CN107861026A - A kind of electrical power distribution network fault location method based on hybrid artificial immune system - Google Patents
A kind of electrical power distribution network fault location method based on hybrid artificial immune system Download PDFInfo
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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Abstract
The invention belongs to intelligent power grid technology field, discloses a kind of electrical power distribution network fault location method based on hybrid artificial immune system, and source of trouble both candidate nodes are screened using immunological network method；If obtained source of trouble candidate solution only includes a node, the source of trouble is exported；If obtained source of trouble candidate solution is multiple, the failure subset primarily determined that is further diagnosed using immune algorithm, determines malfunctioning node.Accuracy that the present invention solves the positioning of distribution network failure in the prior art is poor, slow problem, has reached the technique effect of the speed and efficiency that improve fault location.
Description
Technical field
The present invention relates to intelligent power grid technology field, more particularly to a kind of distribution network failure based on hybrid artificial immune system to determine
Position method.
Background technology
Distribution automation is to improve the intelligent important means with self-healing property of power distribution network.Wherein, feeder automation is its master
Want one of function, i.e., after distribution network failure, the fault message that is reported according to ca bin be quickly found out fault section and every
From the rapid power supply for recovering non-faulting dead electricity load.The fault section location of power distribution network is the basis of feeder automation, for carrying
High power supply reliability is significant.
The method of the fault location of techniques of feeder terminal unit in power distribution network unit reporting fault information progress at present is broadly divided into two classes：It is a kind of
It is based on the Fault Locating Method for perfecting information, its principle is fault current diagnostic method, and main results are matrix algorithms, square
Battle array algorithm can realize the fault location of power distribution network rapidly, and still, distribution terminal equipment is installed with outdoor more, and running environment is disliked
It is bad so that fault message fails to report, the possibility reported by mistake is higher, therefore limits the application based on the matrix algorithm for perfecting information；
Another kind of is based on the non-Fault Locating Method for perfecting information, and main results are that the failure based on intelligent algorithm is determined
Position, wherein improved adaptive GA-IAGA can carry out global optimizing solution in the fault location of power distribution network, have good use mistake,
But the genetic algorithm is in the process of implementation, it is iterative search random, without guidance, is as a result easily trapped into local optimum,
Therefore in order to improve the accuracy of fault location and rapidity, it is necessary to improve intelligent algorithm.
The content of the invention
The embodiment of the present application solves existing by providing a kind of electrical power distribution network fault location method based on hybrid artificial immune system
The accuracy that has distribution network failure in technology to position is poor, slow problem.
The embodiment of the present application provides a kind of electrical power distribution network fault location method based on hybrid artificial immune system, using immunological network
Method is screened to source of trouble both candidate nodes；If obtained source of trouble candidate solution only includes a node, the source of trouble is exported；If
Obtained source of trouble candidate solution is multiple, then the failure subset primarily determined that is further diagnosed using immune algorithm, really
Determine malfunctioning node.
Preferably, the immunological network method includes：
(1) node for defining the propagation path that each measuring point is included combines；
(2) obtain signal and be shown as abnormal measuring point, read set of node { xi}；
(3) abnormal measuring point is shown as with the presence or absence of other signals, if it is present return to step (2)；
(4) the common factor ∩ i { x of each abnormal measuring point set of node are calculatedi}；
(5) obtain signal and be shown as normal measuring point, read its set of node { yi}；
(6) normal measuring point is shown as with the presence or absence of other signals, if it is present return to step (5)；
(7) the union ∪ j { y of each normal measuring point set of node are calculatedi}；
(8) operation result in the operation result in step (4) and step (7) is subjected to intersection operation, i.e. (∩ i { xi}
∩∪j{yi), and set S=(∩ i { xi}∩∪j{yj})；
(9) try to achieve source of trouble collection and be combined into ∩ i { xi}-S。
Preferably, the immune algorithm includes：
(1) arrange parameter, fault current is encoded；
(2) initial antibodies group is generated；
(3) antibody population is evaluated；
(4) to memory cells；
(5) parent antibody population is formed；
(6) judge whether to meet termination condition, if meeting termination condition, export optimal antibody, position malfunctioning node；If
Termination condition is unsatisfactory for, then into step (7)；
(7) selected, intersected based on parent antibody population caused by step (5), mutation operation, forming first antibody group；
Memory antibody is obtained from data base, the first antibody group and the memory antibody collectively form secondary antibody group；
(8) step (3) is transferred to, iterations adds 1, and the secondary antibody group is evaluated.
