Intelligent site selection and line selection planning method, system and equipment for transformer substation and medium

Abstract

The invention relates to a transformer substation intelligent site selection and line selection planning method, system, equipment and medium, and relates to the field of transformer substation site selection, and the method comprises the steps: carrying out the information collection of a target site selection region through employing the oblique photography of an unmanned plane, generating a three-dimensional model, determining a plurality of environment sensitive regions and the sensitive weights of the environment sensitive regions based on the three-dimensional model, and meanwhile, load demand data and construction cost data of the region are obtained. Establishing a multi-target evaluation model, optimizing the plurality of obtained candidate site selection schemes to obtain an optimal site selection scheme, performing line selection planning on the optimal site selection scheme based on an adaptive algorithm, and adjusting a line selection strategy in real time to obtain an optimal line selection scheme. The technical problem of low planning precision and efficiency caused by lack of intelligent algorithm support in substation site selection and line selection planning in the prior art is solved, and the technical effect of improving the precision and efficiency of substation engineering site selection and line selection planning through collaborative optimization of the intelligent algorithm is achieved.

Description

Technical Field

[0001] The present invention relates to the field of substation site selection, and in particular to a method, system, equipment and medium for intelligent substation site selection and line selection planning. Background Art

[0002] With the acceleration of urbanization and the continuous growth of electricity demand, the site selection and line selection planning of substation projects are becoming increasingly important. The site selection and line selection planning of substations are key links in the construction of power systems, and their rationality directly affects the reliability, economy and environmental impact of the entire power system.

[0003] At present, the site selection and line selection planning of substations mainly rely on manual experience for judgment and decision-making. This traditional manual planning method has the following shortcomings: First, planners need to comprehensively consider multiple factors such as geographical environment, load demand, construction cost, etc. Manual processing of such complex multi-dimensional information is prone to deviations, resulting in low planning accuracy; second, manual planning methods require a lot of time to compare and adjust the schemes, and the planning efficiency is low; third, in actual projects, environmental conditions and load demands often change dynamically, and traditional manual planning methods are difficult to respond to these changes in time and make flexible adjustments. Therefore, the lack of intelligent algorithm support for substation site selection and line selection planning in the existing technology leads to low planning accuracy and efficiency. Summary of the invention

[0004] The present invention aims to solve the technical problem that substation site selection and line selection planning in the prior art lacks intelligent algorithm support, resulting in low planning accuracy and efficiency, and provides a substation intelligent site selection and line selection planning method, system, equipment and medium to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a method for intelligent site selection and line planning of a substation, comprising: using unmanned aerial vehicle oblique photography to collect information on a target site selection area, obtaining geographic information data of the target site selection area, and generating a three-dimensional model of the target area based on the geographic information data; determining multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtaining regional sensitivity weights of the multiple environmentally sensitive areas; obtaining load demand data and construction cost data of the target site selection area; establishing a multi-objective evaluation model, based on geographic information data, regional sensitivity weights of multiple environmentally sensitive areas, load demand data and construction cost data, combining a multi-objective evaluation model and an improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area, and optimizing the multiple candidate site selection schemes to obtain an optimal site selection scheme; performing line selection planning for the optimal site selection scheme based on an adaptive algorithm, and obtaining the optimal line selection scheme by adjusting the line selection strategy in real time.

[0007] In a second aspect, the present invention provides a substation intelligent site selection and line selection planning system, including: a three-dimensional model establishment module, which is used to use unmanned aerial vehicle oblique photography to collect information on the target site selection area, obtain geographic information data of the target site selection area, and generate a three-dimensional model of the target area according to the geographic information data; an environmental impact determination module, which is used to determine multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtain regional sensitivity weights of multiple environmentally sensitive areas; a regional data acquisition module, which is used to obtain load demand data and construction cost data of the target site selection area; an optimal site selection determination module, which is used to establish a multi-objective evaluation model, based on geographic information data, regional sensitivity weights of multiple environmentally sensitive areas, load demand data and construction cost data, combined with a multi-objective evaluation model and an improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area, and optimize the multiple candidate site selection schemes to obtain the optimal site selection scheme; an optimal line determination module, which is used to perform line selection planning for the optimal site selection scheme based on an adaptive algorithm, and obtain the optimal line selection scheme by adjusting the line selection strategy in real time.

[0008] In a third aspect, the present application provides an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute the intelligent site selection and line selection planning method for substations provided in the present application.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which is used to execute the substation intelligent site selection and line selection planning method provided in the present application.

