Power distribution network anomaly prediction method, device, equipment, medium and program product

Abstract

The application provides a power distribution network anomaly prediction method, device, equipment, medium and program product. In the method, the line length, line model, power supply capacity and power consumption capacity corresponding to each line level in the target line from the transformer node to the to-be-installed power node are determined according to the acquired power node connection line diagram of the transformer area and the position of the to-be-installed power node. The grid-connected capacity is determined according to the installation purpose and installation capacity of the to-be-installed power node. Then, whether the power distribution network is abnormal is predicted in combination with the current capacity of the transformer and the node type of the to-be-installed power node. The line length, line model, power supply capacity and power consumption capacity corresponding to each line level in the target line determined, the position, installation capacity, node type and installation purpose of the to-be-installed power node are used for power distribution network anomaly prediction, and the prediction accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a method, device, equipment, medium and program product for predicting anomalies in distribution networks. Background Technology

[0002] A distribution area contains various power nodes, such as transformers, meters, and distributed power sources. These power nodes, along with transmission lines, constitute the distribution network. Users may request the installation of power nodes such as meters or distributed power sources. To ensure that the distribution network does not experience anomalies after the installation of these power nodes, distribution network anomaly prediction is necessary.

[0003] In existing technologies, distribution network anomaly prediction is typically achieved by predicting whether transformers will experience severe overload. This is done by determining whether the available capacity of the transformer is less than the registered capacity of the power node. If the available capacity is less than the registered capacity, a severe overload of the transformer is predicted, thus indicating an anomaly in the distribution network.

[0004] In summary, existing technologies predict whether anomalies will occur in the distribution network based solely on the available capacity of transformers and the reported capacity of power nodes, resulting in low accuracy in predicting distribution network anomalies. Summary of the Invention

[0005] The distribution network anomaly prediction method, apparatus, equipment, medium, and program products provided in this application are used to solve the problem that the accuracy of distribution network anomaly prediction is low because the prior art only predicts whether the distribution network will have anomalies based on the available capacity of transformers and the installed capacity of power nodes.

[0006] In a first aspect, embodiments of this application provide a method for predicting power distribution network anomalies, including:

[0007] Obtain the power node connection diagram of the distribution area, the current capacity of the transformer, and the location, application capacity, node type, and application purpose of the power node to be installed;

[0008] Based on the power node connection diagram and the location of the power node to be installed, determine the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed;

[0009] The grid connection capacity is determined based on the application purpose and the application capacity.

[0010] Based on the grid-connected capacity, the current capacity of the transformer, the node type, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level, predict whether the distribution network will experience anomalies.

[0011] In one possible implementation, determining the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed, based on the power node connection diagram and the location of the power node to be installed, includes:

[0012] Based on the location of the power node to be installed, determine the reference node that is closest to the power node to be installed in the power node connection line diagram;

[0013] The line from the transformer node to the reference node in the power node connection diagram is taken as the target line;

[0014] Based on the power node connection diagram, obtain the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line.

[0015] In one possible implementation, determining the grid connection capacity based on the application purpose and the application capacity includes:

[0016] Based on the preset correspondence between purpose and capacity conversion coefficient, determine the target capacity conversion coefficient corresponding to the application purpose;

[0017] The product of the applied capacity and the target capacity conversion factor is taken as the grid-connected capacity.

[0018] In one possible implementation, predicting whether an anomaly has occurred in the distribution network based on the grid-connected capacity, the current capacity of the transformer, the node type, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level includes:

[0019] Based on the grid-connected capacity and the current capacity of the transformer, predict whether the distribution network will experience transformer overload anomalies;

[0020] Determine the target number of phases based on the grid connection capacity;

[0021] In descending order of line level, for each line level, based on the line length, line type, power supply capacity, power consumption capacity, node type, grid connection capacity, and target number of phases, predict whether the distribution network will experience transmission line overload anomaly, and determine the terminal voltage corresponding to the line level.

[0022] Based on the terminal voltage corresponding to each line level and the target number of phases, predict whether the distribution network will experience voltage anomalies at the power nodes to be installed.

[0023] In one possible implementation, the step of predicting whether the distribution network experiences a transmission line overload anomaly and determining the terminal voltage corresponding to the line level, based on the line length, line type, power supply capacity, and power consumption capacity corresponding to the line level, as well as the node type, grid connection capacity, and target number of phases, includes:

[0024] Based on the target number of phases, determine the voltage parameters, current calculation parameters, and voltage drop calculation parameters;

[0025] Calculate the transmission line current based on the power supply capacity, power consumption capacity, grid connection capacity, node type, voltage parameters, and current calculation parameters corresponding to the line level;

[0026] Based on the preset correspondence between model and current, the safe current corresponding to the circuit model is determined;

[0027] Based on the transmission line current and the safety current, predict whether the power distribution network will experience a transmission line overload anomaly.

[0028] Calculate the voltage drop corresponding to the line level based on the voltage drop calculation parameters, the current calculation parameters, the line length, the line type, the transmission line current, and the starting voltage;

[0029] The sum of the starting voltage and the voltage drop corresponding to the line level is taken as the terminal voltage corresponding to the line level;

[0030] Wherein, if the line level is the highest among all line levels, the starting voltage is a preset transformer voltage; if the line level is not the highest among all line levels, the starting voltage is the end voltage of the next higher level of the line level.

[0031] In one possible implementation, calculating the voltage drop corresponding to the line level based on the voltage drop calculation parameters, the current calculation parameters, the line length, the line type, the transmission line current, and the starting voltage includes:

[0032] Based on the preset correspondence between model and resistance and reactance, determine the target unit resistance and target unit reactance corresponding to the circuit model;

[0033] Calculate the voltage drop corresponding to the line level based on the voltage drop calculation parameters, the current calculation parameters, the line length, the target unit resistance, the target unit reactance, the transmission line current, and the starting voltage.

