CN108389002A - A kind of multiple failure generation method based on N-1 catastrophe failure collection - Google Patents
A kind of multiple failure generation method based on N-1 catastrophe failure collection Download PDFInfo
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Abstract
The present invention discloses a kind of multiple failure generation method based on 1 catastrophe failure collection of N suitable for static security analysis.Method integrates D5000 comprehensive intelligents and alerts platform, for cut-offfing failure under normal climate, builds polynary indicator evaluation system, from two angles of fault severity level and probability of malfunction are cut-off, sets 1 catastrophe failure thresholds of N, form 1 catastrophe failure collection of N；And the faulty equipment occurred frequently under adverse weather condition is filled into 1 catastrophe failure collection of N.The present invention is based on trend sensitivity matrix, calculate failure on-off distribution factor, and then determine the failure domain of influence of 1 catastrophe failures of N；It is quickly estimated cut-offfing trend again, according to the out-of-limit situation of intersection branch heavy duty, determines whether faulty equipment takes in 2 fault sets of N.The present invention generates corresponding 2 multiple failures of N combination to seriously cut-offfing failure emphatically, greatly reduces 2 failure combined numbers of N, so that the scannings of period N 2 is had more specific aim, effectively increases scan efficiency.
Description
Technical field
The present invention designs Automation of Electric Systems dispatching technique field, especially a kind of based on the more of N-1 catastrophe failure collection
Weight failure generation method.
Background technology
Static security analysis according to《Guiding rules of power system safety and stability》Forecast accident is modeled as Load flow calculation
Middle accident equipment N-1 is cut-off, and the out-of-limit journeys of the equipment in the case where cut-offfing failure such as system branch, busbar are provided by Load flow calculation technology
Degree, to ensure that power system mesomeric state operational safety provides analysis calculation basis, the starting point does not lie in processing electric system in real time
Accident, but be to prevent accident, it is power network schedule automation routine application module.In recent years, occur both at home and abroad by chain
Large-scale blackout caused by failure makes spot dispatch operation be no longer satisfied with conventional N-1 and cut-off and safeguarded based on artificial experience more
Weight failure on-off, it is proposed that the N-2 failure automatically scanning demands towards whole network equipment.
For large scale electric network, N-2 combined faults number increases substantially compared to N-1 failures, and calculation amount is notable
Increase, calculating speed becomes the bottleneck for restricting the online intermittent scannings of N-2, even if using concurrent technique, increased calculate takes
It is also considerable.Therefore, how N-2 failures are further generated on the basis of N-1 is scanned, really paid close attention to for user and
There are the N-2 combined faults of certain hidden danger it is the effective means for greatly reducing N-2 failures to safe operation of electric network.
The warning system of existing intelligent grid regulator control system, as D5000 comprehensive intelligents alert platform, it can be achieved that electric power
Stable state monitoring, WAMS, failure wave-recording, protection, the alarm of system direct transfer, and can export following information：
1, device fault information：The unit trips such as generator, transformer, busbar, circuit, breaker, transverter；
2, the system failure or exception information：Low-frequency oscillation, open-phase operation, trend be out-of-limit, frequency out-of-limit, electric current are out-of-limit,
Phase angle difference is out-of-limit；
3, warning information：It is static security analysis, transient stability, voltage stabilization, aid decision, calculation of short-circuit current, small dry
Disturb stable, steady stability；
4, plan information：Hair power supply plan deviation；
5, weather information：Thunder and lightning, mountain fire alarm；
6, other information.
Invention content
The object of the present invention is to provide a kind of multiple failure generation methods based on N-1 catastrophe failure collection, by ignoring shadow
Smaller N-2 failures combination is rung, N-2 failure data splittings are reduced so that the scanning of period N-2 has more specific aim, and effectively improves
Fault scanning efficiency.
The technical solution that the present invention takes is：A kind of multiple failure generation method based on N-1 catastrophe failure collection, including：
S1 obtains current weather information data and historical failure alarm data；
S2 carries out N-1 fault scannings to current whole network equipment (including branch equipment), obtains N-1 fault sets, then basis
Current weather state filters out the catastrophe failure in N-1 fault sets, generates N-1 catastrophe failure collection；
S3 calculates separately branch breaking distribution factor for whole network equipment；
S4 sets sensitivity threshold value, and according to the comparison result for cut-offfing distribution factor and sensitivity threshold value, determination is respectively set
Standby cut-offs the failure domain of influence；
S5 traverses N-1 catastrophe failure collection, device A corresponding for each failure therein, if it cut-offs in the failure domain of influence
Any appliance B belong to N-1 catastrophe failure collection, then device A and equipment B are taken in into N-2 fault sets simultaneously；Otherwise computing device A
Failure domain of influence intersection is cut-off in equipment B, it is out-of-limit according to whether there is any appliance trend in intersection, it is determined whether by equipment
A and equipment B take in N-2 fault sets；
S6 exports the whole network N-2 fault sets.
