CN109447439A - A kind of load flow rectification sample generating method and device based on FP-Growth algorithm - Google Patents
A kind of load flow rectification sample generating method and device based on FP-Growth algorithm Download PDFInfo
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Abstract
The present invention provides a kind of load flow rectification sample generating method based on FP-Growth algorithm, comprising: the mark of each grid equipment to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model；According to the mark of at least one grid equipment to be operated, matching obtains the grid equipment sequence of operation corresponding with the mark of at least one grid equipment to be operated from load flow rectification rule base, and the load flow rectification rule base is to be excavated based on FP-Growth algorithm according to offline load flow rectification historical data；The operational motion for the mark to the grid equipment to be operated recorded with the grid equipment sequence of operation replaces the corresponding operational motion of mark for the grid equipment to be operated recorded in the power grid stability analysis task, and the replaced power grid stability analysis task is a load flow rectification sample.The load flow rectification sample generating method increases the diversity of load flow rectification sample.
Description
Technical field
The present invention relates to bulk power grid security technology areas, and more particularly, to a kind of based on FP-Growth algorithm
Load flow rectification sample generating method and device.
Background technique
With being growing for electric system scale, the device model in power grid is increasingly sophisticated, is emulated based on mathematical model
On-line security and stability analysis (dynamic stability analysis, abbreviation DSA) be faced with it is computationally intensive, calculate it is time-consuming
The problems such as longer, calculated result is influenced by model data accuracy.
In recent years, occur some quickly sentencing bulk power grid steady method based on online historical data sample.It is this kind of
The validity of method is influenced by data sample quality and quantity.In general, the data sample that in-circuit emulation is calculated is usual
It is all located near the normal operation point of network system, therefore, the similar sample in sample database is excessive, and the diversity of sample is insufficient,
The validity of operation of power networks law mining is constrained, therefore, the bulk power grid for also causing mathematical model to emulate quickly sentences steady method
Accuracy is insufficient.
Summary of the invention
For this insufficient problem of diversity for the data sample that in-circuit emulation is calculated, the present invention provides one kind and is based on
The load flow rectification sample generating method and device of FP-Growth algorithm, to increase the diversity of load flow rectification sample.
In a first aspect, the present invention provides a kind of load flow rectification sample generating method based on FP-Growth algorithm, including with
Lower step:
Step S10: each is extracted from the power grid stability analysis task of acquisition generated by power system simulation model and is waited for
The mark of the grid equipment of operation；
Step S20: it according to the mark of at least one grid equipment to be operated, matches and obtains from load flow rectification rule base
The grid equipment sequence of operation corresponding with the mark of at least one grid equipment to be operated, the grid equipment operate sequence
Column record the operational motion to the partly or completely mark of at least one grid equipment to be operated, the power grid
Equipment operation sequence is the rule in the load flow rectification rule base, and the load flow rectification rule base is based on FP-
Growth algorithm is excavated according to offline load flow rectification historical data, the electricity for including in the power grid stability analysis task
The number M of the mark of net equipment more than the mark for the grid equipment for including in the load flow rectification rule base number N, it is described from
The number S of the mark for the grid equipment for including in line load flow rectification historical data, which is more than in the power grid stability analysis task, to be wrapped
The number M of the mark of the grid equipment included, wherein M, N, S are the positive integer greater than 1；
Step S30: with the behaviour for the mark to the grid equipment to be operated that the grid equipment sequence of operation is recorded
The corresponding operational motion of mark for the grid equipment to be operated recorded in the power grid stability analysis task is replaced in work movement,
The replaced power grid stability analysis task is a load flow rectification sample.
Specifically, the method,
It include a plurality of types of grid equipments in the power system simulation model, each type of grid equipment includes at least one
It is a；
The step 10, further includes:
Each electricity to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model
The type information of net equipment；
Correspondingly, the step 20, comprising:
According to the mark of at least one identical grid equipment to be operated of type information, from corresponding with the type information
Grid equipment load flow rectification rule base in matching obtain it is corresponding with the mark of at least one grid equipment to be operated
The grid equipment sequence of operation, the grid equipment sequence of operation record to partly or completely it is described at least one wait operating
Grid equipment mark operational motion, the grid equipment sequence of operation be the grid equipment load flow rectification rule base in
A rule, the grid equipment load flow rectification rule base be based on FP-Growth algorithm according to offline load flow rectification history
What data mining obtained；
Correspondingly, the step S30 includes: with grid equipment sequence of operation record to the power grid to be operated
The operational motion of the mark of equipment replaces the mark for the grid equipment to be operated recorded in the power grid stability analysis task
Corresponding operational motion, the replaced power grid stability analysis task are a load flow rectification sample.