[0034] In one possible implementation, predicting whether the distribution network experiences voltage anomalies at the power nodes to be installed, based on the terminal voltage corresponding to each line level and the target number of phases, includes:

[0035] The lowest level of voltage at the end of all line levels is used as the voltage to be compared.

[0036] Based on the preset correspondence between the number of phases and the voltage threshold range, the target voltage threshold range corresponding to the target number of phases is determined;

[0037] If the voltage to be compared falls within the target voltage threshold range, it is predicted that no voltage anomalies have occurred at the power nodes to be installed in the distribution network.

[0038] If the voltage to be compared does not fall within the target voltage threshold range, it is predicted that the voltage of the power node to be installed in the distribution network will be abnormal.

[0039] Secondly, embodiments of this application provide a power distribution network anomaly prediction device, comprising:

[0040] The acquisition module is used to acquire the power node connection line diagram of the distribution area, the current capacity of the transformer, and the location, application capacity, node type and application purpose of the power node to be installed;

[0041] Processing module, used for:

[0042] Based on the power node connection diagram and the location of the power node to be installed, determine the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed;

[0043] The grid connection capacity is determined based on the application purpose and the application capacity.

[0044] The prediction module is used to predict whether an anomaly will occur in the distribution network based on the grid-connected capacity, the current capacity of the transformer, the node type, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level.

[0045] Thirdly, embodiments of this application provide an electronic device, including:

[0046] Processor, memory, communication interface;

[0047] The memory is used to store the executable instructions of the processor;

[0048] The processor is configured to execute the power distribution network anomaly prediction method according to any one of the first aspects by executing the executable instructions.

[0049] Fourthly, embodiments of this application provide a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the power distribution network anomaly prediction method described in any of the first aspects.

[0050] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, is used to implement the power distribution network anomaly prediction method described in any of the first aspects.

[0051] The distribution network anomaly prediction method, apparatus, equipment, medium, and program products provided in this application determine the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed, based on the obtained power node connection line diagram of the transformer area and the location of the power node to be installed. Furthermore, the grid connection capacity is determined based on the application purpose and application capacity of the power node to be installed. Then, based on the grid connection capacity, the current capacity of the transformer, the node type of the power node to be installed, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level, the distribution network anomaly is predicted. This solution improves prediction accuracy by determining the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line, as well as the location, application capacity, node type, and application purpose of the power node to be installed. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0053] Figure 1 This is a flowchart illustrating an embodiment of the power distribution network anomaly prediction method provided in this application.

[0054] Figure 2 Schematic diagram of power node connection lines provided in this application Figure 1 ;

[0055] Figure 3 Schematic diagram of power node connection lines provided in this application Figure 2 ;

[0056] Figure 4 A flowchart illustrating Embodiment 2 of the power distribution network anomaly prediction method provided in this application;

[0057] Figure 5 This is a schematic diagram of the structure of an embodiment of the power distribution network anomaly prediction device provided in this application;

[0058] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this application.

[0059] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0060] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0061] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0062] The nodes in a distribution area are divided into power consumption nodes and power supply nodes. Power consumption nodes can be electricity meters, charging piles, etc., while power supply nodes can be transformers, distributed power sources, etc. Various power nodes and transmission lines constitute the distribution network. Users have a need to install electricity meters or distributed power sources, which means adding power nodes to the distribution network. In order to ensure that no abnormalities occur in the distribution network after the addition of power nodes, distribution network anomaly prediction is required.

[0063] In existing technologies, distribution network anomaly prediction is typically achieved by predicting whether transformers will experience severe overload. This is done by determining whether the available capacity of the transformer is less than the registered capacity of the power node. If the available capacity is less than the registered capacity, a severe overload is predicted, thus indicating a distribution network anomaly. However, predicting distribution network anomalies based solely on the available transformer capacity and the registered capacity of the power node leads to relatively low accuracy in anomaly prediction.

[0064] To address the problems existing in the prior art, the inventors, during their research on distribution network anomaly prediction methods, discovered that improving the accuracy of distribution network anomaly prediction can be achieved not only by improving the accuracy of predicting whether transformers will experience heavy overload, but also by adding predictions of whether transmission lines will experience heavy overload and whether voltage anomalies will occur at the power nodes to be installed. Based on the obtained power node connection diagram of the transformer substation and the location of the power nodes to be installed, the length, type, power supply capacity, and power consumption capacity of each line level in the target line from the transformer node to the power node to be installed are determined. Furthermore, the grid connection capacity is determined based on the application purpose and capacity of the power nodes to be installed. Then, combining the current capacity of the transformer and the node type of the power nodes to be installed, the anomalies in the distribution network are predicted, specifically whether transformer overload anomalies, transmission line overload anomalies, and voltage anomalies at the power nodes to be installed are predicted. Based on the above inventive concept, the distribution network anomaly prediction scheme of this application was designed.

[0065] The execution subject of the distribution network anomaly prediction method in this application can be a computer, or a server, terminal equipment, etc. This application does not limit it. The following explanation uses a computer as an example.

[0066] The following provides examples illustrating the application scenarios of the power distribution network anomaly prediction method provided in this application.

[0067] For example, in this application scenario, a user needs to install a charging pile. The user sends an installation request to the staff's computer through a terminal device. The installation request includes the location of the power node to be installed, the installed capacity, the node type, and the purpose of the installation.

