CN105808382B - Substation's disorder data recognition based on form factor and restoration methods - Google Patents
Substation's disorder data recognition based on form factor and restoration methods Download PDFInfo
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Abstract
The present invention is based on the substation's disorder data recognitions and restoration methods of form factor, first, obtain the actual sample value of current time electrical quantity, while predicting the prediction samples value at current time in real time according to the electrical quantity actual sample value in the past period.Then the prediction samples value at the actual sample value at current time and current time is compared, judges whether the actual sample value at current time is likely to be abnormal data by the two deviation size.Next, it determines whether the actual sample value at current time is abnormal with the consistent degree of electric system electrical quantity signal waveform theoretical expression can be represented according to the fitted signal of the actual sample value in the actual sample value and a period of time later at current time, and obtains the starting point and end point of abnormal data.The last work that next step is carried out according to the exceptional data point judged selects simply latch-up protection, or selection to be restored to abnormal data according to the actual samples point data before and after abnormal data.
Description
Technical field
The present invention relates to digital transformer substation fields, specially substation's disorder data recognition based on form factor and extensive
Compound method
Background technology
With the construction of Chinese intelligent grid, the digitized degree of substation is higher and higher, information exchange in substation
Main medium becomes optical fiber from cable, wherein the signal transmitted also becomes digital quantity from analog quantity.Electronic mutual inductor and merging
Unit is widely applied in digital transformer substation, and primary voltage and current signal are acquired and are converted to by e-book mutual inductor
Digital signal, collect through combining unit, synchronize after pass to subsequent measurement and protection device and handled.In the process,
Due to external electromagnetic environment interference and electronic equipment itself it is unstable, the electrical quantity signal of transmission may be distorted, table
It is now the mutation of one or more data points, these data points are referred to as exceptional data point.Abnormal data is not primary electrical letter
Number correct reflection, but the quality factor position in its data frame be it is normal, measurement and protection device regard it as normal data into
Row processing, can affect greatly to result, and the malfunction of protection can be caused when serious.So secondary device is to receiving
Before sampling value signal is handled, need whether to judge data point extremely, and abnormal data is carried out if necessary
It repairs, to ensure the reliability of secondary data.
Identification about substation's abnormal data and restoration methods, domestic practitioner had done some researchs.It is published in
《Automation of Electric Systems》The paper of magazine《Electric system electrical quantity abnormal sample value real-time identification method》Propose " sampled value
3 continuous effective diagnostic methods ", the characteristics of analyzing electric current, voltage waveform under electric system normal operation and failure, it is indicated that wave
Shape can continuously be led at any other point in addition to several discontinuous points and the same zonal cooling of derivative, and is adopted using the judgement of this characteristic
Whether sample value is abnormal.The method sensitivity in abnormal data and smaller normal data deviation is insufficient, and None- identified is continuous
The little abnormal data of fluctuating range.It is published in《Automation of Electric Systems》Another paper of magazine《Digital transformer substation
The method of anti-abnormal data》Propose a kind of anti-abnormal data method of sampled value detection based on amplitude com parison.The method can not
The effectively smaller abnormal data of identification absolute value, and the case where for electric network fault, to have certain delay could open and protection,
Part may quickly be protected and be impacted.
Accepted in invention disclosed patent,《Intelligent substation flying spot data processing method》It is adopted by comparing target
The order of magnitude of sampling point data and adjacent two sample point data judges whether it is abnormal, and the method that curve matching is used in combination is restored
Abnormal data.The method will fail in the case where abnormal data absolute value is smaller or continuous abnormal data occurs.《Electric power
System ac flow sampled data validation checking method》It proposes to calculate fundametal compoment electric current by continuous three samples value
The quick amplitude of amount, by the quick amplitude at different sampled point calculating mutually compare and this amplitude and fixed threshold value
Compare to judge whether data are abnormal.The method is effective on condition that when system jam calculated quick amplitude absolute value
It is more than threshold value less than the quick amplitude of threshold value and abnormal data.In fact, the size of abnormal data can not determine, absolutely
Value may belong to the same order of magnitude it could even be possible to being less than the latter, for these exceptions with the sampled point absolute value when system failure
Data, the method then None- identified.
In conclusion existing disorder data recognition method is generally compared using the absolute value of sampled value, continuous sampling point one
The comparison of rank or second order difference value judges whether data are abnormal, is difficult to choose there is threshold value, abnormal data point value
The problems such as None- identified, continuous multiple spot disorder data recognition are difficult when smaller and influence quick operating time of protection.