Preferably, the historical failure alarm data that S1 is obtained includes equipment breakdown tripping time of origin, failure cause, failure
The weather information etc. at facility information and corresponding moment.
In S1, weather information data is obtained by accessing meteorological data platform, and historical failure alarm data is from comprehensive intelligent
It alerts platform to obtain, such as existing D5000 comprehensive intelligents alert platform.
Preferably, S2 includes step：
S21 carries out N-1 fault scannings to current whole network equipment, obtains each equipment in N-1 fault sets and N-1 fault sets
Power Flow Information data when failure；
S22 carries out event according to fault severity level and historical failure probability of happening is cut-off to each equipment in N-1 fault sets
Hinder risk assessment；And failure risk threshold value is set, failure risk assessment result is more than to the N-1 failures of failure risk threshold value
N-1 catastrophe failures are defined as, N-1 catastrophe failure collection is formed；
S23 judges current weather state for normal climate or severe gas according to the current weather information data that S1 is obtained
It waits：Under normal climate, the N-1 catastrophe failure collection that S23 is obtained is final N-1 catastrophe failures collection；Under harsh weather, step is gone to
S24；
S24 under harsh weather, is accused from the historical failure chosen in the historical failure alarm data that S1 is obtained under harsh weather
Alert data, calculate mean failure rate probability of each equipment fault under harsh weather, and set mean failure rate probability threshold value, if certain
The mean failure rate probability of equipment is more than mean failure rate probability threshold value, then using the equipment fault as the event occurred frequently under harsh weather
Barrier, fills into N-1 catastrophe failure collection, forms final N-1 catastrophe failure collection.
In Power System Analysis, normal climate and harsh weather divide into the prior art, and normal climate is i.e. without sleet
Thunder and lightning, without strong wind, the meteorology of without superhigh temperature and extremely cold weather.It is only easy in harsh weather due to partly seriously cut-offfing failure
Lower generation, if therefore all failures of cut-offfing are subjected to risk assessment regardless of weather conditions, the undoubtedly overall hair of these catastrophe failures
Probability of happening under raw probability meeting more only adverse weather condition substantially reduces, and will further make the reality under harsh weather
Catastrophe failure, which is submerged, to be ignored.The factor that the present invention considers weather carries out integrated risk quantitative evaluation to cut-offfing failure so that
The result of N-1 catastrophe failures is more accurate comprehensive.
Further, in step S22, the fault severity level that cut-offs that Serv_i is equipment i is defined, Fault_i is equipment i
The historical failure probability of failure is cut-off, then the quantization integrated risk Risk_i that equipment i cut-offs failure is：
In formula (1), ServsplitSystem sectionalizing status data when failure occurs is cut-off for equipment, is 0/1 value, ServVFor
The out-of-limit rate of busbar voltage, ServloadLoad dead electricity amount, ServbchFor the out-of-limit rate of equipment, w1、w2、w3、w4Respectively system parallel off,
The weight factor that busbar voltage is out-of-limit, load dead electricity and equipment are out-of-limit, and w1+w2+w3+w4=1；P is the history event for cut-offfing failure
Hinder probability.Servsplit、ServV、ServloadAnd ServbchIt can be further using the prior art according to the data that N-1 is scanned
It is calculated.
Failure risk threshold value is set as λrisk, for cut-offfing failure i, if quantization integrated risk meets the following conditions,
This cut-offs failure i income N-1 catastrophe failure collection：
Risk_i ＞ λrisk (2)。
The historical failure alarm data that existing intelligent fault alarm platform can export is when containing each failure to occur
Meteorology data.It is preferred, therefore, that in S24 of the present invention, whether the corresponding moment of failure is cut-off with severe with equipment
Weather is judgment basis, and relevant device, which is cut-off failure, to be classified as in the statistics set of corresponding harsh weather, filters out and meets respectively
The faulty equipment occurred frequently of kind harsh weather, supplements generated N-1 catastrophe failures collection, avoids accident under harsh weather high
Hair equipment is submerged.Harsh weather can there are many, it is thunderous electrical time, rainfall weather, snowfall weather, superhigh temperature weather, extremely cold
Weather etc..
