CN111355231A - Power distribution network topology identification method and system - Google Patents

Power distribution network topology identification method and system Download PDF

Info

Publication number
CN111355231A
CN111355231A CN201811580344.9A CN201811580344A CN111355231A CN 111355231 A CN111355231 A CN 111355231A CN 201811580344 A CN201811580344 A CN 201811580344A CN 111355231 A CN111355231 A CN 111355231A
Authority
CN
China
Prior art keywords
matrix
jacobian matrix
variation
voltage
voltage observation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201811580344.9A
Other languages
Chinese (zh)
Inventor
郭屾
张冀川
王鹏
张明宇
林佳颖
张治明
张明泽
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
Original Assignee
State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, State Grid Zhejiang Electric Power Co Ltd, China Electric Power Research Institute Co Ltd CEPRI filed Critical State Grid Corp of China SGCC
Priority to CN201811580344.9A priority Critical patent/CN111355231A/en
Publication of CN111355231A publication Critical patent/CN111355231A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks

Abstract

A method for identifying topology of a power distribution network comprises the following steps: constructing a Jacobian matrix based on an active change vector, a reactive change vector, a voltage phase angle change vector and an amplitude change vector of a voltage observation node, and calculating to obtain an optimal estimation value of a block matrix in the Jacobian matrix; constructing an integer programming model according to the optimal estimated value of the block matrix; and obtaining the incidence relation of the voltage observation nodes based on the integer programming model, and further obtaining a power grid topological structure. According to the scheme, the more accurate tidal current Jacobian matrix is established by utilizing the synchronism of voltage and power measurement of the synchronous measurement device, and the nodes which are related to each other in the optimization result are obtained by solving the optimization model, so that the topological connection relation of the power distribution network is obtained.

