CN104764980B - A kind of distribution line failure Section Location based on BPSO and GA - Google Patents
A kind of distribution line failure Section Location based on BPSO and GA Download PDFInfo
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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
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- 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
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- 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
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
The present invention relates to a kind of distribution line failure Section Location based on BPSO and GA, the strategy that this method is exchanged using double Evolution of Population and information realizes the mixing of binary system population and genetic algorithm, forms binary system hybrid algorithm.Two sub- populations have respective individual scale, and the evolutionary process per a generation is not interfere with each other, and row information transfer is entered after the completion of the evolution of every generation, select optimum individual to carry out the follow-on optimizing search of two populations respectively, until drawing optimal solution.There is the probability of " immature oils " during reducing fault location in the algorithm, and has certain fault-tolerance, carries out fault section location contrast with single binary system population or genetic algorithm, convergence rate significantly improves.
Description
Technical field
The present invention relates to a kind of distribution line failure Section Location based on BPSO and GA.
Background technology
With world energy sources crisis and the exacerbation of environmental pollution, people begin to focus on a kind of cleaner, renewable, efficient
Power supply --- distributed power source (Distributed Generator, DG).Wind turbines obtain extensively as one of common DG
Grid-connected use.Distribution line is directly connected to customer charge, it is ensured that reliability, security and power supply quality when it runs have
Highly important meaning.But DG access causes distribution system to be changed into answering for and fro flow of power from single supply radial networks
Miscellaneous multiterminal electric power network.In addition, structure and trend of distribution system etc. also change therewith, many parameters of power distribution network all have
There is dynamic characteristic, this exerts a certain influence to the fault section location of distribution line.Therefore, it is so that single supply is radial
The fault section location method of the distribution network on basis needs improve and perfect because being influenceed by DG.
The method for being applied to the distribution line section positioning containing DG at present is broadly divided into two major classes：One kind is with matrix algorithm
For the direct algorithm of Typical Representative, this method is based on graph theory knowledge, by fault signature analysis, with reference to the topological structure of power distribution network
Fault location is carried out, but this method needs complicated matrix multiple, to be also normalized to prevent from judging by accident, operand mistake
Greatly, processing time is long, and the processing to Complicated Distribution Network is relatively complicated, in the case of solving dual power supply or more power supply paired runnings
Fault location under the unified criterion that provides there is also limitation；Another kind of is to be based on artificial intelligent type fault location algorithm, main
To include neutral net, ant group algorithm and genetic algorithm, but use generally existing " immature oils " phenomenon of intelligent algorithm and
Slow two contradictory problems of convergence rate, the two problems of how compromising are into the main direction of studying of innovatory algorithm.
Particle cluster algorithm (Particle Swarm Optimization, PSO) is developed in recent years a kind of new
Intelligent optimization algorithm, there is many advantages, such as simple, easy realization, constringency performance is preferable.General principle is will to be sought in optimization problem
The process of optimal solution is looked for be regarded as the process that bird (particle) finds optimal location in space.During evolution, multiple grains are utilized
For sub optimal location of searching in space simultaneously to improve the search efficiency of algorithm, search of each particle per a generation is all according to certainly
Body optimal location and population optimal location are upgraded in time by certain formula, parameter to particle rapidity and particle position,
After it experienced many generations evolution, the optimal solution of optimal location, i.e. optimization problem is finally searched.It is used by this patent
Binary Particle Swarm Optimization (Binary Particle Swarm Optimization, BPSO), its difference are grain
The history of the every one-dimensional and particle of son in itself is optimal, global optimum is limited to 1 or 0, and speed does not make this limitation.
Genetic algorithm (GeneticAlgorithm, GA) is a kind of reference living nature " survival of the fittest " natural selection process
Random global search and optimized algorithm.It utilizes certain coding techniques, and the possibility solution of problem is encoded into the numeric string in chromosome
On, simulate the evolutionary process of colony's (being possible to solve) being made up of these strings, in colony by selecting, intersecting, make a variation behaviour
Optimal solution is finally searched out after work.GA has the advantages of strong robustness, adaptability are extensive and efficient, can be simultaneously in solution space
Multiple points carry out optimizing, so as to draw globally optimal solution.