Specifically, the method,
When the type information is generator,
Further include step S40:
It is adjusted using the adjustment characteristic quantity of generator corresponding with the mark of the generator to be operated described wait operate
Generator operation data, and the operation data of generator to be operated described in after adjusting is replaced into the grid stability
The corresponding operation data of mark for the generator to be operated recorded in analysis task, the replaced grid stability
Analysis task is a load flow rectification sample；Wherein, the adjustment characteristic quantity of the generator is from offline load flow rectification history
Extraction obtains in data.
Specifically, the method, further includes:
Step S50: being directed to each described load flow rectification sample, judges the electricity corresponding with the load flow rectification sample
The grid stability of network simulation model；
If trend corresponding with the load flow rectification sample be it is convergent, the load flow rectification sample be a power grid it is steady
Random sample sheet；
If trend corresponding with the load flow rectification sample be it is not convergent, the load flow rectification sample be a power grid
Unstable sample.
Specifically, the method,
It is being based on FP-Growth algorithm, when excavating load flow rectification rule base according to offline load flow rectification historical data, setting
Minimum support is 8%；It is 100% that min confidence, which is arranged,.
Second aspect, the load flow rectification sample generating means based on FP-Growth algorithm that the present invention provides a kind of, packet
It includes:
Grid equipment marker extraction module, is used for:
Each electricity to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model
The mark of net equipment；
Operational motion matching module, is used for:
According to the mark of at least one grid equipment to be operated, from load flow rectification rule base matching obtain with it is described to
The corresponding grid equipment sequence of operation of mark of a few grid equipment to be operated, the grid equipment sequence of operation record
To the operational motion of the partly or completely mark of at least one grid equipment to be operated, the grid equipment operation
Sequence is the rule in the load flow rectification rule base, and the load flow rectification rule base is based on FP-Growth algorithm root
It is excavated according to offline load flow rectification historical data, the mark for the grid equipment for including in the power grid stability analysis task
Number M more than the grid equipment for including in the load flow rectification rule base mark number N, the offline load flow rectification goes through
The number S of the mark for the grid equipment for including in history data is more than the grid equipment for including in the power grid stability analysis task
Mark number M, wherein M, N, S are the positive integer greater than 1；
Load flow rectification sample generation module, is used for:
The operational motion for the mark to the grid equipment to be operated recorded with the grid equipment sequence of operation replaces
The corresponding operational motion of mark for the grid equipment to be operated recorded in the power grid stability analysis task is changed, it is replaced
The power grid stability analysis task is a load flow rectification sample.
Specifically, the device,
It include a plurality of types of grid equipments in the power system simulation model, each type of grid equipment includes at least one
It is a；
The grid equipment marker extraction module, is also used to:
Each electricity to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model
The type information of net equipment；
Correspondingly, the operational motion matching module, is used for:
According to the mark of at least one identical grid equipment to be operated of type information, from corresponding with the type information
Grid equipment load flow rectification rule base in matching obtain it is corresponding with the mark of at least one grid equipment to be operated
The grid equipment sequence of operation, the grid equipment sequence of operation record to partly or completely it is described at least one wait operating
Grid equipment mark operational motion, the grid equipment sequence of operation be the grid equipment load flow rectification rule base in
A rule, the grid equipment load flow rectification rule base be based on FP-Growth algorithm according to offline load flow rectification history
What data mining obtained；
Correspondingly, the load flow rectification sample generation module, is used for:
The operational motion for the mark to the grid equipment to be operated recorded with the grid equipment sequence of operation replaces
The corresponding operational motion of mark for the grid equipment to be operated recorded in the power grid stability analysis task is changed, it is replaced
The power grid stability analysis task is a load flow rectification sample.
Specifically, the device,
When the type information is generator,
Further include generator characteristics amount adjustment module, be used for:
It is adjusted using the adjustment characteristic quantity of generator corresponding with the mark of the generator to be operated described wait operate
Generator operation data, and the operation data of generator to be operated described in after adjusting is replaced into the grid stability
The corresponding operation data of mark for the generator to be operated recorded in analysis task, the replaced grid stability
Analysis task is a load flow rectification sample；Wherein, the adjustment characteristic quantity of the generator is from offline load flow rectification history
Extraction obtains in data.
Specifically, the device, further includes:
Load flow rectification sample judgment module, is used for:
For load flow rectification sample described in each, the grid simulation mould corresponding with the load flow rectification sample is judged
The grid stability of type；
If trend corresponding with the load flow rectification sample be it is convergent, the load flow rectification sample be a power grid it is steady
Random sample sheet；
If trend corresponding with the load flow rectification sample be it is not convergent, the load flow rectification sample be a power grid
Unstable sample.
Specifically, the device,
It is being based on FP-Growth algorithm, when excavating load flow rectification rule base according to offline load flow rectification historical data, setting
Minimum support is 8%；It is 100% that min confidence, which is arranged,.
Load flow rectification sample generating method provided by the invention based on FP-Growth algorithm, in conjunction with based on FP-Growth
The load flow rectification rule that algorithm is excavated from offline load flow rectification historical data data, generates load flow rectification sample data, thus
Increase the diversity of load flow rectification sample；Using these load flow rectification samples, be conducive to improve scale grid line emulation quickly
Sentence steady adaptation of methods.
