Optimal configuration method of measuring device generated based on 10kv radial power distribution network parameter matrix

Abstract

The invention discloses an optimal configuration method of a measuring device generated based on a parameter matrix of a 10kv radial power distribution network, which comprises the following steps of: 1, generating a node parameter table of the 10kv radial power distribution network and a branch parameter table of the 10kv radial power distribution network by using original data of the 10kv radial power distribution network; 2, converting the node parameter table for generating the 10kv radial power distribution network and the branch parameter table for generating the 10kv radial power distribution network into a node parameter matrix for generating the 10kv radial power distribution network and a branch parameter matrix for generating the 10kv radial power distribution network, and generating a corresponding topological graph; and 3, taking the maximum node vulnerability, the minimum number of measurement devices and the minimum voltage amplitude width of the 10kv radial power distribution network as objective functions, and constructing a complete observability constraint, a budget constraint and a key node priority constraint so as to establish a multi-objective measurement configuration model. According to the method, the original data can be accurately converted into the required matrix, and the relation of each objective function is balanced, so that the optimal configuration of the measuring device can be realized.

Description

Technical Field

[0001] This invention belongs to the field of measurement device optimization configuration, specifically a method for optimizing the configuration of measurement devices based on the generation of a 10kV radial distribution network parameter matrix. Background Technology

[0002] In traditional measurement device optimization configuration processes, the selection of measurement points typically relies on manual experience, lacking systematic mathematical modeling and optimization methods. Due to the complexity of distribution network structures, numerous nodes, and the high cost of measurement devices, minimizing the number of measurement devices while ensuring parameter identification accuracy has become a key challenge. Existing technologies often employ a uniform distribution strategy, failing to adequately consider the priority of key nodes in the distribution network, the redundancy of measurement data, and the merging of some tower data, resulting in insufficient economic efficiency or limited parameter estimation accuracy. Furthermore, traditional parameter matrix generation methods are highly sensitive to errors in the measurement components, potentially affecting the accuracy and stability of the matrix when measurement data is limited. Therefore, there is an urgent need for a measurement device configuration method based on parameter matrix generation that can improve the accuracy of distribution network state estimation while reducing economic costs and ensuring the priority configuration of key nodes, providing more reliable support for the optimized operation of the distribution network. Summary of the Invention

[0003] This invention addresses the shortcomings of existing technologies by proposing an optimized configuration method for measurement devices based on the generation of a 10kV radial distribution network parameter matrix. The aim is to achieve optimal placement of measurement devices through a combination of mathematical optimization models and algorithms. This will improve the accuracy of distribution network state estimation while reducing economic costs and ensuring priority configuration of key nodes, thus providing more efficient technical support for the intelligent operation and management of distribution networks.

[0004] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: The method for optimizing the configuration of a measuring device based on the generation of a 10kV radial distribution network parameter matrix, as described in this invention, is characterized by the following steps: Step 1: Based on the original data of the 10kV radial distribution network, generate the line parameter table and the tower parameter table of the 10kV radial distribution network. Step 2: Sort the parameters in each row of the line parameter table of the 10kV radial distribution network to obtain the final sorted line parameter table and the final sorted tower parameter table of the 10kV radial distribution network. Step 3: Number the line parameters of the 10kV radial distribution network in ascending order after final sorting, so as to obtain the numbered line parameter table of the 10kV radial distribution network. Step 4: Number the tower parameters after the final sorting of the 10kV radial distribution network in ascending order to obtain the tower parameter table after numbering the 10kV radial distribution network. Step 5: Process the abnormal or missing data in the line parameter table and tower parameter table after numbering the 10kV radial distribution network to obtain the corrected line parameter table and tower parameter table for the 10kV radial distribution network. Step 6: Merge the towers in the corrected tower parameter table of the 10kV radial distribution network where the active power or reactive power consumed by the load is 0, to obtain a new 10kV radial distribution network node parameter table and a new 10kV radial distribution network branch parameter table. Step 7: Convert the new 10kV radial distribution network node parameter table and the new 10kV radial distribution network branch parameter table into a 10kV radial distribution network node parameter matrix A and a 10kV radial distribution network branch parameter matrix B. Step 8: Generate the line topology of the 10kV radial distribution network based on the branch parameter matrix B of the 10kV radial distribution network. Step 9: Using the maximum node vulnerability, the maximum number of measuring devices, and the minimum voltage amplitude width of the 10kV radial distribution network as objective functions, and constructing complete observability constraints, budget constraints, and key node priority constraints, a multi-objective measurement configuration model is established and solved to obtain the optimal configuration scheme of the measuring devices.

