A power distribution network line parameter identification and calibration method and device, and electronic equipment

Abstract

This invention provides a method, apparatus, and electronic device for identifying and calibrating distribution network line parameters. The method includes: acquiring and parsing a CIM file of a target power grid to construct a simplified topology of electrical equipment within the CIM file; constructing node power flow equations based on the node parameters of each node in the simplified topology; constructing a first and a second modified equation based on the node power flow equations and the quasi-Newton-Raphson method; outputting the corresponding target power grid parameters when the first and second modified equations satisfy iteration conditions; constructing a power grid topology of the target power grid based on the target power grid parameters and the simplified topology to obtain high-precision line parameters; and calibrating the CIM file of the target power grid. This invention solves the problem of poor accuracy in identifying distribution network line parameters in existing related technologies.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and in particular to a method, apparatus, and electronic equipment for identifying and calibrating power distribution network line parameters. Background Technology

[0002] With the widespread integration of distributed energy resources and the dynamic evolution of complex load characteristics in distribution networks, traditional grid modeling methods based on fixed parameters face severe challenges. Inaccurate line parameters can negatively impact several key system analysis aspects of distribution networks. In power flow calculations, parameter deviations can lead to significant discrepancies between the calculated results and the actual power distribution and voltage conditions, thus affecting dispatchers' accurate control over the grid's operating status and the rational arrangement of operating modes. In fault location, inaccurate parameters can interfere with the precise determination of fault locations, prolonging fault investigation and repair time and reducing power supply reliability. Similarly, in the state estimation stage, parameter errors make it difficult to accurately reflect the actual operating state of the grid.

[0003] Chinese Patent Publication No. CN117787543A discloses a method and system for calculating distribution network line parameters based on low-voltage measurements. The method includes: acquiring low-voltage measurement data of distribution transformers and performing corresponding preprocessing; wherein, the corresponding preprocessing includes: filtering the measurement data based on adaptive Kalman filtering and moving average filtering, and handling missing values ​​in the filtered data; constructing and simplifying the distribution network topology based on the preprocessed measurement data to obtain a line-point connection model that retains only the lines, distribution transformers, and users; constructing an objective function for parameter solving based on the line-point connection model and combining it with the power conservation of distribution network line parameters, and obtaining the distribution network line parameters by solving the objective function. However, the above scheme relies only on Kalman and moving average filtering, which has poor immunity to phase angle missing values ​​and noise disturbances. Furthermore, the objective function is based only on power conservation, which is prone to ill-conditioned or singular matrices, resulting in insufficient accuracy and reliability, and insufficient accuracy in answering the question.

[0004] There is currently no effective solution to the problem of poor accuracy in identifying power distribution line parameters in existing related technologies. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for identifying and calibrating power distribution network line parameters, in order to solve the defects of poor accuracy in identifying power distribution network line parameters in existing related technologies.

[0006] In a first aspect, the present invention provides a method for identifying and calibrating parameters of a power distribution network, comprising:

[0007] Obtain and parse the CIM file of the target power grid, and construct a simplified topology of the electrical equipment in the CIM file;

[0008] Based on the node parameters of each node in the simplified topology, the node power flow equations are constructed.

[0009] Based on the nodal power flow equations and the quasi-Newton-Raphson method, a first modified equation and a second modified equation are constructed respectively. When the first modified equation and the second modified equation satisfy the iteration conditions respectively, the corresponding target power grid parameters are output.

[0010] Based on the target power grid parameters and the simplified topology, the power grid topology of the target power grid is constructed to obtain high-precision line parameters, and the CIM file of the target power grid is calibrated.

[0011] According to a method for identifying and calibrating distribution network line parameters provided by the present invention, the method acquires and parses the CIM file of the target power grid, and constructs a simplified topology of the electrical equipment in the CIM file, including:

[0012] Extract the initial equipment parameters and topology connections of each electrical device from the CIM file;

[0013] The simplified topology is generated based on the initial parameters of the electrical equipment and the topological connection relationships.

[0014] According to a method for identifying and calibrating power distribution network line parameters provided by the present invention, a simplified topology is generated based on the initial parameters of the electrical equipment and the topology connection relationship, comprising:

[0015] Extract the electrical model and connection terminals from the CIM file; the electrical model includes transmission lines, switches, busbars, transformer windings, and transformers;

[0016] Traverse all connection points in the CIM file and connect electrical models with the same connection points to form an initial topology.

[0017] The initial topology is simplified based on the type of electrical model and the connection relationships of the nodes in the initial topology to obtain the simplified topology.

[0018] According to a method for identifying and calibrating distribution network line parameters provided by the present invention, the initial topology is simplified based on the type of the electrical model and the connection relationship of the nodes in the initial topology to obtain the simplified topology, including:

[0019] Delete all switches in the initial topology and merge the nodes corresponding to the switches;

[0020] Delete the first node in the initial topology; the first node is a node that is not connected to any load and is connected to only one line.

[0021] Delete the second node in the initial topology and merge the two ends of the line corresponding to the second node; the second node is a node that only connects two lines and does not connect to a transformer.

[0022] According to the method for identifying and calibrating distribution network line parameters provided by the present invention, based on the nodal power flow equations and the quasi-Newton-Raphson method, a first modified equation and a second modified equation are constructed, respectively, including:

[0023] The transformer tap ratio, voltage phase angle, and voltage amplitude are used as solution variables, and the first modified equation and the first Jacobian matrix are constructed using the nodal power flow equation and the quasi-Newton-Raphson method.

