CN106451503A - Three-phase unbalanced treatment device with temperature and vibration warning unit and realization method thereof - Google Patents
Three-phase unbalanced treatment device with temperature and vibration warning unit and realization method thereof Download PDFInfo
- Publication number
- CN106451503A CN106451503A CN201610991128.8A CN201610991128A CN106451503A CN 106451503 A CN106451503 A CN 106451503A CN 201610991128 A CN201610991128 A CN 201610991128A CN 106451503 A CN106451503 A CN 106451503A
- Authority
- CN
- China
- Prior art keywords
- data
- voltage
- voltage dip
- electrical network
- rule
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention provides a three-phase unbalanced treatment device with a temperature and vibration warning unit and a realization method thereof. A data obtaining unit obtains current feature data of a power grid. A voltage sag knowledge base forming unit extracts data having an identical feature type with the feature data from historical data, classifies the extracted data based on a preset class attribute to form a sample set, screens out data meeting a predetermined condition from the sample set to form a voltage sag strong association rule knowledge base. An identification unit determines a current voltage sag cause of the power grid in the voltage sag strong association rule knowledge base based on the current feature data of the power grid. And a treatment unit determines a grid power quality treatment compensation scheme based on the current voltage sag cause and carries out compensation on a voltage at a user side based on the grid power quality treatment compensation scheme. With the device, dependence on a waveform is avoided. On the basis of the analysis on historical data, the strong association rule in the historical voltage sag is dug out, thereby realizing prediction and treatment of future voltage sag possibility.
Description
Technical field
The present invention relates to quality of power supply three-phase imbalance Treatment process field, more particularly, to carry temperature and vibrating alert list
The three-phase imbalance controlling device of unit and its implementation.
Background technology
With science and technology and industrial expansion, the requirement to the quality of power supply for the very high industrial user of many automaticities
More and more higher.In production or IT Enterprises, due to most of device and SCM Based digitial controller or electric power
Electronic device is all very sensitive to various electromagnetic interference, and the therefore very slight disturbance of electrical network all can lead to the said goods can not be just
Often work or some function reduction are so that enterprise suffers huge loss.
Currently preferably electric power signal has the three phase sine waveform of fixed frequency and amplitude, but in actual life,
Due to the asymmetry of fault, circuit and equipment, startup of bulk loads etc. so that electric power signal produces the phenomenon deviateing
It is referred to as power quality problem.
In the research of the quality of power supply, most important analysis the reason be exactly quality of power supply event, if can quickly know
The reason event occurs, has important meaning for prevention and failure removal.In conventional art, to the quality of power supply analysis of causes
Research be based primarily upon experiment and the emulation of electrical network three-phase imbalance, but the conventional art Shortcomings based on experiment and emulation
Part, it is disadvantageous in that：First, electrical network is much complicated than analogue system, the model of simplification cannot simulated real system completely,
Thus producing error；2nd, the testing equipment in electrical network is only able to detect the temporary range of decrease degree of voltage dip and duration it is impossible to note
The waveform of record super large data volume, thus deviation in the reason lead to quality of power supply event analysis.
In addition, the scene temperature of three-phase imbalance controlling device directly affects its operation stability it is therefore necessary to
Its temperature is carried out detect control, and scene alarm, to improve its operation stability, additionally, three-phase imbalance controlling device
Scene, when not having attendant on duty, needs anti-illegal-inbreak, and the means of more effectively anti-illegal-inbreak are at present
Using vibration detecting, the vibration signal sending is perceived by vibration detector, by circuit to vibration signal during illegal invasion
Scene alarm is sent, to improve its operation stability after being processed..
Content of the invention
Embodiment of the present invention technical problem to be solved is, provides three-phase with temperature and vibrating alert unit not
Balance controlling device and implementation method, it is to avoid the dependence to waveform, based on the analysis of historical data, excavate in history voltage dip
Strong association rule, reach the prediction to following voltage dip possibility and improvement.
In order to solve above-mentioned technical problem, the three-phase embodiments providing with scene temperature alarm function is uneven
Weighing apparatus controlling device, described three-phase imbalance controlling device includes：
Data capture unit, the characteristic current for obtaining electrical network, the characteristic type of described characteristic includes electricity
The pressure time that temporarily the three-phase temporary range of decrease degree of fall, three phase durations of voltage dip and voltage dip occur；
Voltage dip knowledge base forms unit, has same characteristic features for extracting from historical data with described characteristic
The data of type, and according to default category attribute, classification formation sample set is carried out to the data of described extraction, and further
The data formation voltage dip Strong association rule knowledge base meeting predetermined condition is filtered out in described sample set；
Recognition unit is for according to the described electrical network current signature data getting, strong in the voltage dip of described formation
In correlation rule knowledge base, determine the current voltage sag reason of electrical network；
Governance unit, for temporarily dropping reason according to the current voltage of described determination, determines that electrical network power quality controlling compensates
Scheme, and according to the electrical network power quality controlling compensation scheme of described determination, user side voltage is compensated；
Temperature and vibrating alert unit, include audible-visual annunciator, temp probe, vibration signal detector, RC wave filter
With alarm master controller, temp probe is used for detecting temperature the output temperature signal of three-phase imbalance controlling device, described
Audible-visual annunciator is connected on alarm master controller, and the detection signal of described temp probe is connected to by CAN input
Alarm master controller, this master controller is processed to the detection signal of temp probe, and controls audible-visual annunciator action, vibration
Signal sensor detects live vibration signal and is input to alarm master controller by RC wave filter, described alarm main control
Device is connected with the audible-visual annunciator for scene alarm.