[0010] The beneficial effects of the present invention are:

[0011] Use UAV oblique photography to collect information on the target site selection area, obtain geographic information data of the target site selection area, generate a three-dimensional model of the target area based on the geographic information data, and accurately grasp the geographical environment characteristics of the site selection area; determine multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtain the regional sensitivity weights of multiple environmentally sensitive areas to quantitatively evaluate the environmental impact of the site selection; obtain load demand data and construction cost data of the target site selection area to provide important parameters for subsequent optimization calculations; establish a multi-objective evaluation model, combine the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection plans in the target site selection area, and optimize the multiple candidate site selection plans to obtain the optimal site selection plan, and realize automatic optimization of the site selection plan; based on the adaptive algorithm, the optimal site selection plan is selected for line planning, and the optimal line selection plan is obtained by adjusting the line selection strategy in real time, ensuring that the optimal line planning result is obtained in a complex dynamic environment. Through the above technical solution, a variety of intelligent algorithms are used to cooperate with each other, and the technical effect of improving the accuracy and efficiency of substation project site selection and line selection planning through intelligent algorithm collaborative optimization is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of a process flow of a substation intelligent site selection and line selection planning method provided by the present invention;

[0013] Figure 2 A schematic diagram of the structure of a substation intelligent site selection and line selection planning system provided by the present invention;

[0014] Figure 3 A schematic diagram of the structure of an electronic device provided by the present invention;

[0015] Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0016] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0017] 11. Three-dimensional model building module; 12. Environmental impact determination module; 13. Regional data acquisition module; 14. Optimal address determination module; 15. Optimal line determination module; 200. Electronic device; 210. Memory; 220. Processor; 211. First computer program; 300. Computer-readable storage medium; 311. Second computer program. DETAILED DESCRIPTION

[0018] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0019] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0020] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0021] Embodiment 1:

[0022] like Figure 1 As shown, an embodiment of the present invention provides a method for intelligent site selection and line selection planning of a substation, including:

[0023] S100: Collect information on the target site selection area by using drone oblique photography to obtain geographic information data of the target site selection area, and generate a three-dimensional model of the target area according to the geographic information data.

[0024] Specifically, firstly, the geographic information of the target site selection area is collected through the oblique photography technology of unmanned aerial vehicles, and the oblique photography data collected is processed using geographic information system software (such as ArcGIS, QGIS) to obtain geographic information data including terrain, land use type, road and building distribution, with an accuracy of less than 0.1 meters. Then, a three-dimensional model of the target site selection area is generated based on the acquired geographic information data. The three-dimensional model can be used to identify the location of key environmental sensitive areas such as ecological protection areas, residential areas, rivers, etc., and provide data support for subsequent environmental impact assessments. At the same time, the three-dimensional model can also be used to analyze the slope, flatness and obstacles of the site, providing a basis for estimating construction costs.

[0025] By obtaining a three-dimensional model of the target area, geographical constraints are provided for subsequent site selection and line selection planning to ensure the applicability and scientificity of the site selection process.

[0026] S200: determining a plurality of environmentally sensitive areas based on the three-dimensional model of the target area, and obtaining regional sensitivity weights of the plurality of environmentally sensitive areas.

[0027] Specifically, firstly, by analyzing the generated three-dimensional model of the target area, the locations of key environmentally sensitive areas such as ecological protection areas, residential areas, and rivers are identified, and multiple environmentally sensitive areas are determined. Then, different protection weights are set for different types of environmentally sensitive areas. Specifically, weights are allocated based on the nature of the area. For example, the weight of the ecological protection area can be set to 1.0, the residential area to 0.6, and the industrial area to 0.3.

[0028] By establishing regional sensitivity weights for multiple environmentally sensitive areas, environmental constraints are provided for subsequent site selection evaluation to ensure that different environmentally sensitive areas can be reasonably protected during the site selection process.

[0029] S300: Obtain load demand data and construction cost data of the target site selection area.

[0030] Specifically, for load demand data, we first collect historical power grid data and urban planning data as basic data, then use time series analysis methods to test the stationarity of historical load data, and use the ARIMA model to capture the trend and seasonal characteristics of load changes. At the same time, machine learning methods such as random forests are used to incorporate multi-dimensional influencing factors such as climate conditions and urban development plans into the prediction model. Finally, prediction models of three time scales of 5 years, 10 years, and 15 years are established to determine the load demand data.