Preferably, in S24, defining somewhere component equipment i mean failure rate probability under certain harsh weather isIt can
It is calculate by the following formula to obtain：
In formula (3),For the failure frequency of somewhere equipment i under corresponding harsh weather, n is corresponding severe gas
The equipment sum of failure occurred for this areas Hou Xia；
Set mean failure rate probability threshold value λweather, meet the following conditions, you can corresponding equipment is classified as emphasis and is examined
The faulty equipment occurred frequently under object i.e. harsh weather is examined, N-1 catastrophe failure collection is filled into：
Preferably, in S3, it is based on trend sensitivity matrix, calculates branch breaking distribution factor.Specific calculating process content
It refers to《High electric network analysis》Middle 8.3.3 sections, are the prior art.
Preferably, in S4, sensitivity threshold value is set as λdomain；
Any appliance A ∈ S concentrated for N-1 catastrophe failuresseriousIf there are an equipment B, and device A is to equipment B's
Branch breaking distribution factor DBA＞ λdomain, then what equipment B belonged to device A cut-offs the failure domain of influence, i.e. B ∈ Domain_A.
Cut-off distribution factor DBAIt is expressed as fault branch A and cut-offs influence on tidal flow to branch B.DBA＞ λdomain, it is believed that branch
Road B is cut-off by branch A to be affected, and power flow transfer increases, and is easiest to that out-of-limit situation occurs, therefore determines B ∈ Domain_A；Instead
It, DBA＜ λdomain, then it is assumed that fault branch device A, which cut-offs the influence to branch B, to be ignored, then branch B does not belong to branch
The domain of influence of device A, but the value still participates in subsequent trend estimation.
Preferably, in S5, if the equipment B in the failure domain of influence of device A is not belonging to N-1 catastrophe failure collection, to device A
The intersection Domain_A ∩ Domain_B of the failure domain of influence are calculated with equipment B；
Each equipment in device A and equipment B failure domain of influence intersections is traversed, for any appliance C ∈ in intersection
Domain_A ∩ Domain_B carry out Load flow calculation, including：
S51 cut-offs failure, then the trend estimated value of equipment C simultaneously according to device A and equipment BFor：
In formula (5),Respectively device A, the initial trend of B, C, DCAFailure is cut-off to equipment for device A
C's cut-offs distribution factor, DCBFailure is cut-off for equipment B, and distribution factor is cut-off to equipment C；
If the trend estimated value of equipment CIt is out-of-limit, then branch equipment A and branch B are taken in into N-2 fault sets；Otherwise it is transferred to
S52；
S52 cut-offs failure, then the trend estimated value of equipment C in succession according to device A and equipment BFor：
In formula (6),Item has reacted device A, cascading failure occurs for B on the influence of the power flow transfer of equipment C；
If the trend estimated value of equipment CIt is out-of-limit, then device A and equipment B are taken in into N-2 fault sets；Otherwise it device A and sets
Standby B is not belonging to N-2 fault sets.
Advantageous effect
Compared with prior art, the present invention has the following advantages and improves：
1) special consideration should be given to climatic factors, have targetedly taken into account and have been gone through under harsh weather (thunder and lightning, typhoon, snowstorm etc.)
The N-2 combined faults of history faulty equipment occurred frequently so that the N-2 failures that N-2 fault sets cover are more comprehensively accurate, exist for dispatcher
The Emergency Preparedness for cut-offfing failure under harsh weather to the whole network N-1, N-2 provides foundation；
2) polynary indicator evaluation system, more compared to the out-of-limit evaluation index of current D5000 static systems safety analysis individual event
Add comprehensively, and the history data of comprehensive intelligent alarm platform such as D5000 can be efficiently used, risk is cut-off to N-1 and is commented
Estimate more directive significance；
3) be directed to N-1 catastrophe failure collection, in conjunction with based on cut-off sensitivity determination the failure domain of influence, consider simultaneous faults and
Two kinds of successive failure cut-offs type, can significantly reduce the N-2 failures combination that noncritical failure is brought, effectively improve N-2 groups
Close catastrophe failure collection scan efficiency.
Description of the drawings
Fig. 1 show a kind of specific embodiment flow diagram of the method for the present invention.
Specific implementation mode
It is further described below in conjunction with the drawings and specific embodiments.
The present invention is based on the multiple failure generation methods of N-1 catastrophe failure collection, including：
S1 obtains current weather information data and historical failure alarm data；
S2 carries out N-1 fault scannings to current whole network equipment, obtains N-1 fault sets, the data then obtained according to S1,
The catastrophe failure in N-1 fault sets is filtered out, N-1 catastrophe failure collection is generated；
S3 calculates separately branch breaking distribution factor for whole network equipment；
S4 sets sensitivity threshold value, and according to the comparison result for cut-offfing distribution factor and sensitivity threshold value, determination is respectively set
Standby cut-offs the failure domain of influence；
S5 traverses N-1 catastrophe failure collection, device A corresponding for each failure therein, if it cut-offs in the failure domain of influence
Any appliance B belong to N-1 catastrophe failure collection, then device A and equipment B are taken in into N-2 fault sets simultaneously；Otherwise computing device A
Failure domain of influence intersection is cut-off in equipment B, it is out-of-limit according to whether there is any appliance trend in intersection, it is determined whether by equipment
A and equipment B take in N-2 fault sets；
S6 exports the whole network N-2 fault sets.
