Distribution network line autonomous planning method and system based on heuristic algorithm

Abstract

The invention relates to a distribution network line autonomous planning method and system based on a heuristic algorithm, and the method comprises the following steps: planning an unmanned plane route according to a distribution network planning design project, collecting an aerial image of a planning region, and making an orthoimage and a normalized digital surface model of the planning region; carrying out region division on the orthoimage of the planning region, and confirming a feasible region; according to the design parameters of the power distribution network, determining an area meeting the requirement of the crossing distance in the normalized digital surface model, and obtaining a union set of the feasible area and the area meeting the requirement of the crossing distance to obtain a power channel buffer area; and in combination with the power channel buffer area and the power distribution network design parameters, with the goal of minimizing the construction investment and operation cost of the power distribution network, obtaining a predicted power distribution network line by using a heuristic genetic algorithm.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network planning, and more specifically, to a method and system for autonomous planning of distribution network lines based on a heuristic algorithm. Background Art

[0002] In the field of distribution network planning, with the continuous growth of power 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 a large number of on-site surveys. This method is not only time-consuming and labor-intensive, but also easily affected by subjective factors, making it difficult to ensure the accuracy and scientificity of the planning results.

[0003] With the rapid development of UAV technology, digital image processing technology and optimization algorithms, new ideas and methods have been provided for distribution network line planning. Using UAVs to collect image data of the planning area can obtain high-resolution geographic information and provide richer data support for line planning.

[0004] Prior art, such as the Chinese patent application with publication number "CN116109024A", discloses a method for automatic planning of distribution network lines, which includes: obtaining the positions of network nodes in the road network and the connection relationship between the network nodes; obtaining the starting position and the end position of the distribution network line to be planned; performing route planning in the road network according to the starting position, the end position, the position of the network nodes and the connection relationship between the network nodes, obtaining a target route, which is a route formed by multiple connected network node pairs; and determining the distribution network line to be planned according to the target route. The efficiency and accuracy of distribution network line planning are improved.

[0005] The problem with the above-mentioned existing technology is that it lacks consideration of terrain and landform information and only considers the connection between the starting point and the end point, which may lead to high costs in the construction and operation of the planned lines; the algorithm of this method is relatively simple, and when dealing with large-scale and complex distribution network planning scenarios, it may not be able to quickly find the optimal or near-optimal line solution, and the degree of intelligence is relatively low. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a method and system for autonomous planning of distribution network lines based on a heuristic algorithm.

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

[0008] The present invention proposes a distribution network line autonomous planning method based on a heuristic algorithm, comprising the following steps:

[0009] Step S1, planning the UAV route according to the distribution network planning and design project, collecting aerial images of the planning area, and making orthophotos and normalized digital surface models of the planning area;

[0010] Step S2, dividing the area in the orthophoto of the planning area to confirm the feasible area; confirming the area that meets the cross-span distance requirement in the normalized digital surface model according to the distribution network design parameters, and obtaining the power channel buffer by taking the union of the feasible area and the area that meets the cross-span distance requirement;

[0011] Step S3, combining the power channel buffer and distribution network design parameters, taking the distribution network construction investment and operation cost as the minimum, using a heuristic genetic algorithm to obtain the predicted distribution network line.

[0012] As a preferred implementation, the distribution network design parameters include: tower height, distance between adjacent towers, service life of equipment, equipment discount rate and cross-span distance requirements.

[0013] As a preferred implementation, the method of using a heuristic genetic algorithm to obtain a predicted distribution network line includes the following steps:

[0014] Chromosome coding: The towers are coded with chromosomes using the node coding method of the leading method. Each chromosome corresponds to a distribution network line solution.

[0015] Initialize the population: Use Tent chaotic mapping to initialize the population. The formula of Tent mapping is as follows:

[0016]

[0017] Where: x n is the variable value at the nth iteration; a is the mapping parameter;

[0018] Individual fitness calculation: construct the objective function with the minimum construction investment and operation cost of the distribution network, normalize the objective function value, and establish the fitness function;

[0019] Crossover and mutation are used to complete the selection and inheritance of the best individuals: the roulette method is used to select individuals to be inherited into the offspring population, and then the diversity of the population is increased through chromosome crossover and mutation operations. The calculation formulas for the crossover probability pc and mutation probability pb based on the Sigmoid function are as follows:

[0020]

[0021] Where: g is the current iteration number; G is the total iteration number.

