TW201834382A - System and method able to determine a condition of a photovoltaic module over time and related media - Google Patents

Abstract

Apparatus and methods are presented for determining the condition of photovoltaic modules at one or more points in time, in particular using line-scanning luminescence imaging techniques. One or more photoluminescence and/or electroluminescence images of a module are acquired and processed using one or more algorithms to provide module data, including the detection of defects that may cause or have caused module failure. Also presented is a system and method for determining the condition of photovoltaic modules, preferably throughout the production, transport, installation and service life of the photovoltaic modules.

Description

 System and method and associated medium for determining the condition of a photovoltaic module over time  

The present invention relates to an apparatus and method for determining the condition of a photovoltaic module, in particular, using luminescence imaging techniques. Some embodiments of the present invention have been developed to inspect or otherwise determine the condition of a photovoltaic module including a germanium photovoltaic cell, and are described with reference to the present application. However, it should be understood that the invention is not limited to this particular field of use.

Any discussion of prior art throughout the specification should not be considered in any way as an admission that such prior art is well-known or part of the common general knowledge in the art.

Photovoltaic modules (hereafter referred to as "modules" or "plural modules") are becoming an increasingly important part of the global power generation portfolio. It is estimated that the number of modules currently installed in the world exceeds 1 billion and is growing at a rate of 10% to 20% per year. Most installed modules contain a rectangular array of sixty or seventy-two single-crystal germanium or polycrystalline germanium photovoltaic cells (hereinafter referred to as "batteries" or "plurality of cells"), although thin film-based modules such as deuteration Cadmium, copper indium gallium selenide (CIGS) or amorphous germanium are also relatively common, such as modules with more or fewer germanium batteries. 1 shows a typical module 100 in a schematic plan view, the module comprising a rectangular array of 60 neodymium cells 102 wired into three groups 104 of 20 cells connected in series, and having a Electrical contact 106 of the charge carriers generated by the absorption of solar radiation in the battery. Each set of columns 104 has bypass diodes 108 connected in parallel for limiting the expanded effects of defective or temporarily shadowed cells. The module 100 will have a total width 110 of about 1.0 meter and a total length 112 of about 1.65 meters by sixty 150 x 150 mm cells arranged in a rectangular grid of six by ten closely packed. As shown in the schematic plan view of FIG. 2, the film module 200 typically includes an array of narrow strip cells 202 connected in series with electrical contacts 106 at each end. The thin film module is typically formed into a wide width by depositing a doped semiconductor material on a substrate 204 (such as a glass coated with a transparent conductive oxide) using a thin film deposition technique using a battery structure typically produced using laser light scribing techniques. The size of the range.

Modules are typically intended to have a service life of approximately twenty or twenty five years and typically have a warranty covering these time scales. However, there are several failure modes that can affect not only the performance of individual cells in a module, but also the performance of surrounding cells or even entire modules. Some failure modes can also cause hot spots with the risk of associated fire or further damage to the module. It is said that in some cases, up to 10% of the modules in the installation process will fail during their warranty period, which is a huge business problem. A module's "fault" may be a complete failure in the absence of power generation or a power generation that is normally calculated according to a formula that allows a fixed percentage reduction per year to fall below a guaranteed level.

Examples of failure modes for a single battery include cracks, shunts, and excessive series resistance of localized regions that may be associated with breakage of the metal contact pattern or poor contact between the metal pattern and the crucible or other battery material. Disconnection of the electrical connections between the batteries may also completely or partially isolate one or more of the modules. Such failure modes may be caused by, for example, battery or module manufacturing errors or due to improper handling during module shipping or installation. They can also be induced and/or grown in the field for months and years, such as by the ingress of water and oxygen, or the inevitable thermal cycling and UV degradation of organic materials in the module. Cracks are a particularly concealed failure mode because they tend to grow over time. For example, small cracks in the battery caused during module manufacture or shipment may have no appreciable impact on performance when the module is installed, but may grow, for example, due to thermal cycling or other environmental stresses. Various so-called light-induced degradation mechanisms are known which reduce the electrical performance of the illumination module over time during illumination. Many physical mechanisms of such degradation have been discovered, such as boron-oxygen defects that are ubiquitous in single crystal germanium cells. Another degradation mechanism is potential-induced degradation, which is a result of the large voltage difference between the glass surface of the cell and the module and the frame. Another possible degradation mechanism is turbidity caused by oxidation of ethylene vinyl acetate (EVA) polymers, which are commonly used to seal tantalum cells within modules.

Therefore, especially for the purpose of warranty, in order to identify or determine the condition of the module not only in the factory but also before, during and after the installation, to identify defective or isolated modules Battery or any other feature related to undesired changes in power generation performance. It is especially desirable to be able to determine the root cause of any identified problem.

The best known method for detecting modules is the current and voltage (IV) test, which measures the current (I) and voltage (V) characteristics of a particular module under simulated or actual solar conditions. A detailed description of solar energy conversion capabilities and efficiencies. Understanding the I-V characteristics of a module, especially its maximum power point (MPP), is critical to determining its expected output performance and solar efficiency and its value. As a general part of module manufacturing, all modules are tested for I-V performance.

Other common module detection techniques include visual inspection, thermal imaging, and electroluminescence using a camera under ultraviolet or visible light, M. Köntges, Photovoltaic, Colorado, Colorado, February 25-26, 2014. The latter two modes are described in "Reviewing the practicality and utility of electroluminescence and thermography images" on page 362-388 of the Module Reliability Symposium. Thermal imaging, which basically looks for temperature differences within or between modules, is currently the most common technique for inspecting modules at the site, ie after installation. It may not necessarily have sufficient resolution to determine the cause of the failure, but the defective module can be removed for further investigation in the module's "anatomy" laboratory, for example using I-V testing or electroluminescence imaging. Another drawback of thermal imaging is that it only identifies faults that have caused significant degradation in electrical performance. In other words, it does not apply to identifying the more subtle effects that can be used to predict module failure. For example, thermal imaging cannot detect cracks in the battery that have not grown to impede current flow.

Another method for field monitoring modules is to record their real-time performance using a dedicated circuit integrated with the module or in the inverter, such as the power output of the measurement module and its operating current and voltage. The test measures the power production of the module over the extended period of time and can alert the operator to a fault in the module or even a fault in the queue within the module, but is uncertain of the cause of the fault. Similar to thermal imaging, this approach typically only finds faults that have evolved to levels that cause the electrical output to significantly deviate from the performance of the rated module.

Full field electroluminescence (EL) imaging can be used to detect and locate various defects in a single cell and to infer the presence of disconnection or erroneous connections between cells, where CCD cameras, etc. are used in full field electroluminescence (EL) imaging. The spatial distribution of the band illumination caused by the radiation recombination of the charge carriers injected by the contact terminals of the forward biasing module is measured. 3 shows, in a schematic side view, a typical system 300 for acquiring full field EL images from a photovoltaic module 100, the exemplary system including a power source 302 for injecting current into the module by contact terminals, for An area camera 304 that detects the EL 306 emitted from the battery 102 in the module and a memory 308 for storing images read from the camera are detected. Since helium is a very poor illuminator, a full field EL imaging system typically also requires a light-proof housing 310 for removing ambient light. Due to the large working distance 312 required by the zone camera 304, the full field EL imaging system is typically bulky, which is one reason why it is typically limited to modular anatomy laboratories or factory inspections rather than field module inspections. If a plurality of zone cameras 304 are used to capture the ELs emitted from different portions of the module 100, the working distance 312 can be slightly reduced, but this adds to the cost of the device.

Full-field EL imaging is sensitive to many defects associated with module failure, including cracks, shunts and fractures in the metal contact pattern of the cell, as well as carrier recombination defects such as dislocations and impurities, which reduce charge carrier lifetime and therefore Degrade battery performance. In fact, all defects reduce EL emission and are therefore darker in EL images than materials without background defects, so it is difficult to distinguish between different types of defects. Image processing algorithms can be used to automatically distinguish between dark features and other features with different intensities, positions, shapes, sizes, but the accuracy and precision of these algorithms may be affected if there are a large number of feature types that may overlap.

A general property of EL imaging is that illumination can only be produced from areas of the battery that are electrically accessible. This effect is illustrated in FIG. 4, which shows an EL image of a module 100 having sixty polycrystalline germanium cells 102 taken with a device of the type illustrated in FIG. Several batteries appear to be completely dark, probably because they are shunted outside, such as interconnect errors during manufacturing, such that charge carriers cannot be injected into the battery. While this illumination mode is useful in revealing the presence of a module failure, dark batteries may contain defects such as cracks that are clearly not detected. In another example, if the interconnection between any two batteries is completely broken, the entire module will look completely dark under EL imaging. Often, the absence of illumination in some or all of the batteries in the module limits the amount of information available for defect detection or troubleshooting.

Another luminescence-based technique that can be applied to detect cells and modules is photoluminescence (PL) imaging, which differs from EL imaging in that charge carriers are generated by injecting high intensity light rather than by electricity. A PL-based module inspection technique is described in the published U.S. Patent Application No. 2015/0155829 A1. In this technique, when the operating point of the module is electrically modulated at a selected frequency, the module to be tested is illuminated by sunlight and imaged with an area camera. This applies a similar modulation to the PL emitted from the illuminated battery, enabling the locking technique to separate the PL signal from the ambient light. It appears that the ability of the technology to operate depends on the amount of sunlight available, and like full field EL imaging, the device is typically bulky. In addition, because of the significant intensity of sunlight over a wide spectral range, even with the best locking techniques, the spatial resolution of the image is relatively poor. Such low resolution images are generally useless to isolate a single defect, but only batteries with low PL emissions that may have low power generation.

There is an improved need for equipment and methods for inspecting or determining the condition of a photovoltaic module in a factory, prior to installation, in use, and in a modular anatomy laboratory to reliably detect and locate the performance of a photovoltaic module The occurrence of a failure mode that adversely affects. There is also a need for a system for determining one or more conditions, such as features or defects, of a photovoltaic module throughout the life of the photovoltaic module, such as a system for determining whether a failure mode has occurred or when a failure mode may occur.

It is an object of the present invention to overcome or ameliorate at least one of the disadvantages of the prior art or to provide a useful alternative. It is an object of the present invention to provide improved apparatus and methods for inspecting or determining the state of a photovoltaic module in a factory, prior to installation, in use, or in a modular anatomy laboratory. Preferably, another object of the present invention is to provide a system and method for determining one or more conditions, such as features or defects, of a photovoltaic module throughout the production, transportation, installation, and service life of a photovoltaic module in a preferred form. .

According to a first aspect of the present invention, there is provided an apparatus for inspecting a photovoltaic module, the apparatus comprising: a power source for applying an electrical stimulus to the photovoltaic module to generate electroluminescence from the photovoltaic module; and a detector for Detecting electroluminescence emitted by the first region of the photovoltaic module; scanning mechanism for scanning the first region along the photovoltaic module when applying the electrical excitation; and computing means for programming by executable instructions The electroluminescence image emitted from at least a portion of the photovoltaic module is received by the detector when the first region is scanned along the photovoltaic module.

In some embodiments, the detector comprises a line camera or a TDI camera. In other embodiments, the detector includes a contact imaging sensor.

In some embodiments, the scanning mechanism includes a mechanism for moving the photovoltaic module. In other embodiments, the scanning mechanism includes a mechanism for moving the detector. In other embodiments, the scanning mechanism includes 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 aforementioned first region and the detector remains substantially constant as the first region is scanned along the photovoltaic module.

In a preferred embodiment, the apparatus further includes one or more temperature sensors for monitoring the temperature of the photovoltaic module in the vicinity of the first region when scanning the first region along the photovoltaic module to enable Temperature correction is performed on the electroluminescence signal detected by the detector.

Preferably, the foregoing apparatus further includes a light source for illuminating the second region of the photovoltaic module with light suitable for generating photoluminescence by the photovoltaic module, such that when scanning the second region along the photovoltaic module, Obtaining a photoluminescence image emitted by at least a portion of the photovoltaic module. In some embodiments, the light source and detector are configured such that a photoluminescence image can be acquired with the detector. In other embodiments, the aforementioned apparatus further includes a second detector for acquiring an image of the photoluminescence.

In some embodiments, the aforementioned device is configured to acquire IV test data from a photovoltaic module or to acquire an optical image of at least a portion of the photovoltaic module or to acquire at least a portion of the photovoltaic module due to application of electrical excitation to the photovoltaic module. An image of the heat radiation.

In a preferred embodiment, the apparatus further includes a computer for processing one or more electroluminescent images and/or photoluminescence images acquired by the aforementioned device, the computer being programmed to classify or distinguish between different types of features or Defects, or generating one or more overlapping images for highlighting one or more types of features or defects, or calculating one or more indicators of one or more types of features or defects, or based on The aforementioned photovoltaic modules are mass categorized by the expected performance of the various types of features or defects identified in the photovoltaic module. In some embodiments, the aforementioned apparatus further includes a computer for comparing two or more images of the photovoltaic module acquired using the aforementioned device, the two or more images including electroluminescence images, photoinduced Choose from a group of illuminating images, optical images, or thermal images.

According to a second aspect of the present invention, there is provided an apparatus for inspecting a photovoltaic module, the apparatus comprising: a light source for illuminating a second region of the photovoltaic module with light suitable for generating photoluminescence from the photovoltaic module; a detector for detecting photoluminescence emitted from a first region of the photovoltaic module; a scanning mechanism for scanning the first region and the second region along the foregoing photovoltaic module; and a computing device executable by The instructions are programmed to receive a photoluminescence image emitted by at least a portion of the photovoltaic module from the detector when the first region and the second region are scanned along the photovoltaic module.

