CN111463778A - Active power distribution network optimization reconstruction method based on improved suburb optimization algorithm - Google Patents

Active power distribution network optimization reconstruction method based on improved suburb optimization algorithm Download PDF

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CN111463778A
CN111463778A CN202010311393.3A CN202010311393A CN111463778A CN 111463778 A CN111463778 A CN 111463778A CN 202010311393 A CN202010311393 A CN 202010311393A CN 111463778 A CN111463778 A CN 111463778A
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suburb
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温东翰
徐敏
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention discloses an active power distribution network optimization reconstruction method based on an improved suburb optimization algorithm, which comprises the following steps of: and establishing a multi-objective optimization reconstruction model of the power distribution network containing the distributed power supply, which takes the minimum active network loss and the optimal voltage stability index as targets. The method has the advantages that the overall optimizing capability of the suburb algorithm is improved by introducing a hunting mechanism of the whale algorithm, and the method has practical value for improving the economical efficiency and the safety of the operation of the active power distribution network.

Description

Active power distribution network optimization reconstruction method based on improved suburb optimization algorithm
Technical Field
The invention relates to an active power distribution network optimization reconstruction method based on an improved suburb optimization algorithm, particularly considers the influence caused by the fact that distributed power sources such as wind power and photovoltaic power are connected into a power grid, and belongs to the technical field of active power distribution network reconstruction.
Background
An Active Distribution Network (ADN) is a Distribution network with distributed or decentralized energy sources inside and control and operation capabilities, and the Active management capability of the Distribution network is considered as a development direction of future Distribution networks. After a distributed power supply represented by clean energy is connected to a power distribution network, the network structure of the power distribution network changes, and more possibilities and uncertainties are brought for optimizing the network structure. The distribution network reconfiguration enables the distribution network to run more safely and reliably by changing the switching states of the section switch and the interconnection switch, and has important significance for reducing network loss and improving power supply quality.
The wolf optimizing algorithm (COA) is a meta-heuristic algorithm for global optimization proposed by Juliano et al in the 2018 IEEE evolutionary computing Consortium (CEC), is inspired by North American wolf population, randomly groups wolf individuals, and represents an iterative process of each group of solutions for birth, growth and death of each group of wolfs, and has few adjusting parameters and good global optimizing capability. However, in the original picnic algorithm, the picnic takes a group as a unit to explore a solution space, and although the diversity of the group is ensured, the local search capability of the algorithm is not strong to a certain extent, and the convergence accuracy is not high.
Disclosure of Invention
The invention aims to provide an active power distribution network optimization reconstruction method based on an improved suburb wolf optimization algorithm, which aims to reduce the network loss and improve the voltage stability, performs topology identification on a power distribution network added with DGs, eliminates a loop network structure and ensures the open-loop operation of the power distribution network. Aiming at the weak global optimizing capability of the original suburb optimization algorithm, a whale searching idea is introduced for improvement, population diversity is improved, and the optimal solution of a multi-objective function is calculated.
The purpose of the invention is realized by adopting the following technical scheme. An active power distribution network optimization reconstruction method based on an improved suburb optimization algorithm comprises the following steps:
step 1: performing network analysis on the active power distribution network;
step 2: according to the target function and the constraint condition, a fitness function is given, and an active power distribution network reconstruction mathematical model is established;
and step 3: and optimizing the optimal solution of the optimal reconstruction of the active power distribution network by adopting an improved suburb optimization algorithm, and outputting an optimal switch set and a fitness function value.
Further, the step 1 specifically comprises: and establishing a network topological structure, numbering nodes and branches, forming a node hierarchical matrix by adopting a breadth-first search method, wherein a switch number is the number of the branch where the switch is located, and adding load data at a specified node to realize grid connection of the distributed power supply.
