CN110175413A - Reconstruction method of power distribution network and device based on R2 index multi-objective particle swarm algorithm - Google Patents
Reconstruction method of power distribution network and device based on R2 index multi-objective particle swarm algorithm Download PDFInfo
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Abstract
The present invention relates to a kind of reconstruction method of power distribution network and device based on R2 index multi-objective particle swarm algorithm, the described method comprises the following steps: S1, the initial data for obtaining power distribution network carry out particle group coding according to network structure；S2, optimal reconstructed network topology is obtained using particle swarm algorithm iterative solution；In the iterative process, population is updated based on R2 index, specifically, by elite population and updates swarm of particles as candidate disaggregation RR, using equally distributed weight vector Λ and ideal point z*Candidate disaggregation RR is trimmed, is sorted from large to small by R2 contribution margin of the candidate solution to the weight vector Λ, sets the population that number candidate solution forms next iteration in the past.Compared with prior art, the present invention has many advantages, such as that convergence is high, diversity is good.
Description
Technical field
The present invention relates to power distribution network reconfiguration optimisation technique fields, are based on R2 index multi-objective particle swarm more particularly, to one kind
The reconstruction method of power distribution network and device of algorithm.
Background technique
Distribution system is responsible for providing the pith of energy to ultimate consumer, produces a large amount of function in this stage
Rate loss and extra cost, power distribution network reconfiguration refer in the premise for meeting the basic demands such as system voltage, electric current, capacity of trunk
Under, optimize power distribution network operating structure by changing the folding condition of distribution web switch, to reach balanced load, improve section
One or more targets such as point variation, elimination overload, reduction network active power loss are optimal, are a multiple constraints, more
Target, nonlinear combinatorial optimization relationship.
Power distribution network reconfiguration is a complicated large-scale non-designated polynomial combinatorial optimization problem, at present for power distribution network
The research method of reconstruct can be roughly divided into two types: one is traditional mathematics optimization algorithms, i.e., by power distribution network reconfiguration problem number
Model is learned to describe, obtains the optimum results for not depending on network initial configuration, but power distribution network reconfiguration problem is to belong to extensive net
The Combinatorial Optimization of network, occupancy calculator memory is big, and operation difficulty is high；But another kind is intelligent algorithm, wherein population
Optimization algorithm is the Swarm Intelligence Algorithm after genetic algorithm, ant group algorithm, simulates migrating during flock of birds is looked for food and group
Collection behavior, in an iterative process, particle are unanimously constantly flown to optimal direction, if encountering local optimum, particle rapidity meeting
It is reduced to zero quickly, particle stagnates and Premature convergence occurs, and is difficult to jump out Local Extremum.Solving, higher-dimension is more
There are many algorithms to be suggested on objective optimisation problems, a kind of algorithm based on evaluation index uses evaluation index alternatively standard
Measure the quality of solution, selection mechanism by comparing solution the better solution of quality selection, this kind of algorithm represent include IBEA and
HypE etc., although above-mentioned algorithm can handle some higher-dimension multi-objective optimization questions very well, before these algorithms are for pareto
The shape on edge is very sensitive, leads to that convergence and diversity cannot be balanced well.
Summary of the invention
It is more based on R2 index that it is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind
The power distribution network reconfiguration of intended particle group's algorithm optimizes forwarding method.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of reconstruction method of power distribution network based on R2 index multi-objective particle swarm algorithm, comprising the following steps:
S1, the initial data for obtaining power distribution network carry out particle group coding according to network structure；
S2, optimal reconstructed network topology is obtained using particle swarm algorithm iterative solution；
In the iterative process, population is updated based on R2 index, specifically,
By elite population and swarm of particles is updated as candidate disaggregation RR, using equally distributed weight vector Λ and reason
Think point z*Candidate disaggregation RR is trimmed, is sorted from large to small by R2 contribution margin of the candidate solution to the weight vector Λ, with
Preceding setting number candidate solution forms the population of next iteration.
Calculating process of the candidate solution to the R2 contribution margin of the weight vector Λ are as follows:
Initialize the R2 contribution margin C of candidate solutiona=0；
Candidate solution is calculated to the TCH value of utility of weight vector
In formula, λj>=0, andX indicates candidate solution；
Candidate solution is calculated to the R2 contribution margin of this group of weight vector Λ:
In the iterative process, if continuously the optimal pBest of setting algebra individual is constant, Gauss learning strategy pair is used
The speed and location information of particle are reset, and formula is as follows:
In formula, the mean value of μ Gauss number, σ2For the variance of Gauss number,Indicate particle i at the t+1 times repeatedly
For when jth dimension position, xgb(j) the global optimum position that jth is tieed up in i-th iteration, x are indicatedPb, i(j) indicate that particle i exists
The personal best particle that jth is tieed up when the t times iteration.