[0005] The method for optimizing the configuration of a measuring device based on the generation of a 10kV radial distribution network parameter matrix, as described in this invention, is also characterized in that step nine includes: Step 9.1: Establish an assessment index system for the vulnerability of distribution network nodes, calculate the comprehensive weight of each node to obtain the vulnerability of each node, and establish the first objective function. ; Step 9.2: Based on the parameter information of the node parameter matrix A and the branch parameter matrix B, obtain the voltage amplitude width of each node through the interval state estimation method, and establish a second objective function. ; Step 9.3: Construct the third objective function using equation (13) : (13) Step 9.4: Use equation (14) to establish constraints for the optimal configuration of the measuring device; (14) In equation (14), C The maximum number of devices that can be configured is set.K These are the top m nodes with the highest vulnerability. Indicates the first i Each node, It is the kth node with the highest vulnerability.

[0006] Step 9.5: After establishing and solving the multi-target measurement configuration model using equation (15), the optimal configuration scheme of the measurement device is obtained: (15) In equation (15), , , This indicates 3 weights.

[0007] Furthermore, step 9.1 includes: Step 9.1.1: Based on the structural topology of the 10kV radial distribution network, establish the vulnerability index matrix of the 10kV radial distribution network. ,in, x it For the first i The node of the first t One vulnerability indicator; n This represents the total number of nodes in a 10kV radial distribution network. Step 9.1.2: Obtain the first step based on the improved entropy weight method. t Objective weights of each vulnerability index ; Step 9.1.3: Use the Analytic Hierarchy Process (AHP) to obtain the... t Subjective weights of each vulnerability index ; Step 9.1.4: Use equation (7) to obtain the first... t The combined weight of each vulnerability index ; (7) Step 9.1.5: Use equation (8) to obtain the vulnerability of each node in the 10kV radial distribution network. And obtain the first objective function. ; (8) (9) In equation (9): n The total number of nodes in a 10kV radial distribution network; x i Indicates the first i Whether the node is equipped with a measuring device, if the node is... i If a measuring device is installed at each node, then... Otherwise, let ; Furthermore, step 9.1.2 includes: Step 9.1.2.1: For x it Dimensionless processing is performed to obtain the preprocessed first dimension. i The node of the first t Vulnerability Indicators r it : Step 9.1.2.2: Calculate the first step using equation (1). i The node of the first t Vulnerability Indicators x it Characteristic proportion h it : (1) Step 9.1.2.3: Calculate the first step using equation (2). t Entropy value of each vulnerability index e t : (2) Step 9.1.2.4: Calculate the first step using equation (3). t Entropy weights of each vulnerability index w 1t : (3) Step 9.1.2.5: Calculate the first step using equation (4). t Variance of each vulnerability index f t : (4) In equation (7): It is the first of all nodes t The mean of each vulnerability index, It is the first i The node of the first t A vulnerability index, n This represents the total number of nodes in a 10kV radial distribution network. Step 9.1.2.6: Calculate the first step using equation (5). t Variance weights of each vulnerability index w 2t : (5) Step 9.1.2.7: Calculate the first step using equation (6). t Objective weights of each vulnerability index : (6) Furthermore, step 9.2 includes: Step 9.2.1: Based on the branch parameter matrix B, obtain the first... i The node and the first j Inter-node impedance parameters Based on the node parameter matrix A, the first... i Inter-node injected active power , No. i Inter-node reactive power injection Passing the exam i Initial voltage range of each node ,in: For the first i The node and the first j Minimum resistance between nodes For the first i The node and the first j The maximum range of resistances between nodes For the first i The node and the first j Minimum inter-node reactance For the first i The node and the first j Maximum inter-node reactance For the first i The minimum value of the interval injection power of each node. For the first i The maximum value of the interval injection power of each node; Represents the imaginary unit; Step 9.2.2: Calculate the branch parameter matrix B. i The node and the first j Interval admittance between nodes This generates an N×N dimensional interval complex admittance matrix. Y ; Step 9.2.3: Establish the interval power equation using equation (10); (10) In equation (11): For the first i The complex power range of each node, For the first i Each node voltage range For the first j Each node voltage range; For conjugate; Step 9.2.4: Use equation (11) to obtain the first... i Voltage amplitude width at each node : (11) In equation (13): For the first i The maximum value of the interval voltage amplitude at each node. For the first i The minimum value of the interval voltage amplitude at each node; Step 9.2.5: Construct the second objective function using equation (12) : (12).

