Power distribution network line parameter reconstruction method and system

Abstract

The invention discloses a power distribution network line parameter reconstruction method and system. The method comprises the steps of performing actual measurement on a power distribution network line, obtaining actual measurement data of a power distribution network, and determining feature points of the power distribution network; according to the topological structure of the power distribution network, carrying out rough estimation on line parameters; determining important feature points in the feature points of the power distribution network; partitioning the power distribution network according to the topological structure of the power distribution network; and respectively calculating the reconstructed power distribution network line parameters of each partition according to the roughly estimated line parameters and the important feature points. According to the method, partition calculation is carried out on the power distribution network, the dimension of the Jacobian matrix during iteration is reduced, the calculation amount is simplified, and meanwhile the iteration convergence and the matrix numerical stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for reconstructing line parameters of a distribution network. Background Art

[0002] With the massive access of new energy and distributed power sources, it is difficult for traditional line parameters based on design data to accurately reflect the actual operating status. The accuracy of distribution network line parameters is crucial for core tasks such as load flow calculation, fault location, and state estimation. Therefore, accurate parameter identification of line parameters is required.

[0003] At present, many scholars have conducted research on the identification of distribution network line parameters at home and abroad. Some scholars use synchronous measurement methods using synchronous phase angle measurement devices (Phasor Measurement Unit, PMU) or micro synchronous phase angle measurement devices (Micro Phasor Measurement Unit, μPMU) and asynchronous measurement methods using asynchronous measurement. An existing method uses the least square method to identify parameters through PMU measurement data, but PMU is expensive and has certain requirements for communication conditions. Another existing method proposes a data-driven three-phase distribution network topology and parameter identification method without phase angle information. By constructing the Jacobian matrix of line impedance parameters and phase angles, the line parameters and node voltage phase angles are obtained by iterative solution. However, as the number of system nodes increases, the dimension of the Jacobian matrix increases, the amount of calculation increases, and it is easy to have non-convergence problems: At the same time, there is still a lot of redundant data used in the current distribution network parameter identification. The introduction of these data will not bring additional information to the calculation but will increase the amount of calculation. Summary of the invention

[0004] In view of the deficiencies in the above-mentioned prior art, the present invention provides a method and system for reconstructing distribution network line parameters, which processes data by finding important feature points to reduce the amount of calculation; uses a partitioning method to reduce the dimension of the parameter matrix, thereby reducing problems such as non-convergence of iterations, ill-conditioned matrices, and numerical instability that occur during the iterative process.

[0005] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical scheme.

[0006] In a first aspect, the present invention provides a method for reconstructing line parameters of a distribution network, comprising:

[0007] Conduct actual measurement of distribution network lines, obtain actual measurement data of distribution network, and determine characteristic points of distribution network;

[0008] According to the topology of the distribution network, the line parameters are roughly estimated;

[0009] Among the characteristic points of the distribution network, determine the important characteristic points;

[0010] According to the topology of the distribution network, the distribution network is partitioned;

[0011] The line parameters of the reconstructed distribution network in each partition are calculated based on the roughly estimated line parameters and important characteristic points.

[0012] Furthermore, the measured data of the distribution network includes active power P, reactive power Q and node voltage amplitude V.

[0013] Furthermore, the method for determining the characteristic point of the distribution network is: when the voltage of a certain node has an extreme point or an inflection point, it is recorded as a characteristic point, and all node data at the current moment are recorded. The expression is:

[0014]

[0015] Among them, A C is the characteristic point set of the distribution network, a i is the voltage of the ith node in the distribution network, a i-1 is the voltage of the i-1th node in the distribution network, a i+1 is the voltage of the i+1th node in the distribution network, and n is the number of nodes in the distribution network.

[0016] Furthermore, the step of roughly estimating the line parameters includes:

[0017] According to the power flow equation in polar coordinates and the measured data of the distribution network, the conductance and susceptance of each node in the distribution network are roughly estimated, and the rough estimation formula is obtained;

[0018] The rough estimation formula is solved according to the least square method to obtain the rough estimated line parameters.

