A power distribution network construction method and system based on a standard digital model

Abstract

The application relates to the technical field of smart grid, in particular to a power distribution network construction method and system based on a standard digital model. The method comprises the following steps: collecting position information, voltage and power of line nodes; screening a local line node set of each line node based on the position information of the line nodes, and calculating a neighboring node density based on the same; determining a weighted power of each line node based on the distance between the line nodes and the power, and combining the power of the line node and its neighboring line nodes and the neighboring node density to obtain a power influence degree of the line node; obtaining a fragile attention degree of the line node based on the difference between a voltage peak factor of each line node in a preset time period and a standard voltage peak factor and the power influence degree; determining an important attention degree based on the fragile attention degree and a node degree; and marking and constructing a node level based on the important attention degree. The application more accurately evaluates the importance of nodes.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, specifically to a method and system for constructing a distribution network based on a standard digital model. Background Technology

[0002] With the rapid development of computer technology, standardized digital monitoring models have emerged for power distribution network systems. Monitoring the operational status of the power distribution network through these standardized digital models allows for the rational allocation of electrical energy within the smart grid. When constructing these standardized digital models, monitoring weights need to be assigned to the nodes in the distribution network. This ensures that when a fault occurs in the smart grid, higher-weighted line nodes are prioritized for repair, thereby improving maintenance efficiency.

[0003] Traditional methods typically assess the importance of line nodes in a smart grid by calculating the nodality of the line nodes in the distribution network. However, with the development of the electrical industry, more and more electrical devices are used in daily life, requiring dedicated power lines. This increases the complexity of the lines in the smart grid. Traditional nodality is calculated by the number of line nodes connected to each other. Due to the fluidity of current in the smart grid, traditional methods fail to fully consider the distribution and power of neighboring line nodes, leading to inaccuracies in the assessment of line node importance. Summary of the Invention

[0004] To address the technical problem of inaccurate node importance assessment, this application provides a distribution network construction method and system based on a standard digital model. The specific technical solution adopted is as follows:

[0005] This application proposes a method and system for constructing a distribution network based on a standard digital model. The method includes the following steps:

[0006] Collect the location information of each line node, as well as the voltage and power at each moment;

[0007] Construct a local set of line nodes for each line node based on the location information of all line nodes; obtain the density of neighboring nodes of each line node based on the number of line nodes connected to each line node and the distance between the line node and all line nodes in the local set of line nodes.

[0008] For each line node, the power of the line nodes in the local line node set is weighted by the distance between line nodes as the weight, and the power of each line node is obtained by weighting the power of the line node and its neighboring line nodes and the weighted power. The power influence of the line node is obtained based on the difference between the power of the line node and its neighboring line nodes and the weighted power, as well as the density of the neighboring line nodes.

[0009] The voltage peak factor is obtained based on the voltage of each line node within a preset time period; a preset standard voltage peak factor is obtained; the vulnerability concern of each line node is obtained based on the difference between the voltage peak factor and the standard voltage peak factor of each line node and the power impact of the line node.

[0010] The importance of a node is determined based on its vulnerability level and node degree; nodes are then labeled based on their importance to construct a node hierarchy.

[0011] In the aforementioned scheme, this application analyzes the local distribution state of line nodes to obtain their local density state, which characterizes the state of line nodes in the distribution network and accurately determines their impact on the smart grid, thus providing accurate basic data for assessing the level of attention given to line nodes. By analyzing the power distribution state of line nodes, the impact of power flow on the smart grid after a fault occurs can be obtained, allowing for a more accurate evaluation of the impact of local line nodes on other line nodes. By calculating the voltage operating state and local distribution state of line nodes, the vulnerability level of line nodes can be calculated, effectively determining the impact of voltage and neighboring line nodes. Compared to traditional methods, this application, through line node density assessment, can more accurately evaluate the importance of line nodes in the smart grid, facilitating remote monitoring of the smart grid and providing strong support for the long-term stable operation of the power grid.

[0012] In one embodiment, the method for constructing a local set of line nodes for each line node based on the location information of all line nodes is as follows:

[0013] The Euclidean distance between any two line nodes is calculated based on location information, and the square root of the product of the maximum and minimum Euclidean distances is taken as the average distribution distance.