Wherein, described voltage dip knowledge base forms unit and includes：
Data preprocessing module, for extracting the number with described characteristic with same characteristic features type from historical data
According to, and the described data extracted is carried out sliding-model control；
Sample set forms module, for according to default category attribute, the data of described sliding-model control is carried out point
Class, forms sample set；
Voltage dip knowledge base forms module, for carrying out Apriori calculating to the sample set of described formation, filters out
Support that the data that angle value is more than the first preset value forms sample relation integration, and the sample relation integration of described formation is entered again
Row Apriori calculates, and filters out the data formation voltage dip Strong association rule knowledge base that confidence value is more than the second preset value.
Wherein, described recognition unit includes：
Decision tree setup module, for the voltage dip Strong association rule knowledge base according to described formation and default classification
Attribute, by ID3 algorithm, obtains the minimum decision tree of the corresponding comentropy of each pre-set categories attribute；
Voltage sag reason identification module, for according to the described electrical network current signature data getting, obtaining from described
The minimum decision tree of the corresponding comentropy of each pre-set categories attribute in, determine the reason electrical network current voltage temporarily drops.
Wherein, described voltage sag reason identification module includes：
Voltage dip matched sub-block, for using CBR inference method by the described electrical network current signature data getting
The decision tree minimum with the described corresponding comentropy of each category attribute obtaining is mated；
Detection sub-module, for when there is matching condition, detecting whether the matching condition of described presence is unique rule；
First identification submodule, for when being uniquely rule when the matching condition described presence is detected it is determined that described
The reason uniquely rule temporarily drops for described electrical network current voltage；
Second identification submodule, during for corresponding to many rules when the matching condition described presence is detected, obtains described
To support angle value be ranked up with confidence value, determine support angle value be corresponding regular conduct during maximum with confidence value
The reason described electrical network current voltage temporarily drops.
Wherein, when described default category attribute includes that voltage dip is separate, voltage dip amplitude, voltage dip continue
Between, voltage dip time of origin and transmission characteristic；Wherein, described voltage dip separate include one mutually temporarily fall two-phase temporarily rise, two-phase
Temporarily fall one mutually temporarily rises, three-phase temporarily drops and the temporary fall one of two-phase is mutually constant；Described voltage dip amplitude is the current electricity of voltage dip
Ratio between pressure value and standard voltage value；Described voltage dip time of origin includes the working time model of default bulk loads
Enclose the run time scope leading to fault with thunderstorm, and the working time scope of described bulk loads is led to described thunderstorm
The run time scope sum of fault is one day；It is 1 that described transmission characteristic includes active event that value is 0 and value
Passive event.
The embodiment of the present invention additionally provides the realization side of the three-phase imbalance controlling device with scene temperature alarm function
Method, it is realized on aforesaid device, and methods described includes：
S1, obtain the current characteristic of electrical network, the three-phase that the characteristic type of described characteristic includes voltage dip is temporary
The time that range of decrease degree, three phase durations of voltage dip and voltage dip occur；
S2, extract from historical data, with described characteristic, there is the data of same characteristic features type, and according to default
Category attribute, carries out classification formation sample set to the data of described extraction, and filters out in described sample set further
The data meeting predetermined condition forms voltage dip Strong association rule knowledge base；
S3, according to the described electrical network current signature data getting, know in the voltage dip Strong association rule of described formation
Know in storehouse, determine the current voltage sag reason of electrical network；And
S4, reason is temporarily dropped according to the current voltage of described determination, determine electrical network power quality controlling compensation scheme, and according to
The electrical network power quality controlling compensation scheme of described determination compensates to user side voltage.
Wherein, described step S2 specifically includes：
Extract from historical data and with described characteristic there is the data of same characteristic features type, and extract described
Data carries out sliding-model control；
According to default category attribute, the data of described sliding-model control is classified, form sample set；
Apriori calculating is carried out to the sample set of described formation, filters out the number supporting angle value to be more than the first preset value
According to formation sample relation integration, and again Apriori calculating is carried out to the sample relation integration of described formation, filter out confidence level
Value forms voltage dip Strong association rule knowledge base more than the data of the second preset value.
Wherein, described step S3 specifically includes：
Voltage dip Strong association rule knowledge base according to described formation and default category attribute, by ID3 algorithm, obtain
To the decision tree that the corresponding comentropy of each pre-set categories attribute is minimum；
According to the described electrical network current signature data getting, from the described corresponding letter of each pre-set categories attribute obtaining
In the minimum decision tree of breath entropy, determine the reason electrical network current voltage temporarily drops.
Wherein, the described voltage dip Strong association rule knowledge base according to described formation and default category attribute, pass through
ID3 algorithm, the concrete steps obtaining the minimum decision tree of the corresponding comentropy of each pre-set categories attribute include：
A, using the data in the voltage dip Strong association rule knowledge base of described formation as training data；
B, the letter according to each pre-set categories attribute comprising in the described default category attribute described training data of calculating
Breath gain, selects information gain maximum as the Split Attribute of root node, and calculates decision rule and pass to ground floor
Prefix information；
C, judge whether to create new decision rule；If it is, execution next step d；If it is not, then, jump to
Step e；
D, the new decision rule of described generation is saved in rule set, deletes simultaneously and in described training data, comprise institute
State the sample of the new decision rule of generation, produce new data set, and using the new data set of described generation as described instruction
After practicing data, return to step b；
E, continue to determine whether to produce new prefix information；If it is, execution next step f；If it is not, then, redirect
To step i；
F, the decision tree number of plies add one；
G, judge the number of plies of described decision tree whether less than the sum of all properties comprising in described training data；If
It is then to execute next step h；If it is not, then, jump to step i；
H, calculate under described new prefix information, the information of each pre-set categories attribute comprising in described training data
Gain, selects the Split Attribute of the number of plies corresponding node as described current decision tree for the information gain maximum, and calculates decision-making
Rule and the prefix information passing to next layer, return to step c；
I, training terminate, and build decision tree according to described calculated decision rule.