[0031] For construction cost data, we first analyze the slope, flatness and obstacle distribution of the site based on the acquired three-dimensional model, evaluate the construction conditions and difficulty coefficient, and then estimate the land acquisition cost in combination with local land use planning and market prices. At the same time, we obtain the latest price data of construction materials, equipment, etc. through market research, and obtain relevant planning requirements from the urban planning department to determine the construction cost data.

[0032] By obtaining load demand data and construction cost data, it provides important basis for the subsequent establishment of multi-objective evaluation models and scheme optimization.

[0033] S400: Establish a multi-objective evaluation model, based on geographic information data, regional sensitivity weights of multiple environmentally sensitive areas, load demand data and construction cost data, combine the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area, and optimize the multiple candidate site selection schemes to obtain the optimal site selection scheme.

[0034] Specifically, first, a multi-objective evaluation model for substation site selection is established, including a construction cost evaluation model, an environmental impact evaluation model, and a power supply reliability evaluation model. The construction cost evaluation model mainly considers land acquisition costs, construction costs, and material costs; the environmental impact evaluation model is based on the weight and distance of environmentally sensitive areas; and the power supply reliability evaluation model is evaluated through load coverage and power supply loss.

[0035] Then, the improved multi-objective genetic algorithm is used to optimize the site selection scheme. During the optimization process, the initial scheme population is first generated, and the schemes in the population are evaluated through the multi-objective evaluation model, and then non-dominated sorting and congestion calculation are performed, and new schemes are generated through dynamic mutation and generalized mutation strategies. After multiple iterative optimizations, the optimal site selection scheme is finally obtained, realizing the automatic optimization of the site selection scheme.

[0036] S500: Performing line selection planning for the optimal location solution based on an adaptive algorithm, and obtaining the optimal line solution by adjusting the line selection strategy in real time.

[0037] Specifically, after obtaining the optimal site selection scheme, an adaptive algorithm is used to plan and design the substation line.

[0038] First, perform initialization and parameter setting to set initial weights for all candidate paths i→j , where i and j represent the starting point and end point of the path respectively; select the number of candidate path solutions m; set the weight iteration rate And weight upper and lower limits .

[0039] At the same time, the objective function of line selection and positioning is determined using the following formula:

[0040]

[0041] Among them, C is the total cost of the line, E is the impact of the line on the environment, and R is the power supply reliability. , , is the target weight, which is used to balance the priorities of different targets.

[0042] Then, the path is constructed and selected. The next node j is selected by probability based on the following formula:

[0043]

[0044] in, is the weight of the current path at time t, For heuristic information (such as the inverse of the path length), are weight factors, which control the relative importance of weight and heuristic information respectively. Represents the set of optional next nodes for node i.

[0045] After completing the selection of path i→j, the weight on the path needs to be updated:

[0046]

[0047] in, is the control factor for weight update, is the newly added weight value on the path. To ensure the stability of the algorithm, it is necessary to ensure that the updated weight remains within the set range, that is, .

[0048] In the actual execution process, the algorithm perceives external changes by acquiring dynamic data such as new load demand and changes in geographical obstacles in real time. To adapt to environmental changes, the weight iteration rate is dynamically adjusted:

[0049]

[0050] Where t is the current iteration number, This is the preset maximum number of iterations. This design ensures that the iteration rate gradually decreases in the later search process, which is conducive to the convergence of the algorithm.

[0051] In order to improve the reliability of route planning, a redundant optimization model is introduced:

[0052]

[0053] in, represents the optimization cost of line redundancy, is the actual length of the ith line, is the number of backup paths in the ith line, is the minimum number of backup paths required by the system, Represents the redundancy cost coefficient, which is used to balance the relationship between line length and the number of backup paths.

[0054] During the iterative optimization process, when it is detected that the current path plan is affected by a significant environmental impact (such as new geographical obstacles or load changes), the algorithm will perform local perturbations to re-search for a better solution. The entire optimization process will be completed after the maximum number of iterations is reached. Or when the optimal path remains unchanged for multiple times in a row, it stops and outputs the final optimal route plan. If environmental conditions continue to change, the system will re-execute the path planning process based on the latest environmental data obtained.

[0055] Through the application of adaptive algorithms, dynamic optimization of route planning is achieved, and the adaptability of planning schemes to complex environments is improved.

[0056] Furthermore, the embodiment of the present application also includes:

[0057] S410: Establish construction cost assessment model, environmental impact assessment model, and power supply reliability assessment model;

[0058] S420: Summarizing the construction cost assessment model, the environmental impact assessment model and the power supply reliability assessment model to generate the multi-objective assessment model.