[0014] Draw a circle with each line node as the center and the average distribution distance as the radius, and obtain all line nodes within the circle as the local line node set for each line node.

[0015] In one embodiment, the density of neighboring nodes is positively correlated with the number of line nodes connected to the line node, and negatively correlated with the distance between the line node and all line nodes in the local line node set.

[0016] In one embodiment, the method of using the distance between line nodes as a weight to weight the power of line nodes in the local line node set as the weighted power of each line node is as follows:

[0017] Calculate the reciprocal of the Euclidean distance between a line node and each line node in its corresponding local set of line nodes. Normalize all derivatives and use them as the power influence weight of each line node in the local set of line nodes. Multiply the power influence weight of each line node in the local set of line nodes with its corresponding power and sum them up to obtain the weighted power of the line node.

[0018] In one embodiment, the power influence of the line node is positively correlated with the difference in power and weighted power between the line node and its neighboring line nodes, and negatively correlated with the density of neighboring nodes; the neighboring line nodes are the line nodes that are closest to each line node.

[0019] In one embodiment, the voltage peak factor is the ratio of the maximum voltage value to the effective voltage value within a preset time period.

[0020] In one embodiment, the vulnerability concern is positively correlated with the difference between the voltage peak factor and the standard voltage peak factor of the line node, and negatively correlated with the degree of power impact.

[0021] In one embodiment, the importance concern is the product of the vulnerability concern and the node degree, where the node degree is the number of edges connected to the linear node.

[0022] In one embodiment, the method for constructing node rankings based on node labels according to importance is as follows:

[0023] Sort all line nodes by importance in order, and divide all line nodes into 4 equal intervals according to their importance. Calculate the mean of importance in the 4 intervals. The line nodes in the interval with the largest mean are marked as key nodes and set in red; the line nodes in the interval with the second largest mean are marked as important nodes and set in orange; the line nodes in the interval with the third largest mean are marked as ordinary nodes and set in blue; and the line nodes in the interval with the smallest mean are marked as auxiliary nodes and set in green.

[0024] Secondly, embodiments of this application also provide a power distribution network construction system based on a standard digital model, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the power distribution network construction method based on a standard digital model described above.

[0025] The beneficial effects of this application are as follows:

[0026] This application analyzes the local distribution of line nodes to obtain their local density, which characterizes the state of line nodes in the distribution network and accurately assesses their impact on the smart grid, thus providing accurate basic data for determining the level of attention given to line nodes. By analyzing the power distribution of line nodes, the application obtains the impact of power flow on the smart grid after a fault occurs, allowing for a more accurate evaluation of the impact of local line nodes on other line nodes. Furthermore, by calculating the voltage operating state and local distribution of line nodes, the application calculates their vulnerability level, effectively determining the impact of voltage and neighboring line nodes. Compared to traditional methods, this application, through line node density assessment, more accurately evaluates the importance of line nodes in the smart grid, facilitating remote monitoring and providing strong support for the long-term stable operation of the power grid. Attached Figure Description

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating a distribution network construction method based on a standard digital model, provided as an embodiment of this application. Detailed Implementation

[0029] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a power distribution network construction method and system based on a standard digital model proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0031] A method and system embodiment for constructing a power distribution network based on a standard digital model:

[0032] The following description, in conjunction with the accompanying drawings, details a specific scheme for a distribution network construction method and system based on a standard digital model provided in this application.

[0033] Please see Figure 1 This document illustrates a method and system flowchart for constructing a power distribution network based on a standard digital model, according to an embodiment of this application. The method includes the following steps:

[0034] Step S001: Collect the location information, voltage, and power of the line nodes.

[0035] Using CAD modeling software, a digital model of the power distribution network is constructed based on the CAD drawings of the power topology wiring. The construction of the digital model of the power distribution network is a well-known technology, and the specific calculation steps will not be described in detail here.

[0036] Smart meters are installed at each line node of the distribution network to monitor and collect electricity consumption data. The latitude and longitude coordinates of the smart meters are obtained through the Beidou positioning device built into the smart meters, and the voltage and power of the line nodes are collected through the smart meters. The collected data is then normalized. In this embodiment, the data collection frequency is 1kHz.