[0068] To determine whether any abnormalities will occur in the power distribution network after the installation of power nodes, staff input the power node connection diagram and the current transformer capacity of the distribution area into the computer, which can then obtain the power node connection diagram and the current transformer capacity.

[0069] Then, based on the power node connection diagram and the location of the power node to be installed, the computer determines the line length, line type, power supply capacity, and power consumption capacity of each line level in the target line from the transformer node to the power node to be installed.

[0070] Then, the grid connection capacity is determined based on the application purpose and application capacity.

[0071] Finally, based on the grid connection capacity, the current capacity of the transformers, the node type, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level, it is predicted whether the distribution network will experience anomalies. Specifically, it predicts whether the distribution network will experience transformer overload anomalies, whether the distribution network will experience transmission line overload anomalies, and whether the distribution network will experience voltage anomalies at power nodes awaiting installation.

[0072] When a computer predicts an anomaly in the power distribution network, it can output anomaly information, anomaly type, and corresponding mitigation measures. Staff then perform maintenance on the power distribution network according to these measures to ensure that the installation of charging stations by users does not cause further anomalies.

[0073] It should be noted that the above scenario is only an example of an application scenario provided by the embodiments of this application. The embodiments of this application do not limit the actual form of the various devices included in the scenario, nor do they limit the interaction method between devices. In the specific application of the solution, it can be set according to actual needs.

[0074] The technical solution of this application will now be described in detail through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0075] Figure 1 This is a flowchart illustrating an embodiment of the power distribution network anomaly prediction method provided in this application. This embodiment describes how a computer predicts whether an anomaly will occur in the power distribution network based on the power node connection diagram, the current transformer capacity, and the location, applied capacity, node type, and application purpose of the power node to be installed. The method in this embodiment can be implemented through software, hardware, or a combination of both. Figure 1 As shown, the distribution network anomaly prediction method specifically includes the following steps:

[0076] S101: Obtain the power node connection diagram of the distribution area, the current capacity of the transformer, and the location, application capacity, node type and application purpose of the power node to be installed.

[0077] In this step, in order to predict whether the distribution network will experience any abnormalities after the installation of power nodes, the computer needs to obtain the power node connection diagram of the distribution area, the current capacity of the transformer, and the location, application capacity, node type, and application purpose of the power nodes to be installed.

[0078] The power node connection diagram includes power nodes and transmission lines within the transformer area, for example, Figure 2 Schematic diagram of power node connection lines provided in this application Figure 1 ,like Figure 2As shown in the figure, black dots mark power nodes, and thicker solid lines, thinner solid lines, and dashed lines represent transmission lines. Power node A is a transformer node.

[0079] It should be noted that the node type is either power supply type or power consumption type.

[0080] It should be noted that the application for installation can be for large industrial electricity use, large industrial power supply, residential use, general industrial use, commercial use, or other purposes. This application does not limit the application for installation purpose, which can be determined according to the actual situation.

[0081] It should be noted that the method for obtaining the power node connection map of the transformer area can be as follows: first, obtain the location of each power node in the transformer area and the connection relationship between the power nodes; then, generate a power node connection topology map based on the location of the power nodes and the connection relationship between the power nodes; and finally, combine the actual geographical path to generate a power node connection route map.

[0082] S102: Based on the power node connection diagram and the location of the power node to be installed, determine the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed.

[0083] In this step, after the computer obtains the power node connection diagram and the information of the power node to be installed, in order to improve the accuracy of the prediction, it determines the line length, line type, power supply capacity and power consumption capacity of each line level in the target line from the transformer node to the power node to be installed, based on the power node connection diagram and the location of the power node to be installed.

[0084] Specifically, based on the location of the power node to be installed, the reference node closest to the power node to be installed is determined in the power node connection line diagram.

[0085] The line from the transformer node to the base node in the power node connection diagram is taken as the target line.

[0086] Based on the power node connection diagram, obtain the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line.

[0087] It should be noted that the target line is a section of transmission line. Since the power node connection line diagram also includes a scale, as well as the line level, line type, and capacity of each power node of each section of transmission line, the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line can be determined.

[0088] It should be noted that line levels can include first level, second level, and third level, etc. The smaller the number of the line level, the higher the line level.

[0089] For example, in Figure 2 On this basis, Figure 3 Schematic diagram of power node connection lines provided in this application Figure 2 ,like Figure 3 As shown, rhombus E represents the location of the power node to be installed. In the power node connection diagram, the power node closest to the power node to be installed is power node B, so the reference node is power node B. The target route is the route starting from power node A, passing through connection point C and connection point D, and ending at power node B.

[0090] In the diagram, the thicker solid lines, thinner solid lines, and dashed lines represent transmission lines of different line levels. The line levels decrease sequentially from the thicker solid lines to the dashed lines to the thinner solid lines. The thicker solid lines represent the first level, the dashed lines represent the second level, and the thinner solid lines represent the third level.

[0091] The target route is classified into three levels: Level 1, Level 2, and Level 3.

[0092] The power supply capacity corresponding to level N in the target line refers to the sum of the capacities of the power nodes of the power supply type that the level N transmission line intersects with the target line can connect to.

[0093] The power capacity corresponding to level N in the target line refers to the sum of the capacities of the power nodes of the power consumption type that the level N transmission line intersects with the target line can connect to.

[0094] The line length corresponding to level N in the target line refers to the actual length of the transmission line at level N in the target line.

[0095] The line model corresponding to level N in the target line refers to the line model of the transmission line of level N that intersects with the target line.

[0096] exist Figure 3 In the diagram, there is a first-level transmission line on the left and right sides of power node A. The power supply capacity of the first level is the sum of the capacities of the power nodes of the power supply type that the first-level transmission line on the left side of power node A can connect to.