[0059] In a preferred implementation, first, three evaluation models are established, namely, a construction cost evaluation model, an environmental impact evaluation model, and a power supply reliability evaluation model.

[0060] The construction cost assessment model uses the following formula:

[0061]

[0062] in, is the total cost, Land acquisition costs (depending on location and size), is the construction cost (determined by the complexity of the terrain and the construction conditions), For material cost.

[0063] The environmental impact assessment model uses the following formula:

[0064]

[0065] in, is the environmental sensitivity weight of region i, and di is the distance from the substation to region i.

[0066] The power supply reliability assessment model uses the following formula:

[0067]

[0068] Among them, R is the power supply reliability, f is the covered load demand, F is the total load demand, and L is the power supply loss.

[0069] Preferably, in order to achieve a balance between multiple objectives, a risk assessment model is introduced:

[0070]

[0071] in, is the total risk assessment value of the site selection plan, is the probability of occurrence of the i-th risk event, is the impact degree of the i-th risk event.

[0072] At the same time, a dynamic weight adjustment model is adopted:

[0073]

[0074] in, is the dynamic weight of the i-th target at time t, is the initial weight, is the sensitivity coefficient, is the current constraint variable, is the maximum value of the constraint variable.

[0075] Through the comprehensive application of the above models, a complete multi-objective evaluation model is formed to provide support for subsequent site selection plan evaluation.

[0076] Furthermore, the embodiment of the present application also includes:

[0077] S430: generating a plurality of first site selection schemes based on a preset population size to form a first population;

[0078] S440: Evaluate the plurality of first site selection schemes respectively according to the multi-objective evaluation model to obtain a plurality of first scheme scores;

[0079] S450: performing non-dominated sorting and congestion calculation on the plurality of first site selection schemes according to the plurality of first scheme scores, and determining a plurality of first to-be-mutated schemes based on the non-dominated sorting results and the congestion calculation results;

[0080] S460: performing mutation operations on the plurality of first to-be-mutated solutions based on the mutation probability to obtain a plurality of first mutation solutions, and respectively evaluating the plurality of first mutation solutions according to the multi-objective evaluation model to obtain a plurality of first mutation solution scores;

[0081] S470: Selecting a plurality of second site selection schemes from the plurality of first site selection schemes and the plurality of first variation schemes according to the plurality of first scheme scores and the plurality of first variation scheme scores to form a second population;

[0082] S480: Iterative execution, when a preset number of iterations is reached, obtaining an Nth population, wherein the Nth population has a plurality of Nth site selection schemes;

[0083] S490: Use the multiple Nth site selection schemes as multiple candidate site selection schemes.

[0084] In a preferred embodiment, first, according to a preset population size, multiple first site selection schemes are generated in the target site selection area. Each site selection scheme contains information such as the site location, the area occupied, the distance relationship with the environmentally sensitive area, and the load coverage. These first site selection schemes constitute the first population. Then, each site selection scheme in the first population is evaluated using the established multi-objective evaluation model. The specific evaluation process includes calculating the construction cost score, the environmental impact score, and the power supply reliability score, and combining the dynamic weight to obtain the comprehensive score of each scheme as the first scheme score. Then, based on the obtained first scheme score, non-dominated sorting is performed, and the site selection schemes in the first population are stratified according to their merits. At the same time, the congestion of each site selection scheme is calculated to ensure the diversity of the population. According to the non-dominated sorting results and the congestion calculation results, multiple first mutated schemes are selected.

[0085] After that, the selected first scheme to be mutated is mutated to generate multiple first mutation schemes. Then, these mutation schemes are evaluated using a multi-objective evaluation model to obtain multiple first mutation scheme scores. Next, according to the first scheme score and the first mutation scheme score, high-quality schemes are selected from the first site selection scheme and the first mutation scheme to form a second population. The selection process follows the elite strategy to ensure that excellent individuals are retained. Repeat the above steps and iterate the population until the preset number of iterations is reached to obtain the Nth population containing multiple Nth site selection schemes. Finally, the site selection schemes in the Nth population are used as candidate site selection schemes to lay the foundation for subsequent scheme optimization.

[0086] Through the coordinated cooperation of the above steps, the site selection scheme optimization process based on the improved multi-objective genetic algorithm is realized, and multiple candidate site selection schemes are determined to provide support for obtaining the most optimal site selection scheme.

[0087] Furthermore, the mutation probability gradually increases with the increase in the number of iterations.

