Line processing method, device, electronic device, computer readable storage medium and computer program product

Abstract

The present application provides a line processing method, device, electronic device, computer program product and computer-readable storage medium; the method includes: obtaining line data of a power distribution system, constructing an initial line model based on the line data, wherein the initial line model includes multiple power nodes; dividing the initial line model to obtain multiple line sub-models, merging the power nodes in each line sub-model to obtain a first line model; optimizing the first line model to obtain a second line model; setting a virtual series compensation device in the second line model based on the node voltage and preset voltage of the power node in the second line model to obtain a target line model; setting a series compensation device in the power distribution system based on the position information of the virtual series compensation device in the target line model. Through the present application, the efficiency and accuracy of line processing can be improved.

Description

Technical Field

[0001] The present application relates to power system technology, and in particular to a line processing method, device, electronic equipment, computer-readable storage medium and computer program product. Background Art

[0002] For transmission lines, the voltage of the transmission line gradually decreases from the substation end to the power consumption end. When the voltage at a power consumption node in the line drops below the threshold, a series compensation device needs to be installed in front of the power consumption node to solve the voltage drop problem. Among them, the installation position and series compensation capacity of the series compensation device can be determined by series compensation simulation, but a large number of power consumption nodes are involved in the real line, so it is necessary to merge multiple power consumption nodes in the real line to reduce the number of nodes. Manual node merging is inefficient and inaccurate, which will affect the efficiency and accuracy of series compensation simulation. Summary of the invention

[0003] The embodiments of the present application provide a line processing method, device, electronic device, computer-readable storage medium and computer program product, which can improve the efficiency and accuracy of line processing.

[0004] The technical solution of the embodiment of the present application is implemented as follows:

[0005] The present application provides a line processing method, the method comprising:

[0006] Acquire line data of a power distribution system, and construct an initial line model based on the line data, wherein the initial line model includes a plurality of power consumption nodes;

[0007] Dividing the initial line model to obtain a plurality of line sub-models, and merging the power consumption nodes in each of the line sub-models to obtain a first line model;

[0008] Performing tuning processing on the first line model to obtain a second line model;

[0009] Based on the node voltage of the power node in the second line model and the preset voltage, a virtual series compensation device is set in the second line model to obtain a target line model;

[0010] Based on the position information of the virtual series compensation device in the target line model, a series compensation device is arranged in the power distribution system.

[0011] The present application provides a line processing device, including:

[0012] An acquisition module, used to acquire line data of a power distribution system, and construct an initial line model based on the line data, wherein the initial line model includes a plurality of power consumption nodes;

[0013] A processing module, configured to divide the initial line model to obtain a plurality of line sub-models, and merge the power consumption nodes in each of the line sub-models to obtain a first line model;

[0014] The processing module is further used to perform tuning processing on the first line model to obtain a second line model;

[0015] a setting module, configured to set a virtual series compensation device in the second line model based on a node voltage of a power node in the second line model and a preset voltage, so as to obtain a target line model;

[0016] The setting module is further used to set a series compensation device in the power distribution system based on the position information of the virtual series compensation device in the target line model.

[0017] An embodiment of the present application provides an electronic device, the electronic device comprising:

[0018] A memory for storing computer executable instructions or computer programs;

[0019] The processor is used to implement the method provided in the embodiment of the present application when executing the computer executable instructions or computer program stored in the memory.

[0020] An embodiment of the present application provides a computer-readable storage medium storing a computer program or a computer-executable instruction for implementing the line processing method provided in the embodiment of the present application when executed by a processor.

[0021] An embodiment of the present application provides a computer program product, including a computer program or computer executable instructions. When the computer program or computer executable instructions are executed by a processor, the line processing method provided in the embodiment of the present application is implemented.

[0022] The embodiments of the present application have the following beneficial effects:

[0023] First, an initial line model of a real distribution network system is built according to the line conditions of the real distribution system, so that the initial line model conforms to the real situation of the distribution network system. Then, the initial line model is divided to obtain multiple line sub-models, and the power consumption nodes in each line sub-model are merged to obtain a first line model, thereby reducing the number of power consumption nodes. After that, the first line model is optimized so that the obtained second line model is closer to the real situation of the distribution network system. Then, according to the node voltage and preset voltage of the power consumption node in the second line model, a virtual series compensation device is set in the second line model to obtain a target line model, so that the series compensation device is set in the distribution system according to the position information of the virtual series compensation device in the target line model. In this way, by dividing the initial line model and then merging the power consumption nodes, the efficiency and accuracy of the merging process can be improved. Through the optimization process, the power consumption nodes in the second line model conform to the real situation of the distribution network system, thereby improving the accuracy of setting the virtual series compensation device in the second line model, which is convenient for the subsequent setting of the series compensation device in the distribution system according to the position information of the virtual series compensation device in the target line model, thereby improving the efficiency and accuracy of line processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a schematic diagram of the architecture of a line processing system 100 provided in an embodiment of the present application;

[0025] Figure 2 is a schematic diagram of the structure of the terminal 400 provided in an embodiment of the present application;

[0026] Figure 3A It is a flow chart of a line processing method provided in an embodiment of the present application;

[0027] Figure 3B is a schematic diagram of a process for determining a first line model provided in an embodiment of the present application;

[0028] Figure 3C It is a flowchart of a method for merging main line power nodes provided in an embodiment of the present application;

[0029] Figure 3D It is a flowchart of the tuning processing method provided in the embodiment of the present application;

[0030] Figure 4 is a schematic diagram of an initial circuit model provided in an embodiment of the present application;

[0031] Figure 5 is a schematic diagram of a circuit sub-model provided in an embodiment of the present application;

[0032] Figure 6 This is a schematic diagram of the line sub-model after initial merging provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0034] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0035] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0036] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0037] Unless otherwise defined, all technical and scientific terms used in the embodiments of the present application have the same meanings as those commonly understood by those skilled in the art. The terms used in the embodiments of the present application are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0038] The relevant data collection and processing in the embodiments of this application should be strictly in accordance with the requirements of relevant laws and regulations when applied in examples, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of authorization of laws and regulations and the personal information subject.

[0039] Before further describing the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0040] 1) Series compensation device: A series compensation device is a device used in power systems to improve system stability and transmission efficiency. Its main function is to compensate for the reactive power of transmission lines, improve the power factor of the system, reduce line losses, and improve the transmission capacity of transmission lines under certain circumstances.

[0041] 2) K-means clustering algorithm (k-means): A typical unsupervised machine learning algorithm used to divide a data set into K clusters. The purpose is to make the data points in each cluster as similar as possible, while the data points in different clusters are as different as possible.

[0042] 3) Power consumption nodes: points in the power grid where electricity is consumed, i.e., access points of power loads. These nodes may be homes, commercial buildings, industrial facilities, or any other locations where electricity is consumed.

[0043] 4) Load value: The amount of power or electrical energy consumed by electrical equipment or nodes in the power system within a specific period of time.

[0044] Series compensation devices are particularly important for connecting long-distance power sources or supplying power to large industrial users. For 10kV transmission lines, the voltage of the transmission line gradually decreases from the substation end to the power consumption end. When the voltage at a power consumption node in the line drops below 9.6kV, a series compensation device needs to be installed in front of the power consumption node to solve the voltage drop problem. The installation location and series compensation capacity of the series compensation device can be determined by series compensation simulation, but a large number of power consumption nodes are involved in the real line, so it is necessary to merge multiple power consumption nodes in the real line to reduce the number of power consumption nodes.