Embodiment 1
In S1, current weather information data are obtained by accessing meteorological data platform, and historical failure alarm data is from synthesis
Intelligent alarm platform obtains, and such as existing D5000 comprehensive intelligents alert platform.The historical failure alarm data that S1 is obtained includes setting
Standby emergency stop valve trip time of origin, failure cause, faulty equipment information and the weather information etc. at corresponding moment.
S2 includes step：
S21 carries out N-1 fault scannings to current whole network equipment, obtains each equipment in N-1 fault sets and N-1 fault sets
Power Flow Information data when failure；
S22 carries out event according to fault severity level and historical failure probability of happening is cut-off to each equipment in N-1 fault sets
Hinder risk assessment；And failure risk threshold value is set, failure risk assessment result is more than to the N-1 failures of failure risk threshold value
N-1 catastrophe failures are defined as, N-1 catastrophe failure collection is formed；
S23 judges current weather state for normal climate or severe gas according to the current weather information data that S1 is obtained
It waits：Under normal climate, the N-1 catastrophe failure collection that S23 is obtained is final N-1 catastrophe failures collection；Under harsh weather, step is gone to
S24；
S24 under harsh weather, is accused from the historical failure chosen in the historical failure alarm data that S1 is obtained under harsh weather
Alert data, calculate mean failure rate probability of each equipment fault under harsh weather, and set mean failure rate probability threshold value, if certain
The mean failure rate probability of equipment is more than mean failure rate probability threshold value, then using the equipment fault as the event occurred frequently under harsh weather
Barrier, fills into N-1 catastrophe failure collection, forms final N-1 catastrophe failure collection.
In Power System Analysis, normal climate and harsh weather divide into the prior art, and normal climate is i.e. without sleet
Thunder and lightning, without strong wind, the meteorology of without superhigh temperature and extremely cold weather.It is only easy in harsh weather since part N-1 cut-offs failure
Lower generation, if therefore by all failures of cut-offfing regardless of weather conditions progress risk assessment, undoubtedly the overall generation of these failures is general
Probability of happening under rate meeting more only adverse weather condition substantially reduces, and will further make practical serious under harsh weather
Failure is ignored.The factor that the present invention considers weather carries out integrated risk quantitative evaluation to cut-offfing failure so that N-1 seriously events
The result of barrier is more accurate comprehensive.
Further, in step S22, the fault severity level that cut-offs that Serv_i is equipment i is defined, Fault_i is equipment i
The historical failure probability of failure is cut-off, then the quantization integrated risk Risk_i that equipment i cut-offs failure is：
In formula (1), ServsplitSystem sectionalizing status data when failure occurs is cut-off for equipment, is 0/1 value, ServVFor
The out-of-limit rate of busbar voltage, ServloadLoad dead electricity amount, ServbchFor the out-of-limit rate of equipment, w1、w2、w3、w4Respectively system parallel off,
The weight factor that busbar voltage is out-of-limit, load dead electricity and equipment are out-of-limit, and w1+w2+w3+w4=1；P is the history event for cut-offfing failure
Hinder probability.
Failure risk threshold value is set as λrisk, for cut-offfing failure i, if quantization integrated risk meets the following conditions,
This cut-offs failure i income N-1 catastrophe failure collection：
Risk_i ＞ λrisk (2)。
It is whether adjoint to cut-off failure and its corresponding moment based on the historical failure alarm data under harsh weather in S25
Harsh weather is judgment basis, corresponding outage contingencies is classified as in the statistics set of corresponding adverse weather condition, and symbol is filtered out
The faulty equipment occurred frequently for closing various adverse weather conditions supplements the N-1 catastrophe failure collection generated under normal climate.
In S3, each N-1 catastrophe failures of calculating based on trend sensitivity matrix concentrate the branch breaking point for respectively cut-offfing failure
The cloth factor.Specific calculating process content refers to《High electric network analysis》Middle 8.3.3 sections, are the prior art.
In S4, sensitivity threshold value is set as λdomain, for N-1 catastrophe failures concentrate any to cut-off failure corresponding
Branch equipment A ∈ SseriousIf there are branch B, and branch equipment A is to the branch breaking distribution factor D of branch BBA＞ λdomain,
Then branch B belongs to the failure domain of influence Domain_A of branch equipment A, namely it is corresponding cut-off failure cut-off the failure domain of influence.
