WO2018098516A1 - Détermination de l'état de modules photovoltaïques - Google Patents

Détermination de l'état de modules photovoltaïques Download PDF

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Publication number
WO2018098516A1
WO2018098516A1 PCT/AU2016/051183 AU2016051183W WO2018098516A1 WO 2018098516 A1 WO2018098516 A1 WO 2018098516A1 AU 2016051183 W AU2016051183 W AU 2016051183W WO 2018098516 A1 WO2018098516 A1 WO 2018098516A1
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WIPO (PCT)
Prior art keywords
photovoltaic module
module
images
detector
data
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PCT/AU2016/051183
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English (en)
Inventor
Thorsten Trupke
Ian Andrew Maxwell
Robert Andrew Bardos
Juergen Weber
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Bt Imaging Pty Ltd
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Publication date
Application filed by Bt Imaging Pty Ltd filed Critical Bt Imaging Pty Ltd
Priority to PCT/AU2016/051183 priority Critical patent/WO2018098516A1/fr
Priority to AU2016431057A priority patent/AU2016431057A1/en
Priority to CN201721649915.0U priority patent/CN207743935U/zh
Priority to TW106142224A priority patent/TW201834382A/zh
Priority to TW106142223A priority patent/TW201834381A/zh
Publication of WO2018098516A1 publication Critical patent/WO2018098516A1/fr
Priority to AU2018101083A priority patent/AU2018101083B4/en
Priority to AU2018101283A priority patent/AU2018101283A4/en

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Classifications

    • 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
    • H02S50/15—Testing of PV devices, e.g. of PV modules or single PV cells using optical means, e.g. using electroluminescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6489—Photoluminescence of semiconductors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/66—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light electrically excited, e.g. electroluminescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/9501—Semiconductor wafers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/6456—Spatial resolved fluorescence measurements; Imaging
    • G01N2021/646—Detecting fluorescent inhomogeneities at a position, e.g. for detecting defects
    • 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 present invention relates to apparatus and methods for determining conditions of photovoltaic modules, in particular using luminescence imaging techniques.
  • Photovoltaic modules (hereafter 'module' or 'modules') are becoming an increasingly significant part of the global power generation mix. It is estimated that there are more than a billion modules currently installed worldwide, a figure that is growing by 10 to 20% per annum. The majority of installed modules contain a rectangular array of sixty or seventy -two monocrystalline or multicrystalline silicon photovoltaic cells (hereafter 'cell' or 'cells'), although modules based on thin film materials such as cadmium telluride, copper indium gallium selenide (CIGS) or amorphous silicon are also relatively common as are modules with larger or smaller numbers of silicon cells.
  • CIGS copper indium gallium selenide
  • FIG 1 shows in schematic plan view a typical module 100 comprising a rectangular array of sixty silicon cells 102 wired as three strings 104 of twenty cells connected in series, and with electrical contacts 106 for extracting the charge carriers generated by absorption of solar radiation (or similar) in the cells.
  • Each string 104 has a by-pass diode 108 connected in parallel to limit the extended influence of defective or temporarily shaded cells.
  • sixty 150 x 150 mm cells arranged in a six-by-ten close-packed rectangular grid a module 100 will have a total width 110 of about 1.0 m and a total length 112 of about 1.65 m.
  • a thin film module 200 typically comprises an array of narrow strip-shaped cells 202 connected in series, with electrical contacts 106 at each end.
  • Thin film modules are typically formed in a wide range of sizes by depositing doped semiconductor materials using thin film deposition techniques on a substrate 204 such as glass coated with a transparent conductive oxide, with cell structure usually created using laser scribing techniques.
  • Modules are typically intended to have an operational life of around twenty or twenty five years, with warranties typically covering those time scales.
  • warranty typically covering those time scales.
  • failure modes that can compromise the performance not only of individual cells within a module, but also of surrounding cells or even an entire module. Some failure modes can also cause hot spots, with an associated risk of fire or further damage to the module. It has been claimed that in some cases up to 10% of modules in an installation will fail during their warrantied lifetime, representing a large commercial problem.
  • 'Failure' of a module can be an outright fail where no power is generated, or a drop in power generation to below the warrantied level, usually calculated according to a formula that allows for a fixed percentage drop per annum.
  • failure modes for individual cells include cracks, shunts and localised regions of excessive series resistance that may be associated with breaks in the metal contact pattern or poor contact between the metal pattern and the silicon or other cell material. Breaks in the electrical connections between cells can also fully or partially isolate one or more cells in a module. Such failure modes may be induced for example by cell or module manufacturing errors, or by improper handling during module transport or installation. They may also be initiated and/or grow over months and years in the field, e.g. by ingress of water and oxygen, or the inevitable thermal cycling and UV degradation of organic materials in the module. Cracks are a particularly insidious failure mode because of their propensity to grow over time.
  • a small crack in a cell initiated during module manufacture or shipping may have no discernible effect on performance at the time of module installation, but can grow because of thermal cycling or other environmental stress for example.
  • Various so-called light-induced degradation mechanisms are known, which decrease the electrical performance of an illuminated module over time upon illumination.
  • a number of physical mechanisms for this degradation have been identified, involving for example the Boron-Oxygen defect prevalent in monocrystalline silicon cells.
  • Another degradation mechanism is potential-induced
  • I-V testing measures the current (I) and voltage (V) characteristics of a particular module under simulated or actual solar illumination conditions, giving a detailed description of its solar energy conversion ability and efficiency. Knowing the I-V characteristics of a module, especially its maximum power point (MPP), is critical in determining its expected output performance and solar efficiency, and hence its value. All modules are tested for I-V performance as a routine part of their manufacture.
  • modules include visual inspection with cameras under UV or visible illumination, thermography and electroluminescence, with the latter two described in M. Kontges 'Reviewing the practicality and utility of electroluminescence and thermography images', 2014 Photovoltaic Module Reliability Workshop, Golden, Colorado, 25-26 February 2014, pp 362-388.
  • Thermography which essentially looks for temperature differences within or between modules, is presently the most common technique for inspecting modules in the field, i.e. after installation. It may not necessarily have sufficient resolution to determine the cause of a fault, but defective modules can be removed for further investigation in module 'autopsy' labs, e.g. using I-V testing or electroluminescence imaging.
  • thermography Another shortcoming of thermography is that it can only identify faults that are already causing significant degradation of the electrical performance. In other words it is not suitable for identifying more subtle effects that could be used to predict module failure. For example thermography cannot detect cracks in cells that have not yet grown to impede current flow.
  • Another method for monitoring modules in the field is to log their real time performance using special circuitry integrated with the module or in the inverter that measures, for example, a module's power output as well as its operational current and voltage.
  • This test measures the power production of a module throughout an extended period and can alert an operator to a fault in a module or even within a string within a module, but does not determine a cause of a fault. Similar to thermography, this method generally only finds faults that have evolved to a level where they lead to significant deviations of the electrical output from the rated module performance.
  • FIG. 3 shows in schematic side view a typical system 300 for acquiring full field EL images from a photovoltaic module 100, comprising a power supply 302 for injecting current into the module through contact terminals 106, an area camera 304 for detecting EL 306 emitted from the cells 102 within the module, and a memory 308 for storing the image read out from the camera.
  • a power supply 302 for injecting current into the module through contact terminals 106
  • an area camera 304 for detecting EL 306 emitted from the cells 102 within the module
  • a memory 308 for storing the image read out from the camera.
  • full field EL imaging systems generally also require a light- proof enclosure 310 for excluding ambient light.
  • Full field EL imaging systems are generally bulky because of the large working distance 312 required by the area camera 304, which is one reason why they are usually confined to module autopsy labs or factory inspection rather than in-the-field module inspection.
  • the working distance 312 can be reduced somewhat if multiple area cameras 304 are used to capture EL emitted from different portions of a module 100, but this increases the cost of the apparatus.
  • Full field EL imaging is sensitive to many defects related to module failure, including cracks, shunts and breaks in the metal contact pattern of a cell, as well as carrier recombination defects such as dislocations and impurities that reduce the charge carrier lifetime and hence degrade cell performance.
  • Virtually all defects tend to reduce EL emission and hence appear darker than the background defect-free material in EL images, so it can be difficult to distinguish between different types of defects.
  • Image processing algorithms can be used to distinguish automatically between dark features with different intensities, positions, shapes, sizes and other properties, but the accuracy and precision of such algorithms can be compromised if there are a large number of types of features that may also be overlapping.
  • a general property of EL imaging is that luminescence is only generated from cell regions that can be accessed by the electrical excitation. This effect is illustrated in Figure 4, showing an EL image of a module 100 with sixty multicrystalline silicon cells 102 acquired with an apparatus of the type shown in Figure 3. Several of the cells appear completely dark, probably because they are externally shunted, e.g. by interconnection errors during manufacture, so that no charge carriers can be injected into them. While this sort of luminescence pattern is useful in revealing the presence of a module fault, the dark cells could contain defects such as cracks that clearly will not be detected. In another example, an entire module will appear completely dark under EL imaging if the interconnections between any two cells are completely broken. In general, the absence of luminescence from some or all cells in a module limits the amount of information available for defect detection or fault diagnosis.
  • PL imaging Another luminescence-based technique that can be applied to inspection of cells and modules is photoluminescence (PL) imaging, which differs from EL imaging in that charge carriers are generated optically, by injection of high intensity light, rather than electrically.
  • PL-based module inspection technique is described in published US patent application No 2015/0155829 Al .
  • a module under test is illuminated by the sun and imaged with an area camera while the working point of the module is electrically modulated at a selected frequency. This imposes a similar modulation on the PL emitted from the illuminated cells, enabling lock-in techniques to separate the PL signal from ambient light. It would appear that the ability of this technique to operate depends on the amount of sunlight available, and as with full field-EL imaging the apparatus is generally bulky.
  • an apparatus for inspecting a photovoltaic module comprising:
  • a power supply for applying electrical excitation to a photovoltaic module to generate electroluminescence from said photovoltaic module
  • a detector for detecting electroluminescence emitted from a first area of said photovoltaic module
  • a computing device programmed by executable instructions to receive, from said detector as said first area is scanned along said photovoltaic module, an image of
  • the detector comprises a line camera or a TDI camera. In other embodiments the detector comprises a contact imaging sensor.
  • the scanning mechanism comprises a mechanism for moving the photovoltaic module. In other embodiments the scanning mechanism comprises a mechanism for moving the detector. In yet other embodiments the scanning mechanism comprises an optical element operatively associated with the detector, the optical element being adapted to move along the photovoltaic module while the detector remains stationary. Preferably, the scanning mechanism is configured such that the optical path length between the first area and the detector remains substantially constant as the first area is scanned along the photovoltaic module.
  • the apparatus further comprises one or more temperature sensors for monitoring the temperature of the photovoltaic module in the vicinity of the first area as the first area is being scanned along the photovoltaic module, for enabling a temperature correction to be applied to the electroluminescence signal detected by the detector.
  • the apparatus further comprises a light source for illuminating a second area of the photovoltaic module with light suitable for generating photoluminescence from the photovoltaic module, such that an image of photoluminescence emitted from at least a portion of the photovoltaic module can be acquired as the second area is scanned along the photovoltaic module.