Detailed description of the invention
By reference to the following drawings, exemplary embodiments of the present invention can be more fully understood by:
Fig. 1 is a kind of load flow rectification sample generating method based on FP-Growth algorithm of one embodiment of the invention
Schematic diagram；
Fig. 2 is a kind of load flow rectification sample generating means based on FP-Growth algorithm of one embodiment of the invention
Schematic diagram；
Fig. 3 is the schematic diagram of the load flow rectification sample generating method of one embodiment of the invention.
Specific embodiment
Exemplary embodiments of the present invention are introduced referring now to the drawings, however, the present invention can use many different shapes
Formula is implemented, and is not limited to the embodiment described herein, and to provide these embodiments be at large and fully disclose
The present invention, and the scope of the present invention is sufficiently conveyed to person of ordinary skill in the field.Show for what is be illustrated in the accompanying drawings
Term in example property embodiment is not limitation of the invention.In the accompanying drawings, identical cells/elements use identical attached
Icon note.
Bulk power grid is closely connected, and operation has the characteristics that periodical, repeatability, in grid operation mode and management and running
Under the background that difficulty is deepened increasingly, the mass data for making full use of bulk power grid longtime running to accumulate finds out power grid on this basis
Incidence relation between the method for operation and power grid security and stability is conducive to the security and stability of rapid evaluation power grid.
Being widely popularized and applying with grid operation mode cooperated computing platform, has accumulated in platform including basic number
According to the mass datas such as parameter, steady state data, dynamic data, calculation procedure, regulating measures, adjustment user behaviors log, calculated result, energy
Enough multifarious effective sample is provided for data mining research.
On the other hand, user implies it to the understanding of target power system, experience and correlation to the adjustment behavior of trend
Knowledge.These embodying informations are in the data record of magnanimity, it is difficult to be expressed with language, and with the difference of target power system
And it changes.These information are explicitly modeled and using there is larger difficulty.Such as, it is modeled, then needed using rule base mode
It wants the knowledge engineering of flood tide to work, and effective expression is difficult to the experience of some similar " intuition ".
Therefore, the behavior pattern of user's load flow rectification is analyzed directly from data record based on data analysis technique, is used
The mode implicitly modeled establishes relevant index system, and the difficulty of knowledge application will can be effectively reduced and promote application effect.
Correlation rule theory is put forward by scholars such as U.S. Agrawal in the last century 90's, is mainly used for describing
Correlation between data middle term and item, essence are exactly by frequency that item collection occurs jointly come between mining data
It is contacting, is disclosing the dependence and correlation between event or entity.
FP-Growth algorithm and Apriori algorithm are to implement 2 kinds of methods of correlation rule theory.When the frequent episode of processing
Measure excessive, mode is too long or the threshold value comparison hour of minimum support, FP-Growth algorithm have compared to Apriori algorithm
Many advantages:
1) Apriori algorithm must generate a large amount of candidate.If excavating the frequent mode that a length is 100,
It is about 2 that Apriori algorithm will generate altogether100A candidate subset.Also, no matter which kind of implementation method is used, this is all Apriori
Algorithm generates the essential consumption of Candidate Set.
2) Apriori algorithm needs repeatedly scan data file, this not only expends the time, but also makes at data
The I/O cost of the Rule Extraction system of reason increases.
Compared to Apriori algorithm, FP-Growth algorithm can avoid the generation of a large amount of candidates and only need scan data
File twice, so an order of magnitude faster than the speed of Apriori algorithm；And at the same time be suitable for long frequent mode and it is short frequently
Mode.
Different from Apriori algorithm, FP-Growth algorithm is by all Information Compressions in transaction database into a FP-
In tree tree, the strategy divided and rule is taken, excavates condition database associated by each frequent episode respectively, and by the knot of excavation
Fruit is integrated, so as to excavate all frequent item sets in the case where not generating candidate.
That is, the process of FP-Growth algorithm Mining Frequent Patterns can be divided into two steps: the construction of FP-tree tree
With the Frequent Pattern Mining based on FP-tree tree, which is not described herein again.
As shown in Figure 1, being somebody's turn to do the load flow rectification sample generating method based on FP-Growth algorithm, comprising the following steps:
Step S10: each is extracted from the power grid stability analysis task of acquisition generated by power system simulation model and is waited for
The mark of the grid equipment of operation；
Step S20: it according to the mark of at least one grid equipment to be operated, matches and obtains from load flow rectification rule base
The grid equipment sequence of operation corresponding with the mark of at least one grid equipment to be operated, the grid equipment operate sequence
Column record the operational motion to the partly or completely mark of at least one grid equipment to be operated, the power grid
Equipment operation sequence is the rule in the load flow rectification rule base；
Step S30: with the behaviour for the mark to the grid equipment to be operated that the grid equipment sequence of operation is recorded
The corresponding operational motion of mark for the grid equipment to be operated recorded in the power grid stability analysis task is replaced in work movement,
The replaced power grid stability analysis task is a load flow rectification sample.
