CN107733366A - Photovoltaic module Failure Assessment and its Forecasting Methodology based on accelerated test case - Google Patents
Photovoltaic module Failure Assessment and its Forecasting Methodology based on accelerated test case Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
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- H02S50/15—Testing of PV devices, e.g. of PV modules or single PV cells using optical means, e.g. using electroluminescence
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention discloses a kind of photovoltaic module Failure Assessment and its Forecasting Methodology based on accelerated test case, Weibull distribution and obtained Weibull Function are utilized, it can be estimated that failure probability of the prediction component in the component in different accelerated test stages.Reliability of the photovoltaic module under the experiment of humiture cyclical acceleration, and the reliability of the Reliability Function evaluation component of Weibull distribution are assessed using Weibull Function.The present invention can change at present to the simple single present situation of component accelerated test reliability estimation method, by the analysis to accelerated test data, propose the application of method reliability and its failure probability under component accelerated test using Weibull distribution；By means of the invention it is also possible to obtain photovoltaic module in accelerated test different phase failure probability, reliability of the photovoltaic module under accelerated test is assessed with this, certain reference significance is provided so as to the prediction of the service life to photovoltaic module.
Description
Technical field
The present invention relates to a kind of photovoltaic module Failure Assessment and its Forecasting Methodology based on accelerated test case, belong to photovoltaic skill
Art field.
Background technology
Photovoltaic module is influenceed in running by a variety of environmental factors out of doors, wherein irradiation level, temperature and humidity
It is the three kinds of main factors for influenceing component open air runnability.Most of photovoltaic module manufacturers need to ensure produced light
Lying prostrate component has the service life of 25 years and the above, and research worker establishes real example base out of doors both at home and abroad at present, from outward appearance,
The decay of EL and power end observation assembly performance out of doors, however, under conventional environment, environmental stress agent is to photovoltaic module
The influence of performance is smaller, must carry out long-time observation and test and collect the failure mode of the clear and definite photovoltaic module of related data ability
And the problems such as reliability.Some detection certification authorities and research institute analyze photovoltaic module using the method for indoor accelerated test
Failure mechanism and reliability, mostly using being tested on the basis of IEC61215 standards, common adds accelerated test method
Speed experiment has thermal cycling test, wet-heat test, wet-jelly experiment and ultraviolet test etc..
Laboratory interior focusing at present lies prostrate component photovoltaic before and after the appraisal procedure of accelerated test performance is used to experiment mostly
The change of component power is compared, and the comparison to component facade and EL, and appraisal procedure is relatively simple directly, it is impossible to group
Attenuation trend and failure probability of the part under accelerated test carry out analysis and assessment.
The content of the invention
For the deficiency of performance change appraisal procedure under the existing accelerated test to photovoltaic module above, the present invention proposes
A kind of photovoltaic module Failure Assessment and its Forecasting Methodology based on accelerated test case, by power under photovoltaic module accelerated test
The processing of attenuation data, failure probability of the component under the different acceleration time can be obtained, so as to evaluation component reliability and
Its service life.
A kind of photovoltaic module Failure Assessment and its Forecasting Methodology based on accelerated test case, are comprised the following steps that：
(1) test specimen screens：Visual examination is carried out to test specimen component, and is tested using electroluminescent to test sample
Product are checked, filter out the intact n block crystalline silicon components of original state；
(2) initial power is determined：Before the test standard test condition lower component output is carried out according to IEC61215 standards
The test of power, obtain initial power data a (0,1), a (0,2) ... a (0, n)；
(3) humiture cyclic test：Using the test design method of Censoring, existed using temperature, humidity as accelerated stress
Accelerated test is carried out in humiture environmental test chamber, wherein, in IEC61215 standard bases, regulation humiture circulation environment examination
The temperature of tryoff is -40 ± 2 DEG C~85 ± 2 DEG C, when assembly temperature is higher than 25 DEG C, controls the relative of humiture environmental test chamber
Humidity is 85%, and assembly temperature is not controlled when being less than 25 DEG C to the humidity of humiture environmental test chamber, the one cycle time
For 24h, accelerated test deadline is 2400h, i.e., 100 times circulations, every 10 circulate test specimen taking-up carry out standard survey
The test of power output under the conditions of examination, obtain power attenuation data a (x, 1), a (x, 2) ... a (x, n), x=of test specimen
1,2…10；
(4) data processing：The test data obtained using step (3), according to Weibull distribution model founding mathematical models,