Preferably, the parameter in step (1) includes：The population scale N set according to the scale of power distribution network0；Memory
Cell quantity Nm, the memory cell is to retain the number of optimal antibody after each iteration；Maximum iteration M；According to power network
Topological structure setting antibody coding length L；Crossover probability Pc；Mutation probability Pm；Antibody similarity evaluation parameter e；Iteration time
Number T.
Preferably, described in step (1) is encoded specially to fault current：Gene pairs answers single feeder line section
State；Antibody is made up of the gene, and the antibody is the morphogenetic vector of shape of all feeder line sections in power distribution network；It is if described
Feeder line section fault, then the state of the feeder line section is 1, if the feeder line section does not have failure, the feeder line section
State is 0.
Preferably, the generation scale of the initial antibodies group in step (2) is the kind set according to the scale of power distribution network
Group's scale N0With memory cell quantity NmSummation.
Preferably, described in step (3) carries out evaluation to antibody population includes：Construct evaluation function；Calculating antibody is with resisting
Affinity between original, the affinity between antibody and antibody, antibody concentration, the expectation breeding potential of antibody；
The evaluation function is：
In formula：Fit(SB) it is evaluation function corresponding to each antibody in antibody population；SBFor single antibody, SB(i) it is antibody
Each gene；N1For the sum of feeder switch in power distribution network, N2For the sum of feeder line section in power distribution network, IjFor in power distribution network
The electric current of individual feeder switch gets over limit information；For the state of the feeder switch determined in power distribution network by feeder line sector status；
ω is weight coefficient,Represent that weight coefficient is multiplied by the sum of fault feeder hop count；
Affinity between the antibody and antigen is：
Av=1/Fit(SB) (2)
In formula：AVAffinity between antibody and antigen；
Affinity between the antibody and antibody is：
In formula：KV,SRepresent the affinity between antibody v and antibody s, SV,SFor antibody v be in antibody s it is mutually homogenic
Position and gene identical digit；L is antibody coding length；
The antibody concentration is：
In formula：CvFor antibody concentration, e is antibody similarity evaluation parameter, HV,SRepresent whether antibody v and antibody s is similar, N
For antibody sum；
The expectation breeding potential of the antibody is：
Pv=Av/Cv (6)
In formula：PvFor the expectation breeding potential of antibody.
Preferably, it is specially to memory cells described in step (4)：Antibody population is entered according to the affinity with antigen
Row descending arranges, and takes preceding NmIndividual antibody adds memory cell.
Preferably, parent antibody population is formed described in step (5) is specially：By antibody population, desirably breeding potential is dropped
Sequence arranges, and takes preceding N0Individual antibody forms the parent antibody population.
Preferably, the termination condition in step (6) is：It is expected that the maximum antibody of breeding potential exceedes during evolution
Affinity between the iterations, or the antibody and antigen of adjacent generations immunological network cell, which no longer changes or changed, to be less than
Preset value.
The one or more technical schemes provided in the embodiment of the present application, have at least the following technical effects or advantages：
In the embodiment of the present application, it is proposed that the distribution network failure positioning of hybrid artificial immune system, the failure of power distribution network is determined
Position is divided into the progress of two steps.First step coarse positioning：According to the network structure of fault- traverse technique, carried out using the method for immunological network
The screening of source of trouble both candidate nodes.If obtaining source of trouble candidate solution according to the result of coarse diagnosis only includes a node,
Trouble point determines；Otherwise second step Precise Diagnosis is carried out.Second step Precise Diagnosis：Using immune algorithm to the possibility that primarily determines that
Failure subset carries out further diagnosis to determine the malfunctioning node.The application is matched somebody with somebody using immunological network to more power supply open loop operations
Power network carries out fault location, carries out tentative diagnosis to power distribution network by the method for immunological network, reduces the computing of immune algorithm
Amount, improves the efficiency to fault location.Individual is evaluated using affinity of the immune algorithm according to individual, according to individual
Affinity and the concentration selection individual that is intersected, made a variation, increase population diversity, avoid algorithm and be quickly absorbed in part
It is optimal, improve the speed and efficiency of fault location.