[0045] When merging power consumption nodes, the following principles should generally be followed:

[0046] 1. Merge the places where the load values ​​are concentrated into one power consumption node, and try to merge the power consumption nodes to the location where the large load is located;

[0047] 2. For the main line of the power distribution system, the span of the power consumption nodes should not be too large, generally about 2 to 3 kilometers;

[0048] 3. For the branch line of the distribution system, if it is a branch line close to the front end of the main line and the length of the branch line does not exceed 5km, the branch line will be merged into the main line; if the branch line is at the end of the main line, it is necessary to merge it into the power node according to the length of the branch line and the load size.

[0049] When the above principles are followed manually to merge power nodes, the merging efficiency is low and it is difficult to ensure the accuracy of the merging. It should be noted that the above content is only used to introduce background technology that may be related to this application, which helps readers better understand this application, and does not constitute an admission of the prior art.

[0050] The embodiments of the present application provide a line processing method, device, equipment, computer-readable storage medium, and computer program product, which can improve the efficiency and accuracy of merging power nodes, thereby improving the efficiency and accuracy of line processing. The following describes an exemplary application of the electronic device provided by the embodiments of the present application. The electronic device provided by the embodiments of the present application can be implemented as various types of terminals such as laptops, tablet computers, desktop computers, smart phones, etc., and can also be implemented as a server. Below, an exemplary application when the device is implemented as a terminal will be described.

[0051] See also Figure 1 , Figure 1 It is a schematic diagram of the architecture of the line processing system 100 provided in an embodiment of the present application. The terminal 400 includes a display interface 401. The terminal 400 is connected to the server 200 via a network 300. The network 300 may be a wide area network or a local area network, or a combination of the two.

[0052] The terminal 400 is used to obtain line data of the power distribution system. The line information can be obtained through user input information, or the line data can be directly obtained from the server 200. An initial line model is constructed based on the line data, and the initial line model is divided to obtain multiple line sub-models, and the power consumption nodes in each line sub-model are merged to obtain a first line model. Then, the first line model is optimized to obtain a second line model, and based on the node voltage and the preset voltage of the power consumption node in the second line model, a virtual series compensation device is set in the second line model to obtain a target line model. The target line model is displayed through the display interface 401, and the user can actually install the series compensation device in the power distribution system according to the location information of the virtual series compensation device in the displayed target line model.

[0053] Alternatively, the line data of the power distribution system is obtained through the terminal 400, and then the line data is sent to the server 200. The server 200 receives the line data and constructs an initial line model, divides the initial line model to obtain multiple line sub-models, and merges the power consumption nodes in each line sub-model to obtain a first line model, and then optimizes the first line model to obtain a second line model. Based on the node voltage and the preset voltage of the power consumption node in the second line model, a virtual series compensation device is set in the second line model to obtain a target line model, and the obtained target line model is sent to the terminal 400. The terminal 400 displays the target line model through the display interface 401, so that the user can actually install the series compensation device in the power distribution system according to the location information of the virtual series compensation device in the displayed target line model.

[0054] See also Figure 2 , Figure 2 is a schematic diagram of the structure of the terminal 400 provided in an embodiment of the present application, Figure 2 The terminal 400 shown includes: at least one processor 410, a memory 450, at least one network interface 420 and a user interface 430. The various components in the terminal 400 are coupled together via a bus system 440. It is understood that the bus system 440 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 440 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, the bus system 440 is not shown in FIG. Figure 2 Various buses are labeled as bus system 440 .

[0055] The processor 410 may be an integrated circuit chip having signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., wherein the general-purpose processor may be a microprocessor or any conventional processor, etc.

[0056] The user interface 430 includes one or more output devices 431 that enable presentation of media content, including one or more speakers and / or one or more visual display screens. The user interface 430 also includes one or more input devices 432, including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.

[0057] The memory 450 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard disk drives, optical disk drives, etc. The memory 450 may optionally include one or more storage devices that are physically remote from the processor 410.

[0058] The memory 450 includes a volatile memory or a non-volatile memory, and may also include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), and the volatile memory may be a random access memory (RAM). The memory 450 described in the embodiments of the present application is intended to include any suitable type of memory.

[0059] In some embodiments, memory 450 can store data to support various operations, examples of which include programs, modules, and data structures, or a subset or superset thereof, as exemplarily described below.

[0060] Operating system 451, including system programs for processing various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks;

[0061] A network communication module 452, used to reach other electronic devices via one or more (wired or wireless) network interfaces 420, exemplary network interfaces 420 include: Bluetooth, Wireless LAN (WiFi), and Universal Serial Bus (USB), etc.;

[0062] a presentation module 453 for enabling presentation of information via one or more output devices 431 (e.g., display screen, speaker, etc.) associated with the user interface 430 (e.g., a user interface for operating peripherals and displaying content and information);

[0063] The input processing module 454 is used to detect one or more user inputs or interactions from one of the one or more input devices 432 and translate the detected inputs or interactions.

[0064] In some embodiments, the device provided in the embodiments of the present application can be implemented in software. Figure 2 The line processing device 455 stored in the memory 450 is shown, which can be software in the form of a program and a plug-in, etc., including the following software modules: an acquisition module 4551, a processing module 4552, and a setting module 4553. These modules are logical, so they can be arbitrarily combined or further divided according to the functions implemented. The functions of each module will be described below.

[0065] In other embodiments, the device provided in the embodiments of the present application can be implemented in hardware. As an example, the device provided in the embodiments of the present application can be a processor in the form of a hardware decoding processor, which is programmed to execute the line processing method provided in the embodiments of the present application. For example, the processor in the form of a hardware decoding processor can adopt one or more application specific integrated circuits (Application Specific Integrated Circuit, ASIC), digital signal processors (Digital Signal Processor, DSP), programmable logic devices (Programmable Logic Device, PLD), complex programmable logic devices (Complex Programmable Logic Device, CPLD), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other electronic components.

[0066] The line processing method provided in the embodiment of the present application will be explained in combination with the exemplary application and implementation of the terminal provided in the embodiment of the present application.

[0067] The following describes the line processing method provided by the embodiment of the present application. As mentioned above, the electronic device implementing the line processing method of the embodiment of the present application can be a terminal, a server, or a combination of the two. Therefore, the execution subject of each step will not be repeatedly described below.

[0068] It should be noted that in the examples of line processing below, the lines of the distribution network are used as an example. Those skilled in the art, based on their understanding of the following, can apply the line processing method provided in the embodiments of the present application to the processing of other types of lines.

[0069] See also Figure 3A , Figure 3A is a flow chart of the line processing method provided in the embodiment of the present application, which will be combined with Figure 3A The steps shown are explained.

[0070] In step 101, line data of a power distribution system is acquired, and an initial line model is constructed based on the line data.