  • the light source and the detector are configured such that the image of photoluminescence can be acquired with the detector.
  • the apparatus further comprises a second detector for acquiring the image of photoluminescence.
  • the apparatus is configured to acquire I-V test data from the photovoltaic module, or to acquire an optical image of at least a portion of the photovoltaic module, or to acquire an image of thermal radiation emitted from at least a portion of the photovoltaic module as a result of the application of electrical excitation to the photovoltaic module.
  • the apparatus further comprises a computer for processing one or more electroluminescence images and/or photoluminescence images acquired with the apparatus, the computer being programmed to classify or distinguish between different types of features or defects, or generate one or more overlay images for highlighting one or more types of features or defects, or calculate one or more metrics of the occurrence of one or more types of features or defects, or apply a quality classification to the photovoltaic module, based on expected performance as estimated from the occurrence of various types of features or defects identified in the photovoltaic module.
  • the apparatus further comprises a computer for comparing two or more images of the photovoltaic module acquired with the apparatus, the images being selected from the group comprising electroluminescence images, photoluminescence images, optical images or thermal images.
  • an apparatus for inspecting a photovoltaic module comprising:
  • a light source for illuminating a second area of a photovoltaic module with light suitable for generating photoluminescence from said photovoltaic module; a detector for detecting photoluminescence emitted from a first area said photovoltaic module;
  • a computing device programmed by executable instructions to receive, from said detector as said first and second areas are scanned along said photovoltaic module, an image of photoluminescence emitted from at least a portion of said photovoltaic module.
  • the apparatus is preferably configured such that, in use, the first and second areas are at least partially overlapping.
  • the detector comprises a line camera or a TDI camera. In other embodiments the detector comprises a contact imaging sensor.
  • the scanning mechanism comprises a mechanism for moving the photovoltaic module. In other embodiments the scanning mechanism comprises a mechanism for moving the detector and/or the light source. In yet other embodiments the scanning mechanism comprises an optical element operatively associated with the detector, the optical element being adapted to move along the photovoltaic module while the detector remains stationary. Preferably, the scanning mechanism is configured such that the optical path length between the first area and the detector remains substantially constant as the first and second areas are scanned along the photovoltaic module.
  • electroluminescence emitted from at least a portion of the photovoltaic module as a result of the application of electrical excitation to the photovoltaic module or to acquire I-V test data from the photovoltaic module, or to acquire an optical image of at least a portion of the photovoltaic module, or to acquire an image of thermal radiation emitted from at least a portion of the photovoltaic module as a result of the application of electrical excitation to the photovoltaic module.
  • the apparatus further comprises a computer for processing one or more
  • the apparatus further comprises a computer for comparing two or more images of the photovoltaic module acquired with the apparatus, the images being selected from the group comprising electroluminescence images, photoluminescence images, optical images or thermal images.
  • a method for inspecting a photovoltaic module comprising the steps of:
  • the step of scanning the first area comprises moving the photovoltaic module. In other embodiments the step of scanning the first area comprises moving the detector. In yet other embodiments the step of scanning the first area comprises moving an optical element operatively associated with the detector while the detector remains stationary. Preferably, the optical path length between the first area and the detector remains substantially constant as the first area is scanned along the photovoltaic module.
  • the method further comprises the steps of: monitoring the temperature of the photovoltaic module in the vicinity of the first area as the first area is being scanned along the photovoltaic module; and applying a temperature correction to the electroluminescence signal detected by the detector.
  • the method further comprises the steps of: illuminating a second area of the photovoltaic module with light suitable for generating photoluminescence from the photovoltaic module; and
  • the method further comprises the step of acquiring I-V test data from the photovoltaic module, or the step of acquiring an optical image of at least a portion of the photovoltaic module, or the step of acquiring an image of thermal radiation emitted from at least a portion of the photovoltaic module as a result of the application of electrical excitation to the module.
  • the method further comprises the step of processing one or more electroluminescence images and/or photoluminescence images acquired from the photovoltaic module, to classify or distinguish between different types of features or defects, or generate one or more overlay images for highlighting one or more types of features or defects, or calculate one or more metrics of the occurrence of one or more types of features or defects, or apply a quality classification to the photovoltaic module, based on expected performance as estimated from the occurrence of various types of features or defects identified in the photovoltaic module.
  • the method further comprises the step of comparing two or more images acquired from the photovoltaic module, the images being selected from the group comprising electroluminescence images, photoluminescence images, optical images or thermal images.
  • a method for inspecting a photovoltaic module comprising the steps of:
  • the first and second areas are at least partially overlapping.
  • the step of scanning the first and second areas comprises moving the photovoltaic module. In other embodiments the step of scanning the first and second areas comprises moving the detector and/or the light source. In yet other embodiments the step of scanning the first and second areas comprises moving an optical element operatively associated with the detector while the detector remains stationary. Preferably, the optical path length between the first area and the detector remains substantially constant as the first and second areas are scanned along the photovoltaic module.
  • the method further comprises the step of acquiring an image of electroluminescence emitted from at least a portion of the photovoltaic module as a result of the application of electrical excitation to the photovoltaic module, or the step of acquiring I-V test data from the photovoltaic module, or the step of acquiring an optical image of at least a portion of the photovoltaic module, or the step of acquiring an image of thermal radiation emitted from at least a portion of the photovoltaic module as a result of the application of electrical excitation to the photovoltaic module.
  • the method further comprises the step of processing one or more
  • the method further comprises the step of comparing two or more images acquired from the photovoltaic module, the images being selected from the group comprising electroluminescence images, photoluminescence images, optical images or thermal images.
  • a system able to determine a condition of a photovoltaic module over time comprising:
  • processors one or more processors; and a memory storing computer-executable program code including instructions which, when executed by the one or more processors, configure the one or more processors to:
  • the one or more items of metadata including information about at least one of the module data or the photovoltaic module;
  • the module data preferably comprises one or more of electroluminescence images, photoluminescence images, optical images, thermal images, or I-V test data.
  • a detector for detecting at least one of photoluminescence emitted from the
  • photovoltaic module or electroluminescence emitted from the photovoltaic module
  • a computing device programed by executable instructions to receive, from the detector, as the module data, at least one of a photoluminescence image or an electroluminescence image of at least a portion of the photovoltaic module.
  • the one or more processors are further configured to:
  • the one or more processors are further configured to determine the condition of the photovoltaic module by comparing the module data with prior module data generated for the photovoltaic module at an earlier time.
  • the one or more processors are further configured to determine, based on the condition, at least one of: a grade for the photovoltaic module; whether the photovoltaic module has a fault; whether the photovoltaic module is likely to develop a fault; or a cause of a fault in the photovoltaic module.
  • the one or more processors are further configured to send, based on the condition, a communication to a computing device of at least one entity associated with manufacture, transport, installation, operation or examination of the photovoltaic module, the
  • the one or more processors are further configured to send, to a computing device of an interested party, at least one of:
  • the module data, prior module data, or analysis data determined with respect to the photovoltaic module or
  • a method able to determine a condition of a photovoltaic module over time comprising:
  • module data generated by an inspection apparatus at a first point in time wherein the inspection apparatus is configured for generating the module data for the photovoltaic module
  • the module data preferably comprises one or more of electroluminescence images, photoluminescence images, optical images, thermal images, or I-V test data.
  • the inspection apparatus comprises: a detector for detecting at least one of photoluminescence emitted from the photovoltaic module or electroluminescence emitted from the photovoltaic module;
  • a computing device programed by executable instructions to receive, from the detector, as the module data, at least one of a photoluminescence image or an electroluminescence image of at least a portion of the photovoltaic module.
  • the method further comprises the steps of:
  • determining the condition of the photovoltaic module at the second point in time based at least partially on comparing the module data from the first point in time with the additional module data.
  • determining the condition of the photovoltaic module comprises comparing the module data with prior module data generated for the photovoltaic module at an earlier time.
  • the method further comprises the step of determining, based on the condition, at least one of: a grade for the photovoltaic module; whether the photovoltaic module has a fault; whether the photovoltaic module is likely to develop a fault; or a cause of a fault in the photovoltaic module.
  • the method further comprises the step of sending, based on the condition, a communication to a computing device of at least one entity associated with manufacture, transport, installation, operation or examination of the photovoltaic module, the communication indicating the determined condition.
  • the method further comprises the step of sending, to a computing device of an interested party, at least one of:
  • the module data, prior module data, or analysis data determined with respect to the photovoltaic module or
  • Fig 1 shows in schematic plan view a silicon cell-based module.
  • Fig 2 shows in schematic plan view a thin film module.
  • Fig 3 illustrates in schematic side view a conventional apparatus for acquiring EL images of a module.
  • Fig 4 shows an EL image of a silicon cell-based module acquired with an apparatus of the type shown in Fig 3.
  • Figs 5A, 5B and 5C respectively show a schematic plan view, a schematic side view and a 3D rendered image of an apparatus for inspecting a module, according to an embodiment of the present invention.
  • Fig 5D shows in schematic side view a variation of the apparatus shown in Figs 5A to 5C.
  • Figs 6A and 6B show in schematic plan and side views an apparatus for inspecting a module, according to another embodiment of the present invention.
  • Fig 6C shows in schematic side view a compact PL line-scanning head.
  • Fig 7A shows in schematic side view an apparatus for inspecting a module, according to another embodiment of the present invention.
  • Fig 7B shows in schematic side view a variation of the apparatus shown in Fig 7A.
  • Fig 8A shows in schematic side view an apparatus for inspecting a module, according to another embodiment of the present invention.
  • Fig 8B shows in schematic side view a variation of the apparatus shown in Fig 8A.
  • Fig 9 shows in schematic side view an apparatus for inspecting a module, according to yet another embodiment of the present invention.
  • Fig 10A shows a line-scanning PL image of a module containing multicrystalline silicon cells.
  • Fig 10B shows an image of a single cell extracted from the image of Fig 10A.
  • Figs 11 A shows an image of a multicrystalline silicon cell extracted from a line-scanning EL image of a complete module.
  • Fig 1 IB shows an image of the same cell as in Fig 11 A, extracted from a line-scanning PL image of the complete module.
  • Figs l lC and 1 ID respectively show line-scanning EL and line-scanning PL images of the corner regions of four silicon cells in a module.
  • Fig 12 illustrates a high-level example of a system for determining conditions of photovoltaic modules, such as throughout their useful life.
  • Fig 13 illustrates a cloud-based Software as a Service (SaaS) model for operation of the system of Fig 12.
  • SaaS Software as a Service
  • Fig 14 illustrates an example physical and logical architecture of the system of Fig 12 according to some implementations.
  • Fig 15 is a flow diagram illustrating an example process for determining conditions of modules over time according to some implementations.
  • Figs 16, 17, 18 and 19 are flow diagrams illustrating example processes for generating module data according to some implementations.
  • Figures 5 A and 5B show in schematic plan and side views an apparatus 500 according to an embodiment of the present invention, for inspecting or determining the condition of a module 100 comprising a two-dimensional array of sixty silicon cells 102.
  • a 3-D rendered image of the apparatus 500 is shown in Figure 5C.
