CN107154783A - Using the method for photovoltaic system fault electric arc in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING - Google Patents
Using the method for photovoltaic system fault electric arc in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING Download PDFInfo
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Abstract
The invention discloses a kind of method of photovoltaic system fault electric arc in the case of application independent component analysis and S-transformation detecting system PROCESS COUPLING, based on photovoltaic system output current signal, the main source signal of independence that electric current is obtained by independent component analysis, make variance processing to the frequency information after the signal Fourier transformation and obtain fisrt feature amount, electric current is handled by S-transformation, second feature amount is formed after the time and high fdrequency component integration to gained time-frequency matrix.The corresponding given threshold of characterizing magnitudes relatively after, reuse the output result of determination of upper two characterizing magnitudes of weight coefficient weighted decision layer, the real-time detection of completion photovoltaic system fault electric arc.The present invention is compared by dynamic threshold, weight coefficient weights two results of decision, substantially excavation betides the essential difference of photovoltaic system fault electric arc among systematic procedure, the photovoltaic system fault electric arc under coupling condition can be quickly and accurately cut off, the safe and stable operation ability of photovoltaic system is improved.
Description
Technical field
The invention belongs to the electric fault detection technique field of photovoltaic, and in particular to one kind application independent component analysis and S become
Get in return to two characterizing magnitudes, characterizing magnitudes are compared into two results of decision of acquisition with corresponding dynamic given threshold, dynamic is used
The weight coefficient of setting weights the result of decision of two characterizing magnitudes, carries out photovoltaic system fault electric arc and detects in real time, hence it is evident that excavates
Photovoltaic system fault electric arc under the essential difference of photovoltaic system fault electric arc among systematic procedure, lifting coupling condition is betided to examine
The rapidity and reliability of survey, to ensure no matter when photovoltaic system can be stablized, safety, the economic method for exporting operation.
Background technology
The problems such as global energy crisis and climate warming, is increasingly serious so that the novel green such as photovoltaic, wind-force, fuel cell
Regenerative resource is increasingly widely applied.In recent years, with the continuous reduction of photovoltaic products cost, photovoltaic both domestic and external
Industry grows at top speed.The expansion of photovoltaic generating system scale improves photovoltaic system DC terminal output voltage, typically from tens volts
To several hectovolts, large-scale photo-voltaic power generation station even can reach the high direct voltage of kilovolt, when major photovoltaic plants put into operation
Between extension also increase insulation ag(e)ing degree so that photovoltaic system failure occur more and more frequently, photovoltaic system DC side
Fault electric arc be exactly one of them.Once photovoltaic system fault electric arc is produced, due to the zero crossing without AC fault electric arc
And seem more dangerous, and photovoltaic system fault electric arc safeguard measure can not be such as taken in time, will be to photovoltaic module and power transmission line
Road causes huge damage or even to trigger fire, causes the safety problems such as serious economic loss and casualties.Occur earliest
Photovoltaic system fault electric arc can trace back to Sweden's Mont Soleil photovoltaic plants of last century the nineties, particularly from
Over 2006, photovoltaic system fire incident is more and more by media report, and fire spot includes house photovoltaic facility, commercialization
Photovoltaic facility and large-scale photovoltaic power station.The photovoltaic system fire incident for betiding 2006 is due to being connect in BP solar energy
Occur in line box caused by photovoltaic system fault electric arc, BP companies also make because recalling and substituted for most defective photovoltaic modulies
Into huge economic loss.Therefore, fully and effectively photovoltaic system fault electric arc examinations are controlled in rudiment
State, to ensureing that the safe and reliable operation of photovoltaic generating system is significant.
At present, what domestic and international correlative study object was directed to is non-coupling photovoltaic system fault electric arc, i.e., in photovoltaic system
Unite when fault electric arc occurs and systematic procedure is not present.However, in actually detected, photovoltaic system, which frequently experiencings, comes from photovoltaic battle array
The transient processes such as changed power, the startup of side or load-side are arranged, so that photovoltaic system electricity, which experiencings, frequently changes temporary
State.The time of origin of photovoltaic system fault electric arc is uncontrollable, thus photovoltaic system fault electric arc also has certain probability meeting
Occur among these systematic procedures.For example, in the systematic procedures such as photovoltaic system startup, the system power of increase, photovoltaic system
System output current constantly increases, and on the other hand, tandem photovoltaic system failure electric arc can then reduce photovoltaic system output current.Cause
This, in the photovoltaic system fault electric arc of systematic procedure coupling, photovoltaic system failure output current macroscopically can't be with
The normal output current of photovoltaic system produces difference, and the requirement to photovoltaic system fault electric arc detection algorithm is more harsh.Existing inspection
If method of determining and calculating can not be detected in time from the visual angle for being appropriately determined the normal output current of photovoltaic system, photovoltaic system fault electric arc,
There is tripping in corresponding photovoltaic system dc side fault arc detection device, fails the photovoltaic system fault electric arc of row elimination and can cause
Photovoltaic system fire incident, bring life and property loss；If from the visual angle for being appropriately determined photovoltaic system failure output current, light
Normally operation will be judged by accident volt system, and malfunction occurs in corresponding photovoltaic system dc side fault arc detection device, and these are wrong
The normal condition sentenced can cause photovoltaic system to stop transport and reduce system generating efficiency.Therefore, detection algorithm must extraction system mistake
Photovoltaic system fault electric arc basic feature under journey coupling, accurately identifies photovoltaic system fault electric arc and occurs the moment, to light
Volt system output current state is accurate, reliable, rapidly recognize, and is achieved in installing the detection of photovoltaic system DC side fault electric arc
The functional requirement of device.
The content of the invention
The photovoltaic system failure electricity among systematic procedure is betided it is an object of the invention to accurate, reliable, Fast Identification
There is provided photovoltaic system fault electric arc in the case of a kind of application independent component analysis and S-transformation detecting system PROCESS COUPLING for arc
Method.
To reach above-mentioned purpose, present invention employs following technical scheme：
1) sample frequency f is pressed to photovoltaic system output current signal by multiple current sensorssPointwise sampling is carried out, is obtained
To multichannel current signal xi,j, wherein, i is that current sensor represents sequence number, and i ∈ N and i ＞ 1, j are the analysis period to represent sequence number, j
∈N+, for any two difference i values, when j takes same value, xi,jEqual sampling number N is respectively provided with, when N reaches analysis
After the requirement of section, step 2 is gone to) carry out Fisrt fault arc characteristic analysis；
2) by the multichannel current signal collected formation higher-dimension mixed signal matrix X=[x1,j,x2,j,…,xi,j]T, to institute
Mixed signal matrix is obtained to carry out removing average and whitening processing, then by the way that the mixed matrix W of solution just can be obtained after fast independent component analysis,
Calculate source signal matrix S=WX=[s1,j,s2,j,…,si,j]T, wherein, S includes effective source signal and noise signal, and selection has
The independent main source signal s of effect1,j, to s1,jFast Fourier Transform (FFT) is carried out, the variance of one-dimensional frequency matrix in frequency domain is calculated, the is obtained
One characterizing magnitudes r1,j, go to step 3)；
3) fisrt feature amount threshold value is set in the present analysis period as A1×μ1,j–A2×σ1,j, wherein, μ1,jFor from first point
Analyse the Estimation of Mean of period to present analysis period all fisrt feature values, σ1,jFor from the first analysis period to present analysis
The standard deviation of period all fisrt feature values, A1∈ Z, A2∈ Z, by fisrt feature value and the fisrt feature amount threshold value of setting
Compare, export corresponding electrical level judging result：If r1,j≥A1×μ1,j–A2×σ1,j, then result of determination 0, deposit to first are exported
Fault electric arc trip current out1[j]；If r1,j<A1×μ1,j–A2×σ1,j, then result of determination 1, deposit to Fisrt fault electricity are exported
Arc trip current out1[j], goes to step 4) carry out the second fault electric arc signature analysis；
4) signal all the way in selection multichannel current signal carries out S-transformation, obtains the two-dimensional complex number time-frequency square in time-frequency domain
Battle array, calculates integration of the high fdrequency component absolute value of frequency dimension along the time, obtains second feature value r2,j, go to step 5)；
5) second feature amount threshold value is set in the present analysis period as A3×μ2,j–A4×σ2,j, wherein, μ2,jFor from first point
Analyse the Estimation of Mean of period to present analysis period all second feature values, σ2,jFor from the first analysis period to present analysis
The standard deviation of period all second feature values, A3∈ Z, A4∈ Z, by second feature value and the second feature amount threshold value of setting
Compare, export corresponding electrical level judging result：If r2,j≥A3×μ2,j–A4×σ2,j, then result of determination 0, deposit to second are exported
Fault electric arc trip current out2[j]=0；If r2,j<A3×μ2,j–A4×σ2,j, then result of determination 1, deposit to the second event are exported
Hinder electric arc trip current out2[j]=1, goes to step 6) carry out two characteristic quantity decision-making levels on output result of determination weighting at
Reason；
6) using Dynamic Weights coefficient weighting independent component analysis and the output result of determination of S-transformation, weighted results are obtained
outtempj=C1,j×out1[j]+C2,j×out2[j], then carries out preliminary state judgement：If outtempj>N, wherein, n is
Weighted results threshold value, then export result of determination 1, deposit to preliminary state determination results matrix outt [j]；Otherwise output judges knot
Really 0, be stored in preliminary state determination results matrix outt [j], go to step 7) carry out photovoltaic system state differentiation；
7) set and judge precision p, a photovoltaic system state is judged per p period：Count preliminary state determination results square
The number that battle array outt is 1 to j-th of element to-p elements of jth, from -2p elements of jth from -3p elements of jth, if being counted
The numerical value of number is all higher than p, then confirms occur photovoltaic system fault electric arc in jth -2p to the-p periods of jth, take phase
The photovoltaic system fault electric arc safeguard measure answered；Otherwise it is assumed that photovoltaic system is in just in jth -2p to the-p periods of jth
Normal running status, return to step 1) current signal in next analysis period is analyzed.