Cut-off distribution factor DBAIt is expressed as fault branch A and cut-offs influence on tidal flow to branch B.DBA＞ λdomain, it is believed that branch
Road B is cut-off by branch A to be affected, and power flow transfer increases, and is easiest to that out-of-limit situation occurs, therefore determines B ∈ Domain_A；Instead
It, DBA＜ λdomain, then it is assumed that fault branch device A, which cut-offs the influence to branch B, to be ignored, then branch B does not belong to branch
The domain of influence of device A, but the value still participates in subsequent trend estimation.
In S5, if the equipment B in the failure domain of influence of device A is not belonging to N-1 catastrophe failure collection, to device A and equipment B
Calculate the intersection Domain_A ∩ Domain_B of the failure domain of influence；
Each equipment in device A and equipment B failure domain of influence intersections is traversed, for any appliance C ∈ in intersection
Domain_A ∩ Domain_B carry out Load flow calculation, including：
S51 cut-offs failure, then the trend estimated value of equipment C simultaneously according to device A and equipment BFor：
In formula (5),Respectively device A, the initial trend of B, C, DCAFailure is cut-off to equipment for device A
C's cut-offs distribution factor, DCBFailure is cut-off for equipment B, and distribution factor is cut-off to equipment C；
If the trend estimated value of equipment CIt is out-of-limit, then branch equipment A and branch B are taken in into N-2 fault sets；Otherwise it is transferred to
S52；
S52 cut-offs failure, then the trend estimated value of equipment C in succession according to device A and equipment BFor：
In formula (6),Item has reacted device A, cascading failure occurs for B on the influence of the power flow transfer of equipment C；
If the trend estimated value of intersection equipment CIt is out-of-limit, then device A and equipment B are taken in into N-2 fault sets；Otherwise device A
It is not belonging to N-2 fault sets with equipment B.
Embodiment 2
As shown in Figure 1, it is an object of the invention to propose that a kind of N-2 combined faults suitable for static security analysis are quick
Generation method.Polynary indicator evaluation system is built, platform historical data is alerted based on D5000 comprehensive intelligents, it is tight from failure is cut-off
Two angles of weight degree and probability of malfunction carry out risk assessment to N-1 forecast failures, set N-1 risk thresholds, it is tight to form N-1
Weight fault set；Meteorological data is forecast according to third party, using the faulty equipment occurred frequently forecast under meteorological condition as paying close attention to pair
As supplementing N-1 catastrophe failure collection；Based on trend matrix information, branch breaking sensitivity is calculated, threshold is screened in setting,
The failure domain of influence is generated to N-1 catastrophe failure concentrating equipments；Based on the failure domain of influence of catastrophe failure collection equipment, from simultaneous faults
It with two levels of successive failure, is quickly estimated cut-offfing trend, according to the out-of-limit situation of intersection branch heavy duty, it is determined whether raw
Produce N-2 fault sets.
Above-mentioned a kind of multiple failure rapid generation based on N-1 catastrophe failure collection, which is characterized in that including following
Step：
1) current weather information data are obtained from meteorological data platform, historical failure number is obtained from comprehensive intelligent alarm platform
According to,
2) consider current weather conditions, form the N-1 catastrophe failure collection under normal climate, or comprising under normal climate
The N-1 catastrophe failure collection of failure occurred frequently under catastrophe failure and harsh weather；
3) it is based on trend sensitivity matrix information, branch breaking distribution factor is calculated to whole network equipment；
4) sensitivity threshold is set, according to distribution factor is cut-off, determine each equipment or branch cut-offs the failure domain of influence；
5) N-1 catastrophe failure collection is traversed, to respectively cut-offfing the equipment in the failure domain of influence of failure, according to N-1 faulty equipments
Itself whether belong to the friendship of both catastrophe failure collection and N-1 faulty equipments and equipment in its failure domain of influence the failure domain of influence
Collect the whether heavily loaded out-of-limit generation N-2 combined faults of equipment；
6) the whole network N-2 combined fault set is exported.
In the present embodiment, the step 2) specifically includes：
Current climate is to build polynary indicator evaluation system under normal weather conditions, is accused based on D5000 platform comprehensive intelligents
Alert historical failure probability data carries out risk from two angles of fault severity level and probability of happening are cut-off to N-1 forecast failures
Assessment sets N-1 risk thresholds, forms N-1 catastrophe failure collection；
Uniformly consider each risk indicator using weight weighting scheme, establishes quantization accident risk index system：
In formula, Serv_i is to cut-off failure i severities；w1,w2,w3,w4Respectively system parallel off, busbar voltage are out-of-limit, negative
The out-of-limit weight factor of lotus dead electricity, equipment, and w1+w2+w3+w4=1；Fault_i is the historical failure probability cut-off；Risk_
I is integrated risk quantizating index.