[0022] As a preferred implementation, the objective function is constructed by minimizing the construction investment and operation cost of the distribution network, the objective function value is normalized, and a fitness function is established, 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 is the operation and maintenance cost in year t;

[0026] The fitness function is as follows:

[0027]

[0028] Where: F max and F min are the upper and lower bounds of the objective function respectively; X is the individual chromosome.

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

[0030] The data acquisition and preprocessing module plans the UAV route according to the distribution network planning and design project, collects aerial images of the planning area, and produces orthophotos and normalized digital surface models of the planning area;

[0031] The power channel buffer generation module divides the area in the orthophoto of the planning area and confirms the feasible area; confirms the area that meets the cross-span distance requirements in the normalized digital surface model according to the distribution network design parameters, and obtains the power channel buffer by taking the union of the feasible area and the area that meets the cross-span distance requirements;

[0032] The distribution network line planning module combines the circuit channel buffer and distribution network design parameters, takes the distribution network construction investment and operation cost as the minimum, and uses the heuristic genetic algorithm to obtain the predicted distribution network line.

[0033] As a preferred implementation, in the power channel buffer generation module, the distribution network design parameters include: tower height, distance between adjacent towers, service life of equipment, equipment discount rate and cross-span distance requirements.

[0034] As a preferred implementation, in the distribution network line planning module, a heuristic genetic algorithm is used to obtain a predicted distribution network line, comprising the following steps:

[0035] Chromosome coding: The towers are coded with chromosomes using the node coding method of the leading method. Each chromosome corresponds to a distribution network line solution.

[0036] Initialize the population: Use Tent chaotic mapping to initialize the population. The formula of Tent mapping is as follows:

[0037]

[0038] Where: x n is the variable value at the nth iteration; a is the mapping parameter;

[0039] Individual fitness calculation: construct the objective function with the minimum construction investment and operation cost of the distribution network, normalize the objective function value, and establish the fitness function;

[0040] Crossover and mutation are used to complete the selection and inheritance of the best individuals: the roulette method is used to select individuals to be inherited into the offspring population, and then the diversity of the population is increased through chromosome crossover and mutation operations. The calculation formulas for the crossover probability pc and mutation probability pb based on the Sigmoid function are as follows:

[0041]

[0042] Where: g is the current iteration number; G is the total iteration number.

[0043] As a preferred implementation, the objective function is constructed by minimizing the construction investment and operation cost of the distribution network, the objective function value is normalized, and a fitness function is established, 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 is the operation and maintenance cost in year t;

[0047] The fitness function is as follows:

[0048]

[0049] Where: F max and F min are the upper and lower bounds of the objective function respectively; X is the individual chromosome.

[0050] On the other hand, the present invention further provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for autonomous planning of distribution network lines based on a heuristic algorithm as described in any embodiment of the present invention is implemented.

[0051] On the other hand, the present invention also provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement a method for autonomous planning of distribution network lines based on a heuristic algorithm as described in any embodiment of the present invention.

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

[0053] 1. Combining the distribution network design parameters for regional division and power channel buffer zone extraction can fully consider actual environmental factors such as terrain and landforms, so as to plan distribution network lines that are more in line with the actual situation, effectively avoid obstacles, and improve the accuracy of line planning.

[0054] 2. With the goal of minimizing the construction investment and operation cost of the distribution network, the objective function and fitness function are constructed by comprehensively considering factors such as tower height, distance between adjacent towers, equipment service life, discount rate and cross-span distance requirements, and the heuristic genetic algorithm is used for optimization. This method can comprehensively weigh the cost-effectiveness of different schemes, help select the line planning scheme with the lowest cost and the highest benefit, and reduce the total cost of distribution network construction and operation.

[0055] 3. The use of heuristic algorithms for distribution network line planning reduces dependence on manual experience, improves planning efficiency, can process large amounts of complex data in a relatively short period of time, quickly generate feasible line planning solutions, and adapt to the needs of rapid development and frequent updates of 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a schematic diagram of the process of the present invention;

[0059] Figure 2 This is the flow chart of the improved genetic algorithm for distribution network line planning. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

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

[0063] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0064] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0065] Embodiment 1:

[0066] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be combined with the specific embodiments of the present application and refer to the attached Figure 1 , clearly and completely describe the technical solution of the present invention.