Brief description of the drawings
It is required in being described below to embodiment to use in order to illustrate more clearly of the technical scheme in the present embodiment
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are one embodiment of the present of invention, for this area
For those of ordinary skill, on the premise of not paying creative work, other accompanying drawings can also be obtained according to these accompanying drawings.
Fig. 1 is a kind of flow of the electrical power distribution network fault location method based on hybrid artificial immune system provided in an embodiment of the present invention
Figure；
Fig. 2 is double electricity in a kind of electrical power distribution network fault location method based on hybrid artificial immune system provided in an embodiment of the present invention
The schematic diagram of source open loop operation power distribution network.
Embodiment
The embodiment of the present application solves existing by providing a kind of electrical power distribution network fault location method based on hybrid artificial immune system
The accuracy that has distribution network failure in technology to position is poor, slow problem.
The technical scheme of the embodiment of the present application is in order to solve the above technical problems, general thought is as follows：
A kind of electrical power distribution network fault location method based on hybrid artificial immune system, source of trouble candidate is saved using immunological network method
Point is screened；If obtained source of trouble candidate solution only includes a node, the source of trouble is exported；If obtained source of trouble candidate
Solve to be multiple, then the failure subset primarily determined that is further diagnosed using immune algorithm, determine malfunctioning node.
The present invention proposes the distribution network failure positioning of hybrid artificial immune system, and the fault location of power distribution network is divided into two steppings
OK.First step coarse positioning：According to the network structure of fault- traverse technique, source of trouble candidate's section is carried out using the method for immunological network
The screening of point.If obtaining source of trouble candidate solution according to the result of coarse diagnosis only includes a node, trouble point determines；It is no
Then carry out second step Precise Diagnosis.Second step Precise Diagnosis：The possible breakdown subset primarily determined that is carried out using immune algorithm
Further diagnose to determine the malfunctioning node.The application carries out failure using immunological network to the power distribution network of more power supply open loop operations
Positioning, tentative diagnosis is carried out to power distribution network by the method for immunological network, reduces the operand of immune algorithm, improves a pair event
Hinder the efficiency of positioning.Individual is evaluated using affinity of the immune algorithm according to individual, according to the affinity and dense of individual
The individual that degree selection is intersected, made a variation, increases population diversity, avoids algorithm and be quickly absorbed in local optimum, improve
The speed and efficiency of fault location.
In order to be better understood from above-mentioned technical proposal, below in conjunction with Figure of description and specific embodiment to upper
Technical scheme is stated to be described in detail.
A kind of electrical power distribution network fault location method based on hybrid artificial immune system is present embodiments provided, using immunological network method
Source of trouble both candidate nodes are screened；If obtained source of trouble candidate solution only includes a node, the source of trouble is exported；If
The source of trouble candidate solution arrived is multiple, then the failure subset primarily determined that is further diagnosed using immune algorithm, it is determined that
Malfunctioning node.
Wherein, the immunological network method includes：
(1) node for defining the propagation path that each measuring point is included combines；
(2) obtain signal and be shown as abnormal measuring point, read set of node { xi}；
(3) abnormal measuring point is shown as with the presence or absence of other signals, if it is present return to step (2)；
(4) the common factor ∩ i { x of each abnormal measuring point set of node are calculatedi}；
(5) obtain signal and be shown as normal measuring point, read its set of node { yi}；
(6) normal measuring point is shown as with the presence or absence of other signals, if it is present return to step (5)；
(7) the union ∪ j { y of each normal measuring point set of node are calculatedi}；
(8) operation result in the operation result in step (4) and step (7) is subjected to intersection operation, i.e. (∩ i { xi}
∩∪j{yj), and set S=(∩ i { xi}∩∪j{yj})；
(9) try to achieve source of trouble collection and be combined into ∩ i { xi}-S。
Wherein, the immune algorithm includes：
(1) arrange parameter, fault current is encoded；
(2) initial antibodies group is generated；
(3) antibody population is evaluated；
(4) to memory cells；
(5) parent antibody population is formed；
(6) judge whether to meet termination condition, if meeting termination condition, export optimal antibody, position malfunctioning node；If
Termination condition is unsatisfactory for, then into step (7)；
(7) selected, intersected based on parent antibody population caused by step (5), mutation operation, forming first antibody group；
Memory antibody is obtained from data base, the first antibody group and the memory antibody collectively form secondary antibody group；
(8) step (3) is transferred to, iterations adds 1, and the secondary antibody group is evaluated.