Wherein, the electrical network current signature data getting described in described basis, belongs to from the described each pre-set categories obtaining
Property the minimum decision tree of corresponding comentropy in, determine that the concrete steps of the reason electrical network current voltage temporarily drops include：
For using CBR inference method by the described electrical network current signature data getting and the described each classification obtaining
Minimum decision tree is mated the corresponding comentropy of attribute；
When there is matching condition, detect whether the matching condition of described presence is unique rule；
When the matching condition described presence is detected is uniquely rule it is determined that described unique rule is worked as described electrical network
The reason front voltage dip；
When the corresponding many rules of the matching condition described presence is detected, by the described support angle value obtaining and confidence level
Value is ranked up, and determines and supports that angle value is that corresponding rule during maximum temporarily drops as described electrical network current voltage with confidence value
The reason.
Implement the embodiment of the present invention, have the advantages that：
The analysis based on historical data for the three-phase imbalance controlling device in the present invention, by carrying out to voltage dip event
Causality classification, the Strong association rule being excavated using Apriori algorithm in history voltage dip forms voltage dip Strong association rule
Knowledge base, then using voltage dip Strong association rule knowledge base as training set, determine that current voltage temporarily drops reason, and corresponding
Electrical network power quality controlling compensation scheme compensates to user side voltage, thus avoiding the dependence to waveform, reaches to future
The purpose of the prediction of voltage dip possibility.
In addition, the present invention also has the alarm of temperature scene and the live alarm function of vibration, the advantage of good operation stability.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
Have technology description in required use accompanying drawing be briefly described it should be apparent that, drawings in the following description be only this
Some embodiments of invention, for those of ordinary skill in the art, without having to pay creative labor, according to
These accompanying drawings obtain other accompanying drawings and still fall within scope of the invention.
Fig. 1 is the system structure diagram of three-phase imbalance controlling device provided in an embodiment of the present invention；
Fig. 2 is the system structure diagram that in Fig. 1, voltage dip knowledge base forms unit；
Fig. 3 is the system structure diagram of recognition unit in Fig. 1；
Fig. 4 is the system structure diagram of voltage sag reason identification module in Fig. 3；
Fig. 5 is the flow chart of three-phase imbalance controlling device implementation method provided in an embodiment of the present invention；
Fig. 6 is the flow chart of step S3 in Fig. 5；
Fig. 7 is the flow chart of step S31 in Fig. 6；
Fig. 8 is the flow chart of step S32 in Fig. 6；
Fig. 9 is the application scenario diagram of step S3 in Fig. 6；
Figure 10 is the theory diagram of temperature and vibrating alert unit.
Specific embodiment
For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing, the present invention is made into one
Step ground describes in detail.
As shown in figure 1, administering dress for the three-phase imbalance with scene temperature alarm function provided in an embodiment of the present invention
Put, described device includes：
Data capture unit 110, the characteristic current for obtaining electrical network, the characteristic type of described characteristic includes
The time that the three-phase temporary range of decrease degree of voltage dip, three phase durations of voltage dip and voltage dip occur；
Voltage dip knowledge base forms unit 120, has identical for extraction from historical data and described characteristic
The data of characteristic type, and according to default category attribute, classification formation sample set is carried out to the data of described extraction, and enters
One step filters out the data formation voltage dip Strong association rule knowledge base meeting predetermined condition in described sample set；
Recognition unit 130, for according to the described electrical network current signature data getting, in the voltage dip of described formation
In Strong association rule knowledge base, determine the current voltage sag reason of electrical network；
Governance unit 140, for temporarily dropping reason according to the current voltage of described determination, determines that electrical network power quality controlling is mended
Repay scheme, and according to the electrical network power quality controlling compensation scheme of described determination, user side voltage is compensated.
As shown in Fig. 2 described voltage dip knowledge base forms unit 120 including：
Data preprocessing module 1201, has same characteristic features type for extracting from historical data with described characteristic
Data, and the described data extracted is carried out sliding-model control；
Sample set forms module 1202, for according to default category attribute, entering to the data of described sliding-model control
Row classification, forms sample set；
Voltage dip knowledge base forms module 1203, for Apriori calculating is carried out to the sample set of described formation, sieve
Select the data supporting angle value to be more than the first preset value and form sample relation integration, and to the sample relation integration of described formation again
Secondary carry out Apriori calculating, filter out confidence value be more than the second preset value data formed voltage dip Strong association rule know
Know storehouse.
As shown in figure 3, described recognition unit 130 includes：
Decision tree setup module 1301, for the voltage dip Strong association rule knowledge base according to described formation and default
Category attribute, by ID3 algorithm, obtains the minimum decision tree of the corresponding comentropy of each pre-set categories attribute；
Voltage sag reason identification module 1302, for according to the described electrical network current signature data getting, from described
In the decision tree of each pre-set categories attribute corresponding comentropy minimum obtaining, determine the reason electrical network current voltage temporarily drops.
