CN108832615A - A kind of reconstruction method of power distribution network and system based on improvement binary particle swarm algorithm - Google Patents
A kind of reconstruction method of power distribution network and system based on improvement binary particle swarm algorithm Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
Composition of Switching State of Distribution Network data are acquired based on the reconstruction method of power distribution network and system of improving binary particle swarm algorithm the present invention relates to a kind of, and generate the topological structure of the corresponding power distribution network of switch state data；The topological structure of power distribution network is set as particle, calculating is iterated using the binary system particle algorithm based on step-by-step system, obtains the position and speed of more new particle；According to the position and speed of more new particle, the topological structure of the power distribution network is reconstructed.The present invention improves the particle swarm optimization algorithm of existing binary form, to guarantee the radial structure of power distribution network, reduces optimizing searching times, enhances global optimizing ability.
Description
Technical field
The present invention relates to power distribution network reconfiguration technical fields, and in particular to a kind of matching based on improvement binary particle swarm algorithm
Reconfiguration of electric networks method and system.
Background technique
Being currently applied to some intelligent algorithms of power distribution network reconfiguration, to remain convergence rate slow and fall into office
The problem of portion's inferior solution, and to the processing method of infeasible solution also without good efficiency.Existing improvement particle swarm algorithm is past
Toward population is divided into multiple groups while being scanned for, every group of particle re-starts grouping after carrying out iteration several times again, particle
Iterative manner increases the utilization to each group's optimal particle information in normal binary population, and Shi Ge group just utilizes not
Same iterative formula is evolved, and selection crossover operation can be carried out between particle, ensure that interparticle otherness.Firstly, existing
Method is to avoid the sequence of operations of algorithm precocity progress partially complicated, also will cause efficiency of algorithm reduction；Secondly, existing method is simultaneously
Do not consider that the case where being unsatisfactory for radial constraint occurs in particle in iterative process, per generation in iterative process can so occurred greatly
Infeasible particle is measured, delays the time for optimal solution occur significantly, so that iteration efficiency substantially reduces.
Summary of the invention
Consider that the case where being unsatisfactory for radial constraint occurs in particle in iterative process in the prior art for solution is above-mentioned,
Per generation in iterative process can be made a large amount of infeasible particles occur in this way, delay the time for optimal solution occur significantly, so that iteration
The problem of efficiency substantially reduces, the purpose of the present invention is to provide a kind of based on the power distribution network weight for improving binary particle swarm algorithm
Structure method and system improve the particle swarm optimization algorithm of existing binary form, to guarantee the radial of power distribution network
Structure reduces optimizing searching times, enhances global optimizing ability.
The purpose of the present invention is adopt the following technical solutions realization：
The present invention provides a kind of reconstruction method of power distribution network based on improvement binary particle swarm algorithm, and improvements exist
In：
Composition of Switching State of Distribution Network data are acquired, and generate the topological structure of the corresponding power distribution network of switch state data；
The topological structure of power distribution network is set as particle, be iterated using the binary system particle algorithm by step-by-step system based on
It calculates, obtains the position and speed of more new particle；
According to the position and speed of more new particle, the topological structure of the power distribution network is reconstructed.
Further：The acquisition composition of Switching State of Distribution Network data, and generate the corresponding power distribution network of switch state data
Topological structure, including：
Acquire composition of Switching State of Distribution Network data；
The state of branch where obtaining switch according to composition of Switching State of Distribution Network data；
The topological structure of power distribution network is obtained according to place membership.
Further：It is described that the topological structure of power distribution network is set as particle, using the binary system particle based on step-by-step system
Algorithm is iterated calculating, obtains the position and speed of more new particle, including：
The topological structure of each power distribution network is set as particle, and the particle is initialized, by distribution net topology knot
Composition of Switching State of Distribution Network in structure is assigned to the position of particle；
The particle after initializing is calculated for the fitness of objective function；
Compare the fitness of particle fitness Yu particle individual optimal value, it, will if current particle is optimal better than individual
Current particle position is set as individual optimal value；And compare the fitness of the particle group optimal value of particle fitness and iteration,
If current particle is optimal better than group, group's optimal value is set by current particle position；
The speed of the particle is updated according to the position of particle, individual optimal value and group's optimal value after update.