The aforementioned apparatus is preferably configured such that, in use, the aforementioned first area and the aforementioned second area at least partially overlap.

In some embodiments, the detector comprises a linear camera or a TDI camera. In other embodiments, the detector includes a contact imaging sensor.

In some embodiments, the scanning mechanism includes a mechanism for moving the photovoltaic module. In other embodiments, the scanning mechanism includes a mechanism for moving the detector and/or light source. In other embodiments, the scanning mechanism includes 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 aforementioned first region and the detector remains substantially constant as the first region and the second region are scanned along the photovoltaic module.

In a preferred embodiment, the apparatus is configured to acquire an image of the electroluminescence emitted by at least a portion of the photovoltaic module as a result of applying electrical excitation to the photovoltaic module, or to obtain IV test data from the photovoltaic module or Obtaining an optical image of at least a portion of the photovoltaic module or acquiring an image of thermal radiation emitted by at least a portion of the photovoltaic module due to electrical excitation applied to the photovoltaic module.

Preferably, the apparatus further includes a computer for processing one or more photoluminescence images and/or electroluminescence images acquired by the aforementioned device, the computer being programmed to classify or distinguish different types of features or defects, or Generating one or more overlapping images for highlighting one or more types of features or defects or calculating one or more indicators of one or more types of features or defects, or based on being in a photovoltaic module The aforementioned photovoltaic modules are mass categorized by identifying the expected performance of the various types of features or defects that occur. In some embodiments, the aforementioned apparatus further includes a computer for comparing two or more images of the photovoltaic module acquired using the aforementioned device, the two or more images including electroluminescence images, photoinduced Choose from a group of illuminating images, optical images, and thermal images.

According to a third aspect of the present invention, there is provided a method for inspecting a photovoltaic module, the method comprising the steps of: applying an electrical excitation to the photovoltaic module to generate electroluminescence from the photovoltaic module; detecting with the detector from the foregoing Electroluminescence emitted by the first region of the photovoltaic module; scanning the first region along the photovoltaic module when the electrical excitation is applied; and detecting from the detector when scanning the first region along the photovoltaic module Receiving an image of the electroluminescence emitted from at least a portion of the aforementioned photovoltaic module.

In some embodiments, the step of scanning the first region comprises moving the photovoltaic module. In other embodiments, the step of scanning the first region includes moving the detector. In other embodiments, the step of scanning the first region includes moving the optical element operatively associated with the aforementioned detector while the detector remains stationary. Preferably, the optical path length between the aforementioned first region and the detector remains substantially constant as the first region is scanned along the photovoltaic module.

In a preferred embodiment, the method further includes the steps of: monitoring the temperature of the photovoltaic module in the vicinity of the first region while scanning the first region along the photovoltaic module; and detecting the electroluminescence detected by the detector The signal is temperature corrected.

Preferably, the method further includes the steps of: illuminating the second region of the photovoltaic module with light suitable for generating photoluminescence from the photovoltaic module; and scanning the second region along the photovoltaic module. Obtaining a photoluminescence image emitted from at least a portion of the aforementioned photovoltaic module.

In some embodiments, the foregoing method further includes the steps of: acquiring an IV test data from the photovoltaic module or acquiring an optical image of at least a portion of the photovoltaic module or acquiring an image of the thermal radiation emitted from at least a portion of the photovoltaic module; The steps serve as a result of applying electrical excitation to the aforementioned module.

In a preferred embodiment, the method further includes the steps of: processing one or more electroluminescent images and/or photoluminescence images acquired from the photovoltaic module to classify or distinguish different types of features or defects, or generate one Or a plurality of overlapping images for highlighting one or more types of features or defects, or for calculating one or more indicators of one or more types of features or defects, or for identifying by a photovoltaic module The expected performance of the various types of features or defects that occur is estimated to classify the aforementioned photovoltaic modules. In some embodiments, the method further includes the step of comparing two or more images acquired from the photovoltaic module from the group comprising electroluminescent images, photoluminescence images, optical images, and thermal images. Choose among.

According to a fourth aspect of the present invention, there is provided a method for inspecting a photovoltaic module, the method comprising the steps of: illuminating a second region of the photovoltaic module with light suitable for generating photoluminescence from the photovoltaic module; utilizing Detecting, by the detector, photoluminescence emitted from a first region of the photovoltaic module; scanning the first and second regions along the photovoltaic module; and scanning the first region and the second region along the photovoltaic module A photoluminescence image emitted from at least a portion of the photovoltaic module is received from the detector.

Preferably, the aforementioned first area and second area at least partially overlap.

In some embodiments, the step of scanning the first region and the second region comprises moving the photovoltaic module. In other embodiments, the step of scanning the first region and the second region includes moving the detector and/or the light source. In other embodiments, the step of scanning the first and second regions includes moving the optical element operatively associated with the aforementioned detector while the detector remains stationary. Preferably, the optical path length between the aforementioned first region and the detector remains substantially constant as the first region and the second region are scanned along the photovoltaic module.

In some embodiments, the method further includes the step of acquiring an image of the electroluminescence emitted from at least a portion of the photovoltaic module as a result of applying electrical excitation to the photovoltaic module, or obtaining an IV test data from the photovoltaic module. The step of either obtaining an optical image of at least a portion of the photovoltaic module or obtaining an image of thermal radiation emitted from at least a portion of the photovoltaic module as a result of applying electrical excitation to the photovoltaic module.

Preferably, the foregoing method further comprises the steps of: processing one or more photoluminescence images and/or electroluminescence images acquired from the photovoltaic module to classify or distinguish different types of features or defects, or generate one or more Overlapping images for highlighting one or more types of features or defects, or calculating one or more indicators of one or more types of features or defects, or based on various occurrences identified by photovoltaic modules The expected performance of the type of feature or defect is estimated to classify the aforementioned photovoltaic modules. In some embodiments, the method further includes the step of comparing two or more images acquired from the photovoltaic module from the group comprising electroluminescent images, photoluminescence images, optical images, and thermal images. Choose among.

According to a fifth aspect of the present invention, there is provided a system capable of determining a condition of a photovoltaic module over time, the system comprising: one or a plurality of processors; and a memory storing computer executable program code including instructions, The instructions, when executed by the one or more processors, configure the one or more processors to: receive module data generated by the inspection device at a first point in time, wherein the inspection device is configured to generate the aforementioned photovoltaic module Module data; receiving one or more metadata items associated with the module data, the one or more metadata items including information about the module data or at least one of the foregoing photovoltaic modules; The foregoing module data and the one or more metadata items are stored in a network accessible storage device; and the status of the photovoltaic module is determined based at least in part on the module data and the one or more metadata items.

The aforementioned module data preferably includes one or more of an electroluminescence image, a photoluminescence image, an optical image, a thermal image, and an I-V test data.

In a preferred embodiment, the inspection apparatus includes: a detector for detecting at least one of photoluminescence emitted from the photovoltaic module and electroluminescence emitted from the photovoltaic module; a scanning mechanism for Scanning an area of the photovoltaic module during the inspection; and computing means programmed by the executable instructions to receive at least one of the photoluminescence image and the electroluminescence image of at least a portion of the photovoltaic module from the detector as a mode Group information.

Preferably, the one or more processors are further configured to: receive additional module data generated by the inspection device or different inspection devices at a second point in time; and based at least in part on the mode to be from the first point in time The group data is compared with the additional module data to determine the status of the photovoltaic module at the second time point.

In a preferred embodiment, the one or more processors are further configured to determine the photovoltaic module by comparing the module data with the previous module data generated by the photovoltaic module at an earlier time. situation. Preferably, the one or more processors are further configured to determine at least one of the following based on the foregoing conditions: a level of the photovoltaic module; whether the photovoltaic module is faulty; and whether the photovoltaic module is likely to fail; The cause of the failure in the photovoltaic module.

Preferably, the one or more processors are further configured to transmit communication information to the computing device of at least one entity associated with the manufacture, transportation, installation, operation or inspection of the aforementioned photovoltaic module based on the foregoing conditions, the communication information Indicates the status determined. In a preferred embodiment, the one or more processors are further configured to transmit to the computing device of the interested party at least one of the following: the determined module data about the photovoltaic module, the previous module data. Or analysis data, and cumulative module data received for multiple PV modules.

According to a sixth aspect of the present invention, there is provided a method for determining a condition of a photovoltaic module over time, the method comprising: receiving, by one or more processors, module data generated by the inspection device at a first time point, The foregoing inspection device is configured to generate module data of the foregoing photovoltaic module; and receive one or more metadata items associated with the module data by one or more processors, the one or more metadata items Include information about at least one of the foregoing module data and the aforementioned photovoltaic module; storing the module data and the one or more metadata items in a network accessible storage device by one or more processors Determining the condition of the aforementioned photovoltaic module by one or more processors based at least in part on the aforementioned module data and the aforementioned one or more metadata items.

The aforementioned module data preferably includes one or more of an electroluminescence image, a photoluminescence image, an optical image, a thermal image, and an I-V test data.

In a preferred embodiment, the inspection apparatus includes: a detector for detecting at least one of photoluminescence emitted from the photovoltaic module or electroluminescence emitted from the photovoltaic module; a scanning mechanism for Scanning an area of the photovoltaic module during the inspection; and computing means programmed by the executable instructions to receive at least one of the photoluminescence image or the electroluminescence image of at least a portion of the photovoltaic module from the detector as a mode Group information.

Preferably, the foregoing method further comprises the steps of: receiving additional module data generated by the inspection device or different inspection devices at a second point in time; and based at least in part on the module data and the additional mode to be from the first time point The group data are compared to determine the status of the PV module at the second time point.

Preferably, determining the condition of the photovoltaic module comprises comparing the module data with the previous module data generated by the aforementioned photovoltaic module at an earlier time. In a preferred embodiment, the method further includes the step of determining at least one of the following: a level of the photovoltaic module; a failure of the photovoltaic module; a failure of the photovoltaic module; and a photovoltaic module The cause of the failure in the group.

In some embodiments, the method further includes transmitting communication information to the computing device of the at least one entity associated with the manufacture, transportation, installation, operation, or inspection of the aforementioned photovoltaic module based on the foregoing conditions, the communication information indicating the determined situation. In some embodiments, the method further includes the step of transmitting to the computing device of the interested party at least one of: the determined module data about the photovoltaic module, the previous module data or the analysis data, and Accumulated module data received by a plurality of photovoltaic modules.

100‧‧‧ modules

102, 202‧‧‧Battery

104‧‧‧Group

106‧‧‧Electrical contacts

108‧‧‧Bypass diode

110‧‧‧Width

112‧‧‧ Length (full length)

200‧‧‧film module

204‧‧‧Substrate

300‧‧‧ system

302‧‧‧Power supply

304, 502, 502A, 522, 528, 704‧‧‧ camera

306‧‧‧Electroluminescence (EL)

308‧‧‧ memory

310‧‧‧ Shell

312‧‧‧Working distance

500, 600, 700, 800, 900‧‧‧ equipment

504, 804‧‧ ‧ luminous

506‧‧‧First area

508‧‧‧ scanning agency

510‧‧‧ computing device

512‧‧‧ Terminal

514, 520‧‧‧ light source

516‧‧‧Second area

518‧‧‧Soft brush

524‧‧‧Light

526‧‧‧temperature sensor

602‧‧‧ imaging sensor

604‧‧‧PL line scan head

606‧‧‧ Output window

608‧‧‧Micro Bar Lens Array

610‧‧‧Micro Optical Array

702, 702-A‧‧‧ arrows

706‧‧‧ Mid-infrared radiation

708‧‧‧ components

802‧‧‧ optical components

806‧‧‧ turning mirror

902‧‧‧still position

904‧‧‧Sunlight Simulator

906‧‧‧Power and controller

908‧‧‧Power Monitoring Unit

1000, 1002, 1100, 1102, 1112, 1114‧‧ images

1004, 1206, 1404‧‧‧ network

1006‧‧‧ stripes

1008‧‧‧ bus bar

1104‧‧‧Dislocation cluster

1106‧‧‧Dark lines

1108‧‧‧Battery clip

1110‧‧‧ crack

1116‧‧‧Metal contact fingers

1118‧‧‧ grain structure

1200‧‧‧ system

1202, 1444‧‧‧ storage devices

1204‧‧‧ entity

1208‧‧‧ interested parties

1210‧‧‧Manufacturer

1212‧‧‧Transporter

1214‧‧‧Installers

1216‧‧‧Modular operators

1218‧‧‧Modular Anatomy Laboratory

1220‧‧‧Solar financial entity

1222‧‧‧ solar insurance entity

1224‧‧‧Project owner

1226‧‧‧Solar Market Reporting Group

1228‧‧‧Quality Assurance Agency

1300‧‧‧Service Provider

1302‧‧‧ arrow

1304‧‧‧ Upload

1306‧‧‧Storage

1310‧‧‧ charges

1312‧‧ Search

Provided by 1314‧‧

1400‧‧‧ architecture

1402‧‧‧Service Computing Unit

1406‧‧‧Client computing device

1408‧‧‧Modular data

1410‧‧‧Control program

1412, 1420, 1432, 1450, 1462‧‧‧ computer readable media

1414, 1422, 1434, 1448, 1460‧‧ ‧ processors

1416, 1424, 1436, 1452, 1464‧‧‧ communication interface

1418‧‧‧ client application

1426‧‧‧Service Program

1428‧‧‧Analytical program

1438‧‧‧Analytical data

1440‧‧‧Image data

1442‧‧‧ metadata

1446‧‧‧Storage Controller

1454‧‧‧Storage management program

1458‧‧‧ Computing device

1466‧‧‧ interested parties application

1500, 1600, 1700, 1800, 1900 ‧ ‧ process

1502~1512, 1602~1608, 1702~1708‧‧‧ steps

1802~1808, 1902~1904‧‧‧ steps

The benefits and advantages of the present invention will become apparent to those skilled in the art from this invention. In the drawings, the same reference numerals are used to refer to the

Figure 1 shows a battery based module in a schematic plan view.

