Distribution circuit diagram drawing method and system, electronic equipment and storage medium

Abstract

The invention discloses a distribution line drawing method and system, electronic equipment and a storage medium, and the method comprises the following steps: S1, obtaining the ledger information of distribution line equipment, and constructing a distribution line model according to the ledger information of the distribution line equipment; s2, optimizing the distribution line model by using different algorithms according to different distribution line drawing requirements to obtain an optimal distribution line: optimizing the distribution line model through a shortest path algorithm for the requirements of drawing the same type of equipment distribution lines; optimizing a distribution line model through a genetic algorithm for drawing a distribution line meeting power transmission logic requirements; and S3, drawing the obtained optimal distribution line through a GIS map. The method solves the problem that a traditional distribution circuit diagram mostly depends on manual field acquisition of equipment positioning information and is drawn according to experience, and the optimization capability for specific requirements is lacked.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a method, system, electronic equipment and storage medium for drawing a distribution line diagram. Background Art

[0002] Distribution lines are a vital part of the power system, responsible for transmitting electricity from substations to end users, ensuring safe, stable and economical power supply. Traditional distribution line maps rely on manual on-site collection of equipment location information and are drawn based on experience, lacking optimization capabilities for specific needs (such as the shortest path or transmission logic). At the same time, existing distribution line drawing methods lack flexible optimization methods when dealing with complex constraints, especially in the drawing of large-scale distribution networks, and it is often difficult to achieve a global optimal solution. In addition, traditional methods lack automated model optimization and precise GIS integration capabilities, which often leads to errors in the drawing results. Therefore, there is an urgent need for a more intelligent, automated distribution line drawing method that can comprehensively consider multiple constraints and optimization objectives.

[0003] The Chinese patent with publication number CN108377034A discloses a transmission line inspection and line map drawing system based on a multi-rotor drone, including: a multi-rotor drone for inspection of transmission lines, wherein the multi-rotor drone includes: a drone body; a GPS positioning system; a remote sensing monitoring device; an image transmission module; a command receiving module; a power module; a flight control module; a ground information receiving station communicates with the multi-rotor drone to obtain inspection results based on remote sensing image data and video information, and correct the line map based on the inspection results, wherein the ground information receiving station includes: a ground control module; an image receiving module and a data processing module to obtain inspection results based on remote sensing image data and video information, and draw a line map based on the inspection results. The invention obtains remote sensing image data and video information through drone inspection to draw a line map, which can provide more accurate line information, but it is time-consuming and has a high investment cost. Summary of the invention

[0004] The present invention provides a distribution line drawing method, system, electronic device and storage medium, aiming to solve the problem that traditional distribution line diagrams mostly rely on manual on-site collection of equipment positioning information and are drawn based on experience, and lack optimization capabilities for specific needs.

[0005] In order to solve the above technical problems, the present invention provides a method for drawing a distribution line, comprising the following steps:

[0006] S1: Obtain the distribution line equipment ledger information and build a distribution line model based on the distribution line equipment ledger information.

[0007] S2: Use different algorithms to optimize the distribution line model according to different distribution line drawing requirements to obtain the optimal distribution line: for the distribution line drawing requirements of the same type of equipment, the distribution line model is optimized by the shortest path algorithm; for the distribution line drawing requirements that meet the transmission logic, the distribution line model is optimized by the genetic algorithm.

[0008] S3: Draw the obtained optimal distribution line through GIS map.

[0009] Preferably, the distribution line model constructed in step S1 specifically includes: line length cost, load limit penalty, safety distance penalty and current carrying capacity penalty.

[0010] The line length cost is expressed as:

[0011]

[0012] Where, L total is the line length cost; N is the total number of devices, i, j∈[1, N]; d ij is the Euclidean distance between device i and device j; x ij is a binary variable, indicating whether device i is connected to device j. If so, x ij =1, otherwise x ij =0.

[0013] The load limit penalty is expressed as:

[0014]

[0015] Where P load is the load limit penalty; L ij is the load from device i to device j; L max,ij is the maximum load limit from device i to device j, which is determined by the carrying capacity of the cable and the equipment.

[0016] The safety distance penalty is expressed as:

[0017]

[0018] Where P dist For safety distance penalty; d min The minimum safe distance between devices.

[0019] The current carrying capacity penalty is expressed as:

[0020]

[0021] Where P current is the current carrying capacity penalty; I ij is the current from device i to device j; Imax,ij The maximum current carrying capacity limit for the cable from device i to device.

