CN104123439B - Network system cascading failure online simulation method and device based on genetic algorithm - Google Patents
Network system cascading failure online simulation method and device based on genetic algorithm Download PDFInfo
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Abstract
The present invention provides a kind of network system cascading failure online simulation method and device based on genetic algorithm, and method therein includes: generates power network topology according to the system status information read, and obtains the flow data of current system conditions；According to the flow data of current system conditions, calculate the adaptive value of each system mode in the system mode set generated；According to system mode set, generate initial population；The size of the adaptive value according to individuality each in initial population, selects to generate new population；New population according to the hybridization of genetic algorithm and variation, generates new progeny population as parent population；The progeny population generated constantly updates the cut-off condition reaching default, the cascading failure result of output electrical network.The present invention carries out process by simulating biology based on natural selection, electrical network carries out online cascading failure assessment, the cascading failure path that Fast Discovery System is possible under current state, sends fault pre-alarming, provide aid decision for dispatcher.
Description
Technical field
The present invention relates to cascading failure in power system risk analysis field, more specifically, relate in power system a kind of
Network system cascading failure online simulation method and device based on genetic algorithm.
Background technology
Along with the continuous expansion of Power System Interconnection scale, electric power resource is distributed rationally further, but the reliable fortune of electrical network
Row has but welcome new challenge.After the having a power failure on a large scale for twice of US West's electrical network in 1996, there occurs again a lot of in world wide
Large area blackout.
Cascading failure in power system genesis mechanism is: when electrical network is properly functioning, each element is with certain original negative
Lotus, when causing trouble occurs because of overload for some or certain several elements, can change the balance of trend, and cause load to exist
Redistributing on other nodes, transfers to unnecessary load on other elements.If the element of these original normal work
Unnecessary load can not be processed, new load once will be caused to redistribute, thus cause chain overload fault, and
Cause large area paralysis and the generation of massive blackout accident of network eventually.Although cascading failure occurrence frequency is relatively low, but, one
Denier occurs, and the society, the economic impact that cause will be great, and the research for cascading failure is the most necessary.
At present, research to cascading failure in power system both at home and abroad can be generally divided into three classes: based on Complex Networks Theory
Method, self-organizing Critical Theory method based on complication system and pattern search method.In the past to cascading failure in power system
Research in the middle of, complexity theory mainly analyzes the power law relation of cascading failure scale and probability of happening from macroscopic perspective, by system
Count analysis system weakness and the cascading failure risk of electrical network；The method theoretical based on pattern search is micro-from cascading failure
Sight mechanism is set out, and analyzes cascading failure generation, developing power flow transfer, automatic safety device, protective relaying device action feelings
Condition, it is intended to the evolutionary process that research cascading failure is concrete, but along with system scale increases, amount of calculation will be exponentially increased, time-consumingly
It is greatly increased.Meanwhile, electrical network, from Electric Power Network Planning angle, the static characteristic of selective analysis electrical network, is entered by these method emphasis
Row cascading failure is assessed, it is thus impossible to be efficiently applied to the operation instruction of electrical network.
Accordingly, it is desirable to provide a kind of new method, Operation of Electric Systems finds electrical network weak link in time, sends
Cascading failure early warning, and solve failure problems.
Summary of the invention
In view of the above problems, it is an object of the invention to provide a kind of network system cascading failure based on genetic algorithm online
Analogy method and device, carry out online cascading failure assessment to electrical network, and Fast Discovery System is possible chain under current state
Failure path, sends fault pre-alarming, provides aid decision for dispatcher.
On the one hand, the present invention provides a kind of network system cascading failure online simulation method based on genetic algorithm, including:
Read network system status information, generate power network topology according to the network system status information read, according to described
Power network topology calculates the system load flow of system mode, obtains the flow data of system mode；
According to the flow data of described system mode, transfer generates system mode set, and calculates the system mode of generation
The adaptive value of each system mode in set；Wherein, according to the flow data of current system conditions, the transfer that system is possible is analyzed
State, each transfering state all can be as the follow-up developments state of this state；The data run in conjunction with current system, calculate transfer
The conditional probability of state, and the cutting load amount caused due to fault, and record in corresponding state matrix；Calculate individuality
Adaptive value, simulates natural selection pressure, the situation of the cascading failure of the system mode that described adaptive value reflection system is current；
According to the described system mode set generated after transfer, generate initial population, simultaneously and calculate described initial population
In the adaptive value of each individuality；Wherein, in each population, each individuality represents a kind of system mode；
According to the adaptive value of individuality each in described initial population, select to generate new population, and calculate in described new population
The adaptive value of each individuality；
Described new population passes through hybridization and the variation of genetic algorithm as parent population, generates progeny population；Wherein, described
Individuality in parent population is individual as parent, and the individuality in described progeny population is as offspring individual, by relatively described father
Adaptive value after hybridizing for individual adaptive value and described parent individuality, make a variation, obtains described offspring individual；
When the renewal of the progeny population of described generation reaches default cut-off condition, the cascading failure knot of output electrical network
Really.