[0037] At this point, the location information of the line nodes, as well as the voltage and power at each moment, have been obtained.

[0038] Step S002: Based on the location information of the line nodes, filter the local line node set of each line node, and calculate the density of neighboring nodes accordingly.

[0039] The more electrical appliances used in a given area, the more line nodes are needed to ensure the lines can handle the electrical load and guarantee the normal operation of the equipment. Furthermore, a greater number of line nodes means more detours are possible in the event of a line fault, reducing the area affected by the outage and ensuring the stability of the power supply in the region.

[0040] Therefore, the Euclidean distance between the latitude and longitude coordinates of any two line nodes is calculated, and the maximum and minimum Euclidean distances are multiplied. The square root of the product is taken as the average distribution distance of the line nodes, which is used to characterize the average distribution state between line nodes. This value can reflect the average density distance of all line nodes.

[0041] Furthermore, a circle is drawn with the line node as the center and the average distribution distance as the radius. All line nodes within the circle are obtained to form a local line node set, which is used to characterize the local distribution of line nodes.

[0042] Because the electrical equipment and power consumption vary in different areas, the number of power lines required in each area also varies to meet the local power demand. Therefore, the more power lines there are in an area, the smaller the distance between power line nodes, the higher the density of neighboring power lines, and the greater the load required in that area.

[0043] Therefore, the density of neighboring nodes of a line node is obtained based on the number of line nodes connected to each line node and its distance from all line nodes in the local line node set.

[0044] The density of neighboring nodes is positively correlated with the number of line nodes connected to the line node, and negatively correlated with the distance between it and all line nodes in the local line node set.

[0045] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large. The specific relationship is determined by the actual application, and this application does not impose any special restrictions.

[0046] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by practical application, and this application does not impose any special restrictions.

[0047] Preferably, in this embodiment, the expression for the density of neighboring nodes is:

[0048] , This represents the number of line nodes directly connected to the i-th line node. Let represent the average distance between the i-th line node and all line nodes in its corresponding local set of line nodes. This represents a very small positive number, and its function is to prevent the denominator from being 0. In this embodiment, its value is taken as 0.01. This represents the density of neighboring nodes of the i-th line node.

[0049] The greater the density of neighboring nodes, the greater the load required in the line node area. Therefore, in order to ensure the stability and security of the smart grid operation, the importance of paying attention to line nodes is even greater.

[0050] At this point, the density of neighboring nodes for each line node has been obtained.

[0051] Step S003: Determine the weighted power of each line node based on the distance and power between line nodes, and combine the power of the line node and its neighboring line nodes as well as the density of neighboring nodes to obtain the degree of power influence of the line node.

[0052] Regarding the power of a line node, the flow of current causes neighboring line nodes to influence the current line node. The greater the power of a neighboring line node, the greater its impact on the current line node. When a line node fails, the power flowing from neighboring nodes to the current node is greater, resulting in greater energy loss and a wider range of power impact. This strengthens the importance of line nodes in a smart grid. The neighboring line node is the line node closest to each existing line node.

[0053] Therefore, the closer the neighboring line nodes are to the current node, the greater the impact of the neighboring line node's power.

[0054] Therefore, the reciprocal of the Euclidean distance between a line node and each element in its corresponding local line node set is obtained. These reciprocals are then summed and normalized to obtain the power influence weight. The weighted power of each line node is obtained by multiplying this power influence weight by the power of each element in the local line node set and summing the results. This summation represents the local power variation of the line node. The calculation of the summation and normalization is a well-known technique, and the specific calculation steps will not be elaborated here.

[0055] When a line node in a smart grid fails, current from neighboring line nodes is diverted to the failed node, affecting the operation of electrical equipment in those neighboring nodes. Furthermore, the closer the neighboring line nodes are to the current node, the shorter the current flow path, and the greater the impact on neighboring nodes. Therefore, in this scenario, the greater the power distribution between a line node and its neighboring line nodes during normal smart grid operation, the more important that line node is. Additionally, the more neighboring line nodes a line node has, the greater its neighboring node density, resulting in a greater influence on power distribution and thus increasing its importance to the smart grid.