The current sensor is not required for same type, but its bandwidth should be greater than 100kHz, should be installed in photovoltaic system
The diverse location of system is to show the difference between sampled current signals, it is considered to accurate to obtain electric current independently main source signal reduces hard simultaneously
The principle of part testing cost, the span of current sensor is 2~4；Sample frequency fsIt should be greater than twice of photovoltaic system
The fault electric arc characteristic spectra upper limit, span is 200~500kHz；Consider quickly and accurately to obtain photovoltaic system failure electricity
The principle of arc feature, sampling number N span is 8000~12000.
(being based on negentropy maximization) in the fast independent component analysis, parameters foundation quickly obtains betiding system
During photovoltaic system fault electric arc notable feature depending on, nonlinear function can select g1(u)=u3、g2(u)=u2、g3(u)
=arctan (q1×u)、g4(u)=u × e^ (- q2 2×u2/ 2), wherein, q1And q2For constant, preferably g1(u)=u3, it is maximum
The span of iterations is 950~1050, and the span of iteration precision is 0.00006~0.00015.
The main source signal number of the independence i.e. way of sampled current signals that the fast independent component analysis is obtained, based on letter
Number impact most strong principle selects an effective independent main source signal to carry out follow-up Fast Fourier Transform (FFT) processing：Calculate each
The difference of peak-to-peak value of the independent main source signal within the analysis period, selection difference is that the maximum main source signal of independence is effective independent main
Source signal；Based on reducing the negative of photovoltaic system fault electric arc under spectral leakage reduction fisrt feature amount detection coupling condition as far as possible
Face rings, and the numerical value that points are converted in Fast Fourier Transform (FFT) is chosen to be the corresponding numerical value of sampling number N.
Farthest to find that the photovoltaic system fault electric arc time-frequency difference in systematic procedure is principle from multichannel electric current
The input all the way as S-transformation is chosen in signal：The corresponding current signal of sensitivity highest current sensor is preferably selected, when
During this kind of current sensor more than one, preferably select and occur the nearest current sensor in position apart from photovoltaic system fault electric arc
Corresponding current signal, when occurring the nearest current sensor more than one in position apart from photovoltaic system fault electric arc, preferably
Select photovoltaic system fault electric arc current sensor with minimum number of components into current sensor propagation path corresponding
Current signal；Based on identical principle, window width Dynamic gene is preferably 1 in the S-transformation.
Absolute value processing is made to gained two-dimensional complex number time-frequency matrix element after S-transformation, the frequency dimension based on the time-frequency matrix
Degree component builds the principle of second feature amount to be presented significant downward trend when photovoltaic system fault electric arc occurs, and with compared with
Small amplitude form shows photovoltaic system fault electric arc and the difference of systematic procedure before, photovoltaic system fault electric arc characteristic spectra
Elect as 40~100kHz and with sample frequency fsValue it is uncorrelated.
The fisrt feature amount threshold value A1×μ1,j–A2×σ1,jIt is relevant with the fisrt feature value of all analysis periods before
And fisrt feature amount r is followed in real time1Dynamic change, wherein, coefficient A1With A2It is related to fisrt feature amount output characteristics, according to logical
The fisrt feature amount threshold value for crossing setting compares with fisrt feature value and can be correctly obtained depending on corresponding photovoltaic system state, A1With
A2Preferably 1；Estimation of Mean μ1,jAnd standard deviation sigma1,jOutput result of determination according to fisrt feature amount is corrected in real time：For
The fisrt feature value r that first analysis period obtains1,1, make correction rtemp1,1=r1,1, Estimation of Mean μ1,1=r1,1, mark
Quasi- difference σ1,1=0；For the fisrt feature value r of j-th of analysis period1,j, wherein, j ∈ N and j ＞ 1, if the present analysis period
When interior fisrt feature value is more than or equal to upper one analysis period fisrt feature amount threshold value, correction rtemp is made1,j=r1,j, average
Estimate and the calculation formula of standard deviation is
Wherein, k represents sequence number, k=1,2 ... j, j ∈ N and j ＞ 1 for the analysis period in cumulative process, if during present analysis
When fisrt feature value is less than upper one analysis period fisrt feature amount threshold value in section, correction rtemp is made1,j=μ1,j-1–σ1,j-1,
The calculation formula of Estimation of Mean and standard deviation is
The second feature amount threshold value A3×μ2,j–A4×σ2,jIt is relevant with the second feature value of all analysis periods before
And second feature amount r is followed in real time2Dynamic change, wherein, coefficient A3With A4It is related to second feature amount output characteristics, according to logical
The second feature amount threshold value for crossing setting compares with second feature value and can be correctly obtained depending on corresponding photovoltaic system state, A3With
A4Preferably 1；Estimation of Mean μ2,jAnd standard deviation sigma2,jOutput result of determination according to second feature amount is corrected in real time：For
The second feature value r that first analysis period obtains2,1, make correction rtemp2,1=r2,1, Estimation of Mean μ2,1=r2,1, mark
Quasi- difference σ2,1=0；For the second feature value r of j-th of analysis period2,j, wherein, j ∈ N and j ＞ 1, if the present analysis period
When interior second feature value is more than or equal to upper one analysis period second feature amount threshold value, correction rtemp is made2,j=r2,j, average
Estimate and the calculation formula of standard deviation is
Wherein, k represents sequence number, k=1,2 ... j, j ∈ N and j ＞ 1 for the analysis period in cumulative process, if during present analysis
When second feature value is less than upper one analysis period second feature amount threshold value in section, correction rtemp is made2,j=μ2,j-1–σ2,j-1,
The calculation formula of Estimation of Mean and standard deviation is
Based on the quick principle for calculating each given threshold in the present analysis period, when obtaining present analysis using recurrence relation
The calculation formula of Estimation of Mean and standard deviation is in section
Wherein, μm,j、σm,jEstimation of Mean and standard deviation respectively in the present analysis period, μm,j-1、σm,j-1Before respectively
Estimation of Mean and standard deviation in one analysis period, rtempm,jFor the correction in the present analysis period, wherein, the m amounts of being characterized
Sequence number is represented, value is 1 or 2, j ∈ N and j ＞ 1.
Output result of determination after two characterizing magnitudes are compared with given threshold, corresponding each spy are weighted using Dynamic Weights coefficient
The weight coefficient of the amount of levying output result of determination judges that the statistical property of correctness is true according to characteristic quantity to historical analysis period state
It is fixed, i.e., characteristic quantity the historical analysis period is made correct status judgement the analysis period it is more, this feature amount is in present analysis
The weight coefficient that section is obtained is then bigger, specifically, constructs fisrt feature amount and second feature amount institute respectively based on below equation
Belong to weight coefficient C1,jAnd C2,j：
Wherein, σ2 out1And σ2 out2Respectively Fisrt fault electric arc trip current and the second fault electric arc trip current is from
One element to j-th of element variance, i.e.,
Wherein, out1And out2Respectively Fisrt fault electric arc trip current and the second fault electric arc trip current, k is square
The counting sequence number of array element element, k=1,2 ... j, j ∈ N and j ＞ 1,WithRespectively Fisrt fault electric arc trip current and
Estimation of Mean of the two fault electric arc trip currents from first element to j-th of element；If Fisrt fault electric arc trip current,
Two fault electric arc trip currents from first element to j-th of element be 0, i.e. two characteristic quantities judge it is all analysis the periods as
Normal operating condition, indirect assignment C1,j=0, C2,j=0, then it is weighted the follow-up photovoltaic system failure electric arc of two characteristic quantities progress
Detection；If jth -2p are in normal operating condition to photovoltaic system in the-p periods of jth, to Fisrt fault under this p period
Make element exchange processing in the position of electric arc trip current, the second fault electric arc trip current respective element not etc..
Based on the principle of the photovoltaic system fault electric arc accurately identified in systematic procedure, the weighted results threshold value n's takes
It is 0.45~0.55 to be worth scope；The principle of reliability and quick-action is detected based on photovoltaic system fault electric arc, it is to avoid too small p value is drawn
The photovoltaic system fault electric arc that the systematic procedure nonaction of hair and excessive p value trigger acts phenomenon, the judgement essence not in time
The span for spending p is 2~5.