[0024] Using conductance, susceptance, voltage phase angle, and voltage magnitude as solution variables, and through the nodal power flow equations and the quasi-Newton-Raphson method, the second modified equation and the second Jacobian matrix are constructed.

[0025] According to the present invention, a method for identifying and calibrating distribution network line parameters outputs corresponding target network parameters when the first correction equation and the second correction equation respectively satisfy the iteration conditions, including:

[0026] When the transformer tap ratio deviation, voltage amplitude deviation, and voltage phase angle deviation are all less than the first preset iteration threshold during the iteration process, the current transformer tap ratio is taken within the preset range, and the target transformer tap is output.

[0027] When the conductance deviation, susceptance deviation, voltage amplitude deviation, and voltage phase angle deviation during the iteration process are all less than the second preset iteration threshold, the current conductance and current susceptance are output as the target conductance and target susceptance.

[0028] According to the method for identifying and calibrating power distribution line parameters provided by the present invention, when the current conductance and current susceptance are output as target conductance and target susceptance, the target conductance and target susceptance are limited to the initial value range of conductance and susceptance in the initial parameters of the electrical equipment.

[0029] According to the method for identifying and calibrating distribution network line parameters provided by the present invention, the underdetermined equations in the nodal power flow equations are solved iteratively by the minimum norm and singular value decomposition method; the underdetermined equations are nodal power flow equations when the number of rows in the Jacobian matrix is ​​greater than the number of columns.

[0030] The overdetermined equations in the nodal power flow equations are solved iteratively using the least squares method and singular value decomposition method; the overdetermined equations are the nodal power flow equations when the number of rows in the Jacobian matrix is ​​less than the number of columns.

[0031] Secondly, the present invention also provides a device for identifying and calibrating parameters of a power distribution network, comprising:

[0032] The module is used to obtain and parse the CIM file of the target power grid, and construct a simplified topology of the electrical equipment in the CIM file;

[0033] The processing module is used to construct node power flow equations based on the node parameters of each node in the simplified topology.

[0034] The computation module is used to construct a first modified equation and a second modified equation based on the nodal power flow equation and the quasi-Newton-Raphson method, respectively, and output the corresponding target power grid parameters when the first modified equation and the second modified equation satisfy the iteration conditions.

[0035] The calibration module is used to construct the power grid topology of the target power grid based on the target power grid parameters and the simplified topology, obtain high-precision line parameters, and calibrate the CIM file of the target power grid.

[0036] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for identifying and calibrating power distribution line parameters as described in the first aspect above.

[0037] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying and calibrating power distribution line parameters as described in the first aspect above.

[0038] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for identifying and calibrating power distribution line parameters as described in the first aspect above.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The method for identifying and calibrating distribution network line parameters provided by this invention obtains the distribution network topology by parsing the CIM file of the target power grid, and performs node cleaning and line reduction to simplify the network model, resulting in a simplified topology that reduces network complexity and dependence on high-density measurement equipment. Then, based on the node parameters of each node in the simplified topology, nodal power flow equations are constructed. Next, data is iteratively updated based on the nodal power flow equations and the quasi-Newton-Raphson method, tracking the time-varying characteristics of line and transformer parameters in real time to ensure rapid convergence of the iteration process and obtain the target power grid parameters. This effectively resists data noise and outliers and avoids iterative divergence caused by Jacobian matrix singularities. Finally, based on the target power grid parameters and the simplified topology, a power grid topology and high-precision line parameters consistent with actual operation are obtained, significantly improving the accuracy and reliability of power flow calculation and state estimation, and solving the problem of poor accuracy in identifying distribution network line parameters in existing related technologies. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the method for identifying and calibrating power distribution network line parameters provided by the present invention;

[0043] Figure 2 This is a graph showing the average error change trend of the three power distribution networks during the iteration process in this embodiment of the invention;

[0044] Figure 3 This is a schematic diagram comparing the impedance identification result of phase A line with the initial value provided by the present invention.

[0045] Figure 4 This is a schematic diagram comparing the impedance identification results of phase B line provided by the present invention with the initial value;

[0046] Figure 5 This is a schematic diagram comparing the C-phase line impedance identification results with the initial values ​​provided by the present invention;

[0047] Figure 6 This is a structural block diagram of the power distribution line parameter identification and calibration device provided by the present invention;

[0048] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0050] This invention provides a method for identifying and calibrating parameters of power distribution network lines. Figure 1 This is a flowchart of the method for identifying and calibrating distribution network line parameters provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0051] Step S101: Obtain and parse the CIM file of the target power grid, and construct a simplified topology of the electrical equipment in the CIM file;

[0052] Step S102: Based on the node parameters of each node in the simplified topology, construct the node power flow equations;

[0053] Step S103: Based on the nodal power flow equations and the quasi-Newton-Raphson method, construct the first and second modified equations respectively. When the first and second modified equations satisfy the iteration conditions respectively, output the corresponding target power grid parameters.

[0054] Step S104: Based on the target power grid parameters and simplified topology, construct the power grid topology of the target power grid, obtain high-precision line parameters, and calibrate the CIM file of the target power grid.