Set risk threshold λrisk, meet the following conditions, then enter N-1 catastrophe failure collection：
Risk_i ＞ λrisk (2)
Current climate is that it is tight to form N-1 in the same polynary indicator evaluation system using formula (1) under adverse weather condition
On the basis of weight fault set, it is based on intelligent alarm historical failure probability data, is tripped with equipment breakdown and its whether the corresponding moment
It is judgment basis with harsh weather, which is classified as in the statistics set of corresponding weather, filters out and meet currently
Faulty equipment occurred frequently under adverse weather condition, the N-1 catastrophe failure collection formed to normal climate supplement, and avoid severe gas
Lower accident equipment occurred frequently is waited to be submerged；
By taking thunder and lightning weather as an example, somewhere component equipment i mean failure rate probability under thunder and lightning weatherFor：
In formula (3),For the failure frequency of somewhere equipment i under thunder and lightning weather, n is the ground under thunder and lightning weather
The equipment sum of failure occurred for area；
Set mean failure rate probability threshold value λweather, meet the following conditions, you can corresponding equipment is classified as emphasis and is examined
The faulty equipment occurred frequently under object i.e. harsh weather is examined, N-1 catastrophe failure collection is filled into：
In the present embodiment, step 3) calculates branch breaking distribution factor, tool using the sensitivity coefficient based on trend matrix
Hold in vivo and refers to《High electric network analysis》Middle 8.3.3 sections.
In the present embodiment, the determination process of the failure domain of influence is in the step 4)：
Set threshold λdomain, for catastrophe failure collection SseriousIn branch equipment A ∈ SseriousDetermine that its failure influences
Domain.It such as cut-offs distribution factor and is more than threshold value, DBA＞ λdomain, it is believed that branch B is cut-off by branch A to be affected, and power flow transfer increases
Greatly, it is easiest to that out-of-limit situation occurs, then branch B is belonged to the failure domain of influence of branch A；It such as cut-offs distribution factor and is less than threshold
Value, DBA＜ λdomain, it is believed that its influence can be ignored, then branch B does not belong to the domain of influence of branch A, but the value still participates in subsequently
Trend estimation；
Wherein：DBAIt is expressed as fault branch A and cut-offs influence on tidal flow to branch B；
In the present embodiment, the step 5) specifically includes：
The equipment B in the serious forecast accident A failure domains of influence is defined, if B is under the jurisdiction of serious forecast failure collection, i.e. A ∈
Sserious,B∈Sserious, B ∈ Domain_A are directly added into N-2 forecast failure collections；
The equipment B in the serious forecast accident A failure domains of influence is defined, if B is under the jurisdiction of not serious forecast failure collection, root
According to ground state power flow solutions and distribution factor is cut-off to the intersection equipment C in the failure domain of influence of A and B, i.e. A ∈ Sserious,B ∈ Domain_A, C ∈ Domain_A ∩ Domain_B carry out trend according to simultaneous faults, successive failure respectively
Estimation, if equipment C is out-of-limit, A and B enters N-2 fault sets, if C is not out-of-limit, continues to carry out tide to other intersection equipment
Stream estimation, if all intersection equipment are not out-of-limit, branch A and B does not take in N-2 fault sets.
After cut-offfing failure simultaneously according to branch A and B, then branch C trends estimated value is：
Successive failure successively occurs according to branch A and B, branch C trend estimated values are：
Wherein,Item has reacted A, B and cascading failure occurs on the influence of the power flow transfer of branch C.
Serious forecast accident A failure domains of influence traversal is terminated, then continue to go to step (1) generate it is next serious pre-
Think the N-2 failures combination of accident.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be in the protection domain being defined in the patent claims.
Claims (7)
1. a kind of multiple failure generation method based on N-1 catastrophe failure collection, characterized in that including：
S1 obtains current weather information data and historical failure alarm data；
S2 carries out N-1 fault scannings to current whole network equipment, obtains N-1 fault sets, the data then obtained according to S1, screening
Go out the catastrophe failure in N-1 fault sets, generates N-1 catastrophe failure collection；
S3 calculates separately branch breaking distribution factor for whole network equipment；
S4, setting sensitivity threshold value determine each equipment according to the comparison result for cut-offfing distribution factor and sensitivity threshold value
Cut-off the failure domain of influence；
S5 traverses N-1 catastrophe failure collection, device A corresponding for each failure therein, if it cut-offs appointing in the failure domain of influence
One equipment B belongs to N-1 catastrophe failure collection, then device A and equipment B is taken in N-2 fault sets simultaneously；Otherwise computing device A is in setting
Standby B's cut-offs failure domain of influence intersection, out-of-limit according to whether there is any appliance trend in intersection, it is determined whether by device A and
Equipment B takes in N-2 fault sets；
S6 exports the whole network N-2 fault sets.