[0088] Specifically, in the process of improving the execution of the multi-objective genetic algorithm, in order to improve the global search ability of the algorithm, the initial value of the mutation probability is set to 0.1. As the number of iterations increases, the mutation probability will gradually increase. By gradually increasing the mutation probability with the increase in the number of iterations, in the initial stage of the algorithm, a smaller mutation probability can be used to maintain the basic characteristics of the population and avoid excessive mutations that lead to the loss of excellent genes; in the middle stage of the algorithm, the mutation probability gradually increases, enhancing the diversity of the population, expanding the search space, and improving the algorithm's ability to jump out of the local optimum; in the later stage of the algorithm, a larger mutation probability can inject new vitality into the population, prevent the algorithm from converging prematurely, and continue to maintain the ability to search for new solutions.

[0089] Through dynamic mutation probability, the global search capability of the algorithm is enhanced while maintaining population stability, thereby improving the optimization effect of the site selection scheme.

[0090] Furthermore, the mutation operation process adopts a generalized mutation strategy, and performs multiple perturbation mutations on each to-be-mutated scheme according to a preset number of perturbations.

[0091] In a preferred embodiment, a generalized mutation strategy is adopted when performing mutation operations on the scheme to be mutated. For example, the preset number of disturbances is set to 10, and multiple disturbance mutations are performed on each scheme to be mutated. Through generalized mutation, in each mutation operation, the same scheme to be mutated is disturbed multiple times to different degrees, thereby generating multiple mutation schemes. Through multiple disturbances, exploration can be carried out in different directions of the scheme to be mutated, thereby increasing the diversity of mutation results. Each disturbance makes slight changes to the original scheme, including fine-tuning of the site selection location, changes in the area occupied, etc. This method of multiple small disturbances can explore the solution space more comprehensively and increase the possibility of finding a better solution.

[0092] By applying the generalized mutation strategy, the effectiveness of the mutation operation is improved and the optimization performance of the algorithm is enhanced.

[0093] Furthermore, the embodiment of the present application also includes:

[0094] S4100: Selecting the best candidate site selection scheme based on multiple candidate site selection schemes;

[0095] S4110: Randomly select two candidate site selection schemes from the multiple candidate site selection schemes as a first random candidate site selection scheme and a second random candidate site selection scheme;

[0096] S4120: Based on a preset variation formula, a variation candidate site selection plan is generated by combining the optimal candidate site selection plan, the first random candidate site selection plan, and the second random candidate site selection plan;

[0097] S4130: Evaluate the optimal candidate site selection scheme and the variant candidate site selection scheme according to the multi-objective evaluation model to determine whether the variant candidate site selection scheme is better than the optimal candidate site selection scheme;

[0098] S4140: Cross the variant candidate site selection scheme and the optimal candidate site selection scheme to obtain an updated candidate site selection scheme;

[0099] S4150: When the updated candidate site selection scheme is better than the optimal candidate site selection scheme, taking the updated candidate site selection scheme as the optimal candidate site selection scheme;

[0100] S4160: When the updated candidate site selection scheme is not better than the optimal candidate site selection scheme, maintaining the optimal candidate site selection scheme;

[0101] S4170: Iterative execution, when the preset number of evolution times is reached, the optimal candidate site selection scheme is used as the optimal site selection scheme.

[0102] In a preferred embodiment, first, from a plurality of candidate site selection schemes, according to the scoring results of the multi-objective evaluation model, the scheme with the highest score is selected as the optimal candidate site selection scheme. Then, two schemes are randomly selected from the remaining candidate site selection schemes, as the first random candidate site selection scheme and the second random candidate site selection scheme, respectively. The purpose of random selection is to increase the diversity of scheme search. Subsequently, a preset mutation formula is used to combine the optimal candidate site selection scheme, the first random candidate site selection scheme and the second random candidate site selection scheme to generate a variant candidate site selection scheme. The mutation operation can generate new candidate schemes and expand the search space. Next, the multi-objective evaluation model is used to evaluate the optimal candidate site selection scheme and the variant candidate site selection scheme, and by comparing the evaluation results, it is determined whether the variant candidate site selection scheme is better than the optimal candidate site selection scheme.

[0103] Next, the variant candidate site selection scheme and the optimal candidate site selection scheme are cross-operated to generate an updated candidate site selection scheme. The cross-operation can inherit the excellent features of the two parent schemes. By comparing the evaluation results, if the updated candidate site selection scheme is better than the optimal candidate site selection scheme, the updated candidate site selection scheme will be used as the new optimal candidate site selection scheme; otherwise, the original optimal candidate site selection scheme will remain unchanged. Repeat the above steps for iterative optimization. When the preset number of evolutions is reached, the final optimal candidate site selection scheme will be determined as the optimal site selection scheme.