Description

Power distribution network topology identification method and system
Technical Field
The invention relates to the field of electric power, in particular to a method and a system for identifying topology of a power distribution network.
Background
The topology of the power distribution network is analyzed, identified and verified, wherein the key point is to determine the switch state in the network. In an actual power distribution system, if the switch remote signaling does not match the actual position of the switch, the switch remote signaling is called a network topology error, which causes a problem in network topology identification. Therefore, the analysis and identification of the power distribution network topology based on the unhealthy measurement information are to accurately analyze the on-off state of the network and the corresponding branch connection relationship, and construct the topology relationship of the network.
The power flow Jacobian matrix is not only a premise and a foundation for carrying out power flow analysis on a power system and sensing the current running state of the system, but also is widely applied to the calculation of voltage-power sensitivity parameters and is an important parameter for modeling problems of voltage control, energy management and the like. The traditional manner for obtaining the load flow Jacobian matrix mainly adopts off-line load flow calculation, and has the defects of inaccurate element parameters, untimely information updating, difficulty in tracking system operation points, relevant topological changes and the like, so that larger calculation errors are often caused. The synchronous phasor measurement unit can realize high-precision synchronous measurement of electric quantities such as active power, reactive power, voltage phase angle, voltage amplitude, system frequency and the like, and can be applied to the problem of identification of parameters of an electric power system such as injection transfer factors, line parameters, admittance matrixes and the like. Although the synchronous phasor measurement unit can provide more accurate data measurement, the measured data may still contain bad data due to the influence of links such as data acquisition, conversion and communication, so that the robustness of the jacobian matrix to the bad data is reduced, and therefore the associated nodes in the power distribution network topology structure cannot be accurately screened.
Disclosure of Invention
The invention provides a power distribution network topology identification method and system, and aims to solve the problem that bad data robustness of a jacobian matrix to a synchronous vector is low in the prior art.
The technical scheme provided by the invention is as follows:
a method for identifying topology of a power distribution network comprises the following steps:
constructing a Jacobian matrix based on an active change vector, a reactive change vector, a voltage phase angle change vector and an amplitude change vector of a voltage observation node, and calculating to obtain an optimal estimation value of a block matrix in the Jacobian matrix;
constructing an integer programming model according to the optimal estimated value of the block matrix;
and obtaining the incidence relation of the voltage observation nodes based on the integer programming model, and further obtaining a power grid topological structure.
Preferably, a jacobian matrix is constructed by using the active variation, the reactive variation, the voltage phase angle variation and the amplitude variation, as shown in the following formula:
Figure BDA0001917728990000021
wherein, Δ P is an active variation vector of each voltage observation node, and Δ Q is a reactive variation vector of each voltage observation node; delta theta is a voltage phase angle change vector of each voltage observation node, and delta V is an amplitude change vector of each voltage observation node; j is a jacobian matrix, and the block matrix H, N, which is the jacobian matrix, is the block matrix N, M, which is the jacobian matrix, is the block matrix M, L, which is the jacobian matrix, is the block matrix L of the jacobian matrix.
Preferably, the calculating to obtain the optimal estimation value of the block matrix in the jacobian matrix includes:
constructing a least square estimation model by taking the active variable quantity as a first constraint condition, and solving the optimal estimation values of a block matrix H and a block matrix N;
and constructing a least square estimation model by taking the reactive variable quantity as a second constraint condition, and solving the optimal estimation values of the block matrix M and the block matrix L.
Preferably, the first constraint is as follows:
Figure BDA0001917728990000022
wherein, Δ PiIn order to have the active variation,
Figure BDA0001917728990000023
is a first constraint coefficient, ei,PIndicating the measurement error;
the first constraint factor is expressed by the following formula:
Figure BDA0001917728990000024
wherein the content of the first and second substances,
Figure BDA0001917728990000025
the voltage phase angle variation that is the active variation,
Figure BDA0001917728990000026
the amplitude variation of the active variation is shown.
Preferably, the second constraint is as follows:
Figure BDA0001917728990000027
wherein, is Δ QiIn order to change the amount of the reactive power,
Figure BDA0001917728990000028
is the second constraint coefficient, ei,PIndicating the measurement error;
the second constraint factor is expressed by the following formula:
Figure BDA0001917728990000031
wherein the content of the first and second substances,
Figure BDA0001917728990000032
a voltage phase angle variation amount which is a reactive variation amount,
Figure BDA0001917728990000033
