network and detection of changes of a topological structure, but at present, the power distribution network is frequently updated and complicated in wiring, and mass data of all links of the power distribution network are difficult to identify the topology of the power distribution network along with development of a smart power grid.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a three-phase low-voltage distribution network topology reconstruction method.
In order to achieve the above object, the present invention specifically adopts the following technical solutions.
A three-phase low-voltage distribution network topology reconstruction method is characterized by comprising the following steps: the method comprises the steps of firstly identifying specific phases of buses based on time sequence voltage data correlation analysis, respectively utilizing a Chow-Liu algorithm to reconstruct a topological structure of a three-phase low-voltage distribution network by using the buses belonging to three phases aiming at three groups of different phases, and finally integrating the obtained three networks to obtain a complete topological network structure.
The three-phase low-voltage distribution network topology reconstruction method comprises the following steps:
step 1: acquiring time sequence voltage data from an intelligent electric meter at a user side;
step 2: in each block, the distribution network is abstracted as a graph model G ═ (M, S), the busbars are represented by the nodes of the graph model, i.e., M ═ { i, i ═ 1,2, …, N }, and the branches are represented by the edges of the graph model, i.e., S ═ { l }i,jI, j ∈ M }; wherein G is a graph model of the power distribution network, and M is a section of the power distribution networkPoint set, N is the maximum node number, S is the branch set of the distribution network, li,jA branch connecting node i and node j;
and step 3: given time window TpAnd a time interval T, acquiring a voltage value of the intelligent ammeter at intervals of the time interval T, and collecting D voltage values to form a voltage vector UiWherein D ═ Tp/T，UiAnd (3) representing a vector formed by the voltage of the bus i in the whole time window, and selecting the time sequence voltage of the bus which is closest to the transformer and belongs to the phase A, the phase B and the phase C as a reference, and respectively recording the time sequence voltage as:
Uph＝{uph；ph＝A,B,C}；
and 4, step 4: calculating the time sequence voltage U of the bus iiAnd UA，UB，UCThe correlation coefficients between the two are respectively marked as rhoi,A，ρi,B，ρi,C(ii) a For the bus i, rho is selectedi,A，ρi,B，ρi,CThe phase corresponding to the maximum one of the buses is the identified phase of the bus, and all buses in the power distribution network are calculated in a circulating mode;
and 5: adding all the buses in the step 4 into three categories of the phase A, the phase B and the phase C;
step 6: respectively obtaining three groups of mutual correlation information of the three groups of buses in the step 5
Wherein the information is correlated
According to the weighted value obtained by Chow-Liu algorithm and the cross-correlation information
Respectively reconstructing a topological network;
and 7: and (3) solving correlation coefficients among time sequence voltages of buses respectively belonging to the phase A, the phase B and the phase C as a reference, and integrating three groups of reconstructed topological networks of the phase A, the phase B and the phase C to form a complete topological network structure.
The present invention further includes the following preferred embodiments.
In step 4, the timing voltage U of the bus other than the reference timing voltageiAnd U of reference timing voltage corresponding to phasephThe correlation coefficient of (a) is calculated by the following formula:
wherein Cov (U)i,Uph) Is Ui,UphThe covariance of the variables is determined by the covariance,
is the standard deviation of the two voltage variables.
In step 6, cross-correlation information between different sets of busbars
Calculated according to the following formula:
wherein f isuv(i, j) is P (u)i＝u，ujMaximum likelihood estimation of v), fu(i) Is P (u)iU) maximum likelihood estimation, fv(j) Is P (u)jV) maximum likelihood estimation, P (u)i＝u，ujV) means ui＝u,ujJoint probability when v, P (u)iU) means uiProbability of u, P (u)jV) means ujProbability when v.
In step 6, based on the cross-correlation information
The respectively reconstructing the topological network comprises the following steps:
6.1 selecting a bus a with the maximum correlation coefficient of the bus voltage and the reference voltage for each phase;
6.2 based on the cross-correlation information
Selecting a bus b with the highest cross-correlation information with the bus a;
6.3 repeat step 6.2 until all the busbars belonging to this phase have been connected.
