CN207135065U - Photovoltaic system DC side arc fault detection device - Google Patents
Photovoltaic system DC side arc fault detection device Download PDFInfo
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- CN207135065U CN207135065U CN201720795421.7U CN201720795421U CN207135065U CN 207135065 U CN207135065 U CN 207135065U CN 201720795421 U CN201720795421 U CN 201720795421U CN 207135065 U CN207135065 U CN 207135065U
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
Abstract
The utility model provides photovoltaic system DC side arc fault detection device, by sampling photovoltaic system dc bus current signal, algorithm is decomposed using empirical mode decomposition (EEMD) algorithm is gathered, and extracts characteristic component of the three first layers IMF time serieses for being capable of characterization failure arc characteristic as fault electric arc；Using fuzzy entropy algorithm by time series IMF entropy, fault feature vector E is established1、E2、E3；Recycle Fuzzy C-Means Cluster Algorithm ask for normal working hours according to this and fault electric arc data cluster centre V1, V2, according to the progress fault electric arc detection of the Euclidean distance of signal and different cluster centres；The utility model provides photovoltaic system arc fault detection device, including sampling unit, data processing unit, control unit simultaneously, largely reduces photovoltaic system DC side fault electric arc and is endangered to caused by system.
Description
Technical field
The utility model belongs to grid-connected field of fault detection, more particularly to photovoltaic system DC side arc fault detection
Device and its detection method.
Background technology
With the fast development of photovoltaic generation, the problem of some are serious, is also highlighted, and wherein photovoltaic module aging is brought
Electrical system safety problem it is especially prominent.It has been investigated that photovoltaic system fire incident is mostly relevant with DC Line Fault electric arc.
There is complicated structure and substantial amounts of connection equipment in photovoltaic system, DC terminal voltage can reach more than 1000V, once produce electricity
Arc failure, it is most likely that combustible or photovoltaic module around instantaneous ignition are so as to causing fire incident.To solve such peace
Full problem, No. 690.11 documentation requirements photovoltaic of National Electrical specification NEC (National Electrical Code) is simultaneously
Net system dc bus, which is more than 80V, should be equipped with fault arc detection device and breaker.
The detection of photovoltaic system DC side fault electric arc is not only due to photovoltaic array is complicated, and break down arc position
It is difficult to prediction and brings difficulty to fault detect and circuit protection, also faces the interference of external environment and inverter MPPT is calculated
The internal conditions such as the work interference that method and isolated island detection algorithm, the operation interference of power electronic equipment, class arc load.Therefore, need
A kind of accurate detection method of fault electric arc of multidimensional criterion is wanted, is provided safeguard for the normal operation of photovoltaic system.
Position difference occurs by electric arc, electric arc type can be divided into series, parallel and ground arc, as shown in Figure 1：Wherein
A for group string in serial arc, b, d be group in parallel arc, c parallel arcs between group.Wherein ground arc can be considered a kind of special
Different parallel arc, and be easier to be detected.
Fault electric arc detection currently for photovoltaic system does not obtain in-depth study, does not account for photovoltaic system yet
The complexity structure of system and the characteristic for being vulnerable to external interference, cause erroneous judgement in some cases and fail to judge.For example, photovoltaic system
System when in face of complicated external environments such as noise jamming, Changes in weather, wherein just exist cloud layer shortly past when or flying object skim over
When generated electricity the fluctuation brought to photovoltaic system, if breaking down electric arc at this moment, the arc method for measuring of single criterion may
It can judge by accident, fail to judge.
The content of the invention
The utility model provides photovoltaic system DC side arc fault detection device and its detection method, it is therefore intended that solution
The problem of occurring accurately to detect fault electric arc during direct current arc fault certainly in photovoltaic system.
Photovoltaic array fault arc detection method, comprises the following steps：
(1) electric current is carried out to photovoltaic array output end dc bus using current transformer in photovoltaic system running
Sampling.