Further：The L-expression for updating the particle is as follows：
In formula：Q-th of particle d ties up position when iteration secondary for kth；Q-th of particle when for -1 iteration of kth
The speed of N-dimensional position；Loop L is q-th particle d dimension position when being 1, closes the switch the loop to be formed d-th；B ∈ L is
B dimension belongs to close the switch after the loop L that is formed；The speed of all dimensions when for loop L -1 iteration of kth；It is
Q-th of particle N-dimensional position when k-1 iteration；
The position of particle, individual optimal value and group's optimal value after update are brought into following speed more new formula to particle
Speed be updated：
Wherein：The speed of the d dimension of q-th of particle when being kth time iteration；W is inertia weight；c1, c2It is respectively individual
With global Studying factors；r1, r1It is the random number between [0,1] respectively；It is q-th of particle d after -1 iteration of kth
The history optimal location of dimension,It is the global optimum position that population d is tieed up after -1 iteration of kth；It is -1 iteration of kth
The current location of q-th of particle d dimension afterwards；c3For the incidental learning factor；r3It is the random number between [0,1]；It is kth -1
The history optimal location that i-th of particle d is tieed up after secondary iteration,It is that the history that j-th of particle d is tieed up after -1 iteration of kth is optimal
Position, wherein i, j are 1 to the random number between total number of particles.
Further：The objective function that the setting improves binary system particle algorithm is expressed as：
In formula：F indicates distribution network loss；KijThe switch state variable of branch road between node i and node j is represented, 0 indicates to beat
It opens, 1 indicates closure；RijIndicate the resistance value of branch between node i and node j；PijWith QijThe branch of branch between node i and node j
Road active power and reactive power, when distribution net topology is set as particle, wherein i, j are 1 to the random number between total number of particles.
Further：The objective function further includes following constraint condition：
The constraint of distribution power flow equation：
Node voltage constraint：
Ui,min≤Ui≤Ui,max
Tributary capacity constraint：
|Pij|≤Pij,max
Branch current constraint：
|Iij|≤Iij,max
The radial constraint of power distribution network；
Network need to be kept radial after reconstruct, and there can be no loops and isolated island；
In formula：F indicates distribution network loss；KijThe switch state variable of branch road between node i and node j is represented, 0 indicates to beat
It opens, 1 indicates closure；RijIndicate the resistance value of branch between node i and node j；PijWith QijThe branch of branch between node i and node j
Road active power and reactive power；PiWith QiInjection for node i is active and reactive power；PDGiWith QDGiIt is distributed in node i
The active and idle power output of power supply DG；PLiWith QLiFor the active and reactive requirement of load in node i；UiWith UjRespectively node i
With the voltage magnitude of node j；GijWith BijNetwork conductance and susceptance respectively between node i and node j；θijFor node i and node
Phase angle difference between j；Ui,min、Ui,maxThe respectively node voltage amplitude lower and upper limit of node i；Pij、Pij,maxRespectively node i
The branch power of branch and the branch power amplitude upper limit between node j；Iij、Iij,maxBranch respectively between node i and node j
Branch current and the branch current magnitudes upper limit.
Further：The topological structure by each power distribution network is set as particle, and initializes to the particle, packet
It includes：
According to the radial constraint of objective function and each particle corresponding D dimension speed data setting particle populations scale,
Maximum number of iterations, Studying factors and inertia constant；Each example in the particle populations meets the radial constraint of power distribution network；
According to particle populations scale, maximum number of iterations, Studying factors and inertia constant generate initial kind of population iteration
Group, according to the random formation speed matrix of population iteration initial population size.
Further：It is described calculate initialization after particle for objective function fitness, including：
Each particle is brought into objective function, the fitness of each particle is found out.
Further：The position of the basis more new particle, to the topological structure of the power distribution network be reconstructed including：
According to the position of particle after update, the corresponding composition of Switching State of Distribution Network set of each particle is obtained；
The topological structure of corresponding power distribution network is reconstructed according to each composition of Switching State of Distribution Network set.
The present invention also provides a kind of improvement binary system population system applied to power distribution network reconfiguration, improvements exist
In：
Acquisition module for acquiring composition of Switching State of Distribution Network data, and generates the corresponding power distribution network of switch state data
Topological structure；
Update module, for the topological structure of power distribution network to be set as particle, using the binary system particle based on step-by-step system
Algorithm is iterated calculating, obtains the position and speed of more new particle；
Reconstructed module is reconstructed the topological structure of the power distribution network for the position and speed according to more new particle.