In step 7, the following contents are specifically included:
7.1 calculating correlation coefficients among three reference voltages of three phases;
7.2 according to the result, the reference buses which connect the three groups of voltages, i.e., the buses which are used as the reference and have large correlation coefficients between the voltages are connected. The invention has the following beneficial technical effects:
according to the invention, cross-correlation information among all buses is obtained by using a Chow-Liu algorithm to complete topology reconstruction of the network, so that the network connection relation of the low-voltage user side of the power distribution network is obtained, and the method is helpful for repair positioning, power distribution network fault study and judgment, power failure plan optimization and the like. In addition, the phase recognition process is introduced when the network structure is reconstructed, the problem that phase information of a low-voltage user side of the power distribution network is incomplete is solved, the problem of system line loss and energy consumption can be solved by utilizing the problem that the phase information is unbalanced in network detection, and meanwhile, the introduction of regenerated energy into a user network is facilitated.
Drawings
Fig. 1 is a schematic view of a three-phase low-voltage distribution network entering a home;
FIG. 2 is a schematic diagram of a topology reconstruction of a three-phase low-voltage distribution network;
fig. 3 is a schematic flow chart of a three-phase low-voltage distribution network topology reconstruction method of the invention.
Detailed Description
The technical scheme of the invention is further described in detail by combining the drawings and the specific embodiments in the specification.
The invention provides a method for carrying out phase identification and distinguishing on buses and further carrying out topology reconstruction on a power distribution network.
As shown in fig. 1 and 2, in the distribution network, electric energy enters a distribution box of an end user through links of a main distribution room, a primary distribution device and a secondary distribution device. The task of the primary distribution unit is here to convert a medium voltage of 10kV into a low voltage (system voltage) of 400V/230V, where 400V is the voltage value between the three phase (i.e. A, B, C three-phase) lines and 230V is the phase voltage of the three phase lines with respect to the neutral line. Because many end users only need to distribute the electric energy by the primary distribution equipment, the equipment is too large and complex, the secondary distribution equipment is used for secondary distribution of the electric energy, and the output voltage is reduced to 380V/220V (standard voltage) through the secondary distribution equipment. Secondary equipment's at home distribution cable carries the electric energy to end user's measurement kilowatt-hour meter case, wherein at home distribution cable has three phase lines, end user's measurement kilowatt-hour meter case is single-phase electricity, in order not to cause the waste, zero line N can be split two again, thereby link to each other with the three-phase live wire, promptly "live wire a + zero line", "live wire b + zero line", "live wire c + zero line" three groups, and then distribute each end user according to actual conditions with the three group electricity that obtains, the framework that the three-phase was registered one's residence is as shown in figure one.
As shown in fig. 3, the process schematic diagram of the three-phase low-voltage distribution network topology reconstruction method of the invention is shown, and the three-phase low-voltage distribution network topology reconstruction method disclosed by the invention comprises the following steps:
step 1: acquiring time sequence voltage data from an intelligent electric meter at a user side;
since each end user is connected to the transmission line in a single phase and the end user connection information on the low voltage feeder is mostly incomplete or missing, we are not known which phase line is used by most end users. Aiming at the problem, time sequence voltage data of the user intelligent electric meter are obtained, and phase information of an end user is identified by using correlation analysis.
Step 2: abstracting a distribution network into a graph model
In order to describe the network and parameters of the distribution network during phase identification and topology reconstruction, the following definitions are made. In each station, the distribution network is composed of several busbars and branches. This is abstracted as graph model G (M, S), the bus is represented by the nodes of the graph model, i.e., M { i, i ═ 1,2, …, N }, and the branches are represented byEdge representation of graph model, i.e. S ═ { l ═i,j,i,j∈M}。
And step 3: selecting a reference voltage
Since the voltage curves of different phases over time are different in their trend, the correlation between the voltage curves over time of the same phase is stronger than that between the voltage curves over time of different phases. Therefore, the correlation coefficient between the bus and the time sequence voltage data belonging to the A phase, the B phase and the C phase can be obtained by a correlation analysis method according to the correlation between the voltages, the degree of correlation is higher when the correlation coefficient is larger, and the phase identification is carried out by selecting the largest correlation coefficient.