is the amplitude variation of the reactive variation.
Preferably, the constructing an integer programming model according to the optimal estimation value of the blocking matrix includes:
selecting matrix elements above a diagonal line of a block matrix H in the block matrix to form a vector;
and constructing an integer programming model by taking the maximum value of the decision variable as an objective function and the number of voltage observation nodes as a constraint condition based on the vector.
Preferably, the objective function is as follows:
Figure BDA0001917728990000034
the constraint is represented by the following formula:
Figure BDA0001917728990000035
wherein a islFor decision variables, N is the number of voltage observation nodes.
Preferably, the obtaining of the association relationship of the voltage observation nodes based on the integer programming model to further obtain a power grid topology structure includes:
solving the integer programming model, and screening out voltage observation nodes corresponding to matrix elements with the calculation result of 1 as voltage observation nodes which are correlated in the power grid topology;
and obtaining a topological connection relation of the power distribution network according to the voltage observation nodes and the mutual association relation of the voltage observation nodes.
A power distribution network topology identification system, comprising:
an estimated value calculation module: constructing a Jacobian matrix based on an active change vector, a reactive change vector, a voltage phase angle change vector and an amplitude change vector of a voltage observation node, and calculating to obtain an optimal estimation value of a block matrix in the Jacobian matrix;
an integer model construction module: constructing an integer programming model according to the optimal estimated value of the block matrix;
a topology construction module: and obtaining the incidence relation of the voltage observation nodes based on the integer programming model, and further obtaining a power grid topological structure.
Preferably, the jacobian matrix constructed in the estimation value calculation module is as follows:
Figure BDA0001917728990000041
wherein, Δ P is an active variation vector of each voltage observation node, and Δ Q is a reactive variation vector of each voltage observation node; delta theta is a voltage phase angle change vector of each voltage observation node, and delta V is an amplitude change vector of each voltage observation node; j is a jacobian matrix, and the block matrix H, N, which is the jacobian matrix, is the block matrix N, M, which is the jacobian matrix, is the block matrix M, L, which is the jacobian matrix, is the block matrix L of the jacobian matrix.
Preferably, the estimation value calculation module includes:
a first calculation submodule: constructing a least square estimation model by taking the active variable quantity as a first constraint condition, and solving the optimal estimation values of a block matrix H and a block matrix N;
a second calculation submodule: and constructing a least square estimation model by taking the reactive variable quantity as a second constraint condition, and solving the optimal estimation values of the block matrix M and the block matrix L.
Compared with the prior art, the invention has the beneficial effects that: according to the scheme, a Jacobian matrix is constructed on the basis of an active change vector, a reactive change vector, a voltage phase angle change vector and an amplitude change vector of a voltage observation node, and an optimal estimation value of a block matrix in the Jacobian matrix is obtained through calculation; constructing an integer programming model according to the optimal estimated value of the block matrix; and obtaining the incidence relation of the voltage observation nodes based on the integer programming model, and further obtaining a power grid topological structure. According to the method, the sparsity of the Jacobian matrix is utilized, the estimation problem is converted into the sparse recovery problem, the sparse recovery problem is solved, the Jacobian matrix is accurately estimated, the integer programming model is established by utilizing the estimated power flow Jacobian matrix, and the topological connection relation of the power distribution network is obtained by completely depending on data identification under the condition that data in the node to be detected contains bad data.
Drawings
FIG. 1 is a flowchart of a method for identifying topology of a power distribution network according to the present invention;
FIG. 2 is a diagram of an IEEE33 node algorithm in accordance with an embodiment of the present invention;
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1:
s1: constructing a Jacobian matrix based on an active change vector, a reactive change vector, a voltage phase angle change vector and an amplitude change vector of a voltage observation node, and calculating to obtain an optimal estimation value of a block matrix in the Jacobian matrix:
obtaining the current active power, reactive power, voltage phase angle and amplitude change vectors delta P, delta Q, delta theta and delta V of each voltage observation node;
linearizing the nonlinear power flow equation at the current operating point of the system, solving a Jacobian matrix of the power flow equation, and extracting a block sub-matrix H, N, M, L of the Jacobian matrix;
Figure BDA0001917728990000051
in the formula, Δ P and Δ Q respectively represent the active power and reactive power variation vectors of each node injection system; delta theta and delta V respectively represent variation vectors of voltage phase angles and amplitudes of the nodes; j isThe jacobian matrix of the trend equation, H, N, M, L are the block submatrices of the jacobian matrix respectively,