Whether the present invention is multiple first by immunological network method failure judgement source as shown in figure 1, after failure generation；If
It is no, then export the source of trouble；If so, then enter immune algorithm.Then in turn through lower column processing：Fault current coding, produce just
Beginning antibody population, carry out antibody evaluation calculate (including antibody concentration and antibody desired value calculate), to memory cells, form father
For colony；Now, judge whether to meet termination condition；If meeting termination condition, failure is exported；If being unsatisfactory for termination condition,
Then parent antibody is selected, intersected and made a variation to form new antibody, is then again introduced into antibody evaluation and is calculated link, is followed
Ring processing, until output failure.
In order to be best understood from the present invention, links are explained respectively below.
1. fault location is carried out to the power network of more power supply open loop operations using immunological network method
The present invention proposes the thought of immunological network structure, and specific method is：During system jam, the influence model of failure
Enclose path of the meeting from the source of trouble along fault propagation and be delivered to each node that may be impacted；Fault impact is along propagation
Path branches spread；According to the source of trouble and the influence path of fault propagation, the failure biography for carrying out fault propagation analysis can be established
Broadcast network.The present invention proposes that immunological network structure to carry out fault location to the region, improves the search efficiency of algorithm.
In more power supply open loop power distribution networks, the fault message of tie point is propagated to a plurality of individual path.As shown in Fig. 2 should
Fault propagation network includes 8 nodes and 3 measuring points, and node A3 has multiple excitation inputs, from node A4 and A6.
It is assumed that failure is propagated from left to right, then measuring point d3, d2, d1 source of trouble candidate point set is respectively：A1, A2,
A3, A6, A7, A8 }, { A1, A2, A3, A4, A5 }, { A1 }.
If measuring point d1 failure signals, then measuring point d2 and d3 failure signals, then ∩ i { xi}={ A1, thereforeThe source of trouble is ∩ i { xi- S={ A1}。
If measuring point d2 failure signals, measuring point d1, measuring point d3 are shown normally, then ∩ i { xi}={ A1, A2, A3,
A5 }, ∪ j { yi}={ A1, A2, A3, A5, A7, A8 }, therefore S=(∩ i { xi})∩(∪j{yi)={ A1, A2, A3 }, failure
Source is ∩ i { xi- S={ A4, A5 }.Then probability sorting is pressed to the selected source of trouble.
Relation between measuring point signal and source of trouble candidate solution is as shown in table 1：
Note：√：Normally；×：It is abnormal；√→×：After the propagation time, state is changed into abnormal from normal
The measurement signal of table 1 and the relation in Candidate Fault source
In more power supply open loop power distribution networks, fault message, while reporting fault information are received by measuring point signal first, then
Fault location is carried out to Candidate Fault source with immune algorithm.
2. the distribution network failure positioning based on immune algorithm
In order to simplify problem, fault location is converted into binary morphological space.Fault location based on immune algorithm is
It is made up of parts such as coding, antibody evaluation, immune operation and decodings.
2.1 antibody coding
In distribution network failure positioning, feeder line section is amount to be asked, and therefore, in immune algorithm, gene pairs answers single section to present
The state of line, antibody have gene composition, i.e., the morphogenetic vector of shape of all feeder line sections in power distribution network.The length of antibody is by presenting
The sector number of line determines that each gene pairs of antibody answers the sector status of feeder line, and 1 represents the feeder line section fault, and 0 represents do not have
It is faulty.Finally, the form of expression of antibody is [0010 ... 00].
2.2 antibody are evaluated
Two parts are included to antibody evaluation：First, the affinity of calculating antibody and antigen describes the excellent of antibody；Second,
Affinity between calculating antibody and antibody describes the similarity between antibody, so as to solve the concentration of antibody to describe antibody
Diversity.Constructed according to the concentration of the affinity and antibody of antibody and antigen and it is expected breeding potential, select parent individuality accordingly.