Further, the active power distribution network reconstruction mathematical model established in the step 2 is as follows:
objective function 1: minimum active network loss:
Figure BDA0002457976020000011
wherein N islFor the number of branches of the distribution network, GkIs the conductance of branch k, UiAnd UjThe voltage amplitudes, θ, of the first and last nodes of the branchijThe voltage phase angle difference of the head node and the tail node is obtained;
the objective function 2: the voltage stability index of the power distribution network is minimum:
f2(L)＝max(Lij) (2)
wherein, LijIs defined as branch bijThe calculation formula of the voltage stability index of (2) is as follows:
Figure BDA0002457976020000021
wherein the load of the injection node j is Pj+jQjBranch bijHas an impedance of Rij+jXij，UiIs the voltage amplitude of node i;
a fitness function is obtained using a linear weighting method:
minf＝w1f1+w2f2(4)
wherein, w1And w2Is a weight coefficient, and the sum of the weight coefficient and the weight coefficient is 1;
simultaneously, the constraint conditions are met: (a) keeping power balance after the distributed power supply is connected; (b) the node voltage amplitude is in a reliable range; (c) and the open-loop operation of the power distribution network is ensured.
Further, the step 3 adopts an improved suburb optimization algorithm to optimize the optimal solution of the optimization and reconstruction of the active power distribution network, and comprises the following steps:
s1, setting the number N of groups, the maximum iteration number, the number Np of groups and the number Nc of the wolfs in each group, wherein N is Np × Nc;
s2, randomly initializing the wolf individual, wherein the formula is as follows:
Ci＝Cimin+(Cimax-Cimin)×rand (5)
wherein, CiIs the ith vector of the suburb wolf C, CimaxAnd CiminAn upper limit and a lower limit of i dimension, respectively, and rand is a random number between 0 and 1;
s3, calculating the fitness value of each feasible solution according to the formula (4), randomly grouping, and determining the optimum suburb wolf C of the populationbest；
S4, growth of a suburb wolf group:
s4.1, calculating group movement trend:
Tendm＝meidian(Nm，1) (6)
wherein N ismIs the mth group in the group N, is a matrix of Nc rows and D columns, and formula (6) represents taking the median of each column of the matrix;
s4.2 randomly selecting two suburbs C from the groupr1And Cr2The group grows under the combined action of the optimal suburb α, the group movement trend Tend and two random suburbs;
New_Ci＝Ci+r1×(α-Cr1)+r2×(Tend-Cr2) (7)
wherein r1 and r2 are random weight coefficients between [0,1 ];
s4.3, calculating the fitness of the grown suburb wolf, and if the fitness of the grown suburb wolf is higher, replacing the original suburb wolf, otherwise, keeping the fitness unchanged;
s5, the birth and death of the suburb wolf:
s5.1 the new suburb is determined by the parents and random variation, the parents are randomly selected from the group, and the new suburb inherits the suburbs of the parentsVariables, the remaining position variables are mapped by the probability PsDetermining mutation probability with the associated probability Pr;
in the D dimension variable of the new suburb wolf pup, two dimensions are randomly selected, wherein one dimension is from a father wolf, and the other dimension is from a mother wolf, and the formula is shown as follows:
a，b＝randperm(D，2) (8)
in the formula, a and b are two numbers randomly selected from integers [1 and D ];
pupa＝Cr3a(9)
pupb＝Cr4b(10)
wherein, Cr3aThe a-th dimension variable of the random father suburb wolf; cr4bIs the b-dimension variable of the random suburb wolf;
(D-2) variables of new suburb wolf are mapped by probability PsAnd the associated probability PrJointly determining:
Ps＝1/D (11)
Pr＝(1-Ps)/2 (12)
Figure BDA0002457976020000031
wherein rand is [0, 1]]Random number of (2), RiThe variation value is in the range of upper and lower lines;
s5.2, calculating the fitness value pup (fit) of the new suburb wolf, and replacing the suburb wolf with the new suburb wolf with the highest age, wherein the fitness value pup (fit) is compared with other suburbs in the group; the age of the suburb is represented by year and increases with the increase of the iteration number;
s6, introducing a whale hunting mechanism, taking the position where the optimal population of the wolf is as the position of the prey, and enabling the whole wolf population to be close to the prey;
s7, calculating the fitness value of the updated wolf population, and updating the best wolf population CbestAnd judging whether the maximum iteration number is reached, if so, ending the loop, otherwise, returning to the step S4 to continue the iteration.