[0008] The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the optimized configuration method, and the processor is configured to execute the program stored in the memory.

[0009] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program is executed by a processor to perform the steps of the optimization configuration method.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention generates a parameter matrix from raw data, simplifies the parameter matrix by merging towers, and comprehensively considers the influence of distribution network topology and electrical parameters. It overcomes the problems of traditional methods relying on empirical rules and low configuration efficiency, and achieves optimal placement of measurement devices. It reduces equipment investment costs while ensuring the accuracy of the parameter matrix. 2. This invention introduces a mathematical model to optimize the configuration scheme globally. By dynamically adjusting the weight and position of the measurement points, it breaks through the limitations of the traditional fixed configuration mode, improves the reliability of the measurement system and the protection of key nodes, and provides reliable technical support for the intelligent upgrading of the power distribution network. Attached Figure Description

[0011] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a partial line parameter table for a 10kV radial distribution network according to the method of the present invention; Figure 3 This is a table of partial tower parameters for a 10kV radial distribution network according to the method of the present invention; Figure 4 This is a table of partial line parameters following the numbering of the 10kV radial distribution network according to the method of the present invention; Figure 5 This is a table of partial tower parameters following the numbering of the 10kV radial distribution network according to the method of the present invention; Figure 6 This is a partial node parameter matrix A of a 10kV radial distribution network according to the method of the present invention; Figure 7 This is a partial branch parameter matrix B of the 10kV radial distribution network according to the method of the present invention. Detailed Implementation

[0012] In this embodiment, an optimized configuration method for a measurement device based on the generation of a 10kV radial distribution network parameter matrix is ​​described, such as... Figure 1 As shown, the procedure is as follows: Step 1: Based on the raw data of the 10kV radial distribution network, extract the line resistance, line reactance, active power consumed by the load, and reactive power consumed by the load between the starting and ending towers. Generate the line parameter table and tower parameter table for the 10kV radial distribution network. (Partial example follows...) Figure 2 and Figure 3 As shown; Step 2: Based on the characteristics of the tower names, classify the tower names into "line", "branch line", "branch line" and other categories. Sort the parameters in each row of the line parameter table of the 10kV radial distribution network to obtain the final sorted line parameter table and the final sorted tower parameter table of the 10kV radial distribution network.

[0013] Step 3: Number the line parameters of the 10kV radial distribution network in ascending order after final sorting, thus obtaining the numbered line parameter table of the 10kV radial distribution network, as shown below. Figure 4 As shown; Step 4: Number the tower parameters in ascending order after the final sorting of the 10kV radial distribution network, thus obtaining the tower parameter table after numbering the 10kV radial distribution network, as shown below. Figure 5 As shown; Step 5: Process the abnormal or missing data in the line parameter table and tower parameter table of the 10kV radial distribution network after numbering to obtain the corrected line parameter table and tower parameter table of the 10kV radial distribution network.

[0014] Step Six: Merge the towers with zero active power or zero reactive power consumption in the corrected tower parameter table of the 10kV radial distribution network to obtain a new node parameter table and a new branch parameter table of the 10kV radial distribution network. In the actual power grid, many towers only serve as support lines and cannot be used as key nodes, so some towers are merged to simplify the distribution network.