Because GA has quick global search capability, chromosome shares mutually information, and whole population ratio is relatively evenly to most
There is region movement, but do not made full use of for feedback information in system, often result in redundancy iteration, convergence rate is slow.PSO leads to
The renewal to position and speed is crossed, makes full use of individual optimal value and colony's optimal value to converge on optimal value, there is convergence rate
The advantages of fast, but " immature oils " phenomenon is easily trapped into simultaneously.So this patent combines two algorithms, advantage is mutual
Mend, form a kind of fast convergence rate and the binary system hybrid optimization algorithm of appearance " immature oils " probability can be reduced
(BPSOGA), and it is used in the distribution line failure section positioning containing Wind turbines.
The content of the invention
It is an object of the invention to provide a kind of distribution line failure Section Location based on BPSO and GA, its feature
It is to comprise the following steps：
Step S1：Obtain the SOT state of termination coding I for treating section positioning distribution linej, j=1,2,3..., D, D is that terminal is total
Number；
Step S2：Using BPSOGA search spaces solution dimension as treat section position distribution line in total number of terminals D and area
The sum of section circuit, initialized using the dimension state value of space solution as sector status encoded radio, and to parameter：BPSO
Population scale N1, inertia weight ω, Studying factors c1And c2；GA population scales N2, crossover probability PX, mutation probability PM, insert again
Enter factor GGAP, maximum iteration T, take N1=N2=N；
Step S3：The sub- populations of BPSO and the initialization of GA populations：Particle position in the sub- populations of random initializtion BPSO
Pop1 (i) and particle rapidity vi, and calculate particle fitness value fit1 (i), particle optimal location popbest (i) and particle most
Big fitness value pbest(i), colony's optimal location gpopbest and colony maximum adaptation angle value gbest；Random initializtion GA is planted
Group individual solution pop2 (i) and ideal adaptation angle value fit2 (i), i=1,2,3...N；In wherein BPSO particle position pop1 (i) with
Individual solution pop2 (i) uses binary coding in GA, has identical dimension, all characterizes the area of respective segments in distribution line
Section state encoding；
Step S4：K=1 is made, is iterated computing；
Step S5：To the particle position pop1 (i) and particle rapidity v of the sub- populations of BPSOiIt is updated, forms a new generation
The sub- populations of BPSO, and update the particle fitness value fit1 (i) of the sub- population of BPSO of new generation；Meanwhile to GA populations
Genetic manipulation is carried out, obtains the GA populations of a new generation, and update the ideal adaptation angle value of the GA populations of new generation
fit2(i)；
Step S6：The particle fitness value fit1 (i) of population more sub- than the BPSO of newer generation and corresponding GA of new generation
The ideal adaptation angle value fit2 (i) of sub- population, the particle or individual that fitness value is big are taken as the evolution of future generation of two populations
Parent particle and parent individuality：J=1 is made, is iterated computing；
Step S7：If judging fit1 (j) ＜ fit2 (j), fit1 (j)=fit2 (j), pop1 (j)=pop2 (j),
Otherwise fit2 (j)=fit1 (j), pop2 (j)=pop1 (j), step S8 is continued executing with；
Step S8：Carry out the renewal of the sub- population particle maximum adaptation angle value of BPSO：Compare the sub- population particle fitness of BPSO
The size of value and particle maximum adaptation angle value：If fit1 (j) ＞ pbest(j), then pbest(j)=fit1 (j), popbest (j)=
Pop1 (j), continues executing with step S9, is otherwise transferred to and performs step S10；
Step S9：Carry out the renewal of the sub- population maximum adaptation angle value of BPSO：Compare the sub- population particle fitness values of BPSO and
The size of colony's maximum adaptation angle value：Judge fit1 if (j) ＞ gbest, then gbest=fit1 (j), gpopbest=pop1 (j),
Continue executing with step S10；Otherwise step S10 is directly performed；
Step S10：J=j+1 is made, if judging j ＞ N, performs step S11, otherwise returns and performs step S7；
Step S11：K=k+1 is made, is judged whether if k≤T, step S5 is performed if satisfied, then returning, continues to evolve；If
It is unsatisfactory for, then terminates to evolve, exports colony optimal location gpopbest.