[0071] Here, the distribution system is the distribution network, which is responsible for distributing the electric energy output by the substation to various types of users (residential, commercial and industrial, etc.). The distribution system connects the substation and the user end and is the last level of the power supply network. The line data includes various information and parameters related to the lines in the distribution system, such as the length of each line, the location of multiple power nodes included on the line, the load value of each power node, etc. The initial line model includes multiple power nodes, as well as components such as substations, transformers, distribution rooms, switches, inductors, etc. Among them, the power node refers to the point where electric energy is consumed in the distribution system, that is, the final destination of the power supply, such as any power consumer such as residential households, commercial buildings, industrial facilities, etc. When building the initial line model based on the line data, professional power system analysis software can be used to build the model, such as ETAP, PSCAD, PSS / E, etc. According to the line data, the parameters required by the software are input to enable the software to automatically build the line model, and then the line model is adjusted to obtain the initial line model by comparing the difference between the line model built by the software and the actual distribution system.

[0072] In step 102, the initial line model is divided to obtain a plurality of line sub-models, and the power consumption nodes in each line sub-model are merged to obtain a first line model.

[0073] Here, the initial line model is divided by dividing the main line in the initial line model, thereby obtaining multiple line sub-models. Then, for each line sub-model, the power consumption nodes in the line sub-model are merged, and the merged multiple line sub-models are combined together as the first line model.

[0074] In some embodiments, Figure 3B As shown, step 102 can be implemented through steps 1021 to 1026, which are described in detail below:

[0075] In step 1021, the main line and branch lines in the initial line model are determined.

[0076] Here, in the initial line model, the main line and the branch line are two basic components, which together constitute the transmission network of electricity from the substation to the end user. The main line is the main power supply line in the distribution network. The main line usually extends from the substation. The branch line is a smaller line branched from the main line, which distributes electric energy from the main line to a specific user or area. Among them, since the power system simulation software has the function of automatically identifying the main line and the branch line, the main line and the branch line in the generated initial line model usually have corresponding data identifications, for example, the main line and the branch line are assigned different identifiers or colors, and the main line and the branch line can be distinguished in the initial line model according to the identification information.

[0077] In step 1022, the initial line model is divided based on the first preset length and the main line to obtain a plurality of line sub-models.

[0078] Here, the first preset length is set according to the span requirement of the power consumption node merger. According to the distance requirement of the power consumption node merger span, the main line of the initial line model is evenly divided into multiple segments, and the length of each segment is the first preset length, which is equivalent to dividing the main line into a segment every first preset length starting from the substation, wherein the length of the last segment is less than or equal to the first preset length, and each segment corresponds to a line sub-model. For example, taking the power consumption node merger span of 2 kilometers as an example, the first preset length can be set to N×2 kilometers, where N is an integer from 2 to 4. Taking N as 3 as an example, it is equivalent to dividing the main line into a segment every 6 kilometers starting from the substation, and each segment corresponds to a line sub-model.

[0079] For example, see Figure 4 , Figure 4 It is the initial line model provided in the embodiment of the present application. The main line is divided at division position 1 and division position 2 to obtain three segments (segment 1, segment 2 and segment 3), and each segment corresponds to a line sub-model. Therefore, the line sub-model includes the segment, all branch lines on the segment, and all components on the segment and branch lines, such as transformers, power nodes, etc.

[0080] In step 1023, for each line sub-model, a first length of the longest line in each branch line in the line sub-model is determined.

[0081] Here, the longest line is usually a line extending from a connection point between the branch line and the main line to the farthest user node on the branch line, and the first length represents the length of the longest line segment in the branch line.

[0082] For example, see Figure 5 , Figure 5 It is a schematic diagram of the line sub-model provided in an embodiment of the present application. Taking branch line 1 as an example, node 1 in branch line 1 is the farthest node of branch line 1, node 2 is the connection point between the branch line and the main line, the line between node 1 and node 2 is the longest line, and the length of the line between node 1 and node 2 is the first length.

[0083] Here, for each branch line, it is determined whether the first length of the longest line of the branch line is greater than the second preset length.

[0084] In step 1024, when the first length corresponding to the branch line is less than or equal to the second preset length, a node connecting the branch line and the main line is determined as a first target node.

[0085] Here, if the first length is not greater than the second preset length, that is, the first length is less than or equal to the second preset length, the load of the branch line needs to be merged to the first target node connecting the branch line and the main line.

[0086] In step 1025, the load value of each power consumption node in the branch line is added to the load value of the first target node to obtain a preliminary merged line sub-model.

[0087] Here, the load value is an important parameter in the operation and planning of the power system. It directly affects the stability and power supply quality of the power grid. Each power node has a corresponding load value. The load value of the power node refers to the amount of electric energy consumed by the power node within a specific time. The load value can be an instantaneous load value, such as 5 kilowatts (kW), or a daily load value, such as 20 kilowatt-hours (kWh). The load value of each power node in the branch line is added to the load value of the first target node, and the original power node is deleted, so as to realize the merging processing of each power node in the branch line. For example, the load values ​​of the power nodes to be merged are 20kWh and 500kWh respectively, and the load value of the first target node is 100kWh. Then, 20kWh and 500kWh are added to 100kWh, and the power nodes of 20kWh and 500kWh are deleted in the model to obtain the line sub-model after preliminary merging. The load value of the first target node of the line sub-model after preliminary merging is 620kWh.

[0088] In step 1026, a plurality of power consumption nodes included in the main line in the initially merged line sub-model are merged.

[0089] Here, the branch lines that can be merged into the main line in the line sub-model after the preliminary merging have been merged, so it is necessary to further merge the multiple power consumption nodes included in the main line, which will be explained in detail below.

[0090] In the above steps 1021 to 1026, the main line and branch line in the initial line model are first determined, and the initial line model is divided based on the first preset length and the main line to obtain multiple line sub-models. The first length of the longest line in each branch line in the line sub-model is determined for each line sub-model. When the first length corresponding to the branch line is less than or equal to the second preset length, the load value of each power node in the branch line is accumulated to the load value of the first target node to obtain a preliminary merged line sub-model, and the multiple power nodes included in the main line in the preliminary merged line sub-model are merged. In this way, by dividing the initial line model into several small line sub-models, the process of merging power nodes can be simplified and the accuracy of the merging process can be improved.

[0091] In some embodiments, when the first length corresponding to the branch line is greater than the second preset length, the branch line is used as the line to be processed, the longest line in the lines to be processed is determined as a new main line, and the lines other than the longest line in the lines to be processed are determined as new branch lines. Then, based on the new main line and the new branch line, multiple power nodes in the lines to be processed are merged separately.

[0092] For example, if the second preset length is 5 kilometers, if the first length corresponding to the branch line is not greater than 5 kilometers, the load of the branch line needs to be merged to the first target node connecting the branch line and the main line; if it is greater than 5 kilometers, a series compensation device must be deployed for the branch line alone, and the load of the branch line cannot be merged to the first target node. In this way, it can avoid large errors caused by simply merging complex branch lines directly, thereby improving the accuracy of the merging process and making the merged line closer to the real line.

[0093] In some embodiments, Figure 3C As shown, step 1026 can be implemented through steps 10261 to 10264, which are described in detail below:

[0094] In step 10261, first attributes and second attributes of multiple power consumption nodes included in the main line are obtained.

[0095] Here, each power consumption node has a first attribute and a second attribute. The first attribute is used to characterize the distance between the power consumption node and the starting position of the preliminary merged line sub-model, and the second attribute is used to characterize the load value of the power consumption node.

[0096] In step 10262, clustering is performed on the plurality of power consumption nodes based on the first attribute to obtain N target clusters and cluster centers corresponding to the N target clusters.