Further, the step S6 is specifically:
D＝|M·Cbest-C(t)| (14)
M＝2·rand1(15)
N＝h×(2·rand2-1) (16)
d is a distance vector between the suburb individual and the optimal suburb, t is the current iteration number, and M and N are coefficient vectors; rand1And rand2Is [0, 1]]H is a convergence factor, and the linear decrement from 2 to 0 in the iteration process; the suburb wolf is in the enclosure taking the wolf as the core, and is close to the wolf in two modes of shrinkage enclosure and spiral motion to update the position of the suburb wolf, and the formula is as follows:
Figure BDA0002457976020000032
wherein P is [0, 1]]Random number between them, formula (18) being a spiral mathematical formula simulating whale swimming, Dp＝|Cbest-C(t)I, b is constant and represents the spiral radian of the curve, and lambda is [ -1,1 [ ]]Constant in between.
Further, in the improved suburb optimization algorithm, each dimension variable of the suburb individual represents one operation switch, each suburb represents one switch combination state, and the improved suburb optimization algorithm is to find an optimal operation switch set meeting constraint conditions.
Compared with the prior art, the invention has the beneficial effects that:
the method improves the original wolf algorithm by introducing a whale hunting mechanism so as to improve the global search capability of the algorithm, uses the improved wolf algorithm for the optimized reconstruction of the active power distribution network, has higher convergence speed and better optimal solution adaptability, and effectively solves the problem of the optimized reconstruction after the power distribution network is added with a distributed power supply.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of an improved suburb algorithm;
FIG. 3 is the distribution network after DG is added;
FIG. 4 is a graph of convergence curves solved using the original Turkish algorithm;
fig. 5 is a convergence graph solved with the improved suburb algorithm.
Detailed Description
The invention is further illustrated with reference to the figures and examples.
According to the active power distribution network optimization reconstruction method based on the improved suburb wolf optimization algorithm, after a wind driven generator, a fuel cell and a photovoltaic generator are added to a 69-node power distribution network, active output and reactive power of the nodes are changed, the structure of the power distribution network is changed from passive to active, and more uncertainty is brought to power distribution and supply. By using a whale hunting mechanism for reference, the suburb wolf population can swim around the wolf once, the search strength near the optimal solution is increased, and the network structure of the active power distribution network tends to be stable. The method comprises the following steps:
step 1: and carrying out network analysis on the active power distribution network.
And establishing a network topological structure, numbering nodes and branches, forming a node hierarchical matrix by adopting a breadth-first search method, wherein a switch number is the number of the branch where the switch is located, and adding load data at a specified node to realize grid connection of the distributed power supply.
Step 2: and giving a fitness function according to the target function and the constraint condition, and establishing an active power distribution network reconstruction mathematical model.
Constructing a fitness function:
objective function 1: minimum active network loss:
Figure BDA0002457976020000041
wherein N is1For the number of branches of the distribution network, GkIs the conductance of branch k, UiAnd UjThe voltage amplitudes, θ, of the first and last nodes of the branchijThe voltage phase angle difference of the head node and the tail node. The sum of the active loss of each branch is the active network loss.
The objective function 2: the voltage stability index of the power distribution network is minimum:
f2(L)＝max(Lij) (2)
wherein, LijIs defined as branch bijThe calculation formula of the voltage stability index of (2) is as follows:
Figure BDA0002457976020000042
wherein the load of the injection node j is Pj+jQjBranch bijHas an impedance of Rij+jXij，UiIs the voltage magnitude at node i.
A fitness function is obtained using a linear weighting method:
minf＝w1f1+w2f2(4) wherein, w1And w2Is a weight coefficient, and the sum of the weight coefficient and the weight coefficient is 1; preferably, w1，w2All are 0.5.
