A heuristic algorithm-based distribution network line autonomous planning method and system

Abstract

The application relates to a distribution network line autonomous planning method and system based on a heuristic algorithm, which comprises the following steps: planning a UAV route according to a distribution network planning design engineering, collecting aerial images of a planning area, and manufacturing an orthographic image and a normalized digital surface model of the planning area; dividing the orthographic image of the planning area, and confirming a feasible area; confirming an area meeting a cross-span distance requirement in the normalized digital surface model according to distribution network design parameters, and obtaining a power channel buffer area by performing a set operation on the feasible area and the area meeting the cross-span distance requirement; and combining the power channel buffer area and the distribution network design parameters, taking the minimum distribution network construction investment and operation cost as a target, and using a heuristic genetic algorithm to obtain a predicted distribution network line.

Description

Technical Field

[0001] This application relates to the field of distribution network planning technology, and more specifically, to a method and system for autonomous planning of distribution network lines based on heuristic algorithms. Background Technology

[0002] In the field of distribution network planning, with the continuous growth of electricity demand and the increasing complexity of power systems, efficient and accurate distribution network line planning methods have become crucial. Traditional distribution network line planning methods often rely on manual experience and extensive field surveys. This approach is not only time-consuming and labor-intensive but also easily influenced by subjective factors, making it difficult to guarantee the accuracy and scientific validity of the planning results.

[0003] With the rapid development of drone technology, digital image processing technology, and optimization algorithms, new ideas and methods have been provided for power distribution network line planning. Utilizing drones to collect image data of the planning area can acquire high-resolution geographic information, providing richer data support for line planning.

[0004] Existing technologies, such as the Chinese patent application with publication number "CN116109024A", disclose an automatic planning method for distribution network lines. This method includes: obtaining the locations of network nodes and the connection relationships between them; obtaining the starting and ending points of the distribution network line to be planned; performing route planning within the network based on the starting and ending points, the locations of network nodes, and the connection relationships between them to obtain a target route, which is a route formed by multiple connected pairs of network nodes; and determining the distribution network line to be planned based on the target route. This improves the efficiency and accuracy of distribution network line planning.

[0005] The problems with the existing technologies mentioned above are that they lack consideration of terrain and land features, and only consider the connection between the starting point and the end point, which may lead to higher costs during the construction and operation of the planned lines; the algorithm of this method is relatively simple, and when dealing with large-scale and complex power distribution network planning scenarios, it may not be able to quickly find the optimal or near-optimal line scheme, and the level of intelligence is relatively low. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a method and system for autonomous planning of distribution network lines based on heuristic algorithms.

[0007] The technical solution of this invention is as follows:

[0008] This invention proposes a method for autonomous planning of distribution network lines based on heuristic algorithms, comprising the following steps:

[0009] Step S1: Plan the drone flight path according to the power distribution network planning and design project, collect aerial images of the planned area, and create orthophotos and normalized digital surface models of the planned area.

[0010] Step S2: Divide the planned area into regions based on the orthophoto of the planning area and confirm the feasible areas; confirm the areas that meet the crossing distance requirements in the normalized digital surface model according to the power distribution network design parameters; obtain the power channel buffer by taking the union of the feasible areas and the areas that meet the crossing distance requirements.

[0011] Step S3: Combining the power channel buffer and distribution network design parameters, and with the goal of minimizing the investment and operating costs of the distribution network construction, a heuristic genetic algorithm is used to obtain the predicted distribution network lines.

[0012] As a preferred embodiment, the power distribution network design parameters include: tower height, distance between adjacent towers, equipment service life, equipment discount rate, and crossover distance requirements.

[0013] In a preferred embodiment, the method of obtaining the predicted distribution network lines using a heuristic genetic algorithm includes the following steps:

[0014] Chromosome coding: The towers are coded using a leader-based node coding method, with each chromosome corresponding to a distribution network line scheme;

[0015] Population initialization: The population is initialized using the Tent chaotic map. The formula for the Tent map is as follows:

[0016]

[0017] In the formula: x n Here is the value of the variable at the nth iteration; 'a' is the mapping parameter.

[0018] Individual fitness calculation: Construct an objective function with the goal of minimizing the investment and operating costs of the power distribution network, normalize the objective function value, and establish a fitness function;

[0019] The optimal individual is selected and inherited using crossover and mutation: A roulette wheel selection method is used to select individuals to inherit into the offspring population. Then, chromosome crossover and mutation operations are used to increase population diversity. The formulas for calculating the crossover probability pc and mutation probability pb based on the Sigmoid function are as follows:

[0020]

[0021] In the formula: g is the current iteration number; G is the total number of iterations.