[0055] Among the methods described above, the Newton-Raphson method is an iterative algorithm used to solve nonlinear equation systems, commonly employed in parameter iterative updates for distribution network parameter identification. Its core idea is to linearize the nonlinear equations using Taylor expansion, constructing a Jacobian matrix to gradually approximate the true solution. In distribution network parameter identification, this method constructs a nonlinear equation system based on the error between measured data (such as voltage and power) and the power grid model, and iteratively updates parameters (such as line resistance, reactance, and transformer ratio) to minimize the error. However, this method places certain requirements on data integrity and specific data values. On one hand, the Newton-Raphson method is sensitive to initial values; if the initial values ​​deviate significantly from the true values, it may lead to non-convergence or convergence to an incorrect solution. In some cases, improper initial value selection may even cause the algorithm to diverge. On the other hand, convergence is also a problem in the application of the Newton-Raphson method in parameter identification. Although the Newton-Raphson method theoretically possesses quadratic convergence, in practical applications, its convergence speed may be affected by factors such as the choice of initial values ​​and changes in function curvature. Meanwhile, the algorithm may fail to converge when the Jacobian matrix is ​​singular or nearly singular.

[0056] When executing the above method, the following characteristics must be met:

[0057] 1. This method is applicable to situations where low-voltage side voltage phase angle data and voltage measurement data are missing;

[0058] 2. This method has the ability to identify and dynamically correct parameters online, and it has good convergence.

[0059] 3. This method is highly robust to data noise and outliers. However, most existing methods cannot simultaneously satisfy all four characteristics.

[0060] For example, traditional methods for identifying and calibrating distribution network parameters rely on high-precision measuring devices or synchronous phase angle data, but low-voltage distribution networks generally lack such equipment, resulting in a lack of phase information and dynamic measurement data. Traditional iterative algorithms (such as the Newton-Raphson method) require multiple constructions of the Jacobian matrix and solutions to high-dimensional equations when dealing with large-scale distribution networks, leading to high computational complexity. As the number of measurement data sets increases, the iteration time increases significantly, making it difficult to meet the real-time requirements of online identification. Furthermore, the Newton-Raphson method is sensitive to initial values; if the initial settings deviate significantly from the true values ​​(e.g., the line length does not match the actual value), it can easily lead to iteration divergence or convergence to a local optimum.

[0061] For example, firstly, the CIM file of the target power grid is parsed to obtain the distribution network topology. Node cleanup and line reduction are then performed to simplify the network model, resulting in a simplified topology that reduces network complexity and reliance on high-density measurement equipment. Next, based on the node parameters of each node in the simplified topology, node power flow equations are constructed. Then, data is iteratively updated based on the node power flow equations and the quasi-Newton-Raphson method, tracking the time-varying characteristics of line and transformer parameters in real time to ensure rapid convergence of the iteration process. This yields the target power grid parameters, effectively resisting data noise and outliers, and avoiding iterative divergence caused by Jacobian matrix singularities. Finally, based on the target power grid parameters and the simplified topology, a power grid topology consistent with actual operation and high-precision line parameters are obtained, significantly improving the accuracy and reliability of power flow calculation and state estimation, and solving the problem of poor accuracy in identifying distribution network line parameters in existing related technologies.

[0062] In some embodiments, step S101, obtaining and parsing the CIM file of the target power grid, and constructing a simplified topology of the electrical equipment in the CIM file, includes: extracting the initial parameters and topology connection relationships of each electrical equipment in the CIM file; and generating a simplified topology based on the initial parameters and topology connection relationships of the electrical equipment.

[0063] In this embodiment, the CIM file provides a standard logical view of the target power grid. The power grid topology connection relationship in the CIM file is composed of two models: connection ends and connection points. The connection end model encompasses two key elements: one pointing to a specific electrical model and the other pointing to a common connection point. In the CIM file, the electrical topology connection relationship is presented as a topology structure with connection end models as edges and connection points and electrical models as vertices. To achieve high-precision parameter identification when low-voltage side voltage phase angle data and voltage measurement data are missing, the distribution network topology structure is obtained by parsing the CIM file, and node cleanup and line reduction are performed to simplify the network model and reduce network complexity. Subsequent processing only requires measurement data from key nodes, reducing the dependence on high-density measurement equipment.

[0064] Specifically, a simplified topology is generated based on the initial parameters and topological connections of the electrical equipment. This includes: extracting the electrical models and connection terminals from the CIM file; the electrical models include transmission lines, switches, busbars, transformer windings, and transformers; traversing all connection terminals in the CIM file and connecting electrical models with the same connection points to form an initial topology; and simplifying the initial topology based on the types of electrical models and the connection relationships of nodes in the initial topology to obtain a simplified topology.

[0065] More specifically, the initial topology is simplified based on the type of electrical model and the connection relationship of nodes in the initial topology to obtain a simplified topology, including: deleting all switches in the initial topology and merging the nodes corresponding to the switches; deleting the first node in the initial topology; the first node is a node that is not connected to a load and is connected to only one line; deleting the second node in the initial topology and merging the two lines corresponding to the second node; the second node is a node that is connected to only two lines and is not connected to a transformer.

[0066] In this implementation scheme, considering that the switching impedance is negligible, all switching nodes are deleted and their two endpoints are merged to improve computational efficiency, thereby simplifying the initial topology. For nodes that are not connected to a load and are only connected to one line, the line parameters cannot be identified due to a lack of voltage measurement data; therefore, such nodes and their connected lines are deleted. For nodes that are only connected to two lines and are not connected to a transformer, the line parameters cannot be identified due to a lack of voltage measurement data; therefore, such nodes are deleted and their two endpoints are merged.

[0067] Based on this, the nodal power flow equations are constructed, and the polar coordinate form of the nodal power flow equations is as follows:

[0068]

[0069] Where, Δ P i Represents a node i Active power deviation, i Indicates the node number. P i Indicates injection into the node i active power, V i Represents a node i voltage amplitude, j Represents nodes i Different nodes, n This represents the total number of nodes. V j Represents a node j voltage amplitude, G ij Represents a node i With nodes j The electrical conductance between them B ij Represents a node i With nodes j The susceptance between them θ ij This represents the voltage phase angle difference between node i and node j. This represents the reactive power deviation at node i. Qi Indicates injection into the node i The reactive power.