Photovoltaic module accelerated test crash rate and its reliability are assessed, three parameter probability density functions of Weibull distribution represent
For：
In formula：T is the accelerated test time；θ is scale parameter；β is form parameter；ν is location parameter；
As ν=0, formula (1) is converted into two parameter Weibull probability density function, then corresponding two parameter Weibull adds up
Distribution function is：
Then its Reliability Function R (t) is：
According to the aufbauprinciple of Weibull probability paper, formula (2) is calculated：
OrderX=lnt, a=β, b=- β ln θ, then formula (4) can be converted into：
Y=ax+b (5)
In X-Y rectangular coordinate systems, formula (5) is that a slope is a, and intercept is b straight line, rectangular co-ordinate X and Y scale
All it is equidistant, (t is done using Weibull probability paperi,F(ti)) line, it is t axles below probability paper now, the left side is F (t) axles,
The relation that t axles and X-axis can be obtained by conversion above is t=eX, F (t) and the relation of Y-axis are F (t)=1-exp (- expY)；
F (t are calculated using bernard's approximate equationi), see formula (6)：
Wherein, i represents i-th piece of photovoltaic module, out-of-service time tiWhen decaying to 5% for i-th piece of photovoltaic module generated output
The cycle-index of wet-jelly accelerated test, F (ti) it is tiThe numerical value of corresponding Weibull Function；
Using Maximum Likelihood Estimation Method be calculated the maximum likelihood estimation of Weibull distribution parameters, scale parameter
θ=76.43756, form parameter β=4.50455, scale parameter and form parameter are substituted into formula (2), then the humiture accelerates
Shown in Weibull Function such as formula (7) under cyclic test：
In formula, t represents the accelerated test time, and under different stress levels, the form parameter β of Weibull distribution is kept not
Become；
It is corresponding, shown in Reliability Function such as formula (8) of the component under humiture circulation：
Preferably, crystalline silicon component block number n=16.
Beneficial effect：The invention discloses a kind of based on the photovoltaic module Failure Assessment of accelerated test case and its prediction side
Method, thus it is possible to vary at present to the simple single present situation of component accelerated test reliability estimation method, by accelerated test data
Analysis, propose using Weibull distribution method reliability and its failure probability under component accelerated test application；Pass through
The method of the present invention, photovoltaic module can be obtained in accelerated test different phase failure probability, photovoltaic module is assessed with this and added
Reliability under speed experiment, certain reference significance is provided so as to the prediction of the service life to photovoltaic module.
Brief description of the drawings
Fig. 1 is the experimental design figure of the present invention；
Fig. 2 is humiture cyclic test flow chart；
Fig. 3 is each component power attenuation curve figure；
Fig. 4 is the busbar etch figures occurred after test cycle 10 times；
Fig. 5 is that the backboard occurred after test cycle 10 times is brittle and hierarchical diagram；
Fig. 6 is the Weibull distribution Median rank tropic.
Embodiment
In order that those skilled in the art more fully understand the technical scheme in the application, it is real below in conjunction with the application
The accompanying drawing in example is applied, the technical scheme in the embodiment of the present application is clearly and completely described, it is clear that described implementation
Example only some embodiments of the present application, rather than whole embodiments.It is common based on the embodiment in the application, this area
The every other embodiment that technical staff is obtained under the premise of creative work is not made, it should all belong to the application protection
Scope.
A kind of photovoltaic module Failure Assessment and its Forecasting Methodology based on accelerated test case, are comprised the following steps that：
(1) test specimen screens：Before the test is conducted, in order to avoid test specimen self-defect causes result of the test
Error, visual examination is carried out to test specimen component, and using electroluminescent (Electroluminescenc, EL) test to examination
Test sample to be checked, filter out original state intact n (n=16) block crystalline silicon component；
(2) initial power is determined：Before the test (STC) the following group under standard test condition is carried out according to IEC61215 standards
The test of part power output, initial power data a (0,1), a (0,2) ... a (0, n) are obtained, wherein, n=16；
(3) humiture cyclic test：The experiment flow figure shown in experimental design scheme and Fig. 2 according to Fig. 1, use
The test design method of Censoring, accelerated test is carried out in humiture environmental test chamber as accelerated stress using temperature, humidity,
Wherein, in IEC61215 standard bases, the temperature of regulation humiture circulation environmental test chamber is -40 ± 2 DEG C~85 ± 2 DEG C,
When assembly temperature is higher than 25 DEG C, the relative humidity for controlling humiture environmental test chamber is 85%, when assembly temperature is less than 25 DEG C
The humidity of humiture environmental test chamber not being controlled, the one cycle time is 24h, and accelerated test deadline is 2400h,
Test specimen is taken out the test for carrying out power output under STC, obtains the work(of test specimen by i.e. 100 times circulations, every 10 circulations
Rate attenuation data a (x, 1), a (x, 2) ... a (x, n), x=1, wherein 2 ... 10, n=16；By obtained power attenuation data by
Listed according to table 1.