[0022] In a preferred embodiment, the objective function is constructed by minimizing the investment and operating costs of the power distribution network. The objective function value is then normalized to establish a fitness function, wherein:

[0023] The objective function is as follows:

[0024]

[0025] Where: Z is the construction cost of the distribution network line; m is the service life of the equipment; i is the discount rate; C t The operating and maintenance costs for year t;

[0026] The fitness function is as follows:

[0027]

[0028] In the formula: F max and F min , respectively, represent the upper and lower bounds of the objective function; X represents the individual chromosome.

[0029] On the other hand, the present invention also provides a distribution network line autonomous planning system based on heuristic algorithms, comprising:

[0030] The data acquisition and preprocessing module plans the drone flight path according to the power distribution network planning and design project, collects aerial images of the planned area, and produces orthophotos and normalized digital surface models of the planned area.

[0031] The power channel buffer generation module divides the planned area into regions based on the orthophoto of the planning area and confirms feasible regions. Based on the power distribution network design parameters, it confirms regions that meet the crossing distance requirements in the normalized digital surface model. The power channel buffer is obtained by taking the union of the feasible regions and the regions that meet the crossing distance requirements.

[0032] The distribution network line planning module, combining circuit channel buffer and distribution network design parameters, uses a heuristic genetic algorithm to predict distribution network lines with the goal of minimizing distribution network construction investment and operating costs.

[0033] In a preferred embodiment, the power channel buffer generation module includes power distribution network design parameters such as: tower height, distance between adjacent towers, equipment service life, equipment discount rate, and crossover distance requirements.

[0034] In a preferred embodiment, the distribution network line planning module uses a heuristic genetic algorithm to obtain predicted distribution network lines, including the following steps:

[0035] Chromosome coding: The towers are coded using a leader-based node coding method, with each chromosome corresponding to a distribution network line scheme;

[0036] Population initialization: The population is initialized using the Tent chaotic map. The formula for the Tent map is as follows:

[0037]

[0038] In the formula: x n Here is the value of the variable at the nth iteration; 'a' is the mapping parameter.

[0039] Individual fitness calculation: Construct an objective function with the goal of minimizing the investment and operating costs of the power distribution network, normalize the objective function value, and establish a fitness function;

[0040] The optimal individual is selected and inherited using crossover and mutation: A roulette wheel selection method is used to select individuals to inherit into the offspring population. Then, chromosome crossover and mutation operations are used to increase population diversity. The formulas for calculating the crossover probability pc and mutation probability pb based on the Sigmoid function are as follows:

[0041]

[0042] In the formula: g is the current iteration number; G is the total number of iterations.

[0043] In a preferred embodiment, the objective function is constructed by minimizing the investment and operating costs of the power distribution network. The objective function value is then normalized to establish a fitness function, wherein:

[0044] The objective function is as follows:

[0045]

[0046] Where: Z is the construction cost of the distribution network line; m is the service life of the equipment; i is the discount rate; C t The operating and maintenance costs for year t;

[0047] The fitness function is as follows:

[0048]

[0049] In the formula: F max and F min , respectively, represent the upper and lower bounds of the objective function; X represents the individual chromosome.

[0050] In another aspect, the present invention also provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements a method for autonomous planning of distribution network lines based on a heuristic algorithm as described in any embodiment of the present invention.

[0051] In another aspect, the present invention also provides a computer-readable medium for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement a heuristic algorithm-based autonomous planning method for distribution network lines as described in any embodiment of the present invention.

[0052] The present invention has the following beneficial effects:

[0053] 1. By combining distribution network design parameters to divide areas and extract power channel buffer zones, actual environmental factors such as terrain and land features can be fully considered, thereby planning distribution network lines that are more in line with actual conditions, effectively avoiding obstacles, and improving the accuracy of line planning.

[0054] 2. With the goal of minimizing the investment and operating costs of the distribution network, this approach comprehensively considers factors such as tower height, distance between adjacent towers, equipment lifespan, discount rate, and crossing distance requirements. An objective function and fitness function are constructed, and a heuristic genetic algorithm is used for optimization. This method comprehensively weighs the cost-effectiveness of different schemes, helping to select the lowest-cost and most efficient line planning scheme, thereby reducing the total cost of distribution network construction and operation.

[0055] 3. Using heuristic algorithms for distribution network line planning reduces reliance on human experience, improves planning efficiency, and can process large amounts of complex data in a short time to quickly generate feasible line planning schemes, adapting to the needs of rapid development and frequent updates in modern power systems.

[0056] 4. It can flexibly adjust parameters and algorithms according to different planning areas and design requirements, adapt to various complex geographical environments and constraints, and has stronger versatility than traditional methods. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0059] Figure 2 The flowchart shows an improved genetic algorithm for distribution network line planning. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0062] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0063] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0064] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0065] Example 1:

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, specific embodiments of this application will be described below, with reference to the accompanying drawings. Figure 1 The technical solution of the present invention will be clearly and completely described.

