CN112906283A - Cable layout method and electronic equipment - Google Patents

Abstract

The invention provides a cable layout method and electronic equipment, wherein the method comprises the following steps: the method comprises the steps that a transformer substation is used as a first vertex, a plurality of fans are respectively used as second vertices, at least two of the fans are different in power, a connecting line between the first vertex and the second vertex and a connecting line between the two second vertices are respectively used as edges, and the weight of each edge is obtained; acquiring a first vertex, a second vertex, an edge and a set of weights of the edge; constructing a spanning tree of the weight G according to the set; finding G with the smallest total weight among the weights G_TThe minimum spanning tree of (3); constructing a single cable layout objective function of an optimization model according to the minimum spanning tree; establishing a constraint condition of an optimization model; introducing multiple kinds in a single cable layout objective functionObtaining a final objective function of the optimization model according to the function corresponding to the type of the fan; and solving the optimization model by adopting a preset algorithm to obtain an optimized layout strategy of the cable. According to the method, a more reasonable cable connection layout scheme can be obtained.

Description

Cable layout method and electronic equipment

Technical Field

The invention belongs to the technical field of fans, and particularly relates to a cable layout method and electronic equipment.

Background

Wind energy is one of the fastest-developing sustainable energy sources at present. However, the installation, operation and maintenance costs of the wind power plant are high, and the optimization of the cable connection layout is an effective way for reducing the cost. However, the existing technologies are all configured for wind farms with only one type of wind turbine, and cannot solve the problem of cable connection configuration of multiple wind turbine types. In addition, in the process of cable layout, the current technology judges the proximity relationship between fans according to the length of the geometric distance between fans (or substations), and then determines the priority order of fan connection and obtains a cable connection layout scheme. However, the method cannot judge the proximity relation between the wind turbines (or the substations) from the perspective of the global space, and a comprehensive and reliable cable connection layout scheme is obtained.

Disclosure of Invention

One objective of the present application is to provide a new technical solution of a cable layout method, which can simultaneously achieve micro-site selection of a substation and acquisition of a reasonable cable connection layout scheme.

In a first aspect of the present invention, a cable layout method is provided, which includes the following steps: taking a transformer substation as a first vertex, taking a plurality of fans as second vertices respectively, taking at least two of the fans with different corresponding powers, taking a connecting line between the first vertex and the second vertex and a connecting line between the two second vertices as edges respectively, and acquiring the weight of each edge, wherein the first vertex is adjustable within a preset range so as to realize micro address selection; acquiring a set of the first vertex, the second vertex, the edge and the weight of the edge; constructing a spanning tree of the weight G according to the set; finding G with the smallest total weight among the weights G_TMinimum spanning tree of G_TIs a subfigure of G; constructing a single cable layout objective function of an optimization model according to the minimum spanning tree; establishing constraint conditions of the optimization model; introducing functions corresponding to various types of fans into the single cable layout objective function to obtain a final objective function of the optimization model; solving the optimization model by adopting a preset algorithm,and obtaining an optimized layout strategy of the cable.

According to the cable layout method, the proximity relation between the fans (or the transformer substation) can be judged from the perspective of the global space, and a comprehensive and reliable cable connection layout scheme is obtained.

According to one embodiment of the invention, the preset algorithm comprises the following steps:

initializing a particle population according to a particle coding strategy;

according to a preset decoding method, acquiring a transformer substation address and a Thiessen polygon distance matrix to obtain a fitness value of each particle;

updating the position, the speed and the corresponding fitness value of the particle according to a self-adaptive particle swarm algorithm;

updating the current global optimal particle through a neighborhood search strategy;

and repeating the particle updating step until the stopping standard is reached, and outputting the optimal cable connection layout and the corresponding cable cost.

According to an embodiment of the invention, the definition of the Thiessen polygon map comprises:

defining discrete points distributed on the Voronoi diagram as Voronoi sites;

station E₁And site E₂The corresponding Voronoi regions are respectively defined as VR (E)₁) And VR (E)₂)；

The station E₁And said E₂A Voronoi distance between, defined as connecting said sites E₁And said station E₂The number of Voronoi edges intersected by the path of (1);

the station E₁Is defined as the distance from the site E₁Is denoted as KVNS (E)₁,k)。

According to one embodiment of the invention, the particle coding strategy comprises position information of the substation, connection information of cable layout and model information corresponding to each cable.

According to one embodiment of the present invention, the decoding method includes:

obtaining the position information of the transformer substation according to the front 2-dimensional position information of the particles;

combining the position information of the fan to obtain a Thiessen polygon distance matrix;

acquiring connection information based on cable layout under the current transformer substation by combining position information of other dimensions of the particles;

and selecting the cable model according to the connection information of the current cable layout.

According to one embodiment of the invention, the operation of the neighborhood search strategy comprises:

executing a first neighborhood search strategy under the condition that rand () < 0.3;

executing a second neighborhood search strategy under the condition that rand () is more than or equal to 0.3 and less than 0.6;

and executing the third neighborhood search strategy under the condition that the range () is more than or equal to 0.6.

If none of the steps can be updated to the better global optimum particle, the steps are executed again until the number of searching times reaches T_LS。

According to one embodiment of the invention, the first neighborhood search strategy comprises:

and randomly changing the position information of the transformer substation, keeping the connection information of the cable layout unchanged, and if the new cable connection layout is more optimal, performing an inverse coding strategy to obtain new particles to replace the original globally optimal particles.