It is described using particle swarm algorithm iterative solution when, every time obtain population when to the corresponding network of each particle into
Row topology checks judge whether topology is qualified, if so, continuing to solve, if it is not, then reconstituting population.
The topology is carried out to the corresponding network of each particle using depth-first search to check.
It is described topology check specifically includes the following steps:
101) the corresponding distribution net work structure of particle is obtained, node incidence matrix is generated；
102) circuitry number n and effective number of nodes N is obtained based on the node incidence matrix, judges whether there is n=N-1,
If so, thening follow the steps 103), if it is not, then judging that topology is unqualified；
103) distribution net work structure is judged with the presence or absence of isolated island, if so, determining that topology is unqualified, if it is not, then distribution
Net is radial, judgement topology qualification.
The judgement distribution net work structure whether there is isolated island specifically:
The node at a branch both ends is obtained according to node incidence matrix；
A certain branch is disconnected, using the node of branch one end as starting point, another end node is terminal, utilizes the side of deep search
Method judges whether that another end node can be searched out from an end node, if so, assert that two nodes are connection；
All nodes are traversed, judge the whether equal connection of all nodes, if so, isolated island is not present, if it is not, then there is orphan
Island.
A kind of power distribution network reconfiguration device based on R2 index multi-objective particle swarm algorithm, including memory and processor, institute
It states memory and is stored with computer program, processor calls the computer program to execute such as the step of the above method.
Compared with prior art, the present invention have with following the utility model has the advantages that
(1) present invention carries out power distribution network reconfiguration using based on R2 index multi-objective particle swarm algorithm, and R2 index passes through building
Utility function appropriate carrys out the superiority and inferiority of the numerous and confused candidate solution in quantification area, selects the biggish candidate solution of effectiveness, R2 index can be with synthetic weights
It weighs and integrates the convergence and diversity of candidate solution, and can be with the precision and uncertainty of balanced algorithm, and calculation amount is few, operation
Speed is fast.
(2) present invention can be improved restructuring procedure to the selective power of candidate solution, obtains convergence and diversity is all preferable
Candidate solution, assist population to jump out the local forward position Pareto using Gauss learning strategy, solve to occur in power distribution network reconfiguration
The technical issues of a large amount of infeasible solutions.
(3) present invention assists population to jump out local optimum forward position using Gauss learning strategy, particle to individual it is optimal and
Group's optimal direction is easily trapped into local optimum and Premature Convergence during flying, and will sink into part most using this learning strategy
The population in excellent forward position carries out speed and location information resetting.
(4) present invention has comprehensively considered population using the comprehensive particle swarm algorithm for using R2 index and Gauss learning strategy
The convergence and diversity of individual, ensure that the convergence and diversity of candidate solution entirety, the calculation amount of algorithm is few, effectively improves
The speed and precision of optimizing.
(5) present invention can reduce the via net loss of distribution system by power distribution network reconfiguration optimization method, be effectively improved electricity
The benefit of net improves the quality of power supply, the comprehensive safety and economic benefit for improving power distribution network operation.
Detailed description of the invention
Fig. 1 is algorithm flow chart of the invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
Embodiment 1
The present embodiment provides a kind of reconstruction method of power distribution network based on R2 index multi-objective particle swarm algorithm, and this method is first
The initial data for obtaining power distribution network carries out particle group coding according to network structure；Then it is obtained using particle swarm algorithm iterative solution
Optimal reconstructed network topology is obtained, in the iterative process, population is updated based on R2 index, specifically,
By elite population and swarm of particles is updated as candidate disaggregation RR, using equally distributed weight vector Λ and reason
Think point z*Candidate disaggregation RR is trimmed, is sorted from large to small by R2 contribution margin of the candidate solution to the weight vector Λ, with
Preceding setting number candidate solution forms the population of next iteration.
As shown in Figure 1, above-mentioned reconstruction method of power distribution network specifically includes the following steps:
S1: initialization population encodes population according to network structure.
S2: carrying out topological inspection to the corresponding network of each particle, whether qualified judge topology, if so, continue to solve,
Step S3 is executed, if it is not, then return step S1, reconstitutes population.