The present invention has following beneficial technique effect：
1) recognition capability of the photovoltaic system to electric current normal state is the method increase, solves and is exported with photovoltaic system failure
The photovoltaic system DC side fault electric arc inspection that the transient processes such as electric current visual angle detection algorithm changes in face of system power, startup are produced
Error action of device problem is surveyed, by the way that systematic procedure correctly is determined as into normal operating condition, the normal of photovoltaic system is significantly extended
Run time, significantly improves the generating efficiency of photovoltaic system, enhances the stabilizing power that photovoltaic system is normally run；
2) this method can accurately catch the photovoltaic system fault electric arc basic feature betided among systematic procedure, solve
The photovoltaic system fault electric arc production occurred with the normal output current visual angle detection algorithm of photovoltaic system in face of and then systematic procedure
Raw photovoltaic system DC side fault arc detection device tripping problem, by correctly by the photovoltaic system failure under coupling condition
Electric arc is determined as malfunction, has ensured the validity of photovoltaic system fault electric arc detection, and this kind of photovoltaic system event is eliminated in time
Hinder the harm such as photovoltaic fire incident, life and property loss that electric arc triggers, expand current photovoltaic system fault electric arc detection side
The scope of application of method；
3) this method has wide photovoltaic system fault electric arc inspection range, not by photovoltaic system failure under coupling condition
No matter the variation tendency aspect effect that electric arc is caused to photovoltaic system output current, occur the moment in photovoltaic system fault electric arc
Photovoltaic system output current becomes big or diminishes, it is constant to be also to maintain, detection algorithm can reliably, checkout system mistake exactly
Photovoltaic system fault electric arc in journey；
4) this method has a very strong quick-action, single analysis period of detection photovoltaic system fault electric arc for 40~
60ms, it is 2~5 to set precision, that is, it is 0.3s to detect the photovoltaic system fault electric arc most long cost time, and most bob is out of order electric arc
The time that circuit cutting-off controlling signal is spent is 0.08s, the judgement of photovoltaic system fault electric arc under reliable detection coupling condition
Duration is much smaller than the 2s standards of existing American Standard UL1699B defineds；
5) this method uses characterizing magnitudes and threshold value using Estimation of Mean and standard deviation the construction characteristic quantity threshold value of characteristic quantity
Comparison procedure realizes the normalization of each characteristic quantity output, solves different characteristic amount output magnitude differences and multi-characteristicquantity quantity is detected
The interference of photovoltaic system fault electric arc, is also beneficial to realize follow-up multi-characteristicquantity quantity decision-making level weighting, threshold value and weight coefficient exist
Carry out dynamic change processing in different analytical cycle, and photovoltaic system fault electric arc standard is set up, and is conducive to detection
What algorithm more reliably provided system mode within each analysis period is appropriately determined result；
6) this method detects that the requirement of hardware change is not high to existing photovoltaic system fault electric arc, it is only necessary in original light
Current sensor in volt system rationally needed for laying, is added in original photovoltaic system DC side fault arc detection device
Signal input port is detected, then the software program of photovoltaic system DC side fault arc detection device need to only calculate two methods
Under characteristic quantity, carry out dynamic threshold setting, dynamic weighting coefficient and calculate, finally realize that the weighting of two characteristic quantities in decision-making level is real
Existing photovoltaic system fault electric arc detection, Programming Principle is simple, and cost of implementation is cheap.
Further, when assert that photovoltaic system fault electric arc occurs, Estimation of Mean and standard deviation in being set to threshold value
Calculating need to be modified, it is to avoid the fluctuation of threshold value is caused because Feature change is larger；When identification photovoltaic system is normal
Operation and two characteristic quantities output result of determination not wait when, to two fault electric arc trip currents accordingly grade element do not make exchange processing,
Correct weight coefficient dynamic change is realized, the normal operation malfunction problem caused by external interference is eliminated, effectively increases
The reliability of photovoltaic system fault electric arc detection, adds the economic benefit of photovoltaic system operation.
Brief description of the drawings
Fig. 1 a are photovoltaic system fault arc detection method flow chart of the invention；
Fig. 1 b are dynamic threshold setting process figure in the photovoltaic system fault arc detection method of the present invention；
Fig. 2 is the present invention in including the specific of the photovoltaic system DC side fault arc detection device for being integrated in bus bar
Theory diagram when photovoltaic system application hardware is realized；
Fig. 3 a are that the application present invention carries out the pointer in fault electric arc that photovoltaic system fault electric arc is detected under coupling condition
The photovoltaic system output current signal of standby constant trend；
Fig. 3 b be application independent component analysis carry out coupling condition under photovoltaic system fault electric arc detect characteristic quantity and its
Given threshold waveform；
Fig. 3 c are the characteristic quantity and its given threshold that application S-transformation carries out that photovoltaic system fault electric arc is detected under coupling condition
Waveform；
Fig. 3 d are that the application system mode of the invention for carrying out photovoltaic system fault electric arc detection under coupling condition judges output
Signal；
Fig. 4 a are that the application present invention carries out the pointer in fault electric arc that photovoltaic system fault electric arc is detected under coupling condition
The photovoltaic system output current signal of standby reduction trend；
Fig. 4 b be application independent component analysis carry out coupling condition under photovoltaic system fault electric arc detect characteristic quantity and its
Given threshold waveform；
Fig. 4 c are the characteristic quantity and its given threshold that application S-transformation carries out that photovoltaic system fault electric arc is detected under coupling condition
Waveform；
Fig. 4 d are that the application system mode of the invention for carrying out photovoltaic system fault electric arc detection under coupling condition judges output
Signal；
Fig. 5 a are that the application present invention carries out the pointer in fault electric arc that photovoltaic system fault electric arc is detected under coupling condition
The photovoltaic system output current signal of standby increase tendency；
Fig. 5 b be application independent component analysis carry out coupling condition under photovoltaic system fault electric arc detect characteristic quantity and its
Given threshold waveform；
Fig. 5 c are the characteristic quantity and its given threshold that application S-transformation carries out that photovoltaic system fault electric arc is detected under coupling condition
Waveform；
Fig. 5 d are that the application system mode of the invention for carrying out photovoltaic system fault electric arc detection under coupling condition judges output
Signal；
In figure：1st, photovoltaic system；2nd, photovoltaic system DC side fault arc detection device；3rd, trip gear；4th, breaker；
5th, load；6th, current sensor；7th, photovoltaic system fault electric arc；8th, photovoltaic module.
Embodiment
Explanation is described in detail to the inventive method with reference to the accompanying drawings and examples.
With reference to Fig. 1 a, in the case of application independent component analysis of the present invention and S-transformation detecting system PROCESS COUPLING
The step of photovoltaic system fault electric arc method, is specifically described.
Step 1: Parameter Initialization procedure includes sample frequency f of the setting electric current sensor to current signals, analysis when
Sampling number N in section, judge precision p, weighted results threshold value n, reset each variable for asking for Estimation of Mean and standard deviation, independence into
Parameters in analysis and two kinds of fault electric arc signature analysis instruments of S-transformation etc..Current sensor is according to set sampling
Frequency fsParallel sampling is carried out to the multichannel current signal needed for photovoltaic system DC side fault arc detection device, multichannel is obtained
Current signal xi(the expression sequence number i ∈ N and i ＞ 1 of current sensor), once the sampling number of these current signals reach it is N number of,
Just inputted through multiple ports to photovoltaic system DC side fault arc detection device, go to step 2 and extract photovoltaic system failure electricity
The multi-party region feature of arc.
Current sensor of the present invention is not required for same type, as long as the current sensor bandwidth parameter selected
More than 100kHz, ensure that it can obtain photovoltaic system fault electric arc characteristic spectra.Current sensor should use multiple, pass through
The diverse location of photovoltaic system is installed on to reflect photovoltaic system fault electric arc to different sample point photovoltaic system output currents
The influence of signal.After optimization arrangement of the current sensor in photovoltaic system is carried out, electric current independently main source letter is accurately being obtained
Number while, the number that current sensor is used can be also reduced as far as possible, a whole set of photovoltaic system fault electric arc hardware is thus reduced
Testing cost.The current sensor that the present embodiment is used is 4.
In the photovoltaic system DC side fault arc detection device course of work, with sample frequency fsElectricity is exported to photovoltaic system
Signal pointwise sampling is flowed, too high sample frequency can increase the hardware cost of current sensor, and too low sample frequency can not be contained
The photovoltaic system fault electric arc characteristic frequency that lid current signal is reflected.Therefore, in present invention photovoltaic system failure of interest
The arc characteristic frequency range upper limit is under 100kHz selection, to reduce the hardware realization requirement of current sensor, the present embodiment determines
Sample frequency fs=200kHz.Remember that the current signal that i-th of sensor is collected j-th of analysis period is xI, j(electric current is passed
The expression sequence number i ∈ N and i ＞ 1 of sensor；Analyze the expression sequence number j ∈ N of period+), for any two difference i values, when j takes together
During one value, xi,jEqual sampling number N is respectively provided with, i.e., photovoltaic system fault electric arc detection algorithm proposed by the invention is to more
The analysis of the periods such as road current signal progress.Sampling number N values conference increase photovoltaic system fault electric arc detection algorithm analysis
The time of operation, it is unfavorable for the Rapid Detection of photovoltaic system fault electric arc, sampling number N values are too small to be not sufficient to ensure that in system mistake
The Detection results of accurate detection photovoltaic system fault electric arc among journey.Therefore, the present embodiment considers quickly and accurately to obtain photovoltaic
The principle of system failure arc characteristic, it is determined that sampling number N=10000.