Figure BDA0001917728990000052
extracting historical measurement of power and voltage of all PMUs of C groups of the system, and subtracting the historical measurement from the current measurement to obtain power and voltage variation delta P of each PMU nodei[k]、ΔQi[k]、Δθi[k]、ΔVi[k],k＝1,2...C
By using the elements in the trend jacobian matrix, each set of measurements approximately satisfies the following relationship:
Figure BDA0001917728990000053
Figure BDA0001917728990000054
in the formula, omegaLRepresents the set, Ω, of all PQ nodes in the power distribution systemVRepresenting the collection of all PV nodes in the power distribution system.
Let Δ Pi＝[ΔPi[1],...,ΔPi[C]]T，ΔQi＝[ΔQi[1],...,ΔQi[C]]T，Δθi＝[Δθi[1],...,Δθi[C]]T，ΔVi＝[ΔVi[1],...,ΔVi[C]]TWhen C > 2| omegaL|+|ΩVIn the case of l, the number of the terminal,
Figure BDA0001917728990000055
Figure BDA0001917728990000056
in the formula (I), the compound is shown in the specification,
Figure BDA0001917728990000061
respectively representing each block subarray of the Jacobian matrixI line, | Ω ofL| and | ΩVRespectively represents a set omegaLAnd ΩVThe number of the elements in (B).
Establishing a least square estimation model and solving the least square estimation model,
order to
Figure BDA0001917728990000062
Establishing a least square estimation model as follows:
Figure BDA0001917728990000063
Figure BDA0001917728990000064
in the formula, ei,PIndicating the measurement error.
To obtain Hi、Ni、Mi、LiIs optimized to estimate
Figure BDA0001917728990000065
By solving the least squares estimate, H can be obtainedi、NiOptimal estimated value of (a):
Figure BDA0001917728990000066
in the formula (I), the compound is shown in the specification,
Figure BDA0001917728990000067
represents HiIs determined based on the estimated value of the measured,
Figure BDA0001917728990000068
represents NiThe optimal estimated value of (a).
M can be obtained by the same methodi、LiIs optimized to estimate
Figure BDA0001917728990000069
And estimating to obtain the whole Jacobian matrix.
S2, constructing an integer programming model according to the optimal estimated value of the block matrix:
and sequentially forming vectors by using elements above diagonal lines in the estimated Jacobian matrix according to the estimated power flow Jacobian matrix, and constructing an integer programming model.
And according to the estimated power flow Jacobian matrix, sequentially forming a vector f by using elements above a diagonal line of H in the estimated Jacobian matrix, and constructing an integer programming model.
An objective function:
Figure BDA00019177289900000610
constraint conditions are as follows:
Figure BDA00019177289900000611
in the formula, alThe decision variables are 0-1, and N is the number of nodes in the power grid topological structure. Wherein the constraints indicate that the distribution network needs to be guaranteed radial operation during operation.
S3, obtaining the incidence relation of the voltage observation nodes based on the integer programming model, and further obtaining a power grid topological structure:
and solving the optimized integer programming model, wherein the corresponding element of the H matrix with the value of 1 in the obtained optimized result is the node with correlation, and further the topological connection relation of the power distribution network is obtained.
Example 2:
the IEEE33 node power distribution system is adopted to test the algorithm, and a node algorithm diagram is shown in figure 2. The voltage class of an IEEE33 node system is 12.66kV, a node 1 is a balanced node, nodes 2-33 are PQ type nodes, the maximum degree of the network is 3, and the reference capacity is 1 MVA.
In order to simulate the measurement data of the PMU, the kth set of active power of the node i is first generated in the following manner.
Figure BDA0001917728990000071
In the formula (I), the compound is shown in the specification,
Figure BDA0001917728990000072
the random numbers are normally distributed with a mean value of 0 and standard deviations of 0.01 and 0.025% respectively and are used for simulating power change and measurement error of different measurement moments relative to the current moment respectively.
In the same way, reactive measurements can be obtained. After the k-th group of active power and reactive power measurement of each node is obtained, the corresponding voltage phase angle theta is obtained through load flow calculationi(k) Sum amplitude Vi(k) The kth set of voltage phase angle and magnitude measurements are taken as node i.
The mean square error of the jacobian estimate is calculated using the following equation.
Figure BDA0001917728990000073
In the formula, NJ＝|ΩV|+2|ΩLL represents the dimension of the jacobian matrix,
Figure BDA0001917728990000074
representing estimated values of the parameters of the Jacobian matrix, Ji,jThe theoretical value of an accurate line parameter meter is adopted.
1) Scene 1
The measurement group numbers are set to be 25, 30, 35, 40, 45, 50, 55 and 60 respectively, the conservative estimation value of the network maximum degree is set to be 4, LS, OMP, ROMP, CoSaMP, CohCoSocAMP and a correlation-based compression sampling matching pursuit algorithm (Conrrentpitch CohCohCoSocAMP and CCoSocAMP) for maximizing correlation entropy improvement are respectively adopted to carry out Jacobian matrix estimation, and the table 1 respectively gives the line number of the Jacobian matrix which cannot be successfully estimated by various algorithms under different measurement group numbers. As can be seen from the results, when the number of measurement sets is less than 64, an over-determined equation that reflects the relationship between the power measurement variation and the voltage measurement variation cannot be formed, and thus, the general least square method cannot estimate any line of the jacobian matrix. The OMP, ROMP, and CoSaMP algorithms enable the estimation of certain rows of the jacobian matrix, and as the number of metrology lots increases, the number of rows for which the estimation succeeds increases, but even if the number of metrology lots increases to 60, the estimation of the entire jacobian matrix cannot be achieved. The CohCoSeaMP and CCohCoSeaMP algorithms can realize the estimation of the whole Jacobian matrix when the number of measurement groups is 30. Meanwhile, due to the influence of data randomness, when the number of measured data groups is 40, the CohCoSeMaMP and CCohCoSeMaMP algorithms also generate Jacobian matrix rows which cannot realize estimation.