[0070] Active power deviation Δ P i Deviation from reactive power Essentially reflects the nodes i The difference between the actual injected power and its load demand and network losses. This can be minimized by introducing an appropriate correction mechanism. P i and This can effectively improve the accuracy of power flow calculation and enhance the reliability and stability of system scheduling. Therefore, this problem can be reduced to a numerical optimization problem with the objective of minimizing the deviation between active and reactive power.

[0071] In some embodiments, step S103, based on the nodal power flow equations and the quasi-Newton-Raphson method, constructs a first modified equation and a second modified equation, respectively, including: using the transformer tap ratio, voltage phase angle and voltage amplitude as solution variables, and constructing the first modified equation and the first Jacobian matrix through the nodal power flow equations and the quasi-Newton-Raphson method.

[0072] Specifically, in this step, the power grid topology information in the simplified topology is read and k The measurement data were analyzed using the quasi-Newton-Raphson method with the transformer tap ratio. t Voltage phase angle θ and voltage amplitude V To solve for the variables, the first corrected equation is obtained, and the transformer tap ratio is iteratively updated when... , , The maximum value is less than The iteration is considered complete when the result is set to 10⁻⁷ (typically 10⁻⁷). To match the actual operating conditions of the transformer, the closest value obtained from the iteration is found within the range of 1 ± (5 × 1.25%) and used as the tap ratio of the transformer.

[0073] The expression for the first corrected equation is as follows:

[0074]

[0075] in, k Indicates the number of the measurement data. i Taking 1, 2, and 3 respectively represents taking the data for phases A, B, and C; Indicates the first i Xiang, with the first k The active power deviation is calculated during the iterative process of using the group of measurement data as observations. This indicates the reactive power deviation under the same conditions; This indicates the deviation in the tap ratio between the high and low voltage sides of the transformer during the iteration process; Indicates the first i Xiang, with the first k The voltage phase angle deviation was calculated based on the group of measurement data. This indicates the deviation of the voltage amplitude during the corresponding iteration process. The aforementioned deviation reflects the degree of correction of each physical quantity during the state estimation or power flow calculation iteration process, and is of great significance for evaluating the consistency between the system state and the observed data.

[0076] First Jacobian Matrix The expression is:

[0077]

[0078] in, Indicates the first i The phase transformer tap ratio, Indicates the first i Xiang, with the first k The node voltage phase angle during the iteration process when the set of data is used as the measurement value. Indicates the first i Xiang, with the first k The node voltage amplitude during the iterative process when the set of data is used as the measurement value.

[0079] The conductivity, susceptance, voltage phase angle, and voltage magnitude are used as solution variables. The second modified equation and the second Jacobian matrix are constructed by using nodal power flow equations and the quasi-Newton-Raphson method.

[0080] Specifically, in this step, the conductivity is measured using the quasi-Newton-Raphson method. g susceptance b Voltage phase angle θ and voltage amplitude V To solve for the variables and obtain the second corrected equation, the conductance and susceptance of the line and transformer, as well as the phase angle and magnitude of the node voltage, are iteratively updated. To avoid abnormal situations such as negative impedance values ​​in the iteration results, the conductance needs to be updated in each iteration. g and susceptance b To keep the value within a certain range from the initial value, the specific formula is as follows:

[0081]

[0082] in, Indicates the first iteration during the iteration process i Conductivity during phase conductance iteration Indicates the first iteration during the iteration process i The deviation value of phase susceptance. The second Jacobian matrix. The expression is as follows:

[0083]

[0084] in, Indicates the first iteration during the iteration process i Phase conductivity, Indicates the first iteration during the iteration process i The susceptance of the phase.

[0085] In this embodiment, when the first and second modified equations satisfy the iteration conditions respectively, the corresponding target grid parameters are output, including: when the transformer tap ratio deviation, voltage amplitude deviation, and voltage phase angle deviation during the iteration process are all less than the first preset iteration threshold, the current transformer tap ratio is taken within the preset value range, and the target transformer tap is output; when the conductance deviation, susceptance deviation, voltage amplitude deviation, and voltage phase angle deviation during the iteration process are all less than the second preset iteration threshold, the current conductance and current susceptance are output as the target conductance and target susceptance.

[0086] Specifically, when the current conductance and current susceptance are output as the target conductance and target susceptance, the target conductance and target susceptance are limited to the initial values ​​of conductance and susceptance in the initial parameters of the electrical equipment.

[0087] To achieve online parameter identification and dynamic correction capabilities with good convergence, reasonable initial values ​​were set using asset data, reducing initial value sensitivity and improving iteration stability. Based on the quasi-Newton-Raphson method, the conductance and susceptance parameters of the line and transformer were iteratively updated using multiple sets of low-voltage side measurement data to adapt to scenarios where parameters change over time.