The crystalline silicon component power output of table 1 accelerates die-away test data (unit：W)
Above-mentioned each component power attenuation data is depicted as curve map, as shown in figure 3, being circulated 40 times in accelerated test
Afterwards, the power fall rate of members becomes big.Fig. 4 is the busbar etch figures occurred after test cycle 10 times, and Fig. 5 is examination
Test that the backboard that occurs after circulation 10 times is brittle and hierarchical diagram, this and the Weibull life-span corresponding fatigue of materials of distribution and corrosion damage
Failure mechanism it is similar.
(4) data processing：The test data obtained using step (3), Weibull distribution model founding mathematical models, to light
Volt component accelerated test crash rate and its reliability are assessed, and three parameter probability density functions of Weibull distribution are expressed as：
In formula：T is the accelerated test time；θ is scale parameter；β is form parameter；ν is location parameter；
As ν=0, formula (1) is converted into two parameter Weibull probability density function, then corresponding two parameter Weibull adds up
Distribution function is：
Then its Reliability Function R (t) is：
According to the aufbauprinciple of Weibull probability paper, formula (2) is calculated：
OrderX=lnt, a=β, b=- β ln θ, then formula (4) can be converted into：
Y=ax+b (5)
In X-Y rectangular coordinate systems, above formula is that a slope is a, and intercept is b straight line, rectangular co-ordinate X and Y scale
All it is equidistant.In actual use, it is typically employed on Weibull probability paper and is (ti,F(ti)) line, now, probability paper
Lower section is t axles, and the left side is F (t) axles, and the relation that t axles and X-axis can be obtained by conversion above is t=eX, the relation of F (t) and Y-axis
For F (t)=1-exp (- expY), it is clear that t axles and F (t) axles are non-equidistant scales；
F (t are calculated using bernard's approximate equationi), see formula (6)：
According to IEC61215 standards, acquiescence works as the maximum power attenuation of photovoltaic module to 5%, as fails.Obtained by formula (6)
To the out-of-service time t of photovoltaic moduleiAnd F (ti) numerical value it is as shown in table 2,
Wherein, i represents i-th piece of photovoltaic module, out-of-service time tiIt is wet when decaying to 5% for photovoltaic module generated output-to freeze
The cycle-index of accelerated test, F (ti) it is tiThe numerical value of corresponding Weibull Function.
Table 2F (ti) result of calculation
On Weibull probability paper, by (ti, F (ti)) carrying out described point, can to obtain the Weibull distribution Median rank tropic near
Like being straight line, its coefficient R2=0.984, linear regression significant effect, it can thus be assumed that adding in humiture circulation
The power attenuation situation of the lower photovoltaic module of speed experiment obeys the distribution of Weibull life-span, wherein, the parameter of Weibull distribution can adopt
With Maximum Likelihood Estimation Method MLE (Maximum Likelihood Estimation), (Maximum Likelihood Estimation Method is art technology
The conventional technical means that personnel grasp) calculated, now, scale parameter θ=76.43756, form parameter β=
4.50455, scale parameter and form parameter are substituted into formula (2), then the humiture accelerates the Weibull distribution under cyclic test
Shown in function such as formula (7)：
In formula, t represents the accelerated test time, and under different stress levels, the form parameter β of Weibull distribution is kept not
Become；
It is corresponding, shown in Reliability Function such as formula (8) of the component under humiture circulation：
Thus, it is possible to obtain photovoltaic module (examination described in step (3) in the case where the humiture accelerates cyclic test level
Test condition) failure probability distributions, so as to learn the attenuation of component different phase under the experiment, in different stress levels
Under, the form parameter β of Weibull distribution keeps constant.It can realize using Weibull distribution to the mistake under the accelerated test of component
Effect probability and power attenuation situation are assessed.
