CN116777230A - Intelligent substation site selection area division method based on GIS space analysis technology - Google Patents
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Abstract
The application discloses an intelligent dividing method of transformer substation site selection areas based on a GIS space analysis technology, which relates to the technical field of power system transformer substation site selection and comprises the following steps: based on a substation site selection principle, a constraint index system is established according to geographic constraint conditions; generating a candidate site area; and optimizing and screening the candidate site areas of the transformer substation. The intelligent dividing method for the site selection area of the transformer substation based on the GIS space analysis technology improves the accuracy and rationality of site selection of the transformer substation, enables site selection results to more meet the requirements of actual geographic environments and power grids, improves site selection efficiency, reduces time and cost of manual site selection, improves the capability of processing complex geographic environments by introducing an improved sparrow search algorithm, enables site selection results to be more optimized, enables site selection processes of the transformer substation to be more automatic and intelligent, and is beneficial to promoting modernization and intelligent development of a power system.
Description
Technical Field
The application relates to the technical field of substation site selection of an electric power system, in particular to an intelligent substation site selection area dividing method based on a GIS space analysis technology.
Background
The site selection of the transformer substation is an important link of power grid planning construction, and the economic efficiency, rationality and reliability of power grid system construction are directly influenced by a scientific site selection result of the transformer substation, so that the site selection work of the transformer substation has very important significance for power grid planning construction.
Geographic Information Systems (GIS) are a powerful tool designed for spatial analysis that can collect, analyze, operate on, etc., geographic information in a local spatial range. The GIS technology has strong space analysis capability and is very suitable for being applied to site selection work of a transformer substation. And analyzing and processing massive geographic information data by utilizing a GIS technology, so that a substation site candidate area can be obtained. The GIS technology and the power grid planning work are organically combined, so that the accuracy of site selection work can be improved, the range of selectable target areas can be greatly reduced, and the high efficiency of the planning process is greatly improved.
At present, the actual site area division of the transformer substation only determines a plurality of candidate areas in a relatively large range according to map information, and then exploration is carried out on site by exploration personnel to finally determine. The method often causes inaccurate site selection of the transformer substation due to lack of specific geographic information, and the site selection result is not ideal.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems.
Therefore, the technical problems solved by the application are as follows: the existing transformer substation site selection lacks specific geographic information to cause inaccurate site selection and how to improve the site selection accuracy.
In order to solve the technical problems, the application provides the following technical scheme: a substation site selection area intelligent dividing method based on GIS space analysis technology comprises the following steps:
based on a substation site selection principle, a constraint index system is established according to geographic constraint conditions;
generating a candidate site area;
and optimizing and screening the candidate site areas of the transformer substation.
As a preferable scheme of the intelligent dividing method for the site selection area of the transformer substation based on the GIS spatial analysis technology, the geographic constraint condition comprises the following steps: terrain grade, land type, distance constraints.
As a preferable scheme of the intelligent dividing method of the substation site selection area based on the GIS space analysis technology, the generating the candidate site area comprises the following steps:
basic geographic data corresponding to constraint conditions are obtained, and distance analysis is conducted on constraint index attributes so as to divide the suitability of the region;
reclassifying the attribute values of the constraint indexes, determining the coefficients of the constraint indexes and performing superposition analysis;
covering and eliminating the areas which cannot be built up to obtain the suitability grade division result of the research area;
and selecting the region with highest suitability as a candidate site region of the transformer substation according to the suitability grade division result.
As a preferable scheme of the intelligent dividing method of the substation site selection area based on the GIS space analysis technology, the reclassifying operation of the attribute values of the constraint indexes comprises the following steps:
when reclassifying constraint indexes, the suitability of each constraint index is classified into five grades: unsuitable region, low suitable region, medium suitable region, high suitable region, and optimum region, and different scoring values are set for these five classes.
As a preferable scheme of the intelligent dividing method of the substation site selection area based on the GIS space analysis technology, the optimizing and screening comprises the following steps:
solving the area problem of the largest inscribed rectangular transformer substation of the polygonal candidate area based on an improved sparrow search algorithm, judging whether the selected candidate area meets the building requirement or not by comparing the area parameter of the transformer substation, and optimizing and screening the transformer substation candidate area.