(2) high-pass filtering is carried out by hardware filtering circuit to above-mentioned current sampling signal, filters out below 20kHz letter
Number component, obtain current sample high-frequency signal；
(3) to above-mentioned current sample high-frequency signal, analog signal is switched into data signal by ADC sampling A/D chips and is sent into
Carry out substituting into arc fault detection algorithm progress arc fault detection after DSP, wherein ADC sampling A/D chips are adopted
Sample rate is 180kHz.
(4) utilize and gather empirical mode decomposition (ensemble empirical mode decomposition, EEMD) calculation
Method is decomposed to above-mentioned current sample high-frequency signal, obtains some intrinsic modal components (intrinsic mode
Function, IMF)；
(5) each time series IMF is quantified using the fuzzy entropy algorithm for characterizing signal complexity, selection can distinguish electric arc
High frequency partial component IMF1, IMF2, IMF3 of failure fuzzy entropy E1、E2、E3As characteristic vector；
(6) DC side bus current letter of the multi collect photovoltaic system in normal state and under fault electric arc state
Number, and handled by above-mentioned steps, extract normal and fault electric arc characteristic vector.
(7) Fuzzy C-Means Cluster Algorithm (fuzzy C means clustering, FCM) is utilized by the electricity of multi collect
The normal and fault electric arc characteristic vector for flowing signal and being extracted by EEMD and fuzzy entropy algorithm carries out clustering processing, and draws just
Often with cluster centre V1, V2 of fault electric arc, as shown in Figure 4.
(8) when carrying out arc fault detection, the photovoltaic system DC bus current signal collected every time is handled
Afterwards, after extraction characteristic vector normalizes, the distance progress event with fault electric arc state clustering center V1, V2 with normal condition is calculated
Hinder electric arc identification.
(9) if detection photovoltaic array has fault electric arc, failure is sent by DSP and carries out fault alarm；If it is not present
Fault electric arc, then repeat step 8.
The beneficial effects of the utility model are：
The utility model proposes a kind of based on set empirical mode decomposition (EEMD) and fuzzy C-means clustering (FCM)
Combined fault detection method detects fault electric arc signal, can more accurately detect photovoltaic array electric arc whether occurs therefore
Barrier, prevents erroneous judgement from failing to judge.
Brief description of the drawings
Fig. 1 is that the utility model photovoltaic system DC side fault electric arc detects block diagram；
Fig. 2 is the utility model photovoltaic system DC side fault electric arc overhaul flow chart；
Fig. 3 is four layers of result before normal condition and fault electric arc state straight edge line electric current EEMD decomposition；
Fig. 4 is photovoltaic system DC side fault electric arc recognition result；
Embodiment
In order to more specifically describe the utility model, hereby with preferred embodiment, and accompanying drawing is coordinated to be described in detail below：
Fig. 2 is photovoltaic array fault arc detection method, is comprised the following steps：
(1) electric current is carried out to photovoltaic array output end dc bus using current transformer in photovoltaic system running
Sampling.
(2) high-pass filtering is carried out by hardware filtering circuit to above-mentioned current sampling signal, filters out below 20kHz letter
Number component, obtain current sample high-frequency signal；
(3) to above-mentioned current sample high-frequency signal, analog signal is switched into data signal by ADC sampling A/D chips and is sent into
Carry out substituting into arc fault detection algorithm progress arc fault detection after DSP, wherein ADC sampling A/D chips are adopted
Sample rate is 180kHz.