[0088] In this step, a dual iterative threshold method using the transformer tap ratio and line parameters ensures that the corresponding target parameters are output only when their respective deviations are less than the preset threshold, avoiding premature or excessive iteration. The transformer tap ratio is locked first, followed by the output of conductance and susceptance, achieving inherent coupling and decoupling in parameter identification and improving convergence speed and stability. For underdetermined equations (number of rows in the Jacobian matrix > number of columns), minimum norm and Singular Value Decomposition (SVD) are used to ensure the minimum norm and least bias in the solution vectors. For overdetermined equations (number of rows in the Jacobian matrix < number of columns), least squares and SVD are used to optimize the sum of squared residuals, balancing overall fitting accuracy and local robustness. All output target conductance and susceptance are strictly limited to the initial values ​​of the equipment, avoiding parameter out-of-bounds errors caused by noise or model errors during iteration. Combined with initial constraints from equipment manufacturers or design data, these parameters can be directly used for online state estimation, fault location, etc., without secondary verification. The combined use of hierarchical threshold control and singular value decomposition significantly reduces the risk of iterative divergence when the condition number of the Jacobian matrix is ​​high. By selecting values ​​within a preset range and using multiple threshold judgments, the number of invalid iteration steps is reduced, accelerating the overall convergence process. The thresholding and hierarchical iteration logic gives the system higher self-healing and disturbance resistance capabilities against sudden measurement loss or outliers.

[0089] Furthermore, for the underdetermined equations in the nodal power flow equations, the least norm and singular value decomposition methods are used for iterative solutions; the underdetermined equations are the nodal power flow equations when the number of rows in the Jacobian matrix is ​​greater than the number of columns. For the overdetermined equations in the nodal power flow equations, the least squares method and singular value decomposition method are used for iterative solutions; the overdetermined equations are the nodal power flow equations when the number of rows in the Jacobian matrix is ​​less than the number of columns.

[0090] To reduce dependence on initial values ​​and enhance robustness to noise and outliers, the singular value decomposition method is used to solve for the minimum norm solution or the least squares solution, ensuring numerical stability. This method is suitable for scenarios with insufficient or redundant data in actual power distribution networks.

[0091] Based on the above embodiments, the CIM file is input and parsed to extract model parameters and connection relationships of electrical equipment (lines, switches, transformers, etc.). Initial equipment parameters (resistance, reactance, etc.) and a topology connection table are output. A topology structure is generated based on the initial equipment parameters and the topology connection table. The topology network structure is optimized by deleting switch nodes, cleaning up end nodes, and merging redundant nodes. The initial conductance from the initial parameters is utilized. g ij susceptance b ij Using measured data (balance node voltage, measured node power), the Jacobian matrix is ​​constructed by substituting it into the nodal power flow equations, and the initial voltage phase angle is solved based on the singular value decomposition method. θAnd the voltage amplitude V of the unmeasured nodes. Read the power grid topology information and k sets of measurement data, substitute them into the first correction equation, and iteratively update the transformer tap ratio. , , The maximum value is less than The iteration is considered complete when the result is typically set to 10⁻⁷. To reflect actual transformer operating conditions, the closest value obtained from the iteration is found within the range of 1 ± (5 × 1.25%) as the transformer tap ratio. The latest iterative data is substituted into the second correction equation, and the conductance and susceptance of the line and transformer, as well as the phase angle and amplitude of the node voltage, are iteratively updated using the singular value decomposition method. This iterative update of the line and transformer parameters ultimately converges to a uniquely reasonable three-phase distribution network topology and high-precision line parameters.

[0092] In this embodiment, the distribution network topology is obtained by parsing the CIM file, and node cleaning and line reduction are performed to simplify the network model, reduce network complexity, and reduce dependence on high-density measurement equipment. Furthermore, it eliminates the need for low-voltage side voltage phase angle or complete voltage measurement, requiring only a small amount of key node data to complete parameter identification. Simultaneously, based on the quasi-Newton-Raphson method, multiple sets of measurement data are iteratively updated, which can track the time-varying characteristics of line and transformer parameters in real time, ensuring rapid convergence of the iteration process, effectively resisting data noise and outliers, and avoiding iteration divergence caused by Jacobian matrix singularities. Ultimately, a power grid topology and high-precision line parameters that match actual operation are obtained, significantly improving the accuracy and reliability of power flow calculation and state estimation.

[0093] To verify the effectiveness of the above method, a test analysis was conducted using a real 10kV distribution network in a certain region. In this example, the distribution network CIM file was first parsed, and the switches and lines were reduced in size. The parameters of the line and transformer models in the CIM file were used as the initial values ​​for parameter correction. Load data was obtained by uploading three-phase voltage and power measurement data, totaling 288 sets, every 15 minutes within 24 hours from the smart meters on the low-voltage side of the transformer.

[0094] The distribution network topology after CIM file parsing and reduction has 91 nodes, 58 lines, and 32 transformers.

[0095] This test case includes 91 nodes, 58 lines, and 32 transformers. The total number of nodes is... n =91, Number of measurement nodes m =33 (including 32 measurement points on the low-voltage side of 10kV transformers and 1 measurement point on the low-voltage side of 35kV transformers). Based on the Jacobian matrix... J Analysis of the relationship between the number of rows and columns reveals that the Jacobian matrix... JThe number of rows is always less than the number of columns, which indicates that in the actual solution process of this example, as k increases, the system of equations of the second correction equation is always underdetermined.

[0096] To analyze the numerical stability of the proposed method during the iterative process, the average error of each iteration of the three-phase distribution network is calculated, and the iterative conductance is determined. g and susceptance b The trend of the average error change during the process is as follows: Figure 2 As shown, Figure 2 This is a graph showing the trend of the average error of the three power distribution networks during the iteration process in this embodiment of the invention. It can be seen that the error tends to stabilize as the number of iterations increases.

[0097] like Figures 3 to 5 As shown, Figure 3 This is a schematic diagram comparing the impedance identification results of phase A line provided by the present invention with the initial value. Figure 4 This is a schematic diagram comparing the impedance identification results of phase B line provided by the present invention with the initial value. Figure 5 This is a schematic diagram comparing the C-phase line impedance identification results with the initial values ​​provided by this invention. To determine the rationality of the parameter identification results, the parameter identification results after 50 iterations are compared with the initial values ​​set according to the model.