  • the apparatus 500 comprises: a power supply 302 for applying electrical excitation to the module 100 via the contacts 106 to generate electroluminescence 306 from the module; a detector 502 in the form of a line or time delay integration (TDI) camera for detecting EL emitted from a first area 506 of the module; a scanning mechanism 508, such as a conveyer, rollers or air bearings, for moving the module 100 such that the first area 506 is scanned along the module; and a suitably programmed computing device 510 for reading out the camera 502 line by line in synchronisation with the scanning to obtain an image of EL emitted from at least a portion of the module.
  • TDI time delay integration
  • the first area 506 extends across the width 110 of the module as shown, and is scanned along the full length 112 of the module, so that the entire front surface of the module 100 is imaged.
  • the luminescence 306 generated by the electrical excitation will primarily be band- to-band EL from the cells 102, but the possibility of generating EL from other components of a module should not be excluded.
  • Suitable cameras for detecting band-to-band luminescence from silicon cells include silicon and InGaAs cameras.
  • Figure 5C also shows a terminal 512 for operator control of the apparatus 500 or for presentation of acquired images to an operator.
  • the line-scanning EL imaging apparatus 500 depicted in Figures 5 A to 5C may be much more compact than the area-imaging EL apparatus 300 of the prior art, as shown in Figure 3.
  • the generated EL 306 it is preferred for the generated EL 306 to be detected with a multi-pixel detector such as a line or TDI camera 502 as shown, it could alternatively be detected with a single element detector configured to move back and forth in the direction perpendicular to the direction in which the module 100 is moved.
  • Standard band-to-band EL can be generated from the silicon cells 102 of a module 100 by applying a relatively modest forward bias to the terminals 106, typically slightly above the open circuit voltage (V oc )- For example a forward bias of around 40 V to 50 V is generally adequate for generating EL from a module with sixty silicon cells 102 each having V oc ⁇ 0.63 V.
  • V oc open circuit voltage
  • a reverse bias to a module, since it is known that at large reverse bias silicon cells display breakdown behaviour which manifests as luminescence from the active cell area, potentially providing additional information on the module.
  • the voltages required are significantly higher than for forward biased EL, typically at least 5 to 10 V and up to more than 15 V per cell i.e. several hundred to more than 1000 V for a sixty cell module, which may raise safety concerns.
  • This together with the possibility of large reverse biases actually causing damage to the cells, may confine reverse bias EL to use in module autopsy labs unless the modules being inspected contain far fewer cells.
  • To apply a sufficiently large reverse bias for generating breakdown behaviour it will generally be necessary to disconnect or otherwise disable any by-pass diodes 108 of the subject module 100. This should be possible since by-pass diodes are usually located in a junction box, but further suggests that testing based on reverse bias EL imaging would be reserved for special cases such as module autopsy rather than for mass testing of modules.
  • the apparatus further comprises a light source 514 for illuminating a second area 516 of the module 100 with light suitable for generating PL from the cells 102, and possibly also from other components of the module such as the backsheet polymer.
  • the light source 514 may for example comprise a laser diode array or LED array emitting light in the red or near IR region, e.g. in the range of 600 nm to 980 nm.
  • the light source 514 and camera 502 are configured such that the camera acquires an image of PL emitted from at least a portion of the module 100 as the second, illuminated, area 516 and first, imaged, area 506 are scanned along the module by the scanning mechanism 508.
  • the second area 516 extends across the width 110 of the module as shown, and is scanned along the full length 112 of the module, so that the entire front surface of the module 100 is imaged.
  • the light source and camera are preferably configured such that, in use, the first and second areas 506, 516 are at least partially overlapping as shown in Figure 5A, although this is not essential if a sufficient fraction of the photo-generated charge carriers are able to migrate out of the illuminated area 516, as discussed further below.
  • Additional optics may also be included in the apparatus 500, such as a rod lens for focussing light from the light source 514 onto the second area 516, a short-pass filter in front of the light source 514 to prevent long wavelength tail radiation from reaching the camera 502 and a long-pass filter in front of the camera 502 to block stray excitation light.
  • One or more interchangeable filters may be provided in front of the light source 514 and/or the camera 502 for selective excitation and/or detection of PL from the base material of the cells 102 on the one hand, or from some other material in the module, such as the backsheet polymer, on the other hand.
  • the apparatus 500 may contain additional light sources or detectors with different excitation or detection bands for excitation or detection of PL from various components of a module.
  • a single camera 502 is used to detect the generated PL or EL, as shown in Figures 5A and 5B, in which case a module 100 could be passed through the apparatus 500 twice, e.g. forwards then backwards, for sequential acquisition of PL and EL images.
  • a module could also be passed through the apparatus more than once to acquire two or more PL images, e.g. where the PL is generated using different illumination intensities, illumination wavelengths or detection wavelengths, or two or more EL images, e.g. with different applied voltages.
  • Figure 5D shows in schematic side view a variation in which the apparatus 500 contains a first camera 502A for detecting EL generated by the power supply 302, and a second camera 502 for detecting PL 504 generated by the light source 514. Both cameras could be read out by the same computing device 510 as shown, or by separate computing devices. Having two cameras enables acquisition of separate PL and EL images without having to pass the module through the apparatus twice or reverse the direction of the scanning mechanism 508.
  • the two cameras 502, 502A are separated in the scanning direction by a distance equal to or greater than the module length 112, or the module width if the scanning is parallel to that dimension, so that optical and electrical excitation can each be applied in isolation.
  • the power supply 302 would only be activated once the module 100 has passed through the illumination zone of the light source 514.
  • the camera 502 and the light source 514 are mounted within a substantially light-proof enclosure 310 as shown in Figures 5A and 5B, to keep ambient light out of the camera or to contain the excitation light 524.
  • the bottom edges of the enclosure 310 have soft brushes 518 or similar, e.g. dark cloth, for improving the light seal.
  • a single enclosure could be provided covering both cameras 502 and 502A, or separate enclosures could be provided for each camera.
  • the electrical excitation from the power supply 302 used to generate electroluminescence will tend to heat the cells 102, which can influence their luminescence efficiency. Consequently, when acquiring a line-scanning EL image of a module 100 a temperature gradient effect could be imposed on the image if EL collected later in the scan has been generated from cells at a higher temperature.
  • Such an artefact can be ameliorated by monitoring the temperature of the module 100 in the vicinity of the first area 506 during the scan with one or more temperature sensors 526 such as infrared thermometers spaced apart within the enclosure 310.
  • the computing device 510 or another computing device could then apply a temperature correction to the electroluminescence signal detected by the camera 502, e.g. using a known
  • the apparatus 500 may include a vision system comprising a light source 520 such as a linear array of white light LEDs and a suitable line or TDI camera 522 for acquiring an optical (i.e. reflection) image of at least a portion of the module 100 as it is scanned through the apparatus.
  • a vision system comprising a light source 520 such as a linear array of white light LEDs and a suitable line or TDI camera 522 for acquiring an optical (i.e. reflection) image of at least a portion of the module 100 as it is scanned through the apparatus.
  • the camera 522 may be read out by the same computing device 510 or a different computing device.
  • the optical images acquired by this vision system can provide further information on defects or other features in a module 100.
  • the light source 514 and camera 502 used for PL imaging can be adapted to acquire optical images, e.g. by reducing the intensity with a neutral density filter and removing any cut-off or band-pass filters that would otherwise separate the illumination and detection bands.
  • the apparatus 500 may include a near IR transmission vision system having a suitable light source 520 and line or TDI camera 528 on opposite sides of the module 100. Such a transmission vision system could be used for example for micro-crack detection in modules containing bifacial cells and having glass on both sides.
  • the power supply 302 may be operated to inject current into the module 100 while the light source 514 is illuminating the module.
  • the injection or extraction of current encourages the movement of charge carriers during luminescence image acquisition, for even further discrimination between carrier lifetime defects and series resistance defects. Some potential applications of this are discussed below in the 'Image Analysis' section.
  • the power supply 302 is omitted from the apparatus 500, so that luminescence is generated solely by optical excitation.
  • an EL image can be simulated by configuring the apparatus 500 such that, in use, the first area 506, i.e.
  • the apparatus 500 is equipped with a mechanism for varying the extent to which the first and second areas 506, 516 overlap on the module 100.
  • Acquired luminescence images can be stored on the computing device 510 for subsequent processing on the same or a different computing device, or displayed on a monitor 512 for interpretation by an operator.
  • luminescence images are processed using one or more software algorithms, e.g. to highlight various types of defects or features, before being presented to an operator for interpretation, or for triggering an automatic alert, or for transfer to a database for later viewing or comparison with images acquired from the module at different times.
  • luminescence generated from the module 100 is detected with a detector 502 in the form of a line or TDI camera that is considerably shorter in lateral extent than the module width 110.
  • Line and TDI cameras with enhanced near IR response for greater sensitivity to silicon band-to-band luminescence are readily available, and TDI cameras are particularly advantageous because of the gain enhancement provided by the summing of signals from the multiple pixel rows.
  • this configuration also has disadvantages, such as the need for a relatively large working distance, of order tens of centimetres, and a roll-off in detected intensity from the edges of the field of view
  • the path length of the luminescence to a line or TDI camera 502 may be considerably longer than is shown schematically in Figure 5B, and it will be appreciated that one or more folding mirrors may be included as required to contain the optical path within an appropriately sized enclosure 310.
  • the luminescence is detected using a detector in the form of a contact imaging sensor 602 for read out by a computing device 510 in synchronisation with the scanning to obtain an image of luminescence emitted from at least a portion of the module.
  • the contact imaging sensor may for example comprise a pixel array with an integrated micro-rod lens array sufficiently long to span the full width 110 of a module 100.
  • Contact imaging sensors of virtually unlimited length can be constructed by butting together a number of shorter CMOS sensor chips, e.g. as described in US patent No 8,058,602. While CMOS sensor chips are commonly used for contact imaging sensors, it is also possible to use other types of sensor, e.g. CCD sensors.
  • a contact imaging sensor 602 can readily be placed as close as a few mm to the cover glass of a module.
  • a light source 514 could be tightly integrated with a contact imaging sensor 602 to provide a highly compact PL line-scanning head 604 that could be placed as close as a few mm to the cover glass of a module.
  • a light source 514 having an output window 606 with a width in the range of 0.1 to a few mm could be located directly adjacent to, or within a few mm laterally of, the micro-rod lens array 608 of the contact imaging sensor 602.
  • the light source 514 could have a micro-optical array 610, which may for example have the same pitch as the micro-rod lens array 608.
  • a contact imaging sensor 602 enables a compact module inspection apparatus.
  • the detector could be in the form of separate CMOS sensor chips provided for detecting the luminescence from each row of cells.
  • Commercial contact imaging sensor systems are generally designed for operation in the visible spectral region, and would only be sensitive to the short wavelength end of the silicon luminescence band. This reduction in sensitivity can be offset by using arrays of rectangular sensor pixels with long axis parallel to the scan direction, preferably in combination with a micro-optical array having elements that gather light onto the rectangular sensor pixels from sample areas that have an approximately 1 : 1 aspect ratio (length to width), or are essentially circular.
  • the insensitivity to long wavelength luminescence can in fact be advantageous in improving spatial resolution for reasons discussed in published PCT patent application No WO 2011/017776 Al .
