CN108537369A - Improvement population algorithm for distribution network reconfiguration based on local search - Google Patents
Improvement population algorithm for distribution network reconfiguration based on local search Download PDFInfo
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- CN108537369A CN108537369A CN201810234227.0A CN201810234227A CN108537369A CN 108537369 A CN108537369 A CN 108537369A CN 201810234227 A CN201810234227 A CN 201810234227A CN 108537369 A CN108537369 A CN 108537369A
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses the improvement population algorithm for distribution network reconfiguration based on local search, using loss minimization as Optimization goal.This algorithm slightly has insufficient feature for particle cluster algorithm local optimal searching ability, it proposes to accelerate local iteration on the basis of particle cluster algorithm and expands the method and step of search space two, not only increase particle cluster algorithm global optimizing ability, the local optimal searching ability and speed for also enhancing algorithm ensure that algorithm in global and local optimizing ability.Meanwhile the characteristics of for power distribution network radial networks structure, utilizes spanning tree algorithm in restructuring procedure, the distribution net work structure for meeting network radiativity structural constraint is directly obtained according to network edge weights to avoid generating a large amount of infeasible solutions.Present invention improves over particle cluster algorithms, improve computational efficiency, update network structure by optimization algorithm iteration, play the role of reducing network loss.
Description
Technical field
The present invention relates to technical field of electric power, and in particular to a kind of improvement population power distribution network reconfiguration based on local search
Algorithm.
Background technology
Power distribution network reconfiguration is exactly the assembled state by changing block switch, interconnection switch, to change network topology structure
With the supply path of user.Current derivation algorithm can be divided into traditional mathematics algorithm, heuritic approach and intelligent algorithm etc..
Wherein, the calculating time of mathematical algorithm is long；Heuritic approach influenced by the original state and network size of network it is bigger, no
One surely obtains optimal solution；Intelligent algorithm is not influenced by network initial state, but some convergence speed of the algorithm are slow,
It is easily trapped into local optimum, to influence the efficiency and accuracy of power distribution network reconfiguration.
Particle cluster algorithm (Particle Swarm Optimization, PSO) is a kind of optimization skill based on swarm intelligence
Art.Particle cluster algorithm is adjusted the position of its own by the speed of particle, to seek the optimal solution in space.Population
The global space optimizing ability of algorithm is relatively good, some are insufficient for local search ability and iterative convergence speed.
Invention content
It is an object of the invention to overcome the shortcomings of existing intelligent algorithm, the improvement particle based on local search is proposed
Group's algorithm for distribution network reconfiguration, the algorithm can simultaneously improve ability of searching optimum and part searched to avoid a large amount of infeasible solutions
Suo Nengli improves the algorithm of convergence rate, to improve the efficiency and accuracy of power distribution network reconfiguration.
To achieve the above object, the technical solution adopted by the present invention is that：
Improvement population algorithm for distribution network reconfiguration based on local search, the method includes：
Step 1：The initial parameter of particle cluster algorithm, random initializtion population are set；
Step 2：The target function value of each individual of population is calculated using object function；
Step 3：Individual is updated using particle cluster algorithm, and calculates the object function of individual；
Step 4：The optimal solution P of the corresponding individual of updateiWith globally optimal solution Pg；
Step 5：Individual in step 4 is further updated, and updates the optimal solution P of corresponding individualiWith it is complete
Office optimal solution Pg；
Step 6：All number of individuals are combined as a matrix Xnsw×Nbranch=[X1；X2；…；Xi], wherein nsw is
Number of individuals；NbranchFor network number of edges；Xnsw×NbranchIn submatrix Xnsw×nswTransposition operation is carried out, it is then random to generate separately
One submatrix Xnsw×(Nbranch-nsw)In element；
Step 7：Judge whether to reach greatest iteration number, is to terminate, obtains optimal solution, otherwise return to step 3；
Step 8：Export result.