[0019] Furthermore, the rough estimation formula is expressed as:

[0020] [P / V] M =[G # ]·[V] M

[0021] [Q / V] M =-[B # ]·[V] M

[0022] Where, [P / V] M =[P1 / V1,…,P M / V M ], [Q / V] M =[Q1 / V1,…,Q M / V M ],[V]M =[V1,…,V M ], P M is the measured active power of the Mth group of distribution network, Q M is the measured reactive power of the Mth group of distribution network, V M is the measured node voltage amplitude of the Mth group of distribution network;

[0023] represents the rough estimated distribution network conductivity matrix, represents the roughly estimated distribution network susceptance matrix, G # and B # Together they serve as coarse estimates of line parameters.

[0024] Furthermore, the method for determining important feature points is: sorting the number of nodes with extreme points or inflection points at the same time, determining the feature points corresponding to the k moments with the largest number of nodes with extreme points or inflection points as the important feature point set, and each moment corresponds to an important feature point.

[0025] Furthermore, the strategies for partitioning the distribution network are: averaging the number of nodes in each zone, keeping the number of partition points as small as possible (no more than 20 nodes in each zone), and giving priority to T-type nodes.

[0026] Furthermore, the step of calculating the line parameters of the distribution network after reconstruction of each partition includes:

[0027] According to the roughly estimated line parameters and important characteristic points, Newton method iteration is performed on each partition to obtain the node parameter calculation results of each partition;

[0028] Calculate the parameter matrix of the distribution network;

[0029] The node parameter calculation results of each partition are merged, and according to the parameter matrix of the distribution network, the line parameters of the reconstructed distribution network of each partition are obtained.

[0030] In a second aspect, the present invention provides a distribution network line parameter reconstruction system, comprising:

[0031] Characteristic point determination unit: conducts actual measurement on the distribution network lines, obtains actual measurement data of the distribution network, and determines characteristic points of the distribution network;

[0032] Line parameter rough estimation unit: rough estimation of line parameters based on the topology of the distribution network;

[0033] Important feature point determination unit: determining important feature points among the feature points of the distribution network;

[0034] Partitioning unit: partition the distribution network according to the topological structure of the distribution network;

[0035] Distribution network line parameter calculation unit: Calculates the distribution network line parameters after reconstruction of each partition based on the roughly estimated line parameters and important feature points.

[0036] The present invention has the following beneficial effects:

[0037] 1. Process data by finding feature points, reducing the amount of calculation;

[0038] 2. The partitioning method is used to reduce the dimension of the parameter matrix, thereby reducing problems such as non-convergence of iterations, ill-conditioned matrices, and numerical instability that occur during the iteration process. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of the method for reconstructing the line parameters of the power distribution network of the present invention;

[0040] Figure 2 A schematic diagram of an IEEE 33 node power distribution network used in an embodiment of the present invention;

[0041] Figure 3 This is a feature point extraction result diagram in one embodiment of the present invention;

[0042] Figure 4 This is a diagram showing the extraction result of important feature points in one embodiment of the present invention;

[0043] Figure 5 This is a diagram showing the partitioning results of the IEEE 33-node power distribution network used in one embodiment of the present invention;

[0044] Figure 6 A comparison diagram of the roughly estimated distribution network branch conductance and the true value in one embodiment of the present invention;

[0045] Figure 7 A comparison diagram of the roughly estimated branch susceptance of a distribution network and the true value in one embodiment of the present invention;

[0046] Figure 8 A diagram showing the conductance and susceptance errors after rough estimation in one embodiment of the present invention;

[0047] Fig. 9 A comparison diagram of the reconstructed branch conductance and the true value in one embodiment of the present invention;

[0048] Fig.10 A comparison diagram of the reconstructed branch susceptance and the true value in one embodiment of the present invention;

[0049] Fig.11 is a diagram of the reconstructed conductance and susceptance error results in one embodiment of the present invention;