Simultaneously, the constraint conditions are met: (a) keeping power balance after the distributed power supply is connected; (b) the node voltage amplitude is in a reliable range; (c) and the open-loop operation of the power distribution network is ensured.
And step 3: and optimizing the optimal solution of the optimal reconstruction of the active power distribution network by adopting an improved suburb optimization algorithm, and outputting an optimal switch set and a fitness function value.
The flow of the improved suburb optimization algorithm is specifically described with reference to fig. 1:
s1, setting the number N of the groups, the maximum iteration number, the number Np of the groups and the number Nc of the wolfs in each group, wherein N is Np × Nc;
s2: randomly initializing the suburb individual, wherein the formula is as follows:
Ci＝Cimin+(Cimax-Cimin)×rand (5)
wherein, CiIs the ith vector of the suburb wolf C, CimaxAnd CiminI-dimensional upper and lower limits, respectively, and rand is a random number between 0 and 1.
S3: calculating the fitness value of each feasible solution according to the formula (4), randomly grouping, and determining the optimum suburb wolf C of the populationbest。
S4: the growth of a suburb group;
calculating a group movement trend;
Tendm＝meidian(Nm,1) (6)
wherein N ismIs the mth group in the group N, and is a matrix of Nc rows and D columns, and equation (6) represents taking the median per column of the matrix.
Randomly selecting two suburbs C from the groupr1And Cr2The group grows under the combined action of the optimal suburb α, the group movement trend Tend and two random suburbs;
New_Ci＝Ci+r1×(α-Cr1)+r2×(Tend-Cr2) (7)
wherein r is1And r2Is [0, 1]]Random weight coefficients in between.
And calculating the fitness of the developed suburb wolf, and replacing the original suburb wolf if the fitness of the developed suburb wolf is higher, otherwise, keeping the fitness unchanged.
S5: the life and death of suburbs;
the new-born suburb is determined by the combination of the parent suburb and random variation, the parent suburb is randomly selected from the group, the new-born suburb inherits the variable of the parent suburb, and the rest position variables are determined by the mapping probability PsAnd the associated probability PrThe probability of variation is determined.
In the D dimension variable of the new suburb wolf pup, two dimensions are randomly selected, wherein one dimension is from a father wolf, and the other dimension is from a mother wolf, and the formula is shown as follows:
a,b＝randperm(D,2) (8)
in the formula, a and b are two numbers randomly selected from integers [1 and D ].
pupa＝Cr3a(9)
pupb＝Cr4b(10)
Wherein, Cr3aThe a-th dimension variable of the random father suburb wolf; cr4bIs the b-dimension variable of the random suburb wolf.
(D-2) variables of new suburb wolf are mapped by probability PsAnd the associated probability PrJointly determining:
Ps＝1/D (11)
Pr＝(1-Ps)/2 (12)
Figure BDA0002457976020000051
wherein rand is [0, 1]]Random number of (2), RiThe variation values in the upper and lower line ranges.
Calculating the fitness value pup (fit) of the new suburb, and replacing the suburb with the new suburb with the highest group fitness capability (the suburb age is represented by year and increases with the increase of the iteration number) compared with other suburbs in the group.
S6: introducing a whale hunting mechanism, taking the position of the optimal population of the suburb as the position of a prey, and enabling the whole suburb population to approach the prey; the specific operation steps are as follows:
D＝|M·Cbest-C(t)| (14)
M＝2·rand1(15)
N＝h×(2·rand2-1) (16)
wherein D is a distance vector between the suburb individual and the optimal suburb, t is the current iteration number, and M and N are coefficient vectors. rand1And rand2Is [0, 1]]H is a convergence factor, the linear decrease from 2 to 0 in the iteration process, and h is 2-2t/tmax. The suburb wolf is in the enclosure taking the wolf as the core, and is close to the wolf in two modes of shrinkage enclosure and spiral motion to update the position of the suburb wolf, and the formula is as follows:
Figure BDA0002457976020000061
p is [0, 1]]Random number between them, formula (18) being a spiral mathematical formula simulating whale swimming, Dp＝|Cbest-C(t)I, b is constant and represents the spiral radian of the curve, and lambda is [ -1,1 [ ]]Constant in between.