Specifically, before step 2 calculates the target function value of each individual of population using object function, generation is utilized
Tree algorithm generates radial networks structure, specific as follows：
The data of network topology of the input with side right value, vertex set V, line set E；
Opposite side weights are ranked up with the principle not subtracted, and obtain the sequence list on a side；The side that selection side right value ranked first
e1；Initialize vertex set and line set Vnew={ x }, Enew={ e1}={ E1E2, x is the head end vertex on side, E1=E2={ }；
By the sequence list on side, lower one side e is selectedk=(uk,vk), add vkTo Vnew；If ukAnd vkPositioned at different subgraphs,
EkIt is added to E1, otherwise it is added to E2；ukAnd vkIt is side e respectivelykHead end and end；
Aforesaid operations are repeated until Vnew=V；Finally obtain Enew={ E1E2, wherein E1={ e1,e2,…,eMIndicate spoke
Set branch, E in the side for penetrating shape struc-ture2={ eN,…,eNbranchThen indicate chord；Enew={ e1,e2,…,eM,eN,…,
eNbranch}。
To minimize network loss as object function：
Wherein, NbranchFor the quantity of network edge；IbFor the electric current of b branches；RbFor the resistance of b branches；kbThe switch of branch b
State is worth and indicates that switch is closed for 1, is worth and indicates that switch disconnects for 0.
In steps of 5, the individual in step 4 is further updated by the way of accelerating local iteration, is had
Body renewal process is as follows：
Xi,new=Xi+rand(0,1)*(Xbest-Mutual_Vector*BF)
Mutual_Vector=(Xi+Xj)/2
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold；
Or
Xi,new=Xi+rand(-1,1)*(Xbest-Xj)
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold；
Wherein, XiIndicate individual i；XjIndicate individual j not identical with individual i；XbestIndicate best individual；rand(0,
1) random number in (0,1) section is indicated；Mutual-Vector indicates the mean value of two individual i and j；BF expressions randomly generate
1 or 2；, XnewThe new individual generated for local search；XoldFor original individual.
In step 3, individual is updated using particle cluster algorithm, and the process for calculating the object function of individual is：
Pi k=[Pi k,Pi k,…Pi k]T
In formula, Veli kIt is the speed of particle；Xi kIt is the position of particle；Pi kIt is individual optimal value array；Pg kIt is global optimum
Value；Nsw is the number of individuals of each iteration；c1And c2For accelerated factor；ρ is coefficient；r1And r2Respectively in (0,1) section with
Machine number；T indicates transposition.
The object function will meet maximum current constraint, maximum voltage constraint, trend constraint and radiativity network structure
Pact
Compared with prior art, the present invention advantage is：
In calculating process, embedded spanning tree algorithm directly generates the network for meeting the constraint of radiativity network structure, avoids
The verification of a large amount of infeasible solutions, improves search speed.It can retain simultaneously by the same of the ability of searching optimum of particle cluster algorithm
When, accelerate local search ability and speed, further increase optimizing ability, and then the efficiency of reconfiguration of electric networks and accurate can be improved
Property.
Description of the drawings
Fig. 1 is the flow of the improvement population algorithm for distribution network reconfiguration provided in an embodiment of the present invention based on local search
Figure.
Specific implementation mode
As shown in fig.1, being the improvement population power distribution network reconfiguration based on local search provided in this embodiment provided
The flow chart of algorithm, the algorithm specifically comprise the following steps：
Step 1：The initial parameter of particle cluster algorithm, random initializtion population are set；
Step 2：The target function value of each individual is calculated using object function；
To minimize network loss as object function：
Wherein, NbranchFor the quantity of network edge；IbFor the electric current of b branches；RbFor the resistance of b branches；kbThe switch of branch b
State is worth and indicates that switch is closed for 1, is worth and indicates that switch disconnects for 0.
Meanwhile object function will meet maximum current constraint, maximum voltage constraint, trend constraint and radiativity network structure
Constraint.
Step 3：Individual is updated using particle cluster algorithm, and calculates the object function of individual；
Pi k=[Pi k,Pi k,…Pi k]T
In formula, Veli kIt is the speed of particle；Xi kIt is the position of particle；Pi kIt is individual optimal value array；Pg kIt is global optimum
Value；Nsw is the number of individuals of each iteration；c1And c2For accelerated factor；ρ is coefficient, generally 1；r1And r2Respectively (0,1) area
Interior random number；T indicates transposition.