Because voltage drop along the line is considered by the time sequence voltage data of the bus, for different buses along the line, the positions of the bus and the voltage amplitude of the bus are different from the position of the transformer, and the time sequence voltage amplitude of the bus closest to the electrical distance of the transformer is higher. Given time window TpAnd a time interval T, selecting the time sequence voltages of the buses which are closest to the transformer and belong to the phase A, the phase B and the phase C respectively as reference, and recording the time sequence voltages as reference
Uph＝{uph；ph＝A,B,C}，
And 4, step 4: acquiring node voltage of each bus, and solving correlation coefficient of each bus and reference voltage to perform phase identification
Selecting the same time window TpAnd a time interval T is obtained, and time sequence voltage data of the rest buses is recorded as Ui＝{ui(ii) a i ═ 1,2, …, N }, where N is the number of all buses.
The correlation coefficients of X, Y are calculated according to the cross-correlation principle, i.e. for two variables X, Y, as follows:
wherein Cov (X, Y) ═ E [ (X-mu)X)(X-μY)]Is the covariance of the X, Y variables; mu.sX，μYRespectively, the average value of the variable X and the average value of the variable Y; sigmaX，σYEach of variable XStandard deviation and standard deviation of variable Y. The larger the correlation coefficient is, the stronger the correlation between the two variables is, and the smaller the correlation coefficient is, the smaller the correlation between the two variables is. So that the time sequence voltage U of each bus can be calculated respectivelyiAnd UA，UB，UCCoefficient of correlation between
Are respectively denoted as ρi,A，ρi,B，ρi,C. For each bus i, rho is selectedi,A，ρi,B，ρi,CThe largest one, the corresponding phase, is the phase in which the bus is identified.
And 5: the phases are identified as A, B, C three groups
According to the method of the correlation analysis, all the buses are respectively added into three categories of the phase A, the phase B and the phase C, and then the buses belonging to the three phases are respectively subjected to network topology reconstruction. The reconstruction process is specifically explained by taking the bus belonging to the phase A as an example, and the reconstruction principle of the bus belonging to the phases B and C is the same as that of the phase A.
Step 6: three groups of cross-correlation information matrixes are obtained by using Chow-Liu algorithm, and then topology reconstruction is carried out on three groups of buses
And reconstructing the topological structure of the three-phase low-voltage distribution network based on a Chow-Liu algorithm.
Chow-Liu algorithm according to cross-correlation information
The maximum weight spanning tree is constructed using the Kruskal algorithm. One edge is constructed at a time in descending order of weight, and if all weights are greater than 0, a concatenated result is obtained.
The specific process is as follows: the Chow-Liu algorithm is a finite sample in a given dataset, using a tree model to estimate the n-dimensional discrete probability distribution. For n-dimensional vectors
Each xiAre all one variable, P (x) is n discrete variables x1,x2,…,xnWe want to approximate the true joint probability distribution with a tree model of the form:
xπ(i)is the parent node of variable i, if i is the root node, P (x)i|xπ(i))＝P(xi) The tree model takes into account the interrelationships between variables in the dataset. For variable xiAnd xjDefining the cross-correlation information between two variables as
Namely, it is
Wherein P (x)i,xj) Is a variable xiAnd xjJoint probability distribution, for a limited sample set, we estimate the probability distribution function using the maximum likelihood method, in particular when used
Namely, it is
Wherein f isuv(i, j) is P (x)i＝u，xjV) and n is the number of samples, i.e.
fu(i) Is P (x)iU) maximum likelihood estimation, i.e.
fu(i)＝∑vfuv(i,j) (6)
Obtaining cross-correlation information
The cross-correlation matrix can be established, and then the establishment of the tree model is completed.
For the topological reconstruction of our three-phase low-voltage distribution network, the Chow-Liu algorithm can be applied to obtain a reconstructed undirected network. Firstly, the time sequence voltage of a limited number of buses belonging to three different phases can be obtained, and then the cross-correlation information among the buses with the same phase can be obtained
Voltage fluctuation curves between buses with close electrical distance are similar, namely the correlation degree is high; the voltage fluctuation curves between buses with long electrical distance have low similarity, namely low correlation. The correlation degree between two continuously changing voltage values is measured by the mutual correlation information of the time sequence voltages among the buses. Then, a maximum weight spanning tree is constructed by using a Kruskal algorithm, so that a topological network structure with the maximum correlation coefficient among the buses connected with each other can be obtained.