2.2.1 evaluation function
The key that antibody calculates with antigen affinity is the construction of evaluation function, and evaluation function is based upon feeder line area
Section state determines feeder switch state and the out-of-limit difference minimum of the actual electric currents that upload of FTU to construct.What the present invention used
Evaluation function is：
In formula：Fit(SB) fitness, i.e. evaluation of estimate of the evaluation function to feasible solution corresponding to each antibody in the antibody population of position；
SBFor single antibody, i.e., the solution vector of all feeder line sector status compositions, SB(i) each gene of antibody is represented, correspond to match somebody with somebody
The state in each feeder line section in power network, value are 1 expression failure, and value is 0 expression normal condition；N1Opened for feeder line in power distribution network
The sum of pass, N2For the sum of feeder line section in power distribution network.IjThe electric current gone out in power distribution network feeder switch, which gets over limit information, (to be had
Electric current is in limited time more 1, is not then 0)；It is then the shape of the feeder switch determined in power distribution network by feeder line sector status
State, it is the individual feeder line sector status or function of switch downstream, for example switchs S1There is feeder line section b in downstream, thenIt is 1 that feeder line section a, b state, which have one, thenFor 1；ω is the positive power system that value is less than 1
Number, weight coefficient value are 0.4,Represent that weight coefficient is multiplied by the sum of fault feeder hop count.
2.2.2 the affinity of antibody and antigen
Affinity between antibody and antigen, is represented by：
AV=1/Fit(SB) (2)
In formula, Fit(SB) represent the object function of minimum optimization problem；AVFor representing the matching between antibody and antigen
Excellent, the A of caused feasible solution in degree, i.e. initial solution group or iterative processVBigger, the feasible solution is better, AVIt is smaller, should
Feasible solution is poorer.
2.2.3 the affinity of antibody and antibody
The similarity between affinity reflection feasible solution and feasible solution between antibody and antibody, two antibody have more than n
Position or continuous n positions coding are identical, then it represents that two kinds of antibody are approximately the same, otherwise represent two kinds of antibody differences, i.e.,：
In formula：SV,SIt is that identical gene location and gene identical digit are in antibody v and antibody s；L is antibody coding
Length.Affinity K between antibody v and antibody sV,SBigger, then two antibody are more similar；It is conversely, more dissimilar.
2.2.4 antibody concentration
Antibody concentration CVSimilar antibodies proportion i.e. in antibody population, has reacted the diversity of antibody population, and calculation formula is：
In formula：E is antibody similarity evaluation parameter；SV,SRepresent to be in identical gene location and base in antibody v and antibody s
Because of identical digit；HV,SRepresent whether antibody v and antibody s is similar, is to take 1, otherwise takes 0；N is antibody sum.
2.2.5 the expectation breeding degree of antibody
In antibody population, the expectation breeding potential of each antibody is by the affinity between antibody and antigen and antibody concentration two
Divide and together decide on, i.e.,：
Pv=Av/Cv (6)
In formula：AvRepresent the affinity between antibody and antigen；CvFor antibody concentration.Antibody is with resisting it can be seen from formula (6)
Former affinity AvIt is bigger, it is selected as intersecting, the possibility for the individual that makes a variation is bigger；Antibody concentration CvIt is bigger, then it is expected breeding
Rate PvIt is smaller, it is selected as intersecting, the possibility of variation is smaller.The antibody high with antigen affinity was so both promoted, together
When also inhibits the high antibody of concentration, ensure that the diversity of antibody.
Immune algorithm when suppressing high concentration antibody, with antigen affinity higher antibody may it is high because its concentration and
It is suppressed, so as to cause the optimal solution tried to achieve to be lost, therefore uses excellent individual retention strategy, increase data base, every
, will antibody deposit data bases some with antigen affinity highest during secondary renewal data base.The establishment of data base also avoids simultaneously
Intersection, mutation process make colony degenerate.
2.3 immune operation
Immune operation process includes selection, intersection and mutation operation process.The selected probability of individual calculates with formula (6)
Expectation breeding potential PvPositive correlation.Crossover operation, crossover probability P are carried out from single-point interior extrapolation methodc0.8 is taken, random selection becomes dystopy
Enter row variation mutation probability Pm0.05 is taken, makes a variation and provides chance for the generation of new explanation, is changed into 1 by 0 or is changed into 0 by 1.
2.4 immune algorithm distribution network failure position fixing process
(1) parameter setting.Population scale N is set according to the scale of power distribution network0；Memory cell quantity Nm, i.e., after each iteration
Retain the number of optimal antibody；Maximum iteration M；Structure determination antibody coding length L is mended according to opening up for power network；Intersection generally carries
Rate Pc；Mutation probability Pm；Antibody similarity evaluation parameter e；Iterations T=1.