Figure 2 shows a film module in a schematic plan view.

Figure 3 shows, in a schematic side view, a conventional apparatus for acquiring an EL image of a module.

Figure 4 shows an EL image of a helium-based battery module taken with a device of the type shown in Figure 3.

5A, 5B, and 5C respectively show a schematic plan view, a schematic side view, and a 3D image of an apparatus for inspecting a module according to an embodiment of the present invention.

Figure 5D shows a variation of the apparatus shown in Figures 5A through 5C in a schematic side view.

6A and 6B show, in a schematic plan view and a side view, an apparatus for inspecting a module in accordance with another embodiment of the present invention.

Figure 6C shows a compact PL line scan head in a schematic side view.

Figure 7A shows, in a schematic side view, an apparatus for inspecting a module in accordance with another embodiment of the present invention.

Figure 7B shows a variation of the apparatus shown in Figure 7A in a schematic side view.

Figure 8A shows, in a schematic side view, an apparatus for inspecting a module in accordance with another embodiment of the present invention.

Figure 8B shows a variation of the apparatus shown in Figure 8A in a schematic side view.

Figure 9 shows, in a schematic side view, an apparatus for inspecting a module in accordance with yet another embodiment of the present invention.

Figure 10A shows a line scan PL image of a module comprising a polycrystalline germanium battery.

FIG. 10B shows an image of a single battery extracted from the image of FIG. 10A.

Figure 11A shows an image of a polycrystalline silicon battery extracted from a line scan EL image of a complete module.

FIG. 11B shows an image of the same battery as that of the battery of FIG. 11A extracted from the line scan PL image of the complete module.

11C and 11D respectively show the line scan EL and the line scan PL image of the corner areas of the four xenon cells in the module.

Figure 12 shows a high level example of a system for determining the state of a photovoltaic module, for example, throughout its useful life.

Figure 13 illustrates a cloud-based software as a service (SaaS) model for the operation of the system of Figure 12.

FIG. 14 illustrates an exemplary physical and logical architecture of the system of FIG. 12, in accordance with some embodiments.

15 is a flow chart showing an example process for determining the condition of a module over time, in accordance with some embodiments.

16, 17, 18, and 19 are flow diagrams illustrating example processes for generating module material, in accordance with some embodiments.

Preferred embodiments of the present invention will now be described, by way of example only, with reference to the drawings.

Illumination imaging device for module inspection

5A and 5B show, in a schematic plan view and a side view, an apparatus 500 for inspecting or determining a condition of a module 100 including a two-dimensional array of sixty neon cells 102, in accordance with an embodiment of the present invention. A 3D image of device 500 is shown in Figure 5C. Apparatus 500 includes a power source 302 for applying electrical excitation to module 100 via electrical contacts 106 to generate electroluminescence 306 from the module; linear camera or Time Delay and Integration (TDI) camera detection 502 for detecting EL emitted from the first region 506 of the module; a scanning mechanism 508, such as a conveyor belt, roller or air bearing, for moving the module 100 such that the first region 506 is scanned along the module; A suitably programmed computing device 510 is used to read the camera 502 line by line in synchronization with the scan to obtain an image of the EL emitted from at least a portion of the module. Preferably, as shown, the first region 506 extends across the width 110 of the module and is scanned along the full length 112 of the module such that the entire front surface of the module 100 is imaged. In general, the electroluminescence 306 generated by electrical excitation will be primarily from the belt to the strip EL of the battery 102, but the possibility of generating EL from other components of the module should not be excluded. Suitable cameras for detecting belt-to-band illumination from neodymium batteries include neodymium and InGaAs cameras. Figure 5C also shows the terminal 512 for the operator's control of the device 500 or for presenting the acquired image to the operator. It should be understood that the line scanning EL imaging apparatus 500 illustrated in FIGS. 5A through 5C may be more compact, such as the prior art area imaging EL apparatus (system 300) illustrated in FIG. Although the generated EL 306 is preferably detected by a multi-pixel detector such as a linear camera or TDI camera 502 as shown, it may be configured to move back and forth in a direction perpendicular to the direction of the mobile module 100. A single component detector performs the test.

A standard strip to strip EL can be generated from the tantalum cell 102 of the module 100 by applying a relatively moderate forward bias (typically slightly above the open circuit voltage (Voc)) to the electrical contact 106 (terminal). For example, a forward bias of about 40V to 50V is typically sufficient for generating an EL from a module having sixty neon cells 102 each having a Voc of about 0.63V. It is also possible to apply a reverse bias to the module because it is known that under a large reverse bias, the battery exhibits a breakdown behavior that manifests itself as illumination from the active battery area, potentially providing additional on the module. News. However, the required voltage is significantly higher than the forward biased EL, typically at least 5V to 10V and up to 15V per battery, ie hundreds to over 1000V for sixty battery modules, which may Causes security issues. This, along with the possibility of a large reverse bias that actually causes battery damage, can limit the reverse bias EL for use in a modular anatomy lab unless the module being inspected contains fewer batteries. In order to apply a sufficiently large reverse bias for generating a breakdown behavior, it is often necessary to disconnect or otherwise disable any of the bypass diodes 108 of the module under test 100. This should be possible because the bypass diodes are usually located in the junction box, but it is further recommended that tests based on reverse bias EL imaging be retained for special cases such as module anatomy, rather than for modules. Quality inspection.

In a preferred embodiment, the apparatus further includes a light source 514 for utilizing a second of the light illumination module 100 adapted to generate a PL from the battery 102 and possibly also from other components of the module, such as a backplane polymer. Area 516. For neon cells, light source 514 may, for example, comprise a laser photodiode array or an array of LEDs that emit red light or emit light in a near infrared region, for example, in the range of 600 nm to 980 nm. Light source 514 and camera 502 are configured such that the camera acquires an image of the PL emitted from at least a portion of module 100 because scanning mechanism 508 scans second region 516 and first region 506 along the module. Preferably, as shown, the second region 516 extends across the width 110 of the module and is scanned along the full length 112 of the module such that the entire front surface of the module 100 is imaged. As further discussed below, as shown in Figure 5A, the light source and camera are preferably configured such that, in use, the first region 506 and the second region 516 at least partially overlap, although if a sufficient proportion of the photogenerated charge carriers are capable of The illumination area 516 is removed, but this is not important. Additional optics may also be included in device 500, such as a rod lens for focusing light from source 514 onto second region 516, a short pass filter in front of source 514 to prevent long wavelength tail radiation from reaching camera 502. And a long pass filter in front of the camera 502 that blocks stray excitation light. One or more interchangeable filters may be disposed in front of the light source 514 and/or the camera 502 for selectively energizing and/or detecting the substrate material from the battery 102 on the one hand or from the module on the other hand. The PL of some other materials (such as backsheet polymers). Alternatively, device 500 can include additional light sources or detectors with different excitation or detection bands for energizing or detecting PL from various components of the module.

As shown in Figures 5A and 5B, in some embodiments, a single camera 502 is used to detect the generated PL or EL, in which case the module 100 can pass through the device 500 twice, for example, forward and then The PL and EL images are acquired in order in the backward direction. The module may also pass through the device more than once to acquire two or more PL images, for example, where different illumination intensities, illumination wavelengths or detection wavelengths or two or more EL images are used to generate the PL, for example by application Different voltages. FIG. 5D shows in a schematic side view a variation of apparatus 500 comprising a first camera 502A for detecting EL generated by power source 302 and a second camera 502 for detecting PL 504 generated by light source 514. As shown, the two cameras can be read by the same computing device 510 or by a separate computing device. Having two cameras is capable of acquiring separate PL and EL images without having to pass the module through the device twice or reverse the direction of the scanning mechanism 508. Preferably, if the scan is parallel to the module length 112 or the module width, 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, so that the light excitation and the electrical excitation can be Isolation is applied separately. For example, once the module 100 has been illuminated by the light source 514, only the power source 302 will be activated.

In the preferred embodiment, camera 502 and light source 514 are mounted within substantially light-proof housing 310 as shown in Figures 5A and 5B to maintain ambient light outside of the camera or camera includes excitation light 524. As shown in Figure 5C, in certain embodiments, the bottom edge of the outer casing 310 has a soft brush 518 or the like, such as a black cloth, for improved light sealing. In the variation shown in Figure 5D, a single housing covering cameras 502 and 502A may be provided, or a separate housing may be provided for each camera.

The electrical excitation of the power source 302 used to generate electroluminescence will tend to heat the battery 102, which can affect their luminous efficiency. Therefore, when the line scan EL image of the module 100 is acquired, if the EL collected later in the scan is generated from the battery at a higher temperature, a temperature gradient effect can be applied to the aforementioned image. This artifact can be improved by monitoring the temperature of the module 100 near the first region 506 during scanning of one or more temperature sensors 526 such as infrared thermometers spaced within the housing 310. The computing device 510 or another computing device can then apply temperature correction to the electroluminescent signal detected by the camera 502, for example, using the known illumination temperature coefficient of the battery 102. This temperature gradient effect is less likely to occur in the area imaging EL imaging system as shown in FIG. 3, where the camera 304 simultaneously collects EL from all portions of the module 100. This temperature gradient effect is also less likely to occur when acquiring a line scan PL image of a module, as any localized heating from source 514 should be equally applied to each portion of the module as it is being imaged.

Alternatively, as shown in Figure 5B, device 500 can include a vision system that includes a light source 520 (such as a linear array of white LEDs) and a suitable linear camera or TDI camera 522 for use with the aforementioned device When scanned, an optical (ie, reflected) image of at least a portion of the module 100 is acquired. Camera 522 can be read by the same computing device 510 or a different computing device. As explained in more detail below, the optical image acquired by the vision system can provide further information regarding defects or other features in the module 100. In other embodiments, the light source 514 and camera 502 for PL imaging may be adapted to acquire an optical image, such as by reducing the intensity with a neutral density filter and removing any cut-off or bands that would otherwise separate the illumination from the detection band. Pass filter. In another variation, apparatus 500 can include a near-infrared transmission vision system having a suitable light source 520 and a linear camera or TDI camera 528 on opposite sides of module 100. This transmission vision system can be used, for example, to perform microcrack detection in a module that includes a double-sided battery and has glass on both sides.

Luminescence can be generated by a combination of light and electrical excitation. For example, power source 302 can be operated to inject current into module 100 as light source 514 illuminates module 100. Broadly speaking, injecting or extracting current during acquisition of a luminescent image promotes the movement of charge carriers for further differentiation between carrier lifetime defects and series resistance defects. Some potential applications are discussed below in the "Image Analysis" section. In an alternate embodiment, power source 302 is omitted from device 500 such that illumination is only generated by optical excitation. In this context, and as explained in more detail in the published U.S. Patent Application No. 2015/0168303 A1, the EL image can be simulated by configuring the device 500 such that, in use, the first region 506, ie "imaging" stripes and The second regions 516, ie the "lighting" stripes do not overlap, but instead are displaced from each other. In this context, luminescence is detected from the photogenerated charge carriers that laterally migrate out of the "light" fringes (second region 516) before the radiation recombines. In general, the main contribution of this lateral migration will be that most of the carriers are transported through the emitter layer and the base of the cell 102, and current flows through the front and back surface metallization layers, as well as through the diffusion of the matrix material. The minority carriers also play a small role. In some embodiments, device 500 is equipped with a mechanism for varying the extent to which first region 506 and second region 516 overlap on module 100. One advantage of simulating an EL image via light excitation rather than simply applying a voltage to the module is that the device is designed to acquire EL and PL images from the aforementioned modules when the module is single-passed. Since the effect of the light excitation applied to the narrow region (second region) 516 of the module is more localized than the effect of the electrical excitation applied to the electrical contact 106, the two light source/camera units can be placed relatively close together, This results in a more compact device and faster scanning. In contrast, in the apparatus 500 shown in FIG. 5D, the "PL" camera 502 and the "EL" camera 502A should be separated by a distance equal to or larger than the module size in the scanning direction so that the electric excitation can be assisted by The EL image is acquired in the case of the light emission 504 captured by the "PL" camera 502.

The acquired illuminating images can be stored on computing device 510 for subsequent processing on the same or different computing devices or displayed on monitor 512 for interpretation by the operator. As described above, in the preferred embodiment, one or more software algorithms are used to process the illuminating image, for example, presented to the operator for interpretation or triggering an automatic alert or transmitted to a database for later viewing or at a different time. The images acquired by the module are highlighted to highlight various types of defects or features.

As shown in the plan view of FIG. 5A, the illumination generated from the module 100 is detected by a linear camera in the lateral range or a detector 502 in the form of a TDI camera that is much shorter than the module width 110. It is easy to obtain a line with an enhanced near-infrared response and a TDI camera with higher sensitivity for silicon band-to-band luminescence, and the TDI camera is particularly advantageous in that it is due to a plurality of pixels The summation of the lines of signals provides an increased gain. However, this configuration also has disadvantages such as requiring a relatively large working distance of the order of tens of centimeters, and a detected drop in intensity from the edge of the field of view corresponding to the "imaged" fringes (first region 506). The path length of the illumination to the linear camera or TDI camera 502 can be much longer than the length of the illumination path schematically shown in Figure 5B, and it should be understood that one or more folding mirrors can be included as needed to include the optical path in An appropriately sized outer casing 310.