Further：The update module, including：
Initialization submodule for the topological structure of each power distribution network to be set as particle, and carries out the particle initial
Change, the composition of Switching State of Distribution Network in power distribution network topological structure is assigned to the position of particle；
Computational submodule, for calculating the particle after initializing for the fitness of objective function；
Comparative sub-module, for comparing the fitness of particle fitness Yu particle individual optimal value, if current particle is excellent
It is optimal in individual, then individual optimal value is set by current particle position；
Comparative sub-module, the fitness of the particle group optimal value for comparing particle fitness and iteration, if currently
Particle is optimal better than group, then sets group's optimal value for current particle position；
Submodule is updated, for updating the grain according to the position of particle, individual optimal value and group's optimal value after update
The speed of son.
Further：The initialization submodule, including：
Setting unit, for according to the radial constraint of objective function and the corresponding D dimension speed data setting of each particle
Particle populations scale, maximum number of iterations, Studying factors and inertia constant；Each particle in the particle populations meets distribution
Net radial constraint；
Generation unit, for generating particle according to particle populations scale, maximum number of iterations, Studying factors and inertia constant
Group's iteration initial population, according to the random formation speed matrix of population iteration initial population size.
Further：The computational submodule is also used to bring each particle in objective function into, finds out each grain
The fitness of son.
Further：It further include setup module, for the objective function and constraint item that improve binary system particle algorithm to be arranged
Part.
Further：The reconstructed module, including：
It obtains submodule and show that the corresponding power distribution network of each particle is opened for the position and speed according to particle after update
Off status set；
Submodule is reconstructed, the topology for reconstructing corresponding power distribution network according to each composition of Switching State of Distribution Network set is tied
Structure.
Compared with the immediate prior art, technical solution provided by the invention is had an advantageous effect in that：
The present invention provides the improvement binary system population method and system for being applied to power distribution network reconfiguration, acquisition power distribution network switch
Status data, and generate the topological structure of the corresponding power distribution network of each switch state data；By the topological structure of each power distribution network
It is set as particle, calculating is iterated using the binary system particle algorithm based on step-by-step system, obtains the position of more new particle；According to
The topological structure of the power distribution network is reconstructed in the position of more new particle.The present invention optimizes existing binary system population
Algorithm is improved, and ensure that the radial constraint for not violating power distribution network in iterative process, and obtain the position of more new particle,
It is reconstructed with the topological structure to power distribution network, guarantees to generate in iterative process without infeasible solution, relatively other algorithm iteration mistakes
The a large amount of infeasible solutions occurred in journey, the present invention can greatly speed up Searching efficiency.
And each particle is effective particle in technical solution of the present invention, greatly reduces the number of iterations.It can be effective
Solve the problems, such as that traditional binary population global convergence ability is poor, calculating speed is fast, and convergence is good.
Detailed description of the invention
Fig. 1 is a kind of simple stream based on the reconstruction method of power distribution network for improving binary particle swarm algorithm provided by the invention
Cheng Tu；
Fig. 2 is a kind of detailed stream based on the reconstruction method of power distribution network for improving binary particle swarm algorithm provided by the invention
Cheng Tu；
Fig. 3 is the network structure of specific embodiment provided by the invention；
Fig. 4 is that depth-first search is carried out on the basis of Fig. 3, the expression figure after being named to branch.
Specific embodiment
Specific embodiments of the present invention will be described in further detail with reference to the accompanying drawing.
The following description and drawings fully show specific embodiments of the present invention, to enable those skilled in the art to
Practice them.Other embodiments may include structure, logic, it is electrical, process and other change.Embodiment
Only represent possible variation.Unless explicitly requested, otherwise individual component and function are optional, and the sequence operated can be with
Variation.The part of some embodiments and feature can be included in or replace part and the feature of other embodiments.This hair
The range of bright embodiment includes equivalent obtained by the entire scope of claims and all of claims
Object.Herein, these embodiments of the invention can individually or generally be indicated that this is only with term " invention "
For convenience, and if in fact disclosing the invention more than one, the range for being not meant to automatically limit the application is to appoint
What single invention or inventive concept.