Utilize Weibull distribution and obtained Weibull Function (formula (7)), it can be estimated that prediction component is in difference
The failure probability of the component in accelerated test stage.Using Weibull distribution Reliability Function R (t) (formula 8) evaluation component can
By degree, distributed constant θ and β therein can determine by preliminary accelerated test, determine photovoltaic module in accelerated test with this
The failure probability of different phase, the failure probability based on distribution function prediction photovoltaic module under accelerated test, compensate for light
Single direct appraisal procedure is tested only with power and EL etc. after volt component accelerated test, photovoltaic module accelerated test is commented
It is significant to estimate method.
The foregoing description of the disclosed embodiments, professional and technical personnel in the field are enable to realize or using the present invention.
Two kinds of modifications to these embodiments will be apparent for those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.
Claims (2)
1. a kind of photovoltaic module Failure Assessment and its Forecasting Methodology based on accelerated test case, it is characterised in that specific steps are such as
Under：
(1) test specimen screens：Visual examination is carried out to test specimen component, and test specimen is entered using electroluminescent test
Row checks, filters out the intact n block crystalline silicon components of original state；
(2) initial power is determined：Before the test standard test condition lower component power output is carried out according to IEC61215 standards
Test, obtain initial power data a (0,1), a (0,2) ... a (0, n)；
(3) humiture cyclic test：It is accelerated stress warm and humid using temperature, humidity using the test design method of Censoring
Accelerated test is carried out in degree environmental test chamber, wherein, in IEC61215 standard bases, regulation humiture circulation environmental test chamber
Temperature be -40 ± 2 DEG C~85 ± 2 DEG C, when assembly temperature be higher than 25 DEG C when, control humiture environmental test chamber relative humidity
For 85%, assembly temperature is not controlled when being less than 25 DEG C to the humidity of humiture environmental test chamber, and the one cycle time is
24h, accelerated test deadline are 2400h, i.e., 100 times circulations, test specimen is taken out in every 10 circulations carries out standard testing
Under the conditions of power output test, obtain power attenuation data a (x, 1), a (x, 2) ... a (x, n), x=1 of test specimen,
2…10；
(4) data processing：The test data obtained using step (3), according to Weibull distribution model founding mathematical models, to light
Volt component accelerated test crash rate and its reliability are assessed, and three parameter probability density functions of Weibull distribution are expressed as：
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In formula：T is test period；θ is scale parameter；β is form parameter；ν is location parameter；
As ν=0, formula (1) is converted into two parameter Weibull probability density function, then corresponding two parameter Weibull cumulative distribution
Function is：
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Then its Reliability Function R (t) is：
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According to the aufbauprinciple of Weibull probability paper, formula (2) is calculated：
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OrderX=lnt, a=β, b=- β ln θ, then formula (4) can be converted into：
Y=ax+b (5)
In X-Y rectangular coordinate systems, formula (5) is that a slope is a, and intercept is b straight line, and rectangular co-ordinate X and Y scale are all
It is equidistant, (t is done using Weibull probability paperi,F(ti)) line, it is t axles below probability paper, the left side is F (t) axles, by preceding now
It is t=e that the conversion in face, which can obtain t axles and the relation of X-axis,X, F (t) and the relation of Y-axis are F (t)=1-exp (- expY)；
F (t are calculated using bernard's approximate equationi), see formula (6)：
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Wherein, i represents i-th piece of photovoltaic module, out-of-service time tiIt is wet when decaying to 5% for i-th piece of photovoltaic module generated output-to freeze
The cycle-index of accelerated test, F (ti) it is tiThe numerical value of corresponding Weibull Function；
Using Maximum Likelihood Estimation Method be calculated the maximum likelihood estimation of Weibull distribution parameters, scale parameter θ=
76.43756, form parameter β=4.50455, scale parameter and form parameter are substituted into formula (2), then the humiture accelerates to follow