[0050] Fig.12A comparison diagram of the reconstructed phase angle and the true value in one embodiment of the present invention;

[0051] Fig.13 A diagram showing the phase angle error result after reconstruction in one embodiment of the present invention;

[0052] Fig.14 A graph showing changes in average line parameter errors during an iteration process in one embodiment of the present invention;

[0053] Fig.15 This is an architecture diagram of the distribution network line parameter reconstruction system of the present invention. DETAILED DESCRIPTION

[0054] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0055] like Figure 1 and Figure 2 As shown, in one embodiment of the present invention, taking the IEEE 33-node distribution network as an example, a distribution network line parameter reconstruction method includes:

[0056] S1, measure the distribution network lines, obtain the measured data of the distribution network, and determine the characteristic points of the distribution network;

[0057] S2, based on the topology of the distribution network, roughly estimates the line parameters;

[0058] S3, determining important characteristic points among the characteristic points of the distribution network;

[0059] S4, partitioning the distribution network according to the topological structure of the distribution network;

[0060] S5, calculating the line parameters of the distribution network after reconstruction of each partition according to the roughly estimated line parameters and important feature points.

[0061] Furthermore, in this embodiment, the measured data of the distribution network includes active power P, reactive power Q and node voltage amplitude V.

[0062] Preferably, in this embodiment, characteristic value extraction is also performed on the measured data of the distribution network, and redundant data is eliminated, so as to reduce the amount of calculation while ensuring the calculation accuracy.

[0063] Furthermore, the method for determining the characteristic point of the distribution network is: when the voltage of a certain node has an extreme point or an inflection point, it is recorded as a characteristic point, and all node data at the current moment are recorded. The expression is:

[0064]

[0065] Among them, A C is the characteristic point set of the distribution network, a i is the voltage of the ith node in the distribution network, a i-1 is the voltage of the i-1th node in the distribution network, a i+1 is the voltage of the i+1th node in the distribution network, and n is the number of nodes in the distribution network.

[0066] In this embodiment, 500 sets of measured data were obtained. After feature point extraction, more than 100 sets of redundant data were excluded and more than 300 sets of data were retained. The results are as follows: Figure 3 shown.

[0067] Furthermore, the step of roughly estimating the line parameters includes:

[0068] According to the power flow equation in polar coordinates and the measured data of the distribution network, the conductance and susceptance of each node in the distribution network are roughly estimated, and the rough estimation formula is obtained;

[0069] The rough estimation formula is solved according to the least square method to obtain the rough estimated line parameters.

[0070] The rough estimation formula is expressed as:

[0071] [P / V] M =[G # ]·[V] M

[0072] [Q / V] M =-[B # ]·[V] M

[0073] Where, [P / V] M =[P1 / V1,…,P M / V M ], [Q / V] M =[Q1 / V1,…,Q M / V M ],[V] M =[V1,…,V M ], P M is the measured active power of the Mth group of distribution network, Q M is the measured reactive power of the Mth group of distribution network, V M is the measured node voltage amplitude of the Mth group of distribution network;

[0074] represents the rough estimated distribution network conductivity matrix, represents the roughly estimated distribution network susceptance matrix, G # and B # Together they serve as coarse estimates of line parameters.

[0075] The specific steps of obtaining the rough estimation formula include:

[0076] According to the tidal flow equation in polar coordinates, we can obtain equations (2) and (3):

[0077]

[0078] When making a rough estimate, assume that the angle θ is very small and can be ignored. In this case, sinθ≈0 and cosθ≈1. Rewriting the above equations yields equations (4) and (5):

[0079] [p / v]=[G # ]·[v] (4)

[0080] [q / v]=-[B # ]·[v] (5)

[0081] Where v represents the amplitude of the node voltage, [p / v] = [p1 / v1,…,p n / v n ] T , [q / v] = [q1 / v1,…,q n / v n ] T ,[v]=[v1,…,v n ] T ;

[0082]

[0083] Assuming that there are M sets of observation data after actual measurement, the above equation becomes:

[0084] [P / V] M =[G # ]·[V] M (6)

[0085] [Q / V] M =-[B # ]·[V] M (7)

[0086] Where, [P / V] M =[P1 / V1,…,P M / V M ], [Q / V]M =[Q1 / V1,…,Q M / V M ],[V] M =[V1,…,V M ].