Further, in the step S3, the particle fitness values of the sub- populations of described BPSO and GA populations
The calculating process of ideal adaptation angle value is as follows：
In formula：Ij(s) it is the SOT state of termination function of j-th of terminal, j=1,2,3...D, D are total number of terminals, and j-th eventually
Circuit between end and power supply is referred to as the lines upstream of the terminal, and the circuit between line end is referred to as the line downstream of the terminal
Road, siEncoded for the sector status of the i-th section, a total of D section circuit, Π is logic or computing, then above formula is expressed as jth
Individual terminal downstream line state at least one be 1 when, Ij(s) it is just 1, is otherwise just 0；
Above formula is substituted into following formula fitness function：
In formula：Fit (n) represents the fitness value of n-th of individual, and n=1,2,3...N, M take twice of total number of terminals, i.e. M
The arithmetic number of=2D, η between [0,1], referred to as weight coefficient.
Further, in the step S5, to the particle position pop1 (i) and particle rapidity v of the sub- populations of BPSOiEnter
Row renewal, it is so as to form the process of the sub- populations of BPSO of a new generation：
Step SA51：I=1 is made, starts interative computation；
Step SA52：According to previous generation particle optimal location popbest (i), colony optimal location gpopbest and particle
Position pop1 (i) calculates the particle rapidity v of the BPSO population at individual of a new generationiWith particle position pop1 (i), circular
It is as follows：
In formula:I=1,2,3...N, k represent that current iteration number, ω are inertia weight, c1And c2For Studying factors, ξ1、
ξ2WithRandom number all between section [0,1],Represent to tie up in d during i-th of particle iteration kth time respectively
Particle rapidity and particle position；Represent the particle optimal location tieed up during i-th of particle iteration kth time in d；Represent the colony's optimal location tieed up during iteration kth time in d；WhereinFunction is as follows：
Step SA53：I-th of particle of the sub- populations of BPSO of a new generation is substituted into calculating particle in fitness value function to fit
Answer angle value pop1 (i)；
Step SA54：I=i+1 is made, if judging i ＞ N, stops iteration, performs step S6；Otherwise return and perform step
SA52。
Further, the process of the genetic manipulation of GA populations is as follows in the step S5：
Step SB51：Selection：N*GGAP individual is selected from N number of particle, according to the size of each ideal adaptation angle value,
The bigger individual selected probability of adaptive value is bigger, otherwise smaller.The selected probability of an individual is given by:
In formula：Fit (i) is the ideal adaptation angle value of i-th of individual, and F (i) is the selected probability of the individual, i=1,2,
3...,N；
Step SB52：Intersect：Mix into each individual in GA populations is random pair, it is general to intersect to every a pair of individuals
Rate PX exchanges the chromosome dyad between them, produces offspring individual；
Step SB53：Variation：An individual is randomly choosed in GA populations, it is individual with mutation probability PM for what is chosen
It is other allele to change the genic value on certain some chromogene seat；
Step SB54：Insert again：N*GGAP offspring individual is inserted into N number of parent by the sequence based on fitness size
In body, instead of most unconformable parent individuality, so as to form the population of a new generation；
Step SB55：The population at individual of a new generation is substituted into fitness function respectively and calculates its ideal adaptation angle value.
To achieve the above object, the present invention adopts the following technical scheme that：
The present invention has the advantages that compared with prior art：
1st, BPSOGA of the present invention exchanges shared strategy, two son kinds of BPSO and GA using double Evolution of Population and information
Group first separates and evolved alone, compares individual of two groupy phases with numbering successively after a generation of having evolved, and selects optimum individual to make
The advantages of parent individuality evolved for the next generation, forming information sharing, and taking full advantage of each algorithm；
2nd, the present invention can supervise jointly in evolution engineering, i.e., wherein a kind of algorithm tend to be absorbed in it is another during local convergence
Kind algorithm helps it to jump out, and is drawn close to globally optimal solution, and " immature oils " phenomenon is reduced while convergence rate is improved
Occur；
3rd, the present invention has certain fault-tolerance, and event can be still accurately positioned out for the distortion information of SOT state of termination coding
Hinder section.
Brief description of the drawings
Fig. 1 is inventive algorithm flow chart.
Fig. 2 is the distribution line schematic diagram containing Wind turbines of the embodiment of the present invention.