Invention content
For problems of the prior art, the present invention provides a kind of substation's abnormal data knowledge based on form factor
Other and restoration methods can make abnormal data effectively identification and restore, and required data window is shorter, and identification is quick accurate.
The present invention is to be achieved through the following technical solutions：
Substation's disorder data recognition based on form factor and restoration methods, include the following steps：
Step 1 obtains the actual sample value y at electrical quantity signal current timek, while according in the past period
Electrical quantity actual sample value predicts the prediction samples value y ' at current time in real timek；
Step 2, to the prediction samples value y ' at current timekWith the actual sample value y at current timekIt is compared and judges
Whether this actual sample value is likely to be abnormal data；
If judging, the actual sample value at this current time is normal, thens follow the steps one；
If judging, the actual sample value at this current time is likely to be abnormal data, using the sampled point at current time as
Potential abnormal data starting point, and execute step 3；
Step 3 is believed according to the fitting of the actual sample value in the actual sample value at current time therewith rear a period of time
Number, judge whether current time actual sample value is different with the similarity degree of the electrical quantity signal in ideally electric system
Often；
If judging, this current time actual sample value is normal, and previous moment actual sample value is normal, then removes potential different
Regular data starting point, and execute step 1；
If judging this current time actual sample value for abnormal data, update subsequent time sample point data be it is current when
Sample point data is carved, and repeats step 3, until updated current time actual sample value is normal, then by potential exception
Data starting point is as abnormal data starting point, using updated current time actual samples point as abnormal data end point,
And execute step 4；
Step 4, according to the abnormal data starting point and end point judged, selection carries out latch-up protection, or selection basis
Sample point data before and after abnormal data restores abnormal data.
Preferably, in step 1, the prediction samples value y ' at current timekIt can be calculated by following formula：
y′k=-yk-4+2yk-3-2yk-2+2yk-1+2cosωTs(yk-3-2yk-2+yk-1)；
In formula, yk-4~yk-1For the actual sample value of the first four sampled point at current time, ω is power frequency component angular frequency,
TsFor the sampling period.
Preferably, in step 2, to the prediction samples value y ' at current timekWith the actual sample value y at current timekIt carries out
Relatively and the Rule of judgment of judgement is shown below：
In formula, ImFor electric system nominal current magnitude, ε1Compare threshold value for prediction；
If the formula of Rule of judgment is unsatisfactory for, judge that the sampled value at this current time is normal, return to step one；If judging
The formula of condition meets, then using the sampled point at current time as potential abnormal data starting point, and executes step 3.
Preferably, in step 3, the method for discrimination of electrical quantity signal similarity degree is specific as follows：
Step 3.1, the actual sample value y at current time is chosenkN-1 sampled value after it is as data window
Form factor R is calculated,
In formula, N is the sampled point number calculated used in form factor, and value range is N >=5, and N is integer；
Step 3.2, if R ＜ ε2It sets up, ε2For form factor threshold value, then y is judgedkIt is not abnormal data, executes step
One；If R ＜ ε2It is invalid, then judge ykIt for abnormal data starting point, and updates sample point data and repeats method of discrimination, directly
To formula R ＜ ε2It sets up, judges that sampled point at this time for abnormal data end point, that is, has recorded one section of continuous abnormal data
Point, and execute step 4.
Further, the integer between N desirable 5 to 41.
Further, ε2Value determined by following steps：
First, choosing continuous N number of sampled point in a segment standard power frequency component, will wherein as the data segment location chosen
First sampled value replaces with abnormal data, and the degrees of offset of abnormal data is by ε1It determines, by the form factor expression formula defined
Calculate the form factor R of this section of sample values；
Then, the data segment location for changing selection in same segment standard power frequency component chooses N number of sampled point again, in weight
Final election makes the position of first sampled point traverse the entire signal period during taking, calculate different R values, takes wherein minimum
R be assigned to ε2。
Preferably, in step 4, when selection according to the sample point data before and after abnormal data to abnormal data into
When row restores, restoration methods are as follows：
It is y " that note, which restores post-sampling value,k, calculation formula is：
y″k=-yk+4+2yk+1-2yk+2+2yk+3+2cosωTs(yk+1-2yk+2+yk+3)；
In formula, yk+1~yk+4To restore the actual sample value of four sampled points after the corresponding sampling instant of post-sampling value,
ω is power frequency component angular frequency, TsFor the sampling period；After completion to be restored, all records, return to step one are removed.
Compared with prior art, the present invention has technique effect beneficial below：
1, it is to judge data according to the fitting effect of destination sample point and its follow-up sampled point rather than its amplitude size
No exception can effectively distinguish abnormal data and the sample point data when system failure.