[0067] To solve the problems in the prior art, the present invention provides a method for autonomous planning of distribution network lines based on a heuristic algorithm, comprising the following steps:

[0068] Step S1, planning the UAV route according to the distribution network planning and design project, collecting aerial images of the planning area, and making orthophotos and normalized digital surface models of the planning area;

[0069] According to the distribution network planning and design project, the UAV flight route is planned in the corresponding survey area, and the UAV image data and other related data are obtained in the survey area by using the UAV photogrammetry method to obtain the orthophoto and normalized digital surface model of the survey area from the image data and other related data collected by the UAV.

[0070] Step S2, dividing the area in the orthophoto of the planning area to confirm the feasible area; confirming the area that meets the cross-span distance requirement in the normalized digital surface model according to the distribution network design parameters, and obtaining the power channel buffer by taking the union of the feasible area and the area that meets the cross-span distance requirement;

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

[0072] The features in the planned area are classified on the orthophoto, such as basic farmland, buildings, roads, trees, etc., and the feasible area is divided according to the design specifications and requirements. On the other hand, combined with the design parameters such as the tower height, the area that meets the cross-span distance requirements is extracted in the normalized digital surface model, and the union with the feasible area on the orthophoto is used to obtain the power channel buffer zone.

[0073] Step S3, combining the power channel buffer and distribution network design parameters, taking the distribution network construction investment and operation cost as the minimum, using a heuristic genetic algorithm to obtain the predicted distribution network line.

[0074] The method of using a heuristic genetic algorithm to obtain a predicted distribution network line comprises the following steps:

[0075] Step S301, chromosome encoding: chromosome encoding is performed on the poles by using the node encoding method of the leading method, and each chromosome corresponds to a distribution network line scheme; in the above-mentioned power channel buffer, given the positions of some power poles and the distances between adjacent poles and other conditions, the poles are numbered by integers, and chromosome encoding is performed on the poles by using the node encoding method of the leading method, and each chromosome corresponds to a distribution network line scheme;

[0076] Step S302, initializing the population: using Tent chaotic mapping to initialize the population, the formula of Tent mapping is as follows:

[0077]

[0078] Where: x n is the variable value at the nth iteration; a is the mapping parameter, a∈(0,1), generally 0.5;

[0079] Step S303, individual fitness calculation: construct an objective function with the minimum construction investment and operation cost of the 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 is the operation and maintenance cost in year t;

[0083] The fitness function is as follows:

[0084]

[0085] Where: F max and F min are the upper and lower bounds of the objective function respectively; X is the individual chromosome. At the same time, the constraint condition that the lines between towers must be included in the power channel buffer is set, and the solutions that do not meet the constraint condition are directly discarded.

[0086] Step S304, using crossover and mutation to complete the selection and inheritance of the best individuals: using the roulette method to select individuals to be inherited into the offspring population, and then increasing the diversity of the population through chromosome crossover and mutation operations, wherein the calculation formulas for the crossover probability pc and the mutation probability pb based on the Sigmoid function are as follows:

[0087]

[0088] Where: g is the current iteration number; G is the total iteration number.

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

[0090] Embodiment 2:

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

[0092] The data acquisition and preprocessing module plans the UAV route according to the distribution network planning and design project, collects aerial images of the planning area, and produces orthophotos and normalized digital surface models of the planning area;

[0093] The power channel buffer generation module divides the area in the orthophoto of the planning area and confirms the feasible area; confirms the area that meets the cross-span distance requirements in the normalized digital surface model according to the distribution network design parameters, and obtains the power channel buffer by taking the union of the feasible area and the area that meets the cross-span distance requirements;

[0094] The distribution network line planning module combines the circuit channel buffer and distribution network design parameters, takes the distribution network construction investment and operation cost as the minimum, and uses the heuristic genetic algorithm to obtain the predicted distribution network line.

[0095] Embodiment three:

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

[0097] Embodiment 4:

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

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 autonomous planning of distribution network lines based on heuristic algorithm, characterized in that: The following steps are involved: Step S1, planning the UAV route according to the distribution network planning and design project, collecting aerial images of the planning area, and making orthophotos and normalized digital surface models of the planning area; Step S2, dividing the area in the orthophoto of the planning area and confirming the feasible area; According to the design parameters of the distribution network, the area that meets the cross-span distance requirement is confirmed in the normalized digital surface model, and the power channel buffer zone is obtained by taking the union of the feasible area and the area that meets the cross-span distance requirement; Step S3, combining the power channel buffer and distribution network design parameters, taking the distribution network construction investment and operation cost as the minimum, using a heuristic genetic algorithm to obtain the predicted distribution network line.