In order to solve the problems, such as that bulk power grid quickly sentences the diversity deficiency of online data sample set in steady, this method utilizes electricity
Network users adjust these off-line datas of the daily record data of trend accumulation, extract user based on FP-Growth algorithm and adjust trend
Rule base, and then effective online data sample is generated according to rule base, is provided largely to quickly sentence steady work for power grid
, various effective sample.
Be construed as, herein for grid equipment to be operated operational motion include two kinds: be linked into power grid or
It is exited from power grid.That is, operational motion is investment or exits if grid equipment to be operated is generator；If wait operate
Grid equipment be load, then operational motion be open or break.
It is construed as, the load flow rectification rule base is based on FP-Growth algorithm according to offline load flow rectification history
What data mining obtained.The number M of the mark for the grid equipment for including in the power grid stability analysis task is more than described
The number N of the mark for the grid equipment for including in load flow rectification rule base includes in the offline load flow rectification historical data
The number S of the mark of grid equipment is more than the number M of the mark of the grid equipment in the power grid stability analysis task included,
Wherein, M, N, S are the positive integer greater than 1.
That is, the trend tune including less grid equipment excavated from numerous offline load flow rectification historical datas
Whole rule；Load flow rectification rule is applied to generate and load flow rectification sample and then is used in power grid stability analysis task, it can be with
Randomness when generating load flow rectification sample is reduced, so that the load flow rectification sample generated more meets true history operation of power networks
Rule.
In general, in the power system, including a plurality of types of grid equipments, and each type of grid equipment at least one
It is a.It similarly, also include a plurality of types of grid equipments in Bulk power system simulation model, each type of grid equipment includes extremely
It is one few.
When establishing load flow rectification rule base, the type of grid equipment is distinguished, is distinguished for different types of grid equipment
Grid equipment load flow rectification rule base is established, then is more conducive to reduce the quantity of rule, improves the efficiency of rule match.
Preferably, in the step 10 of this method, further includes:
Each electricity to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model
The type information of net equipment；
Correspondingly, the step 20, comprising:
According to the mark of at least one identical grid equipment to be operated of type information, from corresponding with the type information
Grid equipment load flow rectification rule base in matching obtain it is corresponding with the mark of at least one grid equipment to be operated
The grid equipment sequence of operation, the grid equipment sequence of operation record to partly or completely it is described at least one wait operating
Grid equipment mark operational motion, the grid equipment sequence of operation be the grid equipment load flow rectification rule base in
A rule, the grid equipment load flow rectification rule base be based on FP-Growth algorithm according to offline load flow rectification history
What data mining obtained；
Correspondingly, the step S30 includes: with grid equipment sequence of operation record to the power grid to be operated
The operational motion of the mark of equipment replaces the mark for the grid equipment to be operated recorded in the power grid stability analysis task
Corresponding operational motion, the replaced power grid stability analysis task are a load flow rectification sample.
Carrying out power grid stability analysis or when tidal current analysis, except the throwing of regulator generator move back, power generation can also be adjusted
Machine has work value and without work value.
Specifically, when the type information is generator, this method
Further include step S40:
It is adjusted using the adjustment characteristic quantity of generator corresponding with the mark of the generator to be operated described wait operate
Generator operation data, and the operation data of generator to be operated described in after adjusting is replaced into the grid stability
The corresponding operation data of mark for the generator to be operated recorded in analysis task, the replaced grid stability
Analysis task is a load flow rectification sample；Wherein, the adjustment characteristic quantity of the generator is from offline load flow rectification history
Extraction obtains in data.
When it is implemented, as shown in table 2, the adjustment characteristic quantity of the generator is from offline load flow rectification historical data
Carry out what data statistics obtained.
Specifically, the method, further includes:
Step S50: being directed to each described load flow rectification sample, judges the electricity corresponding with the load flow rectification sample
The grid stability of network simulation model；
If trend corresponding with the load flow rectification sample be it is convergent, the load flow rectification sample be a power grid it is steady
Random sample sheet；
If trend corresponding with the load flow rectification sample be it is not convergent, the load flow rectification sample be a power grid
Unstable sample.
Specifically, the method,
It is being based on FP-Growth algorithm, when excavating load flow rectification rule base according to offline load flow rectification historical data, setting
Minimum support is 8%；It is 100% that min confidence, which is arranged,.
To sum up, the rule base of adjustment trend behavior is excavated based on FP-Growth algorithm, and then forms load flow rectification sample
This, can add to trend and sentence in steady sample set, be conducive to improve the performance that in-circuit emulation carries out grid stability judgement.