[0097] Here, N is a positive integer. In some embodiments, according to the first attribute of each power consumption node, N is the number of clusters, and a clustering algorithm such as K-means clustering algorithm (k-means), hierarchical clustering algorithm (Hierarchical clustering), mean shift clustering algorithm (Mean shift clustering), etc. is used to cluster the power consumption nodes in the line sub-model after preliminary merging to obtain N target clusters, wherein each target cluster has a cluster center, which represents the center point or representative point of the target cluster, and is usually the coordinate mean of all power consumption nodes in the target cluster.

[0098] In step 10263, N second target nodes are determined from multiple power consumption nodes based on the second attribute.

[0099] Here, according to the second attribute, N power nodes with the largest load values ​​are determined from multiple power nodes as the second target nodes. For example, sorting functions in various programming languages, such as sorted() or sort() functions in the programming language Python, can be used to sort multiple power nodes in descending order according to the load values ​​from large to small, and the first N power nodes are selected as the second target nodes from the sorted multiple power nodes. Therefore, the load value of the second target node is greater than the load value of the power nodes other than the second target node in the line sub-model after the preliminary merger.

[0100] In step 10264, based on the cluster center and the second target node, N merged positions are determined in the main line in the line sub-model after the preliminary merge.

[0101] Here, firstly, N cluster centers and N second target nodes are combined to obtain multiple combination results, each of which includes N target points, and the target points are cluster centers or second target nodes. For example, N is 2, the two cluster centers are the first cluster center C1 and the second cluster center C2, the two second target nodes are the first node N1 and the second node N2, and the order of the four target points from left to right in the main line is the first cluster center, the second cluster center, the first node, and the second node. Then, the combination results include six: {C1, C2}; {N1, N2}; {C1, N1}; {C1, N2}; {C2, N1}; {C2, N2}.

[0102] Then, for each combination result, the spacing information corresponding to the combination result is determined based on the position information of N target points in the combination result, a target combination result is determined from multiple combination results based on the spacing information, and the target point in the target combination result is determined as the merged position.

[0103] Among them, the spacing information includes N+1 distance values. The target points in each combination result are sorted according to the position information from left to right on the main line. The distance value between the first target point on the left and the starting position of the line sub-model after the initial merger is calculated, and the distance value between the last target point on the right and the ending position of the line sub-model after the initial merger is calculated. The distance value between every two adjacent target points is calculated, and these N+1 distance values ​​are used as spacing information. For example, the combination result is: the first cluster center, the second cluster center, and the third cluster center; calculate the distance value d1 between the first cluster center and the starting position, the distance value d2 between the second cluster center and the first cluster center, the distance value d3 between the third cluster center and the second cluster center, and the distance value d4 between the ending position and the third cluster center, and use the distance values ​​d1, d2, d3, and d4 as the spacing information corresponding to this combination result.

[0104] In this way, N cluster centers and N second target nodes are combined to obtain multiple combination results. For each combination result, the spacing information is determined based on the position information of the N target points. Then, the target combination result is determined from the multiple combination results based on the spacing information, and the target point in the target combination result is determined as the merge position. In this way, the places where the load values ​​are relatively concentrated can be merged into one power node, and the power nodes are merged to the location where the large load is located as much as possible, thereby improving the accuracy of the merge processing.

[0105] Among them, the target combination result can be determined from multiple combination results through the following process:

[0106] First, for each combination result, the first standard deviation of the combination result is determined based on the N+1 distance values, the discount coefficient corresponding to the combination result is determined based on the number of second target nodes included in the combination result, and the second standard deviation of the combination result is determined based on the first standard deviation and the discount coefficient. After obtaining the second standard deviations of multiple combination results, the combination result with the smallest second standard deviation among the multiple combination results is determined as the target combination result.

[0107] Here, in order to meet the merging principle of "merging places where the load is relatively concentrated into one power consumption node, and merging the power consumption nodes to the location with larger load value as much as possible", the number of second target nodes is set to be negatively correlated with the discount coefficient. The specific correspondence between the two is set in advance. The more second target nodes there are in the combination result, the greater the discount strength corresponding to the combination result, that is, the smaller the discount coefficient. For example, the number of second target nodes in combination result 1 is 0, so the discount coefficient is 1, the first standard deviation of combination result 1 is multiplied by the discount coefficient 1 to obtain the second standard deviation, and the second standard deviation of combination result 1 is equal to the first standard deviation; the number of second target nodes in combination result 2 is 1, so the discount coefficient is 0.9, and the first standard deviation of combination result 2 is multiplied by the discount coefficient 0.9 to obtain the second standard deviation.

[0108] In this way, the first standard deviation of the combination result is determined based on the distance value, the discount coefficient is determined based on the number of second target nodes, the second standard deviation of the combination result is determined according to the first standard deviation and the discount coefficient, and then the combination result with the smallest second standard deviation among multiple combination results is determined as the target combination result, thereby improving the accuracy and efficiency of determining the target combination result.

[0109] In step 10265, multiple power consumption nodes are merged into N merge positions.

[0110] Here, for each power node, the second distance between the power node and each merge position is determined; the merge position corresponding to the smallest second distance is the target merge position corresponding to the power node, and then the load value of this power node is accumulated to the load value of the target merge position, and this power node is deleted to realize the merge processing of the power node. For example, the three merge positions are: the first node, the second node, and the third cluster center. The distances between the power node n and the first node, the second node, and the third cluster center are: 0.7km, 1.1km, and 0.1km, respectively. The distance between the power node n and the third cluster center is the shortest, so the load value of the power node n is merged into the third cluster center, and the power node n is deleted. Assuming that the load value of the power node n is 10kWh and the load value of the third cluster center is 200kWh, then the load value of the third cluster center after merging is 210kWh.

[0111] In the embodiment of the present application, the first attribute and the second attribute of the multiple power nodes included in the main line are obtained, N cluster centers are determined based on the first attribute, N second target nodes are determined based on the second attribute, and N merged positions are determined in the main line in the line sub-model after preliminary merging based on the cluster centers and the second target nodes, and then the multiple power nodes are merged into the N merged positions. In this way, the places where the load values ​​are relatively concentrated can be merged into one node, and the power nodes can be merged to the location where the large load is located as much as possible, thereby improving the accuracy of node merging.

[0112] In the embodiment of the present application, the main line and branch line in the initial line model are determined, the initial line model is divided based on the first preset length and the main line to obtain multiple line sub-models, the first length of the longest line in each branch line in the line sub-model is determined for each line sub-model, and when the first length is less than or equal to the second preset length, the branch line is directly merged to the first target node to obtain a preliminary merged line sub-model, and then the multiple power nodes included in the main line in the preliminary merged line sub-model are merged. In this way, the power nodes on the branch lines are merged first, and then the power nodes on the main lines are merged, and the hierarchical processing reduces the amount of calculation and processing difficulty, and improves the efficiency and accuracy of the merging processing.

[0113] In step 103, the first line model is optimized to obtain a second line model.

[0114] In some embodiments, Figure 3D As shown, step 103 can be implemented through steps 1031 to 1035, which are described in detail below:

[0115] In step 1031, the real maximum outlet current of the power distribution system and the virtual maximum outlet current of the first line model are obtained.