As a preferable scheme of the intelligent dividing method of the substation site selection area based on the GIS space analysis technology, the improved sparrow searching algorithm comprises the following steps: a mixed strategy of Circle chaotic mapping and refraction reverse learning mechanism is introduced when the population is initialized;
the Circle chaotic map is expressed as:
wherein ,represents the position of the ith individual in the sparrow population in the j-th dimension at the t-th iteration, k is a scaling factor, l j and uj Minimum and maximum of the j-th dimensional space, respectively,>is X i,j Is a refractive inversion solution of (a).
As a preferable scheme of the intelligent dividing method of the substation site selection area based on the GIS space analysis technology, in the improved sparrow searching algorithm, self-adaptive weights are introduced in a finder position updating stage, and the self-adaptive weights are expressed as follows:
wherein, iter max For the maximum iteration number, t is the current iteration number, and the weight value omega (t) is subjected to nonlinear change on the value range.
As a preferable scheme of the intelligent dividing method of the substation site selection area based on the GIS space analysis technology, the improved position updating formula of the discoverer is as follows:
wherein ω (t) is an adaptive weight,representing the position of the ith individual in the jth dimension in the nth iteration of the population; alpha represents (0, 1)]Random numbers over the interval; itermax represents the maximum number of iterations; q represents a random number subject to normal distribution; l represents a 1 x d matrix with element values of all 1; r is R 2 Represents the early warning value and is [0,1 ]]A uniform random number thereon; ST is a preset security value.
A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method as described above when executing the computer program.
A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method as described above.
The application has the beneficial effects that: the intelligent dividing method for the site selection area of the transformer substation based on the GIS space analysis technology improves the accuracy and rationality of site selection of the transformer substation, enables site selection results to more meet the requirements of actual geographic environments and power grids, improves site selection efficiency, reduces time and cost of manual site selection, improves the capability of processing complex geographic environments by introducing an improved sparrow search algorithm, enables site selection results to be more optimized, enables site selection processes of the transformer substation to be more automatic and intelligent, and is beneficial to promoting modernization and intelligent development of a power system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is an overall flowchart of a substation site selection area intelligent dividing method based on a GIS spatial analysis technology according to an embodiment of the present application;
fig. 2 is a schematic diagram of candidate area generation in the intelligent substation site selection area dividing method based on the GIS spatial analysis technology according to the first embodiment of the present application;
fig. 3 is a refractive reverse learning schematic diagram in the intelligent dividing method of the substation site selection area based on the GIS spatial analysis technology according to the first embodiment of the present application;
fig. 4 is a flowchart of a method for intelligently dividing a site selection area of a transformer substation based on a GIS spatial analysis technology according to a first embodiment of the present application based on solving a modeling type by an improved sparrow search algorithm.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present application have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1
1-4, for one embodiment of the present application, there is provided a substation site selection area intelligent dividing method based on GIS space analysis technology, including:
step 1: the substation site selection work needs to consider the influence of external geographic factors on site selection results, and mainly comprises various geographic factors such as topography, land type, traffic conditions, gradient, elevation, frequent areas far away from natural disasters, protection areas, residential areas, nearby water areas and the like. These constraints are represented in the GIS geographic information system as corresponding geographic data, which is collected as needed in the area of investigation.
Step 2: based on the substation site selection principle, a proper standard system needs to be established to measure in consideration of different geographical factors and different degrees of site selection influence. For example, areas with higher altitudes can have adverse effects in equipment transportation and later manual operation and maintenance, so in substation site selection work, the lower the altitude is generally required, the better; good traffic conditions can provide great convenience for material equipment transportation and staff in the transformer substation construction process, so that the closer the construction area is to a highway or a railway, the better the construction area is, but a certain buffer area is required to be arranged for safety; the terrain gradient of most of the transformer substation construction in China does not exceed 10 degrees, and the method is an important index for measuring whether the transformer substation site selection is proper or not. Therefore, the relevant investigation of the site selection factors of the transformer substation is used for determining the division standard of each influence factor, and a corresponding constraint index system is established.