As shown in Fig. 2, additionally providing a kind of load flow rectification sample generating means based on FP-Growth algorithm, comprising:
Grid equipment marker extraction module 100, is used for:
Each electricity to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model
The mark of net equipment；
Operational motion matching module 200, is used for:
According to the mark of at least one grid equipment to be operated, from load flow rectification rule base matching obtain with it is described to
The corresponding grid equipment sequence of operation of mark of a few grid equipment to be operated, the grid equipment sequence of operation record
To the operational motion of the partly or completely mark of at least one grid equipment to be operated, the grid equipment operation
Sequence is the rule in the load flow rectification rule base, and the load flow rectification rule base is based on FP-Growth algorithm root
It is excavated according to offline load flow rectification historical data, the mark for the grid equipment for including in the power grid stability analysis task
Number M more than the grid equipment for including in the load flow rectification rule base mark number N, the offline load flow rectification goes through
The number S of the mark for the grid equipment for including in history data is more than the grid equipment for including in the power grid stability analysis task
Mark number M, wherein M, N, S are the positive integer greater than 1；
Load flow rectification sample generation module 300, is used for:
The operational motion for the mark to the grid equipment to be operated recorded with the grid equipment sequence of operation replaces
The corresponding operational motion of mark for the grid equipment to be operated recorded in the power grid stability analysis task is changed, it is replaced
The power grid stability analysis task is a load flow rectification sample.
Specifically, the device,
It include a plurality of types of grid equipments in the power system simulation model, each type of grid equipment includes at least one
It is a；
The grid equipment marker extraction module 100, is also used to:
Each electricity to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model
The type information of net equipment；
Correspondingly, the operational motion matching module 200, is used for:
According to the mark of at least one identical grid equipment to be operated of type information, from corresponding with the type information
Grid equipment load flow rectification rule base in matching obtain it is corresponding with the mark of at least one grid equipment to be operated
The grid equipment sequence of operation, the grid equipment sequence of operation record to partly or completely it is described at least one wait operating
Grid equipment mark operational motion, the grid equipment sequence of operation be the grid equipment load flow rectification rule base in
A rule, the grid equipment load flow rectification rule base be based on FP-Growth algorithm according to offline load flow rectification history
What data mining obtained；
Correspondingly, the load flow rectification sample generation module 300, is used for:
The operational motion for the mark to the grid equipment to be operated recorded with the grid equipment sequence of operation replaces
The corresponding operational motion of mark for the grid equipment to be operated recorded in the power grid stability analysis task is changed, it is replaced
The power grid stability analysis task is a load flow rectification sample.
Specifically, the device,
When the type information is generator,
Further include generator characteristics amount adjustment module 400, be used for:
It is adjusted using the adjustment characteristic quantity of generator corresponding with the mark of the generator to be operated described wait operate
Generator operation data, and the operation data of generator to be operated described in after adjusting is replaced into the grid stability
The corresponding operation data of mark for the generator to be operated recorded in analysis task, the replaced grid stability
Analysis task is a load flow rectification sample；Wherein, the adjustment characteristic quantity of the generator is from offline load flow rectification history
Extraction obtains in data.
Specifically, the device, further includes:
Load flow rectification sample judgment module 500, is used for:
For load flow rectification sample described in each, the grid simulation mould corresponding with the load flow rectification sample is judged
The grid stability of type；
If trend corresponding with the load flow rectification sample be it is convergent, the load flow rectification sample be a power grid it is steady
Random sample sheet；
If trend corresponding with the load flow rectification sample be it is not convergent, the load flow rectification sample be a power grid
Unstable sample.
Specifically, the device,
It is being based on FP-Growth algorithm, when excavating load flow rectification rule base according to offline load flow rectification historical data, setting
Minimum support is 8%；It is 100% that min confidence, which is arranged,.
The device and this method technical solution having the same, technical effect having the same, which is not described herein again.
When it is implemented, the step of should generating load flow rectification sample set based on FP-Growth algorithm, is as follows:
1) acquisition user adjusts flow data
In general, adjusting trend in electric system generallys use nearby principle, by adjusting the power output of generator, transformer
Capacity, increase route etc. carry out the stability that measure meets network system.
The trend needs that user adjusts electric system operate on the calculation procedure interface of turn-key system, these operations
Trigger data library is increased into data, deletes data, the modification movement such as data, these action events be all stored in the two of MySQL into
In journal file processed.By parsing binary log file, available user adjusts the time series of the behavior event of trend,
And it obtains user and adjusts trend behavioral data.
It is text that the user that finally extraction and analysis obtains from the binary log file of MySQL, which adjusts trend behavioral data,
The data of form, specifically include that the title of various kinds of equipment, and the type etc. of state and equipment is moved back in the throwing of various kinds of equipment.
Wherein, equipment includes generator, transformer, load, route/series/parallel capacity reactance device, static reactive power compensation
Device, inverter etc..
A kind of format of preferred data file is as shown in table 1.When being the primary adjustment trend of user shown in table 1
Operation.