[0097] The first-level power capacity is the sum of the capacities of all power nodes of the power type that the first-level transmission line to the left of power node A can connect to.

[0098] The line length corresponding to the first level is the actual length from power node A to connection point C.

[0099] The line type corresponding to the first level is: the line type of the first-level transmission line to the left of power node A.

[0100] It should be noted that the capacity of the power node of the power supply type refers to the product of the actual capacity of the power node of the power supply type and the preset inverter conversion efficiency. The preset inverter conversion efficiency can be 0.85, 0.9, 0.95, etc. This application embodiment does not limit the preset inverter conversion efficiency, and it can be determined according to the actual situation.

[0101] S103: Determine the grid connection capacity based on the application purpose and application capacity.

[0102] In this step, after the computer obtains the location, applied capacity, node type, and application purpose of the power node to be installed, it determines the grid connection capacity based on the application purpose and applied capacity in order to improve the accuracy of prediction.

[0103] Since the power nodes to be installed generally do not operate at full power after being added to the distribution network, the grid connection capacity needs to be determined based on the application purpose and application capacity.

[0104] Specifically, the target capacity conversion factor corresponding to the application purpose is determined based on the pre-defined correspondence between the intended use and the capacity conversion factor.

[0105] For example, the capacity conversion factor for large industrial electricity consumption is 1, for large industrial power supply it is -0.8, for residential electricity consumption it is 0.35, for general industrial electricity consumption it is 0.7, for commercial electricity consumption it is 0.4, and for other applications it is 0.4. This application does not limit the correspondence between the intended use and the capacity conversion factor; it can be determined according to the actual situation.

[0106] The product of the applied capacity and the target capacity conversion factor is then used as the grid-connected capacity.

[0107] It should be noted that the execution order of steps S102 and S103 can be as follows: step S102 can be executed first, followed by step S103; step S103 can be executed first, followed by step S102; or steps S102 and S103 can be executed simultaneously. This embodiment does not limit the execution order of steps S102 and S103, and it can be determined according to the actual situation.

[0108] S104: Based on the grid-connected capacity, the current capacity of the transformer, the node type, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level, predict whether there will be any abnormalities in the distribution network.

[0109] In this step, after the computer determines the grid-connected capacity and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level, it predicts whether there will be any abnormalities in the distribution network based on the current capacity of the transformer and the node type.

[0110] Based on the grid-connected capacity and the current transformer capacity, it is possible to predict whether the distribution network will experience transformer overload anomalies. After determining the transmission line current and terminal voltage for each line level based on the grid-connected capacity, node capacity, and the corresponding line length, line type, power supply capacity, and power consumption capacity for each line level, it is possible to predict whether the distribution network will experience transmission line overload anomalies and whether the distribution network will experience voltage anomalies at power nodes awaiting installation, based on the transmission line current and terminal voltage.

[0111] The distribution network anomaly prediction method provided in this embodiment determines the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed, based on the obtained power node connection line diagram of the transformer area and the location of the power node to be installed. It then determines the grid connection capacity based on the application purpose and application capacity of the power node to be installed. Finally, based on the grid connection capacity, the current capacity of the transformer, the node type of the power node to be installed, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level, it predicts whether anomalies will occur in the distribution network. This solution improves prediction accuracy and avoids anomalies in the distribution network after the installation of power nodes by determining the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line, as well as the location, application capacity, node type, and application purpose of the power node to be installed.

[0112] Figure 4 This is a flowchart illustrating a second embodiment of the power distribution network anomaly prediction method provided in this application. Based on the above embodiments, this application describes how a computer predicts whether an anomaly will occur in the power distribution network. Figure 4 As shown, the distribution network anomaly prediction method specifically includes the following steps:

[0113] S401: Based on the grid-connected capacity and the current capacity of the transformer, predict whether the distribution network will experience transformer overload anomalies.

[0114] In this step, after the computer obtains the grid-connected capacity and the current transformer capacity, it can predict whether the distribution network will experience transformer overload anomalies based on the grid-connected capacity and the current transformer capacity.

[0115] Specifically, when the sum of the grid-connected capacity and the current transformer capacity is greater than or equal to the product of the transformer's rated capacity and a first preset ratio, it is predicted that the distribution network will experience a transformer overload anomaly; when the sum of the grid-connected capacity and the current transformer capacity is less than the product of the transformer's rated capacity and the first preset ratio, it is predicted that the distribution network will not experience a transformer overload anomaly.

[0116] Furthermore, when the sum of the grid-connected capacity and the current transformer capacity is greater than or equal to the product of the transformer's rated capacity and a first preset ratio, but less than the product of the transformer's rated capacity and a second preset ratio, a transformer overload anomaly is predicted in the distribution network; when the sum of the grid-connected capacity and the current transformer capacity is greater than or equal to the product of the transformer's rated capacity and a second preset ratio, a transformer overload anomaly is predicted in the distribution network; when the sum of the grid-connected capacity and the current transformer capacity is less than the product of the transformer's rated capacity and a first preset ratio, no transformer overload anomaly is predicted in the distribution network.

[0117] It should be noted that the second preset ratio is greater than the first preset ratio. The first preset ratio can be 75%, 80%, 85%, etc., and the second preset ratio can be 90%, 95%, 100%, etc. This application does not limit the first and second preset ratios; they can be determined according to actual circumstances.

[0118] By using grid-connected capacity, current transformer capacity, and a first preset ratio, the prediction of whether the distribution network will experience transformer overload is more consistent with the actual situation and can improve the accuracy of the prediction.