[0022] Preferably, in step S2, for drawing a distribution line that meets the power transmission logic requirement, optimizing the distribution line model by genetic algorithm is specifically as follows:

[0023] S21: Encode the distribution line model variables and design the total fitness function according to the distribution line model.

[0024] S22: Initialize the population and randomly generate a certain number of chromosomes to represent the initial population. Each chromosome represents a possible solution for a distribution line.

[0025] S23: For each individual, calculate its fitness value and evaluate its quality based on the design of the fitness function.

[0026] S24: Select and crossover operations based on individual fitness.

[0027] S25: Perform mutation operation according to the preset mutation probability.

[0028] S26: Repeat steps S23 to S25. In each generation, new individuals are continuously generated through selection, crossover and mutation operations, and the population is updated.

[0029] S27: until the maximum number of iterations is reached, the optimal distribution line is output.

[0030] Preferably, the topological structure of the generated child distribution lines is checked to ensure that all nodes are still connected to each other after crossing, and if isolated nodes or disconnected parts are found, the crossing operation is withdrawn.

[0031] On the other hand, the present invention provides a distribution line drawing system, including a data management module, a distribution line optimization module and a distribution line drawing module.

[0032] The data management module is used to obtain the distribution line equipment ledger information and build a distribution line model based on the distribution line equipment ledger information.

[0033] The distribution line optimization module is used to optimize the distribution line model using different algorithms according to different distribution line drawing requirements to obtain the optimal distribution line: for the distribution line drawing requirements of the same type of equipment, the distribution line model is optimized by the shortest path algorithm; for the distribution line drawing requirements that meet the transmission logic, the distribution line model is optimized by the genetic algorithm.

[0034] Preferably, the distribution line model constructed in the data management module specifically includes: line length cost, load limit penalty, safety distance penalty and current carrying capacity penalty.

[0035] The line length cost is expressed as:

[0036]

[0037] Where, L total is the line length cost; N is the total number of devices, i, j∈[1, N]; d ij is the Euclidean distance between device i and device j; x ij is a binary variable, indicating whether device i is connected to device j. If so, x ij =1, otherwise x ij =0.

[0038] The load limit penalty is expressed as:

[0039]

[0040] Where P load is the load limit penalty; L ij is the load from device i to device j; L max,ij is the maximum load limit from device i to device j, which is determined by the carrying capacity of the cable and the equipment.

[0041] The safety distance penalty is expressed as:

[0042]

[0043] Where P dist For safety distance penalty; d min The minimum safe distance between devices.

[0044] The current carrying capacity penalty is expressed as:

[0045]

[0046] Where P current is the current carrying capacity penalty; I ij is the current from device i to device j; I max,ij The maximum current carrying capacity limit for the cable from device i to device.

[0047] Preferably, the distribution line optimization module optimizes the distribution line model through a genetic algorithm to draw a distribution line that meets the transmission logic requirements:

[0048] S21: Encode the distribution line model variables and design the total fitness function according to the distribution line model.

[0049] S22: Initialize the population and randomly generate a certain number of chromosomes to represent the initial population. Each chromosome represents a possible solution for a distribution line.

[0050] S23: For each individual, calculate its fitness value and evaluate its quality based on the design of the fitness function.

[0051] S24: Select and crossover operations based on individual fitness.

[0052] S25: Perform mutation operation according to the preset mutation probability.

[0053] S26: Repeat steps S23-S25. In each generation, new individuals are continuously generated through selection, crossover and mutation operations, and the population is updated.

[0054] S27: until the maximum number of iterations is reached, the optimal distribution line is output.

[0055] Preferably, when performing a crossover operation, the distribution line optimization module checks the topological structure of the generated child distribution lines to ensure that all nodes are still interconnected after the crossover, and withdraws the crossover operation if isolated nodes or disconnected parts are found.

[0056] The distribution line drawing module is used to draw the optimal distribution line through the GIS map.

[0057] On the other hand, the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the distribution line drawing method as described in any embodiment of the present invention is implemented.

[0058] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for drawing a power distribution line as described in any embodiment of the present invention is implemented.

[0059] Compared with the prior art, the present invention has the following technical effects:

[0060] The method of the present invention optimizes the drawing of distribution lines according to different requirements by combining different optimization algorithms (such as the shortest path algorithm and the genetic algorithm). When dealing with complex distribution networks, the genetic algorithm can optimize factors such as line length, load distribution, safety distance, and current carrying capacity on the basis of ensuring the interconnection between devices, thereby obtaining a more reasonable and economical distribution line solution. In addition, the iterative evolution of the genetic algorithm can quickly search for a better solution and complete complex distribution line optimization tasks in a short time. Through the automated optimization process, manual intervention is reduced, design efficiency is improved, and the time required for traditional manual line drawing is significantly reduced.