The results of the estimation of the first 32 elements of the 57 th row of the jacobian matrix by different methods when the number of measurement groups is 50 are given in table 2, and it can be seen from the results that the cases of the OMP, ROMP and CoSaMP methods that cannot accurately search the row where the non-zero element is located exist, while the CohCoSaMP and CCohCoSaMP methods can realize the accurate estimation of the corresponding element.
TABLE 1 number of rows for which Jacobian matrix estimation was unsuccessful
Figure BDA0001917728990000081
Table 2 estimated values in line 57
Figure BDA0001917728990000082
Figure BDA0001917728990000091
2) Scene 2
By adopting the measured data and algorithm parameter setting of the scene 1 and the characteristic of the flow Jacobian matrix, the rows of the Jacobian matrix which cannot be successfully estimated are re-estimated, and the obtained rows of the Jacobian matrix which cannot be successfully estimated are shown in the table 3. As can be seen by comparing Table 1 and Table 3, the number of rows that were not evaluated successfully in the OMP, ROMP, CoSaMP, CohCoSoMaMP, and CCohCoMaMP methods is all reduced by taking advantage of the characteristics of the Jacobian matrix. Table 4 shows the success of estimation of each row by the CoSaMP algorithm when the measured group number is 45 and the trend jacobian matrix characteristic is used. From the results in the table, it can be seen that, for the 33 th, 34 th, 36 th, 41 th, 11 th, 14 th, 47 th, 49 th, 50 th, 52 th, 22 th, 55 th, 25 th, 26 th, 63 th rows which can not be successfully estimated, the successful estimation is realized through the 1 st, 2 nd, 4 th, 43 th, 46 th, 15 th, 17 th, 18 th, 20 th, 54 th, 57 th, 58 th, 31 th rows which can be successfully estimated by utilizing the characteristics of the power flow jacobian matrix.
TABLE 3 number of rows for which Jacobian matrix estimation was unsuccessful
Figure BDA0001917728990000101
TABLE 4 estimated success for each row
Figure BDA0001917728990000102
Figure BDA0001917728990000111
3) Scene 3
The measurement groups are respectively set to be 30, 35, 40, 45, 50, 55 and 60, the conservative estimation value of the network maximum degree is set to be 4, and the measurement of each group is estimated for 100 times. The average jacobian matrix row number and the topology identification success rate that cannot be successfully estimated without considering the specificity of the jacobian matrix are shown in tables 5 and 6, respectively. As can be seen from the table, the CohCoSaMP and CCohCoSaMP methods greatly reduce the number of unsuccessful rows of the tidal current jacobian matrix estimation, and improve the success rate of topology identification.
TABLE 5 number of rows for which Jacobian matrix estimation was unsuccessful
Figure BDA0001917728990000112
Figure BDA0001917728990000121
TABLE 6 topology identification success (%)
Figure BDA0001917728990000122
Considering the particularity of the jacobian matrix, the number of rows of the jacobian matrix and the success rate of the topology identification which cannot be successfully estimated are shown in tables 7 and 8, respectively. By comparing table 7 with table 5, it can be found that the number of unsuccessful rows of jacobian matrix estimation can be reduced by utilizing the particularity of the jacobian matrix; by comparing table 8 and table 6, it can be found that the success rate of topology identification can be improved by utilizing the particularity of the jacobian matrix.
TABLE 7 number of rows for which Jacobian matrix estimation was unsuccessful
Figure BDA0001917728990000123
TABLE 8 topology identification success Rate (%)
Figure BDA0001917728990000124
Figure BDA0001917728990000131
4) Scene 4
Set measurement group number 60, add bad data to the 10 th PMU measurement data at node 30. Table 9 shows the number of rows and estimation errors for which jacobian estimation was unsuccessful with different methods in the presence of bad data. As can be seen from the table, under the influence of bad measurement data, the OMP, ROMP, CoSaMP, and CohCoSaMP methods cannot successfully estimate the rows corresponding to the 30 nodes in the jacobian matrix, and the accuracy of estimation of the jacobian matrix can still be ensured while the estimation of the CCohCoSaMP is successfully achieved.
TABLE 9 estimated results
Figure BDA0001917728990000132
The bad data are further added, and the measured bad data are added to the nodes 15, 16, 24, 25, and 29, respectively, and the obtained estimation results are shown in table 10. It can be seen from the estimation result that, as the number of nodes containing bad measurement data increases, the number of rows of the jacobian matrix which is unsuccessfully estimated by other algorithms increases, and the CCohCoSaMP algorithm can still realize the estimation of all rows of the tidal current jacobian matrix, and the estimation accuracy can still be ensured.
TABLE 10 estimated results
Figure BDA0001917728990000133
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (11)