  • Many other detection configurations are possible for collecting luminescence from close to the surface of a module, for example using optical fibre ribbons or integrated optical waveguides to guide the luminescence to a pixel array, with tapering if necessary to match the dimensions of the pixel array.
  • the apparatus 500 as depicted in Figures 5A to 5C is configured to span the short dimension 110 of a module 100, so that the module is conveyed in the direction parallel to its long dimension 112.
  • a module 100 is moved on a scanning mechanism 508, such as a conveyer, etc., while the camera 502 or contact imaging sensor 602 and the light source 514 remain stationary.
  • the scanning mechanism 508 for scanning the first and second areas 506, 516 along a module comprises a mechanism such as transport belts, rollers or air bearings for moving the module 100.
  • Such an arrangement is advantageous if the detector or light source contain delicate optics, and is generally suitable for module inspection in any situation where modules can be moved, for example during or after manufacture, before or after shipping, before installation or in a module autopsy lab.
  • Figure 7 A shows in schematic side view an apparatus 700 for inspecting or determining the condition of a module 100 according to another embodiment of the invention.
  • the apparatus comprises a light source 514 for generating PL from the cells 102 and possibly other components of the module, a detector 502 in the form of a line or TDI camera for detecting the generated PL, and a suitably programmed computing device 510 for reading out the camera line by line in synchronisation with scanning of the illuminated and imaged areas along the module 100 to obtain an image of PL emitted from at least a portion of the module.
  • the scanning is performed by moving the light source 514 and camera 502 as indicated by the arrow 702.
  • the light source 514 and camera 502 are fixedly attached within a substantially light-proof enclosure 310 adapted to move along the module 100 on a scanning mechanism 508 comprising rails or rollers or the like.
  • This arrangement allows the module 100 to remain stationary, suitable for inspecting modules post- installation where the module is fixed in place, e.g. on a rooftop, or if it is otherwise convenient for the module to be in a fixed position.
  • the generated luminescence it is preferred for the generated luminescence to be detected with a multi-pixel detector such as a line or TDI camera 502 as shown, it could alternatively be detected with a single element detector configured to move back and forth in the direction perpendicular to the direction in which the enclosure 310 is moved.
  • the apparatus 700 also comprises a power supply 302 for injecting current into or extracting current from the module via the contacts 106, e.g. for generating EL.
  • the apparatus may cooperate with existing electrical infrastructure for applying electrical excitation to the module.
  • the light source 514 is omitted, in which case luminescence is generated solely by electrical excitation.
  • the apparatus 700 may include a thermal imaging line or TDI camera 704 for detecting mid-IR radiation 706 emitted from hot spots in the module 100 as a result of the application of electrical excitation to the module. If the field of view of the thermal imaging camera 704 is sufficiently close to the field of view of the camera 502, the thermal imaging camera could also perform the temperature monitoring function of the temperature sensors 526 discussed above with reference to Figure 5B.
  • Figure 7B shows in schematic side view a variation of the apparatus 700 shown in Figure 7A, in which an assembly 708 comprising the light source 514 and the camera 502, as well as the thermal imaging camera 704 if present, is configured to move along the module 100 on a scanning mechanism 508 such as a rail inside a substantially light-proof enclosure 310.
  • a scanning mechanism 508 such as a rail inside a substantially light-proof enclosure 310.
  • FIG 8A shows in schematic side view an apparatus 800 for inspecting or determining the condition of a module 100, according to another embodiment of the invention.
  • a detector 502 in the form of a line or TDI camera is fixed within a substantially light-proof enclosure 310 placed on or around the module 100, while an assembly 708 comprising a light source 514 and an optical element 802 operatively associated with the camera 502 is adapted to move along the module 100 on a scanning mechanism 508 comprising rails or rollers or the like, as indicated by the arrow 702.
  • the optical element 802 which may for example be an off-axis parabolic mirror, is designed to direct luminescence 804 to the camera 502 for detection and successive read-out by a suitably programmed computing device 510 in synchronisation with movement of the assembly 708 on the scanning mechanism 508.
  • a suitably programmed computing device 510 is designed to direct luminescence 804 to the camera 502 for detection and successive read-out by a suitably programmed computing device 510 in synchronisation with movement of the assembly 708 on the scanning mechanism 508.
  • Many other optical elements suitable for directing the luminescence 804 to the camera such as prisms and optical fibre ribbons, will occur to those skilled in the art.
  • luminescence could also be generated from the module 100 via electrical excitation from a power supply 302.
  • Figure 8B shows in schematic side view a variation of the apparatus 800 shown in Figure 8A, in which the distance travelled by the luminescence 804 to the camera 502 is kept substantially constant during scanning.
  • a scanning mechanism 508 enables an assembly 708 comprising a light source 514 and an optical element 802 operatively associated with a line or TDI camera 502 to move along a module 100 while the camera 502 remains stationary, e.g. fixedly attached to a substantially light-tight enclosure 310 placed on or around the module 100.
  • the luminescence 804 generated by the light source 514 or a power supply 302 is directed to the camera 502 via a turning mirror 806 that moves on the scanning mechanism 508 at half the speed of the assembly 708 as suggested by the relative lengths of the arrows 702 and 702-A.
  • This ensures that the distance travelled by the collected luminescence 804 to the camera 502, i.e. the optical path length between the imaged area and the camera, remains substantially constant during scanning, potentially improving the focusing onto the camera. It is noted that this is also the case with the previously described
  • the detected luminescence signal is read out from the camera 502 by a suitably programmed computing device 510 in synchronisation with the movement of the assembly 708 and the turning mirror 806, to obtain an image of luminescence emitted from at least a portion of the module.
  • FIG. 9 shows in schematic side view an apparatus 900 for inspecting or determining the condition of a module 100, according to yet another embodiment of the invention.
  • This embodiment is similar to that shown in Figure 8A in that luminescence 804 generated from the cells 102 and possibly other components of the module by a light source 514 or a power supply 302 is detected by a detector 502 in the form of a stationary line or TDI camera.
  • the movable assembly 708 including the light source 514 and a mirror 802 can be moved away to a resting position 902 to allow the module 100 to be exposed to a sunlight simulator 904, composed of LEDs, halogen lights or similar and controlled by a power source and controller 906.
  • This sunlight simulator 904 can be used to simulate solar illumination of the module 100 at a range of conditions, while a power-monitoring unit 908 measures the power performance of the module including its I-V characteristics.
  • some or all of this data can be transferred to a centralised storage system and/or used locally to make decisions as to, for example, whether to proceed with installing a given module.
  • luminescence images read out from a detector 502 can be stored and/or processed in a computer, which may be identified with or separate from the computing device 510 used to read out the detector, for display, automatic alerts or further analysis.
  • Figure 10A shows a line-scanning PL image 1000 acquired from a substantial portion of a module having sixty multicrystalline silicon cells using an apparatus 500 of the type shown in Figures 5A to 5C.
  • the image 1000 shows forty of the sixty cells in full.
  • Figure 10B shows the image 1002 of a single cell extracted from the image 1000.
  • the PL was generated with near infrared illumination from an LED array and the module image 1000 captured in thirty seconds as the module was moved underneath a light source and camera assembly.
  • the module image 1000 has approximately 70 Megapixels, representing over 1 Megapixels per cell, providing excellent spatial resolution for identifying defects or other features in individual cells as demonstrated by the single cell line-scanning PL image 1002.
  • This image reveals an extensive network of dark lines 1004 associated with cracks, as well as a number of bright stripes 1006 extending perpendicularly to the bus bars 1008, indicative of broken metal contacts. It is a particularly useful feature of line-scanning PL images compared to EL images that defects such as cracks, dislocations or impurities causing local reduction of carrier lifetime appear relatively dark compared to the PL emission from the surrounding material, i.e. the
  • defect detection is the first step, and involves locating candidate defects and segmenting them from their surroundings.
  • the classification step determines the type of defect, e.g. a broken finger, crack, etc.
  • 'metrics For both of these steps it necessary to take measurements of regions of pixels that differ in intensity from the background, with these measurements referred to hereafter as 'metrics'.
  • Example metrics include relative intensity, size, shape, orientation, texture and position. Not all features identified by the image processing techniques will necessarily be defects that will degrade module performance, but it is important for performance-degrading defects to be identified reliably.
  • Image processing algorithms can be used to distinguish automatically between candidate defects with different relative intensities, size, shape, orientation, texture or position, among other metrics.
  • accuracy and precision of such algorithms can be compromised if a sample has several types of candidate defects that can be spatially overlapping, especially if the candidate defects are all darker than the background.
  • the 'contrast inversion' effect in line-scanning PL images is highly beneficial in providing an additional metric that can be used to distinguish between different categories of defects, substantially improving the accuracy and precision of the image processing algorithms.
  • the relative merits of line-scanning PL and EL imaging for cell and module inspection are further discussed with reference to the images shown in Figures 11 A to 1 ID.
  • Figure 11 A shows a line-scanning EL image 1100 of a multicrystalline silicon cell, extracted from a line-scanning EL image of a sixty cell module acquired with an apparatus 500 such as that shown in Figures 5 A to 5C.
  • a forward bias of 39.5 V (equivalent to 1.045 times the open circuit voltage) was applied to the module contacts 106 as the module 100 was moved at a speed of 50 mm/s through the field of view of a silicon CCD line-scanning camera 502 with enhanced NIR response.
  • Figure 1 IB shows a line-scanning PL image 1102 of the same cell acquired with the same camera, where the PL was generated from the moving module with an illumination intensity of approximately 4 Suns from a light source 514 comprising a 1.2 m long array of near infrared LEDs focused to a 6 mm wide stripe 516 across the short side of the module.
  • the line-scanning EL image 1100 shows a large number of features that appear relatively dark compared to the emission from the surrounding material, including an extensive network of lines 1004 associated with cracks, dislocation clusters 1104, several dark stripes 1106
  • each cell is clearly visible in the line-scanning PL image 1114, whereas they are difficult to discern in the line-scanning EL image 1112 because fewer charge carriers are generated by electrical excitation in regions more distant from the metal contact fingers 1116.
  • This effect is particularly significant for the early detection of cracks, which are often initiated at the edges of cells and are therefore more likely to be detected in a line-scanning PL image.
  • Both images reveal a number of other features in the cells, such as several dislocation clusters 1104 in the lower left cell and some crystal grain structure 1118 in the lower right cell.
  • the metal contact fingers 1116 are more easily discerned in the line-scanning PL image 1114.
  • a region of locally high series resistance along one of the fingers in the upper left cell is revealed as a relatively dark stripe 1106 in the line-scanning EL image 1112 and a relatively bright stripe 1006 in the line-scanning PL image 1114, consistent with the previously noted contrast inversion.
  • line-scanning PL images are ideally better suited than EL images for identifying different types of defects in a subject cell or module because of the contrast inversion effect, there are some module failure modes for which EL imaging may be better suited.
  • an otherwise intact cell that is isolated from a module by an interconnection error may appear quite normal in a line-scanning PL image, but will appear completely dark in an EL image as shown by Figure 4.
  • cells which are partially disconnected e.g. if one of several cell interconnects between adjacent cells is interrupted, will show a characteristic pattern with areas around certain bus bars appearing brighter than others in an EL image. Sometimes this type of pattern is sufficient to identify that specific fault mechanism.