(2) initialize.Generation scale is N0+NmInitial antibodies group.Because initial memory cell is sky, need to randomly generate,
So initial antibodies group includes memory cell.
(3) each antibody of above-mentioned colony is evaluated.Cohesion A between calculating antibody v and antigenv, antibody v with
Affinity K between antibody sv,s, and then calculating antibody concentration CvWith expectation breeding potential Pv。
(4) to memory cells.By the N best with antigen affinitymIndividual antibody adds memory cell.
(5) parent colony is formed.The breeding potential of antibody population desirably is subjected to descending arrangement, takes preceding N0Individual antibody is formed
Parent colony.
(6) judge whether to meet termination condition, be to terminate, export optimal antibody, position the section to break down；Otherwise
Continue to operate in next step.
(7) colony updates.Selected, intersected and mutation operation obtains newly based on parent antibody population caused by step (5)
Colony, then memory antibody is obtained from data base, collectively form antibody population of new generation.
(8) step (3), and T=T+1 are gone to.
A kind of electrical power distribution network fault location method based on hybrid artificial immune system provided in an embodiment of the present invention comprises at least such as
Lower technique effect：
In the embodiment of the present application, it is proposed that the distribution network failure positioning of hybrid artificial immune system, the failure of power distribution network is determined
Position is divided into the progress of two steps.First step coarse positioning：According to the network structure of fault- traverse technique, carried out using the method for immunological network
The screening of source of trouble both candidate nodes.If obtaining source of trouble candidate solution according to the result of coarse diagnosis only includes a node,
Trouble point determines；Otherwise second step Precise Diagnosis is carried out.Second step Precise Diagnosis：Using immune algorithm to the possibility that primarily determines that
Failure subset carries out further diagnosis to determine the malfunctioning node.The application is matched somebody with somebody using immunological network to more power supply open loop operations
Power network carries out fault location, carries out tentative diagnosis to power distribution network by the method for immunological network, reduces the computing of immune algorithm
Amount, improves the efficiency to fault location.Individual is evaluated using affinity of the immune algorithm according to individual, according to individual
Affinity and the concentration selection individual that is intersected, made a variation, increase population diversity, avoid algorithm and be quickly absorbed in part
It is optimal, while evolutionary process establishes mnemon, member-retaining portion optimal solution, in order to avoid intersect, colony degenerates after mutation process, guarantee
The result that algorithm obtains when terminating is the individual of the highest affinity occurred in the successive dynasties, and finally trouble point is positioned, and is improved
The speed and efficiency of fault location.
It should be noted last that above embodiment is merely illustrative of the technical solution of the present invention and unrestricted,
Although the present invention is described in detail with reference to example, it will be understood by those within the art that, can be to the present invention
Technical scheme modify or equivalent substitution, without departing from the spirit and scope of technical solution of the present invention, it all should cover
Among scope of the presently claimed invention.
Claims (10)
1. a kind of electrical power distribution network fault location method based on hybrid artificial immune system, it is characterised in that using the event of immunological network method pair
Barrier source both candidate nodes are screened；If obtained source of trouble candidate solution only includes a node, the source of trouble is exported；If obtain
Source of trouble candidate solution is multiple, then the failure subset primarily determined that is further diagnosed using immune algorithm, determine failure
Node.