[0116] Here, the real maximum export current is provided by the power supply bureau and is the maximum current value that can be reached when the substation in the distribution system provides power to external users under normal operating conditions of the distribution system. The virtual maximum export current can be directly obtained from the simulation data of the power system analysis software. The virtual maximum export current is the virtual maximum current value that can be reached when the virtual distribution station in the first line model provides power to external virtual users under normal operating conditions of the first line model.

[0117] In step 1032 , when the real maximum outlet current and the virtual maximum outlet current are different, an error parameter is determined based on the real maximum outlet current and the virtual maximum outlet current.

[0118] Here, when the real maximum export current and the virtual maximum export current are different, it means that there is an error between the first line model and the power distribution system. If the real maximum export current is less than the virtual maximum export current, it means that the load value of each point node in the first line model needs to be reduced. If the real maximum export current is greater than the virtual maximum export current, it means that the load value of each point node in the first line model needs to be increased. The error parameter is determined based on the percentage difference between the real maximum export current and the virtual maximum export current. The error parameter is used to tune the first line model to make the first line model closer to the power distribution system.

[0119] In step 1033, the load value of each power consumption node in the first line model is obtained.

[0120] Here, each power-consuming node has a corresponding load value, which is used to represent the amount of electric energy or power consumed by each power-consuming node within a specific time.

[0121] In step 1034, a new load value of each power consumption node is determined based on the error parameter and the load value.

[0122] Here, if the real maximum export current is less than the virtual maximum export current, the load value of each use point node in the first line model is reduced according to the error parameter, and if the real maximum export current is greater than the virtual maximum export current, the load value of each use point node in the first line model is increased according to the error parameter. For example, if the real maximum export current is less than the virtual maximum export current and the error parameter is 5%, the load value of each use point node in the first line model is reduced by 5% as the new load value; if the real maximum export current is greater than the virtual maximum export current and the error parameter is 7%, the load value of each use point node in the first line model is increased by 7% as the new load value.

[0123] In step 1035, the load value of each power consumption node in the first line model is updated to a new load value.

[0124] For example, the actual maximum export current is greater than the virtual maximum export current, the error parameter is 10%, and the load value of a certain power consumption node is 100kW. The new load value of this power consumption node is determined to be 110kW by 100kW×10%+100kW, and the load value of this power consumption node will be updated to 110kW.

[0125] In an embodiment of the present application, the real maximum export current of the power distribution system and the virtual maximum export current of the first line model are obtained. When the real maximum export current and the virtual maximum export current are different, the error parameter is determined based on the real maximum export current and the virtual maximum export current, and the load value of each power consumption node is updated to obtain a second line model, so that the second line model is closer to the real power distribution system, thereby improving the accuracy of the second line model.

[0126] In step 104, based on the node voltage of the power node in the second line model and the preset voltage, a virtual series compensation device is set in the second line model to obtain a target line model.

[0127] Here, taking the substation end in the second line model as the starting position, the node voltages of the power nodes in the second line model are determined in sequence, and a virtual series compensation device is set at the first power node where the node voltage is lower than the preset voltage to obtain a new second line model; then taking the power node where the virtual series compensation device is set as the new starting position, based on the node voltage of the power node in the new second line model and the preset voltage, a virtual series compensation device is set in the new second line model, and the above process is repeated, and so on, until the node voltage of each power node in the new second line model is greater than or equal to the preset voltage, and the target line model is obtained. In this way, the influence of the series compensation device on the power system is simulated by series compensation simulation, thereby improving the power transmission capacity and voltage stability of the line, and improving the efficiency and accuracy of line processing.

[0128] In step 105, a series compensation device is arranged in the power distribution system based on the location information of the virtual series compensation device in the target line model.

[0129] Here, the user may refer to the location information of the virtual series compensation device in the target line model to determine the location where the series compensation device needs to be set in the power distribution system, thereby actually installing the series compensation device in the power distribution system.

[0130] In an embodiment of the present application, first, an initial line model of a real distribution network system is built according to the line conditions of the real distribution system, so that the initial line model conforms to the real conditions of the distribution network system. Then, the initial line model is divided to obtain multiple line sub-models, and the power consumption nodes in each line sub-model are merged to obtain a first line model, thereby reducing the number of power consumption nodes, facilitating the tuning of the first line model, so that the obtained second line model is closer to the real conditions of the distribution network system. Then, according to the node voltage and the preset voltage of the power consumption node in the second line model, a virtual series compensation device is set in the second line model to obtain a target line model, so that the series compensation device is set in the distribution system according to the position information of the virtual series compensation device in the target line model. In this way, by dividing the initial line model and then merging the power consumption nodes, the efficiency and accuracy of the merging process can be improved, so that the power consumption nodes in the target line model conform to the real conditions of the distribution network system, and the accuracy of setting the virtual series compensation device in the second line model is improved, which is convenient for the subsequent setting of the series compensation device in the distribution system according to the position information of the virtual series compensation device in the target line model, thereby improving the efficiency and accuracy of line processing.

[0131] The following is an explanation of an exemplary application of an embodiment of the present application in a practical application scenario.

[0132] The series compensation device helps maintain the voltage level of the system by compensating for the voltage drop in the transmission line, thereby improving the overall stability of the power system. It is especially important for connecting long-distance power sources or supplying power to large industrial users. For a 10kV transmission line, the voltage of the transmission line gradually decreases from the substation end to the power consumption end. When the voltage at a power consumption node in the line drops below 9.6kV, a series compensation device needs to be installed in front of the power consumption node to solve the voltage drop problem.

[0133] The embodiment of the present application adopts a series compensation simulation method to determine the installation position and series compensation capacity of the series compensation device. Before the series compensation simulation, it is necessary to build a line model corresponding to the real distribution network line according to the actual distribution network line conditions, and optimize the line model according to the simulation operation data and the real data, so that the line model is closer to the actual distribution network conditions. Since a large number of power consumption nodes are involved in the real line, if each power consumption node is retained in the line model, it will lead to great difficulty in optimizing the line model, and the optimization workload will be difficult to converge and close. Therefore, multiple power consumption nodes in the line model are merged to reduce the number of power consumption nodes, so that the merged power consumption nodes meet the actual distribution network conditions.

[0134] When merging power consumption nodes, the following principles should generally be followed:

[0135] 1. Merge the places where the load values ​​are concentrated into one power consumption node, and try to merge the power consumption nodes to the location of the power consumption nodes with large load values;

[0136] 2. For the main line, the span of the power node merger should not be too large, generally around 2 to 3 kilometers;

[0137] 3. For branch lines, if they are close to the front end of the main line and their length does not exceed 5 km, they will be merged into the main line; if they are at the end of the main line, they will need to be merged into the power node according to the length and load of the branch line.

[0138] In the embodiment of the present application, an initial line model is first established, and the initial line model includes all the power consumption nodes in the real distribution network line. Then, the power consumption nodes are merged to obtain the first line model, and the first line model is calculated by simulation software to calculate the virtual maximum export current a of the substation in the first line model, and the calculated virtual maximum export current a is compared with the real maximum export current b provided by the power supply bureau. If a is greater than b, it is necessary to reduce the load rate of each power consumption node in the first line model, and if a is less than b, it is necessary to increase the load rate of each power consumption node in the first line model, so as to further optimize the first line model and obtain the second line model. Starting from the substation end of the second line model and extending backward, when the node voltage at a certain power node is lower than 9.6kV, a virtual series compensation device is set at the power node, and then the second line model after the virtual series compensation device is set is calculated by simulation software to calculate the voltage at each power node after adding the first virtual series compensation device, and then starting from the first virtual series compensation device and extending backward, when the voltage at a certain power node is lower than 9.6kV, another virtual series compensation device is set at the power node, and so on, until the voltage of all power nodes is no less than 9.6kV, and the target line model is obtained.