(4) above-mentioned electric current high-frequency signal is decomposed by EEMD algorithms；
EEMD decomposable process is as follows：
1) the less Gaussian sequence n (t) of root mean square is added in former time series x (t), is expressed as：
xe(t)=x (t)+n (t) (1)
2) to the time series x after reconstructe(t), carry out being decomposed into some IMF components using EMD algorithms, it is all to find it
Maximum and minimum, envelope u (t) up and down, the v (t) of signal are constructed using cubic spline interpolation, obtain bag up and down respectively
The difference of the average of winding thread, primary signal and envelope average：
H (t)=xe(t)-m(t) (3)
3) two conditions whether h (t) meets IMF are judged：The quantity of extreme point is equal with the quantity of zero crossing or differs
One；The average of the envelope up and down of signal is zero.If satisfied, then obtain an IMF components ci(t) walked more than, otherwise repeating
Suddenly；
4) residual components r (t) is primary signal xe(t) c is subtractedi(t), judge whether r (t) meets end condition：Decomposition reaches
It is set as 3 or r (t) as monotonic function herein to the number of plies set.If being unsatisfactory for x is replaced with r (t)e(t) walked more than repeating
Suddenly；
In signal decomposition terminate to obtain some IMF components for EMD be with residual components sum：
5) the intrinsic modal components IMF after the EMD decomposition after m groups add the equal different white noise sequences of root mean square is calculated
(ij), i represents number of plies i≤3 that EMD algorithms decompose；J represents that jth time EMD algorithms decompose, j≤m
6) average is calculated to m EMD decomposition result, and exported obtained result as final decomposition result, i.e.,As shown in Figure 3.
(4) each time series IMF is quantified using the fuzzy entropy algorithm for characterizing signal complexity, selection can distinguish electric arc
High frequency partial component IMF1, IMF2, IMF3 of failure fuzzy entropy E1、E2、E3As characteristic vector；
IMF fuzzy entropy calculating process is as follows：
1) for a N point sequences { u (i)：1≤i≤N }, nonnegative integer m is introduced, reconstruct obtains the vector of m dimensions：
2) introduce exponential function and carry out definition vector XiWith XjSimilarity：
Dij=u (dij, n, r) and=exp [- (dij/r)n] (7)
In formula, dijFor vectorial XiWith XjThe maximum of the difference absolute value of corresponding element；The border that n and r is ambiguity function u is wide
Degree and gradient, n take 2, r to take 0.2；
3) defined function：
4) ambiguity in definition entropy：
When N is finite value, ambiguity in definition entropy is：
E (m, n, r, N)=ln φm(n, r)-ln φm+1(n, r) (10)
Then the fuzzy entropy for three layers of IMF eigencomponents that EEMD is decomposed forms signal in selected fault electric arc feature frequency
The automatic division of one kind in section.
(5) DC side bus current letter of the multi collect photovoltaic system in normal state and under fault electric arc state
Number, and handled by above-mentioned steps, extract normal and fault electric arc characteristic vector.
(6) Fuzzy C-Means Cluster Algorithm (fuzzy C means clustering, FCM) is utilized by the electricity of multi collect
The normal and fault electric arc characteristic vector for flowing signal and being extracted by EEMD and fuzzy entropy algorithm carries out clustering processing, and draws just
Often with cluster centre V1, V2 of fault electric arc, as shown in Figure 4.
(7) when carrying out arc fault detection, the photovoltaic system DC bus current signal collected every time is handled
Afterwards, after extraction characteristic vector normalizes, the distance progress event with fault electric arc state clustering center V1, V2 with normal condition is calculated
Hinder electric arc identification.
The step of calculating cluster centre based on Fuzzy C-Means Cluster Algorithm is as follows：
Fuzzy entropy spy is normally carried out with DC bus current high-frequency signal under arc fault state by sampling 20 groups respectively
Sign vector extraction, obtains data sample X={ xj：1≤j≤20 }, setting cluster centre C=[c1, c2]TWith subordinated-degree matrix A
=[aij]2×20, then must being fulfilled for condition is：
In formula, m ＞ 1 be influence subordinated-degree matrix index weight, general m=2；dijIt is poly- to i-th for j-th of data point
The Euclidean distance at class center；
For such a minimization problem, typically using the necessary condition of local best points is obtained, rotation is carried out
Optimizing.It is shown below：
Photovoltaic system fault arc detection device as shown in Figure 1, including sampling unit, data processing unit.