In an alternative device 600, shown in the schematic plan and side views of Figures 6A and 6B, a detector in the form of a contact imaging sensor 602 is used to detect illumination for readout by the computing device 510 in synchronization with the scan to An image of the illumination emitted from at least a portion of the module is obtained. The contact imaging sensor can, for example, comprise an array of pixels having an integrated microrod lens array that is long enough to span the full width 110 of the module 100. An almost unlimited length of contact imaging sensor can be constructed by docking a plurality of shorter CMOS sensor chips together as described in U.S. Patent No. 8,058,602. Although CMOS sensor chips are commonly used for contact imaging sensors, other types of sensors, such as CCD sensors, can also be used. In an EL-only configuration, i.e., without the light source 514, the contact imaging sensor 602 can be easily placed a few millimeters from the cover glass of the module. If a slightly larger spacing is desired, additional or alternative focusing optics may be used, for example, to provide better access to illumination 524 of light source 514 for generating PL from the aforementioned modules. Alternatively, as shown in the schematic side view in FIG. 6C, the light source 514 can be tightly integrated with the contact imaging sensor 602 to provide a highly compact PL line scan head 604 that can be placed at a distance from the PL line scan head. The module cover glass is close to a few millimeters. In one example, the light source 514 having an output window 606 having a width in the range of 0.1 to a few millimeters can be directly adjacent to or within a few microns of the microrod lens array 608 of the contact imaging sensor 602. To focus the output of the light source 514, the light source 514 can have a miniature optical array 610 that can have, for example, the same pitch as the microrod lens array 608.

Whether configured for EL imaging or PL imaging, the use of contact imaging sensor 602 enables a compact module inspection device. In another variation suitable for use with a module comprising a two-dimensional battery array, the detector may be in the form of a separate CMOS sensor chip that is configured to detect illumination from each row of cells. Commercial contact imaging sensor systems are typically designed to operate in the visible region of the spectrum and will only be sensitive to the short wavelength end of the xenon band. The reduction in sensitivity can be compensated by using a rectangular sensor pixel array having a long axis parallel to the scan direction, preferably with having a light from about 1:1 aspect ratio (aspect ratio) or substantially a circle The shaped sample regions are gathered into a micro-optical array combination of elements on the rectangular sensor pixels. Insensitivity to long wavelength illumination can actually facilitate improved spatial resolution for the reasons discussed in the published PCT patent application no. WO 2011/017776 A1.

Many other detection configurations for collecting illumination from near the surface of the module are also possible, such as using fiber optic ribbon or integrated optical waveguides to direct illumination to the pixel array, tapering as needed to match the size of the pixel array.

It should be noted that the apparatus 500 as depicted in Figures 5A-5C is configured to span the short dimension 110 of the module 100 such that the module is transported in a direction parallel to its long dimension 112. The above configuration is a more convenient configuration for several reasons than alternatives that span long dimensions, such as a simpler optical design or the ease of connecting power supply 302 to electrical contacts 106. However, the reason why the line scan illuminating imaging device cannot be designed to scan the module in a direction parallel to the short dimension 110 is that there is no root cause, such as using a contact imaging sensor system that is long enough, and in terms of speed, It is actually beneficial. Under all other conditions, a 1.0 m x 1.65 m module scans 40% faster along its short dimension.

In the apparatus 500, 600 shown in Figures 5A through 6C, when the camera 502 or the contact imaging sensor 602 and the light source 514 remain stationary, the module 100 moves over a scanning mechanism 508, such as a conveyor belt or the like. In some examples, scanning mechanism 508 for scanning first region 506 and second region 516 along the module includes mechanisms for moving module 100 such as a conveyor belt, rollers, or air bearings. This arrangement is advantageous if the detector or source contains precision optics and is generally suitable for use in modules that may be, for example, during or after manufacture, before or after shipment, prior to installation, or in a modular anatomy laboratory. How to perform a module check when moving.

FIG. 7A shows, in a schematic side view, an apparatus 700 for inspecting or determining the state of a module 100 in accordance with another embodiment of the present invention. As previously mentioned, the apparatus includes a light source 514 for generating a PL from the battery 102 and possible other components of the module, a detector 502 in the form of a linear camera or TDI camera for detecting the generated PL, and suitably programmed A computing device 510 for reading the camera line by line in synchronization with the illumination of the module 100 and the scanning of the imaging region to obtain an image of the PL emitted from at least a portion of the module. However, in this case, scanning is performed by moving the light source 514 and the camera 502 as indicated by arrow 702. In the illustrated embodiment, light source 514 and camera 502 are fixedly attached within a substantially light-resistant housing 310 that is adapted to move along module 100 on a scanning mechanism 508 that includes a track or roller. This arrangement allows the module 100 to remain stationary, suitable for installation when the module is fixed in place, such as on a roof, or if the module is in a fixed position inspection module if convenient. Although the generated illumination is preferably detected by a multi-pixel detector such as a line array or TDI camera 502 as shown, it is also possible to use a unit that is configured to move back and forth in a direction perpendicular to the direction of the moving housing 310. The detector performs the test. In some embodiments, device 700 further includes a power source 302 for injecting current into or out of the module via electrical contacts 106 for generating EL. In an alternate embodiment, such as when inspecting installed modules, the aforementioned devices can cooperate with existing electrical infrastructure to apply electrical excitation to the modules. In other embodiments, the source 514 is omitted where illumination is only generated by electrical excitation. Alternatively, device 700 may include a thermal imaging linear camera or TDI camera 704 for detecting mid-infrared radiation 706 emitted from hot spots in module 100 as a result of applying 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 can also perform the temperature monitoring function of the temperature sensor 526 discussed above with respect to FIG. 5B.

Figure 7B shows, in a schematic side view, a variation of the apparatus 700 shown in Figure 7A, wherein the component 708 comprising the light source 514 and the camera 502 and the thermal imaging camera 704 (if present) is configured to be at the scanning mechanism 508 as in The track within the substantially light-resistant housing 310 moves along the module 100.

For reasons of mechanical stability, it may be desirable to keep the detector stationary. FIG. 8A shows, in a schematic side view, an apparatus 800 for inspecting or determining the state of a module 100 in accordance with another embodiment of the present invention. In this embodiment, as indicated by arrow 702, a detector 502 in the form of a linear camera or TDI camera is secured within a substantially light-proof housing 310 placed on or around the module 100, including the light source 514 and The assembly 708 of the camera 502 operative associated optical component 802 is adapted to move along the module 100 on a scanning mechanism 508 that includes a rail or roller. Optical element 802, which may be an off-axis parabolic mirror, for example, is designed to direct illumination 804 to camera 502 for detection and continuous readout by synchronous programming of device 708 on scanning mechanism 508 by appropriately programmed computing device 510. Those skilled in the art will appreciate many optical components such as prisms and fiber optic ribbons suitable for directing the illumination 804 to the camera. Light emission from the module 100 can also be generated via electrical excitation from the power source 302 as previously described.

FIG. 8B shows a variation of the apparatus 800 shown in FIG. 8A in a schematic side view in which the distance traveled by the illumination 804 to the camera 502 during the scan remains substantially constant. As previously described, while the camera 502 remains stationary, for example, fixedly attached to a substantially opaque outer casing 310 placed on or around the module 100, the scanning mechanism 508 includes a light source 514 and a linear camera or TDI Component 708 of optical component 802 operatively associated with camera 502 moves along module 100. However, in this embodiment, illumination 804 generated by light source 514 or power source 302 is directed to camera 502 via steering mirror 806, which is indicated on scanning mechanism 508 as indicated by the relative lengths of arrows 702 and 702-A. The speed of component 708 moves at half the speed. This ensures that the collected illumination 804 travels to the camera 502 at a distance that the optical path length between the imaging area and the camera remains substantially constant during the scan, potentially improving focus on the camera. It is to be noted that this is also the case of the foregoing embodiment as shown in FIGS. 5B, 5D, 6B, 7A, and 7B. The detected illuminating signal is read from camera 502 by appropriately programmed computing device 510 in synchronization with movement of component 708 and turning mirror 806 to obtain an image of the illuminating light emitted from at least a portion of the module.

It should be understood that it is also possible to keep the device with the light source similar to that shown in Figures 8A and 8B in addition to or in lieu of the camera, for example using a moving mirror to scan the illuminated area along the module to be tested.

Figure 9 shows, in a schematic side view, an apparatus 900 for inspecting or determining the state of a module 100 in accordance with yet another embodiment of the present invention. This embodiment is similar to the embodiment shown in Figure 8A in that the illumination 804 generated by the light source 514 or the power source 302 from the battery 102 and possibly other components of the module is detected by a detector 502 in the form of a still linear camera or TDI camera. However, in this embodiment, the movable component 708 including the light source 514 and the mirror (optical element 802) can be moved to the rest position 902 to allow the module 100 to be exposed to LEDs, halogen lamps, or the like and consist of A power and controller 906 controls the solar simulator 904. The solar simulator 904 can be used to simulate the solar illumination of the module 100 under a range of conditions, while the power monitoring unit 908 measures the power performance of the module including its I-V characteristics. As described in detail below, some or all of these materials may be communicated to the centralized storage system and/or used locally to make decisions regarding, for example, whether to continue installing a given module.

In each of the embodiments shown in FIGS. 5A-9, the illuminating image and possible optical or thermal images read from the detector 502 can also be stored and/or processed in a computer, which can be identified as being used. The computing device 510 for reading out the detector, for display, automatic alarming or further analysis is together or separately.

Image analysis

FIG. 10A illustrates a line scan PL image 1000 taken from a majority of a module having sixty polysilicon cells using apparatus 500 of the type illustrated in FIGS. 5A-5C. Image 1000 shows a complete representation of forty of the sixty batteries. FIG. 10B shows an image 1002 of a single battery extracted from image 1000. When the module is moved under the light source and camera assembly, the near-infrared illumination from the LED array is used to generate the PL and capture the module image 1000 within 30 seconds. The module image 1000 has about 7 million pixels, representing more than 1 megapixel per cell, providing excellent spatial resolution for identifying defects or other features in a single cell, as shown by the single cell line scan PL image 1002. The image reveals a network 1004 of broad dark lines associated with cracks and a plurality of bright stripes 1006 extending perpendicular to the bus bars 1008 indicating the broken metal contacts. A particularly useful feature of line-scanned PL images compared to EL images is that defects such as cracks, dislocations, or impurities that cause localized decrease in carrier lifetime appear relatively dark compared to the surrounding material, ie, the background PL emission. The local increase in series resistance appears to be relatively bright. This "contrast inversion" effect facilitates the differentiation of different types of defects and occurs due to the obstruction of lateral transport of photogenerated charge carriers to and along the metal conductive path in cell regions having local high series resistance. This increases the local concentration of the carriers and thus increases the amount of luminescence of these regions. For example, in regions of high density having carrier recombination sites associated with the presence of cracks, impurities or dislocations, the number of carriers is reduced by local recombination such that these regions appear relatively dark.

Once one or more of the illumination or other images of the module have been acquired, image processing techniques can be used to identify and quantify defects or other features present in the battery or other portions of the module. There are two main tasks: defect detection and defect classification. Detection is the first step and it involves finding candidate defects and separating them from the surroundings. The sorting step then determines the type of defect, for example, a cracked finger pattern, a crack, and the like. For these two steps, it is necessary to measure the pixel areas with different intensities from the background, which are referred to below as "indicators". Example metrics include relative strength, size, shape, orientation, texture, and position. Not all features that are not recognized by image processing techniques are necessarily defects that degrade the performance of the module, but it is important to reliably identify defects in performance degradation.

One of the most common metrics for defect detection and classification is the relative intensity, ie how dark or bright the candidate defect is compared to its surroundings. This leads to a fundamental limitation of EL-based imaging of batteries in which all defects appear darker than the surrounding area. In this case, the "relative strength" indicator does not have a strong distinguishing ability, that is, it is not a robust indicator for distinguishing one type of defect from another. In contrast, and as shown in FIG. 10B, in a line scan PL image, certain defect types have an inverted contrast. Specifically, series resistance defects appear bright, while composite defects appear dark. In this case, the "relative strength" indicator has a strong distinguishing ability and can be used to stably distinguish between different types of defects.

In addition to other metrics, image processing algorithms can use different relative intensities, sizes, shapes, orientations, textures, or positions to automatically distinguish candidate defects. However, it should be understood that the accuracy and precision of such an algorithm may be affected if the sample has several types of candidate defects that may overlap spatially, especially if the candidate defects are all darker than the background. In this case, the "contrast inversion" effect in the line scan PL image is very useful in providing additional indicators that can be used to distinguish between different types of defects, thereby greatly improving the accuracy and precision of the image processing algorithm. The relative advantages of line scan PL and EL imaging for battery and module inspection are further discussed with reference to the images shown in Figures 11A-11D.

FIG. 11A shows a line scan EL image 1100 of a polycrystalline silicon battery extracted from a line scan EL image of 60 battery modules acquired with the apparatus 500 shown in FIGS. 5A to 5C. When the module 100 is moved at a speed of 50 mm/s by the field of view of the CCD CCD line scan camera 502 having an enhanced near-infrared response, a forward bias of 39.5 V is applied to the electrical contacts 106 of the module (equivalent to 1.045 times the open circuit voltage). Figure 11B shows a line scan PL image 1102 of the same battery taken with the same camera, wherein PL is generated from the moving module by a light intensity of about 4 Sun from source 514, which includes focusing on the short side of the module. A 1.2 m long near-infrared LED array of 6 mm wide strips 516.