  • injecting current into a module 100 while the light source 514 is applying illumination to the module will result in both electrical and optical excitation contributing to the luminescence 504 detected by the camera 502.
  • the result will be a 'biased' line-scanning PL image that will show some characteristics of an EL image such as that shown in Figure 11 A, and some characteristics of a normal 'unbiased' line-scanning PL image such as that shown in Figure 1 IB, with the mix depending on the relative magnitudes of the electrical and optical excitations.
  • the PL imaging mode may for example enable the PL imaging mode to detect cell interconnection errors that it would not otherwise be able to detect, so that a module might only need to be passed through the inspection apparatus 500 once if EL imaging is not required for any other reason.
  • the level of electrical excitation applied when acquiring a biased line-scanning PL image should be enough to reveal cell interconnection errors, without losing the 'contrast inversion' effect discussed above with reference to Figures 11 A to 1 ID.
  • Another possibility is to extract current through the module terminals 106, e.g. with a resistor or an active load, while the light source 514 is applying illumination to the module 100.
  • this will only yield useful information, such as an enhancement of the 'contrast inversion' effect, if all cells 102 in a string 104 are at least partially illuminated while one or more cells in that string are being imaged.
  • useful information such as an enhancement of the 'contrast inversion' effect, if all cells 102 in a string 104 are at least partially illuminated while one or more cells in that string are being imaged.
  • this could be achieved if the module 100 were being scanned in the direction parallel to its short dimension 110 and the light from the light source 514 defocused such that the 'illuminated' stripe 516 is sufficiently wide to at least partially illuminate all cells in a string 104.
  • the luminescence used for module inspection will be primarily generated from the cell materials, e.g. the silicon diode materials in silicon cell-based modules
  • an unexpected and desirable feature of the present invention is that under some circumstances it is possible to generate and detect luminescence from other materials in a module, in particular by careful selection of the light source, detector or associated optics.
  • the backsheet polymeric material that is behind the cells which may be for example be polyethylene terephthalate, polyvinylidene fluoride, polyamide or composites thereof, may be caused to emit PL. This can provide a contrasting background to the cells, and also to the metal interconnects between cells which will generally appear darker due to the lower levels of PL from metallic materials.
  • oxidation-induced cloudiness of the ethylene vinyl acetate (EVA) polymer that encapsulates silicon cells within a module may be detectable from blurring of features in a luminescence or optical image, an effect that will likely be more noticeable from comparison of images acquired at different times.
  • EVA ethylene vinyl acetate
  • One use of the unexpected contrast in the PL emitted by various components of a module is to provide an alignment test of the metal interconnects and the cells, or more specifically between the metal interconnects and the printed bus bars on the cells. Another application is to look for breaks in the metal interconnect structures. Yet another application is to probe each of the PL emitting materials for inhomogeneities in their PL emission, which can be correlated to varying material properties that may be indicative of actual or potential defects.
  • a module inspection or condition determining apparatus is configured to acquire optical (i.e. reflection or transmission) images in addition to EL or PL images, e.g. by having an additional light source 520 and line or TDI camera 522 or 528 as shown in Figure 5B, for obtaining further information on a module under test.
  • optical images i.e. reflection or transmission
  • a comparison between an optical image and a luminescence image can be useful for distinguishing carrier recombination defects such as dislocations, which will generally not be visible in an optical image, from grain boundaries which will generally be visible in both images.
  • an optical image may reveal a crack that might otherwise be hidden by a dislocation cluster.
  • a high resolution optical image may reveal defects in metal lines that can be correlated with a high series resistance region shown in a line-scanning PL image, or with the degree of darkness of the region in an EL image. Additionally, optical images may highlight defects in module components that do not luminesce, at least in response to the emission band(s) of the available light source(s).
  • Non-luminescing module components may include packaging components such as the cover glass, the edge sealant or the polymeric encapsulant between the cover glass and the cells. Defects in the packaging components may allow the passage of oxygen and/or water to the cells or interconnects which will ultimately lead to power degrading defects such as electrical breaks or carrier recombination defects.
  • a module inspection or condition determining apparatus is additionally configured to acquire images of thermal radiation emitted from at least a portion of a module, e.g. by having a thermal imaging line or TDI camera 704 as shown in Figures 7 A and 7B, for detecting mid-IR radiation 706 emitted from hot spots in a module under test.
  • line-scanning imaging apparatus such as those shown in Figures 5 to 9 could be used to inspect modules, by acquiring images of luminescence generated by photo-excitation or electrical excitation or a combination of both, and optionally optical or thermal images or I-V test data as well.
  • modules could be employed in a module factory to inspect modules during production, e.g. to check strings of cells or lay-ups of cells prior to encapsulation in polymeric materials and glass, for corrective action such as replacement of cells with excessive levels of series resistance-related defects or excessive levels of cracks or other carrier recombination defects.
  • They could also be employed in a module factory as a final test of completed modules for quality control (QC) or quality assurance (QA) purposes.
  • QC quality control
  • QA quality assurance
  • Figure 12 illustrates a high-level example of a system 1200 for determining conditions of photovoltaic modules, such as throughout their useful life.
  • a network accessible storage 1202 where images and other data acquired from a plurality of modules are stored.
  • 'processed images' i.e. images that have been processed using one or more algorithms to detect various defects and other features, may also be stored on the network accessible storage 1202.
  • multiple instances of the module inspection apparatus described herein may be used to determine various types of module data for a plurality of modules.
  • the determined module data may be uploaded or otherwise sent to the network accessible storage 1202 over one or more networks 1206, such as wired or wireless data links, as discussed additionally below.
  • networks 1206 such as wired or wireless data links, as discussed additionally below.
  • Examples of such data may include photoluminescence images and/or electroluminescence images, and possibly also optical images or thermal images, and power generation and I-V test data if the module inspection apparatus is suitably equipped to monitor module power generation after installation, or if the module is inspected with an I-V test system at manufacturing or prior to installation.
  • the data sent to the network accessible storage 1202 may be acquired by various ones of the multiple entities 1204 involved in the supply and operation of modules or the examination of failed modules, including manufacturers 1210, transporters 1212, installers 1214, module operators 1216 and module autopsy labs 1218.
  • Data in the network accessible storage 1202 may be stored and managed by one or more servers or data centres at one or more locations.
  • Photovoltaic modules typically have unique or otherwise individually distinguishable identifying barcodes or numerical codes for ID purposes, which may be discernible in a luminescence or optical image, or entered manually as metadata for upload with the image(s) or other module data, or broadcast wirelessly from the inverter if the inverter is so equipped.
  • a plurality of metadata items associated with a module inspection event are uploaded with the images and other module data, including one or more of image acquisition apparatus ID, operator ID, time and place of image acquisition, imaging mode (e.g. EL, PL, optical or thermal), environmental conditions such as temperature and relative humidity, and operator comments.
  • Metadata items that can be uploaded for storage at the network accessible storage 1202 may include information on the manufacturing of the module, such as the supplier of the cells, serial numbers of the cells, type of the cells and I-V test data of individual cells.
  • the metadata may also include detailed information about materials and processes used for module assembly, e.g. supplier and types of raw materials including wafer feedstock, and cell processing equipment and conditions such as furnaces and wafer cutting equipment.
  • the stored data may span the entire photovoltaic value chain.
  • the records stored in the network accessible storage 1202 could include the geo-position of modules after installation. Combining this information with weather records for specific locations would enable development of algorithms for relating defect types with weather history for example, or to assist in assessing an insurance claim.
  • the module data stored at the network accessible storage 1202 can be made available for access by any of the entities 1204 involved in module supply, operation and/or examination, as well as other interested parties 1208 such as solar finance entities 1220, solar insurance entities 1222, solar energy project owners 1224, solar market reporting groups 1226 and standards and quality assurance agencies 1228, for a variety of purposes. These purposes include for example: determining which entity is at fault when a module fails to deliver its warranted power generation; allowing insurance and finance groups to mine the data to apply risk factors to various entities in the module supply chain; allowing standards or market reporting groups to mine the data to apply quality factors to various entities in the module supply chain;
  • the module data may also provide big data for value-added analysis for any supply, operation and/or examination entity 1204 or interested party 1208, e.g. for the purposes of improvements in manufacturing, potential improvements in cell designs, suitability of specific modules for different environments, the reliability or otherwise of certain module
  • the images uploaded to the network accessible storage 1202 are processed with one or more algorithms on a computer equipped with suitable machine- readable program code, for qualitative or quantitative identification of defects of interest.
  • an edge detection algorithm may be applied to identify localised regions of higher or lower intensity relative to the background, that are generally indicative of defects.
  • Other algorithms may classify or distinguish between different types of defect, e.g.
  • a crack detection algorithm can be applied to calculate one or more metrics such as the number or total length of cracks in each cell in a module under test.
  • Other algorithms may be applied to identify broken fingers and calculate a metric such as the number of broken fingers in each cell, or to identify and enumerate electrically isolated cells or cell regions, or to calculate metrics for carrier recombination defects such as dislocations or impurity-rich cell areas.
  • Yet another algorithm may be used, particularly at the end of module manufacture, to apply a quality classification to a module based on expected performance as estimated from the occurrence of various types of defects identified in the module.
  • image processing algorithms are applied and analytical data calculated by the supply, operation and/or examination entities 1204 that acquired the images, instead of or in addition to a computing device of a service provider associated with the network accessible storage 1202.
  • stored images can be analysed at the request of any of the supply, operation and/or examination entities 1204 and/or the interested parties 1208.
  • images and data of a given module acquired at different times can be compared e.g. by subtraction or by calculation of intensity ratios to highlight any new defects, to assist in determining cause and time of module failure. Additionally or alternatively, comparisons can be made between one or more metrics obtained from those images and data. 'Difference' or 'ratio' images can be particularly useful for distinguishing newly formed defects such as cracks or broken metal fingers from carrier recombination defects such as dislocations that were present in the cell material from the beginning.
  • Image metadata can also provide useful information, e.g. to identify whether a statistically significant number of module failures are associated with specific manufacturers 1210, transporters 1212 or installers 1214.
  • statistical data for various groupings of modules may be calculated by a computing device of the service provider associated with the network accessible storage 1202, either routinely or on request from an interested party 1208 or a supply, operation and/or examination entity 1204. More complex comparisons of processed module data are also possible, including comparing data obtained from images or associated metrics for a selection of one or more modules with data obtained from a general population of modules, e.g.
  • PL images of one or more modules can be segmented into individual cell images that are optionally corrected for distortions before a cell template is calculated by averaging or obtaining the median of the cell images.
  • a module image is segmented into individual cell images, which are optionally corrected for distortions before being fed into the template calculation, and the individual images are then analysed using the average median or any other method to create a cell image of a 'normal cell' .
  • Suspected defective cells i.e.
  • cells for which the PL image deviates strongly from the template according to an ANOVA analysis or similar can then be excluded, to provide an image representative of a 'normal cell' .
  • Individual cell images can then be compared to the 'normal cell' image, which enables quantifying deviations in cell performance from the expected normal performance.