[0119] S402: Determine the target number of phases based on the grid connection capacity.

[0120] In this step, after the computer obtains the grid-connected capacity, in order to ensure the safety of the distribution network, different capacities correspond to different numbers of phases, and the number of phases will affect the accuracy of the prediction. Therefore, it is necessary to determine the target number of phases based on the grid-connected capacity.

[0121] When the grid-connected capacity is greater than the preset capacity, the target number of phases is determined to be three-phase. When the grid-connected capacity is less than or equal to the preset capacity, the target number of phases is determined to be single-phase.

[0122] It should be noted that the preset capacity can be 15kW, 16kW, 19kW, etc. This application does not limit the preset capacity; it can be determined according to actual conditions.

[0123] S403: In descending order of line level, for each line level, based on the line length, line type, power supply capacity, power consumption capacity, node type, grid connection capacity, and target number of phases, predict whether the distribution network will experience transmission line overload anomalies, and determine the terminal voltage corresponding to the line level.

[0124] In this step, after obtaining the target number of phases, the computer, in descending order of line level, predicts whether the distribution network will experience transmission line overload anomalies based on the line length, line type, power supply capacity, power consumption capacity, node type, grid connection capacity, and target number of phases for each line level, and determines the terminal voltage corresponding to the line level.

[0125] Specifically, based on the target number of phases, the voltage parameters, current calculation parameters, and voltage drop calculation parameters are determined.

[0126] Based on the preset correspondence between the number of phases and voltage parameters, the voltage parameters corresponding to the target number of phases are determined. The voltage parameter corresponding to three phases is 0.38, representing 380V. The voltage parameter corresponding to a single phase is 0.22, representing 220V.

[0127] Based on the preset correspondence between the number of phases and current calculation parameters, the current calculation parameters corresponding to the target number of phases are determined. These current calculation parameters are used to calculate the current. The current calculation parameters corresponding to the three phases are as follows: The current calculation parameter for a single item is 1.

[0128] Based on the preset correspondence between the number of phases and the voltage drop calculation parameters, the voltage drop calculation parameters corresponding to the target number of phases are determined. These voltage drop calculation parameters are used to calculate the voltage drop. The voltage drop calculation parameters corresponding to the three phases are as follows: The voltage drop calculation parameter for a single item is 2.

[0129] Then, based on the power supply capacity, power consumption capacity, grid connection capacity, node type, voltage parameters, and current calculation parameters corresponding to the line level, the transmission line current is calculated.

[0130] If the node type is power supply type, then the sum of the power supply capacity and the grid connection capacity is used as the target power supply capacity; the electricity consumption is used as the target electricity consumption capacity.

[0131] If the node type is power consumption type, then the sum of the power consumption capacity and the grid connection capacity is used as the target power consumption capacity; and the power supply consumption is used as the target power supply capacity.

[0132] According to the formula Calculate the supply current, where, Indicates the supply current. Indicates the target power supply capacity. Indicates the preset power factor. Indicates voltage parameters. This represents the parameters used for current calculation.

[0133] According to the formula Calculate the electrical current, where, Indicates the electrical current. Indicates the target power consumption capacity. Indicates the preset power factor. Indicates voltage parameters. This represents the parameters used for current calculation.

[0134] The difference between the current used and the current supplied is then taken as the transmission line current.

[0135] Or, according to the formula Calculate the current in the transmission line, where, Indicates the current in the transmission line. Indicates the target power supply capacity. Indicates the target power consumption capacity. Indicates the preset power factor. Indicates voltage parameters. This represents the parameters used for current calculation.

[0136] It should be noted that the preset power factor can be 0.8, 0.85, 0.9, etc. This application embodiment does not limit the preset power factor, and it can be determined according to the actual situation.

[0137] It is also necessary to determine the safe current corresponding to the circuit model based on the preset correspondence between the model and the current.

[0138] For example, Table 1 is a table showing the correspondence between model number and current provided in this application.

[0139] Table 1

[0140]

[0141] It should be noted that Table 1 is only an example of the correspondence between model and current. The embodiments of this application do not limit the correspondence between model and current, and can be determined according to the actual situation.

[0142] Furthermore, based on the transmission line current and the safe current, it can be predicted whether the distribution network will experience abnormal overload of the transmission lines.

[0143] When the transmission line current is greater than or equal to the product of the safe current and the third preset ratio, it is predicted that the distribution network will experience a transmission line overload anomaly; when the transmission line current is less than the product of the safe current and the third preset ratio, it is predicted that the distribution network will not experience a transmission line overload anomaly.

[0144] Furthermore, when the transmission line current is greater than or equal to the product of the safe current and the third preset ratio, and less than the product of the safe current and the fourth preset ratio, it is predicted that the distribution network will experience a transmission line overload anomaly; when the transmission line current is greater than or equal to the product of the safe current and the fourth preset ratio, it is predicted that the distribution network will experience a transmission line overload anomaly; when the transmission line current is less than the product of the safe current and the third preset ratio, it is predicted that the distribution network will not experience a transmission line overload anomaly.

[0145] It should be noted that the fourth preset ratio is greater than the third preset ratio. The third preset ratio can be 75%, 80%, 85%, etc., and the fourth preset ratio can be 90%, 95%, 100%, etc. This application does not limit the third and fourth preset ratios; they can be determined according to actual circumstances.

[0146] To determine whether there are voltage anomalies at the power nodes to be installed in the distribution network, it is also necessary to determine the end voltage. Based on voltage drop calculation parameters, current calculation parameters, line length, line type, transmission line current, and starting voltage, the voltage drop corresponding to the line level is calculated.