The line scan EL image 1100 shows a number of features that appear relatively dark compared to the emission of surrounding material, including a wide line of networks 1004 associated with cracks, dislocation clusters 1104, and the resulting defects from broken metal fingers. A plurality of dark lines 1106 extending vertically from the bus bar 1008 and a large, completely dark triangular area indicating the electrically isolated battery segment 1108. The cracked network 1004 and dislocation clusters 1104 appear equally dark in the line scan PL image 1102 because they act as a recombination center that locally reduces carrier lifetime. On the other hand, the broken metal fingers now appear as bright stripes 1006, and the isolated battery segments 1108 also appear relatively bright, as the photogenerated charge carriers are laterally or completely blocked from laterally transporting from these regions. This illustrates another significant difference between EL images and line scan PL images. As previously discussed with reference to Figure 4, the EL can only be generated from an electrically energized accessible battery or battery area. In contrast, it can be seen that PL can be produced in all battery areas. The ability to generate PL from a completely isolated battery region, as illustrated by identifying cracks 1110 within the isolated battery segment 1108, provides additional information that may be relevant to determining the cause of battery or module failure.

A similar effect can be demonstrated by comparing FIG. 11C with FIG. 11D, and FIG. 11C and FIG. 11D respectively show a line scan EL image 1112 and a line scan PL image 1114 of the corner regions of the four polysilicon cells 102 in the module. The edges and corners of each cell are clearly visible in the line scan PL image 1114, which is difficult to discern in the line scan EL image 1112 because less is generated by electro-active in regions further away from the metal contact fingers 1116. Charge carriers. This effect is particularly important for early detection of cracks, which typically begin at the edge of the cell and are therefore more likely to be detected in line scan PL images. Both of these images reveal many other features in the battery, such as several dislocation clusters 1104 in the lower left cell and some grain structures 1118 in the lower right cell. The metal contact fingers 1116 are more easily discernible in the line scan PL image 1114. The area of the local high series resistance along one of the upper left cells reveals relatively dark stripes 1106 in the line scan EL image 1112 and relatively bright stripes 1006 in the line scan PL image 1114, as previously mentioned The contrast is inverted and consistent.

It should be noted that although line scan PL images can be said to be more suitable than EL images to identify different types of defects in the tested battery or module due to contrast inversion effects, there are some module failure modes that EL imaging may be more suitable. For example, as shown in Figure 4, another complete battery isolated from the module by interconnect errors may appear to be very normal in a line scan PL image, but will appear completely dark in the EL image. For example, in a similarly-disconnected battery that is partially disconnected, if one of the plurality of battery interconnections between adjacent cells is interrupted, the area around some of the bus bars will be displayed more than the other areas in the EL image. Bright feature pattern. Sometimes this type of pattern is sufficient to identify a particular failure mechanism. However, in other cases, the dark pattern around the bus bar may be caused by other effects, such as dark side regions caused by high impurity concentrations in the polycrystalline silicon wafer cut from the edge or corner brick. This uncertainty can be solved by introducing a line scan PL image in the analysis: if the area around the bus bar in both the EL image and the line scan PL image appears dark, it will be due to enhanced recombination, for example at the edge Or in a corner wafer, and if the same area in the line scan PL image appears normal (ie, there is no reduced intensity), it will be due to battery interconnect problems. It should be understood that the combined line scan PL and EL imaging apparatus as shown in Figures 5A through 9 has considerable value due to the synergy between the two imaging modes, as it can produce an image that is more than any isolated imaging mode. More information. Further information may also be from images of ELs generated by different excitation conditions such as different voltages or current injections or images of PLs generated using different illumination intensities or wavelength bands or detected in different wavelength bands or by Luminescence images generated by various combinations of light and electrical excitation are obtained. Different combinations of illuminating images can be compared, for example, by calculating or highlighting certain defects or other features by pixel-by-pixel intensity differences or ratios.

In one specific example of combined electrical excitation and optical excitation, and with reference to FIG. 5D, when light source 514 applies illumination to the module, injecting current into module 100 will produce illumination 504 that facilitates detection by camera 502. Electrical and optical excitation. The result will be an "offset" line scan PL image that will show some of the characteristics of the EL image as shown in Figure 11A, as well as some of the characteristics of a normal "non-biased" line scan PL image as shown in Figure 11B. The mixing depends on the relative magnitude of the electrical excitation and the optical excitation. This may, for example, enable the PL imaging mode to detect battery interconnect errors that would otherwise be undetectable, such that if EL imaging is not required for any other reason, then only the module may need to be checked by the device 500 once. Ideally, the level of electrical excitation applied when acquiring an offset line scan PL image should be sufficient to reveal a battery interconnect error without losing the "contrast inversion" effect discussed above with reference to Figures 11A-11D. .

Another possibility is to extract current flowing through the electrical contacts 106 (module terminals) of the module, for example by a resistor or active load, while the light source 514 is applying illumination to the module 100. Typically, if all of the batteries 102 in the battery array 104 are at least partially illuminated and one or more of the set of cells are being imaged, this will only produce useful information, such as an enhanced "contrast inversion" effect. Referring to FIG. 5A, if the module 100 is scanned in a direction parallel to its short dimension 110 and the light from the source 514 is defocused such that the "illuminated" stripe 516 is wide enough to at least partially illuminate all of the cells in the set 104, The aforementioned enhanced "contrast inversion" effect can then be achieved.

Although it is generally contemplated that illumination for module inspection will be primarily generated from battery materials, such as germanium diode materials in germanium-based battery modules, an unexpected and desirable feature of the present invention is that, in some cases, it can be generated and detected from Luminescence of other materials in the module, especially by careful selection of light sources, detectors or associated optics. For example, the backing plate polymeric material located behind the battery can be caused to emit PL, and the aforementioned backing plate polymeric material can be, for example, polyethylene terephthalate, polyvinylidene fluoride, polyamidamine or a composite thereof. It can provide a contrasting background for the battery, as well as metal interconnections between the cells. Due to the low content of PL in the metal material, the metal interconnection between the cells will typically appear darker. Another example is a contact finger on a battery that can contain residual organic material from a screen printed metal paste even after firing, which materials can be made to illuminate, thereby again forming a useful contrast with 矽PL. If the metal material has a metal oxide on its surface and this is usually the case, even the metal interconnection can emit light. Module components that are not illuminated can still have a detectable effect on one or more of the module images. In one example, turbidity caused by oxidation of an ethylene vinyl acetate (EVA) polymer that seals a tantalum battery within a module can be detected by blurring of features in the illuminating or optical image, as compared by images acquired at different times. There may be more obvious effects. One use of the unexpected contrast in the PL emitted by the various components of the module is to provide alignment testing between the metal interconnect and the battery, or more specifically the metal interconnect and the bus bar printed on the battery. Another application is to look for fractures in metal interconnect structures. Another application is to detect inhomogeneities in the PL emissions of each PL emissive material, which can be associated with varying material properties that may indicate actual or potential defects.

In some embodiments, the module inspection or condition determination device is configured to acquire an optical (ie, reflected or transmitted) image in addition to acquiring an EL or PL image, such as by having an additional light source 520 as shown in FIG. 5B. And a linear camera or TDI camera 522 or 528 for obtaining further information about the module to be tested. For example, a comparison between an optical image and a luminescent image can be used to distinguish between carrier composite defects that are typically not visible in an optical image, such as dislocations, and particle boundaries that are typically visible in both images. In another example, an optical image can reveal cracks that may be hidden by dislocation clusters. Moreover, high resolution optical images can reveal defects in the metal lines that can be associated with the high series resistance regions shown in the line scan PL image or with the darkness of the regions in the EL image. Additionally, at least in response to the emission band of the available light source, the optical image can highlight defects in the module component that is not illuminated. The non-illuminated module component can include a package component such as a cover glass, an edge sealant, or a polymeric sealant between the cover glass and the battery. Defects in the packaged components may allow oxygen and/or water to pass to the battery or interconnect, which will ultimately lead to defects in power reduction, such as electrical disconnection or carrier recombination defects. By combining the information about the non-illuminating components of the optical image with the information of one or more of the plurality of illuminating images of the faulty module, it is possible to establish an early warning based on the optical image only before the battery and the interconnect are affected. The relationship of module failures.

In other embodiments, the module inspection or condition determination device is additionally configured to acquire an image of thermal radiation emitted from at least a portion of the module, for example, by having a thermal imaging linear camera as shown in Figures 7A and 7B or A TDI camera 704 is provided for detecting mid-infrared radiation 706 emitted from hot spots in the module to be tested.

Module status determination system

In many cases, the line scan imaging apparatus as shown in Figures 5A-9 can be used to obtain a luminescence image generated by light excitation or electrical excitation or a combination of both and optionally by optical or thermal imaging or IV test data to check the module. For example, they can be used in module factories to inspect modules during production, for example, to check the stack of cells or cells for correction before sealing the polymer material and glass, for example to replace series resistors with excess levels A battery with associated defects or excessive levels of cracks or other carrier composite defects. They can also be used as final tests for completed modules in a module factory for quality control (QC) or quality assurance (QA) purposes. Other example applications are to inspect the module after shipping or prior to installation to check for damage due to rough shipping or to check for damage due to rough handling or improper attachment methods immediately after installation. The installed modules can also be inspected during their service life, for example as part of a regular inspection program or after adverse events such as severe hail. Finally, line scan imaging devices can be used to inspect defective modules in a modular anatomy lab, often as a prerequisite for warranty claims. Different versions of the aforementioned devices can be designed for different applications. For example, a smaller, more portable version of the "movable module" device of the type shown in Figures 5A through 6C can be designed to be used outside of a factory or laboratory environment, for example, after shipment and prior to installation. Module.

Among other things, the foregoing apparatus is advantageous for warranty and determination of faults to maintain a record of images taken from a given module at these and possibly other stages from the production line to the end of its useful life.

FIG. 12 illustrates a high level example of a system 1200 for determining a state of a photovoltaic module, such as during its entire useful life. The center of the system is a network accessible storage device 1202 that stores images and other data retrieved from a plurality of modules. In addition, "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 in network accessible storage device 1202. In some examples, multiple instances of the module inspection device described herein can be used to determine various types of module data for a plurality of modules. As discussed further below, the determined module data can be uploaded or otherwise transmitted to the network accessible storage device 1202 by one or more networks 1206, such as wired or wireless data links. Examples of such data may include photoluminescence images and/or if the module inspection equipment is properly equipped to monitor module power generation after installation, or if the module is inspected at the time of manufacture or prior to installation with an IV test system. Or electroluminescence images, and may also include optical or thermal images as well as power generation and IV test data. The data sent to the network accessible storage device 1202 may be obtained by a different entity in a plurality of entities 1204 that relate to the provisioning and operation of the module or the inspection of the failed module and include the manufacturer 1210, the transporter 1212, the installer 1214, module operator 1216 and module dissection laboratory 1218. The data in the network accessible storage device 1202 can be stored and managed by one or more servers or data centers located in one or more locations.

Photovoltaic modules typically have unique or otherwise separately identified identification barcodes or digital codes for ID purposes, which may be discernible in illuminating or optical images or manually entered as metadata for use with images or other module data. Upload, or if the drive is fully equipped, broadcast wirelessly from the drive. In some examples, the plurality of metadata items associated with the module check event are uploaded with the image and other module data, including the image acquisition device ID, the operator ID, the time and location of the image acquisition, and the imaging mode ( For example, EL, PL, optical or thermal), one or more of environmental conditions such as temperature and relative humidity, and operator reviews. Other metadata items that may be uploaded for storage in the network accessible storage device 1202 may include information regarding the manufacture of the module, such as the supplier of the battery, the serial number of the battery, the type of battery, and the IV test of the individual battery. data. The metadata can also include detailed information about the materials and processes used for module assembly, such as the supplier and the type of raw materials including wafer materials, as well as battery processing equipment and states such as electric furnaces, and wafer cutting equipment. Ultimately, in order to get the most value out of the status determination system, the stored data may span the entire PV value chain.

The records stored in the network accessible storage device 1202 may include the geographic location of the modules after installation. Combine this information with weather records at specific locations to develop algorithms that correlate defect types to weather history or to help evaluate warranty claims.

Any entity 1204 that stores module data stored in the network accessible storage device 1202 may be involved in module provisioning, operation, and/or inspection, as well as other interested parties 1208 such as solar financial entity 1220, solar insurance entity 1222, solar project owner 1224, Solar Market Reporting Group 1226 and Standards and Quality Assurance Agency 1228 are accessed for various purposes. These purposes include, for example, determining which entity has failed when the module fails to provide its guaranteed power generation; allowing insurance and finance groups to mine data to apply risk factors to entities in the module supply chain; allowing standards or market reporting Group mining data to apply quality factors to entities in the module supply chain; allowing project owners, installers, insurance companies, or financial institutions to adhere to modules with validated test records prior to installation; and allowing manufacturers Provides pre-certified high quality modules that have been programmed with luminescence based QC and QA programs. The module data can also provide large data for value-added analysis to any supply, operation, and/or inspection entity 1204 or interested party 1208 for improved manufacturing, potentially improved battery design, and suitability of particular modules for different environments. , the reliability or other purposes of certain module manufacturers, carriers or installers, and end-customer marketing. Data records containing the complete history of the module under test (in addition to module manufacturing, including information on wafer and battery manufacturing) can help track specific module failure modes to use specific materials, processes, process equipment, suppliers, and more.

In a preferred embodiment, images uploaded to the network accessible storage device 1202 are processed by one or more algorithms on a computer equipped with suitable machine readable program code to qualitatively or quantitatively identify defects of interest. . For example, for a luminescent image, an edge detection algorithm can be applied to identify a localized region that has a higher or lower intensity relative to the background, typically indicating a defect. Other algorithms may classify or distinguish between different types of defects, for example, based on characteristic shapes, comparisons of two or more illuminating images generated under different excitation conditions, or comparisons of illuminating images and optical images. You can then generate overlapping images that highlight different types of defects. Other algorithms can be used to quantify specific types of defects. In one example, 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 the module under test. Other algorithms can be applied to identify broken fingers and calculate an indicator such as the number of broken fingers in each cell, or identify and enumerate electrically isolated cells or battery regions or calculate indicators of carrier recombination defects, such as Wrong or impurity - rich battery area. It is also possible to use another algorithm, in particular at the end of the module manufacture, which classifies the application quality for the module based on the expected performance estimated from the occurrence of various types of defects identified in the module. These and other image processing results from a given illuminating, optical or thermal image can be stored with the image and any I-V test data.