  • Actionable decisions can be made based on one or more of the image processing and analysis outcomes. Such decisions include for example rating a module as defective, grading a module based on expected performance, determining the likely entity at fault if a module failure is detected, and/or removing the module from service e.g. by deciding not to ship or install it. In some embodiments these decisions may be made at the network accessible storage 1202, which may serve as a centralised image storage and processing service operated as a cloud service, i.e. through an IT network and a backend server/processing unit represented in Figure 12 as a cloud 1202. Actionable decisions can then be conveyed to an appropriate operator. In other embodiments actionable decisions can be made during module production, for example to remove defective cells or strings and replace them prior to the irreversible step of encapsulating the cells in the module packaging.
  • module data such as image data and associated metadata, and analysis data
  • the network accessible storage 1202 the size of an image data file for a module will generally scale with the spatial resolution, i.e. the number of pixels. Higher resolution images may provide superior defect detection outcomes but may be more expensive to store, resulting in a trade-off. If the spatial resolution offered by an imaging system exceeds requirements, pixel binning can be used to reduce the resolution and therefore the image file size.
  • the counts from 2x2 groups of pixels can be combined to reduce the image file size by a factor of four.
  • the Applicant has found that 2x2 pixel binning can be applied to a 70 Megapixel luminescence image of a module, such as that shown in Figure 10A, without markedly affecting the outcomes of the image processing algorithms as compared to the original un-binned images.
  • the Applicant has developed a proprietary data format (with a related codec) that uses 10 bits per pixel. This is decoded to 16-bit before image display or processing, which involves a small computational overhead but provides significant storage savings.
  • Image compression is lossless in terms of resolution, so that processing and/or comparison of images in a processor associated with the network accessible storage 1202 is not compromised.
  • the Applicant has determined that storing two images, e.g. line-scanning EL and line-scanning PL images, in uncompressed form requires approximately 100 Megabytes of storage, compared with only 25 Megabytes for the compressed images.
  • module data can be initially stored in faster access storage until the subject module has been installed, and thereafter moved into less expensive, slower access storage.
  • condition determining system 1200 shown in Figure 12 may be operated as a network-based Software as a Service (SaaS) model.
  • a service provider 1300 responsible for or otherwise associated with the module condition determining system may provide (e.g. lease, sell, etc.) as indicated at 1302, module inspection apparatus 500, 600, 700, 800 or 900 to one or more of the entities 1204 involved in the supply, operation and/or examination of modules.
  • the entity 1204 and/or the inspection apparatus 500-900 uploads module image data 1304 to the service provider 1300 for processing, analysis, and storage 1306 at the network accessible storage 1202 or similar.
  • the service provider 1300 may pay an operator of the network accessible storage 1202 for the data storage and may recoup the cost by charging a fee 1310 to an interested party 1208, such as a solar insurance company assessing a warranty claim, or some other interested party 1208 as enumerated above.
  • the service provider 1300 or the interested party 1208 may retrieve 1312 and provide 1314 the requested module data and/or analysis data.
  • the service provider 1300 may provide the module inspection apparatus 500-900 to a supply, operation and/or examination entity 1204 for no upfront cost, and may charge a fee to the entity 1204 for uploading or otherwise providing the module data 1304.
  • the service provider 1300 and a supply, operation and/or examination entity 1204 that uses the module inspection apparatus 500-900 may negotiate a higher equipment lease or sale cost in exchange for a lower fee for access to the module data.
  • the service provider 1300 provides module inspection equipment to a party 1204 for no upfront cost, and charges a fee for image data upload 1304 and another fee to any other party that wants to access the image data at any time in the future. Other variations of fees and charges can be considered.
  • Figure 14 illustrates an example physical and logical architecture 1400 of a system 1200 for determining conditions of photovoltaic modules (not shown in Figure 14) according to some implementations.
  • the architecture 1400 includes one or more service computing devices 1402 of a service provider, such as the service provider 1300 discussed above with respect to Figure 13 or another service provider.
  • the one or more service computing devices 1402 are able to communicate over one or more networks 1404 with the network accessible storage 1202. Further, the one or more service computing devices 1402 are able to communicate over the one or more networks 1404 with entities 1204 involved in the supply, operation and/or
  • the one or more service computing devices 1402 may communicate with client computing devices 1406 of entities 1204 involved in the supply, operation and/or examination of photovoltaic modules, and/or computing devices 510 associated with module inspection apparatus 500-900.
  • the computing device 510 associated with an inspection apparatus 500-900 may be configured to send module data 1408 directly over the one or more networks 1404 to the service computing device(s) 1402, e.g. as the module data 1408 is obtained in the field.
  • a control program 1410 may be stored or otherwise maintained in one or more computer readable media (CRM) 1412 in the computing device 510.
  • the control program may be stored or otherwise maintained in one or more computer readable media (CRM) 1412 in the computing device 510.
  • the control program 1410 may be executed by one or more processors 1414 of the computing device 510 to obtain the module data 1408 in the field.
  • the control program 1410 may be executed to operate the camera(s), scanning mechanisms, and other components discussed above to obtain module data 1408 regarding one or more photovoltaic modules being inspected by one or more of the inspection apparatus 500-900.
  • the module data 1408 may include one or more PL and/or EL images, optical images, or other types of images, I-V test data and the like. Further, the module data 1408 may include metadata about the photovoltaic module being tested, the test being performed, the inspection apparatus performing the testing, and/or other metadata, as discussed above.
  • Execution of the control program 1410 may cause the processor(s) 1414 to use one or more wireless and/or wired communication interfaces 1416 to connect to the one or more networks 1404 for sending the module data 1408 to the service computing device(s) 1402.
  • the module data 1408 may be sent in real time, e.g. as the inspection apparatus 500-900 generates the module data 1408.
  • the module data 1408 may be sent as a batch, such as after a certain trigger point is reached, after a certain amount of data has been collected, after a certain point in time has passed, or the like.
  • the one or more service computing devices 1402 may receive the module data 1408, store the module data 1408 at the network accessible storage 1202, and perform analysis or other operations on the module data 1408
  • module data 1408 may be received by the client computing device 1406 from the computing device 510 of the inspection apparatus 500-900.
  • the client computing device 1406 may send the module data 1408 to the service computing device(s) 1402 for storage on the network accessible storage 1202.
  • the client computing device 1406 may include a client application 1418 stored or otherwise maintained on one or more CRM 1420.
  • the client application 1418 may be executed by one or more processors 1422 of the client computing device 1406, such as to receive the module data 1408 from the inspection apparatus 500-900 and send the module data 1408 to the service computing device(s) 1402.
  • the client application 1418 may cause the processor(s) 1422 to use one or more wireless and/or wired communication interfaces 1424 to connect to the one or more networks 1404 for sending the module data 1408 to the service computing device(s) 1402.
  • the client application 1418 may be downloaded or otherwise provided to the client device 1406 by the service computing device(s) 1402.
  • the client application 1418 may be a program that specifically configures the client computing device 1406 to receive and process module data 1408 from the inspection apparatus 500-900, and to send the module data 1408 to the service computing device 1402.
  • one or more of the supply, operation and/or examination entities 1204 may each operate an inspection apparatus 500-900 and a client computing device 1406.
  • a module manufacturer may use an inspection apparatus 500-900 to obtain first module data about each manufactured module, and this first module data may be sent to the service computing device(s) 1402 for storage at the network accessible storage 1202.
  • that module may again be inspected using an inspection apparatus 500-900 to obtain second module data about that module, which may be sent to the service computing device(s) 1402 for storage at the network accessible storage 1202. Additionally, following installation that particular module may again be inspected using an inspection apparatus 500-900 to obtain third module data about that module, which may be sent to the service computing device(s) 1402 for storage at the network accessible storage 1202. Additionally, following installation that particular module may be periodically re-inspected using an inspection apparatus 500-900 to obtain additional module data about that module, which may be sent to the service computing device(s) 1402 for storage at the network accessible storage 1202.
  • that particular module may again be inspected using an inspection apparatus 500-900 by a module autopsy lab entity to obtain still additional module data, which may be sent to the service computing device(s) 1402 for storage at the network accessible storage 1202.
  • the module data obtained at different points in time may be compared with each other for determining when an event may have occurred that led to damage, failure or other faulty condition of the module, such as for determining a likely cause of the faulty condition of the module.
  • the service computing device(s) 1402 may include a service program 1426 and an analysis program 1428 stored or otherwise maintained on one or more CRM 1432.
  • the service program 1426 may be executed by one or more processors 1434 to configure the service computing device(s) 1402 to receive and process module data 1408 from an inspection apparatus 500-900 and/or the client device(s) 1406, and to send the module data 1408 to the network accessible storage 1202.
  • the service computing device(s) 1402 may for example include one or more communication interfaces 1436 configured for communicating over the one or more networks 1404 with the inspection apparatus 500-900, the client computing devices 1406, the network accessible storage 1202 and the like.
  • analysis program 1428 may be executed by the one or more processors 1434 for analysing the module data 1408 to determine analysis data 1438.
  • the analysis data 1438 may indicate conditions of particular modules and/or overall trends, causes of failure in individual or multiple modules, or the like.
  • the analysis program 1428 when executed by the one or more processors 1434, may cause the processors to compare module data 1408 received for a particular module at a first point in time with module data 1408 received for that module at a second point in time to determine at least one of a quality grade for the
  • photovoltaic module whether the photovoltaic module has a fault, whether the photovoltaic module is likely to develop a fault, or a cause of a fault in the photovoltaic module.
  • the analysis data 1438 may indicate a point in the manufacturing and installation chain at which a faulty condition was first identified for determining an entity that is likely to be the cause of the faulty condition. Consequently, the analysis data 1438 may enable identification of a cause of failure or other faulty condition to enable improvement of processes for improving quality and/or reliability of modules.
  • the analysis data 1438 and the module data 1408, including image data 1440 and metadata 1442, may be stored on the network accessible storage 1202 on a plurality of storage devices 1444 associated with the network accessible storage 1202.
  • the network accessible storage 1202 may provide storage capacity for the service provider 1300, as well as providing storage services for others in some examples.
  • the network accessible storage 1202 may include storage arrays such as network attached storage (NAS) systems, storage area network (SAN) systems, or storage virtualisation systems. Further, the network accessible storage 1202 may be co-located with one or more of the service computing devices 1402, or may be remotely located or otherwise external to the service computing devices 1402.
  • the network accessible storage 1202 includes one or more storage computing devices referred to as storage controlled s) 1446, which may include one or more servers or any other suitable computing devices, such as any of the examples discussed with respect to the service computing device(s) 1402.
  • the storage controller(s) 1446 may each include one or more processors 1448, one or more computer-readable media 1450 and one or more communication interfaces 1452. Further, the computer-readable media 1450 of the storage controller 1446 may be used to store any number of functional components that are executable by the processor(s) 1448.
  • these functional components comprise instructions, modules, or programs that are executable by the processor(s) 1448 and that, when executed, specifically program the processor(s) 1448 to perform the actions attributed herein to the storage controller 1446.
  • a storage management program 1454 may control or otherwise manage the storage of module data 1408 and analysis data 1438 in a plurality of storage devices 1444 coupled to the storage controller 1446.
  • the storage devices 1444 may in some cases include one or more arrays of physical storage devices.
  • the storage controller 1446 may control one or more arrays, such as for configuring the arrays in a RAID (redundant array of independent disks) configuration or other desired storage configuration.