According to one embodiment of the invention, the second neighborhood search strategy comprises:

randomly selecting a certain edge (BT) of the set of edge information in the connection information of the cable lay-out₁，BT₂) Ordered in (BT)₁，BT₂) Forming a temporary point set by points corresponding to the front edge;

BT (ethylene terephthalate)₁Randomly changing to a certain point BT in temporary point set₃Constitute a new edge (BT)₃,BT₂) Other cable connection information is unchanged, and a new cable connection layout is formed;

and if the new cable connection layout meets the constraint condition and is better than the original cable connection layout, performing an inverse coding strategy to obtain a new particle to replace the original globally optimal particle.

According to one embodiment of the invention, the third neighborhood search strategy comprises:

randomly changing a certain dimension representing cable model selection in the positions of the particles, keeping the connection information of the cable layout unchanged, and replacing the original globally optimal particles with the new particles if the new cable connection layout is more optimal.

In a second aspect of the present invention, there is also provided an electronic device, including a processor, a memory, and a program or an instruction stored in the memory and executable on the processor, where the program or the instruction, when executed by the processor, implements the steps of the cable layout method according to any of the above embodiments.

Further features of the present application and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which is to be read in connection with the accompanying drawings.

Drawings

FIG. 1(a) is a schematic diagram of actual current carrying capacity of cables of wind power plants with the same type of wind turbine according to an embodiment of the application;

FIG. 1(b) is a schematic diagram of actual current carrying capacity of cables of wind power plants with different fan types according to an embodiment of the application;

FIG. 2 is a Voronoi diagram according to an embodiment of the application;

FIG. 3 is a Voronoi diagram of a wind farm with different types of wind turbines according to an embodiment of the application;

FIG. 4 is a schematic diagram of particle position encoding according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a first stage of particle position decoding according to an embodiment of the present application;

FIG. 6 is a diagram illustrating a second stage of particle position decoding according to an embodiment of the present application;

FIG. 7 is a first diagram illustrating a second stage of inverse coding according to an embodiment of the present application;

FIG. 8 is a diagram illustrating a second stage of inverse coding according to an embodiment of the present application

FIG. 9 is a block diagram of a cable placement method according to an embodiment of the present application;

FIG. 10 is a schematic illustration of an optimal cable layout according to experimental results of an embodiment of the present application;

FIG. 11 is a schematic diagram of an electronic device according to an embodiment of the application.

Reference numerals:

an electronic device 100;

a memory 110; an operating system 111; an application 112;

a processor 120; a network interface 130; an input device 140; a hard disk 150; a display device 160.

Detailed Description

Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

First, a cable layout method according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

The cable layout method according to the embodiment of the invention comprises the following steps:

the method comprises the steps of taking a transformer substation as a first vertex, taking a plurality of fans as second vertices respectively, taking at least two of the fans with different corresponding powers, taking a connecting line between the first vertex and the second vertices and a connecting line between the two second vertices as edges respectively, and obtaining the weight of each edge, wherein the first vertex is adjustable in a preset range so as to realize micro address selection. That is, the position of the substation can be changed within a certain range, and micro-addressing is realized.

Obtaining a set of weights for the first vertex, the second vertex, the edge, and the edge.

And constructing a spanning tree of the weight G according to the set.

Finding G with the smallest total weight among the weights G_TMinimum spanning tree of G_TIs a subfigure of G.

And constructing a single cable layout objective function of an optimization model according to the minimum spanning tree.

And establishing constraint conditions of the optimization model.

And introducing functions corresponding to various types of fans into the single cable layout objective function to obtain a final objective function of the optimization model.

And solving the optimization model by adopting a preset algorithm to obtain an optimized layout strategy of the cable.

It should be noted that, in the graph theory, the spanning tree is a subgraph of the undirected graph G. The spanning tree contains all vertices and some edges in G, and all vertices have paths to communicate with each other, but no closed loop. The weighted G spanning tree can be expressed as:

G_T＝(V,B_T,W_T),G_T∈G,B_T∈B,W_T∈W (1)

where V represents the set of all vertices, B is the set of all edges connecting the vertices in V, and W is the set of weights corresponding to all edges in B. G_TIs a subgraph of G, represents a spanning tree of G, and similarly, B_TIs a set of edges in B, W_TIs corresponding to B_TThe weight of all edges in.

According to the above shown definition of spanning tree, if substation OS (in V)_OSIndicated) and fan WT (with V)_WTRepresentation) are vertices like G, then the cost of the cable connecting the two vertices (between OS and WT or WT and WT) is the weight of the edge. Then the cable layout optimization problem is converted into the G with the minimum total weight in the weighted G_TThe minimum spanning tree problem (MST).

Optionally, the cable layout objective function includes:

wherein the content of the first and second substances,

in order to bring the total expenditure to the cable construction,

in order to account for the energy loss of a single cable,

is the sum of the currents through the side m of the cable of type p carrying the electric energy.

That is, the objective function of the cable layout planning problem (CCLP) problem described above includes two parts, cable infrastructure expenditure and energy loss, and the detailed mathematical expression is as follows.

The total expenditure of the basic construction of the cable is as follows:

energy loss of single cable:

wherein the content of the first and second substances,

is the sum of the currents through the side m of the cable of type p carrying the electric energy. However, MWTTOWF (multi-power fan type) has many kinds of power fans, and the currents generated by different types of fans are different under standard voltages. Thus, MWTTOWF is comparable to OWFUTT (single power fan type)

The calculation method of (c) is different.

For convenience of description, the following description is briefly made with reference to specific examples

The method of (3).