As shown in figure 4, described voltage sag reason identification module 1302 includes：
Voltage dip matched sub-block 13021, for using CBR inference method by the described electrical network current signature getting
The decision tree minimum with the described corresponding comentropy of each category attribute obtaining is mated data；
Detection sub-module 13022, for when there is matching condition, detecting whether the matching condition of described presence is unique
Rule；
First identification submodule 13023, for when detect described presence matching condition be uniquely rule when it is determined that
The reason described unique rule temporarily drops for described electrical network current voltage；
Second identification submodule 13024, during for corresponding to many rules when the matching condition described presence is detected, by institute
State the support angle value obtaining to be ranked up with confidence value, determine and support that angle value is corresponding rule during maximum with confidence value
The reason temporarily drop as described electrical network current voltage.
In embodiments of the present invention, default category attribute includes that voltage dip is separate, voltage dip amplitude, voltage dip
Duration, voltage dip time of origin and transmission characteristic；Wherein, voltage dip separate include one mutually temporarily fall two-phase temporarily rise, two
Mutually temporarily fall one mutually temporarily rises, three-phase temporarily drops and the temporary fall one of two-phase is mutually constant；Voltage dip amplitude is the current voltage of voltage dip
Ratio between value and standard voltage value；Voltage dip time of origin includes working time scope and the thunder of default bulk loads
Rain leads to the run time scope of fault, and the working time scope of bulk loads leads to fault with described thunderstorm
Run time scope sum is one day；Transmission characteristic includes the active event that value is 0 and the passive event that value is 1.
As shown in figure 5, the three-phase imbalance with scene temperature alarm function for, in the embodiment of the present invention, providing is administered
The implementation method of device, it is realized on above-mentioned device, and methods described includes：
The current characteristic of step S1, acquisition electrical network, the characteristic type of described characteristic includes the three of voltage dip
The time that mutually temporarily range of decrease degree, three phase durations of voltage dip and voltage dip occur；
Detailed process is to obtain the current characteristic of electrical network, this feature data is by the data shape of multiple characteristic type items
Become, the characteristic type of this feature data includes but is not limited to the three-phase temporary range of decrease degree of voltage dip, the three-phase of voltage dip continues
Time and the time of voltage dip generation.
Step S2, extract from historical data, with described characteristic, there is the data of same characteristic features type, and according to pre-
If category attribute, the data of described extraction is carried out with classification and forms sample set, and sieve in described sample set further
Select the data formation voltage dip Strong association rule knowledge base meeting predetermined condition；
Detailed process is, step S21, extracts the data with characteristic with same characteristic features type from historical data,
And the described data extracted is carried out sliding-model control；
Specifically, mainly pre-processing to historical data, remove redundancy and the data damaged, thus reducing answering of data
Miscellaneous degree and process capacity, then individually extract some attributes needing according to the characteristic type in step S1, and will
Historical data carries out sliding-model control, becomes the multiple data blocks for ease of analyzing and calculate.
Step S22, according to default category attribute, the data of described sliding-model control is classified, formed sample set
Close；
Specifically, pre-setting five features as category attribute, and according to default five features to sliding-model control
Data classified, formed sample set M；Wherein, five features are respectively that voltage dip is separate, voltage dip amplitude, electricity
Duration, voltage dip time of origin and transmission characteristic temporarily drop in pressure.
Because voltage is three-phase, therefore voltage dip separate include one mutually temporarily fall two-phase temporarily rise, two-phase temporarily drop one mutually temporarily rise,
Three-phase temporarily drops and the temporary fall one of two-phase is mutually constant etc..Voltage dip amplitude is current voltage value and the standard voltage value of voltage dip
Between ratio；As an example, temporarily the current voltage value of fall voltage is 198V, and normal voltage is 220V, and calculate is temporary
Range of decrease degree is 0.9.The time that the voltage dip duration terminates for voltage dip deducts the time that it starts.Due to leading to voltage
The fault that temporarily most important two factors of fall start for bulk loads and thunderstorm causes, therefore voltage dip time of origin
Also serve as a key character, and operate between load operationally, thunderstorm all may be distributed in whole day, therefore voltage dip is sent out
The raw time can be set to one day 24 hours, mark off the working time scope of bulk loads as needed and thunderstorm leads in one day
The run time scope of fault；As an example, by AM8:00 to PM5:00 working time being preset as bulk loads
Scope, remaining time (same day PM5:To second day AM8 after 00:Before 00) it is preset as the run time model that thunderstorm leads to fault
Enclose, therefore in AM8:00 to PM5:00 measurable startup for bulk loads the reason voltage dip is caused, and in AM8:
00 to PM5:The reason outside 00, voltage dip in the time period measurable for thunderstorm fault.It is 0 that transmission characteristic includes value
Active event (source event) and the passive event that value is 1.Active event (source event) all has extraneous factor to cause, and quilt
Dynamic event is propagated in electrical network by existing voltage dip event and is caused.
Step S23, the sample set to described formation carry out Apriori calculating, filter out support angle value pre- more than first
If the data of value forms sample relation integration, and carries out Apriori calculating again to the sample relation integration of described formation, screening
Go out the data formation voltage dip Strong association rule knowledge base that confidence value is more than the second preset value.