[0087] Since the line topology is known, that is, the positions of G and B are known, the least squares method can be used to solve equations (6) and (7), and the non-zero elements in the above G# and B# matrices are solved row by row to construct the objective function and As shown in formula (8) and formula (9):

[0088]

[0089] Where W is the connectivity matrix. When node i and node j are connected, w ij =1, otherwise w ij =0. [G # ] (i) , [B # ] (i) , W (i) Respectively represent [P / V] M , [Q / V] M , [G # ]、[B # ], the i-th row data of W.

[0090] By minimizing and To find the best [G # ] (i) and [B # ] (i) , the specific steps are as follows:

[0091] Expand the objective function:

[0092]

[0093] For the objective function and Yes [G # ] (i) and [B # ] (i) Find the reciprocal and set it equal to zero:

[0094]

[0095] After simplifying the above formula, we can get the normal equation:

[0096]

[0097] Solving the above normal equations yields:

[0098]

[0099] Repeat the above process until all [G # ] and [B # ].

[0100] Furthermore, the method for determining important feature points is: sorting the number of nodes with extreme points or inflection points at the same time, determining the feature points corresponding to the k moments with the largest number of nodes with extreme points or inflection points as the important feature point set, and each moment corresponds to an important feature point.

[0101] By extracting important feature points, the 20 data with the most obvious features are selected in descending order and substituted into the subsequent steps, such as Figure 4 shown.

[0102] The important feature points refer to specific data groups, and one point is a data group at a certain moment. The important feature points refer to the data groups with the largest number of data change nodes found on the basis of the feature points. Figure 4 In the figure, each horizontal line represents the voltage change of a node (there are 33 nodes in total, that is, 33 horizontal lines), and the horizontal axis is the number of samples. The purpose of extracting important feature points is to eliminate redundant data and reduce the amount of calculation.

[0103] Since the topology of the distribution network is known (such as Figure 2 ), according to the principle of average number of nodes in each zone, as few partition points as possible (no more than 20 nodes in each partition) and T-type node priority, node 6 is selected as the partition point. Figure 2 The distribution network in is divided into three areas, such as Figure 5 As shown, the number of nodes designed for a single calculation is greatly reduced, and the matrix dimension is reduced.

[0104] The steps of calculating the line parameters of the distribution network after reconstruction of each partition include:

[0105] According to the roughly estimated line parameters and the measured data of the distribution network, Newton's method iteration is performed on each partition to obtain the node parameter calculation results of each partition;

[0106] Calculate the parameter matrix of the distribution network;

[0107] The node parameter calculation results of each partition are merged, and according to the parameter matrix of the distribution network, the line parameters of the reconstructed distribution network of each partition are obtained.

[0108] Preferably, in this embodiment, the Jacobian matrix is ​​used as the parameter matrix, which specifically includes:

[0109] According to the power flow equation of the distribution network, g, b, and θ are used as variables of the power flow equation. Then, when p is the active power P, q is the reactive power Q, and v is the node voltage amplitude V, Newton-Raphson iteration is used to solve g, b, and θ in each zone according to the partition results. Among them, g = G # , b=B # This is the result of a rough estimate.

[0110] 1. Newton-Raphson Iteration Process

[0111] According to the Newton-Raphson iteration method, the following equation can be obtained:

[0112]

[0113] In the formula, h represents the hth partition, is the mth partition of the distribution network h The conductance and susceptance of the branch, is the mth partition of the distribution network h The active power, reactive power and node voltage amplitude of each branch.