[0147] Based on the preset correspondence between model and resistance and reactance, determine the target unit resistance and target unit reactance corresponding to the line model.

[0148] For example, Table 2 is a table showing the correspondence between model numbers and resistors and reactances provided in this application.

[0149] Table 2

[0150]

[0151] It should be noted that Table 2 is only an example of the correspondence between model number and resistance and reactance. The embodiments of this application do not limit the correspondence between model number and resistance and reactance, and can be determined according to the actual situation.

[0152] Calculate the voltage drop corresponding to the line level based on the voltage drop calculation parameters, current calculation parameters, line length, target unit resistance, target unit reactance, transmission line current, and starting voltage.

[0153] According to the formula ,in, I represents voltage drop, L represents transmission line current, and L represents line length. This represents the parameters used in pressure drop calculations. Indicates the starting voltage. Indicates the parameters for current calculation. Indicates the target unit resistance. Indicates the target unit reactance. This represents the first preset angle parameter. This indicates the second preset angle parameter.

[0154] It should be noted that the first preset angle parameter is 0.95, the second preset angle parameter is 0.31, and the unit of the starting voltage is kilovolts.

[0155] The sum of the starting voltage and the voltage drop corresponding to the line level is taken as the terminal voltage corresponding to the line level.

[0156] If the line level is the highest among all line levels, the starting voltage is the preset transformer voltage; if the line level is not the highest among all line levels, the starting voltage is the end voltage of the next higher line level. The preset transformer voltage can be 240V, 245V, 260V, etc.

[0157] S404: Based on the terminal voltage and target number of phases corresponding to each line level, predict whether there will be voltage anomalies at the power nodes to be installed in the distribution network.

[0158] In this step, after the computer obtains the terminal voltage corresponding to each line level, it predicts whether there will be voltage anomalies at the power nodes to be installed in the distribution network based on the terminal voltage and target number of phases corresponding to each line level.

[0159] Specifically, the lowest-level terminal voltage in all line levels is used as the comparison voltage. The comparison voltage is the voltage at the power node to be installed.

[0160] Based on the preset correspondence between the number of phases and the voltage threshold range, the target voltage threshold range corresponding to the target number of phases is determined.

[0161] For example, the voltage threshold range for three-phase and single-phase is (198, 235), and the voltage threshold range for single-phase is (342, 407). This application does not limit the voltage threshold range for three-phase and single-phase, and it can be determined according to actual conditions.

[0162] If the voltage to be compared is within the target voltage threshold range, it is predicted that there is no voltage anomaly at the power node to be installed in the distribution network.

[0163] If the voltage to be compared does not fall within the target voltage threshold range, it is predicted that an abnormal voltage will occur at the power node to be installed in the distribution network.

[0164] The distribution network anomaly prediction method provided in this embodiment predicts whether the distribution network will experience transformer overload anomalies by using grid-connected capacity and current transformer capacity; predicts whether the distribution network will experience transmission line overload anomalies by using transmission line current and safe current; and predicts whether the distribution network will experience voltage anomalies at power nodes to be installed by using the range between the terminal voltage and the target voltage threshold, thereby improving the accuracy of distribution network anomaly prediction.

[0165] The following describes, through Example 3 of the power distribution network anomaly prediction method provided in this application, the situation where the computer determines and outputs mitigation measures.

[0166] When the computer predicts that a transformer overload anomaly occurs in the distribution network, it determines whether the rated capacity of the transformer is less than the preset capacity increase benchmark capacity. If the rated capacity of the transformer is less than the preset capacity increase benchmark capacity, the first preset measure is determined and then the first preset measure is output.

[0167] If the rated capacity of the transformer is greater than or equal to the preset capacity increase benchmark capacity, then the mitigation measure is determined to be the second preset measure, and the second preset measure is then output.

[0168] It should be noted that the preset expansion baseline capacity can be 450kVA, 500kVA, 550kVA, etc. The first preset measure indicates measures to increase the capacity of the transformer, and the second preset measure indicates measures for adding new transformers.

[0169] When the computer predicts that a transmission line is overloaded in the distribution network, it determines whether there is a transmission line with a wire diameter smaller than the preset wire diameter in the target line. If there is a transmission line with a wire diameter smaller than the preset wire diameter in the target line, the third preset measure is determined as the mitigation measure, and then the third preset measure is output.

[0170] If there are no transmission lines with a wire diameter smaller than the preset wire diameter in the target line, then the treatment measure is determined to be the fourth preset measure, and the fourth preset measure is output.

[0171] It should be noted that the preset wire diameter can be 110 square millimeters, 120 square millimeters, 130 square millimeters, etc. The third preset measure is a measure to indicate the replacement of the conductor, and the fourth preset measure is a measure to indicate the construction of a new conductor.

[0172] When the computer predicts that an abnormal voltage occurs at a power node to be installed in the power distribution network, it determines whether the distance between the transformer node and the power node to be installed is less than a preset distance. If the distance between the transformer node and the power node to be installed is greater than or equal to the preset distance, the second preset measure is determined and then output.

[0173] If the distance between the transformer node and the power node to be installed is less than the preset distance, it is determined whether there is a transmission line with a wire diameter smaller than the preset wire diameter in the target line; if there is a transmission line with a wire diameter smaller than the preset wire diameter in the target line, the treatment measure is determined to be the third preset measure, and then the third preset measure is output.

[0174] If there are no transmission lines with a wire diameter smaller than the preset wire diameter in the target line, then the treatment measure is determined to be the fourth preset measure, and the fourth preset measure is output.

[0175] It should be noted that the preset distance can be 450 meters, 500 meters, 600 meters, etc.