[0067] To address the problems in existing technologies, this invention provides a method for autonomous planning of distribution network lines based on heuristic algorithms, comprising the following steps:

[0068] Step S1: Plan the drone flight path according to the power distribution network planning and design project, collect aerial images of the planned area, and create orthophotos and normalized digital surface models of the planned area.

[0069] Based on the power distribution network planning and design project, UAV flight routes are planned within the corresponding survey area. UAVs are then used to acquire UAV imagery data and other relevant data within the survey area. UAV photogrammetry methods are employed to create orthophotos and normalized digital surface models of the survey area from the UAV-collected imagery data and other relevant data.

[0070] Step S2: Divide the planned area into regions based on the orthophoto of the planning area and confirm the feasible areas; confirm the areas that meet the crossing distance requirements in the normalized digital surface model according to the power distribution network design parameters; obtain the power channel buffer by taking the union of the feasible areas and the areas that meet the crossing distance requirements.

[0071] The power distribution network design parameters include: tower height, distance between adjacent towers, equipment service life, equipment discount rate, and crossover distance requirements.

[0072] On the orthophoto, the land features of the planning area are classified, such as basic farmland, buildings, roads, trees, etc., and feasible areas are divided according to design specifications and requirements. On the other hand, combined with design parameters such as tower height, areas that meet the crossing distance requirements are extracted from the normalized digital surface model, and the union of these areas with the feasible areas on the orthophoto is obtained to obtain the power channel buffer zone.

[0073] Step S3: Combining the power channel buffer and distribution network design parameters, and with the goal of minimizing the investment and operating costs of the distribution network construction, a heuristic genetic algorithm is used to obtain the predicted distribution network lines.

[0074] The process of using a heuristic genetic algorithm to predict distribution network lines includes the following steps:

[0075] Step S301, Chromosome Coding: The towers are coded using the node coding method of the preamble method, and each chromosome corresponds to a distribution network line scheme; In the above-mentioned power channel buffer zone, given the location of some power towers and the distance between adjacent towers, the towers are numbered by integers, and the towers are coded using the node coding method of the preamble method, and each chromosome corresponds to a distribution network line scheme.

[0076] Step S302, Initialize the population: Initialize the population using the Tent chaotic mapping. The formula for the Tent mapping is as follows:

[0077]

[0078] In the formula: x n is the value of the variable in the nth iteration; a is the mapping parameter, a∈(0,1), and is generally taken as 0.5;

[0079] Step S303, Individual fitness calculation: Construct an objective function with the goal of minimizing the investment and operating costs of the power distribution network, normalize the objective function value, and establish a fitness function;

[0080] The objective function is as follows:

[0081]

[0082] Where: Z is the construction cost of the distribution network line; m is the service life of the equipment; i is the discount rate; C t The operating and maintenance costs for year t;

[0083] The fitness function is as follows:

[0084]

[0085] In the formula: F max and F min Here, X represents the upper and lower bounds of the objective function, respectively; X is the individual chromosome. Simultaneously, a constraint is set that the lines between towers must be included in the power channel buffer zone; solutions that do not meet this constraint are directly discarded.

[0086] Step S304: Apply crossover and mutation to complete the selection and inheritance of the optimal individual: The roulette wheel method is used to select individuals to be inherited into the offspring population. Then, the diversity of the population is increased through chromosome crossover and mutation operations. The formulas for calculating the crossover probability pc and mutation probability pb based on the Sigmoid function are as follows:

[0087]

[0088] In the formula: g is the current iteration number; G is the total number of iterations.

[0089] Step S305: Apply the improved genetic algorithm obtained above to the distribution network line planning to obtain the predicted distribution network lines.

[0090] Example 2:

[0091] This embodiment provides a distribution network line autonomous planning system based on heuristic algorithms, including:

[0092] The data acquisition and preprocessing module plans the drone flight path according to the power distribution network planning and design project, collects aerial images of the planned area, and produces orthophotos and normalized digital surface models of the planned area.

[0093] The power channel buffer generation module divides the planned area into regions based on the orthophoto of the planning area and confirms feasible regions. Based on the power distribution network design parameters, it confirms regions that meet the crossing distance requirements in the normalized digital surface model. The power channel buffer is obtained by taking the union of the feasible regions and the regions that meet the crossing distance requirements.

[0094] The distribution network line planning module, combining circuit channel buffer and distribution network design parameters, uses a heuristic genetic algorithm to predict distribution network lines with the goal of minimizing distribution network construction investment and operating costs.

[0095] Example 3:

[0096] This embodiment provides an electronic device that stores a computer program. When the computer program is executed by a processor, it implements a method for autonomous planning of distribution network lines based on a heuristic algorithm as described in any embodiment of the present invention.