Step 4：The optimal solution P of the corresponding individual of updateiWith globally optimal solution Pg；
Step 5：Improved method 1
The individual in step 4 is further updated by the way of accelerating local iteration, and updates corresponding
The optimal solution P of bodyiWith globally optimal solution Pg；It is as follows：
Xi,new=Xi+rand(0,1)*(Xbest-Mutual_Vector*BF)
Mutual_Vector=(Xi+Xj)/2
Or
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold。
Xi,new=Xi+rand(-1,1)*(Xbest-Xj)
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold；
Wherein, XiIndicate individual i；XjIndicate individual j not identical with individual i；XbestIndicate best individual；rand(0,
1) random number in (0,1) section is indicated；Mutual-Vector indicates the mean value of two individual i and j；BF expressions randomly generate
1 or 2；, XnewThe new individual generated for local search；XoldFor original individual；
In this way, local search ability and speed can be accelerated by step 5.
Step 6：Improved method 2
All individual arrays be can be combined into a matrix Xnsw×Nbranch=[X1；X2；…；Xi], wherein nsw is
Number of individuals；NbranchFor network number of edges；In general, therefore number of individuals is less than network number of edges, Xnsw×NbranchIn submatrix
Xnsw×nswTransposition operation is carried out, then generates another submatrix X at randomnsw×(Nbranch-nsw)In element.It can by step 6
Further to expand the search space of algorithm in limited number of individuals.
Step 7：Judge whether to reach greatest iteration number, is to terminate, obtains optimal solution, otherwise return to step 3；
Step 8：Export result.
It follows that this method slightly has insufficient feature for particle cluster algorithm local optimal searching ability, in particle cluster algorithm
On the basis of propose to accelerate local iteration and expand search space step, i.e. step 5 and 6 not only increases particle cluster algorithm
Global optimizing ability also enhances the local optimal searching ability and speed of algorithm, ensure that algorithm in global and local optimizing ability.
One kind as the present embodiment preferably, the target letter of each individual of population being calculated in step 2 using object function
Before numerical value, radial networks structure is generated using spanning tree algorithm, it is specific as follows：
The data of network topology of the input with side right value, vertex set V, line set E；
Opposite side weights are ranked up with the principle not subtracted, and obtain the sequence list on a side；The side that selection side right value ranked first
e1；Initialize vertex set and line set Vnew={ x }, Enew={ e1}={ E1E2, x is the head end vertex on side, E1=E2={ }；
By the sequence list on side, lower one side e is selectedk=(uk,vk), add vkTo Vnew；If ukAnd vkPositioned at different subgraphs,
EkIt is added to E1, otherwise it is added to E2；
Aforesaid operations are repeated until Vnew=V.Finally obtain Enew={ E1E2, wherein E1={ e1,e2,…,eMIndicate spoke
Set branch, E in the side for penetrating shape struc-ture2={ eN,…,eNbranchThen indicate chord；Enew={ e1,e2,…,eM,eN,…,
eNbranch}。
In this way, the characteristics of by being directed to power distribution network radial networks structure, is reconstructing to avoid generating a large amount of infeasible solutions
Spanning tree algorithm is utilized in the process, and the power distribution network knot for meeting network radiativity structural constraint is directly obtained according to network edge weights
Structure.
Above-described embodiment simply to illustrate that the present invention technical concepts and features, it is in the art the purpose is to be to allow
Those of ordinary skill cans understand the content of the present invention and implement it accordingly, and it is not intended to limit the scope of the present invention.It is all
It is the equivalent changes or modifications made according to the essence of the content of present invention, should all covers within the scope of the present invention.