[0056] Therefore, the power influence of a line node is obtained based on the difference between the power of the line node and its neighboring line nodes and the local power, as well as the density of neighboring nodes.

[0057] The degree of power influence of the line node is positively correlated with the power and local power differences of the line node and its neighboring line nodes, and negatively correlated with the density of neighboring nodes.

[0058] Preferably, in this embodiment, the expression for the degree of power influence is:

[0059] , This represents the power of the i-th line node. This represents the power of the neighboring line nodes of the i-th line node. This represents the weighted power of the i-th line node. This represents the density of neighboring nodes of the i-th line node. This indicates the degree of power impact of the i-th line node.

[0060] At this point, the power impact of each line node has been obtained.

[0061] Step S004: Based on the difference between the peak voltage factor and the standard peak voltage factor of each line node within a preset time period and the degree of power impact, obtain the vulnerability concern level of the line node.

[0062] In monitoring line nodes in smart grids, it is essential to consider not only their local distribution but also their operational status. The more unstable the voltage at a line node, the more likely it is to experience a fault, such as a short circuit or partial discharge. Therefore, increased attention should be paid to this particular line node.

[0063] Obtain the maximum value and the effective value of the voltage data for each line node within the previous 24 hours. The calculation of the effective voltage value is a well-known technique, and the specific calculation steps will not be elaborated here.

[0064] Ideally, the ratio between the maximum and effective voltage values ​​at a line node is constant, and this ratio is: The ratio between the effective value and the maximum value of the voltage is denoted as the voltage peak factor, which characterizes the relationship between the effective value and the maximum value of the voltage. This is denoted as the standard voltage peak factor. In actual operation, the greater the difference between the ratio of the effective value of the line node voltage to the maximum value of the voltage and the rated ratio, the stronger the voltage instability of the line node and the more prone it is to failure. The smart grid should pay more attention to the changing state of the line node to prevent local power outages caused by line node failures.

[0065] Therefore, the vulnerability level of each line node is obtained based on the difference between the peak voltage factor and the standard peak voltage factor of each line node, as well as the degree of power impact of the line node.

[0066] The vulnerability concern level is positively correlated with the difference between the peak voltage factor and the standard peak voltage factor of the line node, and negatively correlated with the degree of power impact.

[0067] Preferably, in this embodiment, the expression for vulnerability concern is:

[0068] , This represents the maximum voltage at the i-th line node. This represents the effective value of the voltage at the i-th line node. Indicates the standard voltage peak factor. This indicates the degree of power impact of the i-th line node. This represents the vulnerability level of the i-th line node. This represents the adjustment factor, which is used to prevent the factor from being 0. In this embodiment, the adjustment factor is set to 1.

[0069] The higher the local distribution density and the greater the power of line nodes, the greater the influence of power distribution on line nodes. At the same time, the lower the voltage stability coefficient of line nodes, that is, the greater the difference between the ratio of peak value to effective value and the peak factor, the larger the scope and the greater the effect when a line node fails. The vulnerability of line nodes should be given higher attention, and more attention should be paid to line nodes to improve the focus on vulnerable nodes in the smart grid and ensure the safe and stable operation of the smart grid.

[0070] At this point, the vulnerability profile of each line node has been obtained.

[0071] Step S005: Determine the importance of concern based on vulnerability concern and node degree; construct node level by labeling nodes based on importance of concern.

[0072] The vulnerability level of a line node is multiplied by its degree to obtain its importance level; the degree is the number of edges connected to that linear node. The method for obtaining the degree is a well-known technique and will not be elaborated upon in this embodiment.

[0073] Furthermore, all line nodes are sorted by importance in order, and all line nodes are divided into 4 intervals according to their importance. The mean of importance in the 4 intervals is calculated. The line nodes in the interval with the largest mean are marked as key nodes, the line nodes in the interval with the second largest mean are marked as important nodes, the line nodes in the interval with the third largest mean are marked as ordinary nodes, and the line nodes in the interval with the smallest mean are marked as auxiliary nodes.

[0074] Labeling is performed on line nodes in the standard digital model of the distribution network. CAD software is used to add node levels to virtual nodes, setting key nodes to red, important nodes to orange, ordinary nodes to blue, and auxiliary nodes to green, thus constructing the node levels in the standard digital model of the distribution network.