Embodiment one,
The present invention provides a kind of based on the reconstruction method of power distribution network for improving binary particle swarm algorithm, flow chart such as Fig. 1
It is shown, it the described method comprises the following steps：
Composition of Switching State of Distribution Network data are acquired, and generate the topological structure of the corresponding power distribution network of each switch state data；
The topological structure of power distribution network is substantially exactly the synthesis of the disconnection of each branch and closed state in power distribution network.The status representative of switch
The closure and disconnection of branch where switch, therefore can (0 representation switch disconnects, 1 representation switch according to the status data of switch
Closure) state of each branch is immediately arrived at, to obtain the topological structure of power distribution network.
The acquisition composition of Switching State of Distribution Network data, and the topological structure of the corresponding power distribution network of switch state data is generated,
Including：
Composition of Switching State of Distribution Network data are acquired, and generate the topological structure of the corresponding power distribution network of switch state data；
The topological structure of power distribution network is set as particle, be iterated using the binary system particle algorithm by step-by-step system based on
It calculates, obtains the position and speed of more new particle；
According to the position and speed of more new particle, the topological structure of the power distribution network is reconstructed.
The topological structure of power distribution network is set as particle described, updates each particle using binary system particle algorithm is improved
Before speed and position, further include：The objective function and constraint condition for improving binary system particle algorithm are set.
The objective function that the setting improves binary system particle algorithm is expressed as：
It is described setting improve binary system particle algorithm constraint condition include：
The constraint of distribution power flow equation：
Node voltage constraint：
Ui,min≤Ui≤Ui,max
Tributary capacity constraint：
|Pij|≤Pij,max
Branch current constraint：
|Iij|≤Iij,max
The radial constraint of power distribution network；
Network need to be kept radial after reconstruct, and there can be no loops and isolated island；
In formula：F indicates distribution network loss；KijThe switch state variable of branch road between node i and node j is represented, 0 indicates to beat
It opens, 1 indicates closure；RijIndicate the resistance value of branch between node i and node j；PijWith QijThe branch of branch between node i and node j
Road active power and reactive power；PiWith QiInjection for node i is active and reactive power；PDGiWith QDGiIt is distributed in node i
The active and idle power output of power supply DG；PLiWith QLiFor the active and reactive requirement of load in node i；UiWith UjRespectively node i
With the voltage magnitude of node j；GijWith BijNetwork conductance and susceptance respectively between node i and node j；θijFor node i and node
Phase angle difference between j；Ui,min、Ui,maxThe respectively node voltage amplitude lower and upper limit of node i；Pij、Pij,maxRespectively node i
The branch power of branch and the branch power amplitude upper limit between node j；Iij、Iij,maxBranch respectively between node i and node j
Branch current and the branch current magnitudes upper limit.When distribution net topology is set as particle, wherein i, j be 1 between total number of particles with
Machine number.
Now using loss minimization as target, present invention is described, as shown in Fig. 2, it includes the following steps：
1) it initializes：Population scale m is set, and maximum number of iterations, Studying factors, inertia constant generate population iteration
Initial population, according to the random formation speed matrix of population size；
2) particle is evaluated：To each particle, it is evaluated for the fitness of objective function；
3) it updates optimal：The fitness for comparing particle fitness and its individual optimal value pbest, if current particle is excellent
It is optimal in individual, then individual optimal value pbest is set by current particle position.The group for comparing particle fitness with it is optimal
The fitness of value gbest sets group's optimal value for current particle position if current particle is optimal better than group
gbest。
4) population recruitment：The speed of more new particle and position；
The L-expression for updating the particle is as follows：
In formula：Q-th of particle d ties up position when iteration secondary for kth；Q-th of particle when for -1 iteration of kth
The speed of N-dimensional position；Loop L is q-th particle d dimension position when being 1, closes the switch the loop to be formed d-th；B ∈ L is
B dimension belongs to close the switch after the loop L that is formed；The speed of all dimensions when for loop L -1 iteration of kth；It is
Q-th of particle N-dimensional position when k-1 iteration；
The position of particle, individual optimal value and group's optimal value after update are brought into following speed more new formula to particle
Speed be updated：
Wherein：The speed of the d dimension of q-th of particle when being kth time iteration；W is inertia weight；c1, c2It is respectively individual
With global Studying factors；r1, r1It is the random number between [0,1] respectively；It is q-th of particle d after -1 iteration of kth
The history optimal location of dimension,It is the global optimum position that population d is tieed up after -1 iteration of kth；It is -1 iteration of kth
The current location of q-th of particle d dimension afterwards；c3For the incidental learning factor；r3It is the random number between [0,1]；It is kth -1
The history optimal location that i-th of particle d is tieed up after secondary iteration,It is that the history that j-th of particle d is tieed up after -1 iteration of kth is optimal
Position, wherein i, j are 1 to the random number between total number of particles.