1 user of table adjusts the text formatting description of trend behavior
| State is moved back in throwing | Device type | Device name |
| 0, in vain | 5, it is expressed as substation | Jin Here G1 |
| 1, effectively | 6, it is expressed as load | Distant Yingkou factory 110 |
| 1, effectively | 6, it is expressed as load | Shandong Fengtai station 110 |
| … | … | … |
The user for obtaining larger time span or larger geographical expanses adjusts the data file of trend behavior (namely trend tune
Whole sample set) after, it can be carried out rule digging in next step.
2) load flow rectification rule base is generated
It handles collected user and adjusts flow data file, establish FP tree, excavated with FP-Growth algorithm whole
Frequent item set；And support deficiency and the insufficient rule of confidence level are filtered out by minimum support and min confidence, finally
It forms user and adjusts trend rule base.
Preferably, in order to guarantee extract sample set rule reliability, the threshold values of min confidence can be set to
100%.
It, will if the threshold values setting of minimum support is excessively high in the less situation of the sample size that user adjusts trend
It is unable to get effective user and adjusts trend rule, therefore the threshold values of minimum support can be set as 8%.
Such as it is carried out using Liaoning electric power grid in July, 2017 to the daily record data for adjusting trend behavior between in December, 2017
Association Rule Analysis.Specifically, the user collected adjusts in the data of trend behavior, is related to carrying out 66 generators
Adjustment, is adjusted 17 loads.
By adjusting the numerical value of minimum support and the numerical value of min confidence repeatedly, obtains user and adjust trend behavior
Strong association rule, namely the rule base of adjustment trend behavior；Wherein, 66 generators excavate altogether obtains 22288 rules, and 17
Load excavates obtain 151 rules altogether.
Wherein rule base can be expressed as { (' the hot G1 ' in distant Dalian Huaneng Group two, ' distant Zhuanghe factory G1 ', ' distant Mount Taishan G2 ', ' the Liao Dynasty
Gan Jingzi G2 ', ' the red nuclear power G1 ' along the river of the Liao Dynasty, ' distant Yingkou factory G1 ', ' distant Yingkou factory G3 '): ((' Liao great Lian Wanchang ', ' distant Mount Taishan
G1 '), 1.0),
(' east area of the Liao River, coextensive with eastern and southern Liaoning Province Fang Chang G2 ', ' distant Dalian thermoelectricity G2 ', ' Ji Jiangnan thermoelectricity G2 '): ((' lucky Jilin Cogeneration Plant GA ', ' black chicken
Two hot G2 ' of west), 1.0),
...,
....}。
Rule is explained as follows:
Following rule (' the red nuclear power G1 ' along the river of the Liao Dynasty, ' distant Yingkou factory G1 ', ' distant Yingkou factory G3 ', ' the sweet well G2 ' of the Liao Dynasty, ' the Liao Dynasty
Mount Taishan G1 '): (' distant Dalian Bay factory G2 ', ' and the western thermoelectricity G1 ' in Liaoxi-Shenyang), 1.0) it indicates, it is 100% in confidence level, support 8%
In the case where, user adjusts generator combination: the red G1 of nuclear power along the river of the Liao Dynasty, the Liao Dynasty Yingkou factory G1, the Liao Dynasty Yingkou factory G3, distant sweet well G2,
When distant Mount Taishan G1, while can also it adjust generator combination: the distant Dalian Bay factory G2 and western thermoelectricity G1 in Liaoxi-Shenyang.
3) sample is generated
Rule base based on adjustment trend behavior mainly considers generator when generating the online data sample of load flow rectification
Adjustment and load adjustment.
Specifically, the throwing including regulator generator move back, adjust generator have work value and without work value, adjust load open and
It is disconnected.
Using the historical data of generator adjustment amount in adjustment trend behavior, statistics obtains the characteristic of generator adjustment amount
According to the maximum value and minimum value of maximum value and minimum value, idle adjustment amount including active adjustment amount.Further, according to tune
Synchronizing number is 10, calculates respective adjusting step:
Step-length=(maximum value-minimum value)/10.
It is construed as, step number can be adjusted come flexible setting according to the span scope of generator adjustment amount, to obtain
The set-up procedure of different numerical value.The example of the characteristic of generator adjustment amount is shown in table 2.
The example of the adjustment characteristic quantity of 2 generator of table
It, can be when generating load flow rectification sample according to the data of the above generator adjustment amount, make repeated attempts different tune
Whole amount, to determine that the load flow rectification sample adjusts the validity of trend.
The flow chart of the load flow rectification sample generating method based on FP-Growth algorithm is shown in Fig. 3 comprising following
Step:
Obtain the power grid stability analysis task that power system simulation model generates；
For at least one gen-set in the power grid stability analysis task, generator load flow rectification rule is traversed
Library, according to the adjustment rule screened, state is moved back in the throwing for modifying at least one gen-set one by one；And carry out trend meter
It calculates, judge that trend restrains in the secondary load flow rectification?