2, the disorder data recognition of continuous multiple spot can be suitable for.
3, threshold value can be accurately set according to the patient abnormal data departure degree of system institute, the sensitivity of algorithm not by
The influence of abnormal data size itself, the smaller abnormal data of logarithm can also accurately identify.
4, data window needed for algorithm is shorter, 5 sampled points of most short need, sample rate corresponding data window length when being 4kHz
For 1ms.It is general to take data window length for 5ms, it is sufficient to meet the actuation time requirement quickly protected.
5, after disorder data recognition can simple latch-up protection, also it can be restored as needed.
Description of the drawings
Fig. 1 is the flow chart of method described in present example.
Fig. 2 is the schematic diagram of the embodiment of the present invention, wherein (a) indicates the exemplary currents wave of period before and after electric power system fault
Shape, wherein containing different types of abnormal data；(b) indicate this algorithm to the identification situation of abnormal data in (a), " 1 " generation
The sampled point of table corresponding position is abnormal data, and " 0 " represents without exception；(c) be abnormal data is restored after signal wave
Shape.
Specific implementation mode
With reference to specific embodiment, the present invention is described in further detail, it is described be explanation of the invention and
It is not to limit.
The invention discloses a kind of substation's disorder data recognition and restoration methods based on form factor, this method realization
Process is as follows：First, the actual sample value of current time electrical quantity is obtained, while real according to the electrical quantity in the past period
Border sampled value predicts the prediction samples value at current time in real time.Then pre- to the actual sample value at current time and current time
It surveys sampled value to be compared, judges whether the actual sample value at current time is likely to be abnormal number by the two deviation size
According to.Next, according to the fitted signal and energy of the actual sample value in the actual sample value and a period of time later at current time
Enough represent the consistent degree of electric system electrical quantity signal waveform theoretical expression is to determine the actual sample value at current time
No exception, and obtain the starting point and end point of abnormal data.It is last that next step is carried out according to the exceptional data point judged
Work, can select simply latch-up protection, can also select according to the actual samples point data before and after abnormal data
Abnormal data is restored.The present invention can effectively distinguish abnormal data and sample point data when electric power system fault；It is applicable in
In the disorder data recognition of continuous multiple spot；Threshold value can be accurately set, and the smaller abnormal data of logarithm can also accurately identify；Institute
It needs data window short, meets the quick-action requirement of protection.
Specifically, substation's disorder data recognition and restoration methods proposed by the present invention based on form factor include following
Step：
Step 1 updates the actual sample value y at current timek.Meanwhile the reality of the first four sampled point according to current time
Border sampled value yk-4~yk-1The actual sample value at current time is predicted, sampled point is corresponded with sampled value, and prediction is public
Formula is as follows：
y′k=-yk-4+2yk-3-2yk-2+2yk-1+2cosωTs(yk-3-2yk-2+yk-1) (1)
In formula, ω is power frequency component angular frequency, TsFor the sampling period, if judging the data of combining unit output,
According to current standard, TsUsually take 0.25ms.
Step 2, to the prediction samples value y ' at current timekWith the actual sample value y at current timekIt is compared and judges
Whether this actual sample value is likely to be abnormal data, and Rule of judgment is：
In formula, ImFor electric system nominal current magnitude, ε1Compare threshold value for prediction, this threshold value can be according to different electricity
The adaptive change of the patient abnormal data value departure degree of Force system.If electromagnetic environment residing for electrical secondary system is more severe,
The abnormal data type of appearance is various, ε1Can be set as a smaller value, such as 0.5, i.e., the prediction samples value at current time with work as
0.5 times of up-to-date style (2) condition that the deviation of the actual sample value at preceding moment is more than electric system nominal current magnitude meets, after startup
Continuous process flow.When environment is ideal, ε1It can be set as a higher value, if the actual sample value degrees of offset at current time
It is smaller, without being further processed.
If formula (2) is unsatisfactory for, judge that the actual sample value at current time is normal, return to step one.If formula (2) meets,
The actual sample value serial number k at current time is assigned to potential abnormal data starting point startpoint, at this time there are two types of may,
First, the actual sample value at current time is abnormal, second is that electric system generates transient process, current time due to failure and other reasons
The corresponding point of actual sample value be electric system from stable state to the critical point of transient state transition.For both of these case differentiation by
Subsequent step is completed.
Step 3, according to the fitted signal of the actual sample value in the actual sample value at current time therewith rear a period of time
Judge whether current time actual sample value is abnormal with the similarity degree of the electrical quantity signal in ideally electric system.