5) stop condition：Judge whether to reach greatest iteration value, if so, it is out of service, export result；Otherwise step is returned
It is rapid 2).
Above-mentioned steps 1) in, the method for initialization is：
Population scale m, maximum number of iterations, Studying factors, inertia constant are set.By the distribution in power distribution network topological structure
Net switch state is assigned to the position of particle.
Since each switch is in one of closure, disconnection state, disconnected using 0 as switch, 1 closes as switch
It closes, each switch state corresponds to one-dimensional data (0 or 1).M particle is generated at random, and each particle has D dimension data, due to invention
It is required that m particle being initially generated is both needed to meet radial constraint.The corresponding random particle D that generates of each particle ties up number of speed
According to.
Above-mentioned steps 2) in, the method for evaluating particle is：
The corresponding objective function of goal-setting as requested, each particle is brought into target function typeFor objective function, and show that corresponding value as fitness, finds out the fitness of each particle
And it stores.
By taking Fig. 3 as an example, we first pass through depth-first search and are ranked up to each branch of power distribution network in Fig. 3, obtain
As a result such as Fig. 4.L in Fig. 4sThe s articles branch is represented, then the mathematical expression of the topological structure of power distribution network is exactly matrix [l1,l2,
l3,…,l37], ls∈ [0,1], this also corresponds to be a particle.Every number corresponds to corresponding K in particleijValue, such as
l4K is corresponded to45Value, work as l4=1, then corresponding K45=1.
At this point, we are it can be concluded that each K when giving a particleijValue, RijIt is fixed value, Pij、QijWith UiValue
It is the distribution power flow situation that the topological structure according to corresponding to particle obtains.All data, which are brought into objective function, can be obtained
In target function value, and corresponding target function value is obtained, it is known that each particle has a target letter corresponding to it
Numerical value finds out the fitness of each particle and storage as fitness.
Above-mentioned steps 3) in, updating optimal method is：
Compare the fitness of particle fitness and its individual optimal value pbest, if current particle is optimal better than individual,
Then individual optimal value pbest is set by current particle position.Compare the suitable of particle fitness and its group optimal value gbest
Response sets group optimal value gbest for current particle position if current particle is optimal better than group.
Optimal individual is for single particle.In particle swarm algorithm, with iterations going on, particle is continuous
It evolves.And optimal situation (the i.e. objective function of certain particle in that some particle occurs in all iterative process before
Value is minimum) be the particle individual optimal value.
For example, primary totally 20, the individual that study the 3rd particle here is optimal.Assuming that current iteration 2
It is secondary, pass through 2 times after 1 iteration with the 3rd particle then we compare the primary value of the 3rd particle, the 3rd particle
Target function value after iteration when this 3 kinds of particle values, take the smallest particle value of target function value as the 3rd particle individual most
The figure of merit.If the 3rd particle of the particle after 1 iteration is worth the target function value minimum, then the 3rd particle passes through
Particle value after 1 iteration is exactly that the individual of current 3rd particle is optimal.Similarly it can be concluded that the individual of other particles is optimal
Value.And group's optimal value is exactly the optimal value of all particles in an iterative process.
Above-mentioned steps 4) in, the method for population recruitment is：
The position of particle is updated according to formula (2) first, then brings the particle location information after update into formula
(1) speed of particle is updated in.
Above-mentioned steps 5) in, judge that the method for stop condition is：
Judge whether to reach greatest iteration value, if so, it is out of service, export result；Otherwise return step 2), it comments again
Valence particle, and update particle position and speed.
According to the position and speed of more new particle, the topological structure of the power distribution network is reconstructed, including：
According to the position and speed of particle after update, the corresponding composition of Switching State of Distribution Network set of each particle is obtained；
The topological structure of corresponding power distribution network is reconstructed according to each composition of Switching State of Distribution Network set.