1. A method for identifying topology of a power distribution network is characterized by comprising the following steps:
constructing a Jacobian matrix based on an active change vector, a reactive change vector, a voltage phase angle change vector and an amplitude change vector of a voltage observation node, and calculating to obtain an optimal estimation value of a block matrix in the Jacobian matrix;
constructing an integer programming model according to the optimal estimated value of the block matrix;
and obtaining the incidence relation of the voltage observation nodes based on the integer programming model, and further obtaining a power grid topological structure.
2. The method of claim 1, wherein the active variation, the reactive variation, the voltage phase angle variation and the amplitude variation are constructed into a jacobian matrix as shown in the following formula:
Figure FDA0001917728980000011
wherein, Δ P is an active variation vector of each voltage observation node, and Δ Q is a reactive variation vector of each voltage observation node; delta theta is a voltage phase angle change vector of each voltage observation node, and delta V is an amplitude change vector of each voltage observation node; j is a jacobian matrix, and the block matrix H, N, which is the jacobian matrix, is the block matrix N, M, which is the jacobian matrix, is the block matrix M, L, which is the jacobian matrix, is the block matrix L of the jacobian matrix.
3. The method of claim 1, wherein the calculating an optimal estimate of a blocking matrix in the jacobian matrix comprises:
constructing a least square estimation model by taking the active variable quantity as a first constraint condition, and solving the optimal estimation values of a block matrix H and a block matrix N;
and constructing a least square estimation model by taking the reactive variable quantity as a second constraint condition, and solving the optimal estimation values of the block matrix M and the block matrix L.
4. The method of claim 3, wherein the first constraint is expressed by:
Figure FDA0001917728980000012
wherein, Δ PiIn order to have the active variation,
Figure FDA0001917728980000013
is a first constraint coefficient, ei,PIndicating the measurement error;
the first constraint factor is expressed by the following formula:
Figure FDA0001917728980000021
wherein the content of the first and second substances,
Figure FDA0001917728980000022
the voltage phase angle variation that is the active variation,
Figure FDA0001917728980000023
the amplitude variation of the active variation is shown.
5. The method of claim 3, wherein the second constraint is expressed by:
Figure FDA0001917728980000024
wherein, is Δ QiIn order to change the amount of the reactive power,
Figure FDA0001917728980000025
is the second constraint coefficient, ei,PIndicating the measurement error;
the second constraint factor is expressed by the following formula:
Figure FDA0001917728980000026
wherein the content of the first and second substances,
Figure FDA0001917728980000027
a voltage phase angle variation amount which is a reactive variation amount,
Figure FDA0001917728980000028
is the amplitude variation of the reactive variation.
6. The method of claim 1, wherein constructing an integer programming model from the optimal estimates of the blocking matrices comprises:
selecting matrix elements above a diagonal line of a block matrix H in the block matrix to form a vector;
and constructing an integer programming model by taking the maximum value of the decision variable as an objective function and the number of voltage observation nodes as a constraint condition based on the vector.
7. The method of claim 6, wherein the objective function is expressed by the following equation:
Figure FDA0001917728980000029
the constraint is represented by the following formula:
Figure FDA00019177289800000210
wherein a islFor decision variables, N is the number of voltage observation nodes.
8. The method of claim 1, wherein obtaining the association relationship of the voltage observation nodes based on the integer programming model to obtain a power grid topology comprises:
solving the integer programming model, and screening out voltage observation nodes corresponding to matrix elements with the calculation result of 1 as voltage observation nodes which are correlated in the power grid topology;
and obtaining a topological connection relation of the power distribution network according to the voltage observation nodes and the mutual association relation of the voltage observation nodes.
9. A power distribution network topology identification system, comprising:
an estimated value calculation module: constructing a Jacobian matrix based on an active change vector, a reactive change vector, a voltage phase angle change vector and an amplitude change vector of a voltage observation node, and calculating to obtain an optimal estimation value of a block matrix in the Jacobian matrix;
an integer model construction module: constructing an integer programming model according to the optimal estimated value of the block matrix;
a topology construction module: and obtaining the incidence relation of the voltage observation nodes based on the integer programming model, and further obtaining a power grid topological structure.
10. The system of claim 9, wherein the jacobian matrix constructed in the estimate calculation module is represented by the following equation:
Figure FDA0001917728980000031
wherein, Δ P is an active variation vector of each voltage observation node, and Δ Q is a reactive variation vector of each voltage observation node; delta theta is a voltage phase angle change vector of each voltage observation node, and delta V is an amplitude change vector of each voltage observation node; j is a jacobian matrix, and the block matrix H, N, which is the jacobian matrix, is the block matrix N, M, which is the jacobian matrix, is the block matrix M, L, which is the jacobian matrix, is the block matrix L of the jacobian matrix.
11. The system of claim 9, wherein the estimate calculation module comprises:
a first calculation submodule: constructing a least square estimation model by taking the active variable quantity as a first constraint condition, and solving the optimal estimation values of a block matrix H and a block matrix N;
a second calculation submodule: and constructing a least square estimation model by taking the reactive variable quantity as a second constraint condition, and solving the optimal estimation values of the block matrix M and the block matrix L.
CN201811580344.9A 2018-12-24 2018-12-24 Power distribution network topology identification method and system Pending CN111355231A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811580344.9A CN111355231A (en) 2018-12-24 2018-12-24 Power distribution network topology identification method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811580344.9A CN111355231A (en) 2018-12-24 2018-12-24 Power distribution network topology identification method and system