[0104] Through the above iterative execution, the site selection plan is continuously optimized, and finally the optimal site selection plan that meets multiple objective requirements is obtained.

[0105] Furthermore, the preset variation formula is:

[0106]

[0107] in, is the candidate site selection scheme for variation, is the optimal candidate site selection solution. is the variation factor, is the first random candidate site selection plan, is the second random candidate site selection solution.

[0108] In a preferred embodiment, the preset variation formula is:

[0109]

[0110] in, It indicates the mutated candidate site selection scheme generated by the mutation operation, which contains the specific information such as the site selection location and area of ​​the substation; Indicates the current optimal candidate site selection plan, which is the plan with the highest score in the previous optimization process; Represents the variation factor, which is used to control the intensity of variation. Its value range is usually between 0 and 1. represents a first random candidate site selection scheme randomly selected from the candidate site selection schemes; Represents a second random candidate site selection plan randomly selected from the candidate site selection plans.

[0111] The current best candidate site selection plan As a benchmark, two random selection schemes are used and The difference information is used to determine the disturbance direction and magnitude. The term represents a difference vector in the solution space, multiplied by the mutation factor After that, the amplitude of the disturbance can be controlled. By adding the disturbance term to the optimal candidate site selection plan, a new candidate site selection plan can be generated near the optimal candidate site selection plan.

[0112] The optimal candidate site selection scheme is mutated through the above preset mutation formula to ensure that the newly generated scheme will not deviate too far from the current optimal candidate site selection scheme; the difference between two schemes is randomly selected as the disturbance term to increase the randomness and diversity of the search; through the adjustment of the mutation factor, the intensity of the disturbance can be flexibly controlled to achieve a balance between local search and global search, effectively improving the algorithm's search ability and optimization effect.

[0113] The embodiment of the present invention provides a method for intelligent site selection and line selection planning of a substation, which has at least the following technical effects:

[0114] Use drone oblique photography to collect information on the target site selection area, obtain geographic information data of the target site selection area, generate a three-dimensional model of the target area based on the geographic information data, and intuitively display regional characteristics for easy analysis and evaluation. Determine multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtain the regional sensitivity weights of multiple environmentally sensitive areas to ensure that the site selection plan meets environmental protection requirements. Obtain load demand data and construction cost data for the target site selection area to provide important information for subsequent site selection evaluation; establish a multi-objective evaluation model, combine the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection plans in the target site selection area, and optimize multiple candidate site selection plans to obtain the optimal site selection plan and find the best balance point between multiple objectives. Based on the adaptive algorithm, the optimal site selection plan is selected and planned, and the optimal line selection plan is obtained by adjusting the line selection strategy in real time, ensuring that the optimal line plan is found in a complex and changing environment, thereby improving the accuracy and efficiency of substation project site selection and line selection planning.

[0115] Embodiment 2:

[0116] like Figure 2 As shown, based on the same inventive concept as the method for intelligent site selection and line selection planning of a substation provided in Embodiment 1, an embodiment of the present invention further provides a system for intelligent site selection and line selection planning of a substation, comprising:

[0117] The three-dimensional model building module 11 is used to collect information about the target site selection area by using drone oblique photography, obtain geographic information data of the target site selection area, and generate a three-dimensional model of the target area according to the geographic information data;

[0118] An environmental impact determination module 12 is used to determine a plurality of environmentally sensitive areas based on the three-dimensional model of the target area, and obtain regional sensitivity weights of the plurality of environmentally sensitive areas;

[0119] A regional data acquisition module 13 is used to acquire load demand data and construction cost data of the target site selection area;

[0120] The optimal site determination module 14 is used to establish a multi-objective evaluation model, based on geographic information data, regional sensitivity weights of multiple environmentally sensitive areas, load demand data and construction cost data, and to obtain multiple candidate site selection schemes in the target site selection area in combination with the multi-objective evaluation model and the improved multi-objective genetic algorithm, and to optimize the multiple candidate site selection schemes to obtain the optimal site selection scheme;

[0121] The optimal route determination module 15 is used to perform route selection planning for the optimal location solution based on an adaptive algorithm, and obtain the optimal route solution by adjusting the route selection strategy in real time.

[0122] Furthermore, the optimal address determination module 14 includes the following execution steps:

[0123] Establish construction cost assessment model, environmental impact assessment model, and power supply reliability assessment model;

[0124] The construction cost assessment model, the environmental impact assessment model and the power supply reliability assessment model are aggregated to generate the multi-objective assessment model.