Fig. 3 is that section 3 of the present invention breaks down, and when the SOT state of termination coding of upload is not distorted, is calculated according to BPSOGA
The individual adaptation degree Distribution value figure that method is drawn.
Fig. 4 is that section 3 of the present invention breaks down, and when the SOT state of termination coding of upload is not distorted, is calculated according to BPSOGA
The dimension state value figure that method is drawn.
Fig. 5 is that section 3 of the present invention breaks down, when the SOT state of termination coding of upload is distorted, according to BPSOGA algorithms
The individual adaptation degree Distribution value figure drawn.
Fig. 6 is that section 3 of the present invention breaks down, when the SOT state of termination coding of upload is distorted, according to BPSOGA algorithms
The dimension state value figure drawn.
Fig. 7 is tri- kinds of algorithm the convergence speed contrast tables of BPSO, GA, BPSOGA of the present invention.
Embodiment
Below in conjunction with the accompanying drawings and embodiment the present invention will be further described.
Fig. 1 is refer to, the present invention provides a kind of distribution line failure Section Location based on BPSO and GA, its feature
It is to comprise the following steps：
Step S1：Obtain the SOT state of termination coding I for treating section positioning distribution linej, j=1,2,3..., D, D is that terminal is total
Number, the D=30 in the present embodiment, SOT state of termination coding Ij=[1 1100000000000000000
0 0 0 0 0 0 0 0 0 0]；
Step S2：Using BPSOGA search spaces solution dimension as treat section position distribution line in total number of terminals D and area
The sum of section circuit, three is all 30, using the dimension state value of space solution as sector status encoded radio, and parameter is carried out just
Beginningization：The sub- population scale N of BPSO1=50, inertia weight ω=0.9, Studying factors c1And c2, c1=c2=1.5；GA populations are advised
Mould N2=50, crossover probability PX=0.7, again mutation probability PM=0.2, insertion factor GGAP=0.9, maximum iteration T=
100, take N=50；
Step S3：The sub- populations of BPSO and the initialization of GA populations：Particle position in the sub- populations of random initializtion BPSO
Pop1 (i) and particle rapidity vi, and calculate particle fitness value fit1 (i), particle optimal location popbest (i) and particle most
Big fitness value pbest(i), colony's optimal location gpopbest and colony maximum adaptation angle value gbest；Random initializtion GA is planted
Group individual solution pop2 (i) and ideal adaptation angle value fit2 (i), i=1,2,3...50；Particle position pop1 (i) in wherein BPSO
Binary coding is all used with individual solution pop2 (i) in GA, there is identical dimension, all characterizes respective segments in distribution line
Sector status encodes；
The calculating process of the particle fitness value of the sub- populations of described BPSO and the ideal adaptation angle value of GA populations is as follows：
In formula：Ij(s) it is the SOT state of termination function of j-th of terminal, j=1,2,3...30, total number of terminals 30, j-th
Circuit between terminal and power supply is referred to as the lines upstream of the terminal, and the circuit between line end is referred to as the downstream of the terminal
Circuit, siEncoded for the sector status of the i-th section, a total of 30 section circuits, Π is logic or computing, then above formula is expressed as
J-th terminal downstream line state at least one when being 1, Ij(s) it is just 1, is otherwise just 0；
Above formula is substituted into following formula fitness function：
In formula：Fit (n) represents the fitness value of n-th of individual, and n=1,2,3...50, M take twice of total number of terminals, M
The arithmetic number of=60, η between [0,1], referred to as weight coefficient, η=0.5.