Step 2: passing through the multichannel current signal formation higher-dimension mixed signal matrix X=[x collected1,j, x2,j... xi,j
]T, gained mixed signal matrix is carried out to remove average value processing, i.e. x'1,j=x1,j–E(x1,j), wherein, E (x1,j) represent x1,jIt is equal
Value estimation；Then whitening processing is carried out to gained zero-mean signal, makes E=(e1, e2... en) it is with covariance matrix C=E
(x1,j xT 1,j) unit norm characteristic vector for row matrix, make D=diag (e1, e2... en) it is with covariance matrix C
Characteristic value is the diagonal matrix of diagonal element, and V=D can be obtained after linear transformation-1/2ET, the signal through whitening processing is z=after conversion
Vx'1,j.By based on the mixed matrix W of solution just can be obtained after the maximum fast independent component analysis of negentropy, calculating source signal matrix S=
WX=[s1,j, s2,j... si,j]T, wherein, S includes effective source signal and noise signal, the effectively independent main source signal s of selection1,j,
Fast Fourier Transform (FFT) is carried out to it, the variance of one-dimensional frequency matrix in frequency domain is calculated, obtains fisrt feature value r1,j.Selection
Signal x all the way in multichannel current signali,j, S-transformation is carried out to the signal, the two-dimensional complex number matrix distribution in time-frequency domain is obtained,
Integration of the high fdrequency component absolute value of frequency dimension along the time is calculated, second feature value r is obtained2,j, step 3 is gone to corresponding
Given threshold compares the result of determination for obtaining each characteristic quantity within the present analysis period.
The fast independent component analysis that the present embodiment is determined is the fast independent component analysis based on negentropy maximization：Pass through
Negentropy maximization algorithm is sought to a suitable mixed matrix W of solution, finally gives the main source signal of each independence of electric current.In order to as early as possible
Seek to the mixed matrix of suitable solution to accelerate photovoltaic system fault electric arc detection process, the nonlinear function that the present embodiment is selected is
u3, it is determined that the iteration precision value for terminating iterative process is that 0.0001, maximum iteration is 1000.Using quick independent element point
The method of analysis is analyzed multichannel current signal, obtains the current signal way that the number of independent main source signal is analyzed, meter
The difference of these peak-to-peak values of the independent main source signal within the analysis period is calculated, the independence corresponding to the difference of the maximum peak-to-peak value of selection is main
Source signal is effectively independent main source signal.This effective independent main source signal is subjected to Fast Fourier Transform (FFT) processing, excessively
Conversion points can trigger the distortion to primary current spectrum analysis, very few conversion points can then trigger serious spectral leakage
Phenomenon, these factors are all unfavorable for fisrt feature amount and detect photovoltaic system fault electric arc exactly.Therefore, it is fast in the present embodiment
The numerical value that points are converted in fast Fourier transformation is chosen to be 10000.Occur in systematic procedure after photovoltaic system fault electric arc, frequency spectrum
Energy transfer make it that spectral matrix is more uniformly spread in the present analysis period, thus its variance is in photovoltaic system fault electric arc hair
Spike, entirety occurs in the raw moment has smaller amplitude in fault electric arc state compared with normal operating condition, so it possesses accurately
It was found that hiding the photovoltaic system fault electric arc among systematic procedure, fisrt feature amount is chosen to be.
Different from independent component analysis, the input of S-transformation only has current signal all the way.Therefore, selection multichannel electric current is compared
That road current signal of most effective reflection photovoltaic system fault electric arc difference is only the most suitable as input signal in signal.
The distance of sensor mounting location and photovoltaic system fault electric arc is contrasted, current sensor sensitivity is higher as priority
Selective goal, i.e. certain class sensitivity high current sensor more than one when, preferably select apart from photovoltaic system fault electric arc
Occur input of the nearest corresponding current signal of current sensor in position as S-transformation.The matching optimization of each parameter in S-transformation
The feature of photovoltaic system fault electric arc during maximum piece-rate system is also based on, with more reliably identification photovoltaic system
System fault electric arc.After S-transformation, current signal is changed into the two-dimensional complex number matrix on time-frequency domain, and its real part, imaginary part or phase angle are to photovoltaic
The instruction of system failure electric arc is not as the effect of absolute value processing.40~100kHz of the absolute value in frequency dimension possess compared with
Good uniformity, amplitude integrally has significant falling after the generation of photovoltaic system fault electric arc, and systematic procedure is to this frequency
The influence of section is often weaker, thus betides the photovoltaic system fault electric arc among systematic procedure and have in this frequency range preferably
Separating effect, be chosen to be second feature amount.The reliability detected for lifting photovoltaic system fault electric arc, to this time-frequency conversion
40~100kHz photovoltaic system fault electric arc characteristic spectras under instrument are integrated along time shaft makees overlap-add procedure, this feature frequency
Section and sample frequency fsValue it is unrelated, thus using technical scheme when, sample frequency must not be less than 200kHz.
Step 3: after being analyzed and processed by above two method to current signal, characteristic layer in each analysis period
Two characterizing magnitudes of upper acquisition, by the comparison of fisrt feature value, second feature value and respective threshold, by each characteristic quantity
Output result normalizing is to decision-making level.Threshold value carries out dynamic change processing within the different analysis periods, is analyzed when assert in the period
During generation photovoltaic system fault electric arc, to carrying out threshold value setting again after the calculating amendment of Estimation of Mean and standard deviation, first is obtained
Fault electric arc trip current out1, the second fault electric arc trip current out2, go to step 4 and be weighted processing.
In order to overcome the magnitude differences that each characteristic quantity itself is produced, itself flux matched exclusive given threshold of each feature.
Here the step for illustrating so that fisrt feature value and its given threshold compare as an example.By fisrt feature value and the first of setting
Characteristic quantity threshold value compares, and exports corresponding electrical level judging result：If fisrt feature value is more than the threshold value of setting, output judges
As a result 0, deposit to Fisrt fault electric arc trip current out1[j]；If fisrt feature value is less than the threshold value of setting, output is sentenced
Determine result 1, deposit to Fisrt fault electric arc trip current out1[j].Therefore, threshold value comparison procedure is made equivalent to by each characteristic quantity
Normalized, makes weighting procedure not produce significant amplitude fluctuations.Second fault electric arc trip current out2Can be special to second
The amount of levying and second feature amount threshold value are obtained using similar approach.
In order to which the characteristic quantity produced by adapting to the normal assay period is fluctuated, the threshold value of setting is estimated with the average of individual features amount
Meter and standard difference correlation, that is, set in the present analysis period fisrt feature amount threshold value as A1×μ1,j–A2×σ1,j, wherein, μ1,jFor
From the Estimation of Mean of first analysis period to present analysis period all fisrt feature values, σ1,jTo analyze the period extremely from first
The standard deviation of present analysis period all fisrt feature values, A1∈ Z, A2∈Z.When assert the present analysis period in occur photovoltaic
During system failure electric arc, the calculating of Estimation of Mean and standard deviation need to be modified, it is to avoid caused because Feature change is larger
The fluctuation of threshold value, eliminates the normal operation malfunction problem caused by ambient systems process is disturbed, effectively increases coupling
In the case of photovoltaic system fault electric arc detect reliability, add photovoltaic system operation economic benefit.
With reference to Fig. 1 b, by taking fisrt feature amount threshold setting procedure as an example, to being moved in photovoltaic system fault arc detection method
State threshold setting procedure is specifically described.
The fisrt feature amount threshold value A1×μ1,j–A2×σ1,jIt is relevant with the fisrt feature value of all analysis periods before
And fisrt feature amount r is followed in real time1Dynamic change, wherein, coefficient A1With A2It is related to fisrt feature amount output characteristics, according to logical
The fisrt feature amount threshold value for crossing setting compares with fisrt feature value and can be correctly obtained depending on corresponding photovoltaic system state, this reality
Apply example and feature is levied based on the fisrt feature scale that independent component analysis is built, determine coefficient A1=1, A2=1, i.e. fisrt feature amount
Threshold value is μ1,j–σ1,j；Estimation of Mean μ1,jAnd standard deviation sigma1,jOutput result of determination according to fisrt feature amount is corrected in real time：
The fisrt feature amount r obtained for first analysis period1,1, make correction rtemp1,1=r1,1, Estimation of Mean μ1,1=r1,1,
Standard deviation sigma1,1=0, the given threshold accordingly exported is μ1,1–σ1,1；For the fisrt feature amount r of j-th of analysis period1,j, its
In, j ∈ N and j ＞ 1, if fisrt feature value is more than or equal to upper one analysis period fisrt feature amount threshold value in the present analysis period
When, i.e., when the preliminary judgement present analysis period is normal state, make correction rtemp1,j=r1,j, the meter of Estimation of Mean and standard deviation
Calculating formula is
Wherein, k represents sequence number, k=1,2 ... j, j ∈ N and j ＞ 1 for the analysis period in cumulative process, if during present analysis
When characterizing magnitudes are less than upper one analysis period fisrt feature amount threshold value in section, i.e. fault case is presented in the preliminary judgement present analysis period
When, another set of threshold value need to be used to set scheme to ensure the correct judgement of photovoltaic system fault electric arc detection algorithm, correction is made
rtemp1,j=μ1,j-1–σ1,j-1, the calculation formula of Estimation of Mean and standard deviation is
Based on the quick principle for calculating each given threshold in the present analysis period, existing Estimation of Mean and standard deviation are utilized
Initial value μ1,1、σ1,1And correction rtemp1,jEach analysis period obtained from the second analysis period is calculated by following recurrence relations
Estimation of Mean and standard deviation, and then the given threshold accordingly exported is μ1,j–σ1,j：
Wherein, μ1,j、σ1,jEstimation of Mean and standard deviation respectively in the present analysis period, μ1,j-1、σ1,j-1Before respectively
Estimation of Mean and standard deviation in one analysis period, rtemp1,jFor the correction in the present analysis period, j ∈ N and j ＞ 1.