2. the electrical power distribution network fault location method according to claim 1 based on hybrid artificial immune system, it is characterised in that described
Immunological network method includes：
(1) node for defining the propagation path that each measuring point is included combines；
(2) obtain signal and be shown as abnormal measuring point, read set of node { xi}；
(3) abnormal measuring point is shown as with the presence or absence of other signals, if it is present return to step (2)；
(4) the common factor ∩ i { x of each abnormal measuring point set of node are calculatedi}；
(5) obtain signal and be shown as normal measuring point, read its set of node { yi}；
(6) normal measuring point is shown as with the presence or absence of other signals, if it is present return to step (5)；
(7) the union ∪ j { y of each normal measuring point set of node are calculatedi}；
(8) operation result in the operation result in step (4) and step (7) is subjected to intersection operation, i.e. (∩ i { xi}∩∪j
{yj), and set S=(∩ i { xi}∩∪j{yj})；
(9) try to achieve source of trouble collection and be combined into ∩ i { xi}-S。
3. the electrical power distribution network fault location method according to claim 1 based on hybrid artificial immune system, it is characterised in that described
Immune algorithm includes：
(1) arrange parameter, fault current is encoded；
(2) initial antibodies group is generated；
(3) antibody population is evaluated；
(4) to memory cells；
(5) parent antibody population is formed；
(6) judge whether to meet termination condition, if meeting termination condition, export optimal antibody, position malfunctioning node；It is if discontented
Sufficient termination condition, then into step (7)；
(7) selected, intersected based on parent antibody population caused by step (5), mutation operation, forming first antibody group；From note
Recall and memory antibody is obtained in storehouse, the first antibody group and the memory antibody collectively form secondary antibody group；
(8) step (3) is transferred to, iterations adds 1, and the secondary antibody group is evaluated.
4. the electrical power distribution network fault location method according to claim 3 based on hybrid artificial immune system, it is characterised in that step
(1) parameter in includes：The population scale N set according to the scale of power distribution network0；Memory cell quantity Nm, the memory is carefully
Born of the same parents are to retain the number of optimal antibody after each iteration；Maximum iteration M；The antibody set according to the topological structure of power network is compiled
Code length L；Crossover probability Pc；Mutation probability Pm；Antibody similarity evaluation parameter e；Iterations T.
5. the electrical power distribution network fault location method according to claim 3 based on hybrid artificial immune system, it is characterised in that step
(1) described in is encoded specially to fault current：Gene pairs answers the state of single feeder line section；Antibody is by the gene
Form, the antibody is the morphogenetic vector of shape of all feeder line sections in power distribution network；If the feeder line section fault, described
The state of feeder line section is 1, if the feeder line section does not have failure, the state of the feeder line section is 0.
6. the electrical power distribution network fault location method according to claim 3 based on hybrid artificial immune system, it is characterised in that step
(2) the generation scale of the initial antibodies group in is the population scale N set according to the scale of power distribution network0With memory cell number
Measure NmSummation.
7. the electrical power distribution network fault location method according to claim 3 based on hybrid artificial immune system, it is characterised in that step
(3) described in carries out evaluation to antibody population to be included：Construct evaluation function；Affinity between calculating antibody and antigen, antibody with
Affinity between antibody, antibody concentration, the expectation breeding potential of antibody；
The evaluation function is：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>t</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>S</mi>
<mi>B</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mn>1</mn>
</msub>
</msubsup>
<mo>|</mo>
<msub>
<mi>I</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msubsup>
<mi>I</mi>
<mi>j</mi>
<mo>*</mo>
</msubsup>
<mrow>
<mo>(</mo>
<msub>
<mi>S</mi>
<mi>B</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>+</mo>
<mi>&omega;</mi>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mn>2</mn>
</msub>
</msubsup>
<mo>|</mo>
<msub>
<mi>S</mi>
<mi>B</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula：Fit(SB) it is evaluation function corresponding to each antibody in antibody population；SB is single antibody, and SB (i) is the every of antibody
One gene；N1For the sum of feeder switch in power distribution network, N2For the sum of feeder line section in power distribution network, IjFor in power distribution network
The electric current of feeder switch gets over limit information；For the state of the feeder switch determined in power distribution network by feeder line sector status；ω
For weight coefficient,Represent that weight coefficient is multiplied by the sum of fault feeder hop count；
Affinity between the antibody and antigen is：
AV=1/Fit(SB) (2)
In formula：AVAffinity between antibody and antigen；
Affinity between the antibody and antibody is：
<mrow>
<msub>
<mi>S</mi>
<mrow>
<mi>V</mi>
<mo>,</mo>
<mi>S</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>K</mi>
<mrow>
<mi>V</mi>
<mo>,</mo>
<mi>S</mi>
</mrow>
</msub>
<mi>L</mi>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula：KV,SRepresent the affinity between antibody v and antibody s, SV,STo be in identical gene location in antibody v and antibody s
And gene identical digit；L is antibody coding length；
The antibody concentration is：
<mrow>
<msub>
<mi>C</mi>
<mi>V</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mo>&Sigma;</mo>
<mrow>
<mi>V</mi>
<mo>,</mo>
<mi>S</mi>
<mo>&Element;</mo>
<mi>N</mi>
</mrow>
</msub>
<msub>
<mi>H</mi>
<mrow>
<mi>V</mi>
<mo>,</mo>
<mi>S</mi>
</mrow>
</msub>
</mrow>
<mi>N</mi>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula：CvFor antibody concentration, e is antibody similarity evaluation parameter, HV,SRepresent whether antibody v and antibody s is similar, and N is anti-
Body sum；
The expectation breeding potential of the antibody is：
Pv=Av/Cv (6)
In formula：PvFor the expectation breeding potential of antibody.