[0097] Example 4:

[0098] This embodiment provides a computer-readable medium for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement a heuristic algorithm-based autonomous planning method for distribution network lines as described in any embodiment of the present invention.

[0099] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related 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 singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0100] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for autonomous planning of distribution network lines based on heuristic algorithms, characterized in that, Includes the following steps: Step S1: Plan the drone flight path according to the power distribution network planning and design project, collect aerial images of the planned area, and create orthophotos and normalized digital surface models of the planned area. The design parameters for power distribution networks include: tower height, distance between adjacent towers, equipment lifespan, equipment discount rate, and crossover distance requirements; Step S2: Divide the planned area into regions based on the orthophoto of the planning area and confirm the feasible areas; confirm the areas that meet the crossing distance requirements in the normalized digital surface model according to the power distribution network design parameters; obtain the power channel buffer by taking the union of the feasible areas and the areas that meet the crossing distance requirements. Step S3: Combining power channel buffer zones and distribution network design parameters, and aiming to minimize distribution network construction investment and operating costs, a heuristic genetic algorithm is used to predict the distribution network lines. The process of using a heuristic genetic algorithm to predict the distribution network lines includes the following steps: Chromosome coding: The towers are coded using a leader-based node coding method, with each chromosome corresponding to a distribution network line scheme; Population initialization: The population is initialized using the Tent chaotic map. The formula for the Tent map is as follows: ； In the formula: Here is the value of the variable at the nth iteration; 'a' is the mapping parameter. Individual fitness calculation: Construct an objective function with the goal of minimizing the investment and operating costs of the power distribution network, normalize the objective function value, and establish a fitness function; The optimal individual is selected and inherited using crossover and mutation: A roulette wheel selection method is used to select individuals to inherit into the offspring population. Then, chromosome crossover and mutation operations are used to increase population diversity. The formulas for calculating the crossover probability pc and mutation probability pb based on the Sigmoid function are as follows: ； ； In the formula: g is the current iteration number; G is the total number of iterations; The objective function is constructed by minimizing the investment and operating costs of the power distribution network. The objective function value is then normalized to establish a fitness function, where: The objective function is as follows: ； In the formula: Z is the construction cost of the distribution network line; m is the service life of the equipment; i is the discount rate; The operating and maintenance costs for year t; The fitness function is as follows: ； In the formula: and , respectively, represent the upper and lower bounds of the objective function; X represents the individual chromosome.

2. A distribution network line autonomous planning system based on heuristic algorithms, characterized in that, include: The data acquisition and preprocessing module plans the drone flight path according to the power distribution network planning and design project, collects aerial images of the planned area, and produces orthophotos and normalized digital surface models of the planned area. The design parameters for power distribution networks include: tower height, distance between adjacent towers, equipment lifespan, equipment discount rate, and crossover distance requirements; The power channel buffer generation module divides the planned area into regions based on the orthophoto of the planning area and confirms feasible regions. Based on the power distribution network design parameters, it confirms regions that meet the crossing distance requirements in the normalized digital surface model. The power channel buffer is obtained by taking the union of the feasible regions and the regions that meet the crossing distance requirements. The distribution network line planning module, combining circuit channel buffers and distribution network design parameters, aims to minimize distribution network construction investment and operating costs, and uses a heuristic genetic algorithm to predict distribution network lines. The process of using a heuristic genetic algorithm to predict distribution network lines includes the following steps: Chromosome coding: The towers are coded using a leader-based node coding method, with each chromosome corresponding to a distribution network line scheme; Population initialization: The population is initialized using the Tent chaotic map. The formula for the Tent map is as follows: ； In the formula: Here is the value of the variable at the nth iteration; 'a' is the mapping parameter. Individual fitness calculation: Construct an objective function with the goal of minimizing the investment and operating costs of the power distribution network, normalize the objective function value, and establish a fitness function; The optimal individual is selected and inherited using crossover and mutation: A roulette wheel selection method is used to select individuals to inherit into the offspring population. Then, chromosome crossover and mutation operations are used to increase population diversity. The formulas for calculating the crossover probability pc and mutation probability pb based on the Sigmoid function are as follows: ； ； In the formula: g is the current iteration number; G is the total number of iterations; The objective function is constructed by minimizing the investment and operating costs of the power distribution network. The objective function value is then normalized to establish a fitness function, where: The objective function is as follows: ； In the formula: Z is the construction cost of the distribution network line; m is the service life of the equipment; i is the discount rate; The operating and maintenance costs for year t; The fitness function is as follows: ； In the formula: and , respectively, represent the upper and lower bounds of the objective function; X represents the individual chromosome.

3. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the autonomous planning method for distribution network lines based on heuristic algorithms as described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a heuristic algorithm-based autonomous planning method for distribution network lines as described in claim 1.

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