Step 4: the output result of determination to independent component analysis and S-transformation matches corresponding weight coefficient, to current point
The fault electric arc trip current of two characteristic quantities is weighted under the analysis period, obtains weighted results outtempj=C1,j×out1[j]+
C2,j×out2[j], then carries out preliminary state judgement：If outtempj>N, wherein, n is weighted results threshold value, then output judges
As a result 1, deposit to preliminary state determination results matrix outt [j]；Otherwise result of determination 0 is exported, deposit to preliminary state judges
Matrix of consequence outt [j], goes to step 5 and is made whether to send the judgement of fault electric arc cutting-off controlling signal.
The weighted results determined based on the principle of the photovoltaic system fault electric arc accurately identified in systematic procedure, the present embodiment
Threshold value n is 0.5.Output result of determination after two characterizing magnitudes are compared with threshold value, corresponding each spy are weighted using Dynamic Weights coefficient
The weight coefficient of the amount of levying output result of determination judges that the statistical property of correctness is true according to characteristic quantity to historical analysis period state
It is fixed, i.e., characteristic quantity the historical analysis period is made correct status judgement the analysis period it is more, this feature amount is in present analysis
The weight coefficient that section is obtained is then bigger, specifically, constructs fisrt feature amount and second feature amount institute respectively based on below equation
Belong to weight coefficient C1,jAnd C2,j：
Wherein, σ2 out1And σ2 out2Respectively Fisrt fault electric arc trip current and the second fault electric arc trip current is from
One element to j-th of element variance, i.e.,
Wherein, out1And out2Respectively Fisrt fault electric arc trip current and the second fault electric arc trip current, k is square
The counting sequence number of array element element, k=1,2 ... j, j ∈ N and j ＞ 1,WithRespectively Fisrt fault electric arc trip current and
Estimation of Mean of the two fault electric arc trip currents from first element to j-th of element.
If Fisrt fault electric arc trip current, the second fault electric arc trip current are equal from first element to j-th of element
All analysis periods are judged as normal operating condition for 0, i.e. two characteristic quantities, it is in this case final to judge photovoltaic system operation
State is necessarily normal.To avoid the complex calculation of weight coefficient, indirect assignment C1,j=0, C2,j=0, then it is weighted two spies
The amount of levying carries out follow-up photovoltaic system failure arc-detection.
Judge precision p Step 5: setting, the judgement of a photovoltaic system fault electric arc differentiation result is carried out per p period.
Decision principle is accordingly：Preliminary state determination results matrix outt is counted from jth -3p elements to-p elements of jth, from the
The number that j -2p elements are 1 to j-th of element, if the numerical value of counted number is all higher than p, confirms individual to the in jth -2p
In-p periods photovoltaic system fault electric arc occurs for j, takes corresponding photovoltaic system fault electric arc safeguard measure；Otherwise at this
Think that photovoltaic system is in normal operating condition in section, the current signal in a pair of next analysis periods of return to step is sampled
And analysis.If jth -2p is in normal operating condition to photovoltaic system in the-p periods of jth, photovoltaic is thought within the period
System is in normal operating condition, under this p period, if Fisrt fault electric arc trip current, the second fault electric arc judge square
What battle array respective element was exported is 0/1 or 1/0 combination, i.e., when respective element value is not waited, two elements of corresponding position are exchanged
Processing, to ensure the validity of weight coefficient in any case.
If it is too small that setting judges that precision p is set, it may result in and judged by accident when systematic procedure occurs in photovoltaic system, phase
There is malfunction in the photovoltaic system DC side fault arc detection device answered, causes unnecessary photovoltaic system electricity generation power to lose；
If p sets excessive, faulty line can not can be in time cut off after the generation of photovoltaic system fault electric arc, corresponding photovoltaic system is straight
There is tripping in stream side fault arc detection device, causes great economic loss and serious security threat.Based on photovoltaic system
Fault electric arc detects the principle of reliability and quick-action, it is to avoid the systematic procedure nonaction and excessive p value that too small p value triggers draw
The photovoltaic system fault electric arc of hair acts phenomenon not in time, and what the present embodiment was determined judges precision p for 5.
The present invention is illustrated applied to the method for actual photovoltaic system, as shown in Fig. 2 explanation the inventive method is in reality
Operation in the photovoltaic system of border.The photovoltaic system 1 being made up of photovoltaic module 8 exports dc power, through over-current sensor
6th, breaker 4 is input in load 5.
Multichannel photovoltaic system output current signal is inputted to photovoltaic system DC side failure electricity by multiple current sensors 6
Arc detection means 2 carries out above-mentioned photovoltaic system fault electric arc identification process.Because the generation position of photovoltaic system fault electric arc has
There is randomness, multiple current sensors 6 need rationally to be arranged in photovoltaic system, photovoltaic system failure electricity is reduced to greatest extent
Arc missing inspection blind area.Here just this example demonstrates that choosing defeated as S-transformation all the way from multichannel photovoltaic system output current signal
The method entered.Assuming that 6A~6D elects same type current sensor as, photovoltaic system fault electric arc 7A is selected suitably all the way
Photovoltaic system output current signal, according to the principle of distance after first kind of sensor proposed by the invention, should choose electric current biography
The photovoltaic system output current signal that sensor 6A is collected as S-transformation input.Photovoltaic system fault electric arc 7B is selected to close
Suitable photovoltaic system output current signal all the way, it is assumed that the photovoltaic module 8 where current sensor 6C on photovoltaic string is enough, order
Photovoltaic system fault electric arc 7B occur position and current sensor 6C distance much larger than its with current sensor 6B away from
From according to the principle of distance after first kind of sensor proposed by the invention, the photovoltaic that current sensor 6B is collected should be chosen
System output current signal as S-transformation input；Assuming that the photovoltaic module 8 where current sensor 6C on photovoltaic string is enough
It is many, make photovoltaic system fault electric arc 7B occur position with current sensor 6B distance much larger than itself and current sensor 6C
Distance, according to the principle of distance after first kind of sensor proposed by the invention, the photovoltaic that sensor 6C is collected should be chosen
System output current signal as S-transformation input；Assuming that the photovoltaic module 8 where current sensor 6C on photovoltaic string makes just
Photovoltaic system fault electric arc 7B occur position and current sensor 6B distance be equal to its with current sensor 6C away from
From, according to it is proposed by the invention first apart from rear wiring complexity principle, the photovoltaic that current sensor 6B is collected should be chosen
System output current signal as S-transformation input.
In actually detected, in photovoltaic system in addition to photovoltaic system starting transient being undergone in every morning,
Power adjusting transient process can also occur because of normal system operation, so that photovoltaic system electricity experiencings and frequently changed
Transient state.The time of origin of photovoltaic system fault electric arc is uncontrollable, thus photovoltaic system fault electric arc also has certain probability
Can occur among these systematic procedures, will then ultimately result in the photovoltaic system fault electric arc under coupling.For example, exist
In the systematic procedures such as photovoltaic system starts, the system power of increase, photovoltaic system output current constantly increases, and on the other hand,
Tandem photovoltaic system failure electric arc can then reduce photovoltaic system output current.Therefore, the photovoltaic system failure under coupling condition
In electric arc, and then photovoltaic system failure output current the normal output current of photovoltaic system can occur, to photovoltaic system failure electricity
The requirement of arc detection algorithm is more harsh.
Corresponding photovoltaic system fault electric arc detection algorithm must catch photovoltaic system fault electric arc difference in systematic procedure
Basic feature, it is accurate, reliable, rapidly identification photovoltaic system fault electric arc occur the moment, thus can complete that photovoltaic is installed
The functional requirement of system dc side fault arc detection device 2.Correctly, the photovoltaic system failure among reliable checkout system process
The specific requirement of electric arc is：When photovoltaic system is normally run, it is low that photovoltaic system DC side fault arc detection device 2 is exported
Level is failure to actuate breaker 4, and photovoltaic system 1 still stablizes output electric energy to loading 5；If photovoltaic system DC side fault electric arc
Detection means 2 detects the photovoltaic system fault electric arc 7 betided among systematic procedure, then can quickly and accurately send cut-out
Respective branch control signal is to trip gear 3, and final control breaker 4 cut-offs whole photovoltaic system loop, and load is stopped,
Extinguish photovoltaic system fault electric arc and eliminate its operation security threat brought to photovoltaic system, it is to avoid photovoltaic system fault electric arc
The problem of caused photovoltaic system DC side fault arc detection device refused action, it is to avoid photovoltaic system is normally caused by operation
Photovoltaic system DC side fault arc detection device misoperation the problem of, thus expand photovoltaic system detection algorithm and be applicable model
Enclose, potential tripping may be occurred and threaten photovoltaic system stable by solving the photovoltaic system fault electric arc among systematic procedure
The problem of safe operation.
With reference to Fig. 3 a~3d, illustrate that the photovoltaic system fault arc detection method of the present invention is applied to possess fault electric arc hair
Photovoltaic system fault electric arc identification effect under the coupling condition of raw moment invariant features.