[0139] The method for merging power consumption nodes provided in the embodiment of the present application includes the following steps:

[0140] 1. According to the requirement of merging power nodes with a span of 2 to 3 kilometers (taking 2 kilometers as an example), the main line of the initial line model is evenly divided into multiple segments. The length of each segment is equal to N×2 kilometers, and N is an integer from 2 to 4. Taking N as 3 as an example, it is equivalent to dividing the main line into a segment every 6 kilometers starting from the substation, and each segment is equivalent to a line sub-model.

[0141] See also Figure 4 , Figure 4 is the initial line model provided in the embodiment of the present application, which is a 10kV transmission line, including a substation and a main line. Figure 4The lines other than the main line shown in are branch lines. The main line and each branch line include multiple power nodes, as well as isolating switches, transformers, circuit breakers, distribution rooms, switches and other parts. According to the interval of 6 kilometers, the main line is divided at division position 1 and division position 2 to obtain three sections (section 1, section 2 and section 3). Each section corresponds to a line sub-model. Each section is 6 kilometers long, and the length of the last section is less than or equal to 6 kilometers. For each branch line, determine whether the first length of the longest line of the branch line is greater than 5 kilometers. If it is not greater than 5 kilometers, the load of the branch line needs to be merged to the first target node connecting the branch line and the main line; if it is greater than 5 kilometers, a series compensation device must be deployed for the branch line separately, and the load of the branch line cannot be merged to the first target node.

[0142] See also Figure 5 , Figure 5 It is a schematic diagram of a line submodel provided in an embodiment of the present application, which is a 10kV transmission line, including a substation, a main line, branch line 1, branch line 2, branch line 3, branch line 4, branch line 5 and branch line 6. Among them, the main line and each branch line include multiple power nodes, as well as isolating switches, transformers, circuit breakers, distribution rooms, switches and other parts. Figure 5 Node 1 in the middle branch line 1 is the farthest end of branch line 1, and node 2 is the first target node corresponding to the branch line. The line length from node 1 to node 2 is the first length, which exceeds 5 kilometers. Therefore, the load of branch line 1 cannot be merged into node 2; and the first length of the longest line of branch line 2, branch line 3, branch line 4, branch line 5, and branch line 6 does not exceed 5 kilometers. Therefore, all loads on each branch line need to be merged into the corresponding first target node, for example, all loads on branch line 5 need to be merged into node 3, and the preliminary merged segments are obtained, that is, the preliminary merged line sub-model.

[0143] For each initially merged line sub-model, which includes multiple power nodes, each power node has two attributes, the first attribute: the distance from the power node to the starting position of the line sub-model, and the second attribute: the load value of the power node. For each initially merged line sub-model, the multiple power nodes in the initially merged line sub-model are merged in the following manner.

[0144] See also Figure 6 , Figure 6 This is a schematic diagram of the line sub-model after initial merging provided in the embodiment of the present application, including a starting position, an ending position, and multiple power nodes. According to the first attribute of each power node (the distance from the power node to the starting position), the power nodes are clustered using the k-means clustering algorithm with N (N=3) as the number of clusters, and 3 clusters are obtained, each with a cluster center ( Figure 6The black solid triangle in the figure is the first cluster center, the second cluster center and the third cluster center; the circles are power nodes. The larger the circle, the greater the load value of the power node. The three power nodes with the largest load in the line sub-model after the preliminary merger are found as the second target nodes, which are the first node, the second node and the third node.

[0145] For three cluster centers and three second target nodes, a total of 6 target points, 3 target points are grouped together, and without considering the order, a total of the following 20 combination results are generated.

[0146] Combination result 1: the first cluster of hearts, the second cluster of hearts, the third cluster of hearts;

[0147] Combination result 2: the first node, the second cluster center, and the third cluster center;

[0148] Combination result 3: second node, second cluster center, third cluster center;

[0149] Combination result 4: the third node, the second cluster center, the third cluster center;

[0150] Combination result 5: first node, first cluster center, third cluster center;

[0151] Combination result 6: the second node, the first cluster center, the third cluster center;

[0152] Combination result 7: the third node, the first cluster center, the third cluster center;

[0153] Combination result 8: first node, first cluster center, second cluster center;

[0154] Combination result 9: second node, first cluster center, second cluster center;

[0155] Combination result 10: the third node, the first cluster center, the second cluster center;

[0156] Combination result 11: first node, second node, third cluster center;

[0157] Combination result 12: the first node, the third node, the third cluster center;

[0158] Combination result 13: the second node, the third node, the third cluster center;

[0159] Combination result 14: first node, second node, second cluster center;

[0160] Combination result 15: the first node, the third node, the second cluster center;

[0161] Combination result 16: the second node, the third node, the second cluster center;

[0162] Combination result 17: first node, second node, first cluster center;

[0163] Combination result 18: the first node, the third node, the first cluster center;

[0164] Combination result 19: the second node, the third node, the first cluster center;

[0165] Combination result 20: first node, second node, third node;

[0166] For each combination result, the three target points in the combination result are sorted according to their position information from left to right in the segment, and the distance between the first target point on the left and the starting position of the segment is calculated, the distance between the last target point on the right and the ending position of the segment is calculated, and the distance between every two adjacent target points is calculated, and a total of 4 spacing information is obtained. Taking combination result 1 as an example, the distance value d1 between the first cluster center and the starting position of the segment, the distance value d2 between the second cluster center and the first cluster center, the distance value d3 between the third cluster center and the second cluster center, and the distance value d4 between the ending position of the segment and the third cluster center are calculated as the spacing information corresponding to combination result 1.

[0167] After obtaining the four spacing values ​​of each combination result, it is necessary to find the combination result with the most balanced spacing value distribution. The more balanced the spacing value distribution, the more balanced the distribution of each target point in the combination result in the segment. Specifically, for each combination result, calculate the first standard deviation of the four spacing values ​​of the combination result. In addition, in order to meet the merging principle of "merging places where the load is relatively concentrated into one power node, and merging the power node to the location where the large load is located as much as possible", the more second target nodes in the combination result, the greater the discount corresponding to the combination result, that is, the smaller the discount coefficient, and the standard deviation of the combination result is discounted by the discount coefficient.

[0168] For example, the number of second target nodes in combination result 1 is 0, so the discount coefficient is 1, the first standard deviation of combination result 1 is multiplied by the discount coefficient 1 to obtain the second standard deviation, and the second standard deviation of combination result 1 is equal to the first standard deviation; the number of second target nodes in combination results 2 to 10 is 1, so the discount coefficient is 0.9, the first standard deviation of combination results 2 to 10 is multiplied by the discount coefficient 0.9 to obtain the second standard deviation; the number of second target nodes in combination results 11 to 19 is 2, so the discount coefficient is 0.8, the first standard deviation of combination results 11 to 19 is multiplied by the discount coefficient 0.8 to obtain the second standard deviation; the number of large nodes in combination result 20 is 3, so the discount coefficient is 0.7, the first standard deviation of combination result 20 is multiplied by the discount coefficient 0.7 to obtain the second standard deviation.