Furthermore it is preferred that scheme be that a system mode item chromosome represents, each system mode all has it right
The conditional probability Pi of the system mode answered, mistake loading Capi and adaptive value Fitnessi, and conditional probability Pi, mistake loading
Capi and adaptive value Fitnessi information are stored in system mode matrix, every corresponding matrix information of chromosome.
Furthermore it is preferred that scheme be to generate during new population, the computing formula of adaptive value size such as following formula institute
Show:
Wherein, SiRepresent the individual i in population；
CiFor system mistake loading size in this case；
PiFor system condition probability；
P∑For individual condition probability sums all in population.
Furthermore it is preferred that scheme be, at new population as parent population by the hybridization of genetic algorithm and variation, to generate new
Progeny population during；
Parent individuality in new population hybridizes, and randomly chooses two parent individualities, all possible filial generation
In, compare the size of the adaptive value of offspring after parent each hybridization individual, select the system mode conduct of bigger adaptive value
Individuality after hybridization, the parent individuality in described new population the most once hybridizes, then the state square answered parent individual relative
Battle array information is updated；
Individuality in the new population updated is made a variation, compare the individual adaptive value of parent in the new population of renewal with
The size of the adaptive value after described parent individual variation, selects the system mode of bigger adaptive value as the filial generation after variation
Body.
Furthermore it is preferred that scheme be to read before network system status information, also including, genetic parameter is set, heredity
Parameter includes genetic algebra nGeneration, population scale Pop_size, probability of crossover Pc, mutation probability Pm.
On the other hand, the present invention also provides for a kind of network system cascading failure online simulation device based on genetic algorithm,
Including: flow data acquiring unit, it is used for reading network system status information, generates according to the network system status information read
Power network topology, calculates the system load flow of system mode according to described power network topology, obtains the flow data of system mode；
System mode set signal generating unit, for the flow data according to described system mode, transfer generates system mode
Set, and calculate the adaptive value of each system mode in the system mode set of generation；Wherein, according to the tide of current system conditions
Flow data, analyzes the transfering state that system is possible, and each transfering state all can be as the follow-up developments state of this state；In conjunction with working as
The data that front system is run, calculate the conditional probability of transfering state, and the cutting load amount caused due to fault, and record in phase
In the state matrix answered；Calculate individual adaptive value, simulate natural selection pressure, the system that described adaptive value reflection system is current
The situation of the cascading failure of state；
Initial population signal generating unit, for according to described system mode set, generates initial population, simultaneously and described in calculating
The adaptive value of each individuality in initial population；Wherein, in described initial population, each individuality represents a kind of system mode；
New population signal generating unit, for the adaptive value according to individuality each in described initial population, selects to generate new population；
Progeny population signal generating unit, passes through hybridization and the variation of genetic algorithm for described new population as parent population,
Generate progeny population；Wherein, the individuality in described parent population is individual as parent, and the individuality in described progeny population is as son
In generation, is individual, and the adaptive value individual by relatively described parent hybridizes with described parent individuality, adaptive value after variation, obtains described
Offspring individual；
Cascading failure output unit, is used for when the renewal of the progeny population of described generation reaches default cut-off condition,
The cascading failure result of output electrical network.
Knowable to technical scheme above, the network system cascading failure online simulation side based on genetic algorithm of the present invention
Method and device, need angle from operation of power networks, in conjunction with genetic algorithm, proposes a kind of novel cascading failure in power system mould
Intending algorithm, this algorithm can be based on system current operating conditions, it is considered to system operation conditions affects, by simulation based on natural choosing
The biology selected carries out process, and electrical network carries out online cascading failure assessment, the company that Fast Discovery System is possible under current state
Lock failure path, sends fault pre-alarming, and blocks for weak link, provide aid decision for dispatcher.Chain
During failure evolution, the status information of system mode and correspondence thereof is stored in system mode matrix, according to system mode
Information arranges cascading failure risk indicator, and by selection, hybridization, mutation operation, makes system increase to cascading failure risk
Direction is developed.
In order to realize above-mentioned and relevant purpose, one or more aspects of the present invention include will be explained in below and
The feature particularly pointed out in claim.Description below and accompanying drawing are described in detail some illustrative aspects of the present invention.
But, some modes in the various modes of the principle that only can use the present invention of these aspects instruction.Additionally, the present invention
It is intended to include all these aspect and their equivalent.