In other embodiments, instead of or in addition to the computing device of the service provider associated with the network accessible storage device 1202, the image processing algorithm is applied and the analytical data is calculated by the acquisition, operation, and/or inspection entity 1204. . In other embodiments, the stored images may be analyzed at the request of any of the provisioning, operating, and/or inspection entities 1204 and/or interested parties 1208.

It should be understood that images and data for a given module acquired at different times, such as before and after shipping, can be compared, for example, by subtraction or by calculating the intensity ratio to highlight any new defects to aid in determining the module. The cause and time of the failure. Additionally or alternatively, a comparison can be made between one or more of the indicators obtained from the images and materials. "Differential" or "ratio" images may be particularly useful for distinguishing newly formed defects such as cracked or broken metal fingers from carrier recombination defects such as dislocations present in the battery material from the beginning. The image metadata may also provide useful information, such as to identify whether a plurality of module failures that are statistically significant are associated with a particular manufacturer 1210, a carrier 1212, or an installer 1214.

In some embodiments, statistics for various module groupings, such as modules shipped from a particular manufacturer 1210 or by a particular carrier 1212, may be by a service provider associated with the network accessible storage device 1202. The computing device performs the calculations as usual or upon request by the interested party 1208 or the provisioning, operating, and/or inspection entity 1204. More complex comparisons of processed module data are also possible, including, for example, ANOVA (ANOVA) or other statistical analysis known in the art, data obtained from images or used to select one or more modules. Relevant indicators are compared with data obtained from the general module group. A similar statistical analysis can be applied to a single battery. For example, the PL image of one or more modules can be segmented into separate battery images that can optionally be corrected before the battery template is calculated by averaging or obtaining the median of the battery image. For this purpose, the module image is segmented into individual battery images that are optionally corrected for distortion before being fed to the template calculation, and then the individual images are analyzed using average median or any other method to create a "normal battery" Battery image. The suspected defective battery can then be removed according to ANOVA analysis or a similar method, ie, a battery with a PL image strongly deviating from the template to provide an image representative of a "normal battery." A single battery image can then be compared to a "normal battery" image, which enables quantification of battery performance deviations from expected normal performance.

A feasible decision can be made based on one or more image processing and analysis results. Such determinations include, for example, rating the module as defective based on expected performance, scoring the module, determining a possible entity failure if a module failure is detected, and/or removing the module from use, For example, decide not to ship or install it. In some embodiments, these determinations can be made at network accessible storage 1202, which can be used as a centralized image storage and processing service for cloud service operations, ie, represented by FIG. The IT network and back-end server/processing unit act as cloud 1202. The feasible decision can then be communicated to the appropriate operator. In other embodiments, a viable determination can be made during module production, such as removing a defective battery or group of columns and replacing it before replacing the battery in an irreversible step in the module package.

In general, among other factors, the cost associated with storing module data such as image data and associated metadata and analytics data in the network accessible storage device 1202 depends on the size of the stored data file and the required data. Data accessibility and required storage time, which can be expected to be twenty or twenty five years or even longer depending on the useful life of the module. Regardless of any data compression algorithm that can be applied, the size of the image data file of the module will typically scale with spatial resolution, ie the number of pixels. Higher resolution images provide excellent defect detection results, but storage can be more expensive and requires trade-offs. If the spatial resolution provided by the imaging system exceeds the requirements, pixel merging can be used to reduce the resolution and thereby reduce the size of the image file. For example, counts from 2 x 2 pixel groups can be combined to reduce the image file size by a quarter. In one specific example, the Applicant has discovered that 2 x 2 pixels can be combined for a 7 megapixel illuminating image of the module as shown in Figure 10A, which does not significantly affect the original uncombined image. The result of the image processing algorithm.

In another approach to reducing data storage requirements and costs, the Applicant has developed a proprietary data format using 10 bits per pixel (by associated codecs). The foregoing method is decoded to 16 bits prior to image display or processing, which involves small computational overhead but provides significant storage savings. Image compression is lossless in terms of resolution such that processing and/or comparison of images in the processor associated with network accessible storage device 1202 is unaffected. In one particular example, the Applicant determined that storing two images in an uncompressed format, such as a line scan EL and a line scan PL image, requires approximately 100 megabytes of storage space, while compressed images require only 25 megabytes.

In another method for reducing storage costs, module data can be initially stored in faster access storage until the module being tested has been installed and thereafter moved to cheaper, slower access. Stored.

In some examples, the status determination system 1200 shown in FIG. 12 can operate as a network-based software as a service (SaaS) model. In one particular embodiment shown in FIG. 13, as shown at 1302, the service provider 1300 responsible for or otherwise associated with the module state determination system can provide for provisioning, operation, and/or inspection of modules. One or more entities 1204 provide (eg, rent, sell, etc.) module inspection devices 500, 600, 700, 800, or 900. The entity 1204 and/or the inspection device 500-900 uploads the module image material 1304 to the service provider 1300 for processing, analysis, and storage 1306 at the network accessible storage device 1202 or the like. The service provider 1300 can pay for the storage of information to the operator of the network accessible storage device 1202 and can charge a fee 1310 to the interested party 1208, such as a solar insurance company that evaluates the warranty claim or some other interested parties 1208 listed above. To compensate for the cost. The service provider 1300 or the interested party 1208 can retrieve 1312 and provide the module data and/or analysis data requested by 1314. In an alternate embodiment, the service provider 1300 can provide the module inspection device 500-900 to the provisioning, operating, and/or inspection entity 1204 without the need for pre-paid, and can charge the entity 1204 for uploading 1304 or Other methods provide module data. In other embodiments, the service provider 1300 and the provisioning, operation, and/or inspection entity 1204 using the module inspection device 500-900 can negotiate higher equipment rental or sales costs in exchange for lower fees for accessing module data. In an alternate embodiment, the service provider 1300 provides the module check device to the party 1204 without the need for pre-paid, and charges a fee for the video material upload 1304 and charges any other party who wishes to access the video material at any time in the future. Another fee. Other variants of fees and charges can be considered.

FIG. 14 illustrates an example physical and logical architecture 1400 of a system 1200 for determining conditions of a photovoltaic module (not shown in FIG. 14), in accordance with some embodiments. The architecture 1400 includes one or a plurality of service computing devices 1402 of a service provider, such as the service provider 1300 or another service provider discussed above with respect to FIG. One or more of the service computing devices 1402 can communicate via one or more networks 1404 having network accessible storage devices 1202. In addition, one or more of the service computing devices 1402 can communicate with the entity 1204 that is involved in the provisioning, operation, and/or inspection of the photovoltaic modules by one or more of the networks 1404. For example, one or more of the service computing devices 1402 can be associated with the client computing device 1406 of the entity 1204 that is involved in the provision, operation, and/or inspection of the photovoltaic module and/or the computing device 510 associated with the module inspection device 500-900. Communicate.

In some examples, computing device 510 associated with inspection device 500-900 can be configured to send module material 1408, such as module material 1408 obtained in the field, directly to service computing by one or more networks 1404. Device 1402. For example, control program 1410 can be stored or otherwise retained in one or more computer readable media (CRM) 1412 in computing device 510. Control program 1410 can be executed by one or more processors 1414 of computing device 510 to obtain module data 1408 for the field. For example, the control program 1410 can be executed to operate the camera, the scanning mechanism, and other components described above to obtain module data 1408 for one or more photovoltaic modules inspected by one or more of the devices 500-900 being inspected. . As noted above, the module data 1408 can include one or more PL and/or EL images, optical images or other types of images, I-V test data, and the like. Moreover, as described above, the module material 1408 can include metadata and/or other metadata about the photovoltaic module being tested, the test being performed, the inspection device performing the test.

Execution of control program 1410 may cause processor 1414 to connect to one or more of network 1404 using one or more wireless and/or wired communication interfaces 1416 to transmit module material 1408 to service computing device 1402. In some cases, the module data 1408 can be sent in real time, such as when the inspection device 500-900 generates the module data 1408. In other cases, the module material 1408 can be sent in bulk, such as after a particular trigger point is reached/after a certain amount of data has been collected, after a certain point in time has elapsed, and the like. Accordingly, one or more of the service computing devices 1402 can receive the module data 1408, store the module data 1408 at the network accessible storage device 1202, and perform analysis or other operations on the module data 1408.

Additionally or alternatively, the module material 1408 can be received by the client computing device 1406 from the computing device 510 of the inspection device 500-900. The client computing device 1406 can then send the module material 1408 to the service computing device 1402 to store it on the network accessible storage device 1202. For example, client computing device 1406 can include a client application 1418 that is stored or otherwise maintained on one or more CRMs 1420. The client application 1418 can be executed by one or more processors 1422 of the client computing device 1406, such as receiving module data 1408 from the inspection device 500-900 and transmitting the module material 1408 to the service computing device 1402. The client application 1418 can cause the processor 1422 to connect to one or more of the networks 1404 using one or more wireless and/or wired communication interfaces 1424 to transmit the module material 1408 to the service computing device 1402. In some cases, client application 1418 can be downloaded or otherwise provided to client device 1406 by service computing device 1402. For example, the client application 1418 can be a program that specifically configures the client computing device 1406 to receive and process the module data 1408 from the inspection device 500-900 and send the module data 1408 to the service computing device 1402.

In some examples, one or more of the provisioning, operating, and/or inspection entities 1204 can each operate the inspection device 500-900 and the client computing device 1406. For example, the module manufacturer can use the inspection device 500-900 to obtain first module data for each manufacturing module, and the first module data can be sent to the service computing device 1402 for storage on the network. At storage device 1202. Then, after the specific module is transported to the installation location, the module can be inspected again using the inspection device 500-900 to obtain the second module data about the module, and the second module data can be sent to the service. Computing device 1402 is for storage at network accessible storage device 1202. Additionally, after installation, the particular module can be checked again using the inspection device 500-900 to obtain third module data about the module, and the third module data can be sent to the service computing device 1402 for storage. The network can access the storage device 1202. Additionally, after installation, the particular module can be re-examined periodically using inspection equipment 500-900 to obtain additional module information about the module, which can be sent to service computing device 1402 for storage at The network can access the storage device 1202. In addition, if it is determined that the specific module has a fault condition, the module anatomy laboratory entity can check the specific module again to obtain additional module data by using the inspection device 500-900, and the additional module data can be sent to the service. Computing device 1402 is for storage at network accessible storage device 1202. Module data obtained at different points in time can be compared to each other to determine when events that cause damage, failure, or other fault conditions of the module may occur, such as a possible cause for determining a fault condition of the module.

In some examples, service computing device 1402 can include service program 1426 and analysis program 1428 that are stored or otherwise maintained on one or more CRMs 1432. For example, the service program 1426 can be executed by one or more processors 1434 to configure the service computing device 1402 to receive and process the module data 1408 from the inspection device 500-900 and/or the client device 1406 and send the module data 1408 to The network can access the storage device 1202. The service computing device 1402 can, for example, include one or more communication interfaces 1436 configured to communicate with the inspection device 500-900, the client computing device 1406, the network accessible storage device 1202, etc., by one or more networks 1404. .

Additionally, analysis program 1428 can be executed by one or more processors 1434 for analyzing module data 1408 to determine analysis data 1438. The analysis data 1438 can indicate the status and/or overall trend of a particular module, the cause of failure in a single or multiple modules, and the like. For example, the analysis program 1428, when executed by one or more processors 1434, may cause the processor to receive the module data 1408 received for the particular module at the first time point and receive the module data at the second time point. The module data 1408 is compared to determine at least one of a quality level of the photovoltaic module, whether the photovoltaic module has a fault, whether the photovoltaic module is likely to fail, or a cause of a failure in the photovoltaic module. Additionally, the analysis profile 1438 can indicate the point in the manufacturing and installation chain that first identifies the fault condition for use in determining an entity that may be the cause of the fault condition. Thus, the analytical data 1438 can be able to identify the cause of the failure or other fault condition to enable improved procedures for improving the quality and/or reliability of the module.

Analysis data 1438 and module data 1408, including image data 1440 and metadata 1442, may be stored on network accessible storage device 1202 on a plurality of storage devices 1444 associated with network accessible storage device 1202. In some examples, network accessible storage device 1202 can provide storage capacity for service provider 1300 and provide storage services for others. Network accessible storage device 1202 may include a storage array such as a network attached storage (NAS) system, a storage area network (SAN) system, or a storage virtualization system. Moreover, network accessible storage device 1202 can be co-located with one or more of service computing devices 1402, or can be located at a remote location or other external location of service computing device 1402.

In the illustrated example, network accessible storage device 1202 includes one or more storage computing devices, referred to as storage controller 1446, which may include one or more servers or any other suitable computing device. As with any example discussed with respect to service computing device 1402. Storage controller 1446 can each include one or more processors 1448, one or more computer readable media 1450, and one or more communication interfaces 1452. Moreover, computer readable medium 1450 of storage controller 1446 can be used to store any number of functional components that are executable by processor 1448. In many embodiments, these functional components include instructions, modules or programs that are executable by processor 1448, and when executed, the aforementioned instructions, modules or programs specifically program processor 1448 to perform the purposes herein. The action of the controller 1446 is stored. For example, the storage management program 1454 can control or otherwise manage the storage of the module data 1408 and analyze the data 1438 in a plurality of storage devices 1444 coupled to the storage controller 1446.