Table 1 substation site selection constraint index system
Step 3: after the geographic information data are acquired, the index data attributes are divided according to requirements, and distance analysis and reclassification are performed. For example, the distance analysis is carried out on index data of water areas, traffic, residential areas and the like so as to grade the suitability of the research area; the attribute values of the land utilization, elevation and gradient indexes can be directly divided into suitability. The attribute values of the respective indices are thereafter reclassified and scored, and converted into suitable values under the same criteria. For areas where the station cannot be built, such as natural disaster frequent areas and protection areas, the suitability value of the areas can be set to be a larger negative value, so that coverage elimination can be conveniently carried out when the suitability level classification is carried out; it can be set to a small positive value for the settable region. The suitability of each constraint index is divided into five classes: unsuitable region, low suitable region, medium suitable region, high suitable region and optimum region, and the corresponding scoring scale is set as: -999 points, 1 point, 2 points, 3 points, 4 points. The suitability of each constraint index is shown in Table 2.
Table 2 constraint index suitability level table
| Index (I) | -999 | 1 | 2 | 3 | 4 |
| Land use | Wet land | Cultivated land | Grassland | Woodlands | Bare land |
| Highway | ＜300m | ＞20000m | 10000～20000m | 5000～10000m | 300～5000m |
| Gradient of slope | ＞25° | 15°～25° | 5°～15° | 2°～5° | ＜2° |
| Elevation | ＞1500m | 1200～1500m | 800～1200m | 400～800m | ＜400m |
| Residential area | ＜500m | 500～3000m | 3000～8000m | 8000～15000m | ＞15000m |
| Water area | ＜500m | ＞10000m | 6000～10000m | 3000～6000m | 500～3000m |
Step 4: the index coefficient is formulated according to the importance degree of constraint indexes to the site selection of the transformer substation, and when the index coefficient is determined, the index coefficient can be generally evaluated and scored according to the experience of an expert so as to obtain the relative importance and the weight coefficient of the index; after the weight coefficient is determined, superposition analysis can be performed according to a superposition formula, and a fitness grading result is obtained.
The superposition formula is as follows:
Q＝α 1 A 1 +α 2 A 2 +…+α n A n
wherein ,α1 +α 2 +…+α n =1; q is the fitness value after superposition of various indexes, alpha n For the weight coefficient of each index, A n And scoring the suitability of each index.
Step 5: after the superposition analysis is completed, a suitability grade division result of the research area can be obtained, and the transformer substation candidate area can be further determined. The candidate area of the transformer substation is also located in the part of the area with the highest suitability value after superposition analysis, so that the site selection range of the transformer substation is further reduced, support is provided for subsequent further site selection service decision, and the candidate area generation schematic diagram is shown in fig. 2.