Publications (1)

Publication Number Publication Date
CN111355231A true CN111355231A (en) 2020-06-30

Family

ID=71198034

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811580344.9A Pending CN111355231A (en) 2018-12-24 2018-12-24 Power distribution network topology identification method and system

Country Status (1)

Country Link
CN (1) CN111355231A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113221307A (en) * 2021-06-11 2021-08-06 南方电网科学研究院有限责任公司 Power distribution network transient operation topology identification method and device based on compressed sensing algorithm

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102163844A (en) * 2011-03-14 2011-08-24 国网技术学院 Method for detecting state of power system based on phasor measurement unit (PMU)
CN102545205A (en) * 2011-12-22 2012-07-04 河海大学 Distributed power system state estimating method based on automatic differentiation technology
CN103632313A (en) * 2013-12-05 2014-03-12 国家电网公司 Power grid dynamic observing method based on PMU (power management unit) data
CN103840452A (en) * 2014-03-04 2014-06-04 国家电网公司 Large power system state estimating method introducing PMU measure information
CN106229970A (en) * 2016-07-29 2016-12-14 河海大学 Micro-capacitance sensor method for estimating state based on converter Control characteristic
CN106874113A (en) * 2017-01-19 2017-06-20 国电南瑞科技股份有限公司 A kind of many GPU heterogeneous schemas static security analysis computational methods of CPU+
CN108075480A (en) * 2016-11-17 2018-05-25 中国电力科学研究院 The method for estimating state and system of a kind of ac and dc systems
CN108462181A (en) * 2018-01-31 2018-08-28 天津大学 Consider the intelligent distribution network Load Flow Jacobian Matrix robust estimation method of sparsity
CN108649574A (en) * 2018-06-15 2018-10-12 华北电力大学 A kind of power distribution network fast state method of estimation based on three kinds of metric data