Electrical quantity signal expression ideally in electric system is as follows：
In formula, A is the amplitude of power current,For power current initial phase angle, B is the initial value of attenuating dc component, and τ is to decline
Subtract the damping time constant of DC component.
Choose the actual sample value y at current timekN-1 sampled value after it has N number of sampled value altogether as number
Form factor R is calculated according to window, calculation formula is as follows：
And following judgement is done to R：
R ＜ ε2 (5)
If formula (5) is invalid, the actual sample value at current time is updated, repeats calculating and the formula (5) of formula (4)
Judge.When formula (5) is set up, serial number k-N+1 is assigned to abnormal data end point endpoint.If startpoint with
Endpoint represents the same sampled point, illustrates that electric system at this time is undergoing normal transient process, exception does not occur
Data；If startpoint represents different sampled points from endpoint, illustrate that the sampled point between this 2 points is exception
Data point.
In formula (4), when sample rate is 4kHz, the integer between N desirable 5~41, algorithm delay at this time be 1ms~
10ms, it is sufficient to meet the quick-action requirement of protection.
ε2Value by N and ε1It codetermines.It chooses continuous N number of sampled point in a segment standard power frequency component and is used as selection
Data segment location, wherein first sampled value will replace with abnormal data, the degrees of offset of abnormal data is by ε1It determines, by public affairs
Formula (4) calculates the form factor R of this section of sample values.Then, change the data of selection in same segment standard power frequency component
Fragment position chooses N number of sampled point again, and the position of first sampled point is made to traverse entire signal week during repetition is chosen
Phase calculates different R values, and wherein minimum R is taken to be assigned to ε2。
When the value range of N is 5~41, i.e., the algorithm delay (data window is long) when sample rate is 4kHz is 1ms~10ms,
ε1Value range be 0.1~1 when, ε2Value determined by table 1.
1 ε of table2Value
Step 4, by step 3 startpoint and endpoint determine the position of abnormal data, by thereafter just
Abnormal data is restored at constant strong point, and it is y " that note, which restores post-sampling value,k, calculation formula is：
y″k=-yk+4+2yk+1-2yk+2+2yk+3+2cosωTs(yk+1-2yk+2+yk+3) (6)
I.e. from time sequencing, first restore exceptional data point of the last one appearance, then restore to occur before successively
Exceptional data point.After completion to be restored, all records, return to step one are removed.
In conjunction with Fig. 2, the validity of this method is illustrated.The sample rate used is 4kHz, and algorithm is each during realizing
Parameter selection is as follows：Im=10, ε1=0.5, N=21 obtain ε by table 12=0.026.
Fig. 2 (a) indicates the current waveform before and after the electric system failure moment, 1~240 corresponding signal wave of sampled point
Shape expression formula is：
Y=10sin (100 π t) (7)
241~480 corresponding signal waveform expression formula of sampled point is：
The value for changing certain sampled points becomes exceptional data point, shares at 8, often locate abnormal data position number with
Exception Type is as shown in table 2.
2 abnormal data position number of table and type
Fig. 2 (b) indicates this method to the identification situation of abnormal data in (a), and it is different that " 1 ", which represents the sampled point of corresponding position,
Regular data, " 0 " represent without exception.It can be seen that, in addition to No. 101 sampled points since departure degree is less than ε1It is unrecognized outer, remaining
Various types of exceptional data points are effectively recognized.Meanwhile the sampled point of fault moment and being not recognized as abnormal data.
Fig. 2 (c) is that this method identifies the recovery situation after exceptional data point, it can be seen that result is ideal.
Claims (7)
1. substation's disorder data recognition based on form factor and restoration methods, which is characterized in that include the following steps：
Step 1 obtains the actual sample value y at electrical quantity signal current timek, while according to the electrical quantity in the past period
Actual sample value predicts the prediction samples value y ' at current time in real timek；
Step 2, to the prediction samples value y ' at current timekWith the actual sample value y at current timekIt is compared and judges this reality
Whether border sampled value is likely to be abnormal data；
If judging, the actual sample value at this current time is normal, thens follow the steps one；
If judging, the actual sample value at this current time is likely to be abnormal data, using the sampled point at current time as potential
Abnormal data starting point, and execute step 3；
Step 3, according to the actual sample value at current time therewith after actual sample value in a period of time fitted signal, with
Ideally the similarity degree of the electrical quantity signal in electric system judges whether current time actual sample value is abnormal；
If judging, this current time actual sample value is normal, and previous moment actual sample value is normal, then removes potential abnormal number
According to starting point, and execute step 1；
If judging, this current time actual sample value for abnormal data, updates subsequent time sample point data and is adopted for current time
Sampling point data, and repeat step 3, until updated current time actual sample value is normal, then by potential abnormal data
Starting point using updated current time actual samples point as abnormal data end point, and is held as abnormal data starting point
Row step 4；
Step 4, according to the abnormal data starting point and end point judged, selection carries out latch-up protection, or selection according to exception
Sample point data before and after data restores abnormal data.