Accompanying drawing explanation
By with reference to below in conjunction with the explanation of accompanying drawing and the content of claims, and along with to the present invention more comprehensively
Understanding, other purpose of the present invention and result will be more apparent and should be readily appreciated that.In the accompanying drawings:
Fig. 1 is the network system cascading failure online simulation method flow based on genetic algorithm according to the embodiment of the present invention
Schematic diagram；
Fig. 2 is the hybridization schematic flow sheet according to the embodiment of the present invention；
Fig. 3 is the variation schematic flow sheet according to the embodiment of the present invention；
Fig. 4 is the network system cascading failure online simulation method second based on genetic algorithm according to the embodiment of the present invention
Schematic flow sheet；
Fig. 5 is the network system cascading failure online simulation device logic based on genetic algorithm according to the embodiment of the present invention
Structured flowchart；
Fig. 6 is the new individual generation schematic diagram according to the embodiment of the present invention.
The most identical label indicates similar or corresponding feature or function.
Detailed description of the invention
In the following description, for purposes of illustration, in order to provide the comprehensive understanding to one or more embodiments, explain
Many details are stated.It may be evident, however, that these embodiments can also be realized in the case of not having these details.
The most domestic and international research to cascading failure in power system for aforementioned proposition, it is impossible to be effectively applied to electrical network
Operation instruction.The present invention proposes network system cascading failure online simulation method and device based on genetic algorithm, uses
The cascading failure simulation of Genetic evolution algorithm is intended to be arranged, for different system original state, mould by suitable selection pressure
Intend phylogeny process, the cascading failure state that search system is possible, and find system weakness.
Take into full account Operation of Electric Systems situation in the present invention, by effectively selecting system mode, the company of being greatly reduced
Lock failure risk assessment amount of calculation, thus improve calculating speed, make cascading failure risk online evaluation be possibly realized.Can be not
With finding cascading failure path, early warning of being concurrently out of order under original state, find weak link, provide auxiliary certainly for dispatcher
Plan, improves power system security reliability service level.
In order to the network system cascading failure online simulation method based on genetic algorithm that the present invention provides is described, Fig. 1 shows
Go out network system cascading failure online simulation method flow based on genetic algorithm according to embodiments of the present invention.
As it is shown in figure 1, the network system cascading failure online simulation method bag based on genetic algorithm that the present invention provides
Include:
S110: read network system status information, generates power network topology, root according to the system status information of the electrical network read
Calculate the system load flow of system mode according to power network topology, obtain the flow data of system mode.
Wherein it is desired to explanation, online reading POWER SYSTEM STATE information, then generate power network topology, topological structure
Forming the annexation between equipment component, it is the basis of the various application software of analysis of network, and power network topology is that on-line analysis is lost
The basis of the electric power cascading failure of propagation algorithm.
S120: according to the flow data of system mode, transfer generates system mode set, and calculates the system mode of generation
The adaptive value of each system mode in set；Wherein, the situation of the cascading failure of the system mode that adaptive value reflection system is current.
Specifically, according to the flow data of current system conditions, analyze the state migration procedure that system is possible, generate transfer
Rear system mode set, and the conditional probability of each system mode after calculating transfer, lose loading and adaptive value.
Cascading failure in power system is a small probability event, and considers from operation of power networks angle, cascading failure evolutionary process
Being a stochastic process, each succeeding state in evolution is only relevant in system current state, and with the historic state of system
Unrelated.
During network system cascading failure online simulation based on genetic algorithm, according to current individual local environment,
The i.e. flow data of current system conditions, analyzes the transfering state that system is possible, and each transfering state all can be as this state
Follow-up developments state；The data run in conjunction with current system, calculate the conditional probability of transfering state, and owing to fault causes
Cutting load amount, and record in corresponding state matrix；Calculate individual adaptive value, simulate natural selection pressure, ideal adaptation
Value reflects the cascading failure risk situation of system current state.
It is to say, the adaptive value of each system mode, simulate natural selection pressure, reflect the chain of system current state
Failure risk situation.
Every chromosome represents a system mode, each state all have the system mode of its correspondence conditional probability Pi,
System loses loading Capi and adaptive value Fitnessi, and these information are stored in system mode matrix, every chromosome correspondence
Article one, matrix information.System status information as shown in table 1.
Table 1 system status information
| Element number | Conditional probability | Lose loading | Adaptive value |
| i | Pi | Capi | Fitnessi |
S130: according to the system mode set generated after transfer, generates initial population, simultaneously and calculate in initial population every
The adaptive value of individuality；Wherein, in each population, each individuality represents a kind of system mode.
The generation of initial population is to prepare in order to network system generates new population.It should be noted that in the kind generated
In Qun, each individuality represents a status system, not merely refers to initial population, it is also possible to generate other population it can be understood as
Individuality in each population generated represents a status system.And during generating initial population, also obtain simultaneously
Obtained adaptive value individual in initial population.