With sample frequency fs=200kHz obtains multichannel photovoltaic system output current signal, as shown in Figure 3 a, with wherein one
Input waveform explanation is carried out exemplified by the photovoltaic system output current signal of road.Before 3.53s, current signal is in normal state, this
When photovoltaic system by closed circuit supply electricity to load；After 3.53s, current signal is in fault case, but now fault current ripple
Not because of the generation of photovoltaic system series fault arc, dynamic is reduced shape, but maintains normally at the fault electric arc generation moment
The form of electric current, remains constant curent change trend.
After multichannel current signal carries out removing average and whitening processing, multichannel current signal is entered by independent component analysis
Row analysis, selects an effective independent main source signal, calculates the side of one-dimensional frequency matrix after the signal Fast Fourier Transform (FFT)
Difference, obtains the shown in solid of fisrt feature amount such as Fig. 3 b.In order to preferably observe the result of determination that fisrt feature amount is final, accordingly
Fisrt feature amount threshold value be μ1,j–σ1,jAlso shown in fig 3b in the form of dotted line.As seen from the figure, fisrt feature amount is with big arteries and veins
Rush form and indicate the analysis period that photovoltaic system fault electric arc occurs, it is overall that photovoltaic system failure is presented with relatively low amplitude level
Electric arc and the distinctiveness feature normally run before, it is shown that fisrt feature amount has to this kind of photovoltaic system fault electric arc detection
Effect property.Fisrt feature value is compared with the threshold value obtained by construction, corresponding electrical level judging result, deposit to Fisrt fault is exported
Electric arc trip current out1In.Current signal all the way is analyzed by the method for S-transformation, the two dimension obtained in time-frequency domain is answered
Matrix number is distributed, and each element to two-dimensional matrix is carried out after absolute value processing, calculates 40~100kHz components edge of frequency dimension
The integration of time, obtains the shown in solid of second feature amount such as Fig. 3 c.In order to preferably observe the judgement of second feature amount finally
As a result, corresponding second feature amount threshold value is μ2,j–σ2,jAlso shown in figure 3 c in the form of dotted line.As seen from the figure, second is special
Bigger fluctuation form is presented compared with fisrt feature amount in the amount of levying within each analysis period, but it is still in integrally with relatively low amplitude level
Existing photovoltaic system fault electric arc and the distinctiveness feature normally run before, also show second feature amount to this kind of photovoltaic system
The validity of fault electric arc detection.Second feature value is compared with the threshold value obtained by construction, corresponding electrical level judging knot is exported
Really, it is stored in the second fault electric arc trip current out2In.
Two characterizing magnitudes are after dynamic threshold is carried out relatively, and the output for having obtained independent component analysis and S-transformation judges knot
Really, weight coefficient is analyzed in the period depending on decision-making system state correctness statistical result, then according to each characteristic quantity at first j -1
Outtemp is obtained after being weighted in decision-making level using Dynamic Weights coefficientj.Compared by corresponding threshold value, weight two characteristic quantities
The result of determination in each analysis period is obtained, preliminary state determination results matrix outt is obtained.Count preliminary state and judge knot
The number that fruit matrix outt is 1 to j-th of element to-p elements of jth, from -2p elements of jth from -3p elements of jth, if institute
The numerical value of statistics number is all higher than p, then confirms occur photovoltaic system fault electric arc in jth-p to the -2p periods of jth, defeated
It is 1 to go out final result of determination, takes corresponding photovoltaic system fault electric arc safeguard measure；Otherwise it is assumed that photovoltaic system is in normally
Running status, it is 0 to export final result of determination.Result as shown in Figure 3 d, detection algorithm normally runs energy in face of photovoltaic system
Enough provide correct low level to indicate, to correct high electricity can be provided without the photovoltaic system fault electric arc for occurring any change
It is flat to indicate, thus the detection algorithm can detect this faster and betide photovoltaic system fault electric arc among systematic procedure.
With reference to Fig. 4 a~4d, illustrate that the photovoltaic system fault arc detection method of the present invention is applied to possess fault electric arc hair
The raw moment diminish feature coupling condition under photovoltaic system fault electric arc identification effect.
With sample frequency fs=200kHz obtains multichannel photovoltaic system output current signal, as shown in fig. 4 a, with wherein one
Input waveform explanation is carried out exemplified by the photovoltaic system output current signal of road.Before 5.86s, current signal is in normal state, this
When photovoltaic system by closed circuit supply electricity to load；After 5.86s, current signal is in fault case, now because photovoltaic system is total
Line occurs series fault arc and produces the fault electric arc current waveform that dynamic is reduced, and has at the fault electric arc generation moment and reduces
Curent change trend, but this fault electric arc electric current low compared with normal current fail maintain, the photovoltaic system increased at once
Power causes fault current waveform moment to raise, and fault current then consistent with normal levels is maintained.
After multichannel current signal carries out removing average and whitening processing, multichannel current signal is entered by independent component analysis
Row analysis, selects an effective independent main source signal, calculates the side of one-dimensional frequency matrix after the signal Fast Fourier Transform (FFT)
Difference, obtains the shown in solid of fisrt feature amount such as Fig. 4 b.In order to preferably observe the result of determination that fisrt feature amount is final, accordingly
Fisrt feature amount threshold value be μ1,j–σ1,jAlso shown in fig. 4b in the form of dotted line.As seen from the figure, fisrt feature amount is with big arteries and veins
The analysis period that form indicates the generation of photovoltaic system fault electric arc and follow-up of short duration change is rushed, it is overall to be in relatively low amplitude level
Now stablize photovoltaic system fault electric arc and the distinctiveness feature normally run before, it is shown that fisrt feature amount is to this kind of photovoltaic system
The validity that fault electric arc of uniting is detected.Fisrt feature value is compared with the threshold value obtained by construction, corresponding electrical level judging is exported
As a result, it is stored in Fisrt fault electric arc trip current out1In.Current signal all the way is analyzed by the method for S-transformation,
The two-dimensional complex number matrix distribution in time-frequency domain is obtained, each element to two-dimensional matrix is carried out after absolute value processing, calculate frequency dimension
Integration of the 40~100kHz components of degree along the time, obtains the shown in solid of second feature amount such as Fig. 4 c.In order to preferably observe
The final result of determination of second feature amount, corresponding second feature amount threshold value is μ2,j–σ2,jAlso figure is illustrated in the form of dotted line
In 4c.As seen from the figure, bigger fluctuation form, but its entirety is presented within each analysis period compared with fisrt feature amount in second feature amount
Photovoltaic system fault electric arc and the distinctiveness feature normally run before are still presented with relatively low amplitude level, second is also shown
The validity that characteristic quantity is detected to this kind of photovoltaic system fault electric arc.Second feature value is compared with the threshold value obtained by construction,
Export corresponding electrical level judging result, deposit to the second fault electric arc trip current out2In.
Two characterizing magnitudes are after dynamic threshold is carried out relatively, and the output for having obtained independent component analysis and S-transformation judges knot
Really, weight coefficient is analyzed in the period depending on decision-making system state correctness statistical result, then according to each characteristic quantity at first j -1
Outtemp is obtained after being weighted in decision-making level using Dynamic Weights coefficientj.Compared by corresponding threshold value, weight two characteristic quantities
The result of determination in each analysis period is obtained, preliminary state determination results matrix outt is obtained.Count preliminary state and judge knot
The number that fruit matrix outt is 1 to j-th of element to-p elements of jth, from -2p elements of jth from -3p elements of jth, if institute
The numerical value of statistics number is all higher than p, then confirms occur photovoltaic system fault electric arc in jth-p to the -2p periods of jth, defeated
It is 1 to go out final result of determination, takes corresponding photovoltaic system fault electric arc safeguard measure；Otherwise it is assumed that photovoltaic system is in normally
Running status, it is 0 to export final result of determination.Result as shown in figure 4d, detection algorithm normally runs energy in face of photovoltaic system
Enough provide correct low level to indicate, subsequently do not occur to of short duration reduction increase fault electric arc transient state and the light of any change
Volt system failure electric arc can provide correct high level and indicate, thus the detection algorithm can detect this faster and betide and be
Photovoltaic system fault electric arc among system process.
With reference to Fig. 5 a~5d, illustrate that the photovoltaic system fault arc detection method of the present invention is applied to possess fault electric arc hair
Photovoltaic system fault electric arc identification effect under the coupling condition of the big feature of raw moment change.
With sample frequency fs=200kHz obtains multichannel photovoltaic system output current signal, as shown in Figure 5 a, with wherein one
Input waveform explanation is carried out exemplified by the photovoltaic system output current signal of road.Before 1.21s, current signal is in normal state, this
When photovoltaic system by closed circuit supply electricity to load；After 1.21s, current signal is in fault case, but fault electric arc now
Betide in systematic procedure, the degree of photovoltaic system power adjusting lifting fault electric arc electric current is compared with photovoltaic system bus series fault
The degree that reduction fault electric arc electric current occurs for electric arc is much bigger, and occurring curent change of the moment with increase in fault electric arc becomes
The fault electric arc electric current of higher level, is then maintained by gesture.