[0125] Furthermore, the optimal address determination module 14 further includes the following execution steps:

[0126] Generate multiple first site selection plans based on a preset population size to form a first population;

[0127] Evaluate the multiple first site selection plans respectively according to the multi-objective evaluation model to obtain multiple first plan scores;

[0128] Performing non-dominated sorting and congestion calculation on the multiple first site selection plans according to the multiple first plan scores, and determining multiple first to-be-mutated plans based on the non-dominated sorting results and the congestion calculation results;

[0129] Performing mutation operations on the multiple first to-be-mutated solutions based on the mutation probability to obtain multiple first mutation solutions, and respectively evaluating the multiple first mutation solutions according to the multi-objective evaluation model to obtain multiple first mutation solution scores;

[0130] According to the scores of the first solutions and the scores of the first variant solutions, a plurality of second site selection solutions are selected from the plurality of first site selection solutions and the plurality of first variant solutions to form a second population;

[0131] Iterative execution, when a preset number of iterations is reached, obtaining an Nth population, wherein the Nth population has a plurality of Nth site selection schemes;

[0132] The multiple N-th site selection schemes are used as multiple candidate site selection schemes.

[0133] Furthermore, the mutation probability gradually increases with the increase in the number of iterations.

[0134] Furthermore, the mutation operation process adopts a generalized mutation strategy, and performs multiple perturbation mutations on each to-be-mutated scheme according to a preset number of perturbations.

[0135] Furthermore, the optimal address determination module 14 further includes the following execution steps:

[0136] Select the best candidate site selection plan based on multiple candidate site selection plans;

[0137] Randomly selecting two candidate site selection schemes from the plurality of candidate site selection schemes as a first random candidate site selection scheme and a second random candidate site selection scheme;

[0138] Based on a preset mutation formula, a mutation candidate site selection plan is generated by combining the optimal candidate site selection plan, the first random candidate site selection plan, and the second random candidate site selection plan;

[0139] Performing scheme evaluation on the optimal candidate site selection scheme and the variant candidate site selection scheme according to the multi-objective evaluation model to determine whether the variant candidate site selection scheme is better than the optimal candidate site selection scheme;

[0140] Cross the variant candidate site selection plan with the optimal candidate site selection plan to obtain an updated candidate site selection plan;

[0141] When the updated candidate site selection scheme is better than the optimal candidate site selection scheme, taking the updated candidate site selection scheme as the optimal candidate site selection scheme;

[0142] When the updated candidate site selection scheme is not better than the optimal candidate site selection scheme, maintaining the optimal candidate site selection scheme;

[0143] Iterative execution, when reaching the preset evolution times, takes the best candidate site selection scheme as the most preferred site selection scheme.

[0144] Furthermore, the preset variation formula is:

[0145]

[0146] in, is the candidate site selection scheme for variation, is the optimal candidate site selection solution. is the variation factor, is the first random candidate site selection plan, is the second random candidate site selection solution.

[0147] Embodiment three:

[0148] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an electronic device 200 provided by an embodiment of the present invention includes a memory 210, a processor 220, and a first computer program 211 stored in the memory 210 and executable on the processor 220. When the processor 220 executes the first computer program 211, a method for intelligent site selection and line selection planning for a substation is implemented.

[0149] Embodiment 4:

[0150] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 300 on which a second computer program 311 is stored. When the second computer program 311 is executed by a processor, a method for intelligent site selection and line selection planning for a substation is implemented.

[0151] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0152] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0156] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0157] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for intelligent site selection and line selection planning of a substation, characterized in that: include: Using drone oblique photography to collect information about the target site selection area, obtaining geographic information data of the target site selection area, and generating a three-dimensional model of the target area based on the geographic information data; Determine multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtain regional sensitivity weights of the multiple environmentally sensitive areas; Obtaining load demand data and construction cost data of the target site selection area; Establish a multi-objective evaluation model, based on geographic information data, regional sensitivity weights of multiple environmentally sensitive areas, load demand data and construction cost data, combine the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area, and optimize the multiple candidate site selection schemes to obtain the optimal site selection scheme; Based on the adaptive algorithm, the line selection planning is performed for the optimal location solution, and the optimal line selection solution is obtained by adjusting the line selection strategy in real time.

2. The method for intelligent site selection and line selection planning of substation according to claim 1, characterized in that: Establish a multi-objective evaluation model, including: Establish construction cost assessment model, environmental impact assessment model, and power supply reliability assessment model; The construction cost assessment model, the environmental impact assessment model and the power supply reliability assessment model are aggregated to generate the multi-objective assessment model.