And selecting a candidate region with the highest suitability level, extracting and analyzing the candidate region, solving the maximum rectangular area of the transformer substation which can be contained in any polygonal candidate region by utilizing an improved sparrow search algorithm, and realizing the optimization screening process of the polygonal candidate region. The method comprises the following specific steps:
the method comprises the steps of establishing a transformer substation candidate area optimization screening mathematical model, wherein the essence of optimizing and screening the transformer substation candidate area is the process of solving the maximum inscribed transformer substation rectangular area of any polygonal candidate area. Therefore, when the mathematical model is built, the vertex coordinates of the substation rectangle are set to P i (x i ,y i ) (i=1, 2,3, 4), considering the area of a rectangular substation as an objective function, the formula of the maximum rectangular substation area can be expressed as:
max S rec ＝0.5×|x 1 y 2 -x 2 y 1 +x 2 y 3 -x 3 y 2 +x 3 y 4 -x 4 y 3 +x 4 y 1 -x 1 y 4 |
the polygon candidate region can be regarded as a region Q surrounded by m boundary points, the boundary points of which are marked as Q k(k＝1,2,…,m) Four vertexes P of rectangle of transformer substation i(i＝1,2,3,4) The enclosed area is denoted as P r . The constraint conditions corresponding to the mathematical model should satisfy the following requirements:
the 4 vertex coordinates of a rectangular substation must lie on or within the boundary of the polygon candidate area, which constraint can be expressed as:
P i(i＝1,2,3,4) ∈Q
all boundary point coordinates of the polygon candidate area must lie outside the boundary of the substation rectangle, and this constraint condition can be expressed as:
any one of four sides of the quadrangle inscribed in the candidate area must be mutually perpendicular to the corresponding adjacent side, and the constraint condition can be expressed as:
(y 2 -y 1 )(y 4 -y 1 )-(x 2 -x 1 )(x 1 -x 4 )＝0
any two opposite sides of the quadrangle inscribed in the candidate region must be parallel, and this constraint can be expressed as:
(y 4 -y 1 )(x 3 -x 2 )-(y 3 -y 2 )(x 4 -x 1 )＝0
(y 2 -y 1 )(x 4 -x 3 )-(y 4 -y 3 )(x 2 -x 1 )＝0
the substation candidate area optimization screening model is solved by utilizing an improved sparrow search algorithm, wherein the sparrow search algorithm (Sparrow Search Algorithm, SSA) is a population intelligent optimization algorithm, and the foraging process of the sparrows is constructed into the solving process of the algorithm optimal value by simulating foraging and anti-predation behaviors of the sparrows in the nature. The traditional SSA has higher searching capability, strong optimizing capability and stability, but has the defects of easy sinking into local optimum, lower optimizing precision and the like. Therefore, an improved sparrow search algorithm (Improved Sparrow Search Algorithm, ISSA) is adopted to improve the optimization performance of the algorithm, so that the problem of the maximum inscribed rectangular area which can be accommodated by the polygonal candidate area of the transformer substation is solved better.
SSA abstracts sparrow foraging behavior into three identities: discoverers, followers and early warning persons. The finder has high adaptability, and finds the best position with sufficient food in the population and feeds back relevant information to the follower; the follower gets better food by monitoring the discoverer or forges around the discoverer; the early warning person realizes danger in the population, and then adjusts the position in the sparrow population.
In initializing a population, the initial position of the population can be represented by a matrix X:
wherein n represents the number of individuals in the population, x n,d Represents the nth individualAt the d-th dimension.
Selecting the objective function established by the model as the fitness function of the sparrow population, and then in the d-dimensional space, the initial fitness values of all the individuals can be expressed as follows:
the finder location update formula is:
wherein ,representing the position of the ith individual in the jth dimension in the nth iteration of the population; alpha represents (0, 1)]Random numbers over the interval; ter (iter) max Representing a maximum number of iterations; q represents a random number subject to normal distribution; l represents a 1 x d matrix with element values of all 1; r is R 2 Represents the early warning value and is [0,1 ]]A uniform random number thereon; ST is a preset security value.
The follower location update formula is:
wherein ,representing the worst individual position in the t-th iteration; />The optimal individual position in the population; wherein A is + ＝A T (AA T ) -1 A represents a 1×d matrix and its elements are randomly assigned 1 or-1.
The early warning person position updating formula is as follows:
wherein ,representing a current global optimal position; beta represents a random number satisfying a normal distribution; k represents a step control parameter, the value of which is [ -1,1]A random number on the table; f (f) i The fitness value of the current individual; f (f) g The adaptation value corresponding to the current optimal position is obtained; f (f) g The adaptation value corresponding to the current worst position is obtained; epsilon is the minimum constant.
The population initialization stage is a very important stage in the intelligent optimization algorithm of the population, and the main purpose of the initialization is to improve the optimization speed of the algorithm and prevent the algorithm from being converged prematurely to a certain extent. In order to better improve algorithm performance, a mixed strategy based on Circle chaotic mapping and reverse learning is adopted to initialize the population. On the one hand, compared with other types of chaotic mapping, the Circle chaotic mapping is more stable, the population distribution after initialization is more uniform, the population diversity can be increased, and the solution quality can be improved; on the other hand, the reverse learning strategy can improve the convergence accuracy of the algorithm.