[0015] Step 7: Convert the new 10kV radial distribution network node parameter table and the new 10kV radial distribution network branch parameter table into a 10kV radial distribution network node parameter matrix A and a 10kV radial distribution network branch parameter matrix B. Partial representations of these matrices are shown below. Figure 6 and Figure 7 As shown; Step 8: Generate the line topology of the 10kV radial distribution network based on the branch parameter matrix B of the 10kV radial distribution network.

[0016] Step 9: Using the maximum node vulnerability, the maximum number of measuring devices, and the minimum voltage amplitude width of the 10kV radial distribution network as objective functions, and constructing complete observability constraints, budget constraints, and key node priority constraints, a multi-objective measurement configuration model is established. Step 9.1: Establish an assessment index system for the vulnerability of distribution network nodes, calculate the comprehensive weight of each node to obtain the vulnerability of each node, and establish the first objective function. ; Step 9.1.1: Based on the structural topology of the 10kV radial distribution network, establish the vulnerability index matrix of the 10kV radial distribution network. ,in, x it For the first i The node of the first t One vulnerability indicator; n This represents the total number of nodes in a 10kV radial distribution network. Step 9.1.2: To reduce the influence of random factors and improve the accuracy of state estimation, an improved entropy weight method is proposed. Based on the improved entropy weight method, the first... t Objective weights of each vulnerability index ; Step 9.1.2.1: For x it Dimensionless processing is performed to obtain the preprocessed first dimension. i The node of the first t Vulnerability Indicators r it : Step 9.1.2.2: Calculate the first step using equation (1). i The node of the first t Vulnerability Indicators x it Characteristic proportion h it : (1) Step 9.1.2.3: Calculate the first step using equation (2). t Entropy value of each vulnerability index et : (2) Step 9.1.2.4: Calculate the first step using equation (3). t Entropy weights of each vulnerability index w 1t : (3) Step 9.1.2.5: Calculate the first step using equation (4). t Variance of each vulnerability index f t : (4) In equation (7): It is the first of all nodes t The mean of each vulnerability index, It is the first i The node of the first t A vulnerability index, n This represents the total number of nodes in a 10kV radial distribution network.

[0017] Step 9.1.2.6: Calculate the first step using equation (5). t Variance weights of each vulnerability index w 2t : (5) Step 9.1.2.7: Calculate the first step using equation (6). t Objective weights of each vulnerability index : (6) Step 9.1.3: Use the Analytic Hierarchy Process (AHP) to obtain the... t Subjective weights of each vulnerability index ; Step 9.1.4: This invention assumes that subjective weights and objective weights are of equal importance. Combining subjective and objective weights, the first step is obtained using equation (7). t The combined weight of each vulnerability index ; (7) Step 9.1.5: Use equation (8) to obtain the vulnerability of each node in the 10kV radial distribution network. And obtain the first objective function. ; (8) (9) In equation (9):n The total number of nodes in a 10kV radial distribution network; x i It is a binary variable, representing the first... i Whether the node is equipped with a measuring device, if the node is... i If a measuring device is installed at each node, then... Otherwise, let .

[0018] Step 9.2: Based on the parameter information of the node parameter matrix A and the branch parameter matrix B, obtain the voltage amplitude width of each node through the interval state estimation method, and establish a second objective function. ; Step 9.2.1: Based on the branch parameter matrix B, obtain the first... i The node and the first j Inter-node impedance parameters Based on the node parameter matrix A, the first... i Inter-node injected active power , No. i Inter-node reactive power injection Passing the exam i Initial voltage range of each node ,in: For the first i The node and the first j Minimum resistance between nodes For the first i The node and the first j The maximum range of resistances between nodes For the first i The node and the first j Minimum inter-node reactance For the first i The node and the first j The maximum inter-node reactance value. For the first i The minimum value of the interval injection power of each node. For the first i The maximum value of the interval injection power of each node; It represents the imaginary unit.

[0019] Step 9.2.2: Calculate the branch parameter matrix B. i The node and the first j Interval admittance between nodes This generates an N×N dimensional interval complex admittance matrix. Y ; Step 9.2.3: Establish the interval power equation using equation (10); (10) In equation (11): For the first i The complex power range of each node, For the first i Each node voltage range For the first j Each node voltage range; For conjugate.