  • the storage controller 1446 may provide logical units based on the physical storage devices 1444 to the service computing device(s) 1402, and may manage the data stored on the underlying physical devices 1444.
  • the physical devices 1444 may be any type of storage device, such as hard disk drives, solid-state devices, optical devices, magnetic tape and so forth, or combinations thereof.
  • the one or more service computing devices 1402 may be able to communicate over the one or more networks 1404 with computing devices 1458 of one or more interested parties 1208.
  • the interested party computing devices 1458 include one or more processors 1460, one or more computer-readable media (CRM) 1462 and one or more communication interfaces 1464.
  • An interested party (IP) application 1466 may be stored or otherwise maintained on the CRM 1462 and may be executed by the one or more processors 1460, e.g. for communicating with the service computing device(s) 1402 and/or receiving analysis data 1438 from the service computing device(s) 1402.
  • controller(s) 1446 may include a plurality of physical servers or other types of computing devices that may be embodied in any number of ways.
  • the modules, programs, other functional components, and a portion of data storage may be implemented on the servers, such as in a cluster of servers, e.g. at a server farm or data centre, a cloud-hosted computing service, and so forth, although other computer architectures may additionally or alternatively be used.
  • client computing device(s) 1406 and/or the interested party computing device(s) 1458 may be one or more servers, or alternatively, may be personal computers, laptop computers, workstations, tablet computing devices, mobile devices, smart phones, wearable computing devices, or any other type of computing device able to send data over a network.
  • Each of the processor(s) 1414, 1422, 1434, 1448 and/or 1460 may be a single processing unit or a number of processing units, and may include single or multiple computing units or multiple processing cores.
  • the processor(s) may be implemented as one or more central processing units, microprocessors, microcomputers, microcontrollers, digital signal processors, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions.
  • the processor(s) may be one or more hardware processors and/or logic circuits of any suitable type specifically programmed or configured to execute the algorithms and processes described herein.
  • the processor(s) may be configured to fetch and execute computer-readable instructions stored in their respective computer-readable media 1412, 1420, 1432, 1450 and/or 1462, which can program the processor(s) to perform the functions described herein.
  • the computer-readable media 1412, 1420, 1432, 1450 and/or 1462 may include volatile and nonvolatile memory and/or removable and non-removable media implemented in any type of technology for storage of information such as computer-readable instructions, data structures, program modules, or other data.
  • the computer-readable media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, optical storage, solid state storage, magnetic tape, magnetic disk storage, RAID storage systems, storage arrays, network attached storage, storage area networks, cloud storage, or any other media that can be used to store the desired information and that can be accessed by a computing device.
  • the computer-readable media may be a tangible non-transitory medium to the extent that, when mentioned, non-transitory computer-readable media exclude media such as energy, carrier signals, electromagnetic waves, and/or signals per se.
  • the computer-readable media 1412, 1420, 1432, 1450 and/or 1462 may be at the same location as the associated computing device, while in other examples the computer- readable media may be separate or partially remote from the associated computing device. Further, the computer-readable media 1412, 1420, 1432, 1450 and/or 1462 may be used to store any number of functional components that are executable by the respective associated processor(s), as discussed above. In many implementations these functional components, e.g.
  • control program 1410 the client application 1418, the service program 1426, the analysis program 1428, the storage management program 1454, and the interested parties application 1466, comprise instructions, modules, or programs that are executable by the respective processor(s) and that, when executed, specifically program the processor(s) to perform the actions attributed herein to the respective computing devices.
  • the communication interface(s) 1416, 1424, 1436, 1452 and/or 1464 may include one or more interfaces and hardware components for enabling communication with various other devices, such as over the one or more networks 1404.
  • the communication interfaces may include, or may couple to, one or more ports that provide connection to the network(s) 1404 for communication with other computing devices.
  • the communication interface(s) may enable communication through one or more of a LAN (local area network), a WAN (wide area network), the Internet, cable networks, cellular networks, wireless networks (e.g. Wi-Fi) and wired networks (e.g.
  • the one or more networks 1404 may include wired and/or wireless communication technologies. Components used for the network(s) 1404 can depend at least in part upon the type of network, the environment selected, desired
  • a manufacturer 1210 of monocrystalline silicon modules used a line-scanning EL/PL inspection apparatus for quality control testing of completed modules prior to packaging and transport.
  • Specific modules are identifiable in line-scanning PL images by front-facing barcodes and also by numeric codes on the edge of the module frame that can be included in the metadata.
  • Application of automatic image processing algorithms to acquired EL and PL images indicated that a specific module had no cracks, minimal series resistance issues and no interconnect issues. Consequently this module was packaged and shipped, whereas if the level of cracks for example had been above a predetermined threshold it would have been rejected and scrapped.
  • the module was also tested for power output using a solar simulator and found to be in the category of 300 W modules. This rated power output is the basis for pricing the module.
  • the installer 1214 used a portable version of a line-scanning EL/PL inspection apparatus to check each module prior to installation, with the objective of identifying modules that were already defective or likely to fail during the module's service period. Their motivation for doing so is related to the cost of replacing a module.
  • the cost of replacing a single defective solar module at this site was estimated to be US$800, i.e. US$2.67 per Watt, inclusive of a US$1.66 per Watt cost for a module autopsy report on which basis a warranty claim can be made.
  • Many manufacturer warranties require expensive autopsy tests and reports prior to any claim being made, which is aimed as a disincentive for warranty claims.
  • the project owner 1224 who had financed the installation insisted the installers 1214 spend US$1.33, i.e. US$0.0044 per Watt (inclusive of labour), to test each module with a line-scanning EL/PL inspection apparatus prior to installation. Any modules that failed the test were to be returned to the manufacturer 1210 for a refund or a replacement module. This requirement was based on the calculation that if just 0.15% of the modules failed during their 25-year service life, then identifying defective modules before installation was a lower cost option than replacing them after failure.
  • the portable field unit for line- scanning EL- and PL-based module inspection performed the same tests as the factory version, except for I-V testing.
  • module data 1408 was generated at the installation site and uploaded to the service provider 1300: (i) line-scanning PL image; (ii) line-scanning EL image; (iii) time and date of test; (iv) operator ID; (v) module ID; (vi) crack metrics from processed EL and PL images; (vii) series resistance metrics from processed EL and PL images; (viii) cell interconnect metrics from processed EL and PL images; and (ix) carrier
  • An initial test at the installation site for the 'defectiveness' of the subject module was based on results (vi) to (ix) of the above list.
  • the module passed these tests, with each of the defect levels being less than the predetermined thresholds for module rejection.
  • another set of data analyses was undertaken in a computing device 1402 of the service provider 1300 after upload of the module data (i) to (ix) to check for significant variations between the module data before transport and at the point of installation to check for damage that occurred during transport. Difference images were calculated by pixel-by-pixel subtraction of intensities in the 'factory' and 'field' PL images, and likewise for the two EL images.
  • ratio images could be calculated via pixel-by pixel intensity ratios of the 'factory' and 'field' images. These 'difference' images are highly likely to highlight any changes to the module that occurred during shipment, e.g. because of rough handling. Image processing algorithms were run on each of the difference/ratio images to calculate metrics for cracks, series resistance, cell interconnects and carrier recombination defects. Each metric has a predetermined threshold above which the module would be deemed defective and not fit for installation.
  • autopsy lab staff Using a line-scanning EL/PL inspection apparatus and an I-V power test unit, autopsy lab staff generated the following data: (i) line-scanning PL image; (ii) line-scanning EL image; (iii) I-V curve; (iv) time and date of test; (v) operator ID; (vi) autopsy lab ID; (vii) module ID; (viii) crack metrics from processed EL and PL images; (ix) series resistance metrics from processed EL and PL images; (x) cell interconnect metrics from processed EL and PL images; and (xi) carrier recombination defect metrics from processed EL and PL images.
  • the I-V test data confirmed that the module was generating lower than expected power.
  • the solar farm operator 1216 provided the relevant results to the project owner 1224, the project owner eventually claimed the cost of module replacement with insurance.
  • the insurance entity 1222 could, if required, request its own copy of the results from the service provider 1300.
  • a standards and quality assurance agency 1228 engaged a data analytics company to obtain and analyse module data 1408 from the service provider 1300 for all modules of a specific model number from a specific manufacturer that had been on the market for two years, with 20,000,000 units already installed in Europe or Australia.
  • the manufacturer 1210 had set specific 'pass/fail' thresholds for the following metrics based on processed EL and PL images acquired with an in-factory line-scanning inspection apparatus: (i) crack metrics; (ii) series resistance metrics; (iii) cell interconnect metrics; and (iv) carrier recombination defect metrics.
  • the pass/fail threshold was set relatively high, because otherwise the reject rate would have been uneconomically high since the manufacturer 1210 had neither the budget nor the expertise to reduce the incidence of the various defects to close to zero. There was concern in the market that the levels of defects being allowed through by the manufacturer 1210 might result in an unacceptably high incidence of module failure during their service life.
  • the analytics company gathered all available data for these modules, including data from factory testing, pre-installation testing and failed module autopsy reports.
  • the analytics company firstly identified that there were three primary causes of failure in modules that had been sent to module autopsy labs: (i) cell interconnect issues had led to electrical isolation issues and outright module failure in some modules installed in Australia, and much less commonly in modules installed in Europe; (ii) a relatively high level of carrier
  • Figures 15-19 are flow diagrams illustrating example processes according to some embodiments.
  • the processes are illustrated as collections of blocks in logical flow diagrams, which represent a sequence of operations, some or all of which may be implemented in hardware, software or a combination thereof.
  • the blocks may represent computer-executable instructions stored on one or more computer-readable media that, when executed by one or more processors, program the processors to perform the recited operations.
  • computer-executable instructions include routines, programs, objects, components, data structures and the like that perform particular functions or implement particular data types.
  • the order in which the blocks are described should not be construed as a limitation. Any number of the described blocks can be combined in any order and/or in parallel to implement the process, or alternative processes, and not all of the blocks need be executed.
  • the processes are described with reference to the environments, frameworks and systems described in the examples herein, although the processes may be implemented in a wide variety of other environments, frameworks and systems.
  • Figure 15 is a flow diagram illustrating an example process 1500 for determining conditions of modules over time according to some implementations.
  • the process 1500 may be executed by at least one of the service computing devices 1402 or some other suitable computing device.
  • a computing device may receive module data generated by an inspection apparatus at a first point in time, wherein the inspection apparatus is configured for generating the module data for a photovoltaic module.
  • the module data may for example be received from a module inspection apparatus and/or a client computing device of an entity that manufactures, transports, installs or operates modules, or that examines failed modules.
  • the computing device may receive one or more items of metadata associated with the module data, the one or more items of metadata including information about at least one of the module data or the photovoltaic module.
  • the metadata may for example include information about module ID, tests performed, manufacturer information, transporter information, installer information, operator information or the like.
  • the computing device may store the module data and the one or more items of metadata at a network accessible storage.
  • Module data received for the module at a plurality of different points in time may be stored for instance at a network storage location to enable analysis and determination of a condition of the module at the different points in time.