If convergence, which is that a stable online power flow adjusts sample；
If not restraining, in conjunction with the adjustment characteristic quantity of at least one gen-set, gradually long modification this at least one
Gen-set active or without work value；And Load flow calculation is carried out, judge that trend restrains in the secondary load flow rectification?
If convergence, which is that a stable online power flow adjusts sample；
If modify at least one gen-set have work value and/or without work value, power grid is not restrained still；
Then for the multiple combinations of at least one load in the power grid stability analysis task, load uncertainty is traversed respectively
Rule base is adjusted, according to the adjustment rule screened, that modifies at least one load one by one cut-offs state；And carry out trend meter
It calculates, judge that trend restrains in the secondary load flow rectification?
If not restraining, which is that a unstable online power flow adjusts sample.
Finally, adjust the combination of at least one gen-set, repeatedly above step again, until having traversed whole hairs
Motor load flow rectification rule base and load uncertainty adjust rule base.
So far, it is based on the power grid stability analysis task, generates multiple online power flow adjustment samples, these online power flows
Adjustment sample is greatly enriched the diversity of in-circuit emulation sample.
Preferably, in application load flow rectification rule base, appoint for the power grid stability analysis generated by numerical simulation
The mark of grid equipment in business, may be different with the title of generator and load from the off-line data collected in log
The case where cause, can modify the device name in data or file according to the principle of fuzzy matching.
To sum up, it in order to solve the problems, such as that bulk power grid quickly sentences the diversity deficiency of online data sample set in steady, is lifted at
Line number is based on FP- using these off-line datas of the daily record data of power grid user adjustment trend accumulation according to the performance of learning algorithm
Growth algorithm extracts the rule base that user adjusts trend, and then generates effective online data sample according to rule base, from
And steady work is quickly sentenced for power grid, a large amount of, various effective sample is provided.
The present invention is described by reference to a small amount of embodiment above.However, it is known in those skilled in the art,
As defined by subsidiary Patent right requirement, in addition to the present invention other embodiments disclosed above equally fall in this hair
In bright range.
Claims (10)
1. a kind of load flow rectification sample generating method based on FP-Growth algorithm, which comprises the following steps:
Step S10: each is extracted from the power grid stability analysis task of acquisition generated by power system simulation model and waits operating
Grid equipment mark；
Step S20: according to the mark of at least one grid equipment to be operated, matching is obtained and institute from load flow rectification rule base
State the corresponding grid equipment sequence of operation of mark of at least one grid equipment to be operated, the grid equipment sequence of operation note
It is loaded with the operational motion to the partly or completely mark of at least one grid equipment to be operated, the grid equipment
The sequence of operation is the rule in the load flow rectification rule base, and the load flow rectification rule base is calculated based on FP-Growth
Method is excavated according to offline load flow rectification historical data, the grid equipment for including in the power grid stability analysis task
Number N of the number M of mark more than the mark for the grid equipment for including in the load flow rectification rule base, the offline trend tune
The number S of the mark for the grid equipment for including in whole historical data is more than the power grid for including in the power grid stability analysis task
The number M of the mark of equipment, wherein M, N, S are the positive integer greater than 1；
Step S30: the operation for the mark to the grid equipment to be operated recorded with the grid equipment sequence of operation is moved
Make the corresponding operational motion of mark for the grid equipment to be operated recorded in the replacement power grid stability analysis task, replacement
The power grid stability analysis task afterwards is a load flow rectification sample.
2. the method according to claim 1, wherein
It include a plurality of types of grid equipments in the power system simulation model, each type of grid equipment includes at least one；
The step 10, further includes:
Each power grid to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model to set
Standby type information；
Correspondingly, the step 20, comprising:
According to the mark of at least one identical grid equipment to be operated of type information, from electricity corresponding with the type information
Matching obtains power grid corresponding with the mark of at least one grid equipment to be operated in net equipment load flow rectification rule base
Equipment operation sequence, the grid equipment sequence of operation are recorded to partly or completely at least one described electricity to be operated
The operational motion of the mark of net equipment, the grid equipment sequence of operation are one in the grid equipment load flow rectification rule base
Rule, the grid equipment load flow rectification rule base are based on FP-Growth algorithm according to offline load flow rectification historical data
What excavation obtained；
Correspondingly, the step S30 includes: with grid equipment sequence of operation record to the grid equipment to be operated
Mark operational motion replace the grid equipment to be operated recorded in the power grid stability analysis task mark it is corresponding
Operational motion, the replaced power grid stability analysis task is a load flow rectification sample.
3. method according to claim 1 or 2, which is characterized in that
When the type information is generator,
Further include step S40:
The hair to be operated is adjusted using the adjustment characteristic quantity of generator corresponding with the mark of the generator to be operated
The operation data of motor, and the operation data of generator to be operated described in after adjusting is replaced into the power grid stability analysis
The corresponding operation data of mark for the generator to be operated recorded in task, the replaced power grid stability analysis
Task is a load flow rectification sample；Wherein, the adjustment characteristic quantity of the generator is from offline load flow rectification historical data
What middle extraction obtained.