S140: according to the adaptive value of individuality each in initial population, selects to generate new population, and calculates in new population each
Individual adaptive value.
Specifically, have in above-mentioned system mode matrix it is known that system mode matrix comprises system shape after transfer
The conditional probability of state, mistake loading and adaptive value size.
Wherein, conditional probability is system mode probability of malfunction under current state after transfer, when specifically calculating, needs
In conjunction with current system conditions, analysis element condition Dependent Troubles probability.Element condition Dependent Troubles probability i.e. considers element self
Health status, service condition, running environment affect, element probability of malfunction in a short time.
The calculating of adaptive value determines Genetic evolution travel direction, in the present invention, in order to find system at the initial shape of difference
Possible cascading failure state under state, system fault condition conditional probability is relatively big, and malfunction loses the shape that loading is bigger
State needs retained and suitably develop.
It is to say, select to generate new population, refer to compare the size of the adaptive value of each individuality in initial population, select
The individuality that adaptive value is big, gets up individual collections big for these adaptive values, generates a new population.
Below as a example by the population scale size population as Pop_size, explain how to be generated new population by initial population.
In initial population, every individuality has certain adaptive value, is designated as Fitness (Si), in population, the adaptation sum of all individualities is, then each individuality ratio shared by adaptive value in initial population can ask into:。
When generating new population, being sequentially generated Pop_size random number, each random number determines a new individuality.Concrete and
Speech, produces a new random number rand, compares rand and PiSize, if, then Pk+1It is selected.
Then determine the new individuality chosen from initial population；In order to new individual generation is described, Fig. 6 illustrates
New individual generation according to embodiments of the present invention.
As shown in Figure 6, as a example by population scale 6: p1, p2 ... p6 represents in initial population shared by 1 to 6 individual fitness
Ratio.Producing a random number rand, if p1+p2 < rand < p1+p2+p3, then p3 is selected.
Wherein, the computing formula of adaptive value size is shown below:
In above-mentioned computing formula, SiRepresent the individual i, C in populationiBig for system mistake loading in this case
Little, PiFor system condition probability, P∑For individual condition probability sums all in population.
Wherein it is desired to explanation, adaptive value can react cascading failure in power system risk situation, can be as chain event
Barrier risk indicator.
S150: new population passes through hybridization and the variation of genetic algorithm as parent population, generates progeny population；Wherein, father
Individual as parent for the individuality in population, the individuality in progeny population is as offspring individual, by comparing individual the fitting of parent
Should be worth with parent individuality hybridization, variation after adaptive value, obtain offspring individual.
Specifically, pass through hybridization and the variation of genetic algorithm at new population as parent population, generate new progeny population
During；Parent individuality in new population hybridizes, and randomly chooses two parent individualities, all possible filial generation
In, compare the size of the adaptive value of offspring after parent each hybridization individual, select the system mode conduct of bigger adaptive value
Individuality after hybridization, the parent individuality in new population the most once hybridizes, then the state matrix letter answered parent individual relative
Breath is updated.Individuality in the new population updated is made a variation, compares the adaptation that the parent in the new population of renewal is individual
The size of the adaptive value after value and described parent individual variation, selects the system mode of bigger adaptive value as the son after variation
In generation, is individual.
It should be noted that due to two parents hybridize time, different filial generations may be produced, this is by hybridizing
Position difference determines.And each filial generation all has corresponding adaptive value.When hybridizing, only select filial generation tool
Have maximum adaptation value for parent hybridize after result.
In the present invention, electrical network simulation biological evolution process, in order to increase the multiformity of population, produce new individuality, i.e.
New system mode, carries out the operation hybridizing and making a variation.Further, for accelerated evolutionary speed, only retain outstanding offspring and enter
In new genetic procedures.
When network system simulation hybridization, one random number r of stochastic generationcIf, rc<Pc, wherein, Pc is one and sets in advance
Fixed probability of crossover (fixed value), carries out crossover operation；Otherwise, individuality is retained constant.
When hybridizing, make crossover process can produce new individuality as far as possible, and wish that system can be to worse situation
Development, specifically, hybrid individual finds self non-existent malfunction in treating hybrid individual, and selects at this individuality bar
Under part, the malfunction with maximum adaptation value is the individual state after hybridizing.Therefore ratio is hybridized the one before by the individuality after hybridization
Body has more fault element.Often carry out a crossover operation, be required to update the state matrix information answered with individual relative.
In order to describe the flow process of the individual hybridization in new population in detail, Fig. 2 shows hybridization according to embodiments of the present invention
Flow process.
As in figure 2 it is shown, the idiographic flow of the individual hybridization in new population includes: step S201: present two individual a and b；
Specifically, select any two individual in selecting generation new population.