Suppose that there are 1 substation and 4 wind turbines in both wind farms, and their coordinates are identical. Secondly, suppose that the currents generated by 4 fans of OWFUTT are all 20A under standard voltage. Finally, it is assumed that there are 2 types of MWTTOWF, and the currents generated by these 2 types of fans are 20A and 30A respectively at the standard voltage. And the actual current capacity of the cables of the two wind power plants is shown in the figure 1.

In fig. 1(a), there are 4 fans transmitting power to the cables (1,2), and the 4 fans are of the same type. Thus, the actual current capacity of the cables (1,2) of fig. 1(a) is 4 × 20 — 80A. Whereas in fig. 1(b) the fans transmitting power to the cables (1,2) comprise 2 fans of type 1 and 2 fans of type 2. Therefore, the actual current capacity of the cables (1,2) in fig. 1(b) is the sum of the currents generated by the 4 fans under the standard voltage: 20+30+30+20 equals 100A.

It follows that, when only one type of wind turbine is present in the wind farm,

the number of the fans for transmitting the electric energy to the cable is multiplied by the current generated by the type of the fan under the standard voltage.

According to one embodiment of the invention, in the presence of a plurality of said fans of different power,

wherein the content of the first and second substances,

in the representation of G_TThe number of fans transmitting power to the edge m.

That is, when there are fans with various powers in the wind farm, the number of fans carried by each cable and the specific type of each fan must be known, and then the current generated by each fan under the standard voltage is accumulated to obtain the current

Thus of wind farms

The calculation method of (c) is as follows:

wherein the content of the first and second substances,

in the representation of G_TThe number of fans transmitting power to the edge m.

In addition, I_rate,qDepending on the type of fan, at nominal voltage:

P_rate,qrated apparent power, U, of a fan of type q_rate,qIs the rated voltage of a fan of type q.

Obtainable from formula (6), G_TThe PLC of all cables in (a) is:

it follows that the final objective function, i.e. the overall objective function, is:

optionally, the constraint includes:

G_T∈G (9)

the formula (9) is for ensuring G_TIs a spanning tree for G. The formula (10) is that the actual current of each cable cannot exceed the maximum current-carrying capacity which can be borne by the cable on the basis of equipment protection principle. The maximum number of feeders of the transformer substation is constrained to be 10 according to the construction condition of the actual wind power plant, the number of incoming wires of each fan is not more than 2, and the number of outgoing wires of each fan is 1 according to the formula (11), the formula (12) and the formula (13). While equation (14) constrains two edges in the layout

And

) Cross cables are not available because crossing of cables reduces the transmission capability of the cables and increases construction costs.

The cable connection layout is feasible only when the constraints of equations (9) to (14) are performed. Thus, when a spanning tree G is used_TAfter determination, the cost of a feasible cable connection layout can be calculated by equations (1) to (8).

Wherein, the meaning of each parameter in the above formula is shown in the following table 1.

TABLE 1 definition of the parameters

Alternatively, in order to obtain a better solution, the preset algorithm may be an adaptive particle swarm algorithm based on Voronoi distance and neighborhood search as a solving algorithm of the model.

In some embodiments of the invention, the preset algorithm comprises the steps of:

and initializing the particle population according to the particle coding strategy.

According to a preset decoding method, a transformer substation address and a Thiessen polygon (Voronoi) distance matrix are obtained, and then the fitness value of each particle is obtained.

And updating the position, the speed and the corresponding fitness value of the particle according to an Adaptive Particle Swarm Optimization (APSO).

And updating the current global optimal particle through a neighborhood search strategy.

And repeating the particle updating step until the stopping standard is reached, and outputting the optimal cable connection layout and the corresponding cable cost.

The APSO algorithm is described in detail below.

First, the algorithm finds the optimal solution by updating the velocity and position of the particles, which can be expressed as:

v_i,t+1＝ω·v_i,t+r₁·rand()·(LB_i,t-x_i,t)+r₂·rand()·(GB_t-x_i,t) (15)

x_i,t+1＝x_i,t+v_i,t+1 (16)

wherein the weight change formula is as follows:

optionally, the definition of the Thiessen polygon map comprises:

discrete points distributed on the Voronoi diagram are defined as Voronoi sites.

Station E₁And site E₂The corresponding Voronoi regions are respectively defined as VR (E)₁) And VR (E)₂)。

The station E₁And said station E₂A Voronoi distance between, defined as connecting said sites E₁And said station E₂The number of Voronoi edges intersected by the path of (a).

The station E₁Is defined as the distance from the site E₁Is denoted as KVNS (E)₁,k)。

The following describes the Thiessen polygon (Voronoi) distance in detail.

First, the Voronoi diagram-related definition includes the following.

Definition 1 discrete points distributed on a Voronoi diagram are defined as Voronoi sites. Thus, in the present application, the substation as well as the wind turbine are both Voronoi sites.

Definition 2 site E₁And E₂The corresponding Voronoi regions are VR (E) respectively₁)，VR(E₂). If VR (E)₂) And VR (E)₂) Between which a Voronoi edge exists, then E₁And E₂Then they are Voronoi neighbors of each other, for example, in the figure, fig. 1 contains a total of 29 sites, and E₁And E₂Real Voronoi neighbors.

Definition 3 site E₁And E₂Voronoi distance between, defined as connecting two sites E₁And E₂The number of Voronoi edges intersected by the path of (a). For example, E of FIG. 1₁And E₂Has a Thiessen polygon distance of 1.