Step S4：K=1 is made, is iterated computing；
Step S5：To the particle position pop1 (i) and particle rapidity v of the sub- populations of BPSOiIt is updated, forms a new generation
The sub- populations of BPSO, and update the particle fitness value fit1 (i) of the sub- population of BPSO of new generation；Meanwhile to GA populations
Genetic manipulation is carried out, obtains the GA populations of a new generation, and update the ideal adaptation angle value of the GA populations of new generation
fit2(i)；
In the step S5, to the particle position pop1 (i) and particle rapidity v of the sub- populations of BPSOiIt is updated, so as to
The process of the sub- populations of BPSO for forming a new generation is：
Step SA51：I=1 is made, starts interative computation；
Step SA52：According to previous generation particle optimal location popbest (i), colony optimal location gpopbest and particle
Position pop1 (i) calculates the particle rapidity v of the BPSO population at individual of a new generationiWith particle position pop1 (i), circular
It is as follows：
In formula:I=1,2,3...50, k represent current iteration number, and ω is inertia weight, ω=0.9, c1And c2For study
The factor, c1=c2=1.5, ξ1、ξ2WithRandom number all between section [0,1],I-th is represented respectively
In the particle rapidity and particle position of d dimensions during sub- iteration kth time；When representing i-th of particle iteration kth time
The particle optimal location of d dimensions；Represent the colony's optimal location tieed up during iteration kth time in d；WhereinFunction is as follows：
Step SA53：I-th of particle of the sub- populations of BPSO of a new generation is substituted into calculating particle in fitness value function to fit
Answer angle value pop1 (i)；
Step SA54：I=i+1 is made, if judging i ＞ 50, stops iteration, performs step S6；Otherwise return and perform step
Rapid SA52.
The process of the genetic manipulation of GA populations is as follows in the step S5：
Step SB51：Selection：45 individuals are selected from 50 particles, according to the size of each ideal adaptation angle value, are fitted
It is bigger that bigger individual selected probability should be worth, on the contrary it is smaller.The selected probability of an individual is given by:
In formula：Fit (i) is the ideal adaptation angle value of i-th of individual, and F (i) is the selected probability of the individual, i=1,2,
3...,50；
Step SB52：Intersect：Mix into each individual in GA populations is random pair, it is general to intersect to every a pair of individuals
Rate PX=0.7 exchanges the chromosome dyad between them, produces offspring individual；
Step SB53：Variation：An individual is randomly choosed in GA populations, it is individual with mutation probability PM for what is chosen
=0.2 genic value changed on certain some chromogene seat is other allele；
Step SB54：Insert again：45 offspring individuals are inserted into 50 parent individualities by the sequence based on fitness size
In, instead of most unconformable parent individuality, so as to form the population of a new generation；
Step SB55：The population at individual of a new generation is substituted into fitness function respectively and calculates its ideal adaptation angle value.
Step S6：The particle fitness value fit1 (i) of population more sub- than the BPSO of newer generation and corresponding GA of new generation
The ideal adaptation angle value fit2 (i) of sub- population, the particle or individual that fitness value is big are taken as the evolution of future generation of two populations
Parent particle and parent individuality：J=1 is made, is iterated computing；
Step S7：If judging fit1 (j) ＜ fit2 (j), fit1 (j)=fit2 (j), pop1 (j)=pop2 (j),
Otherwise fit2 (j)=fit1 (j), pop2 (j)=pop1 (j), step S8 is continued executing with；
Step S8：Carry out the renewal of the sub- population particle maximum adaptation angle value of BPSO：Compare the sub- population particle fitness of BPSO
The size of value and particle maximum adaptation angle value：If fit1 (j) ＞ pbest(j), then pbest(j)=fit1 (j), popbest (j)=
Pop1 (j), continues executing with step S9, is otherwise transferred to and performs step S10；
Step S9：Carry out the renewal of the sub- population maximum adaptation angle value of BPSO：Compare the sub- population particle fitness values of BPSO and
The size of colony's maximum adaptation angle value：Judge fit1 if (j) ＞ gbest, then gbest=fit1 (j), gpopbest=pop1 (j),
Continue executing with step S10；Otherwise step S10 is directly performed；
Step S10：J=j+1 is made, if judging j ＞ 50, performs step S11, otherwise returns and performs step S7；
Step S11：K=k+1 is made, if judging k≤100, step S5 is performed if satisfied, then returning, continues to evolve；If no
Meet, then terminate to evolve, export colony optimal location gpopbest.
Fig. 2 is the distribution line schematic diagram containing Wind turbines of the embodiment of the present invention, when section 3 breaks down, upload
When SOT state of termination coding is not distorted, the individual adaptation degree Distribution value figure and dimension state value figure that are drawn according to BPSOGA algorithms
Respectively as shown in accompanying drawing 3 and Fig. 4, individual fitness value is up to 59.5, and corresponding individual numbering is 17, according to dimension state
Value figure understands that the 17th dimension state value corresponding to individual is [0 0100000000000000000
000000000 0], it can thus be concluded that section 3 breaks down.