[0098] The ratios of the impedance identification results to the initial values ​​for phase A lines are concentrated between 0.4 and 0.6, indicating that the initially given resistance parameters are generally too high and were significantly reduced to 40%–60% of the initial values ​​after the first identification. Near serial number 1, there is a peak value of approximately 1.2, indicating that the initial parameters for this line are too low and need to be increased by about 20%. There is a set of high values ​​around 1.5 near serial numbers ~25; and near serial number ~43, it even approaches 1.9, almost double the initial value. It is evident that the initial asset database parameters differ significantly from the actual field conditions, with most lines having initial values ​​that are too high and a few that are too low.

[0099] The distribution of the ratio of the impedance identification results to the initial values ​​for phase B lines has shifted upwards, with more lines showing ratios concentrated between 0.8 and 1.5. Compared to the downward trend of 0.4 to 0.6 in phase A, the ratios for most lines have rebounded or are slightly above 1. The peak value at position 7 is approximately 2.4, almost 2.4 times the initial resistance; positions 28 and 43 also show high values ​​of approximately 1.9 times and 2.0 times, respectively. Meanwhile, the ratios for several lines (such as positions 3, 15, and 25) have dropped below 0.5. Some lines (especially extreme value lines) may have oscillated repeatedly during iterations due to measurement noise, model simplification errors, or severely inaccurate initial topology / parameters; other lines have basically converged to the range of 1 ± 10%, indicating that the parameter calibration for them has tended to stabilize.

[0100] The ratios of impedance identification results to initial values ​​for phase C lines mostly fall within the range of 0.9–1.6, showing an overall increase compared to phase A (0.4–0.6) and phase B (0.8–1.5), and further converging within the range of 1 ± 50%. The trend indicates that the iteration is converging towards the initial value (ratio = 1), but still tends towards upward adjustment. A few lines remain significantly higher; several lines (such as near serial number 43) show ratios close to the lower limit of 0.5, indicating possible over-adjustment. Compared to phase B, extremely high values ​​have decreased slightly (only 1–2 lines > 2.0), but those below 0.8 have not been completely eliminated, and the overall variance has slightly decreased. Iteration for phase C lines has been able to bring the ratios closer to 1 for most lines.

[0101] Furthermore, this method can simultaneously satisfy three characteristics:

[0102] 1. This method is applicable to situations where low-voltage side voltage phase angle data and voltage measurement data are missing;

[0103] 2. This method has the ability to identify and dynamically correct parameters online, and it has good convergence.

[0104] 3. This method is highly robust to data noise and outliers.

[0105] To achieve high-precision parameter identification in situations where low-voltage side voltage phase angle and voltage measurement data are missing, this invention obtains the distribution network topology by parsing CIM files, and performs node cleanup and line reduction to simplify the network model and reduce network complexity. Subsequent processing only requires measurement data from key nodes, reducing reliance on high-density measurement equipment. To achieve online parameter identification and dynamic correction capabilities with good convergence, this method sets reasonable initial values ​​using asset data, reducing initial value sensitivity and improving iterative stability. Based on the quasi-Newton-Raphson method, it iteratively updates the conductance and susceptance parameters of lines and transformers using multiple sets of low-voltage side measurement data, adapting to scenarios where parameters change over time. To reduce dependence on initial values ​​and enhance robustness to noise and outliers, this invention uses singular value decomposition to solve for the minimum norm solution or least squares solution, ensuring numerical stability and making it suitable for scenarios with insufficient or redundant data in actual distribution networks.

[0106] In summary, this method simplifies the network model by parsing CIM files to obtain the distribution network topology, and performs node cleanup and line reduction, thereby reducing network complexity and dependence on high-density measurement equipment. Furthermore, it eliminates the need for low-voltage side voltage phase angle or complete voltage measurements, requiring only a small amount of key node data for parameter identification. Simultaneously, based on the quasi-Newton-Raphson method, it iterative updates using multiple sets of measurement data enable real-time tracking of the time-varying characteristics of line and transformer parameters, ensuring rapid convergence of the iteration process, effectively resisting data noise and outliers, and avoiding iterative divergence caused by Jacobian matrix singularities. Ultimately, it obtains a power grid topology and high-precision line parameters consistent with actual operation, significantly improving the accuracy and reliability of power flow calculation and state estimation.

[0107] Furthermore, this method employs dual iterative thresholds for the transformer tap ratio and line parameters to ensure that the corresponding target parameters are only output when their respective deviations are less than the preset thresholds, avoiding premature or excessive iteration. By first locking the transformer tap ratio and then outputting conductance and susceptance, the inherent coupling and decoupling of parameter identification is achieved, improving convergence speed and stability. Moreover, all output target conductance and susceptance are strictly limited to the initial value range of the equipment, avoiding parameter out-of-bounds errors caused by noise or model errors during iteration. At the same time, the combination of hierarchical threshold control and SVD solution significantly reduces the risk of iteration divergence when the condition number of the Jacobian matrix is ​​high, giving the system higher self-healing and anti-disturbance capabilities against sudden measurement loss or outliers.