Embodiment two
As shown in figure 3, a kind of new energy power station model structure based on impedance and controlled AC voltage source is provided, it should
Figure is that electric system simulation often uses example system construction drawing, and each black dot represents a load bus, dark circles in figure
Solid black lines between point represent the normally closed branch of connected load node, and the black dotted lines between black dot represent connected load
The normally opened branch of node, the double circles of the intersection on No. 1 dark node left side represent the distribution transformer of connection the superior and the subordinate power grid.We
Method is applied to the validity of the actual result method of proof of the example by observation, and test macro selects 33 node of ieee standard to calculate
Example, including 37 branches, every branch are respectively arranged with switch, wherein there is 32 block switches (normally closed), 5 interconnection switches are (often
It opens).In the present embodiment, number of particles is 40 in population, maximum number of iterations 30, inertia constant ω=1, Studying factors c1
=2, c2=1, c3=2.
Since initial population is randomly generated, therefore each iterative process can be slightly different, and be the convergence of check algorithm
Property, data that the present invention has carried out 50 operations, and result data and traditional binary particle swarm algorithm (BPSO) are obtained into
Row comparison, the results are shown in Table 1.
The comparison of 1 optimizing success rate of table
The present invention is not directed to other calculating in addition to the update of particle rapidity and location information, and Load flow calculation number is weighing apparatus
The good index of quantity algorithm efficiency, thus the present invention using in restructuring procedure Load flow calculation sum as efficiency comparative's standard.It is seeking
In situation similar in excellent success rate, Load flow calculation number is fewer, then the Searching efficiency of algorithm is also higher.Table 2 lists part
Comparative situation of the intelligent algorithm on Searching efficiency.
The comparison of 2 algorithms of different Searching efficiency of table
From table 2 it is known that for compared with traditional BP SO algorithm, inventive algorithm Load flow calculation sum reduces by 55%, efficiency
Improve 120%.The Load flow calculation number of remaining reconstructing method can be seen that inventive algorithm and have a clear superiority in observation table 2.
And the present invention improves on location Update Strategy, guarantees to generate in iterative process without infeasible solution, relatively other algorithms
The a large amount of infeasible solutions occurred in iterative process, the present invention can greatly speed up Searching efficiency.
Embodiment three,
Based on same inventive concept, the present invention also provides the present invention also provides one kind based on improvement binary system population calculation
The power distribution network reconfiguration system of method, including：
Acquisition module for acquiring composition of Switching State of Distribution Network data, and generates the corresponding power distribution network of switch state data
Topological structure；
Update module, for the topological structure of power distribution network to be set as particle, using the binary system particle based on step-by-step system
Algorithm is iterated calculating, obtains the position and speed of more new particle；
Reconstructed module is reconstructed the topological structure of the power distribution network for the position and speed according to more new particle.
Further：The update module, including：
Initialization submodule for the topological structure of each power distribution network to be set as particle, and carries out the particle initial
Change, the composition of Switching State of Distribution Network in power distribution network topological structure is assigned to the position of particle；
Computational submodule, for the updated particle in calculating position for the fitness of objective function；
Comparative sub-module, for comparing the fitness of particle fitness Yu particle individual optimal value, if current particle is excellent
It is optimal in individual, then individual optimal value is set by current particle position；
Comparative sub-module, the fitness of the particle group optimal value for comparing particle fitness and iteration, if currently
Particle is optimal better than group, then sets group's optimal value for current particle position；
Submodule is updated, for updating the grain according to the position of particle, individual optimal value and group's optimal value after update
The speed of son.
Further：The initialization submodule, including：
Setting unit, for according to the radial constraint of objective function and the corresponding D dimension speed data setting of each particle
Particle populations scale, maximum number of iterations, Studying factors and inertia constant；Each example in the particle populations meets
The radial constraint of power distribution network；
Generation unit, for generating particle according to particle populations scale, maximum number of iterations, Studying factors and inertia constant
Group's iteration initial population, according to the random formation speed matrix of population iteration initial population size.
Further：The computational submodule is also used to bring each particle in objective function into, finds out each grain
The fitness of son.
Further：It further include setup module, for the objective function and constraint item that improve binary system particle algorithm to be arranged
Part.
Further：The reconstructed module, including：
It obtains submodule and show that the corresponding power distribution network of each particle is opened for the position and speed according to particle after update
Off status set；
Submodule is reconstructed, the topology for reconstructing corresponding power distribution network according to each composition of Switching State of Distribution Network set is tied
Structure.