In an alternative embodiment of the invention, same section 3 is broken down, but the SOT state of termination coding uploaded is distorted
For [1 1100000000000000000000000010 0] when, according to BPSOGA
The individual adaptation degree Distribution value figure and dimension state value figure that algorithm obtains are respectively as shown in accompanying drawing 5 and Fig. 6, it is known that this method remains unchanged
Fault section can be accurately determined.
In an alternative embodiment of the invention, same failure is continuously entered respectively using tri- kinds of algorithms of BPSO, GA, BPSOGA
The fault location that row is 30 times, obtained three kinds of algorithms appearance " immature oils " number contrast table is as shown in table 1, convergence rate ratio
It is as shown in Figure 7 compared with figure.
There is " immature oils " number contrast table in 1 three kinds of algorithms of table
| Algorithm types | BPSO | GA | BPSOGA |
| There is " immature oils " number | 3 | 12 | 0 |
The foregoing is only presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, it should all belong to the covering scope of the present invention.
Claims (3)
1. a kind of distribution line failure Section Location based on BPSO and GA, it is characterised in that comprise the following steps：
Step S1：Obtain the SOT state of termination coding I for treating section positioning distribution linej, j=1,2,3..., D, D is total number of terminals；
Step S2：Using BPSOGA search spaces solution dimension as treat section position distribution line in total number of terminals D and section line
The sum on road, initialized using the dimension state value of space solution as sector status encoded radio, and to parameter：The sub- populations of BPSO
Scale N1, inertia weight ω, Studying factors c1And c2；GA population scales N2, crossover probability PX, mutation probability PM, insert again because
Sub- GGAP, maximum iteration T, take N1=N2=N；
Step S3：The sub- populations of BPSO and the initialization of GA populations：Particle position pop1 (i) in the sub- populations of random initializtion BPSO
With particle rapidity vi, and calculate particle fitness value fit1 (i), particle optimal location popbest (i) and particle maximum adaptation degree
Value pbest(i), colony's optimal location gpopbest and colony maximum adaptation angle value gbest；Random initializtion GA population at individual solutions
Pop2 (i) and ideal adaptation angle value fit2 (i), i=1,2,3...N；Individual in particle position pop1 (i) and GA in wherein BPSO
Solution pop2 (i) uses binary coding, has identical dimension, and the sector status for all characterizing respective segments in distribution line is compiled
Code；
Step S4：K=1 is made, is iterated computing；
Step S5：To the particle position pop1 (i) and particle rapidity v of the sub- populations of BPSOiIt is updated, forms the BPSO of a new generation
Sub- population, and update the particle fitness value fit1 (i) of the sub- population of BPSO of new generation；Meanwhile GA populations are carried out
Genetic manipulation, the GA populations of a new generation are obtained, and update the ideal adaptation angle value fit2 of the GA populations of new generation
(i)；
Step S6：The particle fitness value fit1 (i) of population more sub- than the BPSO of newer generation and corresponding GA kinds of new generation
The ideal adaptation angle value fit2 (i) of group, the particle or individual that fitness value is big are taken as two populations parent of future generation evolved
Particle and parent individuality：J=1 is made, is iterated computing；
Step S7：Judge fit1 if (j) ＜ fit2 (j), fit1 (j)=fit2 (j), pop1 (j)=pop2 (j), otherwise
Fit2 (j)=fit1 (j), pop2 (j)=pop1 (j), continue executing with step S8；
Step S8：Carry out the renewal of the sub- population particle maximum adaptation angle value of BPSO：Compare the sub- population particle fitness values of BPSO with
The size of particle maximum adaptation angle value：If fit1 (j) ＞ pbest(j), then pbest(j)=fit1 (j), popbest (j)=pop1
(j) step S9, is continued executing with, is otherwise transferred to and performs step S10；
Step S9：Carry out the renewal of the sub- population maximum adaptation angle value of BPSO：Compare the sub- population particle fitness values of BPSO and colony
The size of maximum adaptation angle value：Judge fit1 if (j) ＞ gbest, then gbest=fit1 (j), gpopbest=pop1 (j), continue
Perform step S10；Otherwise step S10 is directly performed；
Step S10：J=j+1 is made, if judging j ＞ N, performs step S11, otherwise returns and performs step S7；
Step S11：K=k+1 is made, if judging k≤T, step S5 is performed if satisfied, then returning, continues to evolve；If not satisfied,
Then terminate to evolve, export colony optimal location gpopbest；
In the step S5, to the particle position pop1 (i) and particle rapidity v of the sub- populations of BPSOiIt is updated, so as to be formed
The process of the sub- populations of BPSO of a new generation is：