Additionally, in some cases, storage device 1444 can include one or a plurality of physical storage device arrays. For example, storage controller 1446 can control one or more arrays, such as arrays for configuring RAID (redundant array of independent disks) configurations or other desired storage configurations. The storage controller 1446 can provide the logical unit based on the physical storage device 1444 to the service computing device 1402 and can manage the data stored on the underlying physical device (storage device 1444). The physical device (storage device 1444) can be any type of storage device, such as a hard disk drive, a solid state device, an optical device, a magnetic tape, or the like, or a combination thereof.

Additionally, one or more of the service computing devices 1402 can be capable of communicating with one or more computing devices 1458 of interested parties 1208 by one or more networks 1404. The computing device 1458 of the interested party includes one or more processors 1460, one or more computer readable media (CRM) 1462, and one or more communication interfaces 1464. The interested party (IP) application 1466 can be stored or otherwise maintained on the CRM 1462 and can be executed by one or more processors 1460, for example, for communicating with the service computing device 1402 and/or from the service computing device The 1402 receives the analytical data 1438.

In some examples, one or more of service computing device 1402 and storage controller 1446 can include a plurality of physical servers or other types of computing devices, which can be implemented in any manner. In the case of, for example, a server, modules, programs, other functional components, and portions of data storage can be implemented on a server, such as in a server cluster, such as in a server farm or data center, cloud-hosted computing services, and the like. However, other computer architectures may be used additionally or alternatively. Moreover, in some examples, client computing device 1406 and/or interested party computing device 1458 can be one or more servers, or alternatively, can be a personal computer, laptop, workstation, tablet computing device , a mobile device, a smart phone, a wearable computing device, or any other type of computing device capable of transmitting material over a network.

Each of the processors 1414, 1422, 1434, 1448, and/or 1460 can be a single processing unit or a plurality of processing units, and can include a single or multiple computing units or a plurality of processing cores. The aforementioned processor may be implemented as one or more central processing units, microprocessors, microcomputers, microcontrollers, digital signal processors, state machines, logic circuits, and/or any devices that manipulate signals based on operational instructions. For example, the aforementioned processor may be any suitable type of one or more hardware processors and/or logic circuits that are specifically programmed or configured to perform the algorithms and processes described herein. The aforementioned processor may be configured to acquire and execute computer readable instructions stored in its respective computer readable medium 1412, 1420, 1432, 1450, and/or 1462, which may program the processor to perform The features described in this article.

The computer readable media 1412, 1420, 1432, 1450, and/or 1462 can include volatile and nonvolatile memory and/or removable and non-removable media implemented in any type of technology for storage, such as a computer. Information on readable instructions, data structures, program modules or other materials. For example, computer readable media can include, but is not limited to, RAM, ROM, EEPROM, flash memory or other storage memory technology, optical storage devices, solid state storage devices, magnetic tape, magnetic disk storage devices, RAID storage systems, storage arrays, attached networks Storage device, storage area network, cloud storage device or any other medium that can be used to store the required information and be accessible by the computing device. Depending on the configuration of the various computing devices, the computer readable medium can be a tangible, non-transitory medium, to the extent that, when mentioned, non-transitory computer-readable media excludes energy, carrier signals. , electromagnetic waves and / or the signal itself.

In some cases, computer readable media 1412, 1420, 1432, 1450, and/or 1462 can be in the same location as the associated computing device, while in other examples, the computer readable media can be separate or partially remote from the association. Computing device. Moreover, as noted above, computer readable media 1412, 1420, 1432, 1450, and/or 1462 can be used to store any number of functional components that can be executed by a respective associated processor. In many embodiments, these functional components, such as control program 1410, client application 1418, service program 1426, analysis program 1428, storage management program 1454, and interested party application 1466, include instructions, modules, or modules that can be executed by respective processors. The program, and when executed, the aforementioned instructions, modules or programs specifically program the processor to perform the actions attributed to the various computing devices herein.

Communication interfaces 1416, 1424, 1436, 1452, and/or 1464 can include one or more interfaces and hardware components that enable communication with various other devices, such as by one or more networks 1404. Thus, the communication interface can include or can be coupled to one or more ports of the connection provided to network 1404 to communicate with other computing devices. For example, as otherwise enumerated elsewhere herein, the communication interface may allow for LAN (Local Area Network), WAN (Wide Area Network), Internet, wired network, cellular network, wireless network (eg Wi-Fi), and wired network. One or more of (eg, Fibre Channel, fiber optic, Ethernet), direct connection, and near field communication, such as Bluetooth, communicate. Additionally, one or more of the networks 1404 can include wired and/or wireless communication technologies. The components used for network 1404 may depend, at least in part, on the type of network, the environment selected, the desired performance, and the like. The protocols for communication over the network are well known and will not be discussed in detail. Moreover, while an example of a system architecture has been described with reference to FIG. 14, many other software and/or hardware configurations will be apparent to those skilled in the art from this disclosure.

The operation of the module state determination system 1200 shown in Fig. 12 is described in the following examples.

Example 1

The manufacturer of the single crystal germanium module 1210 performs quality control testing of the completed module using a line scan EL/PL inspection device prior to packaging and shipping. The specific module can be identified in the line scan PL image by the front bar code, or by the digital code at the edge of the module frame, and the code can be included in the metadata. Applying an automatic image processing algorithm to the acquired EL and PL images indicates that the particular module has no cracks, minimal series resistance problems, and no interconnect problems. Therefore, the module is packaged and shipped, and if the level of the crack is above a predetermined threshold, it will be rejected and scrapped. The module also used a solar simulator to perform a power output test and found that the module belongs to the 300W module category. This rated power output is the basis for module pricing.

Specific data from the luminescence imaging test and power test is sent to the service provider 1300 of the condition determination system for storage in the network accessible storage device 1202. The transmitted module data 1408 includes: (i) line scan PL image; (ii) line scan EL image; (iii) IV curve; (iv) time and date of the test; (v) operator ID; (vi) Factory and line ID; (vii) module ID; (viii) crack indicator from processed EL and PL images; (ix) series resistance index from processed EL and PL images; (x) from processed Battery interconnect indicators for EL and PL images; and (xi) carrier recombination defect indicators from processed EL and PL images.

At a later time, the same module was unpacked from its packaging at a commercial solar installation site. The installer 1214 uses the portable version of the line scan EL/PL inspection device to inspect each module prior to installation in order to identify modules that have been defective or may have failed during use of the module. Their motivation for doing so is related to the cost of replacing the module. The cost of replacing a single defective solar module at this location is estimated at $800, or $2.67 per watt, including $1.66 per watt for module anatomy reporting, based on which a warranty claim can be filed. Many manufacturers' warranties require expensive anatomical testing and reporting prior to making any claims in order to prevent warranty claims. Because of this cost, project owner 1224, which funds the installation, insists that installer 1214 cost $1.33, or $0.0044 per watt (including labor) to test each module with a line scan EL/PL inspection device prior to installation. Any module that has not been tested will be returned to the manufacturer 1210 for a refund or replacement module. This requirement is based on the calculation that if only 0.15% of the modules fail during their 25-year lifetime, it is a lower cost option to identify defective modules prior to installation than to replace them after failure. The portable field unit for line scan EL and PL module inspection performs the same tests as the factory version in addition to the I-V test. The following module data 1408 is generated at the installation site and uploaded to the service provider 1300: (i) line scan PL image; (ii) line scan EL image; (iii) time and date of the test; (iv) operator ID; v) module ID; (vi) crack indicator from processed EL and PL images; (vii) series resistance index from processed EL and PL images; (viii) battery from processed EL and PL images Interconnect indicators; and (ix) carrier recombination defect indicators from processed EL and PL images.

Preliminary testing of the "defects" of the module under inspection at the installation site is based on the results of the above list (vi) to (ix). The module uses these tests, each defect level is less than the predetermined threshold for module rejection. However, before proceeding with the installation, after uploading the module data (i) to (ix), another set of data analysis is performed in the service computing device 1402 of the service provider 1300 to check the module data before and at the installation point. Significant changes between them to check for damage that occurred during transportation. The difference image is calculated by subtracting the intensity pixel by pixel in the "factory" and "live" PL images, and also for the two EL images. Alternatively, the ratio image can be calculated from the pixel-by-pixel intensity ratio of the "factory" and "live" images. These "difference" images are highly likely to highlight any changes to the module that occurred during shipment, for example due to rough handling. An image processing algorithm was run on each of the difference/ratio images to calculate the crack, series resistance, cell interconnect, and carrier composite defect indicators. Each indicator has a predetermined threshold above which the module will be considered defective and unsuitable for installation.

Example 2

After installing the module in the solar farm (modular operator 1216) for ten years, its power output fell below the guaranteed value, and the guaranteed value was calculated from its original value, allowing a drop of 0.8% per year. The solar farm service personnel removes and replaces the module and sends the defective module to the module dissection laboratory 1218 to identify the cause of the failure and, if possible, identify the faulty entity, as required by the manufacturer's 1210 warranty conditions. . The anatomical laboratory staff used the line scan EL/PL inspection device and the IV power test unit to generate the following data: (i) line scan PL image; (ii) line scan EL image; (iii) IV curve; (iv) test time And date; (v) operator ID; (vi) dissection laboratory ID; (vii) module ID; (viii) crack indicator from processed EL and PL images; (ix) from processed EL and PL The series resistance index of the image; (x) the battery interconnect indicator from the processed EL and PL images; and (xi) the carrier recombination defect indicator from the processed EL and PL images.

The I-V test data confirmed that the module generated lower than expected power. Inspections performed with line scan EL/PL inspection equipment identified many areas of several batteries that may be electrically isolated due to cracks. These areas appear relatively dark in the EL image because no current is pushed into these areas and is automatically detected and reported by series resistance and battery interconnect algorithms. The PL image reveals a number of cracks that appear to cause these isolated regions, where the cracks are automatically detected by the crack detection algorithm and reported as quantitative indicators. At this point, the module dissection lab 1218 can confidently report that module failure is due to cracking in several batteries, although no entity may be identified as an entity that may cause a battery failure. The test material 1408 from the module dissection laboratory is then uploaded to the service provider 1300 to compare the most recently measured data with the data obtained prior to installation and at the module factory.

Several "difference" images or "ratio" images are calculated by the service computing device 1402 of the service provider 1300 as follows: (A) PL images (anatomical laboratory) and PL images (factory); (B) EL images (anatomy laboratory) ) and EL images (factory); (C) PL images (anatomical laboratory) and PL images (pre-installed); and (D) EL images (anatomical laboratory) and EL images (pre-installed). In this case, it was found that no cracks were present in the newly manufactured module before installation or in the factory. The solar farm operator 1216 therefore concludes that the crack is not a failure of the manufacturer 1210 or the transporter 1212, so the warranty claim is not appropriate. Instead, this may be due to hail during installation or use/maintenance or the most recent hail. After the solar farm operator (module operator 1216) provided the relevant results to the project owner 1224, the project owner finally asked the insurance company to replace the cost of the module. The insurance entity 1222 may request its own copy of the result from the service provider 1300 if needed.

Example 3

The Standards and Quality Assurance Agency 1228 employs a data analysis company to obtain and analyze from the service provider 1300 module data 1408 for all modules of a particular model of a particular manufacturer that has been on the market for two years, of which 20 million units have been installed. In Europe or Australia. Manufacturer 1210 has set a specific "pass/fail" threshold based on the processed EL and PL images acquired by the factory at the line scan inspection device: (i) crack indicator; (ii) series resistance index; Iii) battery interconnection indicators; and (iv) carrier recombination defect indicators. In each case, the pass/fail threshold is set relatively high, because otherwise the manufacturer 1210 has neither budget nor expertise to reduce the incidence of various defects to near zero, so the reject rate will be uneconomical. The ground is high. The market is concerned that the level of defects allowed by the manufacturer 1210 may result in an unacceptably high rate of module failure during its useful life.

As a result, the Analytical Company collected all available data for these modules, including factory testing, pre-installed test data, and failed module anatomy reports. The analysis company first discovered that the modules sent to the module anatomy lab had three major causes of failure: (i) Battery interconnect problems have led to electrical isolation problems and complete module failures in some modules installed in Australia. There are fewer modules installed in Europe; (ii) in Australia and Europe, relatively high levels of carrier recombination defects occur in modules with lower power output than expected but not completely failed; (3) less The number of modules has cracks and other failure modes that may be caused by handling events, hail or other "God's behavior."

More in-depth analysis, including a comparison of modular anatomy labs, factory and pre-installed test results, provides further useful information. First, it was observed that battery interconnect problems found primarily in modules that were ineffective in Australia did not exist prior to installation. This indicates a systemic failure mode due to material or processing problems in the plant, which exacerbates the high temperatures of Australian solar installations. Secondly, it was observed that the carrier composite defects did not exist prior to installation and were mainly limited to the outside of the battery at the edge of the module. This is consistent with chemical reactions in those cells that are caused by water intrusion at the edge of the module, again indicating material or processing failures in module fabrication.

Therefore, the module manufacturer 1210 is considered to be at fault and is therefore responsible for replacing all failed modules of the model. The manufacturer is committed to providing a replacement module library to project owner 1224 and investigating the reasons for these system failure modes. The failure mode is ultimately compensated for by using key materials such as alternative suppliers of module edge sealants and soldering processes that modify battery interconnects.