S7: calculating the fitness value of the updated wolf population, and updating the best population wolf CbestAnd judging whether the maximum iteration number is reached, if so, ending the loop, otherwise, returning to the step S4 to continue the iteration.
In the improved suburb optimization algorithm, each dimension variable of a suburb individual represents an operation switch, each suburb represents a switch combination state, and the improved suburb optimization algorithm is to find an optimal operation switch set meeting constraint conditions.
Example (b): in order to verify the effectiveness of the improved suburb wolf algorithm, a photovoltaic generator, a fuel cell and a wind driven generator are respectively added to a power distribution network of a PG & E69 node in the United states as distributed power sources, and the power distribution network after DG addition is shown in FIG. 2, wherein numbers 1 to 68 are sectional switches, and the initial state is closed; 69 to 73 are communication branches, the initial state is open. Information such as DG access position, capacity and the like is shown in a table 1;
TABLE 1 Power Properties and capacities of respective DGs
DG numbering DG power generation technology Rated capacity Capacity of Power factor
1 Photovoltaic power generation 400kW 300kW 0.9
2 Wind power generator 400kW 300kW 0.9
3 Fuel cell 400kW 300kW 0.9
Identifying a network topological structure: the 16 node is connected with a photovoltaic generator, the 38 node is connected with a traditional constant-speed asynchronous wind driven generator, and the 55 node is connected with a fuel cell. In view of the fact that a 69-node network is complex in topological structure and unsuitable to use a matrix method, the invention firstly uses a breadth-first search method to form a node hierarchical matrix, a power distribution network is of a radial structure and runs in an open loop mode, and therefore solutions for forming an island and a loop are removed.
The optimization step of the improved suburb optimization algorithm comprises the following steps:
s1: setting algorithm parameters: the number of the groups N is 100p10, the number of the suburbs N in each group c10, maximum iteration number maximum 50, w1＝w2＝0.5；
S2: randomly initializing the suburb individuals;
s3: calculating the suitability value of the suburb wolf and determining the optimum suburb wolf C of the populationbest；
S4, calculating a group movement trend Tend, wherein the suburb individual grows under the combined action of the group movement trend, the randomly selected suburb and the optimal suburb α in the group, calculating a grown suburb fitness value, replacing the suburb fitness value if the suburb fitness value is better than the original fitness value, and otherwise, keeping the suburb fitness value unchanged;
s5: updating the new suburb pup, calculating the fitness value of the new suburb, and replacing the highest age of the suburb individuals with poorer fitness by comparing the fitness value with the group of suburbs;
s6: introducing a whale hunting mechanism, and improving a suburb algorithm:
D＝|M·Cbest-C(t)| (14)
M＝2·rand1(15)
N＝h×(2·rand2-1) (16)
wherein D is a distance vector between the suburb individual and the optimal suburb, t is the current iteration number, and M and N are coefficient vectors. rand1And rand2Is [0, 1]]H is a convergence factor, h is 2-2t/tmax. The suburb wolf is in the enclosure with the head wolf as the core, and is close to the head wolf in two modes of shrinkage enclosure and spiral motion to update the position of the suburb wolf, and the formula is as follows
Figure BDA0002457976020000071
P is [0, 1]]Random number between them, formula (18) being a spiral mathematical formula simulating whale swimming, Dp＝|Cbest-C(t)I, b is 3, lambda is [ -1, 1]]Constant in between.
S7: calculating the fitness value of the updated wolf population, and updating the best population wolf CbestAnd judging whether the maximum iteration number is reached, if so, ending the loop, otherwise, returning to the step S4 to continue the iteration.
The above steps are programmed with matlab2017a as a software platform. In an active power distribution network containing DGs, the optimization of the network structure is realized by changing a section switch and a tie switch. Fig. 3 is a convergence curve graph solved by an original suburb algorithm, and fig. 4 is a convergence curve graph solved by an improved suburb algorithm, and the result shows that the improved suburb algorithm is used for searching an optimal operation switch set, the convergence time is short, and the optimization result is in line with the expectation.