After multichannel current signal carries out removing average and whitening processing, multichannel current signal is entered by independent component analysis
Row analysis, selects an effective independent main source signal, calculates the side of one-dimensional frequency matrix after the signal Fast Fourier Transform (FFT)
Difference, obtains the shown in solid of fisrt feature amount such as Fig. 5 b.In order to preferably observe the result of determination that fisrt feature amount is final, accordingly
Fisrt feature amount threshold value be μ1,j–σ1,jAlso shown in figure 5b in the form of dotted line.As seen from the figure, fisrt feature amount it is overall with
All photovoltaic system fault electric arc states and the distinctiveness feature normally run before are presented in relatively low amplitude level, and big pulse refers to
Though the increase tendency shown can influence the correct identification of fault electric arc state, the of short duration continual analysis period does not interfere with failure electricity
The overall identification of arc, it is shown that the validity that fisrt feature amount is detected to photovoltaic system fault electric arc.By fisrt feature value with
Threshold value obtained by construction compares, and exports corresponding electrical level judging result, deposit to Fisrt fault electric arc trip current out1In.It is logical
The method for crossing S-transformation is analyzed current signal all the way, the two-dimensional complex number matrix distribution in time-frequency domain is obtained, to two-dimensional matrix
Each element carry out after absolute value processing, calculate integration of 40~100kHz components along the time of frequency dimension, obtain second special
The amount of levying such as Fig. 5 c's is shown in solid.In order to preferably observe the result of determination that second feature amount is final, corresponding second feature amount
Threshold value is μ2,j–σ2,jAlso shown in fig. 5 c in the form of dotted line.As seen from the figure, second feature amount compared with fisrt feature amount at each point
Analyse and bigger fluctuation form is presented in the period, but it is overall still with relatively low all photovoltaic system fault electric arcs of amplitude level presentation
State and the distinctiveness feature normally run before so that correction fisrt feature amount can produce erroneous judgement in some analysis periods to be turned into
May, highlight the necessity weighted using two characteristic quantity of the present invention.By second feature value and the threshold value obtained by construction
Compare, export corresponding electrical level judging result, deposit to the second fault electric arc trip current out2In.
Two characteristic quantities have obtained the result of determination of independent component analysis and S-transformation, weights after dynamic threshold is carried out relatively
Analyzed according to each characteristic quantity at first j -1 in the period depending on decision-making system state correctness statistical result, then in decision-making level
Outtemp is obtained after being weighted using Dynamic Weights coefficientj.Compared by corresponding threshold value, two characteristic quantities of weighting obtain each point
The result of determination in the period is analysed, preliminary state determination results matrix outt is obtained.Count preliminary state determination results matrix outt
The number for being 1 to j-th of element to-p elements of jth, from -2p elements of jth from -3p elements of jth, if counted number
Numerical value is all higher than p, then confirms individual to generation photovoltaic system fault electric arc in the -2p periods of jth, the final judgement of output in jth-p
As a result it is 1, takes corresponding photovoltaic system fault electric arc safeguard measure；Otherwise it is assumed that photovoltaic system is in normal operating condition,
It is 0 to export final result of determination.Result as fig 5d, in face of photovoltaic system, normally operation can provide correct to detection algorithm
Low level indicate, the chromic trouble electric arc transient state that increases again is reduced to increase and follow-up higher magnitude fault electric arc stable state is able to
Remain able to provide correct high level to indicate, thus the detection algorithm can detect this faster and betide among systematic procedure
Photovoltaic system fault electric arc.
As shown in Fig. 1 a~1b, photovoltaic fault arc detection method utilizes characteristic quantity under coupling condition provided by the present invention
Estimation of Mean and standard deviation construction characteristic quantity threshold value, threshold value carries out dynamic change processing in different analytical cycles, when recognizing
When determining the generation of photovoltaic system fault electric arc, the calculating to Estimation of Mean and standard deviation is both needed to be modified.Use characteristic value and threshold
Value comparison procedure realizes the normalization of each characteristic quantity output, solves different characteristic amount output magnitude differences to weighting multiple features
The interference of amount detection fault electric arc, is advantageously implemented the multiple features weighting in decision-making level.Weights by each characteristic quantity, correctly go through by identification
Depending on the statistical law of history system mode, when assert photovoltaic system normally operation and when two characteristic quantities output result of determination is not etc.,
Accordingly do not wait element not make exchange processing two fault electric arc trip currents, be conducive within each analysis period more reliably to
Go out system mode is appropriately determined result, effectively increases the reliability of photovoltaic system fault electric arc detection, adds photovoltaic system
The economic benefit of system operation.
As shown in Fig. 3 a~5d, photovoltaic fault arc detection method passes through two features under coupling condition provided by the present invention
The mode of weight coefficient weighting has grasped the statistical law and core feature of photovoltaic system fault electric arc in amount decision-making level, improves
Photovoltaic system is solved with photovoltaic system failure output current visual angle detection algorithm in face of being to the recognition capability of electric current normal state
The photovoltaic system DC side fault arc detection device malfunction problem that the transient processes such as power adjusting, startup of uniting are produced, by just
Systematic procedure is really determined as normal operating condition, the uptime of photovoltaic system is significantly extended, significantly improves light
The generating efficiency of volt system, enhances the stabilizing power that photovoltaic system is normally run.The present invention, which also accurately can catch to betide, is
Photovoltaic system fault electric arc basic feature among system process, accurately identifies the photovoltaic system failure betided among systematic procedure
Electric arc, the variation tendency aspect effect not caused by photovoltaic system fault electric arc under coupling condition to photovoltaic system output current,
The scope of application of current photovoltaic system fault arc detection method is expanded, is solved with the normal output current visual angle of photovoltaic system
The photovoltaic system DC side fault electric arc that detection algorithm is produced in face of the photovoltaic system fault electric arc that and then systematic procedure occurs
Detection means tripping problem, by the way that the photovoltaic system fault electric arc under coupling condition correctly is determined as into malfunction, is ensured
The validity of photovoltaic system fault electric arc detection, eliminate in time photovoltaic fire incident that this kind of photovoltaic system fault electric arc triggers,
Life and property loss etc. endangers.
Claims (10)
1. the side of photovoltaic system fault electric arc in the case of a kind of application independent component analysis and S-transformation detecting system PROCESS COUPLING
Method, it is characterised in that：The method of photovoltaic system fault electric arc comprises the following steps in the case of the detecting system PROCESS COUPLING：
1) sample frequency f is pressed to photovoltaic system output current signal by multiple current sensorssPointwise sampling is carried out, obtains many
Road current signal xi,j, wherein, i is that current sensor represents sequence number, and i ∈ N and i ＞ 1, j are the analysis period to represent sequence number, j ∈
N+, for any two difference i values, when j takes same value, xi,jEqual sampling number N is respectively provided with, when N reaches the analysis period
Requirement after, go to step 2) carry out Fisrt fault arc characteristic analysis；
2) by the multichannel current signal collected formation higher-dimension mixed signal matrix X=[x1,j,x2,j,…,xi,j]T, it is mixed to gained
Close signal matrix to carry out removing average and whitening processing, then by the way that the mixed matrix W of solution just can be obtained after fast independent component analysis, calculate
Source signal matrix S=WX=[s1,j,s2,j,…,si,j]T, the effectively independent main source signal s of selection1,j, to s1,jCarry out in quick Fu
Leaf transformation, calculates the variance of one-dimensional frequency matrix in frequency domain, obtains fisrt feature value r1,j, go to step 3)；
3) fisrt feature amount threshold value is set in the present analysis period as A1×μ1,j–A2×σ1,j, wherein, μ1,jFor from during the first analysis
Section to present analysis period all fisrt feature values Estimation of Mean, σ1,jTo analyze period to the present analysis period from first
The standard deviation of all fisrt feature values, A1∈ Z, A2∈ Z, fisrt feature value is compared with the fisrt feature amount threshold value set,
Export corresponding electrical level judging result：If r1,j≥A1×μ1,j–A2×σ1,j, then result of determination 0, deposit to Fisrt fault electricity are exported
Arc trip current out1[j]；If r1,j<A1×μ1,j–A2×σ1,j, then result of determination 1 is exported, deposit to Fisrt fault electric arc judges
Matrix out1[j], goes to step 4) carry out the second fault electric arc signature analysis；
4) signal all the way in selection multichannel current signal carries out S-transformation, obtains the two-dimensional complex number time-frequency matrix in time-frequency domain, counts
Integration of the high fdrequency component absolute value of frequency dimension along the time is calculated, second feature value r is obtained2,j, go to step 5)；
5) second feature amount threshold value is set in the present analysis period as A3×μ2,j–A4×σ2,j, wherein, μ2,jFor from during the first analysis
Section to present analysis period all second feature values Estimation of Mean, σ2,jTo analyze period to the present analysis period from first
The standard deviation of all second feature values, A3∈ Z, A4∈ Z, second feature value is compared with the second feature amount threshold value set,
Export corresponding electrical level judging result：If r2,j≥A3×μ2,j–A4×σ2,j, then result of determination 0, deposit to the second failure electricity are exported
Arc trip current out2[j]；If r2,j<A3×μ2,j–A4×σ2,j, then result of determination 1 is exported, deposit to the second fault electric arc judges
Matrix out2[j], goes to step 6) carry out two characteristic quantity decision-making levels on the weighting of output result of determination processing；
6) using Dynamic Weights coefficient weighting independent component analysis and the output result of determination of S-transformation, weighted results are obtained
outtempj=C1,j×out1[j]+C2,j×out2[j], then carries out preliminary state judgement：If outtempj>N, wherein, n is
Weighted results threshold value, then export result of determination 1, deposit to preliminary state determination results matrix outt [j]；Otherwise output judges knot
Really 0, be stored in preliminary state determination results matrix outt [j], go to step 7) carry out photovoltaic system state differentiation；
7) set and judge precision p, a photovoltaic system state is judged per p period：Count preliminary state determination results matrix
Outt is 1 number from -3p elements of jth to-p elements of jth, from -2p elements of jth to j-th of element, if counting individual
Several numerical value is all higher than p, then confirms occur photovoltaic system fault electric arc in jth -2p to the-p periods of jth, take corresponding
Photovoltaic system fault electric arc safeguard measure；Otherwise it is assumed that in jth -2p to the-p periods of jth photovoltaic system be in it is normal
Running status, return to step 1) current signal in next analysis period is analyzed.
