US20090171597A1 - Evaluation method - Google Patents

Evaluation method Download PDF

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Publication number
US20090171597A1
US20090171597A1 US12/316,354 US31635408A US2009171597A1 US 20090171597 A1 US20090171597 A1 US 20090171597A1 US 31635408 A US31635408 A US 31635408A US 2009171597 A1 US2009171597 A1 US 2009171597A1
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Prior art keywords
year
power
daily
curve
evaluation method
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US12/316,354
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Peter Drews
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SMA Solar Technology AG
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SMA Solar Technology AG
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Publication of US20090171597A1 publication Critical patent/US20090171597A1/en
Assigned to SMA SOLAR TECHNOLOGY AG reassignment SMA SOLAR TECHNOLOGY AG ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DREWS, PETER
Priority to US13/278,348 priority Critical patent/US8874392B2/en
Abandoned legal-status Critical Current

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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L31/00—Semiconductor devices sensitive to infrared radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation; Processes or apparatus specially adapted for the manufacture or treatment thereof or of parts thereof; Details thereof
    • H01L31/02—Details
    • 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
    • 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
    • H02S50/10—Testing of PV devices, e.g. of PV modules or single PV cells
    • 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

Definitions

  • the invention relates to an evaluation method for determining a power reduction due to aging of at least one photovoltaic module at constant radiation intensity by measuring an electrical variable that may vary after a while such as cell current, cell voltage and/or cell power without additional sensors for measuring the radiation intensity.
  • the power of solar cells or of photovoltaic modules is subject to aging.
  • the loss of efficiency ranges from 10% to 20% over a period of time of 20 years.
  • solar cells are combined into modules, so-called solar or photovoltaic modules or solar panels. After 20 years, a solar module only has 90% to 80% of the power indicated.
  • the power drop of the solar module is very difficult to follow over time. It is difficult to locate whether the power drop is within the limits indicated by the manufacturer since certain general conditions are needed for this purpose such as a certain outside temperature, precise sensors or calibratable radiation sensors and the like.
  • the invention relies on the observation that comparable days in terms of radiation intensity and temperature of the solar plant can be found over several years, these days allowing a reliable statement with regards to power reduction thanks to their comparability.
  • a power curve P(t) is almost identical for example. Since the radiation intensity and the radiation time fluctuate in the course of a year, the year is divided in several periods, i.e., in several classes k. k may for example be equal to 52 so that the comparison may be made weekly. The actual comparison then only occurs within one class k over the years n.
  • the invention relies on the idea consisting in observing the energy output of the solar plant over several years. Actual data are compared with the values in previous years. This allows locating in which way the energy output decreases over the years.
  • each year n there may be a maximum of k energy values.
  • These energy values can be compared to the energy values of a previous year, for example to those of the previous year or of the first year.
  • the difference between the energy values is for example a measure of the power reduction. This difference can be normalized.
  • the energy amount of the previous year may serve as a standard.
  • the invention allows observing over the years power reduction within one class. Thanks to this accurate measurement, a solar plant can thus be connected for a longer time to a grid without maintenance works. It is also possible to locate early increased power reduction so that photovoltaic modules can be exchanged in time.
  • a variable of the generator such as the generator current, the generator voltage or the generator power can be measured.
  • At least two classes k which are distributed over the year and within which the daily power curves or the portion of the daily power curve are comparable on the basis of the radiation intensity and the outside temperature as well as of the radiation time. This allows for taking into consideration the seasonal fluctuations of the radiation intensity and of the outside temperature as well as of the irradiation time to be expected.