The results of the calculations are shown in Table 2
TABLE 2 simulation calculation results
Operating switch Switch operation number Fitness value Index of voltage stability Active network loss (kw)
Original suburb wolf 10, 15, 68, 50, 45 5 6.7478 0.7221 89.6925
Improved suburb 9, 70, 14, 50, 73 5 5.6509 0.7128 84.2493
The 69-node optimization scheme obtained by the improved suburb algorithm is to disconnect the segmented branches 9, 14 and 50 and close the connection branches 70 and 73, and the fitness after optimization is 5.6509 and is lower than 6.7478 of the original scheme. The result shows that the improved suburb wolf optimization algorithm improves the local optimization capability, obviously reduces the required iteration times, effectively reduces the network loss and improves the voltage stability of the power distribution network.
The foregoing merely represents preferred embodiments of the invention, which are described in some detail and detail, and therefore should not be construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, various changes, modifications and substitutions can be made without departing from the spirit of the present invention, and these are all within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (6)

1. An active power distribution network optimization reconstruction method based on an improved suburb optimization algorithm is characterized by comprising the following steps:
step 1: performing network analysis on the active power distribution network;
step 2: according to the target function and the constraint condition, a fitness function is given, and an active power distribution network reconstruction mathematical model is established;
and step 3: and optimizing the optimal solution of the optimal reconstruction of the active power distribution network by adopting an improved suburb optimization algorithm, and outputting an optimal switch set and a fitness function value.
2. The method for optimizing and reconstructing an active power distribution network based on the improved suburb optimization algorithm according to claim 1, wherein the step 1 specifically comprises: and establishing a network topological structure, numbering nodes and branches, forming a node hierarchical matrix by adopting a breadth-first search method, wherein a switch number is the number of the branch where the switch is located, and adding load data at a specified node to realize grid connection of the distributed power supply.
3. The method for optimizing and reconstructing the active power distribution network based on the improved suburb optimization algorithm according to claim 2, wherein the reconstructed mathematical model of the active power distribution network established in the step 2 is as follows:
objective function 1: minimum active network loss:
Figure FDA0002457976010000011
wherein N is1For the number of branches of the distribution network, GkIs the conductance of branch k, UiAnd UjThe voltage amplitudes, θ, of the first and last nodes of the branchijThe voltage phase angle difference of the head node and the tail node is obtained;
the objective function 2: the voltage stability index of the power distribution network is minimum:
f2(L)＝max(Lij) (2)
wherein, LijIs defined as branch bijThe calculation formula of the voltage stability index of (2) is as follows:
Figure FDA0002457976010000012
wherein the load of the injection node j is Pj+jQjBranch bijHas an impedance of Rij+jXij，UiIs the voltage amplitude of node i;
a fitness function is obtained using a linear weighting method:
min f＝w1f1+w2f2(4)
wherein, w1And w2Is a weight coefficient, and the sum of the weight coefficient and the weight coefficient is 1;
simultaneously, the constraint conditions are met: (a) keeping power balance after the distributed power supply is connected; (b) the node voltage amplitude is in a reliable range; (c) and the open-loop operation of the power distribution network is ensured.