Defining each bus, the corresponding Ui，UjRepresents the timing voltages for bus i and bus j, and i, j e {1,2, …, n } is the total number of all buses connected to the a phase line. Assuming that there are 6 buses connected to the phase A, a 6 x 6 symmetric matrix can be obtained according to the voltage correlation coefficients of the 6 buses, and the method includes
And a isi,j＝aj,iSince both values are a correlation coefficient between the bus i and bus j timing voltages. The matrix form of the obtained correlation coefficient between the time sequence voltages of the bus is as follows:
and selecting a bus i closest to the electrical distance of the transformer as an initial point, then selecting a bus with the largest correlation coefficient with the i from the correlation coefficient matrix, assuming that j is taken as a downstream bus directly connected with the i, and similarly, continuously selecting a bus with the largest correlation coefficient with the j from the correlation coefficient matrix as a downstream bus directly connected with the j until all 6 buses are connected to the line, wherein the obtained bus belongs to the network topology structure of the A-phase user.
The above process is still true when the number of busbars is n. The same way can be achieved for the network topology of the users belonging to phase B and phase C.
And 7: calculating the correlation coefficient between the reference voltages, integrating three groups of networks to obtain a complete network structure
And (3) solving correlation coefficients among time sequence voltages of buses respectively belonging to the phase A, the phase B and the phase C as a reference, and integrating three groups of reconstructed topological networks of the phase A, the phase B and the phase C to form a complete topological network structure. The method specifically comprises the following steps: calculating correlation coefficients among three reference voltages of three phases, wherein a specific calculation formula is given in step 4; according to the result, the reference bus bars having a large correlation coefficient between the voltages connecting the three groups of voltages, i.e., the bus bars as the reference, are connected.
By combining the analysis, the buses are divided into three categories of A phase, B phase and C phase according to the correlation between the correlation analysis voltages, next, the buses belonging to each phase are respectively subjected to topological structure reconstruction of the three-phase low-voltage distribution network according to a Chow-Liu algorithm, and finally, the reconstructed networks of the three phases are integrated to obtain a complete topological structure of the three-phase low-voltage distribution network. The figure comprises a topological schematic diagram of 13 users connected on the low-voltage side of converting 10Kv high voltage to A, B, C three phases through a transformer, wherein a user 1, a user 2 and a user 3 are connected on a phase A; user 4, user 5, user 6, user 7 and user 8 are connected in phase B; user 9, user 10, user 11, user 12, and user 13 are connected at phase C. .
While the best mode for carrying out the invention has been described in detail and illustrated in the accompanying drawings, it is to be understood that the same is by way of illustration and example only and is not to be taken by way of limitation, the scope of the invention should be determined by the appended claims and any changes or modifications which fall within the true spirit and scope of the invention should be construed as broadly described herein.