[0061] 2. The present invention uses GIS maps to draw the optimized distribution lines, so that the design results have a better visualization effect. In addition, GIS maps can provide intuitive geographic information and spatial data to help staff better understand the layout of distribution lines and their relationship with the surrounding environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is an overall flow chart of the distribution line drawing method described in the present invention. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in combination with specific embodiments of the present application and with reference to the accompanying drawings.

[0064] Embodiment 1

[0065] This embodiment provides a method for drawing a power distribution line. Figure 1 As shown, the following steps are included:

[0066] S1: Obtain the distribution line equipment ledger information, and build a distribution line model based on the distribution line equipment ledger information. Specifically, the equipment ledger information includes the basic data of all equipment on the distribution line, such as equipment location (coordinates), type (transformer, switch, pole, etc.), technical parameters (rated voltage, rated current, load capacity, etc.), etc. The equipment ledger information is usually obtained through a distribution network management system (such as a SCADA system) or a manually recorded equipment database.

[0067] As a preferred implementation of this embodiment, the distribution line model constructed in step S1 specifically includes: line length cost, load limit penalty, safety distance penalty and current carrying capacity penalty.

[0068] The line length cost is evaluated by calculating the Euclidean distance between all devices. The line length cost is expressed as:

[0069]

[0070] Where, L total is the line length cost; N is the total number of devices, i, j∈[1, N]; d ij is the Euclidean distance between device i and device j; x ij is a binary variable, indicating whether device i is connected to device j. If so, x ij =1, otherwise x ij =0.

[0071] The load limit penalty is calculated by comparing the difference between the line load and the maximum load limit. The load limit penalty is expressed as:

[0072]

[0073] Where P load is the load limit penalty; L ij is the load from device i to device j; L max，ij is the maximum load limit from device i to device j, which is determined by the carrying capacity of the cable and the equipment.

[0074] The safety distance between devices needs to comply with the corresponding power design specifications. The safety distance penalty is expressed as:

[0075]

[0076] Where P dist For safety distance penalty; d min The minimum safe distance between devices.

[0077] By calculating the difference between the current load and the current carrying capacity, overload operation can be avoided and the safety and stability of the system can be improved. The current carrying capacity penalty is expressed as:

[0078]

[0079] Where P current is the current carrying capacity penalty; I ij is the current from device i to device j; I max，ij The maximum current carrying capacity limit for the cable from device i to device.

[0080] S2: Use different algorithms to optimize the distribution line model according to different distribution line drawing requirements to obtain the optimal distribution line: for the distribution line drawing requirements of the same type of equipment, the distribution line model is optimized by the shortest path algorithm; for the distribution line drawing requirements that meet the transmission logic, the distribution line model is optimized by the genetic algorithm.

[0081] As a preferred implementation of this embodiment, in step S2, for drawing the distribution line requirements of the same type of equipment, the distribution line model is optimized by the shortest path algorithm, and the shortest path algorithm can adopt the Dijkstra algorithm. The Dijkstra algorithm optimizes the layout of the distribution line by finding the shortest path from the starting point to other nodes. Its working principle is as follows:

[0082] Modeling: Each device in the distribution line system is regarded as a node in the graph, the connection between devices is regarded as an edge, and the weight of the edge represents the distance or cost between devices.

[0083] Initialization: Set the distance of the starting device to 0 and the distance of all other devices to infinity. Create a collection to store all devices and mark them as unprocessed.

[0084] Step-by-step update: Select the device closest to the starting point among the currently unprocessed nodes and update the distances of other devices connected to the device. If a shorter path is found, update the distance value and predecessor node of the device.

[0085] Iteration: Repeatedly select the device closest to the starting point until the shortest paths to all devices are found.

[0086] Path backtracking: Trace back from the target device to obtain the shortest path from the starting point to the end point, and then draw the most optimized distribution line.

[0087] As a preferred implementation of this embodiment, in step S2, for drawing a distribution line that meets the power transmission logic requirements, optimizing the distribution line model through a genetic algorithm is specifically as follows:

[0088] S21: Encode the distribution line model variables and design the total fitness function according to the distribution line model.