Specifically, Apriori calculating, and the definition according to the first preset value are carried out to the sample set M being formed, if
Degree of holding is less than this first preset value, then need again to be calculated according to Apriori, therefore the sample set M being formed is being carried out
After Apriori calculates, the support angle value finally giving should be greater than the first preset value, and it is default more than first to filter out support angle value
The data of value forms sample relation integration, and carries out Apriori calculating again to this sample relation integration；In the same manner, according to second
The definition of preset value, if confidence value is less than this second preset value, needs again to enter again on the basis of sample relation integration
Row Apriori calculates, and the confidence value finally giving should be greater than this second preset value.Preset due to supporting that angle value is more than first
Value, and confidence value is more than the second preset value, then show that sample set M has relevance after Apriori calculating, meet pre-
Fixed strength of association, thus forming voltage dip Strong association rule knowledge base, in order to carry out this knowledge base as training set
Build decision tree.
Step S3, according to the described electrical network current signature data getting, associate by force rule in the voltage dip of described formation
Then in knowledge base, determine the current voltage sag reason of electrical network；
Detailed process is, as shown in fig. 6, the concrete steps realization of step S3 is as follows：
Step S31, the voltage dip Strong association rule knowledge base according to described formation and default category attribute, pass through
ID3 algorithm, obtains the minimum decision tree of the corresponding comentropy of each pre-set categories attribute；
Step S32, according to the described electrical network current signature data getting, from the described each pre-set categories attribute obtaining
In the minimum decision tree of corresponding comentropy, determine the reason electrical network current voltage temporarily drops.
In step S31, decision tree is built using ID3 algorithm.The fork attribute selecting every time all makes comentropy maximum
Reduce.If all samples belong to same class in a sample set, now comentropy is minimum；If broadly falling into different classes,
Now comentropy is maximum.If S is the set of n data sample, sample set is divided into c different class Ci(i=1,2 ...,
C), each class CiThe number of samples containing is ni, then the comentropy that S is divided into c class is
Wherein, piBelong to the i-th class C for sample in SiProbability, that is,
Assume that the collection of all different values of attribute A is combined into XA, SvThe sample set for v for the value of attribute A in S, i.e. Sv={ s
∈ S | A (s)=v }.Sample set S on each branch node after selecting attribute A, to this nodevThe entropy of classification is E
(Sv).The expectation entropy that A leads to is selected to be defined as each subset SvEntropy weighted sum, weights be belong to SvSample account for original sample
The ratio of this SI.e. expectation entropy is
Wherein, E (Sv) it is by SvIn sample be divided into the comentropy of c class.The information relative to sample set S for the attribute A increases
Beneficial Gain (S, A) is defined as
Gain (S, A)=E (S)-E (S, A)
Select every time to make the maximum attribute of information gain, now, comentropy reduces at most.
Therefore, as shown in fig. 7, step S31 to implement step as follows：
Step S310, using the data in voltage dip Strong association rule knowledge base as training data；
Step S320, the information according to each pre-set categories attribute comprising in default category attribute calculating training data
Gain, selects information gain maximum as the Split Attribute of root node, and before calculating decision rule and passing to ground floor
Sew information；
Step S330, judge whether to create new decision rule；If it is, execution next step S340；If not,
Then jump procedure S350,
Step S340, by produce new decision rule be saved in rule set, simultaneously delete training data in comprise produce
The sample of raw new decision rule, produces new data set, using this new data set as after training data, return to step
S320；
Step S350, continue to determine whether to produce new prefix information；If it is, execution next step S360；If
No, then jump to step S390；
Step S360, the decision tree number of plies add one；
Step S370, judge the number of plies of this decision tree whether less than the sum of all properties comprising in training data；As
Fruit is then to execute next step S380；If it is not, then jumping to step S390；
Step S380, calculate under new prefix information, the information of each pre-set categories attribute comprising in training data
Gain, selects information gain maximum as the Split Attribute of the number of plies corresponding node of current decision tree, and calculates decision rule
And pass to the prefix information of next layer, return to step S330；
Step S390, training terminate, and build decision tree according to calculated decision rule.
In step s 32, as shown in figure 8, the concrete steps realization of step S32 is as follows：
Specifically, step S321, for using CBR inference method by the described electrical network current signature data getting and institute
State the minimum decision tree of the corresponding comentropy of each category attribute obtaining to be mated；
Step S322, when there is matching condition, detect described presence matching condition whether be unique rule；
Step S323, when detect described presence matching condition be uniquely rule when it is determined that described unique rule is
The reason described electrical network current voltage temporarily drops；
Step S324, when the corresponding many rules of the matching condition described presence is detected, by the described support obtaining
Value is ranked up with confidence value, determines and supports that angle value and confidence value are that corresponding rule during maximum is worked as described electrical network
The reason front voltage dip；Wherein, support that angle value is ranked up from high to low with confidence value.
As an example, as shown in figure 9, electrical network is current to be determined in step s 32 to the decision tree obtaining in step S31
The application scenarios of voltage sag reason further illustrate：
Using separate for voltage dip as with reference to attribute, being set to root node, the matching condition of setting to should have a plurality of rule,
Including voltage dip separate temporarily drop for three-phase, voltage dip amplitude>40% and voltage dip time of origin be AM8：00 to
PM5：00 three rules, therefore when finding have a temporary range of decrease degree to be 50%, occur in AM9：When 00 three-phase temporarily drops, permissible
Preliminary judgement is that bulk loads startup leads to.
Step S4, reason is temporarily dropped according to the current voltage of described determination, determine electrical network power quality controlling compensation scheme, and
According to the electrical network power quality controlling compensation scheme of described determination, user side voltage is compensated.