3. The method for intelligent site selection and line selection planning of substation according to claim 1, characterized in that: Combining the multi-objective evaluation model and the improved multi-objective genetic algorithm, multiple candidate site selection schemes are obtained in the target site selection area, including: Generate multiple first site selection plans based on a preset population size to form a first population; Evaluate the multiple first site selection plans respectively according to the multi-objective evaluation model to obtain multiple first plan scores; Performing non-dominated sorting and congestion calculation on the multiple first site selection plans according to the multiple first plan scores, and determining multiple first to-be-mutated plans based on the non-dominated sorting results and the congestion calculation results; Performing mutation operations on the multiple first to-be-mutated solutions based on the mutation probability to obtain multiple first mutation solutions, and respectively evaluating the multiple first mutation solutions according to the multi-objective evaluation model to obtain multiple first mutation solution scores; According to the scores of the first solutions and the scores of the first variant solutions, a plurality of second site selection solutions are selected from the plurality of first site selection solutions and the plurality of first variant solutions to form a second population; Iterative execution, when a preset number of iterations is reached, obtaining an Nth population, wherein the Nth population has a plurality of Nth site selection schemes; The multiple N-th site selection schemes are used as multiple candidate site selection schemes.

4. The method for intelligent site selection and line selection planning of substation according to claim 3 is characterized in that: The mutation probability gradually increases with the increase of the number of iterations.

5. The method for intelligent site selection and line selection planning of substation according to claim 3, characterized in that: The mutation operation process adopts a generalized mutation strategy, and performs multiple perturbation mutations on each to-be-mutated scheme according to a preset number of perturbations.

6. The method for intelligent site selection and line selection planning of substation according to claim 1, characterized in that: Optimizing the multiple candidate site selection schemes to obtain the most optimal site selection scheme includes: Select the best candidate site selection plan based on multiple candidate site selection plans; Randomly selecting two candidate site selection schemes from the plurality of candidate site selection schemes as a first random candidate site selection scheme and a second random candidate site selection scheme; Based on a preset mutation formula, a mutation candidate site selection plan is generated by combining the optimal candidate site selection plan, the first random candidate site selection plan, and the second random candidate site selection plan; Performing scheme evaluation on the optimal candidate site selection scheme and the variant candidate site selection scheme according to the multi-objective evaluation model to determine whether the variant candidate site selection scheme is better than the optimal candidate site selection scheme; Cross the variant candidate site selection plan with the optimal candidate site selection plan to obtain an updated candidate site selection plan; When the updated candidate site selection scheme is better than the optimal candidate site selection scheme, taking the updated candidate site selection scheme as the optimal candidate site selection scheme; When the updated candidate site selection scheme is not better than the optimal candidate site selection scheme, maintaining the optimal candidate site selection scheme; Iterative execution, when reaching the preset evolution times, takes the best candidate site selection scheme as the most preferred site selection scheme.

7. The method for intelligent site selection and line selection planning of substation according to claim 6, characterized in that: The preset variation formula is: in, is the candidate site selection scheme for variation, is the optimal candidate site selection solution. is the variation factor, is the first random candidate site selection plan, is the second random candidate site selection solution.

8. A substation intelligent site selection and line selection planning system, characterized in that: The method for implementing the intelligent site selection and line selection planning of a substation according to any one of claims 1 to 7 comprises: A three-dimensional model building module, the three-dimensional model building module is used to collect information about the target site selection area by using drone oblique photography, obtain geographic information data of the target site selection area, and generate a three-dimensional model of the target area according to the geographic information data; An environmental impact determination module, the environmental impact determination module is used to determine multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtain regional sensitivity weights of the multiple environmentally sensitive areas; A regional data acquisition module, the regional data acquisition module is used to acquire load demand data and construction cost data of the target site selection area; An optimal site determination module, the optimal site determination module is used to establish a multi-objective evaluation model, based on geographic information data, regional sensitivity weights of multiple environmentally sensitive areas, load demand data and construction cost data, combined with a multi-objective evaluation model and an improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area, and optimize the multiple candidate site selection schemes to obtain the optimal site selection scheme; The module for determining the most preferred line is used to plan the line selection for the most preferred site selection scheme based on an adaptive algorithm, and to obtain the most preferred line scheme by adjusting the line selection strategy in real time.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the substation intelligent site selection and line selection planning method described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the method for intelligent site selection and line selection planning of a substation as described in any one of claims 1 to 7 is implemented.

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