Definition 4: E₁The K-th order Thiessen polygon neighbors are distances E₁Is denoted as KVNS (E)₁K). For example, KVNS (E) of FIG. 2₁,1),KVNS(E₁2) and KVNS (E)₁And 3) are each E₁Voronoi neighbors of 1,2,3 orders.

As can be seen from fig. 2, the Voronoi diagram divides the measurement space, and the Voronoi distance obtained on the basis redefines the proximity relationship between the sites, rather than simply determining the proximity relationship between two points by distance, which is a more scientific method for determining the proximity relationship. Because, in the spatial layout, a point is a shorter geometric distance from a site relative to other points, but does not mean that the point is more closely related to the site. For ease of description, the present application explains the above case by way of an example of a wind farm comprising 1 OS and 5 WT (2 fan types in total), as shown in FIG. 2.

As is apparent from FIG. 3, point 6 is closer to OS in geometric distance than point 3, but point 3 is a1 st order neighbor of OS, while point 6 is a2 nd order neighbor of point 1. Thus, point 3 is a neighbor closer to the OS than point 6; while points 4 and 5 are closer to point 6 relative to OS. It follows that in generating a cabling topology, when a fan connected to the OS needs to be selected, point 3 should have priority over point 6, and point 6 should go to either point 4 or point 5.

Therefore, the Voronoi diagram can judge the proximity relation between OS and WT in the wind power plant and between WT and WT more scientifically, and is beneficial to selecting a better edge for cable connection in the layout process.

The following describes a scheme for encoding and decoding particles.

(1) Particle encoding

According to the optimization model, the preset algorithm not only needs to realize micro-site selection of the transformer substation, but also needs to obtain basic cable connection information, and further influence of energy loss on cable model selection in the layout needs to be considered. Therefore, the particle code must simultaneously contain 3 parts of information, the first part is the substation address, the second part contains the connection information of the layout, and the third part contains the model information corresponding to each cable. That is, the particle coding strategy includes location information of the substation, connection information of cable layout, and model information corresponding to each cable. For the above reasons, assuming that the number of fans is M, the position code of the particle of the present application is as shown in fig. 4:

wherein S is₁，S₂Representing x and y axis coordinates of the transformer substation, and selecting the value ranges of x and y according to the wind power plant area; s₂…S_M+2Represents a connection index (connection index), S, in the connection layout₂…S_M+2∈[1,M](ii) a And S_M+3…S_2M+2Representing the CTIN index, S, in the cable model selection process_M+3…S_2M+2∈[1,P]. Of encoded particlesThe specific meaning and method of use will be given in the following decoding process.

(2) Particle decoding

The particle decoding comprises three stages, wherein the first stage obtains the position of the transformer substation, the second stage obtains the cable connection information, and the third stage selects the cable model according to the current cable connection information. That is, the decoding method includes:

and obtaining the position information of the transformer substation according to the front 2-dimensional position information of the particles.

And combining the position information of the fan to obtain a Thiessen polygon distance matrix.

And acquiring connection information based on the cable layout under the current substation by combining position information of other dimensions of the particles, such as position information of the 3 rd dimension and later dimensions of the particles.

And selecting the cable model according to the connection information of the current cable layout.

In the whole decoding process, the adaptive particle swarm algorithm (VDAPSOLS) based on Voronoi distance and neighborhood search needs to use 6 basic sets and 3 matrixes, as follows:

set OS: and x-axis and y-axis coordinates of the transformer substation.

Voronoi distance matrix VAM: voronoi distances between all vertices (the current substation (i.e. the one pointed to by the set OS) and the wind turbines) are stored.

set A contains vertices that have been connected in the MST.

set B-contains vertices that have not yet been connected in the MST.

And set C is the side length of the side corresponding to the vertex containing the set A.

And set D, the actual current of the side corresponding to the vertex containing the set A.

And set E, minimum cable selection of the side corresponding to the vertex containing set A.

And set F, the final cable model of the vertex corresponding edge containing the set A.

And the adjacent matrix AM takes set A as the index of a column and set B as the index of a row to form edges, and the AM comprises the Voronoi distance and the actual side length of the edges. Wherein, the Voronoi distance can be directly obtained from the VAM.

And (3) sequencing all edges in the sequenced AMS from small to large according to the Voronoi distance, and if the Voronoi distances of the two edges are equal, sequencing the two edges from small to large according to the side length, and finally obtaining the sequenced AMS.

Cable matrix: the cable data includes information on the current carrying capacity, resistance, price, etc. of the cable on each side, and the cable data is arranged from small to large according to the current carrying capacity of the cable.

In the first stage: the 1 st dimension of the particle is the x-axis coordinate addressing of the substation, and the 2 nd dimension is the y-axis coordinate addressing of the substation, and is stored in the set OS. Further, combining the site of the transformer substation and the coordinates of all the fans, and according to the definition of the Voronoi distance, calculating to obtain a Voronoi distance matrix VAM, wherein the size of the matrix is (M +1) × (M + 1).

In the second phase, set A contains the OS, set B contains all the fans, and set C, set D, set E and set F are all empty sets. Firstly, an adjacent matrix AM is generated according to the vertexes of set A and set B, and edges in the adjacent matrix AM are sequenced to obtain an adjacent matrix AMS. Then, VDAPSOLS selects a new edge based on the connection index of the particle position and the adjacency matrix AMS, and information on the edge is put into set C. Meanwhile, the vertex corresponding to the edge is deleted from set B and then placed into set A. This phase does not end until set B is empty. It is worth noting that: in the process of selecting the vertex and the corresponding edge thereof, if the accessed cable connection layout of the edge does not satisfy the constraints from the expression (10) to the expression (14), the next edge in the adjacency matrix AMS is sequentially selected until the constraints are satisfied. Wherein the newly selected edge still cannot have the cable crossing condition.