Claims (5)
1. A three-phase low-voltage distribution network topology reconstruction method is characterized by comprising the following steps:
step 1: acquiring time sequence voltage data from an intelligent electric meter at a user side;
step 2: in each block, the distribution network is abstracted as a graph model G ═ (M, S), the busbars are represented by the nodes of the graph model, i.e., M ═ { i, i ═ 1,2, …, N }, and the branches are represented by the edges of the graph model, i.e., S ═ { l }i,jI, j ∈ M }; wherein G is a graph model of the power distribution network, M is a node set of the power distribution network, N is the maximum number of nodes, S is a branch set of the power distribution network, and li,jA branch connecting node i and node j;
and step 3: given time window TpAnd a time interval T, acquiring a voltage value of the intelligent ammeter at intervals of the time interval T, and collecting D voltage values to form a voltage vector UiWherein D ═ Tp/T，UiThe time sequence voltage of the bus closest to the transformer and respectively belonging to the phase A, the phase B and the phase C is selected as a reference and is respectively recorded as a vector representing the voltage constitution of the bus i in the whole time window
Uph＝{uph；ph＝A,B,C}；
And 4, step 4: calculating the time sequence voltage U of the bus iiAnd UA，UB，UCThe correlation coefficients between the two are respectively marked as rhoi,A，ρi,B，ρi,C(ii) a For the bus i, rho is selectedi,A，ρi,B，ρi,CThe phase corresponding to the maximum one of the buses is the identified phase of the bus, and all buses in the power distribution network are calculated in a circulating mode;
and 5: adding all the buses in the step 4 into three categories of the phase A, the phase B and the phase C;
step 6: respectively obtaining three groups of mutual correlation signals of the three groups of buses in the step 5Information processing device
Wherein the information is correlated
According to the weighted value obtained by Chow-Liu algorithm and the cross-correlation information
Respectively reconstructing a topological network;
and 7: and (3) solving correlation coefficients among time sequence voltages of buses respectively belonging to the phase A, the phase B and the phase C as a reference, and integrating three groups of reconstructed topological networks of the phase A, the phase B and the phase C to form a complete topological network structure.
2. The three-phase low-voltage distribution network topology reconstruction method according to claim 1, characterized in that:
in step 4, the timing voltage U of the bus other than the reference timing voltageiAnd U of reference timing voltage corresponding to phasephThe correlation coefficient of (a) is calculated by the following formula:
wherein Cov (U)i,Uph) Is Ui,UphThe covariance of the variables is determined by the covariance,
is the standard deviation of the two voltage variables.
3. The three-phase low-voltage distribution network topology reconstruction method according to claim 1 or 2, characterized in that:
in step 6, cross-correlation information between different sets of busbars
Calculated according to the following formula:
wherein f isuv(i, j) is P (u)i＝u,ujMaximum likelihood estimation of v), fu(i) Is P (u)iU) maximum likelihood estimation, fv(j) Is P (u)jV) maximum likelihood estimation, P (u)i＝u,ujV) means ui＝u,ujJoint probability when v, P (u)iU) means uiProbability of u, P (u)jV) means ujProbability when v.
4. The three-phase low-voltage distribution network topology reconstruction method according to claim 3, characterized in that: in step 6, based on the cross-correlation information
The respectively reconstructing the topological network comprises the following steps:
6.1 selecting a bus a with the maximum correlation coefficient of the bus voltage and the reference voltage for each phase;
6.2 based on the cross-correlation information
Selecting a bus b with the highest cross-correlation information with the bus a;
6.3 repeat step 6.2 until all the busbars belonging to this phase have been connected.
5. The three-phase low-voltage distribution network topology reconstruction method according to claim 1, characterized in that:
in step 7, the following contents are specifically included:
7.1 calculating correlation coefficients among three reference voltages of three phases;
7.2 according to the result, the reference buses which connect the three groups of voltages, i.e., the buses which are used as the reference and have large correlation coefficients between the voltages are connected.
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| CN109738723B (en) * | 2018-12-29 | 2021-02-09 | 重庆邮电大学 | Three-phase automatic identification method for electric energy meter |
| CN110674158B (en) * | 2019-10-15 | 2022-02-15 | 广东电网有限责任公司 | Method and system for automatically updating local area for low-voltage user |
| CN110865328B (en) * | 2019-11-08 | 2021-10-08 | 上海电力大学 | Intelligent electric meter phase identification, topology identification and impedance estimation method based on AMI |
| CN111162533B (en) * | 2020-01-17 | 2022-06-14 | 天津大学 | Smart power grid hidden topology structure identification method based on convex optimization |
| CN111199363B (en) * | 2020-01-20 | 2022-10-18 | 上海电力大学 | Method for realizing topology recognition by maximum correlation screening algorithm |
| CN111505443B (en) * | 2020-05-13 | 2022-08-30 | 广州市奔流电力科技有限公司 | Low-voltage transformer area line-to-user relationship identification method and device and computer equipment |
| CN112182499B (en) * | 2020-10-23 | 2022-10-14 | 国网天津市电力公司 | Low-voltage distribution network topological structure identification method based on time sequence electric quantity data |
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