Shown in Weibull Function such as formula (7) under ring test：
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In formula, t represents the accelerated test time, and under different stress levels, the form parameter β of Weibull distribution keeps constant；
It is corresponding, shown in Reliability Function such as formula (8) of the component under humiture circulation：
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2. a kind of photovoltaic module Failure Assessment and its Forecasting Methodology based on accelerated test case according to claim, it is special
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109149982A (en) * | 2018-08-21 | 2019-01-04 | 南方电网科学研究院有限责任公司 | Reliability evaluation method for power module of modular level converter |
| CN109143098A (en) * | 2018-09-27 | 2019-01-04 | 北京长城华冠汽车科技股份有限公司 | A kind of lithium ion battery life estimation method and device |
| CN109388860A (en) * | 2018-09-17 | 2019-02-26 | 中国人民解放军海军工程大学 | A kind of gamma type cell life estimation of distribution parameters method |
| CN109613431A (en) * | 2018-11-27 | 2019-04-12 | 北京长城华冠汽车科技股份有限公司 | A kind of the reliability verification method and device of lithium ion battery |
| CN110175655A (en) * | 2019-06-03 | 2019-08-27 | 中国科学技术大学 | Data identification method and device, storage medium and electronic equipment |
| CN110261059A (en) * | 2019-06-20 | 2019-09-20 | 广东华矩检测技术有限公司 | A method of influence of the detection photovoltaic module dependent variable to its output characteristics |
| CN110414026A (en) * | 2018-04-28 | 2019-11-05 | 米亚索能光伏科技有限公司 | A kind of appraisal procedure of photovoltaic module service life |
| CN112540297A (en) * | 2020-11-10 | 2021-03-23 | 中车长春轨道客车股份有限公司 | Method for researching overcharge safety redundancy boundary of lithium ion battery |
| CN112924370A (en) * | 2021-01-28 | 2021-06-08 | 无锡市产品质量监督检验院 | Ultraviolet thermal cycle comprehensive test method for photovoltaic module and material |
| CN113168597A (en) * | 2018-11-08 | 2021-07-23 | 施乐百有限公司 | Method and system for predicting failure of a fan group and corresponding fan group |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106093613A (en) * | 2016-05-25 | 2016-11-09 | 广东蓄能发电有限公司 | Pump-storage generator equipment dependability analysis platform and method thereof |
| CN106484011A (en) * | 2016-10-12 | 2017-03-08 | 河海大学常州校区 | A kind of light damp and hot integration aging testing apparatus and method |
-
2017
- 2017-11-08 CN CN201711087752.6A patent/CN107733366A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106093613A (en) * | 2016-05-25 | 2016-11-09 | 广东蓄能发电有限公司 | Pump-storage generator equipment dependability analysis platform and method thereof |
| CN106484011A (en) * | 2016-10-12 | 2017-03-08 | 河海大学常州校区 | A kind of light damp and hot integration aging testing apparatus and method |
Non-Patent Citations (1)
| Title |
|---|
| 王喜炜 等: "光伏组件加速老化试验可靠性及其寿命分布研究", 《可再生能源》 * |
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| CN110414026A (en) * | 2018-04-28 | 2019-11-05 | 米亚索能光伏科技有限公司 | A kind of appraisal procedure of photovoltaic module service life |
| CN110414026B (en) * | 2018-04-28 | 2023-08-22 | 东君新能源有限公司 | Method for evaluating service life of photovoltaic module |
| CN109149982A (en) * | 2018-08-21 | 2019-01-04 | 南方电网科学研究院有限责任公司 | Reliability evaluation method for power module of modular level converter |
| CN109388860A (en) * | 2018-09-17 | 2019-02-26 | 中国人民解放军海军工程大学 | A kind of gamma type cell life estimation of distribution parameters method |
| CN109143098A (en) * | 2018-09-27 | 2019-01-04 | 北京长城华冠汽车科技股份有限公司 | A kind of lithium ion battery life estimation method and device |
| CN113168597A (en) * | 2018-11-08 | 2021-07-23 | 施乐百有限公司 | Method and system for predicting failure of a fan group and corresponding fan group |
| CN109613431A (en) * | 2018-11-27 | 2019-04-12 | 北京长城华冠汽车科技股份有限公司 | A kind of the reliability verification method and device of lithium ion battery |
| CN110175655A (en) * | 2019-06-03 | 2019-08-27 | 中国科学技术大学 | Data identification method and device, storage medium and electronic equipment |
| CN110261059A (en) * | 2019-06-20 | 2019-09-20 | 广东华矩检测技术有限公司 | A method of influence of the detection photovoltaic module dependent variable to its output characteristics |
| CN112540297A (en) * | 2020-11-10 | 2021-03-23 | 中车长春轨道客车股份有限公司 | Method for researching overcharge safety redundancy boundary of lithium ion battery |
| CN112540297B (en) * | 2020-11-10 | 2024-06-11 | 中车长春轨道客车股份有限公司 | Method for researching overcharge safety redundancy boundary of lithium ion battery |
| CN112924370A (en) * | 2021-01-28 | 2021-06-08 | 无锡市产品质量监督检验院 | Ultraviolet thermal cycle comprehensive test method for photovoltaic module and material |
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