In the third stage, first, the current passing through each side (i.e., the cable) is calculated from equation (5) based on set C obtained in the second stage, and the current value is correspondingly placed in set D. Further, according to the current value in set D, the cable model with the minimum current-carrying capacity and meeting the constraint of (10) is selected and correspondingly placed in set E. And finally, adding the CTIN index of the particle position to the cable model of each edge in the set E correspondingly to obtain the finally selected model of each cable, and correspondingly putting the model into the set F. It is worth noting that: and if the added value exceeds the maximum index of the selectable cable models, directly setting the cable models as the maximum index.

In summary, through the three stages of decoding, the Set OS, Set C and Set F obtained form a cable connection layout scheme, and then the cost corresponding to the cable connection layout scheme can be calculated by combining Set D. To better illustrate the two stages of particle encoding, an example is given in this application. The wind power plant in fig. 2 is used as a solving object, the positions of particles i are assumed to be [2.00,2.00,2,4,1,1,2,0,1,2,1,0], and there are 3 optional cable types (the larger the cable index is, the larger the current-carrying capacity of the cable is). The front 2 dimensions of the particles represent substation site selection, the 2-7 dimension positions represent connection indexes, and the rear 5 dimension positions represent CTIN indexes.

First, in the first stage, the first 2 dimensions of the decoded particle positions are available, and the site of the current substation is (2.00 ), i.e. set OS { (2.00 ) }. Further, combining the site selection of the transformer substation and the coordinates of all the fans, and according to the definition of the Voronoi distance, calculating to obtain a Voronoi distance matrix VAM, wherein the size of the matrix is 6 multiplied by 6. Next, the second and third stages of decoding process of the particle i are shown in fig. 5 and 6.

In FIG. 5, the position of particle i has been decoded for the 3 rd dimension, and now set A is [1,2], set B is [3,4,5,6], set C is { {1- -2,1.00} }. First, the adjacency matrix AMS is obtained from set a and set B. Since the 4 th dimension of the particle is 4, it points to the 4 th edge (1,3) of the adjacency matrix AMS. And if the edge meets the constraint of the constraint condition, putting the related information of the edge into set C, and updating the set C into { { 1-2, 1.00}, { 1-3, 2.00} }. Meanwhile, set A is updated to [1,2,3], and set B is updated to [4,5,6 ].

Similarly, the 5 th, 6 th and 7 th dimensions of the particle positions can be decoded according to the above steps, and finally, the results are set A as [1,2,3,4,6,5], set C as { { 1-2, 1.00}, { 1-3, 2.00}, { 1-4, 0.50}, { 4-6, 0.71}, and { 4-5, 1.12} }. Further, assuming that the current value of each cable is calculated from the cable connection information of set C, set D is obtained as [1.0,1.0,3.2,1.2,1.0 ]. Then, assuming that set E is [1,1,2,1,1] under the rule of selecting the cable model with the minimum ampacity. The last 5 dimensions of the position of particle i are [0,1,2,1,0], which, when added in correspondence with set E, gives [1,2,4,2,1 ]. But since there is no cable of cable type 4, the cable type 3 is set directly, resulting in a set F of [1,2,3,2,1 ]. Finally, the cable connection layout corresponding to the position of the particle i and the related information thereof can be obtained from set OS, set C and set F, as shown in FIG. 6.

The neighborhood search strategy is described in detail below

The neighborhood Search strategy (LSS) directly optimizes the globally optimal particle, and the updated globally optimal particle can enhance the social learning ability of the particle, as shown in formula (19):

v_i,t+1＝ω·v_i,t+r₁·rand()·(LB_i,t-x_i,t)+r₂·rand()·(GB_local,t-x_i,t) (19)

wherein, GB_local,tRefers to the globally optimal particle after LS is used in the t generation. It can be seen that: the formula (19) is improved from the formula (15). Therefore, the application provides a neighborhood search strategy according to the characteristics of MWTTOWF and APSO.

The operation of the neighborhood search strategy proposed by the present application is:

if rand () <0.3, executing a first neighborhood search strategy;

if 0.3 is less than or equal to rand () <0.6, executing a second neighborhood searching strategy;

if 0.6 is less than or equal to rand (), a third neighborhood search strategy is executed.

If none of the steps can be updated to the better global optimum particle, the steps are executed again until the number of searching times reaches T_LS. The particle position comprises three parts of substation site selection, connection index and CTIN index. Therefore, the LS provided by the application can simultaneously optimize the substation site selection, the cable connection condition and the cable type selection of the globally optimal particles.

Optionally, the first neighborhood search strategy includes: and randomly changing the position information of the transformer substation, keeping the connection information of the cable layout unchanged, and if the new cable connection layout is more optimal, performing an inverse coding strategy to obtain new particles to replace the original globally optimal particles.

Optionally, the second neighborhood search strategy includes:

randomly selecting a certain edge (BT) of the set of edge information in the connection information of the cable lay-out₁，BT₂) Ordered in (BT)₁，BT₂) The points corresponding to the previous edge constitute a temporary point set.

BT (ethylene terephthalate)₁Randomly changing to a certain point BT in temporary point set₃Constitute a new edge (BT)₃,BT₂) And other cable connection information is unchanged to form a new cable connection layout.