The topology is carried out to the corresponding network of each particle using depth-first search to check, specifically:
101) the corresponding distribution net work structure of particle is obtained, node incidence matrix is generated, the line number of node incidence matrix represents
Number of nodes, matrix column number represent circuitry number, and incidence matrix is Sparse Array, only 0 and 1, and 0 represents node is not connected with branch, and 1
Node is represented to be connected with branch；
102) circuitry number n and effective number of nodes N is obtained based on the node incidence matrix, judges whether there is n=N-1,
If so, thening follow the steps 103), if it is not, then judging that topology is unqualified；
103) distribution net work structure is judged with the presence or absence of isolated island, if so, determining that topology is unqualified, if it is not, then distribution
Net is radial, judgement topology qualification.
The judgement distribution net work structure whether there is isolated island specifically:
The node at a branch both ends is obtained according to node incidence matrix；
A certain branch is disconnected, using the node of branch one end as starting point, another end node is terminal, utilizes the side of deep search
Method judges whether that another end node can be searched out from an end node, if so, assert that two nodes are connection；
Traverse all nodes, judge the whether equal connection of all nodes, if so, be not present isolated island, power distribution network be it is radial,
If one of node can not find corresponding terminal node, there are isolated nodes for power distribution network, do not meet radial judgement
It is required that.
S3: population updates operation.
According to the position and speed of formula (1) and (2) more new particle:
It is crossed the border problem according to formula (3) and (4) amendment particle；
In formula:WithIndicate the particle i position and speed that jth is tieed up in the t+1 times iteration, xub(j) and xlb
(j) upper limit value and lower limit value, coefficient gamma that be respectively particle tie up in jth are that value is 1.
S4: carrying out topological inspection to the corresponding network of each particle, whether qualified judge topology, if so, continue to solve,
Step S5 is executed, if it is not, then return step S3, reconstitutes population.
S5: the objective function f (x) of each particle is calculated；Set each particle current location as the optimal pBest of body, at random
Selecting a gBest is global optimum；Wherein the objective function f (x) of each particle can be indicated are as follows:
Minf (x)=(f1(x), f2(x) ..., fk(x)) (5)
Constraint condition are as follows:
s.t.gi(x)=0 i=1,2 ..., p
hi(x)≤0 i=1,2 ..., p
x∈X∈Rn
In formula: fiIt (x) is k-th of objective function；gi(x)、hiIt (x) is respectively equality constraint and inequality constraints.
In above formula, minf (x) is Pareto optimal solution, it is assumed that there are two decision variable x1And x2If meeting following two
When a condition, claim solution x1Dominate solution x2:
1) forAll meet fi(x1)≤fi(x2)；
2) at least there is 1 i ∈ { 1,2 ..., k } and make fi(x1) < fi(x2)。
Meet the x of above-mentioned condition1It is a Parato optimal solution of multiple-objection optimization.
There are three targets for setting in the present embodiment, then objective function indicates are as follows:
Minf (x)=(f1(x), f2(x), f3(x)) (6)
f3(x)=minf3=min | Ui| (9)
Objective function f1(x) in, L is system branch sum；Ri、Ui、Pi、QiThe resistance of respectively i-th branch, end
Voltage, active power, reactive power, KiIt indicates to disconnect for the state of branch upper switch, 0,1 indicates closure；Objective function f2(x)
In, YiAnd ZjThe state of block switch and interconnection switch after reconstitution is respectively indicated, being closed is 1, is broken as 0, m, n are respectively to match
Block switch and interconnection switch number in power grid；Objective function f3(x) constraint condition includes: that node voltage is constrained to UI, min≤Ui
≤UI, max, UI, min、UI, maxThe respectively upper and lower limit voltage of node i；Tributary capacity is constrained to Si≤SI, max, Si、SI, maxRespectively
It is the capacity of trunk of power and branch that each branch flows through.
S6: evaluation particle simultaneously updates reference point z*；
S7: pBest optimal to individual is updated.
S8: population is trimmed based on R2 index.
Elite population is obtained from current particle group, by elite population and updates swarm of particles as candidate disaggregation
RR, using equally distributed weight vector Λ and ideal point z*Candidate disaggregation RR is trimmed, by candidate solution to the weight
The R2 contribution margin of vector Λ sorts from large to small, and sets the population that number candidate solution forms next iteration in the past.Candidate solution pair
The calculating process of the R2 contribution margin of the weight vector Λ are as follows:
Initialize the R2 contribution margin C of candidate solutiona=0；
Candidate solution is calculated to the TCH value of utility of weight vector
In formula, λj>=0, andX indicates candidate solution；
Candidate solution is calculated to the R2 contribution margin of this group of weight vector Λ:
S9: it is constant to judge whether continuously to set the optimal pBest of algebra individual, if so, learning (GLS) strategy using Gauss
The speed and location information of particle are reset, assist population to jump out local optimum forward position, formula is as follows:
In formula, the mean value of μ Gauss number, σ2For the variance of Gauss number,Indicate particle i at the t+1 times repeatedly
For when jth dimension position, xgb(j) the global optimum position that jth is tieed up in the t times iteration, x are indicatedPb, i(j) indicate that particle i exists
The personal best particle that jth is tieed up when the t times iteration.