[0020] Step 9.2.4: Use equation (11) to obtain the first... i Voltage amplitude width at each node The voltage amplitude width mainly reflects the accuracy of the interval state estimation; the smaller the value, the higher the accuracy. (11) In equation (13): For the first i The maximum value of the interval voltage amplitude at each node. For the first i The minimum value of the interval voltage amplitude of each node.

[0021] Step 9.2.5: Construct the second objective function using equation (12) : (12) Step 9.3: Construct the third objective function using equation (13) This invention primarily reflects cost through the number of equipment units; the smaller the number, the lower the cost. (13) Step 9.4: Use equation (14) to establish constraints for the optimal configuration of the measuring device; (14) In equation (14), C The maximum number of devices that can be configured is set. K These are the top m nodes with the highest vulnerability. Indicates the first i Each node, It is the kth node with the highest vulnerability.

[0022] Step 9.5: After establishing and solving the multi-target measurement configuration model using equation (15), the optimal configuration scheme of the measurement device is obtained: (15) In equation (15), , , It represents 3 weights, which can be adjusted according to specific parameters to achieve dynamic adjustment of the weights of the measurement points, thereby improving the reliability of the measurement system and ensuring the configuration of measurement devices at key nodes.

[0023] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0024] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. A method for optimizing the configuration of a measurement device based on the generation of a parameter matrix for a 10kV radial distribution network, characterized in that, The procedure is as follows: Step 1: Based on the original data of the 10kV radial distribution network, generate the line parameter table and the tower parameter table of the 10kV radial distribution network. Step 2: Sort the parameters in each row of the line parameter table of the 10kV radial distribution network to obtain the final sorted line parameter table and the final sorted tower parameter table of the 10kV radial distribution network. Step 3: Number the line parameters of the 10kV radial distribution network in ascending order after final sorting, so as to obtain the numbered line parameter table of the 10kV radial distribution network. Step 4: Number the tower parameters after the final sorting of the 10kV radial distribution network in ascending order to obtain the tower parameter table after numbering the 10kV radial distribution network. Step 5: Process the abnormal or missing data in the line parameter table and tower parameter table after numbering the 10kV radial distribution network to obtain the corrected line parameter table and tower parameter table for the 10kV radial distribution network. Step 6: Merge the towers in the corrected tower parameter table of the 10kV radial distribution network where the active power or reactive power consumed by the load is 0, to obtain a new 10kV radial distribution network node parameter table and a new 10kV radial distribution network branch parameter table. Step 7: Convert the new 10kV radial distribution network node parameter table and the new 10kV radial distribution network branch parameter table into a 10kV radial distribution network node parameter matrix A and a 10kV radial distribution network branch parameter matrix B. Step 8: Generate the line topology of the 10kV radial distribution network based on the branch parameter matrix B of the 10kV radial distribution network. Step 9: Using the maximum node vulnerability, the maximum number of measuring devices, and the minimum voltage amplitude width of the 10kV radial distribution network as objective functions, and constructing complete observability constraints, budget constraints, and key node priority constraints, a multi-objective measurement configuration model is established and solved to obtain the optimal configuration scheme of the measuring devices.

2. The method for optimizing the configuration of a measuring device based on the generation of a 10kV radial distribution network parameter matrix according to claim 1, characterized in that, Step nine includes: Step 9.1: Establish an assessment index system for the vulnerability of distribution network nodes, calculate the comprehensive weight of each node to obtain the vulnerability of each node, and establish the first objective function. ; Step 9.2: Based on the parameter information of the node parameter matrix A and the branch parameter matrix B, obtain the voltage amplitude width of each node through the interval state estimation method, and establish a second objective function. ; Step 9.3: Construct the third objective function using equation (13) : (13) Step 9.4: Use equation (14) to establish constraints for the optimal configuration of the measuring device; (14) In equation (14), C The maximum number of devices that can be configured is set. K These are the top m nodes with the highest vulnerability. Indicates the first i Each node, It is the kth node with the highest vulnerability. Step 9.5: After establishing and solving the multi-target measurement configuration model using equation (15), the optimal configuration scheme of the measurement device is obtained: (15) In equation (15), , , This indicates 3 weights.