[0114] Since the Jacobian matrix is ​​usually not a square matrix, it is impossible to directly invert it. In this case, its generalized inverse can be solved to calculate g, b, and θ. At this time, the Newton-Raphson iteration method is used to solve g, b, and θ to obtain:

[0115]

[0116] In the formula, ◇ represents the generalized inverse.

[0117] In the iterative process, the line parameters and voltage angle of each iteration can be updated using the calculation results of the above formula until [ΔP h ,ΔQ h ] T The sum of the squares is less than a certain threshold φ, meeting the convergence requirement, and the final calculation result can be obtained.

[0118]

[0119] Finally, the node parameter calculation results of each partition are combined to obtain the overall line parameters. The parameters of the partition points can be easily calculated using the overall line parameter matrix to obtain the final result.

[0120] 2. Calculation of Jacobian Matrix

[0121] According to the above Newton-Raphson iterative equation, it can be seen that the Jacobian matrix can be divided into six block matrices.

[0122]

[0123] In the block matrix of formula (21), H, I, K, L are branch block matrices; J and M are angle block matrices.

[0124] 2.1 Branch Block Matrix

[0125] Assume that branch J connects nodes i and k, and its branch conductance is g J =-G ik =-G ki , similarly, b J =-B ik =-B ki , use g J and b J Replace G ij and B ij You can get:

[0126]

[0127] In the formula, w iJ represents the connectivity between branch J and node i. When branch J is connected to node i, w iJ =1, otherwise, w iJ = 0. Represents the side connected to branch J, which is different from the other side of node i.

[0128] According to the above formula, p, q and g, b satisfy the linear relationship, and the elements of the block matrix H, I, K, L can be obtained as follows:

[0129]

[0130] 2.2 Angle Block Matrix

[0131] p, q and θ do not satisfy a linear relationship. By taking the partial derivative of θ through the power flow equation, we can get J and M:

[0132]

[0133] Finally, the sub-matrices are combined to form the Jacobian matrix.

[0134] 3. Calculate the generalized inverse of the Jacobian matrix

[0135] Unlike the classic Newton-Raphson method, the Jacobian matrix is ​​not a square matrix. For non-square matrices, they are usually not invertible because the definition of the inverse of a matrix is ​​specific to square matrices. However, in some cases, the generalized inverse matrix can be used instead of the ordinary matrix inversion operation.

[0136] In order to solve the problems of numerical instability and ill-conditioned matrix that may occur in the process of calculating the generalized inverse matrix, the present invention adopts a method combining regularization and truncated singular value decomposition to find the generalized inverse matrix. Assume that the matrix W is an m×n matrix, and its singular value decomposition is:

[0137] W=UΓV T (28)

[0138] Where U is an m×m orthogonal matrix, called a left singular matrix. Its column vectors are the left singular vectors of W. Γ is an m×n diagonal matrix, whose diagonal elements are non-negative singular values ​​and are arranged in descending order. V is an n×n orthogonal matrix, called a right singular matrix. Its column vectors are the right singular vectors of W. W T The eigenvectors of W form the V matrix, and the square roots of the eigenvalues ​​are the singular values ​​in Γ. T The eigenvectors of form the U matrix.

[0139] Select a truncation threshold τ, retain the part of the singular value greater than or equal to τ, and construct a new diagonal matrix Γ τ , where the retained singular values ​​are still arranged in descending order, and the rest are set to 0. Correspondingly, the matrices U and V will also be reduced to the same size as Γ τ The dimensions to match.

[0140] Introduce the regularization parameter λ on the remaining non-zero singular values ​​to obtain the regularized singular value matrix Γ τ,λ :

[0141] Γ τ,λ =Γ τ +λI (29)

[0142] Where I is the identity matrix and λ is the regularization parameter.

[0143] Regularized generalized inverse matrix It can be calculated by the following formula:

[0144]

[0145] In the formula, is Γ τ,λ The inverse matrix of τ,λ The inverse of the non-zero singular values ​​in The corresponding positions of , and the rest of the elements are zero.