2. a kind of according to claim 1 apply photovoltaic in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING
The method of system failure electric arc, it is characterised in that：The band of the current sensor is wider than 100kHz, is installed in photovoltaic system
Diverse location to show the difference between sampled current signals, the span of current sensor is 2~4；The sampling frequency
Rate fsSpan be 200~500kHz；The span of the sampling number N is 8000~12000.
3. a kind of according to claim 1 apply photovoltaic in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING
The method of system failure electric arc, it is characterised in that：The fast independent component analysis is preferably based on the quick of negentropy maximization
Nonlinear function in independent component analysis, fast independent component analysis can select g1(u)=u3、g2(u)=u2、g3(u)=
arctan(q1×u)、g4(u)=u × e^ (- q2 2×u2/ 2) in one kind, wherein, q1And q2For constant, maximum iteration
Span is 950~1050, and the span of iteration precision is 0.00006~0.00015.
4. a kind of according to claim 1 apply photovoltaic in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING
The method of system failure electric arc, it is characterised in that：The main source signal number of independence that the fast independent component analysis is obtained is adopted
The way of sample current signal, selects an effective independent main source signal to carry out follow-up fast based on signal impact most strong principle
Fast Fourier transformation processing, that is, calculate the difference of peak-to-peak value of the main source signal of each independence within the analysis period, and selection difference is maximum
The main source signal of independence be effectively independent main source signal；The conversion point value of the Fast Fourier Transform (FFT) is chosen to be sampled point
The corresponding numerical value of number N.
5. a kind of according to claim 1 apply photovoltaic in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING
The method of system failure electric arc, it is characterised in that：Selection multichannel current signal in the method inputted all the way as S-transformation be：
The corresponding current signal of prioritizing selection sensitivity highest current sensor；When this kind of current sensor more than one, preferentially
The nearest corresponding current signal of current sensor in position occurs for chosen distance photovoltaic system fault electric arc；When apart from photovoltaic system
When the nearest current sensor more than one in position occurs for fault electric arc, prioritizing selection photovoltaic system fault electric arc to current sense
There is the corresponding current signal of current sensor of minimum number of components in device propagation path；The S-transformation window width adjustment because
Son is preferably 1.
6. a kind of according to claim 1 apply photovoltaic in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING
The method of system failure electric arc, it is characterised in that：Absolute value processing is made to the two-dimensional complex number time-frequency matrix element obtained by S-transformation,
The time-frequency matrix frequency component for building second feature amount elects 40~100kHz as, the frequency range and sample frequency fsValue not phase
Close.
7. a kind of according to claim 1 apply photovoltaic in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING
The method of system failure electric arc, it is characterised in that：The fisrt feature amount threshold value A1×μ1,j–A2×σ1,jWith all analyses before
The fisrt feature value of period is relevant and follows fisrt feature amount r in real time1Dynamic change, wherein, coefficient A1With A2According to by setting
Fixed fisrt feature amount threshold value compares with fisrt feature value can be correctly obtained depending on corresponding photovoltaic system state；Estimation of Mean
μ1,jAnd standard deviation sigma1,jOutput result of determination according to fisrt feature amount is corrected in real time：Obtained for first analysis period
Fisrt feature value r1,1, make correction rtemp1,1=r1,1, Estimation of Mean μ1,1=r1,1, standard deviation sigma1,1=0；For jth
The fisrt feature value r of individual analysis period1,j, wherein, j ∈ N and j ＞ 1, if fisrt feature value is more than in the present analysis period
During equal to upper one analysis period fisrt feature amount threshold value, correction rtemp is made1,j=r1,j, the calculating of Estimation of Mean and standard deviation
Formula is
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When fisrt feature value is less than upper one analysis period fisrt feature amount threshold value, correction rtemp is made1,j=A1×μ1,j-1–A2×
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The second feature amount threshold value A3×μ2,j–A4×σ2,jWith before it is all analysis the periods second feature values it is relevant and reality
When follow second feature amount r2Dynamic change, wherein, coefficient A3With A4It is special with second according to the second feature amount threshold value by setting
Levying value and comparing can be correctly obtained depending on corresponding photovoltaic system state；Estimation of Mean μ2,jAnd standard deviation sigma2,jAccording to second feature
The output result of determination of amount is corrected in real time：The second feature value r obtained for first analysis period2,1, make correction
rtemp2,1=r2,1, Estimation of Mean μ2,1=r2,1, standard deviation sigma2,1=0；For the second feature value of j-th of analysis period
r2,j, wherein, j ∈ N and j ＞ 1, if second feature value is more than or equal to upper one analysis period second feature in the present analysis period
When measuring threshold value, correction rtemp is made2,j=r2,j, the calculation formula of Estimation of Mean and standard deviation is
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8. a kind of according to claim 7 apply photovoltaic in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING
The method of system failure electric arc, it is characterised in that：Estimation of Mean and standard deviation in the present analysis period are obtained using recurrence relation
Calculation formula be
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<mrow>
<mi>m</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msqrt>
<mrow>
<mfrac>
<mrow>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>j</mi>
<mo>-</mo>
<mn>2</mn>
</mrow>
</mfrac>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>m</mi>
<mo>,</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<mfrac>
<mn>1</mn>
<mi>j</mi>
</mfrac>
<mo>(</mo>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>rtemp</mi>
<mrow>
<mi>m</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>&mu;</mi>
<mrow>
<mi>m</mi>
<mo>,</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
</mrow>
Wherein, μm,j、σm,jEstimation of Mean and standard deviation respectively in the present analysis period, μm,j-1、σm,j-1Respectively previous point
Analyse the Estimation of Mean and standard deviation in the period, rtempm,jFor the correction in the present analysis period, wherein, the m amounts of being characterized are represented
Sequence number, value is 1 or 2, j ∈ N and j ＞ 1.
9. a kind of according to claim 1 apply photovoltaic in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING
The method of system failure electric arc, it is characterised in that：The output of independent component analysis and S-transformation is being weighted using Dynamic Weights coefficient
During result of determination, the weight coefficient of two characteristic quantities output result of determination is judged historical analysis period state according to individual features amount
The analysis period that correct status judgement is made in the statistical property determination of correctness, i.e. characteristic quantity to the historical analysis period is more, should
The weight coefficient that characteristic quantity is obtained in the present analysis period is then bigger, specifically, constructs first respectively based on below equation special
The amount of levying and the affiliated weight coefficient C of second feature amount1,jAnd C2,j：
<mrow>
<msub>
<mi>C</mi>
<mrow>
<mn>1.</mn>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mn>1</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mrow>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mn>1</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mn>2</mn>
</mrow>
<mn>2</mn>
</msubsup>
</mrow>
</mfrac>
</mrow>
3
<mrow>
<msub>
<mi>C</mi>
<mrow>
<mn>2.</mn>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mn>2</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mrow>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mn>1</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mn>2</mn>
</mrow>
<mn>2</mn>
</msubsup>
</mrow>
</mfrac>
</mrow>
Wherein, σ2 out1And σ2 out2Respectively Fisrt fault electric arc trip current and the second fault electric arc trip current is from first member
The plain variance to j-th of element, i.e.,
<mrow>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mn>1</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>j</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>out</mi>
<mn>1</mn>
</msub>
<mo>&lsqb;</mo>
<mi>k</mi>
<mo>&rsqb;</mo>
<mo>-</mo>
<mover>
<mrow>
<msub>
<mi>out</mi>
<mn>1</mn>
</msub>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
<mrow>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mn>2</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>j</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>out</mi>
<mn>2</mn>
</msub>
<mo>&lsqb;</mo>
<mi>k</mi>
<mo>&rsqb;</mo>
<mo>-</mo>
<mover>
<mrow>
<msub>
<mi>out</mi>
<mn>2</mn>
</msub>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
Wherein, out1And out2Respectively Fisrt fault electric arc trip current and the second fault electric arc trip current, k is matrix element
The counting sequence number of element, k=1,2 ... j, j ∈ N and j ＞ 1,WithRespectively Fisrt fault electric arc trip current and second therefore
Hinder Estimation of Mean of the electric arc trip current from first element to j-th of element；If Fisrt fault electric arc trip current, the second event
It is 0 to hinder electric arc trip current from first element to j-th of element, i.e. two characteristic quantities judge all analysis periods to be normal
Running status, indirect assignment C1,j=0, C2,j=0；Normally run if jth -2p is in photovoltaic system in the-p periods of jth
State, to the position of Fisrt fault electric arc trip current, the second fault electric arc trip current respective element under this p period not etc.
Make element exchange processing.
10. a kind of according to claim 1 apply photovoltaic in the case of independent component analysis and S-transformation detecting system PROCESS COUPLING
The method of system failure electric arc, it is characterised in that：The span of the weighted results threshold value n is 0.45~0.55；It is described to sentence
Disconnected precision p span is 2~5.
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