And if the new cable connection layout meets the constraint condition and is better than the original cable connection layout, performing an inverse coding strategy to obtain a new particle to replace the original globally optimal particle.

Optionally, the third neighborhood search strategy includes: randomly changing a certain dimension representing cable model selection in the positions of the particles, keeping the connection information of the cable layout unchanged, and replacing the original globally optimal particles with the new particles if the new cable connection layout is more optimal.

The following describes optimizing the first neighborhood search strategy, the second neighborhood search strategy, and the third neighborhood search strategy in detail, respectively.

(1) For the first neighborhood search strategy, the original cable connection layout information is assumed to be: set OS { (2.00 ) }, set C { { 1-2, 1.00}, { 1-3, 2.00}, { 1-4, 0.50}, { 4-6, 0.71}, { 4-5, 1.12} }, and set F [1,2,3,2,1 ]. Assuming that the address of the substation in the randomly varying set OS becomes set OS { (1.00,0.10) }, the current cable connection layout information is: set OS { (1.00,0.10) }, set C { { 1-2, 1.00}, { 1-3, 2.00}, { 1-4, 0.50}, { 4-6, 0.71}, { 4-5, 1.12} }, and set F [1,2,3,2,1 ]. And if the cable connection layout is more optimal, performing an anti-coding strategy to obtain new particles to replace the original globally optimal particles.

(2) For the second neighborhood search strategy, the original cable connection layout information is assumed to be: set OS { (2.00 ) }, set C { { 1-2, 1.00}, { 1-3, 2.00}, { 1-4, 0.50}, { 4-6, 0.71}, { 4-5, 1.12} }, and set F [1,2,3,2,1 ]. Assuming that { 1-4, 0.50} in setC is selected (i.e., edge (1,4) is selected), the temporary set of points corresponding to the edges ordered before edge (1,4) (i.e., { { 1-2, 1.00}, { 1-3, 2.00} } pointed to edge (1,2) and edge (1,3)) is {1,2,3 }.

Further, assuming that element 2 in the temporary set is randomly selected to form a new edge (2,4), and other information remains unchanged, new cable connection layout information is formed: set OS { (1.25,0.56) }, set C { { 1-2, 1.00}, { 1-3, 2.00}, { 2-4, 0.50}, { 4-6, 0.71}, { 4-5, 1.12} }, and set F [1,2,3,2,1 ]. If the new cabling layout satisfies the constraints of claim 5 and is more optimal than the original cabling layout, then the de-coding strategy is performed to obtain a new particle to replace the original globally optimal particle.

(3) For the third neighborhood search strategy, assume that LS randomly chosen the CTIN index at bit 1 in the particle position and changes this value to 1, resulting in new particles of [1.25,0.56,2,4,1,1,2,1,1, 0 ]. After re-decoding, if the new cable connection topology is more optimal, the new particle replaces the original globally optimal particle.

It is noted that the first neighborhood search strategy and the second neighborhood search strategy both directly search for cable connection layout information. Therefore, the updated relation between the cable connection layout information and the original particle position does not conform to the logic rule of the original coding strategy. In order to solve the problem, the invention obtains the particle positions which accord with the logic rule of the original coding strategy and have one-to-one correspondence with the updated cable connection layout information through the anti-coding strategy. In addition, the anti-coding strategy in the first neighborhood searching strategy and the second neighborhood searching strategy is the same.

The following detailed description of the anti-coding strategy assumes that the current cable connection layout information is: set OS { (1.00,0.10) }, set C { { 1-2, 2.00}, { 1-3, 0.50} }, and set F [1,2 ]. There is also a need to set multiple sets and matrices:

set X1, Set X2, Set X3.

Voronoi distance matrix FVAM: voronoi distances between all vertices (the current substation (i.e. the one pointed to by the set OS) and the wind turbines) are stored.

set FA initial set FA is {1 }.

set FB initial set FB 2, 3.

And set FC is the side length of the side corresponding to the vertex containing set FA.

set FD, the actual current of the vertex-corresponding side containing set FA.

And set FE, namely the minimum cable type of the vertex corresponding edge containing the set FA.

And set FF, namely the final cable model of the vertex corresponding edge containing the set FA.

The adjacency matrix FAM has set FA as the index of the columns and set FB as the index of the rows, and thus forms edges, and the AM includes Voronoi distances and actual side lengths of the edges. Wherein, the Voronoi distance can be directly obtained from the FVAM.

And sequencing all edges in the FAM from small to large according to the Voronoi distance, and if the Voronoi distances of the two edges are equal, sequencing the two edges from small to large according to the side length, and finally obtaining the sequenced FAMS.

It is worth noting that the de-coding strategy also comprises 3 stages:

in the first stage, since Set OS { (1.00,0.10) }, the first 2 dimensions of the particle are 1.00,0.10, respectively, and Set X1 { [1.00,0.10 ].

In the second stage, the side information in set C is selected sequentially from left to right. The first one selects { 1-2, 2.00} in set C first, while the current set FA is {1} and the set FB is {2,3 }. First, AMS is established according to set FA and set FB, and FAMS is obtained after sequencing, as shown in FIG. 7. Further, the sequence number of { 1-2, 2.00} in FAMS, sequence number 2, is returned, and Set X2 ═ 2 is obtained by placing sequence number 2 in Set X2. Further, the rear point (i.e. 2) of the edge (1,2) pointed by { 1-2, 2.00} is put into Set FA, while Set FB deletes 2, Set C deletes { 1-2, 2.00}, resulting in Set FA being {1,2}, Set FB being {3}, Set C being { { 1-3, 0.50 }.