Implement the embodiment of the present invention, have the advantages that：
The analysis based on historical data for the device in the present invention, by carrying out causality classification to voltage dip event, adopts
The Strong association rule that Apriori algorithm is excavated in history voltage dip forms voltage dip Strong association rule knowledge base, then by electricity
Pressure temporarily drops Strong association rule knowledge base as training set, determines that current voltage temporarily drops reason, and the corresponding electrical network quality of power supply
Administer compensation scheme user side voltage is compensated, thus avoiding the dependence to waveform, reaching may to following voltage dip
The purpose of the prediction of property.
As shown in Figure 10, the present invention also has temperature and vibrating alert unit, include audible-visual annunciator, temp probe,
Vibration signal detector, RC wave filter and alarm master controller, temp probe is used for detecting the temperature of three-phase imbalance controlling device
Degree output temperature signal, described audible-visual annunciator is connected on alarm master controller, and the detection of described temp probe is believed
Number by CAN input be connected to alarm master controller, this master controller is processed to the detection signal of temp probe, and
Control audible-visual annunciator action, vibration signal detector detects live vibration signal and is input to alarm master by RC wave filter
Controller, described alarm master controller is connected with the audible-visual annunciator for scene alarm.In addition, described alarm main control
LCD display, keyboard interface and memory cell are also associated with device.Alarm master controller described in the present embodiment adopts NXP public
The ARM7TDMI-S microcontroller LPC2368 of department, described CAN preferably employs Philips company model SJA1000
CAN.
It should be noted that in said apparatus embodiment, each included system unit simply enters according to function logic
Row divides, but is not limited to above-mentioned division, as long as being capable of corresponding function；In addition, each functional unit
Specific name also only to facilitate mutual distinguish, is not limited to protection scope of the present invention.
One of ordinary skill in the art will appreciate that it is permissible for realizing all or part of step in above-described embodiment method
Instruct related hardware to complete by program, described program can be stored in a computer read/write memory medium,
Described storage medium, such as ROM/RAM, disk, CD etc..
Above disclosed be only present pre-ferred embodiments, certainly the right model of the present invention can not be limited with this
Enclose, the equivalent variations therefore made according to the claims in the present invention, still belong to the scope that the present invention is covered.
Claims (10)
1. a kind of three-phase imbalance controlling device with temperature and vibrating alert unit is it is characterised in that described three-phase is uneven
Weighing apparatus controlling device includes：
Data capture unit, the characteristic current for obtaining electrical network, it is temporary that the characteristic type of described characteristic includes voltage
The time that the three-phase temporary range of decrease degree of fall, three phase durations of voltage dip and voltage dip occur；
Voltage dip knowledge base forms unit, has same characteristic features type for extracting from historical data with described characteristic
Data, and according to default category attribute, classification is carried out to the data of described extraction and forms sample set, and further in institute
State and in sample set, filter out the data formation voltage dip Strong association rule knowledge base meeting predetermined condition；
Recognition unit, for according to the described electrical network current signature data getting, associating by force in the voltage dip of described formation
In rule-based knowledge base, determine the current voltage sag reason of electrical network；
Governance unit, for temporarily dropping reason according to the current voltage of described determination, determines electrical network power quality controlling compensation scheme,
And according to the electrical network power quality controlling compensation scheme of described determination, user side voltage is compensated；
Temperature and vibrating alert unit, include audible-visual annunciator, temp probe, vibration signal detector, RC wave filter and announcement
Alert master controller, temp probe is used for detecting temperature the output temperature signal of three-phase imbalance controlling device, described acousto-optic
Alarm is connected on alarm master controller, and the detection signal of described temp probe is connected to alarm by CAN input
Master controller, this master controller is processed to the detection signal of temp probe, and controls audible-visual annunciator action, vibration signal
Detector detects live vibration signal and is input to alarm master controller by RC wave filter, and described alarm master controller is even
It is connected to the audible-visual annunciator for scene alarm.
2. three-phase imbalance controlling device as claimed in claim 1 is it is characterised in that described voltage dip knowledge base forms list
Unit includes：
Data preprocessing module, for extracting the data with described characteristic with same characteristic features type from historical data,
And the described data extracted is carried out sliding-model control；
Sample set forms module, for according to default category attribute, classifying, shape to the data of described sliding-model control
Become sample set；
Voltage dip knowledge base forms module, for carrying out Apriori calculating to the sample set of described formation, filters out support
The data that angle value is more than the first preset value forms sample relation integration, and the sample relation integration of described formation is carried out again
Apriori calculates, and filters out the data formation voltage dip Strong association rule knowledge base that confidence value is more than the second preset value.
3. three-phase imbalance controlling device as claimed in claim 1 is it is characterised in that described recognition unit includes：
Decision tree setup module, belongs to for the voltage dip Strong association rule knowledge base according to described formation and default classification
Property, by ID3 algorithm, obtain the minimum decision tree of the corresponding comentropy of each pre-set categories attribute；
Voltage sag reason identification module, for according to the described electrical network current signature data getting, from described obtain every
In the minimum decision tree of the corresponding comentropy of one pre-set categories attribute, determine the reason electrical network current voltage temporarily drops.
4. three-phase imbalance controlling device as claimed in claim 3 is it is characterised in that described voltage sag reason identification module
Including：
Voltage dip matched sub-block, for using CBR inference method by the described electrical network current signature data getting and institute
State the minimum decision tree of the corresponding comentropy of each category attribute obtaining to be mated；
Detection sub-module, for when there is matching condition, detecting whether the matching condition of described presence is unique rule；
First identification submodule, for when being uniquely rule when the matching condition described presence is detected it is determined that described unique
The reason rule temporarily drops for described electrical network current voltage；
Second identification submodule, for when the corresponding many rules of the matching condition described presence is detected, obtains described
Support that angle value is ranked up with confidence value, determine and support that angle value is corresponding rule during maximum as described with confidence value
The reason electrical network current voltage temporarily drops.