4. the method according to claim 1, wherein further include:
Step S50: being directed to each described load flow rectification sample, judges that the power grid corresponding with the load flow rectification sample is imitative
The grid stability of true mode；
If trend corresponding with the load flow rectification sample be it is convergent, the load flow rectification sample be a stabilization of power grids sample
This；
If trend corresponding with the load flow rectification sample be it is not convergent, the load flow rectification sample be a power grid shakiness
Random sample sheet.
5. the method according to claim 1, wherein
It is being based on FP-Growth algorithm, when excavating load flow rectification rule base according to offline load flow rectification historical data, setting is minimum
Support is 8%；It is 100% that min confidence, which is arranged,.
6. a kind of load flow rectification sample generating means based on FP-Growth algorithm characterized by comprising
Grid equipment marker extraction module, is used for:
Each power grid to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model to set
Standby mark；
Operational motion matching module, is used for:
According to the mark of at least one grid equipment to be operated, matches and obtain and described at least one from load flow rectification rule base
The corresponding grid equipment sequence of operation of the mark of a grid equipment to be operated, the grid equipment sequence of operation are recorded to portion
Point or whole at least one grid equipment to be operated mark operational motion, the grid equipment sequence of operation
For the rule in the load flow rectification rule base, the load flow rectification rule base be based on FP-Growth algorithm according to from
Line load flow rectification historical data is excavated, the number of the mark for the grid equipment for including in the power grid stability analysis task
Number N of the mesh M more than the mark for the grid equipment for including in the load flow rectification rule base, the offline load flow rectification history number
Mark of the number S of the mark for the grid equipment for including in more than the grid equipment for including in the power grid stability analysis task
The number M of knowledge, wherein M, N, S are the positive integer greater than 1；
Load flow rectification sample generation module, is used for:
The operational motion for the mark to the grid equipment to be operated recorded with the grid equipment sequence of operation replaces institute
The corresponding operational motion of mark for the grid equipment to be operated recorded in power grid stability analysis task is stated, it is replaced described
Power grid stability analysis task is a load flow rectification sample.
7. device according to claim 6, which is characterized in that
It include a plurality of types of grid equipments in the power system simulation model, each type of grid equipment includes at least one；
The grid equipment marker extraction module, is also used to:
Each power grid to be operated is extracted from the power grid stability analysis task of acquisition generated by power system simulation model to set
Standby type information；
Correspondingly, the operational motion matching module, is used for:
According to the mark of at least one identical grid equipment to be operated of type information, from electricity corresponding with the type information
Matching obtains power grid corresponding with the mark of at least one grid equipment to be operated in net equipment load flow rectification rule base
Equipment operation sequence, the grid equipment sequence of operation are recorded to partly or completely at least one described electricity to be operated
The operational motion of the mark of net equipment, the grid equipment sequence of operation are one in the grid equipment load flow rectification rule base
Rule, the grid equipment load flow rectification rule base are based on FP-Growth algorithm according to offline load flow rectification historical data
What excavation obtained；
Correspondingly, the load flow rectification sample generation module, is used for:
The operational motion for the mark to the grid equipment to be operated recorded with the grid equipment sequence of operation replaces institute
The corresponding operational motion of mark for the grid equipment to be operated recorded in power grid stability analysis task is stated, it is replaced described
Power grid stability analysis task is a load flow rectification sample.
8. device according to claim 6 or 7, which is characterized in that
When the type information is generator,
Further include generator characteristics amount adjustment module, be used for:
The hair to be operated is adjusted using the adjustment characteristic quantity of generator corresponding with the mark of the generator to be operated
The operation data of motor, and the operation data of generator to be operated described in after adjusting is replaced into the power grid stability analysis
The corresponding operation data of mark for the generator to be operated recorded in task, the replaced power grid stability analysis
Task is a load flow rectification sample；Wherein, the adjustment characteristic quantity of the generator is from offline load flow rectification historical data
What middle extraction obtained.
9. device according to claim 6, which is characterized in that further include:
Load flow rectification sample judgment module, is used for:
For load flow rectification sample described in each, the power system simulation model corresponding with the load flow rectification sample is judged
Grid stability；
If trend corresponding with the load flow rectification sample be it is convergent, the load flow rectification sample be a stabilization of power grids sample
This；
If trend corresponding with the load flow rectification sample be it is not convergent, the load flow rectification sample be a power grid shakiness
Random sample sheet.
10. device according to claim 6, which is characterized in that
It is being based on FP-Growth algorithm, when excavating load flow rectification rule base according to offline load flow rectification historical data, setting is minimum
Support is 8%；It is 100% that min confidence, which is arranged,.
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