  • Sensing or filing a daily curve of the power P of the generator as well as of the energy output E of the photovoltaic generator of the day is particularly advantageous, a day power curve P(t) being determined, the course of which is comparable to at least one day curve Pk,n(t) from previous years. Accordingly, a measurement of the day curve of the power of the solar generator P(t) as well as of the daily energy output Etag, i.e., of the two variables is advantageous. These values can be recorded and filed in a data bank, for example by means of a data logger. Within each class k, one day power curve Pk,n(t) the course of which is comparable to the day power curves Pk,i(t) from previous years can be determined for each year n.
  • one day or one measurement period is determined the power curve P(t) and the energy output E of which is comparable to measurements performed in earlier days.
  • the day classes correspond to time periods that are associated with fixed time intervals and that are distributed over the daily sunshine period to be expected. This comparison then occurs between measurement intervals comprising both the same k and kj classes.
  • One class may also be a class having a same pair of coefficients (k, kj) or a same tuple.
  • comparable days are determined within one class in two steps at most, namely by calculating and evaluating the first derivation Pk′(t) of the function Pk(t).
  • the first derivation Pk′(t) of the data host Pk(t) is hereby calculated and it is checked whether certain limit values have been respected. If these limit values are exceeded or not reached, it can be assumed that this day was cloudless.
  • the first derivation Pk′(t) is calculated and evaluated by testing a curve area included therein for fixed limit values.
  • the first derivation Pk′(t) then enters an evaluation method which yields values that are not allowed to exceed or fall short of imposed limit values.
  • One plausibility criterion may for example be measurement data from the data host Pk(t) lying within tolerance bands such as a tolerance band for a certain region or a tolerance band for summer days and one for winter days. This means that this plausibility criterion is applied to the data host P(t) for example.
  • zero crossings of the first derivation Pk′(t) are advantageously evaluated in another implementation.
  • a day is clearly cloudless if there is only one zero crossing. This evaluation method can be performed in only one step.
  • At least one average value ⁇ Estoff is calculated from the k energy difference values ⁇ Ek,n for the year n as a measure for power reduction.
  • a particular benefit is obtained if a daily power curve is determined, which substantially comprises a daily power maximum. This case occurs if the day is cloudless.
  • a daily power curve is determined, which substantially comprises a daily power maximum. This case occurs if the day is cloudless.
  • only energy values from measurement periods are compared in which there was no shadow. Shadowing can be recognized with the methods mentioned herein above.
  • time periods of shadow are recognized by evaluating the change in luminosity occasioned by passing clouds.
  • one locates the change in luminosity by calculating the derivation of the power generated by the solar plant with respect to time. High values of such a derivation are evaluated by an evaluation algorithm which calculates whether the time range of the measurement was influenced by clouds.
  • An effect of benefit is obtained by implementing an evaluation standard through fix or adaptive threshold values.
  • An evaluation standard with amount averages, quadratic averages or other values can be utilized.
  • An advantage is obtained if a time range with shadowing is recognized by comparing the amount of power or energy generated in the time range with a comparative value. If there are significant negative differences, there were shadows.
  • Such a comparative value can be generated from a model of radiation on a cloudless day.
  • a comparative value can also be calculated from the radiation values of previous days, in particular if these corresponding time intervals were recognized to be cloudless. An effect of benefit is obtained if a comparative value is calculated from values of previous years that have been stored.
  • FIG. 1 a shows a matrix for several years 1 through n as well as several classes 1 through k showing energy outputs
  • FIG. 1 b shows a matrix comparable to that of FIG. 1 a , this matrix now showing the energy differences
  • FIG. 2 a shows a measured curve of the power P of a photovoltaic generator for a cloudless day
  • FIG. 2 b shows a schematic course of the curve shown in FIG. 2 a
  • FIG. 2 c shows the course of the first derivation for the function shown in FIG. 2 b
  • FIG. 3 a shows a measured gradient of the power P of the photovoltaic generator for a day with passing clouds
  • FIG. 3 b shows a schematic curve of the power shown in FIG. 3 a for the day with passing clouds
  • FIG. 3 c shows the course of the first derivation for the function shown in FIG. 3 b
  • FIG. 4 a shows the measured course of the power P of the photovoltaic generator for a cloudy day
  • FIG. 4 b shows the schematic curve of the power P for this day with overcast sky
  • FIG. 4 c shows the curve of the first derivation for the function shown in FIG. 4 b.