[0146] The relationship between the obtained generalized inverse matrix and the reconstructed distribution network line parameters is shown in formula (19). According to the regularized generalized inverse matrix Iterate and you are done.

[0147] Truncation threshold τSelection: Arrange the singular values ​​in Γ in descending order and draw a curve. By observing the curve, we can find that at a certain inflection point value σ k After that, the singular value will decrease rapidly, and this inflection point is selected as the truncation threshold τ.

[0148] Selection of regularization parameter λ: Divide the selected cutoff threshold τ by 100 as the regularization parameter.

[0149] According to the above method, Figure 2 The simulation verification is carried out in the 33-node system shown in the figure, and the processed data is used for rough estimation, and the obtained results are compared with the actual values, as shown in Figure 6 , Figure 7 and Figure 8 shown.

[0150] Then, based on the rough estimate, the refined data is used to perform refined calculation of the road parameters, and the obtained results are compared with the true values, such as Fig. 9 , Fig.10 , Fig.11 , Fig.12 and Fig.13 shown.

[0151] The average error of line parameters during the iteration process changes as follows: Fig.14 As shown, according to Figure 6-14 It can be seen that after refined calculation, the accuracy of line parameters is greatly improved. Fig.14 It can be seen that after about 20 iterations, the iteration accuracy has reached the optimal level and no further iteration is required.

[0152] The present invention proposes a distribution network line parameter reconstruction method, which can iteratively solve the network line parameters only by obtaining the network topology, node power and node voltage amplitude. In view of the fact that there is a large amount of redundant data in the actual measurement data, a data processing method is proposed to eliminate the redundant data and reduce the amount of calculation. In view of the fact that the Jacobian matrix constructed by iteration will increase in dimension as the number of network nodes increases, resulting in the dimensional disaster and triggering the iterative convergence problem, a partition calculation method is adopted to solve it and the generalized inverse matrix is ​​solved in combination with regularization and truncated singular value decomposition method, thereby improving the applicability and accuracy of the distribution network line parameter reconstruction method in different distribution networks.

[0153] Another embodiment of the present invention is a distribution network line parameter reconstruction system, which is composed of a feature point determination unit, a line parameter rough estimation unit, an important feature point determination unit, a partitioning unit and a distribution network line parameter calculation unit. Fig.15 shown.

[0154] The characteristic point determination unit is configured to measure the distribution network lines, obtain the measured data of the distribution network, and determine the characteristic points of the distribution network.

[0155] The line parameter rough estimation unit is used to roughly estimate the line parameters according to the topological structure of the distribution network.

[0156] The important feature point determination unit is configured to determine important feature points among the feature points of the distribution network.

[0157] The partitioning unit partitions the distribution network according to the topological structure of the distribution network.

[0158] The distribution network line parameter calculation unit calculates the distribution network line parameters after reconstruction of each partition according to the roughly estimated line parameters and important characteristic points.

[0159] The distribution network line parameter calculation unit is specifically used to:

[0160] According to the roughly estimated line parameters and important characteristic points, Newton method iteration is performed on each partition to obtain the node parameter calculation results of each partition;

[0161] Calculate the parameter matrix of the distribution network;

[0162] The node parameter calculation results of each partition are merged, and according to the parameter matrix of the distribution network, the line parameters of the reconstructed distribution network of each partition are obtained.

[0163] It should be noted that each unit in the above-mentioned distribution network line parameter reconstruction system can be fully or partially implemented by software, hardware and their combination. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned units. For the specific definition of a distribution network line parameter reconstruction system, please refer to the definition of a distribution network line parameter reconstruction method above. The two have the same functions and effects, which will not be repeated here.

[0164] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for reconstructing distribution network line parameters, characterized in that: include: Conduct actual measurement of distribution network lines, obtain actual measurement data of distribution network, and determine characteristic points of distribution network; According to the topology of the distribution network, the line parameters are roughly estimated; Among the characteristic points of the distribution network, determine the important characteristic points; According to the topology of the distribution network, the distribution network is partitioned; The line parameters of the reconstructed distribution network in each partition are calculated based on the roughly estimated line parameters and important characteristic points.