Step S202:rc<Pc；Specifically, one random number r of stochastic generationcIf, rc<Pc, enter step S203；Otherwise, protect
Staying individual constant, enter step S211, hybridization terminates；Wherein, Pc is individual conditional probability；
Step S203: a, b are hybridized；
Step S204:b exists the fault type not having in a？If b exists the fault type not having in a, enter step
S205；If b does not exist the fault type not having in a, enter step S210；
Step S205: state Load flow calculation, generates state matrix.
Step S206: whether state a loses Fu He？If losing load, enter step S208；
Step S207: revise bus information；Specifically, the information selecting to generate new population is revised；Enter step S208；
Step S208: calculate trend, obtains a state matrix；Specifically, calculate state trend, obtain the state square of individual a
Battle array；
Step S209: select under state a, in b fault type adaptive value maximum for filial generation；
Step S210: all states？Specifically, check whether the individuality in new population meets the individuality after hybridizing by miscellaneous for ratio
Before handing over, individuality has more fault element, is unsatisfactory for this condition, enters above-mentioned steps S204；The condition met, enters step
Rapid S211；
Step S211: hybridization terminates.
When network system simulation variation and preservation are optimum, one random number r of stochastic generationmIf, rm<Pm, carry out variation behaviour
Make；Otherwise, individuality is retained constant.Hereafter, adaptive value individual after comparing hybridization, variation and parent adaptive value size, if variation
Rear individual fitness is more than parent individual fitness, then replace parent individual, become new offspring individual；Otherwise, parent is individual
Become new offspring individual.
In order to describe individual variation in detail and preserve optimum flow process, Fig. 3 shows variation according to embodiments of the present invention
And preserve optimum flow process.
As shown in Figure 3: the idiographic flow of body variation and preservation optimum includes: step S301: select body a one by one；
Step S302:rm<Pm；Specifically, one random number r of stochastic generationmIf, rm<Pm, carry out mutation operation, enter step
Rapid S303；Otherwise, individuality is retained constant.
Step S303:a makes a variation.
Step S304:a loses load？
Step S305: revise bus information；Specifically, the information of the population selecting generation is revised.
Step S306: select variable position；Specifically, the position of individual a variation is selected.
Step S307: calculate state matrix after variation；Specifically, the state matrix after a variation is calculated, including variation
After adaptive value；
Step S308: be better than parent？Specifically, adaptive value individual after comparing variation and parent adaptive value size, if becoming
Different rear individual fitness is more than parent individual fitness, then replace parent individual, become new offspring individual, enter step
S310；Otherwise, parent individuality becomes new offspring individual, enters step S309.
Step S309: retaining parent is new filial generation, is directly entered step S311, and variation terminates.
Step S310: new filial generation；Specifically, the offspring individual after variation replaces parent individual, becomes new filial generation
Body.
Step S311: terminate.
By in Fig. 2 and Fig. 3 it is known that new population is individual according to offspring individual adaptive value and the parent after hybridization, variation
The size of adaptive value, generates new progeny population.It is to say, by the offspring individual after comparing hybridization, making a variation and parent
The adaptive value size of body, if individual fitness is more than parent individual fitness after Bian Yi, then replaces parent individual, becomes new son
In generation, is individual；Otherwise, parent individuality becomes new offspring individual, and new population generates new progeny population by this kind of method.
S160: when the renewal of the progeny population generated reaches default cut-off condition, the cascading failure knot of output electrical network
Really.
Specifically, step S140～step S150 repeatedly, until progeny population meets default cut-off condition.Arrange two
Individual cut-off condition is the Genetic evolution of cascading failure on road, and one, when Genetic evolution reaches genetic algebra set in advance
nGeneration；Its two, new progeny population loses loading relatively parent population and loses loading and be not further added by.
When meeting above-mentioned cut-off condition, by the cascading failure result of output electrical network, the online simulation cascading failure of electrical network
Genetic evolution terminate.
It should be noted that in network system cascading failure online simulation method based on genetic algorithm, reading electricity
Before net system data, genetic parameter to be arranged.Genetic parameter includes: genetic algebra nGeneration, population scale Pop_
Size, probability of crossover Pc, mutation probability Pm.
According to Operation of Electric Systems situation, due to cascading failure in power system online simulation based on genetic algorithm, to electricity
Force system first has to arrange genetic parameter, is respectively as follows: genetic algebra nGeneration, population scale Pop_size, probability of crossover
Pc, mutation probability Pm, these genetic parameters of setting are all used in the analogy method of power system sequence.
Wherein, genetic algebra determines the algebraically that heredity is carried out, and i.e. collectively generates the population that how many times is new, every generation be all through
Cross select, hybridize, make a variation, preserve optimum operation.Population scale determines the individual amount in population.The two parameter is initially
Determine during execution, hereafter keep quantity constant.