5. the three-phase imbalance controlling device as any one of claim 1-4 is it is characterised in that described default classification
Attribute includes that voltage dip is separate, voltage dip amplitude, the voltage dip duration, voltage dip time of origin and transmission special
Property；Wherein, described voltage dip separate include one mutually temporarily fall two-phase temporarily rise, two-phase temporarily drop one mutually temporarily rise, three-phase temporarily drops and two
Mutually temporarily fall one is mutually constant；Described voltage dip amplitude is the ratio between the current voltage value of voltage dip and standard voltage value；
Described voltage dip time of origin includes the working time scope of default bulk loads and thunderstorm leads to the operation of fault
Time range, and the working time scope of described bulk loads leads to the run time scope sum of fault with described thunderstorm
For one day；Described transmission characteristic includes the active event that value is 0 and the passive event that value is 1.
6. a kind of implementation method of the three-phase imbalance controlling device with temperature and vibrating alert function it is characterised in that its
Three-phase imbalance controlling device as any one of claim 1-5 is realized, methods described includes：
The current characteristic of S1, acquisition electrical network, the characteristic type of described characteristic includes the temporary range of decrease of three-phase of voltage dip
The time that degree, three phase durations of voltage dip and voltage dip occur；
S2, extract from historical data, with described characteristic, there is the data of same characteristic features type, and according to default classification
Attribute, carries out classification formation sample set to the data of described extraction, and filters out satisfaction in described sample set further
The data of predetermined condition forms voltage dip Strong association rule knowledge base；
S3, according to the described electrical network current signature data getting, in the voltage dip Strong association rule knowledge base of described formation
In, determine the current voltage sag reason of electrical network；And
S4, reason is temporarily dropped according to the current voltage of described determination, determine electrical network power quality controlling compensation scheme, and according to described
The electrical network power quality controlling compensation scheme determining compensates to user side voltage.
7. method as claimed in claim 6 is it is characterised in that described step S2 specifically includes：
Extract from historical data, with described characteristic, there is the data of same characteristic features type, and by the described data extracted
Carry out sliding-model control；
According to default category attribute, the data of described sliding-model control is classified, form sample set；
Apriori calculating is carried out to the sample set of described formation, filters out the data shape supporting angle value to be more than the first preset value
Become sample relation integration, and again Apriori calculating is carried out to the sample relation integration of described formation, filter out confidence value big
Data in the second preset value forms voltage dip Strong association rule knowledge base.
8. method as claimed in claim 6 is it is characterised in that described step S3 specifically includes：
Voltage dip Strong association rule knowledge base according to described formation and default category attribute, by ID3 algorithm, obtain every
The minimum decision tree of the corresponding comentropy of one pre-set categories attribute；
According to the described electrical network current signature data getting, from the described corresponding comentropy of each pre-set categories attribute obtaining
In minimum decision tree, determine the reason electrical network current voltage temporarily drops.
9. method as claimed in claim 8 is it is characterised in that the described voltage dip Strong association rule according to described formation is known
Know storehouse and default category attribute, by ID3 algorithm, obtain the minimum decision tree of the corresponding comentropy of each pre-set categories attribute
Concrete steps include：
A, using the data in the voltage dip Strong association rule knowledge base of described formation as training data；
B, the information increasing according to each pre-set categories attribute comprising in the described default category attribute described training data of calculating
Benefit, selects information gain maximum as the Split Attribute of root node, and calculates decision rule and the prefix passing to ground floor
Information；
C, judge whether to create new decision rule；If it is, execution next step d；If it is not, then, jump to step
e；
D, the new decision rule of described generation is saved in rule set, deletes simultaneously and in described training data, comprise described product
The sample of raw new decision rule, produces new data set, and using the new data set of described generation as described training number
According to rear, return to step b；
E, continue to determine whether to produce new prefix information；If it is, execution next step f；If it is not, then, jump to step
Rapid i；
F, the decision tree number of plies add one；
G, judge the number of plies of described decision tree whether less than the sum of all properties comprising in described training data；If it is,
Then execute next step h；If it is not, then, jump to step i；
, under described new prefix information, the information of each pre-set categories attribute comprising in described training data increases for h, calculating
Benefit, selects the Split Attribute of the number of plies corresponding node as described current decision tree for the information gain maximum, and calculates decision-making rule
Then and pass to the prefix information of next layer, return to step c；
I, training terminate, and build decision tree according to described calculated decision rule.