[0075] Based on the same inventive concept as the above method, this embodiment of the invention also provides a distribution network construction system based on a standard digital model, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described distribution network construction methods based on a standard digital model.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0077] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for constructing a distribution network based on a standard digital model, characterized in that, The method includes the following steps: Collect the location information of each line node, as well as the voltage and power at each moment; Construct a local set of line nodes for each line node based on the location information of all line nodes; obtain the density of neighboring nodes of each line node based on the number of line nodes connected to each line node and the distance between the line node and all line nodes in the local set of line nodes. For each line node, the power of the line nodes in the local line node set is weighted by the distance between line nodes as the weight, and the power of each line node is obtained by weighting the power of the line node and its neighboring line nodes and the weighted power. The power influence of the line node is obtained based on the difference between the power of the line node and its neighboring line nodes and the weighted power, as well as the density of the neighboring line nodes. The voltage peak factor is obtained based on the voltage of each line node within a preset time period; a preset standard voltage peak factor is obtained; the vulnerability concern of each line node is obtained based on the difference between the voltage peak factor and the standard voltage peak factor of each line node and the power impact of the line node. The importance of a node is determined based on its vulnerability level and node degree; nodes are then labeled based on their importance to construct a node hierarchy.

2. The distribution network construction method based on a standard digital model as described in claim 1, characterized in that, The method for constructing a local set of line nodes for each line node based on the location information of all line nodes is as follows: The Euclidean distance between any two line nodes is calculated based on location information, and the square root of the product of the maximum and minimum Euclidean distances is taken as the average distribution distance. Draw a circle with each line node as the center and the average distribution distance as the radius, and obtain all line nodes within the circle as the local line node set for each line node.

3. The distribution network construction method based on a standard digital model as described in claim 1, characterized in that, The density of neighboring nodes is positively correlated with the number of line nodes connected to the line node, and negatively correlated with the distance between the line node and all line nodes in the local line node set.

4. The distribution network construction method based on a standard digital model as described in claim 1, characterized in that, The method of using the distance between line nodes as a weight to weight the power of line nodes in a local set of line nodes as the weighted power of each line node is as follows: Calculate the reciprocal of the Euclidean distance between a line node and each line node in its corresponding local set of line nodes. Normalize all derivatives and use them as the power influence weight of each line node in the local set of line nodes. Multiply the power influence weight of each line node in the local set of line nodes with its corresponding power and sum them up to obtain the weighted power of the line node.

5. The distribution network construction method based on a standard digital model as described in claim 1, characterized in that, The degree of power influence of the line node is positively correlated with the difference in power and weighted power between the line node and its neighboring line nodes, and negatively correlated with the density of neighboring nodes; the neighboring line node is the line node that is closest to each line node.

6. The distribution network construction method based on a standard digital model as described in claim 1, characterized in that, The voltage peak factor is the ratio of the maximum voltage value to the effective voltage value within a preset time period.

7. The distribution network construction method based on a standard digital model as described in claim 1, characterized in that, The vulnerability concern level is positively correlated with the difference between the peak voltage factor and the standard peak voltage factor of the line node, and negatively correlated with the degree of power impact.

8. The method for constructing a distribution network based on a standard digital model as described in claim 1, characterized in that, The importance level is the product of the vulnerability level and the node degree, where the node degree is the number of edges connected to the node of the line.

9. The distribution network construction method based on a standard digital model as described in claim 1, characterized in that, The method for constructing node levels based on importance and attention is as follows: Sort all line nodes by importance in order, and divide all line nodes into 4 equal intervals according to their importance. Calculate the mean of importance in the 4 intervals. The line nodes in the interval with the largest mean are marked as key nodes and set in red; the line nodes in the interval with the second largest mean are marked as important nodes and set in orange; the line nodes in the interval with the third largest mean are marked as ordinary nodes and set in blue; and the line nodes in the interval with the smallest mean are marked as auxiliary nodes and set in green.

10. A power distribution network construction system based on a standard digital model, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the distribution network construction method based on a standard digital model as described in any one of claims 1-9.

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