Next, {1- -3,0.50} in set C is selected. Therefore, AMS is established according to the set FA and the set FB, and FAMS is obtained after sequencing, as shown in FIG. 8. Further, the sequence number of { 1-3, 0.50} in FAMS, sequence number 1, is returned, and Set X2 is put in sequence number 1, resulting in Set X2 being [2,1 ]. Further, the rear point (i.e. 3) of the edge (1,3) pointed by { 1-3, 0.50} is put into Set FA, while Set FB deletes 3 and Set C deletes { 1-3, 0.50}, resulting in Set FA being {1,2,3}, Set FB, Set C being empty Set. Thus, this stage is ended. Final Set X2 ═ 2,1.

In the third stage, since Set F is [1,2], Set X3 is [1,2 ]. Here, Set X1, Set X2, and Set X3 are combined to obtain [1.00,0.10,2,1,1,2], which is the position of the decompiled particle.

It is worth noting, however, that the neighborhood search strategy for the above three cases will update the information of the globally optimal particle only if the candidate particle has a better solution than the globally optimal solution. Therefore, the strategy can improve the local searching capability of the algorithm while keeping the original global searching capability of the APSO.

That is, the algorithm first initializes the population of particles and their associated parameters according to a particle encoding strategy. And then, according to a decoding method, acquiring the substation address and the Thiessen polygon distance matrix, further acquiring the fitness value of each particle, and taking the fitness value as the basis of later-stage fitness comparison. Further, the position and the speed of the particle and the corresponding fitness value thereof are updated according to the APSO (the fitness value is calculated by equation (8)), and the current global most-available particle is updated through the LS. And repeating the steps until the stopping standard is reached. And finally, outputting the optimal cable wiring layout and the corresponding cable cost.

The cable layout method of the present application will be described in detail with reference to specific embodiments.

An offshore wind farm consisting of 72 wind turbines was selected for this experiment. The wind power station has 3 types of fans, the power of the fans is 3MW, 3.3MW and 6.4MW respectively, and q is set to be 1,2 and 3 respectively. Wind farm related technical and economic information is shown in table a 1. Since the present application assumes that the voltages of all fans are 1p.u.voltage, the full load currents of 3 types of fans can be obtained according to equation (7) as 27.63A, 30.39A, and 59.40A, respectively. In addition, the specification of the cable selected by the application is shown as A2, and the number of the cable is 8.

The results are shown in FIG. 10, Table 2 and Table 3.

TABLE 2 basic data table of wind farm

TABLE 3.66 KV, XRUHAKXS Cable BaseTable

As can be seen from fig. 10, table 2, and table 3, according to the cable layout method in the embodiment of the present application, the proximity relationship between wind turbines (or substations) can be determined from the perspective of a global space, so as to obtain a comprehensive and reliable cable connection layout scheme.

In summary, according to the cable layout method of the embodiment of the application, under the conditions of considering the types of multiple fans, the cost of different cables, the maximum current-carrying capacity constraint of the cables, the energy loss of the cables and the like, the proximity relation of the fans is judged by using the Voronoi distance, a targeted neighborhood search strategy is designed by combining with the adaptive particle swarm algorithm, a cable layout planning method based on the Voronoi distance and the adaptive particle swarm algorithm of neighborhood search is provided, and a reasonable cable connection layout scheme is obtained.

In addition, an embodiment of the present application further provides a computer storage medium, where the computer storage medium includes one or more computer instructions, and when executed, the one or more computer instructions implement any one of the methods described above.

That is, the computer storage medium stores a computer program that, when executed by a processor, causes the processor to perform any of the methods described above.

As shown in fig. 11, an embodiment of the present invention provides an electronic device 100, which includes a memory 110 and a processor 120, where the memory 110 is configured to store one or more computer instructions, and the processor 120 is configured to call and execute the one or more computer instructions, so as to implement any one of the methods described above.

That is, the electronic apparatus 100 includes: a processor 120 and a memory 110, in which memory 110 computer program instructions are stored, wherein the computer program instructions, when executed by the processor, cause the processor 120 to perform any of the methods described above.

Further, as shown in fig. 11, the electronic device 100 further includes a network interface 130, an input device 140, a hard disk 150, and a display device 160.

The various interfaces and devices described above may be interconnected by a bus architecture. A bus architecture may be any architecture that may include any number of interconnected buses and bridges. One or more Central Processing Units (CPUs), represented in particular by processor 120, and one or more memories, represented by memory 110, are coupled together. The bus architecture may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like. It will be appreciated that a bus architecture is used to enable communications among the components. The bus architecture includes a power bus, a control bus, and a status signal bus, in addition to a data bus, all of which are well known in the art and therefore will not be described in detail herein.

The network interface 130 may be connected to a network (e.g., the internet, a local area network, etc.), and may obtain relevant data from the network and store the relevant data in the hard disk 150.

The input device 140 may receive various commands input by the operator and send the commands to the processor 120 for execution. The input device 140 may include a keyboard or a pointing device (e.g., a mouse, a trackball, a touch pad, a touch screen, or the like).

The display device 160 may display the result obtained by the processor 120 executing the instructions.

The memory 310 is used for storing programs and data necessary for operating the operating system, and data such as intermediate results in the calculation process of the processor 120.