  • FIG. 1 a illustrates a matrix with energy output data for several years 1 through n, which are indicated in the matrix lines as well as for several classes 1 through k, which are indicated in the columns.
  • the formulae below the matrix indicate the energy differences for each class k and for the respective year n.
  • E k,n energy values passing clouds can then be compared to the energy values E k,l of the year before i, of any previous year or of the first year.
  • the difference ⁇ E k,n E k,i ⁇ E k,n is taken to measure the power reduction. This difference can be normalized.
  • ⁇ E k,n /E k,i (E k,i ⁇ E k,n )/E k,l
  • ⁇ E 1,n through ⁇ E k,n signify energy differences for each class.
  • ⁇ E* l,n means normalized energy differences for each class; this will be explained in closer detail later.
  • the method is based on the measurement or on the acquisition of the daily curve of the power of the solar generator P(t) as well as on a daily energy output Etag. These data can be stored in a data bank.
  • a power curve Pk,n(t) is determined for each year n, said curve having a course that is comparable with the daily power curves Pk,I of previous years i.
  • a maximum number k of energy output values E k,n are ascertained, which are compared to the energy output values E k,I of the previous years, for example with the energy output values E k,l , so that a maximum number k of values is determined for the energy differences ⁇ E k,n .
  • the energy outputs are related to one day. I.e., within each class one ascertains a day the power curve P(t) and the energy output E of which are comparable with measurements performed in earlier days. For each year n, one then has k energy values E k,n , which relate to one day. These daily energy values E k,n can then be compared with the daily energy values E k,n ⁇ 1 of the year before or with the energy values E k,l of the first year or of any year.
  • FIG. 1 b there is shown another matrix which includes the energy differences for several years 1 through n as well as for all classes 1 through k. Below the matrix, there is shown a measurement graph showing the energy differences ⁇ E related to the years 1 through n.
  • the values ⁇ E sch,n or ⁇ E* kar,n indicate a yearly average value for power reduction of the photovoltaic generator in the respective year n.
  • FIGS. 2 a , 3 a and 4 a show different daily power curves P(t) for different weather conditions.
  • the measured power curve is based on a cloudless day.
  • a measured curve of the power P of the solar generator is shown as a function of the time for a day with passing clouds, the sun irradiating periodically the photovoltaic generator through holes in said clouds.
  • the measured curve of the power P of the solar generator relates to a very cloudy day or to a day with constant weak solar radiation.
  • FIG. 2 a illustrates the measured curve of the power P of a photovoltaic generator or of one or more photovoltaic modules as a function of time t for a cloudless day. As can be seen from the curve, there is only one daily maximum with respect to power P. There is no power break due to passing clouds.
  • FIG. 3 a it can be seen that there are strong power fluctuations.
  • the intensity of the radiation of the photovoltaic modules, which has changed because of the passing clouds, can be seen clearly.
  • a day generates a curve as shown in FIG. 4 a , the day is for example cloudy or rainy and the solar radiation quite low. Typically, this may be a winter day.
  • FIGS. 2 a , 3 a and 4 a show typical measured power curves
  • P(t) for days with different weather conditions these being in discrete form, i.e., they constitute effective measured variables.
  • these functions are shown schematically or as continuous functions in the FIGS. 2 b , 3 b and 4 b .
  • the discrete measurement data can also be transformed into continuous functions through appropriate interpolation methods.
  • the first derivation P′(t) of the power curve P(t) is formed and evaluated for each day, as shown in the FIGS. 2 c , 3 c and 4 c.
  • FIG. 2 b shows the cloudless, sunny day. A plurality of measurement results are filed in a data bank over the day.
  • the curve has a day maximum peak, which is typically about noon.
  • the area below the curves corresponds to the curve integral or to the energy.
  • FIG. 3 b schematically shows the power curve for passing clouds.
  • a kind of harmonics, which are generated by the periodical shadowing through the clouds, are superimposed on a basic curve, which corresponds to the curve in FIG. 2 b.
  • the measurement method preferably uses the power curve P(t) as well as the energy output E that corresponds to the area enclosed by the curve. This area is hatched in the FIGS. 2 b , 3 b and 4 b . Both are evaluated. Through this measurement method, additional information such as outside temperature or radiation data is not needed. Sensors are not needed either since the power data are measured from the variable of the photovoltaic module that has been delivered. A voltage, a current or both can be measured. It is also possible to directly measure the power.
  • FIGS. 2 c through 4 c show the first derivation P′(t) of the functions shown in the FIGS. 2 b through 4 b.