[0108] The present invention also provides a device for identifying and calibrating distribution network line parameters. The device for identifying and calibrating distribution network line parameters provided by the present invention will be described below. The device for identifying and calibrating distribution network line parameters described below can be referred to in correspondence with the method for identifying and calibrating distribution network line parameters described above. Figure 6 This is a structural block diagram of the power distribution network line parameter identification and calibration device provided by the present invention, as shown in the figure. Figure 6 As shown, the device includes:

[0109] Module 601 is used to obtain and parse the CIM file of the target power grid and construct a simplified topology of the electrical equipment in the CIM file;

[0110] Processing module 602 is used to construct node power flow equations based on the node parameters of each node in the simplified topology.

[0111] The computation module 603 is used to construct the first modified equation and the second modified equation based on the nodal power flow equation and the quasi-Newton-Raphson method, respectively, and output the corresponding target power grid parameters when the first modified equation and the second modified equation satisfy the iteration conditions.

[0112] The calibration module 604 is used to construct the power grid topology of the target power grid based on the target power grid parameters and the simplified topology, obtain high-precision line parameters, and calibrate the CIM file of the target power grid.

[0113] In operation, this device first employs a construction module 601 to parse the CIM file of the target power grid, obtain the distribution network topology, and perform node cleanup and line reduction to simplify the network model, resulting in a simplified topology that reduces network complexity and reliance on high-density measurement equipment. Next, the processing module 602 constructs nodal power flow equations based on the node parameters of each node in the simplified topology. The computation module 603 then iteratively updates the data based on the nodal power flow equations and the quasi-Newton-Raphson method, tracking the time-varying characteristics of line and transformer parameters in real time to ensure rapid convergence of the iteration process, obtaining the target power grid parameters, effectively resisting data noise and outliers, and avoiding iterative divergence caused by Jacobian matrix singularities. Finally, the calibration module 604, based on the target power grid parameters and the simplified topology, ultimately obtains a power grid topology and high-precision line parameters that match actual operation, significantly improving the accuracy and reliability of power flow calculation and state estimation, and solving the problem of poor accuracy in identifying distribution network line parameters in existing related technologies.

[0114] This invention provides an electronic device, Figure 7 This is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include: at least one processor 701, at least one network interface 704, a user interface 703, a memory 705, and at least one communication bus 702. The electronic device is used to perform a method for identifying and calibrating distribution network line parameters, the method including:

[0115] Obtain and parse the CIM file of the target power grid, and construct a simplified topology of the electrical equipment in the CIM file;

[0116] Based on the node parameters of each node in the simplified topology, the node power flow equations are constructed.

[0117] Based on the nodal power flow equations and the quasi-Newton-Raphson method, a first modified equation and a second modified equation are constructed respectively. When the first modified equation and the second modified equation satisfy the iteration conditions respectively, the corresponding target power grid parameters are output.

[0118] Based on the target power grid parameters and simplified topology, the power grid topology of the target power grid is constructed to obtain high-precision line parameters, and the CIM file of the target power grid is calibrated.

[0119] The communication bus 702 is used to enable communication between these components.

[0120] The user interface 703 may include a display screen and a camera. Optionally, the user interface 703 may also include a standard wired interface and a wireless interface.

[0121] The network interface 704 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0122] The processor 701 may include one or more processing cores. The processor 701 connects to various parts of the server using various interfaces and lines, and performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 705, and by calling data stored in the memory 705.

[0123] Optionally, the processor 701 can be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 701 can integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem can also be implemented as a separate chip without being integrated into the processor 701.

[0124] The memory 705 may include random access memory (RAM) or read-only memory. Optionally, the memory 705 may include non-transitory computer-readable storage medium. The memory 705 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 705 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 705 may also be at least one storage device located remotely from the aforementioned processor 701. Figure 7 As shown, the memory 705, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a hybrid drive identification and calibration method based on three-phase power distribution network line parameters.

[0125] exist Figure 7 In the electronic device 2 shown, the user interface 703 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 701 can be used to call the application program stored in the memory 705, which is a hybrid drive identification and calibration method based on three-phase power distribution network line parameters. When executed by one or more processors, the electronic device performs one or more methods as described in the above embodiments.

[0126] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the identification and calibration method for power distribution line parameters provided by the above methods, the method comprising:

[0127] Obtain and parse the CIM file of the target power grid, and construct a simplified topology of the electrical equipment in the CIM file;

[0128] Based on the node parameters of each node in the simplified topology, the node power flow equations are constructed.

[0129] Based on the nodal power flow equations and the quasi-Newton-Raphson method, a first modified equation and a second modified equation are constructed respectively. When the first modified equation and the second modified equation satisfy the iteration conditions respectively, the corresponding target power grid parameters are output.

[0130] Based on the target power grid parameters and simplified topology, the power grid topology of the target power grid is constructed to obtain high-precision line parameters, and the CIM file of the target power grid is calibrated.

[0131] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for identifying and calibrating power distribution line parameters provided by the methods described above, the method comprising:

[0132] Obtain and parse the CIM file of the target power grid, and construct a simplified topology of the electrical equipment in the CIM file;

[0133] Based on the node parameters of each node in the simplified topology, the node power flow equations are constructed.

[0134] Based on the nodal power flow equations and the quasi-Newton-Raphson method, a first modified equation and a second modified equation are constructed respectively. When the first modified equation and the second modified equation satisfy the iteration conditions respectively, the corresponding target power grid parameters are output.

[0135] Based on the target power grid parameters and simplified topology, the power grid topology of the target power grid is constructed to obtain high-precision line parameters, and the CIM file of the target power grid is calibrated.