It should be noted that during heredity is carried out, be repeated selection, hybridize, make a variation, the operation of optimal save strategy,
Until progeny population reaches cut-off condition.If progeny population reaches cut-off condition, it is also possible to exit genetic process in advance, i.e. exist
Before not up to specifying genetic algebra, stop Genetic evolution.In order to further illustrate the chain event of network system based on genetic algorithm
Barrier online simulation method, Fig. 4 shows that network system cascading failure based on genetic algorithm according to embodiments of the present invention is online
Analogy method second procedure.
As shown in Figure 4, the network system cascading failure online simulation method second based on genetic algorithm that the present invention provides
Flow process specifically includes: step S401: original state；Specifically refer to the original state of power system；
Step S402: parameter is arranged；Specifically, first have to arrange genetic parameter to power system, be respectively as follows: genetic algebra
NGeneration, population scale Pop_size, probability of crossover Pc, mutation probability Pm.
Step S403: state set；Specifically, read POWER SYSTEM STATE information, generate power network topology, calculate system tide
Stream, according to electric power current system conditions, analyzes the state migration procedure that system is possible, generates system mode set after transfer, and
Calculate the conditional probability of each system mode after shifting, lose loading and adaptive value.
Step S404: initial population；Specifically, initial population is generated according to system mode set.
Step S405: select；Specifically, by the adaptive value size of initial population, select to generate new population.
Step S406: hybridization variation；Specifically, new population carries out hybridizing and making a variation as the individuality in parent population；
Step S407: new progeny population；Specifically, according to hybridization and the result of variation, the filial generation after variation is compared
Body adaptive value and the size of parent individual fitness, generate new progeny population；
Step S408: cut-off？Specifically, the progeny population of generation is constantly updated, if reaching default cut-off condition, enters
Step S409, the cascading failure result of output electrical network, evolution terminates；If not reaching default cut-off condition, enter step S405,
Repeat step S405 to step S407；
Step S409: develop and terminate.
By said method it follows that carry out process by simulating biology based on natural selection, electrical network is carried out
Line cascading failure is assessed, and the cascading failure path that Fast Discovery System is possible under current state sends fault pre-alarming, and for
Weak link blocks, and provides aid decision for dispatcher.
Corresponding with said method, the present invention also provides for a kind of network system online mould of cascading failure based on genetic algorithm
Intending device, Fig. 5 shows network system cascading failure online simulation device based on genetic algorithm according to embodiments of the present invention
Logical structure.
As it is shown in figure 5, the network system cascading failure online simulation device 500 based on genetic algorithm that the present invention provides wraps
Include: flow data acquiring unit 510, system mode set signal generating unit 520, initial population signal generating unit 530, new population generate
Unit 540, progeny population signal generating unit 550, cascading failure output unit 560.
Flow data acquiring unit 510 is used for reading system status information, according to the system status information of the electrical network read
Generate power network topology, calculate the system load flow of system mode according to power network topology, obtain the flow data of system mode.
System mode set signal generating unit 520 generates system mode collection for the flow data according to system mode, transfer
Close, and calculate the adaptive value of each system mode in the system mode set of generation；Wherein, adaptive value reflection system current be
The situation of the cascading failure of system state.
Initial population signal generating unit 530, for according to described system mode set, generates initial population, simultaneously and at the beginning of calculating
The adaptive value of each individuality in beginning population；Wherein, in initial population, each individuality represents a kind of system mode.
New population signal generating unit 540, for the adaptive value according to individuality each in initial population, selects to generate new population, and
Calculate the adaptive value of each individuality in new population.
Progeny population signal generating unit 550 passes through hybridization and the variation of genetic algorithm for new population as parent population, raw
Become progeny population；Wherein, the individuality in parent population is individual as parent, and the individuality in progeny population is as offspring individual, logical
Cross the adaptive value after comparing the individual adaptive value of parent and the hybridization of parent individuality, variation, obtain offspring individual.
Cascading failure output unit 560, for when the renewal of the progeny population generated reaches default cut-off condition, defeated
Go out the cascading failure result of network system.
In present invention network system based on genetic algorithm cascading failure online simulation device, also include that genetic parameter sets
Put unit (not marking in figure).Genetic parameter arranges unit for arranging genetic parameter, and genetic parameter includes genetic algebra
NGeneration, population scale Pop_size, probability of crossover Pc, mutation probability Pm.
Wherein, system mode set signal generating unit 520 is during system mode generates system mode set, according to system
The flow data of system state, analyzes the transfering state that system is possible, and each transfering state all can be as the rear supervention of system mode
Exhibition state；The flow data of coupling system state, generates the condition of each system mode in system mode set after calculating transfer
Probability and the cutting load amount caused due to fault and adaptive value.