[0169] After obtaining the second standard deviation of each combination result, the position of the target point in the combination result with the smallest second standard deviation is selected as the final three merged positions.

[0170] After determining the three merge positions, for each power node in the segment, calculate the distance between each power node and each merge position, and use the merge position with the closest distance as the target merge position of the power node, so as to merge the load of the power node to the target merge position. For example, assuming that the three merge positions are: the first node, the second node, and the third cluster center, and the distances between power node n and the first node, the second node, and the third cluster center are: 0.4km, 1.8km, and 3.1km, respectively, then power node n is closest to the first node, so it is necessary to merge the load of power node n to the first node, and delete power node n.

[0171] In addition, for branch lines that have not been merged into the main line, the longest line among the branch lines is used as the main line, and the lines other than the longest line are determined as branch lines. The branch lines are also merged according to the above-mentioned power node merging process.

[0172] In the embodiment of the present application, the installation position and the series compensation capacity of the virtual series compensation device are determined by series compensation simulation, and the line model is tuned before the series compensation simulation, so that the line model is closer to the actual situation of the distribution network. When establishing the line model of the distribution network, the node merging is performed according to the method of the embodiment of the present application, which has higher merging efficiency and accuracy than manual merging, thereby improving the line processing efficiency.

[0173] The following further describes an exemplary structure of the line processing device 455 provided in the embodiment of the present application implemented as a software module. In some embodiments, for example Figure 2 As shown, the software modules stored in the line processing device 455 of the memory 450 may include:

[0174] An acquisition module 4551 is used to acquire line data of a power distribution system and construct an initial line model based on the line data, wherein the initial line model includes a plurality of power consumption nodes;

[0175] The processing module 4552 is used to divide the initial line model to obtain multiple line sub-models, and merge the power consumption nodes in each of the line sub-models to obtain a first line model;

[0176] The processing module 4552 is further configured to perform tuning processing on the first line model to obtain a second line model;

[0177] The setting module 4553 is used to set a virtual series compensation device in the second line model based on the node voltage of the power node in the second line model and the preset voltage to obtain a target line model.

[0178] In some embodiments, the processing module 4552 is also used to determine the main line and branch line in the initial line model; divide the initial line model based on the first preset length and the main line to obtain the multiple line sub-models; for each of the line sub-models, determine the first length of the longest line in each branch line in the line sub-model; when the first length corresponding to the branch line is less than or equal to the second preset length, determine the node connecting the branch line and the main line as the first target node; add the load value of each power node in the branch line to the load value of the first target node to obtain a preliminary merged line sub-model; merge the multiple power nodes included in the main line in the preliminary merged line sub-model.

[0179] In some embodiments, the processing module 4552 is also used to, when the first length corresponding to the branch line is greater than the second preset length, use the branch line as a line to be processed; determine the longest line in the lines to be processed as a new main line, determine the lines other than the longest line in the lines to be processed as new branch lines, and merge multiple power nodes in the lines to be processed based on the new main line and the new branch lines.

[0180] In some embodiments, the processing module 4552 is also used to obtain the first attribute and the second attribute of the multiple power nodes included in the main line, the first attribute is used to characterize the distance between the power node and the starting position of the line sub-model after the preliminary merger, and the second attribute is used to characterize the load value of the power node; based on the first attribute, the multiple power nodes are clustered to obtain N target clusters and N cluster centers corresponding to the target clusters; based on the second attribute, N second target nodes are determined from the multiple power nodes; wherein the load value of the second target node is greater than the load value of the power nodes other than the second target node in the line sub-model after the preliminary merger; based on the cluster center and the second target node, N merged positions are determined in the main line in the line sub-model after the preliminary merger; and the multiple power nodes are merged into the N merged positions.

[0181] In some embodiments, the processing module 4552 is also used to combine the N cluster centers and the N second target nodes to obtain multiple combination results; wherein each of the combination results includes N target points, and the target points are the cluster centers or the second target nodes; for each of the combination results, the spacing information corresponding to the combination result is determined based on the position information of the N target points in the combination result; based on the spacing information, a target combination result is determined from the multiple combination results; and the target point in the target combination result is determined as the merged position.

[0182] In some embodiments, the processing module 4552 is also used to determine the first standard deviation of the combination result based on the N+1 distance values; determine the discount coefficient corresponding to the combination result based on the number of second target nodes included in the combination result; wherein the number of second target nodes is negatively correlated with the discount coefficient; determine the second standard deviation of the combination result based on the first standard deviation and the discount coefficient; and determine the combination result with the smallest second standard deviation among the multiple combination results as the target combination result.

[0183] In some embodiments, the processing module 4552 is also used to obtain the real maximum export current of the power distribution system and the virtual maximum export current of the first line model; when the real maximum export current and the virtual maximum export current are different, determine the error parameter based on the real maximum export current and the virtual maximum export current; obtain the load value of each of the power consumption nodes in the first line model; determine the new load value of each of the power consumption nodes based on the error parameter and the load value; and update the load value of each of the power consumption nodes in the first line model to the new load value.

[0184] In some embodiments, the setting module 4553 is also used to take the substation end in the second line model as the starting position, determine the node voltages of the power nodes in the second line model in sequence, and set the virtual series compensation device at the first power node where the node voltage is lower than the preset voltage to obtain a new second line model; take the power node where the virtual series compensation device is set as the new starting position, and set the virtual series compensation device in the new second line model based on the node voltage of the power node in the new second line model and the preset voltage, until the node voltage of each power node in the new second line model is greater than or equal to the preset voltage, so as to obtain the target line model.

[0185] The embodiment of the present application provides a computer program product, which includes a computer program or a computer executable instruction, and the computer program or the computer executable instruction is stored in a computer readable storage medium. The processor of the electronic device reads the computer executable instruction from the computer readable storage medium, and the processor executes the computer executable instruction, so that the electronic device executes the line processing method described in the embodiment of the present application.

[0186] The present application embodiment provides a computer-readable storage medium, in which computer executable instructions or computer programs are stored. When the computer executable instructions or computer programs are executed by a processor, the processor will be caused to execute the line processing method provided by the present application embodiment, for example, Figure 3A The line processing method shown.

[0187] In some embodiments, the computer-readable storage medium may be a memory such as RAM, ROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories.

[0188] In some embodiments, computer executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0189] As an example, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored in a portion of a file storing other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0190] As an example, computer executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed at multiple sites and interconnected by a communication network.

[0191] In summary, through the embodiment of the present application, by dividing the initial line model and then merging the power nodes, the efficiency and accuracy of the merging processing can be improved, so that the power nodes in the target line model conform to the actual situation of the distribution network system, meet the accuracy requirements of the series compensation simulation, and improve the accuracy of setting the virtual series compensation device in the second line model, so as to facilitate the subsequent setting of the series compensation device in the distribution system according to the location information of the virtual series compensation device in the target line model, thereby improving the efficiency and accuracy of line processing.

[0192] The above is only an embodiment of the present application and is not intended to limit the protection scope of the present application. Any modifications, equivalent substitutions and improvements made within the spirit and scope of the present application are included in the protection scope of the present application.