3. The method for optimizing the configuration of a measuring device based on the generation of a 10kV radial distribution network parameter matrix according to claim 2, characterized in that, Step 9.1 includes: Step 9.1.1: Based on the structural topology of the 10kV radial distribution network, establish the vulnerability index matrix of the 10kV radial distribution network. ,in, x it For the first i The node of the first t One vulnerability indicator; n This represents the total number of nodes in a 10kV radial distribution network. Step 9.1.2: Obtain the first step based on the improved entropy weight method. t Objective weights of each vulnerability index ; Step 9.1.3: Use the Analytic Hierarchy Process (AHP) to obtain the... t Subjective weights of each vulnerability index ; Step 9.1.4: Use equation (7) to obtain the first... t The combined weight of each vulnerability index ; (7) Step 9.1.5: Use equation (8) to obtain the vulnerability of each node in the 10kV radial distribution network. And obtain the first objective function. ; (8) (9) In equation (9): n The total number of nodes in a 10kV radial distribution network; x i Indicates the first i Whether the node is equipped with a measuring device, if the node is... i If a measuring device is installed at each node, then... Otherwise, let .

4. The method for optimizing the configuration of a measuring device based on the generation of a 10kV radial distribution network parameter matrix according to claim 3, characterized in that, Step 9.1.2 includes: Step 9.1.2.1: For x it Dimensionless processing is performed to obtain the preprocessed first dimension. i The node of the first t Vulnerability Indicators r it : Step 9.1.2.2: Calculate the first step using equation (1). i The node of the first t Vulnerability Indicators x it Characteristic proportion h it : (1) Step 9.1.2.3: Calculate the first step using equation (2). t Entropy value of each vulnerability index e t : (2) Step 9.1.2.4: Calculate the first step using equation (3). t Entropy weights of each vulnerability index w 1t : (3) Step 9.1.2.5: Calculate the first step using equation (4). t Variance of each vulnerability index f t : (4) In equation (7): It is the first of all nodes t The mean of each vulnerability index, It is the first i The node of the first t A vulnerability index, n This represents the total number of nodes in a 10kV radial distribution network. Step 9.1.2.6: Calculate the first step using equation (5). t Variance weights of each vulnerability index w 2t : (5) Step 9.1.2.7: Calculate the first step using equation (6). t Objective weights of each vulnerability index : (6)。 5. The method for optimizing the configuration of a measuring device based on the generation of a 10kV radial distribution network parameter matrix according to claim 4, characterized in that, Step 9.2 includes: Step 9.2.1: Based on the branch parameter matrix B, obtain the first... i The node and the first j Inter-node impedance parameters Based on the node parameter matrix A, the first... i Inter-node injected active power , No. i Inter-node reactive power injection Passing the exam i Initial voltage range of each node ,in: For the first i The node and the first j Minimum resistance between nodes For the first i The node and the first j The maximum range of resistances between nodes For the first i The node and the first j Minimum inter-node reactance For the first i The node and the first j The maximum inter-node reactance value. For the first i The minimum value of the interval injection power of each node. For the first i The maximum value of the interval injection power of each node; Represents the imaginary unit; Step 9.2.2: Calculate the branch parameter matrix B. i The node and the first j Interval admittance between nodes This generates an N×N dimensional interval complex admittance matrix. Y ; Step 9.2.3: Establish the interval power equation using equation (10); (10) In equation (11): For the first i The complex power range of each node, For the first i Each node voltage range For the first j Each node voltage range; For conjugate; Step 9.2.4: Use equation (11) to obtain the first... i Voltage amplitude width at each node : (11) In equation (13): For the first i The maximum value of the interval voltage amplitude at each node. For the first i The minimum value of the interval voltage amplitude at each node; Step 9.2.5: Construct the second objective function using equation (12) : (12)。 6. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the optimized configuration method according to any one of claims 1-5, wherein the processor is configured to execute the program stored in the memory.

7. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the optimization configuration method according to any one of claims 1-5.

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