2. according to the method described in claim 1, it is characterized in that, S2 includes step：
S21 carries out N-1 fault scannings to current whole network equipment, obtains each equipment fault in N-1 fault sets and N-1 fault sets
When Power Flow Information data；
S22 carries out failure wind according to fault severity level and historical failure probability of happening is cut-off to each equipment in N-1 fault sets
Danger assessment；And failure risk threshold value is set, failure risk assessment result is more than to the N-1 fault definitions of failure risk threshold value
For N-1 catastrophe failures, N-1 catastrophe failure collection is formed；
S23 judges current weather state for normal climate or harsh weather according to the current weather information data that S1 is obtained：
Under normal climate, the N-1 catastrophe failure collection that S23 is obtained is final N-1 catastrophe failures collection；Under harsh weather, step is gone to
S24；
Under harsh weather, number is alerted from the historical failure chosen in the historical failure alarm data that S1 is obtained under harsh weather by S24
According to mean failure rate probability of each equipment fault under harsh weather being calculated, and set mean failure rate probability threshold value, if certain equipment
Mean failure rate probability be more than mean failure rate probability threshold value, then using the equipment fault as the failure occurred frequently under harsh weather,
N-1 catastrophe failure collection is filled into, final N-1 catastrophe failure collection is formed.
3. according to the method described in claim 2, it is characterized in that, in step S22, it is cut-off failure i serious to define Serv_i
Degree, Fault_i are the historical failure probability for cut-offfing failure i, then the quantization integrated risk Risk_i for cut-offfing failure i is：
In formula (1), ServsplitSystem sectionalizing status data when occurring for failure is 0/1 value, ServVIt is out-of-limit for busbar voltage
Rate, ServloadLoad dead electricity amount, ServbchFor the out-of-limit rate of equipment, w1、w2、w3、w4Respectively system parallel off, busbar voltage it is out-of-limit,
Load dead electricity and the out-of-limit weight factor of equipment, and w1+w2+w3+w4=1；P is the historical failure probability for cut-offfing failure；
Failure risk threshold value is set as λrisk, failure is cut-off for equipment i, if quantization integrated risk meets the following conditions,
Equipment i is cut-off into failure and takes in N-1 catastrophe failure collection：
Risk_i ＞ λrisk (2)。
4. according to the method described in claim 2, it is characterized in that, in S24, whether the corresponding moment that failure is cut-off with equipment adjoint
Harsh weather is judgment basis, and relevant device, which is cut-off failure, to be classified as in the statistics set of corresponding harsh weather, filters out symbol
The faulty equipment occurred frequently for closing various harsh weather supplements generated N-1 catastrophe failures collection.
5. method according to claim 2 or 4, characterized in that in S24, define somewhere element under certain harsh weather
Equipment i mean failure rate probability isIt can be calculate by the following formula to obtain：
In formula (3),For the failure frequency of somewhere equipment i under corresponding harsh weather, n is under corresponding harsh weather
The equipment sum of failure occurred for this area；
Set mean failure rate probability threshold value λweather, meet the following conditions, you can corresponding equipment is classified as high spot reviews object
Faulty equipment occurred frequently i.e. under harsh weather fills into N-1 catastrophe failure collection：
6. according to the method described in claim 1, it is characterized in that, in S4, set sensitivity threshold value as λdomain；
Any appliance A ∈ S concentrated for N-1 catastrophe failuresseriousIf there are an equipment B, and device A is to the branch of equipment B
Cut-off distribution factor DBA＞ λdomain, then what equipment B belonged to device A cut-offs the failure domain of influence.