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102163844A (en) * 2011-03-14 2011-08-24 国网技术学院 Method for detecting state of power system based on phasor measurement unit (PMU)
CN102545205A (en) * 2011-12-22 2012-07-04 河海大学 Distributed power system state estimating method based on automatic differentiation technology
CN103632313A (en) * 2013-12-05 2014-03-12 国家电网公司 Power grid dynamic observing method based on PMU (power management unit) data
CN103840452A (en) * 2014-03-04 2014-06-04 国家电网公司 Large power system state estimating method introducing PMU measure information
CN106229970A (en) * 2016-07-29 2016-12-14 河海大学 Micro-capacitance sensor method for estimating state based on converter Control characteristic
CN108075480A (en) * 2016-11-17 2018-05-25 中国电力科学研究院 The method for estimating state and system of a kind of ac and dc systems
CN106874113A (en) * 2017-01-19 2017-06-20 国电南瑞科技股份有限公司 A kind of many GPU heterogeneous schemas static security analysis computational methods of CPU+
CN108462181A (en) * 2018-01-31 2018-08-28 天津大学 Consider the intelligent distribution network Load Flow Jacobian Matrix robust estimation method of sparsity
CN108649574A (en) * 2018-06-15 2018-10-12 华北电力大学 A kind of power distribution network fast state method of estimation based on three kinds of metric data

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
郭屾 等: "基于PMU 的配电网潮流雅可比矩阵鲁棒估计与拓扑辨识", 《电力系统及其自动化学报》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113221307A (en) * 2021-06-11 2021-08-06 南方电网科学研究院有限责任公司 Power distribution network transient operation topology identification method and device based on compressed sensing algorithm

Similar Documents

Publication Publication Date Title
CN108173263B (en) Distribution network topology error identification algorithm based on AMI measurement information
CN107453357B (en) Power distribution network state estimation method based on layered solution
CN108199375B (en) Intelligent power distribution network topology identification method based on synchronous phasor measurement
CN101599643B (en) Robust state estimation method in electric power system based on exponential type objective function
CN103944165B (en) A kind of bulk power grid parameter identification method of estimation
CN106505557B (en) Remote measurement error identification method and device
CN107577870A (en) The distribution network voltage power sensitivity robust estimation method measured based on synchronized phasor
CN108448568A (en) Power distribution network admixture method of estimation based on a variety of time cycle measurement data
CN110289613B (en) Sensitivity matrix-based power distribution network topology identification and line parameter identification method
WO2016078477A1 (en) Transformer substation three-phase linear generalized state estimation method
CN112600191B (en) Low-voltage distribution network parameter and topology joint identification method based on nonlinear regression
CN107749627B (en) Improved matching pursuit based intelligent power distribution network power flow Jacobian matrix estimation method
CN107453484B (en) SCADA data calibration method based on WAMS information
CN112865181B (en) Photovoltaic inverter parameter identification method and device based on port impedance characteristics
CN108054757A (en) A kind of embedded idle and voltage N-1 Close loop security check methods
CN106655152B (en) A kind of State Estimation for Distribution Network measuring characteristic based on AMI
CN108462181B (en) Sparse-considered robust estimation method for jacobian matrix of power flow of intelligent power distribution network
CN103793585A (en) Method for improving theoretical line loss computation integrity and accuracy
CN102280877B (en) Method for identifying parameter of poor branch of power system through a plurality of measured sections
CN109858061B (en) Power distribution network equivalence and simplification method for voltage power sensitivity estimation
CN109146336B (en) Robust state estimation method for power system based on t distribution
CN104866714A (en) Self-adaptive nuclear density robust state estimation method for power system
CN111355231A (en) Power distribution network topology identification method and system
CN105932670A (en) Distribution network state estimation method based on semi-definite programming
CN106384298B (en) A kind of intelligent power missing data modification method based on two stages interpolation model

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200630