One system mode item chromosome represents, and each system mode all has the condition of the system mode of its correspondence
Probability P i, lose loading Capi and adaptive value Fitnessi, and described conditional probability Pi, described mistake loading Capi and described suitable
Fitnessi information should be worth be stored in system mode matrix, every corresponding matrix information of chromosome.
Wherein, new population signal generating unit 540, during generating new population, the computing formula of adaptive value size is as follows
Shown in formula:
Wherein, SiRepresent the individual i in population；
CiFor system mistake loading size in this case；
PiFor system condition probability；
P∑For individual condition probability sums all in population.
Wherein, progeny population signal generating unit 550 is during generating progeny population, and progeny population signal generating unit includes miscellaneous
Presentate unit and variation unit.
Hybridised units parent individuality in the new population hybridizes, and randomly chooses two parent individualities, all can
In the filial generation of energy, relatively the size of the adaptive value of offspring after each hybridization individual of described parent, selects bigger adaptation
The system mode of value is as the individuality after hybridization, and the parent individuality in new population the most once hybridizes, then to parent individuality phase
Corresponding state matrix information is updated.
Variation unit, for making a variation the individuality in the new population updated, compares the parent in the new population of renewal
The size of the adaptive value after the adaptive value of body and described parent individual variation, selects the system mode of bigger adaptive value as change
Offspring individual after different.
By above-mentioned embodiment it can be seen that the network system cascading failure based on genetic algorithm that the present invention provides exists
Line analogy method and device, invention needs angle from operation of power networks, in conjunction with genetic algorithm, proposes a kind of novel electrical network system
System cascading failure simulation algorithm, this algorithm can be based on system current operating conditions, it is considered to system operation conditions affects, and passes through mould
Intending biology based on natural selection and carry out process, electrical network carries out online cascading failure assessment, Fast Discovery System is at current shape
Cascading failure path possible under state, sends fault pre-alarming, and blocks for weak link, provide auxiliary for dispatcher
Decision-making.In cascading failure evolutionary process, the status information of system mode and correspondence thereof is stored in system mode matrix, root
Cascading failure risk indicator is set according to system status information, and by selection, hybridization, mutation operation, makes system to cascading failure
The direction that risk increases is developed.
The network system based on genetic algorithm proposed according to the present invention is described in an illustrative manner above with reference to accompanying drawing
Cascading failure online simulation method and device.It will be understood by those skilled in the art, however, that the invention described above is proposed
Network system cascading failure online simulation method and device based on genetic algorithm, it is also possible at the base without departing from present invention
Various improvement is made on plinth.Therefore, protection scope of the present invention should be determined by the content of appending claims.
Claims (10)
1. a network system cascading failure online simulation method based on genetic algorithm, including:
Read network system status information, generate power network topology according to the network system status information read, according to described electrical network
Topology calculates the system load flow of system mode, obtains the flow data of system mode；
According to the flow data of described system mode, transfer generates system mode set, and calculates the system mode set of generation
In the adaptive value of each system mode；Wherein, according to the flow data of current system conditions, the transfer shape that system is possible is analyzed
State, each transfering state all can be as the follow-up developments state of this state；The data run in conjunction with current system, calculate transfer shape
The conditional probability of state, and the cutting load amount caused due to fault, and record in corresponding state matrix；Calculate individual fitting
Should be worth, simulate natural selection pressure, the situation of the cascading failure of the system mode that described adaptive value reflection system is current；
According to the described system mode set generated after transfer, generate initial population, simultaneously and calculate in described initial population every
The adaptive value of individuality；Wherein, in each population, each individuality represents a kind of system mode；
According to the adaptive value of individuality each in described initial population, select to generate new population, and calculate in described new population each
Individual adaptive value；
Described new population passes through hybridization and the variation of genetic algorithm as parent population, generates progeny population；Wherein, described parent
Individuality in population is individual as parent, and the individuality in described progeny population is as offspring individual, by relatively described parent
Adaptive value after the hybridization of the adaptive value of body and described parent individuality, variation, obtains described offspring individual；
When the renewal of the progeny population of described generation reaches default cut-off condition, the cascading failure result of output electrical network.
Network system cascading failure online simulation method based on genetic algorithm the most according to claim 1, wherein,
One system mode item chromosome represents, and each system mode all has the conditional probability of the system mode of its correspondence
Pi, mistake loading Capi and adaptive value Fitnessi, and described conditional probability Pi, described mistake loading Capi and described adaptation
Value Fitnessi information is stored in system mode matrix, every corresponding matrix information of chromosome.