Step SA51：I=1 is made, starts interative computation；
Step SA52：According to previous generation particle optimal location popbest (i), colony optimal location gpopbest and particle position
Pop1 (i) calculates the particle rapidity v of the BPSO population at individual of a new generationiWith particle position pop1 (i), circular is such as
Under：
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</mrow>
<mo>+</mo>
<msub>
<mi>c</mi>
<mn>2</mn>
</msub>
<msub>
<mi>&xi;</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<msubsup>
<mi>gpopbest</mi>
<mi>d</mi>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>pop</mi>
<mi>d</mi>
<mi>k</mi>
</msubsup>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
1
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>pop</mi>
<mi>d</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mn>1</mn>
</mrow>
</mtd>
<mtd>
<mrow>
<msubsup>
<mi>r</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo><</mo>
<mi>s</mi>
<mi>i</mi>
<mi>g</mi>
<mi>m</mi>
<mi>o</mi>
<mi>i</mi>
<mi>d</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>pop</mi>
<mi>d</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mn>0</mn>
</mrow>
</mtd>
<mtd>
<mrow>
<msubsup>
<mi>r</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>&GreaterEqual;</mo>
<mi>s</mi>
<mi>i</mi>
<mi>g</mi>
<mi>m</mi>
<mi>o</mi>
<mi>i</mi>
<mi>d</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
In formula:I=1,2,3...N, k represent that current iteration number, ω are inertia weight, c1And c2For Studying factors, ξ1、ξ2WithRandom number all between section [0,1],The grain tieed up during i-th of particle iteration kth time in d is represented respectively
Sub- speed and particle position；Represent the particle optimal location tieed up during i-th of particle iteration kth time in d；Represent the colony's optimal location tieed up during iteration kth time in d；WhereinFunction is as follows：
<mrow>
<mi>s</mi>
<mi>i</mi>
<mi>g</mi>
<mi>m</mi>
<mi>o</mi>
<mi>i</mi>
<mi>d</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>0.98</mn>
</mtd>
<mtd>
<mrow>
<msubsup>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>></mo>
<mn>4</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mn>1</mn>
<mrow>
<mn>1</mn>
<mo>+</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<msubsup>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
</mrow>
</msup>
</mrow>
</mfrac>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<mn>4</mn>
<mo>&le;</mo>
<msubsup>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>&le;</mo>
<mn>4</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<mn>0.98</mn>
</mrow>
</mtd>
<mtd>
<mrow>
<msubsup>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo><</mo>
<mo>-</mo>
<mn>4</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Step SA53：I-th of particle of the sub- populations of BPSO of a new generation is substituted into particle fitness is calculated in fitness value function
Value pop1 (i)；
Step SA54：I=i+1 is made, if judging i ＞ N, stops iteration, performs step S6；Otherwise return and perform step
SA52。
2. a kind of distribution line failure Section Location based on BPSO and GA according to claim 1, its feature exist
In：In the step S3, the meter of the particle fitness value of the sub- populations of described BPSO and the ideal adaptation angle value of GA populations
Calculation process is as follows：
<mrow>
<msub>
<mi>I</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munder>
<mo>&Pi;</mo>
<mi>j</mi>
</munder>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
</mrow>
In formula：Ij(s) it is the SOT state of termination function of j-th of terminal, j=1,2,3...D, D are total number of terminals, j-th of terminal and electricity
Circuit between source is referred to as the lines upstream of the terminal, and the circuit between line end is referred to as the downstream line of the terminal, si
Encoded for the sector status of the i-th section, a total of D section circuit, Π is logic or computing, then above formula is expressed as j-th of end
Hold downstream line state at least one when being 1, Ij(s) it is just 1, is otherwise just 0；
Above formula is substituted into following formula fitness function：
<mrow>
<mi>f</mi>
<mi>i</mi>
<mi>t</mi>
<mrow>
<mo>(</mo>
<mi>n</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>M</mi>
<mo>-</mo>
<mrow>
<mo>(</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>D</mi>
</munderover>
<mo>|</mo>
<mrow>
<msub>
<mi>I</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msub>
<mi>I</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
</mrow>
<mo>|</mo>
<mo>+</mo>
<mi>&eta;</mi>
<munderover>
<mo>&Sigma;</mo>
<mi>i</mi>
<mi>D</mi>
</munderover>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
In formula：Fit (n) represents the fitness value of n-th of individual, and n=1,2,3...N, M take twice of total number of terminals, i.e. M=
The arithmetic number of 2D, η between [0,1], referred to as weight coefficient.