  • FIG. 4 b shows an example for the curve of the power of the photovoltaic generator as a function of time (t) for a day with overcast sky.
  • FIG. 4 c there is for example shown the associated first derivation with respect to time.
  • the maximum power Pmax can however be significantly less.
  • the function P′(t) is formed.
  • the first derivation P′(t) can be evaluated in different ways. The evaluation clearly indicates whether the day is cloudless or not, as shown in FIG. 3 c.
  • a second step one then analyzes and makes certain whether a cloudless day has indeed been found.
  • the power curve P(t) or its first derivation P′(t) is evaluated.
  • two evaluation steps are utilized in order to reliably acquire a comparable cloudless day.
  • the first derivation P′(t) is evaluated.
  • a first possibility is based on the fact that the evaluation method is based on analyzing a maximum for P′(t). If, as shown in FIG. 2 c , the maximum value P′max is for example below an imposed limit (upper dashed line) or if the minimum value P′min is above an imposed limit P′min (lower dashed line), it is supposed that the day is cloudless.
  • the first derivation of the power curve for a day with passing clouds has a much higher maximum value P′max but also a much lower minimum value P′min than the first derivation of the power curve for a cloudless day shown in FIG. 2 c .
  • the upper limit P′max and the lower limit P′min are also shown as dashed lines.
  • Such limit values can also be defined for certain regions. This is possible because average radiation values are known in principle for all the regions in a country. Since radiation values are not only known for regions but also e.g., for certain cities, fine-tuning is possible.
  • limit values are advantageously acquired and fixed for e.g., a cloudless day in the first year the plant is in operation. Then, verification is performed in the course of the years. Thus, even long-term climatic changes in a region due to climate change can be taken into consideration.
  • the values of the first derivation of the power curve for an overcast day are also below or above the imposed limits.
  • Another criterion can be readily used to undoubtedly and automatically locate a cloudless day. This is advantageous because a completely cloudy day yields a daily power curve P(t) that is similar to that of a cloudless day.
  • P′(t) is also formed from the power curve P(t) for each day.
  • the integral is for example determined
  • the integral I is a measure for the area included in the first derivation P′(t).
  • the number of zero crossings of the curve P(t) is equal to 1. This zero crossing takes place at the time of power maximum, as shown in FIG. 2 c . If more than one zero crossing is located, as is illustrated in FIG. 3 c , it can be assumed that the day is not cloudless.
  • the first possibility is to evaluate the daily power curve P(t) in the second step.
  • the first method for evaluating the daily power curve P(t) consists in determining the daily energy output Etag and in comparing it with an imposed minimum value. If the daily energy output Etag exceeds this minimum value, it is certain that the day is cloudless.
  • the second evaluation method in the second step consists in evaluating the extreme values of the power curve P(t).
  • an absolute value of the power Pabs is acquired from the host of data P(t) measured within one day. It may for example be the maximum value Pmax of the power P(t) for the day observed or also an average of several power maxima. If this value Pabs lies within a tolerance band ranging from Pabs_min to Pabs_max, then it may well be a relatively cloudless day. Indirectly one also considers the radiation intensity and the duration without the need for an additional sensor.
  • This method can be even further improved by using the measurement or the measurement values of the temperature of the modules and/or of the outside temperature.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • Electromagnetism (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Microelectronics & Electronic Packaging (AREA)
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  • Photometry And Measurement Of Optical Pulse Characteristics (AREA)
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7835832B2 (en) 2007-01-05 2010-11-16 Hemisphere Gps Llc Vehicle control system
ITPO20090014A1 (it) * 2009-12-15 2011-06-16 Marco Baroncelli Apparecchio multifunzione per la visualizzazione e il controllo di impianti fotovoltaici
US20110307199A1 (en) * 2010-06-09 2011-12-15 Sma Solar Technology Ag Method of recognizing and assessing shadowing events
CN102854483A (zh) * 2012-08-16 2013-01-02 常州天合光能有限公司 光伏组件测试仪校准方法
AU2011202124B2 (en) * 2010-05-12 2015-09-17 General Electric Company System and method for photovoltaic plant power curve measurement and health monitoring
JP2018007347A (ja) * 2016-06-28 2018-01-11 株式会社関電工 太陽光発電の性能評価方法
WO2019187523A1 (ja) * 2018-03-27 2019-10-03 住友電気工業株式会社 判定装置、天候情報処理装置、判定方法および天候情報処理方法
CN111474429A (zh) * 2020-04-16 2020-07-31 阳光电源股份有限公司 一种光储一体机的老化互助系统及方法
CN111896990A (zh) * 2020-07-10 2020-11-06 成都理工大学 一种基于Frechet距离的放射源活度监测方法