Sampling unit includes current transformer, hardware filtering circuit, ADC sample circuits；Current transformer samples photovoltaic
The current signal of array dc bus, current signal are entered after hardware filtering circuit high-pass filtering using A/D modular converters
Row analog-to-digital conversion, obtain the data signal of corresponding current sampling signal.
Data processing unit is DSP digital processing units, and its inside has arc-detection module, and fault electric arc detection module connects
Data signal corresponding to receiving current sampling signal, judges whether photovoltaic array produces fault electric arc through COMPREHENSIVE CALCULATING.
Although specific embodiment of the present utility model is described in detail in said process, but not the utility model is protected
The limitation of scope, the art personnel should be understood that on the basis of the technical solution of the utility model, art technology
Personnel need not pay the various modifications that creative work can make or deformation still within the scope of protection of the utility model.
Claims (3)
1. photovoltaic system DC side arc fault detection device, it is characterised in that including digital signal processor (1), one
The DC bus current signal of individual current transformer (2) sampling photovoltaic array (5) output end, is filtered by hardware filtering circuit (3)
Except low frequency signal, then it is data signal to sample (4) by analog-signal transitions by ADC, inputs the digital signal processor (1)
Arc fault detection is carried out, the photovoltaic array (5) has fault electric arc if detecting, failure is carried out by fault alarm (6)
Alarm.
2. photovoltaic system DC side arc fault detection device according to claim 1, it is characterised in that described hardware
Filter circuit (3) carries out high-pass filtering, filters out below 20kHz component of signal.
3. photovoltaic system DC side arc fault detection device according to claim 1, it is characterised in that described ADC
The sample frequency for sampling (4) is 180kHz.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107294494A (en) * | 2017-07-03 | 2017-10-24 | 上海岩芯电子科技有限公司 | Photovoltaic system DC side arc fault detection device and its detection method |
| CN108627732A (en) * | 2018-05-15 | 2018-10-09 | 重庆邮电大学 | A kind of photovoltaic battery panel method for diagnosing faults based on crossover voltage detection |
| CN108847686A (en) * | 2018-07-02 | 2018-11-20 | 国电南瑞科技股份有限公司 | A kind of photovoltaic DC-to-AC converter failure prediction method |
| CN109975673A (en) * | 2019-04-23 | 2019-07-05 | 辽宁工程技术大学 | A kind of photovoltaic micro DC side fault electric arc recognition methods |
| CN112671108A (en) * | 2021-01-21 | 2021-04-16 | 云南电力技术有限责任公司 | Line control system for zero crossing point detection |
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2017
- 2017-07-03 CN CN201720795421.7U patent/CN207135065U/en active Active
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107294494A (en) * | 2017-07-03 | 2017-10-24 | 上海岩芯电子科技有限公司 | Photovoltaic system DC side arc fault detection device and its detection method |
| CN108627732A (en) * | 2018-05-15 | 2018-10-09 | 重庆邮电大学 | A kind of photovoltaic battery panel method for diagnosing faults based on crossover voltage detection |
| CN108847686A (en) * | 2018-07-02 | 2018-11-20 | 国电南瑞科技股份有限公司 | A kind of photovoltaic DC-to-AC converter failure prediction method |
| CN108847686B (en) * | 2018-07-02 | 2021-11-30 | 国电南瑞科技股份有限公司 | Photovoltaic inverter fault prediction method |
| CN109975673A (en) * | 2019-04-23 | 2019-07-05 | 辽宁工程技术大学 | A kind of photovoltaic micro DC side fault electric arc recognition methods |
| CN109975673B (en) * | 2019-04-23 | 2021-03-16 | 辽宁工程技术大学 | Method for identifying fault arc at direct current side of photovoltaic microgrid |
| CN112671108A (en) * | 2021-01-21 | 2021-04-16 | 云南电力技术有限责任公司 | Line control system for zero crossing point detection |
| CN112671108B (en) * | 2021-01-21 | 2024-01-30 | 云南电力技术有限责任公司 | Line control system for zero crossing point detection |
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