The present invention provides a kind of improvement Binary Particle Swarm Optimization for power distribution network reconfiguration, to existing binary system
The particle swarm optimization algorithm of form is improved, and to guarantee the radial structure of power distribution network, reduces optimizing searching times, enhancing is complete
Office's optimizing ability.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, although referring to above-described embodiment pair
The present invention is described in detail, those of ordinary skill in the art still can to a specific embodiment of the invention into
Row modification perhaps equivalent replacement these without departing from any modification of spirit and scope of the invention or equivalent replacement, applying
Within pending claims of the invention.
Claims (13)
1. a kind of based on the reconstruction method of power distribution network for improving binary particle swarm algorithm, it is characterised in that：
Composition of Switching State of Distribution Network data are acquired, and generate the topological structure of the corresponding power distribution network of switch state data；
The topological structure of power distribution network is set as particle, calculating is iterated using the binary system particle algorithm based on step-by-step system,
Obtain the position and speed of more new particle；
According to the position and speed of more new particle, the topological structure of the power distribution network is reconstructed.
2. reconstruction method of power distribution network as described in claim 1, it is characterised in that：The acquisition composition of Switching State of Distribution Network data,
And the topological structure of the corresponding power distribution network of switch state data is generated, including：
Acquire composition of Switching State of Distribution Network data；
The state of branch where obtaining switch according to composition of Switching State of Distribution Network data；
The topological structure of power distribution network is obtained according to place membership.
3. reconstruction method of power distribution network as described in claim 1, it is characterised in that：It is described that the topological structure of power distribution network is set as grain
Son is iterated calculating using the binary system particle algorithm based on step-by-step system, obtains the position and speed of more new particle, packet
It includes：
The topological structure of each power distribution network is set as particle, and the particle is initialized, it will be in power distribution network topological structure
Composition of Switching State of Distribution Network be assigned to the position of particle；
The particle after initializing is calculated for the fitness of objective function；
Compare the fitness of particle fitness Yu particle individual optimal value, it, will be current if current particle is optimal better than individual
Particle position is set as individual optimal value；And compare the fitness of the particle group optimal value of particle fitness and iteration, if
Current particle is optimal better than group, then sets group's optimal value for current particle position；
The speed of the particle is updated according to the position of particle, individual optimal value and group's optimal value after update.
4. reconstruction method of power distribution network as claimed in claim 3, it is characterised in that：The L-expression for updating the particle
It is as follows：
In formula：Q-th of particle d ties up position when iteration secondary for kth；Q-th of particle N-dimensional when for -1 iteration of kth
The speed of position；Loop L is q-th particle d dimension position when being 1, closes the switch the loop to be formed d-th；B ∈ L is b dimension
Belong to the loop L formed after closing the switch；The speed of all dimensions when for loop L -1 iteration of kth；It is kth -1 time
Q-th of particle N-dimensional position when iteration；
Bring the position of particle, individual optimal value and group's optimal value after update into speed in following speed more new formula to particle
Degree is updated：
Wherein：The speed of the d dimension of q-th of particle when being kth time iteration；W is inertia weight；c1, c2It is respectively individual and complete
The Studying factors of office；r1, r1It is the random number between [0,1] respectively；It is that q-th of particle d is tieed up after -1 iteration of kth
History optimal location,It is the global optimum position that population d is tieed up after -1 iteration of kth；It is after -1 iteration of kth
The current location of q particle d dimension；c3For the incidental learning factor；r3It is the random number between [0,1]；Be kth -1 time repeatedly
The history optimal location that i-th of particle d is tieed up after generation,It is the optimal position of history that j-th of particle d is tieed up after -1 iteration of kth
It sets, wherein i, j is 1 to the random number between total number of particles.
5. reconstruction method of power distribution network as claimed in claim 3, it is characterised in that：The setting improves binary system particle algorithm
Objective function is expressed as：
In formula：F indicates distribution network loss；KijThe switch state variable of branch road between node i and node j is represented, 0 indicates to open, and 1
Indicate closure；RijIndicate the resistance value of branch between node i and node j；PijWith QijThe branch of branch has between node i and node j
Function power and reactive power, when distribution net topology is set as particle, wherein i, j are 1 to the random number between total number of particles.