[0136] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying and calibrating parameters of a power distribution network line, characterized in that, include: Obtain and parse the CIM file of the target power grid, and construct a simplified topology of the electrical equipment in the CIM file; Based on the node parameters of each node in the simplified topology, the node power flow equations are constructed. Based on the nodal power flow equations and the quasi-Newton-Raphson method, a first modified equation and a second modified equation are constructed respectively. When the first modified equation and the second modified equation satisfy the iteration conditions respectively, the corresponding target power grid parameters are output. Based on the target power grid parameters and the simplified topology, the power grid topology of the target power grid is constructed to obtain high-precision line parameters, and the CIM file of the target power grid is calibrated. Based on the aforementioned nodal power flow equations and the quasi-Newton-Raphson method, a first modified equation and a second modified equation are constructed, including: The transformer tap ratio, voltage phase angle, and voltage amplitude are used as solution variables, and the first modified equation and the first Jacobian matrix are constructed using the nodal power flow equation and the quasi-Newton-Raphson method. The second modified equation and the second Jacobian matrix are constructed by using the conductance, susceptance, voltage phase angle and voltage magnitude as solution variables, and by using the nodal power flow equation and the quasi-Newton-Raphson method. When the first modified equation and the second modified equation respectively satisfy the iteration conditions, the corresponding target power grid parameters are output, including: When the transformer tap ratio deviation, voltage amplitude deviation, and voltage phase angle deviation are all less than the first preset iteration threshold during the iteration process, the current transformer tap ratio is taken within the preset range, and the target transformer tap is output. When the conductance deviation, susceptance deviation, voltage amplitude deviation, and voltage phase angle deviation during the iteration process are all less than the second preset iteration threshold, the current conductance and current susceptance are output as the target conductance and target susceptance.

2. The method for identifying and calibrating distribution network line parameters according to claim 1, characterized in that, Obtain and parse the CIM file of the target power grid, and construct a simplified topology of the electrical equipment in the CIM file, including: Extract the initial equipment parameters and topology connections of each electrical device from the CIM file; The simplified topology is generated based on the initial parameters of the electrical equipment and the topological connection relationships.

3. The method for identifying and calibrating distribution network line parameters according to claim 2, characterized in that, The simplified topology is generated based on the initial parameters of the electrical equipment and the topological connection relationships, including: Extract the electrical model and connection terminals from the CIM file; the electrical model includes transmission lines, switches, busbars, transformer windings, and transformers; Traverse all connection points in the CIM file and connect electrical models with the same connection points to form an initial topology. The initial topology is simplified based on the type of electrical model and the connection relationships of the nodes in the initial topology to obtain the simplified topology.

4. The method for identifying and calibrating distribution network line parameters according to claim 3, characterized in that, The initial topology is simplified based on the type of electrical model and the connection relationships of nodes in the initial topology to obtain the simplified topology, including: Delete all switches in the initial topology and merge the nodes corresponding to the switches; Delete the first node in the initial topology; the first node is a node that is not connected to any load and is connected to only one line. Delete the second node in the initial topology and merge the two ends of the line corresponding to the second node; the second node is a node that only connects two lines and does not connect to a transformer.

5. The method for identifying and calibrating distribution network line parameters according to claim 2, characterized in that, When the current conductance and current susceptance are output as target conductance and target susceptance, the target conductance and target susceptance are limited to the initial values ​​of conductance and susceptance in the initial parameters of the electrical equipment.

6. The method for identifying and calibrating distribution network line parameters according to claim 2, characterized in that, The underdetermined equations in the nodal power flow equations are solved iteratively using the minimum norm and singular value decomposition method; the underdetermined equations are the nodal power flow equations when the number of rows in the Jacobian matrix is ​​greater than the number of columns. The overdetermined equations in the nodal power flow equations are solved iteratively using the least squares method and singular value decomposition method; the overdetermined equations are the nodal power flow equations when the number of rows in the Jacobian matrix is ​​less than the number of columns.

7. A device for identifying and calibrating parameters of a power distribution network line, characterized in that, include: The module is used to obtain and parse the CIM file of the target power grid, and construct a simplified topology of the electrical equipment in the CIM file; The processing module is used to construct node power flow equations based on the node parameters of each node in the simplified topology. The computation module is used to construct a first modified equation and a second modified equation based on the nodal power flow equation and the quasi-Newton-Raphson method, respectively, and output the corresponding target power grid parameters when the first modified equation and the second modified equation satisfy the iteration conditions. The calibration module is used to construct the power grid topology of the target power grid based on the target power grid parameters and the simplified topology, obtain high-precision line parameters, and calibrate the CIM file of the target power grid. Based on the aforementioned nodal power flow equations and the quasi-Newton-Raphson method, a first modified equation and a second modified equation are constructed, including: The transformer tap ratio, voltage phase angle, and voltage amplitude are used as solution variables, and the first modified equation and the first Jacobian matrix are constructed using the nodal power flow equation and the quasi-Newton-Raphson method. The second modified equation and the second Jacobian matrix are constructed by using the conductance, susceptance, voltage phase angle and voltage magnitude as solution variables, and by using the nodal power flow equation and the quasi-Newton-Raphson method. When the first modified equation and the second modified equation respectively satisfy the iteration conditions, the corresponding target power grid parameters are output, including: When the transformer tap ratio deviation, voltage amplitude deviation, and voltage phase angle deviation are all less than the first preset iteration threshold during the iteration process, the current transformer tap ratio is taken within the preset range, and the target transformer tap is output. When the conductance deviation, susceptance deviation, voltage amplitude deviation, and voltage phase angle deviation during the iteration process are all less than the second preset iteration threshold, the current conductance and current susceptance are output as the target conductance and target susceptance.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for identifying and calibrating power distribution line parameters as described in any one of claims 1 to 6.

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