[0089] S22: Initialize the population and randomly generate a certain number of chromosomes to represent the initial population. Each chromosome represents a possible solution for a distribution line.

[0090] S23: For each individual, calculate its fitness value and evaluate its quality based on the design of the fitness function.

[0091] S24: Perform selection and crossover operations based on individual fitness. The selection operation selects individuals with higher fitness to enter the next generation. The crossover operation randomly exchanges part of the genes of two individuals (i.e., the connection relationship between the devices) to generate new individuals.

[0092] S25: Perform a mutation operation according to a preset mutation probability. The mutation operation is used to randomly change a gene (ie, the connection relationship between devices), increase the diversity of the population, and avoid falling into a local optimal solution.

[0093] S26: Repeat steps S23-S25. In each generation, new individuals are continuously generated through selection, crossover and mutation operations, and the population is updated.

[0094] S27: until the maximum number of iterations is reached, the optimal distribution line is output.

[0095] As a preferred implementation of this embodiment, the total fitness function is expressed as:

[0096]

[0097] Where, Fitness is the total fitness function; L total is the line length cost; P load is the load limit penalty; P dist For safety distance penalty; P current is the current carrying capacity penalty; α, β, and γ are weight coefficients of different penalties, which can be adjusted according to actual needs.

[0098] As a preferred implementation of this embodiment, by checking the topological structure of the generated child distribution lines, it is ensured that all nodes are still connected to each other after crossing. If isolated nodes or disconnected parts are found, the crossing operation is withdrawn to avoid generating a solution that does not meet the requirements of the distribution lines.

[0099] S3: Draw the optimal distribution line through GIS map. Specifically, the coordinate information of each device (such as longitude and latitude) and the topological structure of the distribution line (such as the connection relationship between devices) are used as input, and spatial analysis and drawing are performed through the GIS system. In this way, the optimized distribution line data is converted into a visual map through the GIS map.

[0100] Furthermore, when verifying the distribution lines at a later stage, the operation and maintenance personnel can upload the equipment location information to the GIS map through on-site positioning, update the equipment location data in the GIS map, automatically adjust other paths, and mark the equipment with the label "Location verified". The equipment marked with the label "Location verified" can only be modified by uploading on-site positioning information, and cannot be modified by dragging, software simulation calculation, etc., so as to improve the accuracy of distribution line data.

[0101] Embodiment 2

[0102] Accordingly, this embodiment provides a distribution line drawing system, which is used to implement the distribution line drawing method described in Example 1, including a data management module, a distribution line optimization module and a distribution line drawing module. This module is used to implement the function of step S1 in Example 1, which will not be repeated here.

[0103] The data management module is used to obtain the distribution line equipment ledger information and construct a distribution line model based on the distribution line equipment ledger information. This module is used to implement the function of step S2 in Example 1 and will not be repeated here.

[0104] The distribution line optimization module is used to optimize the distribution line model using different algorithms according to different distribution line drawing requirements to obtain the optimal distribution line: for the distribution line drawing requirements of the same type of equipment, the distribution line model is optimized by the shortest path algorithm; for the distribution line drawing requirements that meet the transmission logic, the distribution line model is optimized by the genetic algorithm. This module is used to implement the function of step S3 in Example 1 and will not be repeated here.

[0105] Embodiment 3

[0106] This embodiment provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the distribution line drawing method as described in any embodiment of the present invention is implemented.

[0107] Embodiment 4

[0108] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for drawing a power distribution line as described in any embodiment of the present invention is implemented.

[0109] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0110] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0112] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0113] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for drawing a power distribution line, characterized in that: The following steps are involved: S1: Obtain the equipment ledger information of the distribution line, and build a distribution line model based on the equipment ledger information of the distribution line; S2: Use different algorithms to optimize the distribution line model according to different distribution line drawing requirements to obtain the optimal distribution line: For the distribution line drawing requirements of the same type of equipment, the distribution line model is optimized by the shortest path algorithm; for the distribution line drawing requirements that meet the transmission logic, the distribution line model is optimized by the genetic algorithm; S3: Draw the obtained optimal distribution line through GIS map.