Claims (6)
1. the improvement population algorithm for distribution network reconfiguration based on local search, which is characterized in that the method includes：
Step 1：The initial parameter of particle cluster algorithm, random initializtion population are set；
Step 2：The target function value of each individual of population is calculated using object function；
Step 3：Individual is updated using particle cluster algorithm, and calculates the object function of individual；
Step 4：The optimal solution P of the corresponding individual of updateiWith globally optimal solution Pg；
Step 5：Individual in step 4 is further updated, and updates the optimal solution P of corresponding individualiAnd global optimum
Solve Pg；
Step 6：All number of individuals are combined as a matrix Xnsw×Nbranch=[X1；X2；…；Xi], wherein nsw is individual
Number；NbranchFor network number of edges；Xnsw×NbranchIn submatrix Xnsw×nswTransposition operation is carried out, then generates another at random
Submatrix Xnsw×(Nbranch-nsw)In element；
Step 7：Judge whether to reach greatest iteration number, is to terminate, obtains optimal solution, otherwise return to step 3；
Step 8：Export result.
2. the improvement population algorithm for distribution network reconfiguration based on local search as described in claim 1, which is characterized in that in step
Before rapid 2 calculate the target function value of each individual of population using object function, radial net is generated using spanning tree algorithm
Network structure, it is specific as follows：
The data of network topology of the input with side right value, vertex set V, line set E；
Opposite side weights are ranked up with the principle not subtracted, and obtain the sequence list on a side；The side e that selection side right value ranked first1；Just
Beginningization vertex set and line set Vnew={ x }, Enew={ e1}={ E1E2, x is the head end vertex on side, E1=E2={ }；
By the sequence list on side, lower one side e is selectedk=(uk,vk), add vkTo Vnew；If ukAnd vkPositioned at different subgraphs, ekAdd
Add E1, otherwise it is added to E2；ukAnd vkIt is side e respectivelykHead end and end；
Aforesaid operations are repeated until Vnew=V；Finally obtain Enew={ E1E2, wherein E1={ e1,e2,…,eMIndicate radial
Set branch, E in the side of struc-ture2={ eN,…,eNbranchThen indicate chord；Enew={ e1,e2,…,eM,eN,…,eNbranch}。
3. the improvement population algorithm for distribution network reconfiguration based on local search as claimed in claim 1 or 2, which is characterized in that with
Minimum network loss is object function：
Wherein, NbranchFor the quantity of network edge；IbFor the electric current of b branches；RbFor the resistance of b branches；kbThe switch shape of branch b
State is worth and indicates that switch is closed for 1, is worth and indicates that switch disconnects for 0.
4. the improvement population algorithm for distribution network reconfiguration based on local search as described in claim 1, which is characterized in that in step
In 5, the individual in step 4 is further updated by the way of accelerating local iteration, specific renewal process is as follows：
Xi,new=Xi+rand(0,1)*(Xbest-Mutual_Vector*BF)
Mutual_Vector=(Xi+Xj)/2
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold；
Or
Xi,new=Xi+rand(-1,1)*(Xbest-Xj)
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold；
Wherein, XiIndicate individual i；XjIndicate individual j not identical with individual i；XbestIndicate best individual；Rand (0,1) table
Show the random number in (0,1) section；Mutual-Vector indicates the mean value of two individual i and j；BF indicate randomly generate 1 or
2；, XnewThe new individual generated for local search；XoldFor original individual.
5. the improvement population algorithm for distribution network reconfiguration based on local search as described in claim 1, which is characterized in that in step
In 3, individual is updated using particle cluster algorithm, and the process for calculating the object function of individual is：
Pi k=[Pi k,Pi k,…Pi k]T
In formula, Veli kIt is the speed of particle；Xi kIt is the position of particle；Pi kIt is individual optimal value array；Pg kIt is global optimum；
Nsw is the number of individuals of each iteration；c1And c2For accelerated factor；ρ is coefficient；r1And r2It is respectively random in (0,1) section
Number；T indicates transposition.
6. the improvement population algorithm for distribution network reconfiguration based on local search as claimed in claim 3, which is characterized in that the mesh
Scalar functions will meet maximum current constraint, maximum voltage constraint, the constraint of trend constraint and radiativity network structure.
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