2. substation's disorder data recognition and restoration methods, feature according to claim 1 based on form factor exist
In, in step 1, the prediction samples value y ' at current timekIt can be calculated by following formula：
y′k=-yk-4+2yk-3-2yk-2+2yk-1+2cosωTs(yk-3-2yk-2+yk-1)；
In formula, yk-4~yk-1For the actual sample value of the first four sampled point at current time, ω is power frequency component angular frequency, TsFor
Sampling period.
3. substation's disorder data recognition and restoration methods, feature according to claim 1 based on form factor exist
In in step 2, to the prediction samples value y ' at current timekWith the actual sample value y at current timekIt is compared and judges
Rule of judgment is shown below：
In formula, ImFor electric system nominal current magnitude, ε1Compare threshold value for prediction；
If the formula of Rule of judgment is unsatisfactory for, judge that the sampled value at this current time is normal, return to step one；If Rule of judgment
Formula meet, then using the sampled point at current time as potential abnormal data starting point, and execute step 3.
4. substation's disorder data recognition and restoration methods, feature according to claim 1 based on form factor exist
In in step 3, the method for discrimination of electrical quantity signal similarity degree is specific as follows：
Step 3.1, the actual sample value y at current time is chosenkN-1 sampled value after it is calculated as data window
Form factor R,
In formula, N is the sampled point number calculated used in form factor, and value range is N >=5, and N is integer；
Step 3.2, if R ＜ ε2It sets up, ε2For form factor threshold value, then y is judgedkIt is not abnormal data, executes step 1；If R
＜ ε2It is invalid, then judge ykIt for abnormal data starting point, and updates sample point data and repeats method of discrimination, until formula R ＜
ε2It sets up, judges that sampled point at this time for abnormal data end point, that is, has recorded one section of continuous exceptional data point, and execute step
Rapid four.
5. substation's disorder data recognition and restoration methods, feature according to claim 4 based on form factor exist
In the integer between N desirable 5 to 41.
6. substation's disorder data recognition and restoration methods, feature according to claim 4 based on form factor exist
In ε2Value determined by following steps：
First, data segment location of the continuous N number of sampled point as selection in a segment standard power frequency component is chosen, it will wherein first
A sampled value replaces with abnormal data, and the degrees of offset of abnormal data compares threshold value ε by predicting1It determines, by the waveform defined
Coefficient expressions calculate the form factor R of this section of sample values；
Then, the data segment location for changing selection in same segment standard power frequency component chooses N number of sampled point again, is repeating to select
So that the position of first sampled point is traversed the entire signal period during taking, calculate different R values, takes wherein minimum R
It is assigned to ε2。
7. substation's disorder data recognition and restoration methods, feature according to claim 1 based on form factor exist
In, in step 4, when selection restores abnormal data according to the sample point data before and after abnormal data, recovery
Method is as follows：
It is y " that note, which restores post-sampling value,k, calculation formula is：
y″k=-yk+4+2yk+1-2yk+2+2yk+3+2cosωTs(yk+1-2yk+2+yk+3)；
In formula, yk+1~yk+4For the actual sample value of four sampled points after the corresponding sampling instant of recovery post-sampling value, ω is
Power frequency component angular frequency, TsFor the sampling period；After completion to be restored, all records, return to step one are removed.
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| CN106786336B (en) * | 2016-12-26 | 2019-05-31 | 国电南瑞科技股份有限公司 | The sampling of intelligent substation protective device single-point is abnormal to count greatly anti-error processing method |
| CN113376545A (en) * | 2020-11-13 | 2021-09-10 | 国网江西省电力有限公司柘林水电厂 | Method for detecting relative abnormal signal of leakage current of hydropower station |
| CN112988728A (en) * | 2021-03-26 | 2021-06-18 | 云南电网有限责任公司电力科学研究院 | Power distribution network data cleaning method and device |
| CN113489154B (en) * | 2021-07-16 | 2023-07-28 | 广东电网有限责任公司 | Power grid monitoring system data loss prevention method based on main and standby channel switching technology |
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