S10: judging whether to reach group's maximum number of iterations, if so, output reconstruction and optimization structure；Otherwise step is jumped to
S3。
Embodiment 2
The present embodiment provides a kind of power distribution network reconfiguration devices based on R2 index multi-objective particle swarm algorithm, including memory
And processor, the memory are stored with computer program, it is as described in Example 1 that processor calls the computer program to execute
The step of method.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be within the scope of protection determined by the claims.
Claims (8)
1. a kind of reconstruction method of power distribution network based on R2 index multi-objective particle swarm algorithm, which comprises the following steps:
S1, the initial data for obtaining power distribution network carry out particle group coding according to network structure；
S2, optimal reconstructed network topology is obtained using particle swarm algorithm iterative solution；
In the iterative process, population is updated based on R2 index, specifically,
By elite population and swarm of particles is updated as candidate disaggregation RR, using equally distributed weight vector Λ and ideal point
z*Candidate disaggregation RR is trimmed, sorts from large to small by R2 contribution margin of the candidate solution to the weight vector Λ, set in the past
Determine the population that number candidate solution forms next iteration.
2. the reconstruction method of power distribution network according to claim 1 based on R2 index multi-objective particle swarm algorithm, feature exist
In calculating process of the candidate solution to the R2 contribution margin of the weight vector Λ are as follows:
Initialize the R2 contribution margin C of candidate solutiona=0；
Candidate solution is calculated to the TCH value of utility of weight vector
In formula, λj>=0, andX indicates candidate solution；
Candidate solution is calculated to the R2 contribution margin of this group of weight vector Λ:
3. the reconstruction method of power distribution network according to claim 1 based on R2 index multi-objective particle swarm algorithm, feature exist
In in the iterative process, if continuously the optimal pBest of setting algebra individual is constant, using Gauss learning strategy to particle
Speed and location information reset, formula is as follows:
In formula, the mean value of μ Gauss number, σ 2 is the variance of Gauss number,Indicate particle i in the t+1 times iteration
The position of jth dimension, xgb(j) the global optimum position that jth is tieed up in i-th iteration, x are indicatedPb, i(j) indicate particle i at the t times
The personal best particle that jth is tieed up when iteration.
4. the reconstruction method of power distribution network according to claim 1 based on R2 index multi-objective particle swarm algorithm, feature exist
In, it is described using particle swarm algorithm iterative solution when, every time obtain population when the corresponding network of each particle is opened up
Inspection is flutterred, judges whether topology is qualified, if so, continuing to solve, if it is not, then reconstituting population.
5. the reconstruction method of power distribution network according to claim 4 based on R2 index multi-objective particle swarm algorithm, feature exist
In using depth-first search to each particle corresponding network progress topology inspection.
6. the reconstruction method of power distribution network according to claim 5 based on R2 index multi-objective particle swarm algorithm, feature exist
In, it is described topology check specifically includes the following steps:
101) the corresponding distribution net work structure of particle is obtained, node incidence matrix is generated；
102) circuitry number n and effective number of nodes N is obtained based on the node incidence matrix, judges whether there is n=N-1, if so,
It thens follow the steps 103), if it is not, then judging that topology is unqualified；
103) distribution net work structure is judged with the presence or absence of isolated island, if so, determining that topology is unqualified, if it is not, then power distribution network is
It is radial, determine that topology is qualified.
7. the reconstruction method of power distribution network according to claim 6 based on R2 index multi-objective particle swarm algorithm, feature exist
In the judgement distribution net work structure whether there is isolated island specifically:
The node at a branch both ends is obtained according to node incidence matrix；
A certain branch is disconnected, using the node of branch one end as starting point, another end node is terminal, using the method for deep search,
Judge whether that another end node can be searched out from an end node, if so, assert that two nodes are connection；
All nodes are traversed, judge the whether equal connection of all nodes, if so, isolated island is not present, if it is not, then there is isolated island.
8. a kind of power distribution network reconfiguration device based on R2 index multi-objective particle swarm algorithm, which is characterized in that including memory and
Processor, the memory are stored with computer program, and processor calls the computer program to execute such as claim 1~7
The step of any the method.
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