2. The method for autonomous planning of distribution network lines based on a heuristic algorithm according to claim 1, characterized in that: The distribution network design parameters include: tower height, distance between adjacent towers, equipment service life, equipment discount rate and cross-span distance requirements.

3. The method for autonomous planning of distribution network lines based on heuristic algorithm according to claim 1, characterized in that: The method of using a heuristic genetic algorithm to obtain a predicted distribution network line comprises the following steps: Chromosome coding: The towers are coded with chromosomes using the node coding method of the leading method. Each chromosome corresponds to a distribution network line solution. Initialize the population: Use Tent chaotic mapping to initialize the population. The formula of Tent mapping is as follows: Where: x n is the variable value at the nth iteration; a is the mapping parameter; Individual fitness calculation: construct the objective function with the minimum construction investment and operation cost of the distribution network, normalize the objective function value, and establish the fitness function; Crossover and mutation are used to complete the selection and inheritance of the best individuals: the roulette method is used to select individuals to be inherited into the offspring population, and then the diversity of the population is increased through chromosome crossover and mutation operations. The calculation formulas for the crossover probability pc and mutation probability pb based on the Sigmoid function are as follows: Where: g is the current iteration number; G is the total iteration number.

4. The method for autonomous planning of distribution network lines based on a heuristic algorithm according to claim 3, characterized in that: The objective function is constructed by minimizing the construction investment and operation cost of the distribution network, and the objective function value is normalized to establish a fitness function, wherein: The objective function is as follows: 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 is the operation and maintenance cost in year t; The fitness function is as follows: Where: F max and F min are the upper and lower bounds of the objective function respectively; X is the individual chromosome.

5. A distribution network line autonomous planning system based on heuristic algorithm, characterized in that: include: The data acquisition and preprocessing module plans the UAV route according to the distribution network planning and design project, collects aerial images of the planning area, and produces orthophotos and normalized digital surface models of the planning area; The power channel buffer generation module divides the area based on the orthophoto of the planned area and confirms the feasible area; According to the design parameters of the distribution network, the area that meets the cross-span distance requirement is confirmed in the normalized digital surface model, and the power channel buffer zone is obtained by taking the union of the feasible area and the area that meets the cross-span distance requirement; The distribution network line planning module combines the circuit channel buffer and distribution network design parameters, takes the distribution network construction investment and operation cost as the minimum, and uses the heuristic genetic algorithm to obtain the predicted distribution network line.

6. The distribution network line autonomous planning system based on heuristic algorithm according to claim 5, characterized in that: In the power channel buffer generation module, the distribution network design parameters include: tower height, distance between adjacent towers, service life of equipment, equipment discount rate and cross-span distance requirements.

7. The distribution network line autonomous planning system based on heuristic algorithm according to claim 5, characterized in that: In the distribution network line planning module, a heuristic genetic algorithm is used to obtain a predicted distribution network line, which includes the following steps: Chromosome coding: The towers are coded with chromosomes using the node coding method of the leading method. Each chromosome corresponds to a distribution network line solution. Initialize the population: Use Tent chaotic mapping to initialize the population. The formula of Tent mapping is as follows: Where: x n is the variable value at the nth iteration; a is the mapping parameter; Individual fitness calculation: construct the objective function with the minimum construction investment and operation cost of the distribution network, normalize the objective function value, and establish the fitness function; Crossover and mutation are used to complete the selection and inheritance of the best individuals: the roulette method is used to select individuals to be inherited into the offspring population, and then the diversity of the population is increased through chromosome crossover and mutation operations. The calculation formulas for the crossover probability pc and mutation probability pb based on the Sigmoid function are as follows: Where: g is the current iteration number; G is the total iteration number.

8. The distribution network line autonomous planning system based on heuristic algorithm according to claim 7, characterized in that: The objective function is constructed by minimizing the construction investment and operation cost of the distribution network, and the objective function value is normalized to establish a fitness function, wherein: The objective function is as follows: 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 is the operation and maintenance cost in year t; The fitness function is as follows: Where: F max and F min are the upper and lower bounds of the objective function respectively; X is the individual chromosome.

9. 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 method for autonomous planning of distribution network lines based on a heuristic algorithm as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements a distribution network line autonomous planning method based on a heuristic algorithm as described in any one of claims 1 to 4.

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