3. a kind of distribution line failure Section Location based on BPSO and GA according to claim 1, its feature exist
In：The process of the genetic manipulation of GA populations is as follows in the step S5：
Step SB51：Selection：N*GGAP individual is selected from N number of particle, according to the size of each ideal adaptation angle value, is adapted to
The bigger individual selected probability of value is bigger, otherwise smaller, and the selected probability of an individual is given by:
<mrow>
<mi>F</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mrow>
<mi>f</mi>
<mi>i</mi>
<mi>t</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mi>f</mi>
<mi>i</mi>
<mi>t</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
</mrow>
In formula：Fit (i) is the ideal adaptation angle value of i-th of individual, and F (i) is the selected probability of the individual, i=1,2,
3...,N；
Step SB52：Intersect：Mix into each individual in GA populations is random pair, to every a pair of individuals, with crossover probability PX
The chromosome dyad between them is exchanged, produces offspring individual；
Step SB53：Variation：An individual is randomly choosed in GA populations, is changed for the individual chosen with mutation probability PM
Genic value on certain some chromogene seat is other allele；
Step SB54：Insert again：N*GGAP offspring individual is inserted into N number of parent individuality by the sequence based on fitness size
In, instead of most unconformable parent individuality, so as to form the population of a new generation；
Step SB55：The population at individual of a new generation is substituted into fitness function respectively and calculates its ideal adaptation angle value.
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| CN106054019B (en) * | 2016-05-23 | 2017-09-01 | 河南工程学院 | The online Fault Locating Method of power distribution network high fault tolerance based on failure confactor |
| CN106093713A (en) * | 2016-08-24 | 2016-11-09 | 广东工业大学 | A kind of power distribution network section Fault Locating Method containing distributed power source and system |
| CN106405337B (en) * | 2016-11-09 | 2017-11-28 | 西安理工大学 | Inverse distributed power accesses the Fault Locating Method of power distribution network |
| CN107015112A (en) * | 2017-04-10 | 2017-08-04 | 福州大学 | A kind of distribution line failure Section Location based on improvement harmony algorithm |
| CN107229809B (en) * | 2017-07-14 | 2020-09-18 | 山东电力工程咨询院有限公司 | SPD (Surge protective device) optimal configuration method in power plant low-voltage power distribution system based on particle swarm optimization |
| CN108594079A (en) * | 2018-05-02 | 2018-09-28 | 广东电网有限责任公司 | A kind of electrical power distribution network fault location method, device and computer-readable medium |
| CN108710742B (en) * | 2018-05-14 | 2022-06-14 | 南京工程学院 | PGSA-GA hybrid algorithm-based fault section positioning method |
| CN108957246A (en) * | 2018-09-27 | 2018-12-07 | 广东电网有限责任公司 | A kind of electrical power distribution network fault location method based on population |
| CN109598330B (en) * | 2018-12-06 | 2023-04-21 | 湖北鄂电德力电气有限公司 | Minimum collision set method considering topology characteristics of power distribution network |
| CN110133447B (en) * | 2019-06-12 | 2021-03-23 | 河南工程学院 | Power distribution network fault section positioning method based on complementary relaxation constraint model |
| CN113132125B (en) * | 2019-12-30 | 2022-08-23 | 中国移动通信集团四川有限公司 | Fault cause positioning method and device, electronic equipment and storage medium |
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