[0176] It should be noted that if the treatment measure is determined to be the second preset measure, the candidate area will be drawn and output in the power node connection diagram. The candidate area is a circle with the location of the power node to be installed as the center and a radius of a preset distance. Staff can select the location of the new transformer from the candidate area.

[0177] The distribution network anomaly prediction method provided in this embodiment determines different mitigation measures based on different distribution network anomalies, as well as the rated capacity of transformers, the wire diameter of target lines, and the distance between transformer nodes and power nodes to be installed, thereby improving the safety of the distribution network.

[0178] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0179] Figure 5 This is a schematic diagram of the structure of an embodiment of the power distribution network anomaly prediction device provided in this application. Figure 5 As shown, the power distribution network anomaly prediction device 50 includes:

[0180] The acquisition module 51 is used to acquire the power node connection line diagram of the distribution area, the current capacity of the transformer, and the location, application capacity, node type and application purpose of the power node to be installed;

[0181] Processing module 52 is used for:

[0182] Based on the power node connection diagram and the location of the power node to be installed, determine the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed;

[0183] The grid connection capacity is determined based on the application purpose and the application capacity.

[0184] The prediction module 53 is used to predict whether the distribution network is abnormal based on the grid-connected capacity, the current capacity of the transformer, the node type, and the line length, line model, power supply capacity and power consumption capacity corresponding to each line level.

[0185] Furthermore, the processing module 52 is specifically used for:

[0186] Based on the location of the power node to be installed, determine the reference node that is closest to the power node to be installed in the power node connection line diagram;

[0187] The line from the transformer node to the reference node in the power node connection diagram is taken as the target line;

[0188] Based on the power node connection diagram, obtain the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line.

[0189] Furthermore, the processing module 52 is specifically used for:

[0190] Based on the preset correspondence between purpose and capacity conversion coefficient, determine the target capacity conversion coefficient corresponding to the application purpose;

[0191] The product of the applied capacity and the target capacity conversion factor is taken as the grid-connected capacity.

[0192] Furthermore, the prediction module 53 is specifically used for:

[0193] Based on the grid-connected capacity and the current capacity of the transformer, predict whether the distribution network will experience transformer overload anomalies;

[0194] Determine the target number of phases based on the grid connection capacity;

[0195] In descending order of line level, for each line level, based on the line length, line type, power supply capacity, power consumption capacity, node type, grid connection capacity, and target number of phases, predict whether the distribution network will experience transmission line overload anomaly, and determine the terminal voltage corresponding to the line level.

[0196] Based on the terminal voltage corresponding to each line level and the target number of phases, predict whether the distribution network will experience voltage anomalies at the power nodes to be installed.

[0197] Furthermore, the prediction module 53 is specifically used for:

[0198] Based on the target number of phases, determine the voltage parameters, current calculation parameters, and voltage drop calculation parameters;

[0199] Calculate the transmission line current based on the power supply capacity, power consumption capacity, grid connection capacity, node type, voltage parameters, and current calculation parameters corresponding to the line level;

[0200] Based on the preset correspondence between model and current, the safe current corresponding to the circuit model is determined;

[0201] Based on the transmission line current and the safety current, predict whether the power distribution network will experience a transmission line overload anomaly.

[0202] Calculate the voltage drop corresponding to the line level based on the voltage drop calculation parameters, the current calculation parameters, the line length, the line type, the transmission line current, and the starting voltage;

[0203] The sum of the starting voltage and the voltage drop corresponding to the line level is taken as the terminal voltage corresponding to the line level;

[0204] Wherein, if the line level is the highest among all line levels, the starting voltage is a preset transformer voltage; if the line level is not the highest among all line levels, the starting voltage is the end voltage of the next higher level of the line level.

[0205] Furthermore, the prediction module 53 is specifically used for:

[0206] Based on the preset correspondence between model and resistance and reactance, determine the target unit resistance and target unit reactance corresponding to the circuit model;

[0207] Calculate the voltage drop corresponding to the line level based on the voltage drop calculation parameters, the current calculation parameters, the line length, the target unit resistance, the target unit reactance, the transmission line current, and the starting voltage.

[0208] Furthermore, the prediction module 53 is specifically used for:

[0209] The lowest level of voltage at the end of all line levels is used as the voltage to be compared.

[0210] Based on the preset correspondence between the number of phases and the voltage threshold range, the target voltage threshold range corresponding to the target number of phases is determined;

[0211] If the voltage to be compared falls within the target voltage threshold range, it is predicted that no voltage anomalies have occurred at the power nodes to be installed in the distribution network.

[0212] If the voltage to be compared does not fall within the target voltage threshold range, it is predicted that the voltage of the power node to be installed in the distribution network will be abnormal.

[0213] The power distribution network anomaly prediction device provided in this embodiment is used to execute the technical solution in any of the aforementioned method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0214] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 6 As shown, the electronic device 60 includes:

[0215] Processor 61, memory 62, and communication interface 63;

[0216] The memory 62 is used to store the executable instructions of the processor 61;

[0217] The processor 61 is configured to execute the technical solutions in any of the foregoing method embodiments by executing the executable instructions.

[0218] Optionally, the memory 62 can be either standalone or integrated with the processor 61.

[0219] Optionally, when the memory 62 is a device independent of the processor 61, the electronic device 60 may further include:

[0220] Bus 64, memory 62 and communication interface 63 are connected to processor 61 through bus 64 and complete communication with each other. Communication interface 63 is used to communicate with other devices.

[0221] Optionally, the communication interface 63 can be implemented using a transceiver. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write databases, and read-only databases). The memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0222] Bus 64 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0223] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0224] The electronic device is used to execute the technical solutions in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0225] This application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the technical solutions provided in any of the foregoing method embodiments.