The mathematical model of the Circle chaotic map can be expressed as:
the Circle chaotic map expands the searching range of the sparrow population in the solution space, increases the diversity of the sparrow population positions and improves the optimizing efficiency of the algorithm.
Aiming at the problem of low convergence accuracy of the sparrow search algorithm, a fused refraction reverse learning mechanism is adopted to initialize the sparrow population so as to improve the algorithm solving accuracy. Reverse learning is an optimization strategy that expands the scope of the algorithm search by computing the reverse solution of the current solution. The reverse learning strategy can improve the convergence rate of the algorithm in the early stage of optimizing, but can lead to early convergence of the algorithm in the later stage of optimizing. Therefore, a refraction principle is introduced in the reverse learning optimization strategy to reduce the degree of premature convergence of the algorithm, and the refraction reverse learning principle is shown in fig. 3. The inverse solution of the current solution obtained by refractive inverse learning can be expressed as:
where k is a scaling factor, the expression of which is:
wherein Xi,j Representing the value of the ith individual in the sparrow population in the j-th dimension; l (L) j and uj Respectively the minimum value and the maximum value of the j-th dimensional space;is X i,j Is a refractive inversion solution of (2); h and h * Indicating the length of the incident and refracted rays, respectively.
In addition, in SSA algorithms, adaptive weights are introduced during the finder location update phase to enable balancing the search capabilities of the algorithm. The adaptive weight equation is designed as follows:
the formula is characterized in that the weight value omega (t) varies non-linearly over the range. Although the early weight of the algorithm is small, the change speed is high, and the optimization capacity of the algorithm in a local range can be enhanced; the algorithm weight is large in the later period, the change speed is low, and the global searching capability is improved. The improved finder location update formula is:
the position of the discoverer is continuously and dynamically updated by introducing the self-adaptive weight, so that the algorithm is more flexible. Meanwhile, as the number of iterations increases, sparrows approach in the direction of the optimal position.
And carrying out matching screening on the polygon candidate area. And according to the site area scale of the transformer substation, judging whether the selected candidate area meets the site building requirement or not by calculating the largest inscribed transformer substation rectangular area of the polygonal candidate area, so that the polygonal candidate area is optimized.
Example 2
An embodiment of the present application, which is different from the previous embodiment, is that:
the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 3
For one embodiment of the application, an intelligent dividing method of the site selection area of the transformer substation based on the GIS space analysis technology is provided, and in order to verify the beneficial effects of the application, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
External geographic factors influencing site selection of the transformer substation are determined, a corresponding constraint index system is established according to the principle of site selection of the transformer substation, for example, an index system is established according to rivers, residential areas, elevations, slopes and the like, and geographic data collection is carried out on the area to be studied;
according to the obtained geographic data, carrying out suitability analysis on attribute values of constraint indexes, carrying out superposition analysis on constraint index performance conditions of a research area by utilizing a GIS space analysis technology, and removing infeasible areas to obtain each suitability grade division condition of the feasible areas;
an algorithm is determined that solves the largest inscribed rectangular problem that can be accommodated by the polygon candidate region. By establishing a related mathematical model, the area of the inscribed rectangular transformer substation of the polygonal candidate area is used as the maximum target, and an improved sparrow search algorithm is adopted to solve the related problem.
And screening out candidate areas meeting the requirements according to the area parameters of the transformer substation based on a polygon candidate area maximum inscribed rectangular problem solving algorithm.
To test the optimization performance of the improved SSA algorithm, this embodiment selects the algorithm and a standard Sparrow Search Algorithm (SSA) to perform an optimization test on the 4 benchmark test functions in table 3. Wherein f 1 (x)～f 3 (x) As a unimodal function, f 4 (x) As a multimodal function. The range of values of these 4 functions is known and the minimum value is 0, and the dimensions are set to 30. Each algorithm was run independently 20 times with the optimization results shown in table 4.