10. method as claimed in claim 8 is it is characterised in that the electrical network current signature data that gets described in described basis,
From the minimum decision tree of the described corresponding comentropy of each pre-set categories attribute obtaining, determine what electrical network current voltage temporarily dropped
The concrete steps of reason include：
For using CBR inference method by the described electrical network current signature data getting and the described each category attribute obtaining
Minimum decision tree is mated corresponding comentropy；
When there is matching condition, detect whether the matching condition of described presence is unique rule；
When the matching condition described presence is detected is uniquely rule it is determined that described unique rule is currently electric for described electrical network
The reason pressure temporarily drops；
When the corresponding many rules of the matching condition described presence is detected, the described support angle value obtaining is entered with confidence value
Row sequence, determines and supports that angle value and confidence value are that corresponding rule during maximum is former as the temporary fall of described electrical network current voltage
Cause.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610991128.8A CN106451503A (en) | 2016-11-08 | 2016-11-08 | Three-phase unbalanced treatment device with temperature and vibration warning unit and realization method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610991128.8A CN106451503A (en) | 2016-11-08 | 2016-11-08 | Three-phase unbalanced treatment device with temperature and vibration warning unit and realization method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106451503A true CN106451503A (en) | 2017-02-22 |
Family
ID=58206861
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610991128.8A Pending CN106451503A (en) | 2016-11-08 | 2016-11-08 | Three-phase unbalanced treatment device with temperature and vibration warning unit and realization method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106451503A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108694175A (en) * | 2017-04-05 | 2018-10-23 | 郑州博信知识产权代理有限公司 | A method of electric energy detection big data is analyzed based on data mining algorithm |
| CN111104977A (en) * | 2019-12-16 | 2020-05-05 | 国网新疆电力有限公司信息通信公司 | Power grid three-phase unbalance multi-dimensional time sequence analysis method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400214A (en) * | 2013-08-22 | 2013-11-20 | 华北电力大学 | Multi-dimension and multi-level association rule based voltage sag predicting and analyzing method |
| CN103488802A (en) * | 2013-10-16 | 2014-01-01 | 国家电网公司 | EHV (Extra-High Voltage) power grid fault rule mining method based on rough set association rule |
| CN104463706A (en) * | 2014-12-10 | 2015-03-25 | 深圳供电局有限公司 | Method and system for detecting voltage sag event reason for power grid |
| CN104795891A (en) * | 2015-03-12 | 2015-07-22 | 国网浙江海盐县供电公司 | Intelligent main transformer online monitoring system in distribution line |
| CN105225165A (en) * | 2014-07-02 | 2016-01-06 | 深圳供电局有限公司 | Transformer substation voltage sag characteristic analysis method based on Apriori algorithm |
-
2016
- 2016-11-08 CN CN201610991128.8A patent/CN106451503A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400214A (en) * | 2013-08-22 | 2013-11-20 | 华北电力大学 | Multi-dimension and multi-level association rule based voltage sag predicting and analyzing method |
| CN103488802A (en) * | 2013-10-16 | 2014-01-01 | 国家电网公司 | EHV (Extra-High Voltage) power grid fault rule mining method based on rough set association rule |
| CN105225165A (en) * | 2014-07-02 | 2016-01-06 | 深圳供电局有限公司 | Transformer substation voltage sag characteristic analysis method based on Apriori algorithm |
| CN104463706A (en) * | 2014-12-10 | 2015-03-25 | 深圳供电局有限公司 | Method and system for detecting voltage sag event reason for power grid |
| CN104795891A (en) * | 2015-03-12 | 2015-07-22 | 国网浙江海盐县供电公司 | Intelligent main transformer online monitoring system in distribution line |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108694175A (en) * | 2017-04-05 | 2018-10-23 | 郑州博信知识产权代理有限公司 | A method of electric energy detection big data is analyzed based on data mining algorithm |
| CN111104977A (en) * | 2019-12-16 | 2020-05-05 | 国网新疆电力有限公司信息通信公司 | Power grid three-phase unbalance multi-dimensional time sequence analysis method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104463706B (en) | Method and system for detecting voltage sag event reason for power grid | |
| CN110097297A (en) | A kind of various dimensions stealing situation Intellisense method, system, equipment and medium | |
| Samantaray et al. | Probabilistic neural network based islanding detection in distributed generation | |
| JP2018170006A (en) | Generic framework to detect cyber threats in electric power grid | |
| Serrano-Guerrero et al. | Statistical methodology to assess changes in the electrical consumption profile of buildings | |
| CN105930495A (en) | Product tort monitoring system and monitoring method therefor | |
| CN110297207A (en) | Method for diagnosing faults, system and the electronic device of intelligent electric meter | |
| CN114024829A (en) | Fault repairing method, device, equipment and storage medium of power communication network | |
| CN115128345B (en) | Power grid safety early warning method and system based on harmonic monitoring | |
| CN110147827A (en) | A kind of failure prediction method based on IAALO-SVM and similarity measurement | |
| CN106451503A (en) | Three-phase unbalanced treatment device with temperature and vibration warning unit and realization method thereof | |
| KR102315580B1 (en) | Fire predictive analysis device and method of building | |
| CN116418117A (en) | Equipment detection system for intelligent power grid | |
| CN113409555A (en) | Real-time alarm linkage method and system based on Internet of things | |
| CN106600034A (en) | Three-phase unbalance governance device having in-situ temperature alarm function and realization method thereof | |
| CN106655225A (en) | Three-phase imbalance treatment device and realization method thereof | |
| CN108037739A (en) | The process management and control method and system of PCB product | |
| CN116365519B (en) | Power load prediction method, system, storage medium and equipment | |
| CN106529806A (en) | Three-phase unbalanced governance device with on-site vibration alarm function and realization method thereof | |
| CN117390546A (en) | Multimode database fusion calculation model for instant anti-electricity-theft detection | |
| CN116204846A (en) | Method for rapidly positioning abnormal sensor data of power distribution network based on visible graph | |
| CN106356994A (en) | Grid stability judging method based on grid PMU big data | |
| CN106374514A (en) | Three-phase imbalance governing device with field vibration wireless warning function and implementing method thereof | |
| CN106451502A (en) | Three-phase unbalanced treatment device with wireless field temperature warning function and realization method thereof | |
| CN112800526B (en) | Method and system for selecting type of building floor material |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170222 |