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KR101478019B1 (ko) * 2014-05-12 2015-01-06 주식회사 그랜드 태양광발전 모니터링 및 제어 관리 시스템
DE102014107417B3 (de) * 2014-05-27 2015-10-22 Skytron Energy Gmbh Verfahren zum Erkennen des Verschmutzungsgrads von PV-Modulen
CN109409024B (zh) * 2018-12-25 2022-09-06 福州大学 基于一维深度残差网络的光伏组件电压电流特性建模方法

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US5648731A (en) * 1993-05-11 1997-07-15 Trw Inc. Method of checking solar panel characteristics in an operating solar electrical system
US20040264225A1 (en) * 2003-05-02 2004-12-30 Ballard Power Systems Corporation Method and apparatus for determining a maximum power point of photovoltaic cells

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JP2994812B2 (ja) 1991-09-26 1999-12-27 キヤノン株式会社 太陽電池
DE10222621A1 (de) * 2002-05-17 2003-11-27 Josef Steger Verfahren und Schaltungsanordnung zur Steuer- und Regelung von Photovoltaikanlagen

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US5439533A (en) * 1992-06-30 1995-08-08 Canon Kabushiki Kaisha Photovoltaic device, method of producing the same and generating system using the same
US5648731A (en) * 1993-05-11 1997-07-15 Trw Inc. Method of checking solar panel characteristics in an operating solar electrical system
US20040264225A1 (en) * 2003-05-02 2004-12-30 Ballard Power Systems Corporation Method and apparatus for determining a maximum power point of photovoltaic cells

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7835832B2 (en) 2007-01-05 2010-11-16 Hemisphere Gps Llc Vehicle control system
ITPO20090014A1 (it) * 2009-12-15 2011-06-16 Marco Baroncelli Apparecchio multifunzione per la visualizzazione e il controllo di impianti fotovoltaici
AU2011202124B2 (en) * 2010-05-12 2015-09-17 General Electric Company System and method for photovoltaic plant power curve measurement and health monitoring
CN102364408A (zh) * 2010-06-09 2012-02-29 艾思玛太阳能技术股份公司 用于识别和评价遮蔽的方法
US9112078B2 (en) * 2010-06-09 2015-08-18 Sma Solar Technology Ag Method of recognizing and assessing shadowing events
US20110307199A1 (en) * 2010-06-09 2011-12-15 Sma Solar Technology Ag Method of recognizing and assessing shadowing events
CN102854483A (zh) * 2012-08-16 2013-01-02 常州天合光能有限公司 光伏组件测试仪校准方法
JP2018007347A (ja) * 2016-06-28 2018-01-11 株式会社関電工 太陽光発電の性能評価方法
WO2019187523A1 (ja) * 2018-03-27 2019-10-03 住友電気工業株式会社 判定装置、天候情報処理装置、判定方法および天候情報処理方法
JPWO2019187523A1 (ja) * 2018-03-27 2021-03-25 住友電気工業株式会社 判定装置、天候情報処理装置、判定方法および天候情報処理方法
JP7207401B2 (ja) 2018-03-27 2023-01-18 住友電気工業株式会社 判定装置、天候情報処理装置、判定方法および天候情報処理方法
CN111474429A (zh) * 2020-04-16 2020-07-31 阳光电源股份有限公司 一种光储一体机的老化互助系统及方法
CN111896990A (zh) * 2020-07-10 2020-11-06 成都理工大学 一种基于Frechet距离的放射源活度监测方法

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US8874392B2 (en) 2014-10-28
KR101223502B1 (ko) 2013-01-21
US20120101751A1 (en) 2012-04-26
EP2077453A1 (de) 2009-07-08
KR20090074107A (ko) 2009-07-06
KR20110137272A (ko) 2011-12-22

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