TABLE 3 benchmark test functions
TABLE 4 ISSA and Standard SSA Algorithm optimization results
As can be seen from table 4, for the unimodal test function, although both algorithms can find the theoretical optimal solution of the function, the mean and standard deviation of the ISSA optimization solution are smaller than the standard SSA, which indicates that the improved SSA algorithm is superior to the standard SSA algorithm in terms of stability and convergence accuracy. For multimodal test functions, the improved SSA enhances the local search capability of the algorithm.
The above embodiments are merely illustrative of the principles of the present application and its functions, and are not intended to limit the application. All equivalent modifications and variations are intended to be included within the scope of this disclosure.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Claims (10)
1. The intelligent substation site selection area dividing method based on the GIS space analysis technology is characterized by comprising the following steps of:
based on a substation site selection principle, a constraint index system is established according to geographic constraint conditions;
generating a candidate site area;
and optimizing and screening the candidate site areas of the transformer substation.
2. The intelligent substation site selection area division method based on the GIS space analysis technology as claimed in claim 1, wherein the geographic constraint condition comprises: terrain grade, land type, distance constraints.
3. The intelligent substation site selection area division method based on the GIS spatial analysis technology according to claim 1 or 2, wherein the generating the candidate site area includes:
basic geographic data corresponding to constraint conditions are obtained, and distance analysis is conducted on constraint index attributes so as to divide the suitability of the region;
reclassifying the attribute values of the constraint indexes, determining the coefficients of the constraint indexes and performing superposition analysis;
covering and eliminating the areas which cannot be built up to obtain the suitability grade division result of the research area;
and selecting the region with highest suitability as a candidate site region of the transformer substation according to the suitability grade division result.
4. The intelligent substation site selection area division method based on the GIS spatial analysis technology according to claim 3, wherein the reclassifying the attribute values of the constraint indexes comprises:
when reclassifying constraint indexes, the suitability of each constraint index is classified into five grades: unsuitable region, low suitable region, medium suitable region, high suitable region, and optimum region, and different scoring values are set for these five classes.
5. The intelligent substation site selection area division method based on the GIS space analysis technology as set forth in claim 4, wherein the optimizing and screening includes:
solving the area problem of the largest inscribed rectangular transformer substation of the polygonal candidate area based on an improved sparrow search algorithm, judging whether the selected candidate area meets the building requirement or not by comparing the area parameter of the transformer substation, and optimizing and screening the transformer substation candidate area.
6. The intelligent substation site selection area division method based on the GIS space analysis technology according to claim 5, wherein the improved sparrow search algorithm comprises: a mixed strategy of Circle chaotic mapping and refraction reverse learning mechanism is introduced when the population is initialized;
the Circle chaotic map is expressed as:
wherein ,represents the position of the ith individual in the sparrow population in the j-th dimension at the t-th iteration, k is a scaling factor, l j and uj Minimum and maximum of the j-th dimensional space, respectively,>is X i,j Is a refractive inversion solution of (a).
7. The intelligent substation site selection area division method based on the GIS spatial analysis technology according to claim 4, wherein in the improved sparrow search algorithm, an adaptive weight is introduced in a finder position update stage, expressed as:
wherein, iter max For the maximum iteration number, t is the current iteration number, and the weight value omega (t) is subjected to nonlinear change on the value range.
8. The intelligent substation site selection area division method based on the GIS space analysis technology as set forth in claim 7, wherein the improved location update formula of the discoverer is:
wherein ω (t) is an adaptive weight,representing the position of the ith individual in the jth dimension in the nth iteration of the population; alpha represents (0, 1)]Random numbers over the interval; ter (iter) max Representing a maximum number of iterations;q represents a random number subject to normal distribution; l represents a 1 x d matrix with element values of all 1; r is R 2 Represents the early warning value and is [0,1 ]]A uniform random number thereon; ST is a preset security value.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 8 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 8.
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| CN117537826A (en) * | 2024-01-09 | 2024-02-09 | 中国民航大学 | Track planning method capable of sensing thunderstorm situation |
| CN117537826B (en) * | 2024-01-09 | 2024-03-22 | 中国民航大学 | Track planning method capable of sensing thunderstorm situation |
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