Network system cascading failure online simulation method based on genetic algorithm the most according to claim 1, wherein,
During generating described new population, the computing formula of adaptive value size is shown below:
Wherein, SiRepresent the individual i in population；
CiFor system mistake loading size in this case；
PiFor system condition probability；
P∑For individual condition probability sums all in population.
Network system cascading failure online simulation method based on genetic algorithm the most according to claim 3, wherein,
Pass through hybridization and the variation of genetic algorithm at described new population as parent population, generate the process of new progeny population
In；
Parent individuality in described new population hybridizes, and randomly chooses two parent individualities, all possible filial generation
In, relatively the size of the adaptive value of offspring after each hybridization individual of described parent, selects the system mode of bigger adaptive value
As the individuality after hybridization, the parent individuality in described new population the most once hybridizes, then the shape answered parent individual relative
State matrix information is updated；
Individuality in the new population updated is made a variation, compares the individual adaptive value of the parent in the new population of renewal with described
The size of the adaptive value after parent individual variation, selects the system mode of bigger adaptive value as the offspring individual after variation.
Network system cascading failure online simulation method based on genetic algorithm the most according to claim 1, is reading institute
Before stating network system status information, also include,
Arranging genetic parameter, genetic parameter includes genetic algebra nGeneration, population scale Pop_size, probability of crossover Pc,
Mutation probability Pm.
6. a network system cascading failure online simulation device based on genetic algorithm, including:
Flow data acquiring unit, is used for reading network system status information, generates according to the network system status information read
Power network topology, calculates the system load flow of system mode according to described power network topology, obtains the flow data of system mode；
System mode set signal generating unit, for the flow data according to described system mode, transfer generates system mode set,
And calculate the adaptive value of each system mode in the system mode set of generation；Wherein, according to the trend number of current system conditions
According to, analyze the transfering state that system is possible, each transfering state all can be as the follow-up developments state of this state；In conjunction with current system
The data that system runs, calculate the conditional probability of transfering state, and the cutting load amount caused due to fault, and record accordingly
In state matrix；Calculate individual adaptive value, simulate natural selection pressure, the system mode that described adaptive value reflection system is current
The situation of cascading failure；
Initial population signal generating unit, for according to described system mode set, generates initial population, simultaneously and calculate described initially
The adaptive value of each individuality in population；Wherein, in described initial population, each individuality represents a kind of system mode；
New population signal generating unit, for the adaptive value according to individuality each in described initial population, selects to generate new population, and counts
Calculate the adaptive value of each individuality in described new population；
Progeny population signal generating unit, passes through hybridization and the variation of genetic algorithm, generates for described new population as parent population
Progeny population；Wherein, the individuality in described parent population is individual as parent, and the individuality in described progeny population is as filial generation
Adaptive value after body, the adaptive value individual by relatively described parent and the hybridization of described parent individuality, variation, obtains described filial generation
Individual；
Cascading failure output unit, for when the renewal of the progeny population of described generation reaches default cut-off condition, exporting
The cascading failure result of electrical network.
Network system cascading failure online simulation device based on genetic algorithm the most according to claim 6, wherein,
One system mode item chromosome represents, and each system mode all has the conditional probability of the system mode of its correspondence
Pi, mistake loading Capi and adaptive value Fitnessi, and described conditional probability Pi, described mistake loading Capi and described adaptation
Value Fitnessi information is stored in system mode matrix, every corresponding matrix information of chromosome.
Network system cascading failure online simulation device based on genetic algorithm the most according to claim 6, wherein,
Described new population signal generating unit, is generating during described new population, the computing formula of adaptive value size such as following formula institute
Show:
Wherein, SiRepresent the individual i in population；
CiFor system mistake loading size in this case；
PiFor system condition probability；
P∑For individual condition probability sums all in population.
Network system cascading failure online simulation device based on genetic algorithm the most according to claim 8, wherein,
Described progeny population signal generating unit includes hybridised units and variation unit,
Described hybridised units, the parent individuality in described new population hybridizes, and randomly chooses two parent individualities, in institute
In possible filial generation, relatively the size of the adaptive value of offspring after each hybridization individual of described parent, selects bigger
The system mode of adaptive value is as the individuality after hybridization, and the parent individuality in described new population the most once hybridizes, then to father
The state matrix information answered for individual relative is updated；
Described variation unit, for making a variation the individuality in the new population updated, compares the parent in the new population of renewal
The size of the adaptive value after individual adaptive value and described parent individual variation, selects the system mode conduct of bigger adaptive value
Offspring individual after variation.
Network system cascading failure online simulation device based on genetic algorithm the most according to claim 6, also includes,
Genetic parameter arranges unit, is used for arranging genetic parameter, and genetic parameter includes genetic algebra nGeneration, and population is advised
Mould Pop_size, probability of crossover Pc, mutation probability Pm.
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