6. reconstruction method of power distribution network as claimed in claim 5, it is characterised in that：The objective function further includes following constraint item
Part：
The constraint of distribution power flow equation：
Node voltage constraint：
Ui,min≤Ui≤Ui,max
Tributary capacity constraint：
|Pij|≤Pij,max
Branch current constraint：
|Iij|≤Iij,max
The radial constraint of power distribution network；
Network need to be kept radial after reconstruct, and there can be no loops and isolated island；
In formula：F indicates distribution network loss；KijThe switch state variable of branch road between node i and node j is represented, 0 indicates to open, and 1
Indicate closure；RijIndicate the resistance value of branch between node i and node j；PijWith QijThe branch of branch has between node i and node j
Function power and reactive power；PiWith QiInjection for node i is active and reactive power；PDGiWith QDGiFor distributed generation resource in node i
The active and idle power output of DG；PLiWith QLiFor the active and reactive requirement of load in node i；UiWith UjRespectively node i and section
The voltage magnitude of point j；GijWith BijNetwork conductance and susceptance respectively between node i and node j；θijBetween node i and node j
Phase angle difference；Ui,min、Ui,maxThe respectively node voltage amplitude lower and upper limit of node i；Pij、PIj, maxRespectively node i with
The branch power of branch and the branch power amplitude upper limit between node j；Iij、Iij,maxThe branch of branch respectively between node i and node j
Road electric current and the branch current magnitudes upper limit.
7. reconstruction method of power distribution network as claimed in claim 6, it is characterised in that：The topological structure by each power distribution network is set
For particle, and the particle is initialized, including：
According to the radial constraint of objective function and each particle corresponding D dimension speed data, particle populations scale, maximum are set
The number of iterations, Studying factors and inertia constant；Each particle in the particle populations meets the radial constraint of power distribution network；
According to particle populations scale, maximum number of iterations, Studying factors and inertia constant generate population iteration initial population, root
According to the random formation speed matrix of population iteration initial population size.
8. reconstruction method of power distribution network as claimed in claim 6, it is characterised in that：The particle calculated after initializing is for mesh
The fitness of scalar functions, including：
Each particle is brought into objective function, the fitness of each particle is found out.
9. reconstruction method of power distribution network as claimed in claim 3, it is characterised in that：The position of the basis more new particle, to institute
State power distribution network topological structure be reconstructed including：
According to the position of particle after update, the corresponding composition of Switching State of Distribution Network set of each particle is obtained；
The topological structure of corresponding power distribution network is reconstructed according to each composition of Switching State of Distribution Network set.
10. a kind of based on the power distribution network reconfiguration system for improving binary particle swarm algorithm, it is characterised in that：
Acquisition module for acquiring composition of Switching State of Distribution Network data, and generates the topology of the corresponding power distribution network of switch state data
Structure；
Update module, for the topological structure of power distribution network to be set as particle, using the binary system particle algorithm based on step-by-step system
It is iterated calculating, obtains the position and speed of more new particle；
Reconstructed module is reconstructed the topological structure of the power distribution network for the position and speed according to more new particle.
11. power distribution network reconfiguration system as claimed in claim 10, it is characterised in that：The update module, including：
Initialization submodule for the topological structure of each power distribution network to be set as particle, and initializes the particle, will
Composition of Switching State of Distribution Network in power distribution network topological structure is assigned to the position of particle；
Computational submodule, for calculating the particle after initializing for the fitness of objective function；
Comparative sub-module, for comparing the fitness of particle fitness Yu particle individual optimal value, if current particle is better than a
Body is optimal, then sets individual optimal value for current particle position；
Comparative sub-module, the fitness of the particle group optimal value for comparing particle fitness and iteration, if current particle
It is optimal better than group, then group's optimal value is set by current particle position；
Submodule is updated, for updating the particle according to the position of particle, individual optimal value and group's optimal value after update
Speed.
12. power distribution network reconfiguration system as claimed in claim 11, it is characterised in that：The initialization submodule, including：
Setting unit, for particle to be arranged according to the radial constraint of objective function and the corresponding D dimension speed data of each particle
Population scale, maximum number of iterations, Studying factors and inertia constant；Each example in the particle populations meets distribution
Net radial constraint；
Generation unit, for generating population according to particle populations scale, maximum number of iterations, Studying factors and inertia constant and changing
For initial population, according to the random formation speed matrix of population iteration initial population size.
13. power distribution network reconfiguration system as claimed in claim 10, it is characterised in that：The reconstructed module, including：
Submodule is obtained, for the position and speed according to particle after update, obtains the corresponding power distribution network switch shape of each particle
State set；
Submodule is reconstructed, for reconstructing the topological structure of corresponding power distribution network according to each composition of Switching State of Distribution Network set.
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