7. according to the method described in claim 1, it is characterized in that, in S5, if the equipment B in the failure domain of influence of device A does not belong to
In N-1 catastrophe failure collection, then the intersection Domain_A ∩ Domain_B of the failure domain of influence is calculated device A and equipment B；
Each equipment in device A and equipment B failure domain of influence intersections is traversed, for any appliance C ∈ Domain_A in intersection
∩ Domain_B carry out Load flow calculation, including：
S51 cut-offs failure, then the trend estimated value of equipment C simultaneously according to device A and equipment BFor：
In formula (5),Respectively device A, the initial trend of B, C, DCAFailure is cut-off for device A to open equipment C
Disconnected distribution factor, DCBFailure is cut-off for equipment B, and distribution factor is cut-off to equipment C；
If the trend estimated value of equipment CIt is out-of-limit, then branch equipment A and branch B are taken in into N-2 fault sets；Otherwise it is transferred to S52；
S52 cut-offs failure, then the trend estimated value of equipment C in succession according to device A and equipment BFor：
In formula (6),Item has reacted device A, cascading failure occurs for B on the influence of the power flow transfer of equipment C；
If the trend estimated value of equipment CIt is out-of-limit, then device A and equipment B are taken in into N-2 fault sets；Otherwise device A and equipment B
It is not belonging to N-2 fault sets.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109524982A (en) * | 2018-12-12 | 2019-03-26 | 国网山东省电力公司电力科学研究院 | A kind of alternating current-direct current Power Network Transient Stability methods of risk assessment |
| CN110244577A (en) * | 2019-06-04 | 2019-09-17 | 武汉华飞智能电气科技有限公司 | A kind of power grid surely controls emulation mode and system |
| CN110390078A (en) * | 2019-07-10 | 2019-10-29 | 南瑞集团有限公司 | Failure set creation method is mass-sended under a kind of external disaster based on temporal correlation |
| CN110602037A (en) * | 2019-07-26 | 2019-12-20 | 广州穗科建设管理有限公司 | Remote supervision system |
| CN110932267A (en) * | 2019-12-10 | 2020-03-27 | 国网河北省电力有限公司电力科学研究院 | Energy internet fault set decentralized generation and centralized combination system and method |
| CN111429299A (en) * | 2020-03-10 | 2020-07-17 | 国电南瑞科技股份有限公司 | Method, system and storage medium for generating expected fault set |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101895116A (en) * | 2010-07-16 | 2010-11-24 | 国电南瑞科技股份有限公司 | Online available transmission capacity computing method based on distribution factor |
| CN103279807A (en) * | 2013-05-06 | 2013-09-04 | 国家电网公司 | Static risk assessment method for power grid in severe weather |
| CN103942731A (en) * | 2014-04-21 | 2014-07-23 | 国家电网公司 | Power grid anticipated fault set automatic generating method and system |
| CN104092210A (en) * | 2014-07-14 | 2014-10-08 | 国家电网公司 | Online static security analysis method with N-2 opening rapid scanning function |
-
2018
- 2018-03-13 CN CN201810203846.3A patent/CN108389002B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101895116A (en) * | 2010-07-16 | 2010-11-24 | 国电南瑞科技股份有限公司 | Online available transmission capacity computing method based on distribution factor |
| CN103279807A (en) * | 2013-05-06 | 2013-09-04 | 国家电网公司 | Static risk assessment method for power grid in severe weather |
| CN103942731A (en) * | 2014-04-21 | 2014-07-23 | 国家电网公司 | Power grid anticipated fault set automatic generating method and system |
| CN104092210A (en) * | 2014-07-14 | 2014-10-08 | 国家电网公司 | Online static security analysis method with N-2 opening rapid scanning function |
Non-Patent Citations (2)
| Title |
|---|
| 李传栋等: "基于故障影响域的多重预想故障集筛选方法", 《电力系统保护与控制》 * |
| 王大治等: "配电网风险防控分析及安全预警技术的应用", 《浙江电力》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109524982A (en) * | 2018-12-12 | 2019-03-26 | 国网山东省电力公司电力科学研究院 | A kind of alternating current-direct current Power Network Transient Stability methods of risk assessment |
| CN109524982B (en) * | 2018-12-12 | 2020-06-16 | 国网山东省电力公司电力科学研究院 | AC/DC power grid transient stability risk assessment method |
| CN110244577A (en) * | 2019-06-04 | 2019-09-17 | 武汉华飞智能电气科技有限公司 | A kind of power grid surely controls emulation mode and system |
| CN110390078A (en) * | 2019-07-10 | 2019-10-29 | 南瑞集团有限公司 | Failure set creation method is mass-sended under a kind of external disaster based on temporal correlation |
| CN110602037A (en) * | 2019-07-26 | 2019-12-20 | 广州穗科建设管理有限公司 | Remote supervision system |
| CN110602037B (en) * | 2019-07-26 | 2021-11-16 | 广州穗科建设管理有限公司 | Remote supervision system |
| CN110932267A (en) * | 2019-12-10 | 2020-03-27 | 国网河北省电力有限公司电力科学研究院 | Energy internet fault set decentralized generation and centralized combination system and method |
| CN110932267B (en) * | 2019-12-10 | 2023-04-18 | 国网河北省电力有限公司电力科学研究院 | Energy internet fault set dispersed generation and centralized combination system and method |
| CN111429299A (en) * | 2020-03-10 | 2020-07-17 | 国电南瑞科技股份有限公司 | Method, system and storage medium for generating expected fault set |
| CN111429299B (en) * | 2020-03-10 | 2022-09-06 | 国电南瑞科技股份有限公司 | Method, system and storage medium for generating expected fault set |
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