8. the electrical power distribution network fault location method according to claim 3 based on hybrid artificial immune system, it is characterised in that step
(4) it is specially to memory cells described in：Antibody population is subjected to descending arrangement according to the affinity of antigen, takes preceding NmIt is individual
Antibody adds memory cell.
9. the electrical power distribution network fault location method according to claim 3 based on hybrid artificial immune system, it is characterised in that step
(5) parent antibody population is formed described in is specially：By antibody population, desirably breeding potential carries out descending arrangement, takes preceding N0Individual antibody
Form the parent antibody population.
10. the electrical power distribution network fault location method according to claim 3 based on hybrid artificial immune system, it is characterised in that step
Suddenly the termination condition in (6) is：It is expected that the maximum antibody of breeding potential exceedes the iterations during evolution, or
Affinity between the antibody and antigen of adjacent generations immunological network cell, which no longer changes or changed, is less than preset value.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110287612A (en) * | 2019-06-28 | 2019-09-27 | 成都理工大学 | A kind of hydrogenation stations addressing intelligent algorithm |
| CN112557811A (en) * | 2020-11-19 | 2021-03-26 | 安徽理工大学 | Fault location method based on improved genetic algorithm and used for power distribution network with distributed power supplies |
| CN113285431A (en) * | 2021-06-10 | 2021-08-20 | 云南电网有限责任公司迪庆供电局 | Intelligent power distribution network information physical fusion protection system and method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201666923U (en) * | 2009-12-03 | 2010-12-08 | 天津理工大学 | Device for processing DSP failure diagnosis of electric power system by using immune genetic algorithm |
| CN104598968A (en) * | 2014-10-13 | 2015-05-06 | 芜湖扬宇机电技术开发有限公司 | Fault diagnosis method of transformer |
| CN105954607A (en) * | 2015-11-12 | 2016-09-21 | 北京交通大学 | Method and system for detecting faults of high-speed railway signal system |
| CN106324429A (en) * | 2016-08-02 | 2017-01-11 | 天津大学 | RS-IA data mining-based power distribution network fault locating method |
-
2017
- 2017-11-02 CN CN201711065973.3A patent/CN107861026B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201666923U (en) * | 2009-12-03 | 2010-12-08 | 天津理工大学 | Device for processing DSP failure diagnosis of electric power system by using immune genetic algorithm |
| CN104598968A (en) * | 2014-10-13 | 2015-05-06 | 芜湖扬宇机电技术开发有限公司 | Fault diagnosis method of transformer |
| CN105954607A (en) * | 2015-11-12 | 2016-09-21 | 北京交通大学 | Method and system for detecting faults of high-speed railway signal system |
| CN106324429A (en) * | 2016-08-02 | 2017-01-11 | 天津大学 | RS-IA data mining-based power distribution network fault locating method |
Non-Patent Citations (1)
| Title |
|---|
| 傅龙天 等: "基于人工免疫原理的故障检测模型", 《河南科技学院学报（自然科学版）》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110287612A (en) * | 2019-06-28 | 2019-09-27 | 成都理工大学 | A kind of hydrogenation stations addressing intelligent algorithm |
| CN112557811A (en) * | 2020-11-19 | 2021-03-26 | 安徽理工大学 | Fault location method based on improved genetic algorithm and used for power distribution network with distributed power supplies |
| CN112557811B (en) * | 2020-11-19 | 2024-01-12 | 安徽理工大学 | Distributed power supply-containing power distribution network fault location based on improved genetic algorithm |
| CN113285431A (en) * | 2021-06-10 | 2021-08-20 | 云南电网有限责任公司迪庆供电局 | Intelligent power distribution network information physical fusion protection system and method |
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