2. The method for drawing a power distribution line according to claim 1, characterized in that: The distribution line model constructed in step S1 specifically includes: line length cost, load limit penalty, safety distance penalty and current carrying capacity penalty; The line length cost is expressed as: Where, L total is the line length cost; N is the total number of devices, i,j∈[1,N]; d ij is the Euclidean distance between device i and device j; x ij is a binary variable, indicating whether device i is connected to device j. If so, x ij =1, otherwise x ij =0; The load limit penalty is expressed as: Where P load is the load limit penalty; L ij is the load from device i to device j; L max,ij is the maximum load limit from device i to device j, which is determined by the carrying capacity of the cable and the device; The safety distance penalty is expressed as: Where P dIst For safety distance penalty; d min is the minimum safe distance between devices; The current carrying capacity penalty is expressed as: Where P current I is the current carrying capacity penalty; ij is the current from device i to device j; I max,ij is the maximum current carrying capacity limit of the cable from device i to device j.

3. The method for drawing a power distribution line according to claim 1, characterized in that: In step S2, for drawing a distribution line that meets the requirements of power transmission logic, optimizing the distribution line model through a genetic algorithm is specifically as follows: S21: Encode the distribution line model variables and design the total fitness function according to the distribution line model; S22: Initialize the population and randomly generate a certain number of chromosomes to represent the initial population. Each chromosome represents a possible solution for a distribution line. S23: For each individual, calculate its fitness value and evaluate its quality according to the design of the fitness function; S24: selection and crossover operations based on individual fitness; S25: performing a mutation operation according to a preset mutation probability; S26: Repeat steps S23-S25. In each generation, new individuals are continuously generated through selection, crossover and mutation operations, and the population is updated; S27: until the maximum number of iterations is reached, the optimal distribution line is output.

4. The method for drawing a power distribution line according to claim 3, characterized in that: When performing a crossover operation, the topological structure of the generated child distribution lines is checked to ensure that all nodes are still connected to each other after the crossover. If isolated nodes or disconnected parts are found, the crossover operation is withdrawn.

5. A distribution line drawing system, characterized in that: It includes data management module, distribution line optimization module and distribution line drawing module; The data management module is used to obtain the distribution line equipment ledger information and build a distribution line model based on the distribution line equipment ledger information; The distribution line optimization module is used to optimize the distribution line model using different algorithms according to different distribution line drawing requirements to obtain the optimal distribution line: for drawing distribution line requirements for the same type of equipment, the distribution line model is optimized by the shortest path algorithm; for drawing distribution line requirements that meet the transmission logic, the distribution line model is optimized by the genetic algorithm; The distribution line drawing module is used to draw the optimal distribution line through the GIS map.

6. The power distribution line drawing system according to claim 5, characterized in that: The distribution line model constructed in the data management module specifically includes: line length cost, load limit penalty, safety distance penalty and current carrying capacity penalty; The line length cost is expressed as: Where, L total is the line length cost; N is the total number of devices, i,j∈[1,N]; d ij is the Euclidean distance between device i and device j; x ij is a binary variable, indicating whether device i is connected to device j. If so, x ij =1, otherwise x ij =0; The load limit penalty is expressed as: Where P load is the load limit penalty; L ij is the load from device i to device j; L max,ij is the maximum load limit from device i to device j, which is determined by the carrying capacity of the cable and the device; The safety distance penalty is expressed as: Where P dIst For safety distance penalty; d min is the minimum safe distance between devices; The current carrying capacity penalty is expressed as: Where P current I is the current carrying capacity penalty; ij is the current from device i to device j; I max,ij is the maximum current carrying capacity limit of the cable from device i to device j.

7. The power distribution line drawing system according to claim 5, characterized in that: The distribution line optimization module optimizes the distribution line model through genetic algorithm to draw a distribution line that meets the transmission logic requirements: S21: Encode the distribution line model variables and design the total fitness function according to the distribution line model; S22: Initialize the population and randomly generate a certain number of chromosomes to represent the initial population. Each chromosome represents a possible solution for a distribution line. S23: For each individual, calculate its fitness value and evaluate its quality according to the design of the fitness function; S24: selection and crossover operations based on individual fitness; S25: performing a mutation operation according to a preset mutation probability; S26: Repeat steps S23-S25. In each generation, new individuals are continuously generated through selection, crossover and mutation operations, and the population is updated; S27: until the maximum number of iterations is reached, the optimal distribution line is output.

8. The power distribution line drawing system according to claim 7, characterized in that: When performing a crossover operation, the distribution line optimization module checks the topological structure of the generated child distribution lines to ensure that all nodes are still connected to each other after the crossover. If isolated nodes or disconnected parts are found, the crossover operation is withdrawn.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the power distribution line drawing method according to any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the power distribution line drawing method according to any one of claims 1 to 4 is implemented.

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