4. The method for optimizing and reconstructing the active power distribution network based on the improved suburb optimization algorithm as claimed in claim 3, wherein the step 3 is to optimize the optimal solution of the optimized and reconstructed active power distribution network by using the improved suburb optimization algorithm, and comprises the following steps:
s1, setting the number N of groups, the maximum iteration number, the number Np of groups and the number Nc of the wolfs in each group, wherein N is Np × Nc;
s2, randomly initializing the wolf individual, wherein the formula is as follows:
Ci＝Cimin+(Cimax-Cimin)×rand (5)
wherein, CiIs the ith vector of the suburb wolf C, CimaxAnd CiminAn upper limit and a lower limit of i dimension, respectively, and rand is a random number between 0 and 1;
s3, calculating the fitness value of each feasible solution according to the formula (4), randomly grouping, and determining the optimum suburb wolf C of the populationbest；
S4, growth of a suburb wolf group:
s4.1, calculating group movement trend:
Tendm＝meidian(Nm,1) (6)
wherein N ismIs the mth group in the group N, is a matrix of Nc rows and D columns, and formula (6) represents taking the median of each column of the matrix;
s4.2 randomly selecting two suburbs C from the groupr1And Cr2The group grows under the combined action of the optimal suburb α, the group movement trend Tend and two random suburbs;
New_Ci＝Ci+r1×(α-Cr1)+r2×(Tend-Cr2) (7)
wherein r is1And r2Is [0, 1]]Random weight coefficients in between;
s4.3, calculating the fitness of the grown suburb wolf, and if the fitness of the grown suburb wolf is higher, replacing the original suburb wolf, otherwise, keeping the fitness unchanged;
s5, the birth and death of the suburb wolf:
s5.1 the new suburb is determined by the parents suburbs and random variation, the parents suburbs are randomly selected from the group, the new suburb inherits the variable of the parents suburbs, and the other position variables are determined by the mapping probability PsAnd the associated probability PrDetermining the variation probability;
in the D dimension variable of the new suburb wolf pup, two dimensions are randomly selected, wherein one dimension is from a father wolf, and the other dimension is from a mother wolf, and the formula is shown as follows:
a,b＝randperm(D,2) (8)
in the formula, a and b are two numbers randomly selected from integers [1 and D ];
pupa＝Cr3a(9)
pupb＝Cr4b(10)
wherein, Cr3aThe a-th dimension variable of the random father suburb wolf; cr4bIs the b-dimension variable of the random suburb wolf;
(D-2) variables of new suburb wolf are mapped by probability PsAnd the associated probability PrJointly determining:
Ps＝1/D (11)
Pr＝(1-Ps)/2 (12)
Figure FDA0002457976010000021
wherein rand is [0, 1]]Random number of (2), RiThe variation value is in the range of upper and lower lines;
s5.2, calculating the fitness value pup (fit) of the new suburb wolf, and replacing the suburb wolf with the new suburb wolf with the highest age, wherein the fitness value pup (fit) is compared with other suburbs in the group; the age of the suburb is represented by year and increases with the increase of the iteration number;
s6, introducing a whale hunting mechanism, taking the position where the optimal population of the wolf is as the position of the prey, and enabling the whole wolf population to be close to the prey;
s7, calculating the fitness value of the updated wolf population, and updating the best wolf population CbestAnd judging whether the maximum iteration number is reached, if so, ending the loop, otherwise, returning to the step S4 to continue the iteration.
5. The active power distribution network optimization reconstruction method based on the improved suburb optimization algorithm as claimed in claim 4, wherein the step S6 is specifically:
D＝|M·Cbest-C(t)| (14)
M＝2·rand1(15)
N＝h×(2·rand2-1) (16)
wherein D is suburbThe distance vector between the wolf individual and the optimal wolf, t is the current iteration number, and M and N are coefficient vectors; rand1And rand2Is [0, 1]]H is a convergence factor, and the linear decrement from 2 to 0 in the iteration process; the suburb wolf is in the enclosure taking the wolf as the core, and is close to the wolf in two modes of shrinkage enclosure and spiral motion to update the position of the suburb wolf, and the formula is as follows:
Figure FDA0002457976010000031
wherein P is [0, 1]]Random number between them, formula (18) being a spiral mathematical formula simulating whale swimming, Dp＝|Cbest-C(t)I, b is constant and represents the spiral radian of the curve, and lambda is [ -1,1 [ ]]Constant in between.
6. The method for optimizing and reconstructing the active power distribution network based on the improved suburb wolf optimization algorithm according to claim 4 or 5, wherein in the improved suburb wolf optimization algorithm, each dimension variable of a suburb wolf individual represents an operating switch, each suburb wolf represents a switch combination state, and the improved suburb optimization algorithm is to find an optimal operating switch set meeting constraint conditions.
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