[0226] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the technical solutions provided in any of the foregoing method embodiments.

[0227] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0228] Finally, 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 or all of the technical features therein. 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.

Claims

1. A method for predicting anomalies in a power distribution network, characterized in that, include: Obtain the power node connection diagram of the distribution area, the current capacity of the transformer, and the location, application capacity, node type, and application purpose of the power node to be installed; Based on the power node connection diagram and the location of the power node to be installed, determine the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed; The grid connection capacity is determined based on the application purpose and the application capacity. Based on the grid-connected capacity, the current capacity of the transformer, the node type, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level, predict whether the distribution network will experience anomalies.

2. The method according to claim 1, characterized in that, The step of determining the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed, based on the power node connection diagram and the location of the power node to be installed, includes: Based on the location of the power node to be installed, determine the reference node that is closest to the power node to be installed in the power node connection line diagram; The line from the transformer node to the reference node in the power node connection diagram is taken as the target line; Based on the power node connection diagram, obtain the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line.

3. The method according to claim 1, characterized in that, The determination of grid connection capacity based on the application purpose and the application capacity includes: Based on the preset correspondence between purpose and capacity conversion coefficient, determine the target capacity conversion coefficient corresponding to the application purpose; The product of the applied capacity and the target capacity conversion factor is taken as the grid-connected capacity.

4. The method according to any one of claims 1 to 3, characterized in that, The method of predicting whether an anomaly occurs in the distribution network based on the grid-connected capacity, the current capacity of the transformer, the node type, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level includes: Based on the grid-connected capacity and the current capacity of the transformer, predict whether the distribution network will experience transformer overload anomalies; Determine the target number of phases based on the grid connection capacity; In descending order of line level, for each line level, based on the line length, line type, power supply capacity, power consumption capacity, node type, grid connection capacity, and target number of phases, predict whether the distribution network will experience transmission line overload anomaly, and determine the terminal voltage corresponding to the line level. Based on the terminal voltage corresponding to each line level and the target number of phases, predict whether the distribution network will experience voltage anomalies at the power nodes to be installed.

5. The method according to claim 4, characterized in that, The step of predicting whether the distribution network experiences transmission line overload anomalies based on the line length, line type, power supply capacity, and power consumption capacity corresponding to the line level, as well as the node type, grid connection capacity, and target number of phases, and determining the terminal voltage corresponding to the line level, includes: Based on the target number of phases, determine the voltage parameters, current calculation parameters, and voltage drop calculation parameters; Calculate the transmission line current based on the power supply capacity, power consumption capacity, grid connection capacity, node type, voltage parameters, and current calculation parameters corresponding to the line level; Based on the preset correspondence between model and current, the safe current corresponding to the circuit model is determined; Based on the transmission line current and the safety current, predict whether the power distribution network will experience a transmission line overload anomaly. Calculate the voltage drop corresponding to the line level based on the voltage drop calculation parameters, the current calculation parameters, the line length, the line type, the transmission line current, and the starting voltage; The sum of the starting voltage and the voltage drop corresponding to the line level is taken as the terminal voltage corresponding to the line level; Wherein, if the line level is the highest among all line levels, the starting voltage is a preset transformer voltage; if the line level is not the highest among all line levels, the starting voltage is the end voltage of the next higher level of the line level.

6. The method according to claim 5, characterized in that, The step of calculating the voltage drop corresponding to the line level based on the voltage drop calculation parameters, the current calculation parameters, the line length, the line type, the transmission line current, and the starting voltage includes: Based on the preset correspondence between model and resistance and reactance, determine the target unit resistance and target unit reactance corresponding to the circuit model; Calculate the voltage drop corresponding to the line level based on the voltage drop calculation parameters, the current calculation parameters, the line length, the target unit resistance, the target unit reactance, the transmission line current, and the starting voltage.

7. The method according to claim 4, characterized in that, The step of predicting whether the distribution network will experience voltage anomalies at the power nodes to be installed, based on the terminal voltage corresponding to each line level and the target number of phases, includes: The lowest level of voltage at the end of all line levels is used as the voltage to be compared. Based on the preset correspondence between the number of phases and the voltage threshold range, the target voltage threshold range corresponding to the target number of phases is determined; If the voltage to be compared falls within the target voltage threshold range, it is predicted that no voltage anomalies have occurred at the power nodes to be installed in the distribution network. If the voltage to be compared does not fall within the target voltage threshold range, it is predicted that the voltage of the power node to be installed in the distribution network will be abnormal.

8. A power distribution network anomaly prediction device, characterized in that, include: The acquisition module is used to acquire the power node connection line diagram of the distribution area, the current capacity of the transformer, and the location, application capacity, node type and application purpose of the power node to be installed; Processing module, used for: Based on the power node connection diagram and the location of the power node to be installed, determine the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level in the target line from the transformer node to the power node to be installed; The grid connection capacity is determined based on the application purpose and the application capacity. The prediction module is used to predict whether an anomaly will occur in the distribution network based on the grid-connected capacity, the current capacity of the transformer, the node type, and the line length, line type, power supply capacity, and power consumption capacity corresponding to each line level.

9. An electronic device, characterized in that, include: Processor, memory, communication interface; The memory is used to store the executable instructions of the processor; The processor is configured to execute the power distribution network anomaly prediction method according to any one of claims 1 to 7 by executing the executable instructions.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the distribution network anomaly prediction method according to any one of claims 1 to 7.

11. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, is used to implement the distribution network anomaly prediction method according to any one of claims 1 to 7.

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