  • the computing device may determine a condition of the photovoltaic module, based at least partially on the module data and the one or more items of metadata. For example the computing device may determine the condition of the photovoltaic module by comparing the module data with prior module data generated for the photovoltaic module at an earlier time. Further, the computing device may determine, based on the condition, at least one of: a grade for the photovoltaic module; whether the photovoltaic module has a fault; whether the photovoltaic module is likely to develop a fault; or a cause of a fault in the photovoltaic module. Additionally, as another example, the computing device may receive additional module data generated at a second point in time by the same inspection apparatus or a different inspection apparatus, and the computing device may determine the condition of the
  • the computing device may send, based on the condition, a communication to a computing device of at least one entity associated with manufacture, transport, installation, operation or examination of the photovoltaic module, the communication indicating the determined condition.
  • the computing device may send, to a computing device of an interested party, at least one of the module data, prior module data, analysis data determined with respect to the photovoltaic module, or aggregated module data received for a plurality of photovoltaic modules.
  • module data may for example be generated by an inspection apparatus 500, 600, 700, 800 or 900.
  • an inspection apparatus 500- 900 may be under the control of a computing device 510, the terminal 512 or other computing device. That is, a computing device may operate some or all of the camera 502, light source 514, power supply 302 and scanning mechanism 508, as well as various optional components such as a light source 520 and camera 522 for optical imaging, a thermal imaging camera 704, a sunlight simulator 904 and associated power supply 906 and power monitoring unit 908, and various adjustable optical components such as filters and mirrors that may be present.
  • Figures 16-19 are flow diagrams illustrating example processes 1600, 1700, 1800 and 1900 for generating module data according to some implementations. In some examples, each of the processes 1600-1900 may be executed by a computing device 510 or other suitable computing devices.
  • a computing device may operate a power supply for applying electrical excitation to a photovoltaic module to generate electroluminescence from the photovoltaic module.
  • the computing device may operate a detector for detecting electroluminescence emitted from the photovoltaic module in a first area extending across a first dimension of the photovoltaic module.
  • the computing device may operate a scanning mechanism for scanning the first area along a second dimension of the photovoltaic module whilst applying the electrical excitation.
  • the computing device may receive, from the detector as the first area is scanned along the second dimension, an image of electroluminescence emitted from the photovoltaic module.
  • a computing device may operate a light source for illuminating a first area of a photovoltaic module with light suitable for generating photoluminescence from the photovoltaic module, the first area extending across a first dimension of the photovoltaic module.
  • the computing device may operate a detector for detecting photoluminescence emitted from the photovoltaic module in a second area extending across the first dimension of the photovoltaic module.
  • the computing device may operate a scanning mechanism for scanning the first and second areas along a second dimension of the photovoltaic module.
  • the computing device may receive, from the detector as the first and second areas are scanned along the second dimension, an image of photoluminescence emitted from the photovoltaic module.
  • a computer may process one or more electroluminescence images and/or photoluminescence images acquired with a module inspection apparatus to classify or distinguish between different types of features or defects.
  • the computer may generate one or more overlay images for highlighting one or more types of features or defects.
  • the computer may calculate one or more metrics of the occurrence of one or more types of features or defects.
  • the computer may apply a quality classification to a photovoltaic module, based on expected performance as estimated from the occurrence of various types of features or defects identified in the photovoltaic module.
  • a computer may obtain two or more images of a photovoltaic module acquired with a module inspection apparatus, the images being selected from the group comprising electroluminescence images, photoluminescence images, optical images or thermal images.
  • the computer may compare the two or more images obtained in step 1902.
  • program modules include routines, programs, objects, components, data structures, executable code, etc., for performing particular tasks or implementing particular abstract data types.
  • program modules and the like may be executed as native code or may be downloaded and executed, such as in a virtual machine or other just-in-time compilation execution environment.
  • functionality of the program modules may be combined or distributed as desired in various implementations.
  • An implementation of these modules and techniques may be stored on computer storage media or transmitted across some form of communication media.
  • the index arrangement herein may be implemented on physical hardware, may be used in virtual implementations, may be used as part of overall
  • deduplication system on either physical or virtual machine, and/or may be as a component for other deduplication implementations (e.g. SAN) or in some non-deduplication environments, such as large scale memory indexing.
  • SAN deduplication implementations
  • non-deduplication environments such as large scale memory indexing.
  • PL and EL imaging techniques can generally be applied to inspecting modules based on materials other than silicon by selecting light sources with suitable wavelength bands and illumination intensities, and cameras with suitable sensitivity and detection bands.
  • luminescence imaging techniques may well be easier to apply because of the often much greater luminescence efficiency of these materials compared to silicon.

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  • Health & Medical Sciences (AREA)
  • Biochemistry (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
  • Photovoltaic Devices (AREA)

Abstract

La présente invention concerne un appareil et des procédés de détermination de l'état de modules photovoltaïques à un ou plusieurs moments, en particulier à l'aide de techniques d'imagerie par luminescence à balayage linéaire. Une ou plusieurs images de photoluminescence et/ou d'électroluminescence d'un module sont acquises et traitées à l'aide d'un ou plusieurs algorithmes afin de fournir des données de module, comprenant la détection de défauts qui peuvent provoquer ou ont provoqué une défaillance de module. L'invention concerne également un système et un procédé de détermination de l'état de modules photovoltaïques, de préférence tout au long de la production, du transport, de l'installation et de la durée de vie des modules photovoltaïques.
PCT/AU2016/051183 2016-12-01 2016-12-01 Détermination de l'état de modules photovoltaïques WO2018098516A1 (fr)

Priority Applications (7)

Application Number Priority Date Filing Date Title
PCT/AU2016/051183 WO2018098516A1 (fr) 2016-12-01 2016-12-01 Détermination de l'état de modules photovoltaïques
AU2016431057A AU2016431057A1 (en) 2016-12-01 2016-12-01 Determining the condition of photovoltaic modules
CN201721649915.0U CN207743935U (zh) 2016-12-01 2017-11-30 用于检查光伏模块的设备
TW106142224A TW201834382A (zh) 2016-12-01 2017-12-01 確定光伏模組隨時間推移的狀況的系統和方法和相關介質
TW106142223A TW201834381A (zh) 2016-12-01 2017-12-01 用於檢查光伏模組的設備和方法
AU2018101083A AU2018101083B4 (en) 2016-12-01 2018-08-06 Determining the Condition of Photovoltaic Modules
AU2018101283A AU2018101283A4 (en) 2016-12-01 2018-09-04 Determining the Condition of Photovoltaic Modules

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PCT/AU2016/051183 WO2018098516A1 (fr) 2016-12-01 2016-12-01 Détermination de l'état de modules photovoltaïques

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WO2020137893A1 (fr) * 2018-12-28 2020-07-02 株式会社エンビジョンＡｅｓｃジャパン Dispositif d'inspection
ES2802473A1 (es) * 2019-07-10 2021-01-19 Univ Valladolid Metodo y sistema para inspeccionar paneles fotovoltaicos en funcionamiento
TWI727785B (zh) * 2020-05-06 2021-05-11 有成精密股份有限公司 太陽能模組檢測系統
CN113674150A (zh) * 2021-07-27 2021-11-19 上海洪朴信息科技有限公司 光伏外观组件测距方法
WO2022129172A1 (fr) * 2020-12-16 2022-06-23 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V. Procédé et dispositif permettant l'évaluation de la qualité d'une cellule solaire
CN114705698A (zh) * 2022-06-02 2022-07-05 季华实验室 缺陷检测方法、装置、系统及存储介质
CN114937013A (zh) * 2022-05-19 2022-08-23 武汉光目科技有限公司 一种光伏印刷网版缺陷检测方法及系统
WO2022261719A1 (fr) * 2021-06-18 2022-12-22 Newsouth Innovations Pty Limited Imagerie de photoluminescence extérieure de réseaux photovoltaïques par modulation de chaîne optique
WO2023124585A1 (fr) * 2021-12-31 2023-07-06 广东利元亨智能装备股份有限公司 Procédé et système de détection fondés sur un système de détection de défaut de batterie, et support de stockage
CN117132559A (zh) * 2023-08-16 2023-11-28 佛山职业技术学院 一种光伏组件组装工序管理用监测系统
CN117691948A (zh) * 2024-02-02 2024-03-12 众芯汉创(江苏)科技有限公司 一种智能化光伏组件缺陷巡检系统
WO2024140188A1 (fr) * 2022-12-30 2024-07-04 杭州禾迈电力电子股份有限公司 Procédé, appareil et système de génération de diagramme de disposition pour système de génération d'énergie photovoltaïque, et support de stockage

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DE102020210999A1 (de) 2020-09-01 2022-03-03 Forschungszentrum Jülich GmbH Verfahren und System zur Bewertung von Solarzellen
CN112290886B (zh) * 2020-09-18 2022-10-28 华为数字能源技术有限公司 一种故障检测方法、装置和光伏发电系统
WO2023224886A1 (fr) 2022-05-16 2023-11-23 Onsight Technology, Inc. Machines et procédés de surveillance de systèmes photovoltaïques

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JP7177474B2 (ja) 2018-12-28 2022-11-24 ヨコキ株式会社 検査装置
JP2020106472A (ja) * 2018-12-28 2020-07-09 株式会社エンビジョンＡｅｓｃジャパン 検査装置
WO2020137893A1 (fr) * 2018-12-28 2020-07-02 株式会社エンビジョンＡｅｓｃジャパン Dispositif d'inspection
ES2802473A1 (es) * 2019-07-10 2021-01-19 Univ Valladolid Metodo y sistema para inspeccionar paneles fotovoltaicos en funcionamiento
TWI727785B (zh) * 2020-05-06 2021-05-11 有成精密股份有限公司 太陽能模組檢測系統
WO2022129172A1 (fr) * 2020-12-16 2022-06-23 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V. Procédé et dispositif permettant l'évaluation de la qualité d'une cellule solaire
WO2022261719A1 (fr) * 2021-06-18 2022-12-22 Newsouth Innovations Pty Limited Imagerie de photoluminescence extérieure de réseaux photovoltaïques par modulation de chaîne optique
CN113674150A (zh) * 2021-07-27 2021-11-19 上海洪朴信息科技有限公司 光伏外观组件测距方法
WO2023124585A1 (fr) * 2021-12-31 2023-07-06 广东利元亨智能装备股份有限公司 Procédé et système de détection fondés sur un système de détection de défaut de batterie, et support de stockage
CN114937013A (zh) * 2022-05-19 2022-08-23 武汉光目科技有限公司 一种光伏印刷网版缺陷检测方法及系统
CN114705698A (zh) * 2022-06-02 2022-07-05 季华实验室 缺陷检测方法、装置、系统及存储介质
WO2024140188A1 (fr) * 2022-12-30 2024-07-04 杭州禾迈电力电子股份有限公司 Procédé, appareil et système de génération de diagramme de disposition pour système de génération d'énergie photovoltaïque, et support de stockage
CN117132559A (zh) * 2023-08-16 2023-11-28 佛山职业技术学院 一种光伏组件组装工序管理用监测系统
CN117691948A (zh) * 2024-02-02 2024-03-12 众芯汉创(江苏)科技有限公司 一种智能化光伏组件缺陷巡检系统
CN117691948B (zh) * 2024-02-02 2024-04-26 众芯汉创(江苏)科技有限公司 一种智能化光伏组件缺陷巡检系统

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