Claims

1. A line processing method, characterized in that: The method comprises: Acquire line data of a power distribution system, and construct an initial line model based on the line data, wherein the initial line model includes a plurality of power consumption nodes; Dividing the initial line model to obtain a plurality of line sub-models, and merging the power consumption nodes in each of the line sub-models to obtain a first line model; Performing tuning processing on the first line model to obtain a second line model; Based on the node voltage of the power node in the second line model and the preset voltage, a virtual series compensation device is set in the second line model to obtain a target line model; Based on the position information of the virtual series compensation device in the target line model, setting a series compensation device in the power distribution system; The step of dividing the initial line model to obtain a plurality of line sub-models and merging the power consumption nodes in each of the line sub-models includes: Determine the main line and branch line in the initial line model; divide the initial line model based on the first preset length and the main line to obtain the multiple line sub-models; for each of the line sub-models, determine the first length of the longest line in each branch line in the line sub-model; when the first length corresponding to the branch line is less than or equal to the second preset length, determine the node connecting the branch line and the main line as the first target node; add the load value of each power node in the branch line to the load value of the first target node to obtain a preliminary merged line sub-model; merge the multiple power nodes included in the main line in the preliminary merged line sub-model, including: obtaining the first attribute and the second attribute of the multiple power nodes included in the main line, the first attribute is used to characterize the distance between the power node and the starting position of the preliminary merged line sub-model, and the second attribute is used to characterize the load value of the power node ; Based on the first attribute, cluster the multiple power nodes to obtain N target clusters and N cluster centers corresponding to the target clusters; based on the second attribute, determine N second target nodes from the multiple power nodes; wherein the load value of the second target node is greater than the load value of the power nodes other than the second target node in the line sub-model after the preliminary merger; based on the cluster center and the second target node, determine N merge positions in the main line in the line sub-model after the preliminary merger; merge the multiple power nodes into the N merge positions; when the first length corresponding to the branch line is greater than the second preset length, use the branch line as a line to be processed; determine the longest line in the line to be processed as a new main line, determine the lines other than the longest line in the line to be processed as new branch lines, and merge the multiple power nodes in the line to be processed based on the new main line and the new branch lines; The step of setting a virtual series compensation device in the second line model based on the node voltage of the power node in the second line model and the preset voltage to obtain a target line model includes: Taking the substation end in the second line model as the starting position, determining the node voltages of the power consumption nodes in the second line model in sequence, setting the virtual series compensation device at the first power consumption node where the node voltage is lower than the preset voltage, and obtaining a new second line model; taking the power consumption node where the virtual series compensation device is set as the new starting position, setting the virtual series compensation device in the new second line model based on the node voltage of the power consumption node in the new second line model and the preset voltage, until the node voltage of each power consumption node in the new second line model is greater than or equal to the preset voltage, and obtaining the target line model.

2. The method according to claim 1, characterized in that The determining, based on the cluster center and the second target node, in the main line in the line sub-model after the preliminary merging, N merging positions includes: Combining the N cluster centers and the N second target nodes to obtain a plurality of combination results; wherein each of the combination results includes N target points, and the target points are the cluster centers or the second target nodes; For each of the combination results, determining the spacing information corresponding to the combination result based on the position information of the N target points in the combination result; Based on the spacing information, determining a target combination result from the multiple combination results; Determine the target point in the target combination result as the merged position.

3. The method according to claim 2, characterized in that The distance information includes N+1 distance values, and determining a target combination result from the multiple combination results based on the distance information includes: Determining a first standard deviation of the combined result based on the N+1 distance values; Determining a discount coefficient corresponding to the combination result based on the number of second target nodes included in the combination result; wherein the number of the second target nodes is negatively correlated with the discount coefficient; determining a second standard deviation of the combined result based on the first standard deviation and the discount factor; The combined result with the smallest second standard deviation among the multiple combined results is determined as the target combined result.

4. The method according to claim 1, characterized in that The tuning process of the first line model includes: Obtaining a real maximum outlet current of the power distribution system and a virtual maximum outlet current of the first line model; When the real maximum outlet current and the virtual maximum outlet current are different, determining an error parameter based on the real maximum outlet current and the virtual maximum outlet current; Obtaining a load value of each of the power consumption nodes in the first line model; Determining a new load value of each of the power consumption nodes based on the error parameter and the load value; The load value of each of the power consumption nodes in the first line model is updated to the new load value.

5. A line processing device, characterized in that: The device comprises: An acquisition module, used to acquire line data of a power distribution system, and construct an initial line model based on the line data, wherein the initial line model includes a plurality of power consumption nodes; A processing module, configured to divide the initial line model to obtain a plurality of line sub-models, and merge the power consumption nodes in each of the line sub-models to obtain a first line model; The processing module is further used to determine the main line and branch line in the initial line model; divide the initial line model based on the first preset length and the main line to obtain the multiple line sub-models; for each of the line sub-models, determine the first length of the longest line in each branch line in the line sub-model; when the first length corresponding to the branch line is less than or equal to the second preset length, determine the node connecting the branch line and the main line as the first target node; add the load value of each power node in the branch line to the load value of the first target node to obtain a preliminary merged line sub-model; merge the multiple power nodes included in the main line in the preliminary merged line sub-model; The processing module is further used to obtain the first attribute and the second attribute of the multiple power nodes included in the main line, the first attribute is used to characterize the distance between the power node and the starting position of the line sub-model after the preliminary merger, and the second attribute is used to characterize the load value of the power node; clustering the multiple power nodes based on the first attribute to obtain N target clusters and N cluster centers corresponding to the target clusters; determining N second target nodes from the multiple power nodes based on the second attribute; wherein the load value of the second target node is greater than the load value of the power nodes other than the second target node in the line sub-model after the preliminary merger; based on the cluster center and the second target node, determining N merged positions in the main line of the line sub-model after the preliminary merger; merging the multiple power nodes into the N merged positions; The processing module is further configured to, when the first length corresponding to the branch line is greater than the second preset length, use the branch line as a line to be processed; determine the longest line among the lines to be processed as a new main line, determine lines other than the longest line among the lines to be processed as new branch lines, and merge multiple power nodes in the lines to be processed based on the new main line and the new branch lines; The processing module is further used to perform tuning processing on the first line model to obtain a second line model; a setting module, configured to set a virtual series compensation device in the second line model based on a node voltage of a power node in the second line model and a preset voltage, so as to obtain a target line model; The setting module is further used to take the substation end in the second line model as the starting position, determine the node voltages of the power nodes in the second line model in sequence, and set the virtual series compensation device at the first power node where the node voltage is lower than the preset voltage to obtain a new second line model; take the power node where the virtual series compensation device is set as the new starting position, and set the virtual series compensation device in the new second line model based on the node voltage of the power node in the new second line model and the preset voltage, until the node voltage of each power node in the new second line model is greater than or equal to the preset voltage, so as to obtain the target line model; The setting module is further used to set a series compensation device in the power distribution system based on the position information of the virtual series compensation device in the target line model.

6. An electronic device, characterized in that: The electronic device comprises: A memory for storing computer executable instructions or computer programs; A processor, configured to implement the method according to any one of claims 1 to 4 when executing computer executable instructions or computer programs stored in the memory.

7. A computer-readable storage medium storing computer-executable instructions or a computer program, characterized in that: When the computer executable instructions or computer programs are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising computer executable instructions or a computer program, characterized in that: When the computer executable instructions or computer program are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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