2. The method for reconstructing distribution network line parameters according to claim 1, characterized in that: The measured data of the distribution network include active power P, reactive power Q and node voltage amplitude V.

3. The method for reconstructing distribution network line parameters according to claim 1, characterized in that: The method for determining the characteristic points of the distribution network is: when the voltage of a node reaches an extreme point or an inflection point, it is recorded as a characteristic point, and all node data at the current moment are recorded. The expression is: Among them, A C is the characteristic point set of the distribution network, a i is the voltage of the ith node in the distribution network, a i-1 is the voltage of the i-1th node in the distribution network, a i+1 is the voltage of the i+1th node in the distribution network, and n is the number of nodes in the distribution network.

4. The method for reconstructing distribution network line parameters according to claim 1, characterized in that: The steps for making a rough estimate of line parameters include: According to the power flow equation in polar coordinates and the measured data of the distribution network, the conductance and susceptance of each node in the distribution network are roughly estimated, and the rough estimation formula is obtained; The rough estimation formula is solved according to the least square method to obtain the rough estimated line parameters.

5. The method for reconstructing distribution network line parameters according to claim 4, characterized in that: The rough estimation formula is expressed as: [P / V] M =[G # ]·[V] M [Q / V] M =-[B # ]·[V] M Where, [P / V] M =[P1 / V1,…,P M / V M ], [Q / V] M =[Q1 / V1,…,Q M / V M ],[V] M =[V1,…,V M ], P M is the measured active power of the Mth group of distribution network, Q M is the measured reactive power of the Mth group of distribution network, V M is the measured node voltage amplitude of the Mth group of distribution network; represents the rough estimated distribution network conductivity matrix, represents the roughly estimated distribution network susceptance matrix, G # and B # Together they serve as coarse estimates of line parameters.

6. The method for reconstructing distribution network line parameters according to claim 1, characterized in that: The method for determining important feature points is: sort the number of nodes with extreme points or inflection points at the same time, determine the feature points corresponding to the k moments with the largest number of nodes with extreme points or inflection points as the important feature point set, and each moment corresponds to an important feature point.

7. The method for reconstructing distribution network line parameters according to claim 1, characterized in that: The strategy for partitioning the distribution network is: average the number of nodes in each zone, keep the number of partition points as small as possible, and give priority to T-type nodes.

8. The method for reconstructing distribution network line parameters according to claim 1, characterized in that: The steps of calculating the line parameters of the distribution network after reconstruction of each partition include: According to the roughly estimated line parameters and important characteristic points, Newton method iteration is performed on each partition to obtain the node parameter calculation results of each partition; Calculate the parameter matrix of the distribution network; The node parameter calculation results of each partition are merged, and according to the parameter matrix of the distribution network, the line parameters of the reconstructed distribution network of each partition are obtained.

9. A distribution network line parameter reconstruction system, characterized in that: include: Characteristic point determination unit: conducts actual measurement on the distribution network lines, obtains actual measurement data of the distribution network, and determines characteristic points of the distribution network; Line parameter rough estimation unit: rough estimation of line parameters based on the topology of the distribution network; Important feature point determination unit: determining important feature points among the feature points of the distribution network; Partitioning unit: partition the distribution network according to the topological structure of the distribution network; Distribution network line parameter calculation unit: Calculates the distribution network line parameters after reconstruction of each partition based on the roughly estimated line parameters and important feature points.

10. The distribution network line parameter reconstruction system according to claim 9, characterized in that: The distribution network line parameter calculation unit is specifically used to: According to the roughly estimated line parameters and important characteristic points, Newton method iteration is performed on each partition to obtain the node parameter calculation results of each partition; Calculate the parameter matrix of the distribution network; The node parameter calculation results of each partition are merged, and according to the parameter matrix of the distribution network, the line parameters of the reconstructed distribution network of each partition are obtained.

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