15 through 19 are flow diagrams illustrating example processes in accordance with some embodiments. These processes are illustrated as a collection of blocks in a logic flow diagram that represent a sequence of operations, some or all of which may be implemented in hardware, software, or a combination thereof. In the context of software, the foregoing blocks may represent computer executable instructions stored on one or more computer readable media, and when executed by one or more processors, the processor is programmed to perform the foregoing operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, etc. that perform particular functions or implement particular types of data. The order in which the blocks are described should not be construed as limiting. Any of the foregoing blocks may be combined in any order and/or in parallel to implement the foregoing process or alternative process, rather than all blocks needing to be performed. For purposes of discussion, these processes are described with reference to the environments, frameworks, and systems described in the examples herein, although such processes can be implemented in a wide variety of other environments, frameworks, and systems.

15 is a flow chart showing an example process 1500 for determining a condition of a module over time, in accordance with some embodiments. In some examples, process 1500 can be performed by at least one of service computing devices 1402 or some other suitable computing device.

At 1502, the computing device can receive module data generated by the inspection device at a first point in time, wherein the inspection device is configured to generate module data of the aforementioned photovoltaic module. The module data may be received, for example, from a module inspection device and/or a client computing device that manufactures, transports, installs or operates the module or checks the entity of the failed module.

At 1504, the computing device can receive one or more metadata items associated with the module data, the one or more metadata items including information regarding at least one of the module data or the photovoltaic module. The metadata may include, for example, information about the module ID, the tests performed, the manufacturer information, the carrier information, the installer information, the operator information, and the like.

At 1506, the computing device can store the module data and one or more metadata items in a network accessible storage device. The module data of the modules received at a plurality of different time points can be stored, for example, in a network storage location to enable analysis and determination of the state of the modules at different points in time.

At 1508, the computing device can determine the condition of the photovoltaic module based at least in part on the aforementioned module data and one or more metadata items. For example, the computing device can determine the status of the photovoltaic module by comparing the module data with the previous module data generated by the aforementioned photovoltaic module at an earlier time. In addition, the computing device can determine at least one of the following based on the condition: a level of the photovoltaic module; whether the photovoltaic module is faulty; whether the photovoltaic module is likely to fail; or a cause of the failure in the photovoltaic module. Additionally, as another example, the computing device can receive additional module data generated by the same inspection device or a different inspection device at a second point in time, and the computing device can be based, at least in part, on the module data to be from the first point in time A comparison is made with additional module data to determine the condition of the photovoltaic module at the second point in time.

At 1510, the computing device can transmit a communication indicating the determined condition to the computing device of the at least one entity associated with the manufacture, transportation, installation, operation, or inspection of the aforementioned photovoltaic module based on the condition.

At 1512, the computing device can transmit the module data, the previous module data, the analysis data determined for the photovoltaic module, or the aggregate module data for the plurality of photovoltaic receptions to the computing device of the interested party.

As previously mentioned, the module material can be generated, for example, by the inspection device 500, 600, 700, 800 or 900. In some embodiments, the inspection device 500-900 can be under the control of the computing device 510, terminal 512, or other computing device. That is, the computing device can operate some or all of camera 502, light source 514, power source 302, and scanning mechanism 508, as well as various optional components for optical imaging such as light source 520 and camera 522, thermal imaging camera 704, sunlight. Simulator 904 and associated power and controller 906 and power monitoring unit 908, as well as various adjustable optical components such as filters and mirrors that may be present.

16 through 19 are flow diagrams illustrating example processes 1600, 1700, 1800, and 1900 for generating module material, in accordance with some embodiments. In some examples, each of the processes 1600-1900 can be performed by the computing device 510 or other suitable computing device.

Turning first to the example process 1600 shown in FIG. 16, at 1602, the computing device can operate a power source to apply electrical excitation to the photovoltaic module to generate electroluminescence from the photovoltaic module. At 1604, the computing device is operative to detect a detector of electroluminescence emitted from the photovoltaic module in a first region that extends across a first dimension of the photovoltaic module. At 1606, the computing device can operate the scanning mechanism to scan the first region along a second dimension of the photovoltaic module when electrical excitation is applied. At 1608, the computing device can receive an image of the electroluminescence emitted from the photovoltaic module from the detector when the first region is scanned along the second dimension.

Turning now to the example process 1700 shown in FIG. 17, at 1702, the computing device can operate a light source for illuminating a first region of the photovoltaic module with light suitable for generating photoluminescence from the photovoltaic module, the aforementioned A region extends through the first dimension of the photovoltaic module. At 1704, the computing device is operable to detect a photoluminescent detector emitted from the photovoltaic module in a second region that extends across a first dimension of the photovoltaic module. At 1706, the computing device can operate the scanning mechanism to scan the first region and the second region along a second dimension of the photovoltaic module. At 1708, upon scanning the first region and the second region along the second dimension, the computing device can receive the photoluminescence image emitted from the photovoltaic module from the detector.

Turning now to the example process 1800 shown in FIG. 18, at 1802, the computer can process one or more electroluminescent images and/or photoluminescence images acquired by the module inspection device for different types of features or defects. Sort or differentiate. At 1804, the computer can generate one or more overlapping images for highlighting one or more types of features or defects. At 1806, the computer can calculate one or more indicators of one or more types of features or defects. At 1808, the computer can apply a quality classification to the photovoltaic module based on an estimated expected performance from various types of features or defects identified in the photovoltaic module.

Turning now to the example process 1900 shown in FIG. 19, at 1902, the computer can obtain two or more images of the photovoltaic module acquired by the module inspection device, including the electroluminescent image, the photoluminescence image. Select from a group of optical images or thermal images. At step 1904, the computer can compare the two or more images obtained in step 1902.

The example processes described herein are merely examples of processes that are provided for purposes of discussion. Many other variations will be apparent to those skilled in the art in view of this disclosure. In addition, while the disclosure herein sets forth a few examples of suitable frameworks, architectures, and environments for performing the foregoing processes, the embodiments herein are not limited to the specific examples shown and discussed. Moreover, as described and illustrated in the drawings, the present disclosure provides various example embodiments. However, the present disclosure is not limited to the embodiments described and illustrated herein, but may be extended to other embodiments, as will be appreciated by those skilled in the art or will be known.

The various instructions, processes, and techniques described herein can be considered in the general context of computer-executable instructions, such as program modules stored on a computer readable medium and executed by a processor of the present invention. Typically, a program module includes routines, programs, objects, components, data structures, executable code, and the like for performing particular tasks or implementing particular abstract data types. These program modules and the like can be executed as native code or can be downloaded and executed as in a virtual machine or in other just-in-time compilation execution environments. Typically, the functionality of the program modules can be combined or distributed in various embodiments as desired. Embodiments of these modules and techniques can be stored on a computer storage medium or transmitted by some form of communication medium. Thus, the indexing arrangements herein may be implemented on physical hardware, may be used in a virtual implementation, may be used as part of an overall deduplication system on a physical or virtual machine, and/or may serve as other deduplication implementations ( For example, SAN) or components in large non-repetitive data deletion environments such as large-scale memory indexing.

Although the invention has been described primarily in terms of a neon battery based module, the principles of the invention are not limited to this type of module. In particular, by selecting a light source having a suitable wavelength band and illumination intensity and a camera with appropriate sensitivity and detection bands, PL and EL imaging techniques can generally be used to inspect modules based on materials other than germanium. For thin film modules based on direct bandgap semiconductors such as cadmium telluride, luminescence imaging techniques may be easier to apply because these materials tend to have higher luminous efficiencies than ruthenium.

Although the present invention has been described with reference to certain preferred embodiments thereof, modifications and variations of the invention may be made within the spirit and scope of the appended claims.

Claims (20)

 A system capable of determining a condition of a photovoltaic module over time, comprising: one or more processors; and a memory storing computer executable program code including instructions, when the instructions are executed by one or more of the aforementioned processors Configuring the one or more processors to receive module data generated by the inspection device at a first time point, wherein the inspection device is configured to generate the foregoing module data for the photovoltaic module; One or more metadata items associated with the group data, the one or more metadata items including information about the module data and at least one of the foregoing photovoltaic modules; and the foregoing module data and the foregoing one or more The metadata items are stored in a network accessible storage device; and the status of the aforementioned photovoltaic module is determined based at least in part on the aforementioned module data and the one or more metadata items.    The system as claimed in claim 1 is capable of determining the condition of the photovoltaic module over time, wherein the module data includes electroluminescence images, photoluminescence images, optical images, thermal images, and current and voltage (IV) test data. One or more of them.    A system as claimed in claim 1 or 2, wherein the inspection device comprises: a detector for detecting photoluminescence emitted by the aforementioned photovoltaic module and by the aforementioned photovoltaic At least one of electroluminescence emitted by the module; a scanning mechanism for scanning an area of the photovoltaic module during the detecting; and a computing device programmed by the executable instruction to detect from the foregoing The device receives at least one of a photoluminescence image and an electroluminescence image of at least a portion of the photovoltaic module as the module data.    A system as claimed in claim 1 or 2, wherein the one or more processors are further configured to receive the second time point generated by the inspection device or the different inspection device. Additional module data; and based at least in part on comparing the aforementioned module data from the first point in time with the additional module data to determine the condition of the photovoltaic module at the second time point.    The system as claimed in any one of claims 1 to 4, wherein the one or more processors are further configured to be configured by using the module data with the aforementioned photovoltaic module. The previous module data generated earlier is compared to determine the condition of the aforementioned photovoltaic module.    A system as claimed in any one of claims 1 to 5, wherein the one or more processors are further configured to determine at least one of the following based on the foregoing conditions: The level of the module; whether the aforementioned photovoltaic module is faulty; whether the aforementioned photovoltaic module is likely to malfunction; and the cause of the failure of the aforementioned photovoltaic module.    A system as claimed in any one of claims 1 to 6, wherein the one or more processors are further configured to manufacture and transport the photovoltaic module according to the foregoing conditions. The computing device that installs, operates, or checks the associated at least one entity transmits communication information indicating the determined condition.    A system for determining a condition of a photovoltaic module over time as recited in any one of claims 1 to 7, wherein the one or more processors are further configured to transmit at least one of the following to a computing device of the interested party. The determined module data, the previous module data or the analysis data of the foregoing photovoltaic module, and the accumulated module data received for the plurality of photovoltaic modules.    A method for determining a condition of a photovoltaic module over time, comprising the steps of: receiving, by one or more processors, module data generated by an inspection device at a first time point, wherein the inspection device is configured to generate the foregoing The module data of the photovoltaic module; receiving, by the one or more processors, one or more metadata items associated with the module data, the one or more metadata items including the module data and Information of at least one of the foregoing photovoltaic modules; storing the module data and the one or more metadata items in a network accessible storage device by one or more processors; and based at least in part on The foregoing module data and the one or more metadata items are determined by the one or more processors to determine the condition of the foregoing photovoltaic module.    The method of claim 9, wherein the module data includes electroluminescence images, photoluminescence images, optical images, thermal images, and current and voltage (IV) test data. One or more of them.    A method for determining a condition of a photovoltaic module over time as recited in claim 9 or 10, wherein said inspection device comprises: a detector for detecting photoluminescence emitted by said photovoltaic module and said photovoltaic At least one of electroluminescence emitted by the module; a scanning mechanism for scanning an area of the photovoltaic module during detection; and a computing device programmed by executable instructions to operate from the detector Receiving at least one of a photoluminescence image and an electroluminescence image of at least a portion of the photovoltaic module as the module data.    A method for determining a condition of a photovoltaic module over time as recited in any one of claims 9 to 11, wherein the method further comprises the step of: receiving at a second time point by the aforementioned inspection device or a different inspection device Additional module data; and based at least in part on comparing the module data from the first point in time with the additional module data to determine the condition of the photovoltaic module at the second point in time.    A method for determining a condition of a photovoltaic module over time as recited in any one of claims 9 to 12, wherein determining the condition of the photovoltaic module comprises generating the module data and the photovoltaic module at an earlier time The steps of comparing the previous module data.    A method for determining a condition of a photovoltaic module over time as recited in any one of claims 9 to 13, wherein the method further comprises the step of determining at least one of: the foregoing photovoltaic module based on the foregoing condition Level; whether the aforementioned photovoltaic module is faulty; whether the aforementioned photovoltaic module is likely to malfunction; and the cause of the failure of the aforementioned photovoltaic module.    A method for determining a condition of a photovoltaic module over time as described in any one of claims 9 to 14, wherein the method further comprises the steps of: manufacturing, transporting, and installing the photovoltaic module according to the foregoing conditions; The computing device that operates or checks the associated at least one entity transmits communication information indicating the determined condition.    The method of claim 1, wherein the method further comprises the step of transmitting at least one of: The foregoing module data, the previous module data or the analysis data of the foregoing photovoltaic module, and the accumulated module data received by the plurality of photovoltaic modules.    A non-transitory computer readable medium for storing instructions, wherein when the instructions are executed by one or more processors, the instructions configure one or more processors to receive: generated by the inspection device at a first point in time Module data, wherein the foregoing inspection device is configured to generate module data for the photovoltaic module; and receive one or more metadata items associated with the module data, the one or more metadata items including the foregoing Information of at least one of the module data and the aforementioned photovoltaic module; storing the module data and the one or more metadata items in a network accessible storage device; and based at least in part on the module data And one or more of the foregoing metadata items to determine the condition of the aforementioned photovoltaic module.    The non-transitory computer readable medium of claim 17, wherein the module data comprises one of electroluminescence image, photoluminescence image, optical image, thermal image, and current and voltage (IV) test data. Or more.    The non-transitory computer readable medium of claim 17, wherein the one or more processors are further configured to: receive an additional module generated by the foregoing inspection device or a different inspection device at a second time point And determining, based at least in part on, comparing the module data from the first point in time with the additional module data to determine the condition of the photovoltaic module at the second time point.    The non-transitory computer readable medium of claim 17, wherein the one or more processors are further configured to generate the previous module by using the module data with the aforementioned photovoltaic module at an earlier time. The group data is compared to determine the condition of the aforementioned photovoltaic module.