It will be appreciated that memory 110 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. The memory 110 of the apparatus and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

In some embodiments, memory 110 stores the following elements, executable modules or data structures, or a subset thereof, or an expanded set thereof: an operating system 111 and application programs 112.

The operating system 111 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application 112 includes various applications, such as a Browser (Browser), and the like, for implementing various application services. A program implementing methods of embodiments of the present invention may be included in application 112.

The processor 120, when invoking and executing the application program and data stored in the memory 110, specifically, the application program or the instructions stored in the application program 112, dispersedly sends one of the first set and the second set to the node distributed by the other one of the first set and the second set, where the other one is dispersedly stored in at least two nodes; and performing intersection processing in a node-by-node manner according to the node distribution of the first set and the node distribution of the second set.

The method disclosed by the above embodiment of the present invention can be applied to the processor 120, or implemented by the processor 120. The processor 120 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 120. The processor 120 may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof, and may implement or perform the methods, steps, and logic blocks disclosed in the embodiments of the present invention. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 110, and the processor 120 reads the information in the memory 110 and completes the steps of the method in combination with the hardware thereof.

It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.

For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.

In particular, the processor 120 is further configured to read the computer program and execute any of the methods described above.

In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be physically included alone, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute some steps of the transceiving method according to various embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. A cable routing method, comprising the steps of:

taking a transformer substation as a first vertex, taking a plurality of fans as second vertices respectively, taking at least two of the fans with different corresponding powers, taking a connecting line between the first vertex and the second vertex and a connecting line between the two second vertices as edges respectively, and acquiring the weight of each edge, wherein the first vertex is adjustable within a preset range so as to realize micro address selection;

acquiring a set of the first vertex, the second vertex, the edge and the weight of the edge;

constructing a spanning tree of the weight G according to the set;

finding G with the smallest total weight among the weights G_TMinimum spanning tree of G_TIs a subfigure of G;

constructing a single cable layout objective function of an optimization model according to the minimum spanning tree;

establishing constraint conditions of the optimization model;

introducing functions corresponding to various types of fans into the single cable layout objective function to obtain a final objective function of the optimization model;

and solving the optimization model by adopting a preset algorithm to obtain an optimized layout strategy of the cable.

2. The cable layout method of claim 1, wherein the pre-set algorithm comprises the steps of:

initializing a particle population according to a particle coding strategy;

according to a preset decoding method, acquiring a transformer substation address and a Thiessen polygon distance matrix to obtain a fitness value of each particle;

updating the position, the speed and the corresponding fitness value of the particle according to a self-adaptive particle swarm algorithm;

updating the current global optimal particle through a neighborhood search strategy;

and repeating the particle updating step until the stopping standard is reached, and outputting the optimal cable connection layout and the corresponding cable cost.

3. The cable layout method of claim 2, wherein the defining of the Thiessen polygon map comprises:

defining discrete points distributed on the Voronoi diagram as Voronoi sites;

station E₁And site E₂The corresponding Voronoi regions are respectively defined as VR (E)₁) And VR (E)₂)；

The station E₁And said station E₂A Voronoi distance between, defined as connecting said sites E₁And said E₂The number of Voronoi edges intersected by the path of (1);

the station E₁Is defined as the distance from the site E₁Is denoted as KVNS (E)₁,k)。

4. The cable layout method according to claim 2, wherein the particle coding strategy includes location information of the substation, connection information of the cable layout, and model information corresponding to each cable.

5. The cable arrangement method according to claim 4, wherein the decoding method comprises:

obtaining the position information of the transformer substation according to the front 2-dimensional position information of the particles;

combining the position information of the fan to obtain a Thiessen polygon distance matrix;

acquiring connection information based on cable layout under the current transformer substation by combining the 3 rd dimension of the particles and the position information behind the particles;

and selecting the cable model according to the connection information of the current cable layout.

6. The cable layout method of claim 2, wherein the neighborhood search strategy comprises:

executing a first neighborhood search strategy under the condition that rand () < 0.3;

executing a second neighborhood search strategy under the condition that rand () is more than or equal to 0.3 and less than 0.6;

executing a third neighborhood search strategy under the condition that the rand () is more than or equal to 0.6;

if none of the steps can be updated to the better global optimum particle, the steps are executed again until the number of searching times reaches T_LS。

7. The cable placement method of claim 6, wherein the first neighborhood search strategy comprises:

and randomly changing the position information of the transformer substation, keeping the connection information of the cable layout unchanged, and if the new cable connection layout is more optimal, performing an inverse coding strategy to obtain new particles to replace the original globally optimal particles.

8. The cable placement method of claim 6, wherein the second neighborhood search strategy comprises:

randomly selecting a certain edge (BT) of the set of edge information in the connection information of the cable lay-out₁，BT₂) Ordered in (BT)₁，BT₂) Forming a temporary point set by points corresponding to the front edge;

BT (ethylene terephthalate)₁Randomly changing to a certain point BT in temporary point set₃Constitute a new edge (BT)₃,BT₂) Other cable connection information is unchanged, and a new cable connection layout is formed;

and if the new cable connection layout meets the constraint condition and is better than the original cable connection layout, performing an inverse coding strategy to obtain a new particle to replace the original globally optimal particle.

9. The cable placement method of claim 6, wherein the third neighborhood search strategy comprises:

randomly changing a certain dimension representing cable model selection in the positions of the particles, keeping the connection information of the cable layout unchanged, and replacing the original globally optimal particles with the new particles if the new cable connection layout is more optimal.

10. An electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, the program or instructions when executed by the processor implementing the steps of the cable layout method of claims 1-9.

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