Compressed sensing current mapping spatial characterization of photovoltaic devices

In this work a new measurement technique for current mapping of photovoltaic (PV) devices is developed, utilising the compressed sensing (CS) sampling theory. Conventional current mapping measurements of PV devices are realised using the light beam induced current (LBIC) measurement method. For its realization, a light beam scans a PV device and the induced current is measured for every point, generating the final current map of the device. Disadvantages of the LBIC method are the low measurement speed, the complicated and usually expensive measurement layouts and the impractical application of the method on PV modules. With the development of CS current mapping in this work, the above issues can be mitigated. Instead of applying a raster scan, a series of illumination patterns are projected onto the PV sample, acquiring fewer measurements than the pixels of the final current map. The final reconstruction of the current map is achieved by means of an optimisation algorithm. Spatially resolved electrical simulations of CS current mapping demonstrate that theoretically the proposed method is feasible. In addition, it is shown that current maps can be acquired with even 40% of the measurements a standard LBIC system would require, saving a significant amount of measurement time. The performance of CS current mapping is the same, regardless of the features a sample may contain and measurements can be applied to any type of photovoltaic device. The ability of the method to provide current maps of PV modules is demonstrated. The performance of several reconstruction algorithms is also investigated. An optical measurement setup for CS current mapping of small area PV devices was built at the National Physical Laboratory (NPL), based on a digital micromirror device (DMD). Accurate current maps can be produced with only 40% of the measurements a conventional point by point scan would need, confirming simulation results. The measurement setup is compact, straightforward to realise and uses a small number of optical elements. It can measure a small area of 1cm by 1cm, making it ideal for current mapping of small research samples. A significant signal amplification is achieved, since the patterns illuminate half of the sample. This diminishes the use of lock-in techniques, reducing the cost for current mapping of PV devices. Current maps of an optical resolution up to 27μm are acquired, without the use of any demagnification elements of the projected pattern that the DMD generates. v A scale up of this new current mapping method is demonstrated using Digital Light Processing (DLP) technology, which is based on DMD chips. A commercial DLP projector is utilised for building a proof of concept CS current mapping measurement system at the Centre of Renewable Energy Systems Technology (CREST). Current maps of individual PV cells in encapsulated modules can be acquired, something that is extremely difficult to achieve with conventional LBIC systems. Direct current mapping of a PV module with by-pass diodes is successfully applied for the first time. Specific shading strategies are developed for this purpose in order to isolate the cell under test. Due to the application of compressive sampling, current maps are acquired even if the signal-to-noise-ratio levels are so low that a point by point scan is not possible. Through the above implementations of CS current mapping of this work, the proposed technique is studied and evaluated. The results demonstrate that this novel method can offer a realistic alternative approach for current mapping of PV cells and modules that can be cost effective and straightforward to implement. In addition, this work introduces the application of the CS theory and DLP technology to PV metrology in general

Quantitative electroluminescence measurements of PV devices

Electroluminescence (EL) imaging is a fast and comparatively low-cost method for spatially resolved analysis of photovoltaic (PV) devices. A Silicon CCD or InGaAs camera is used to capture the near infrared radiation, emitted from a forward biased PV device. EL images can be used to identify defects, like cracks and shunts but also to map physical parameters, like series resistance. The lack of suitable image processing routines often prevents automated and setup-independent quantitative analysis. This thesis provides a tool-set, rather than a specific solution to address this problem. Comprehensive and novel procedures to calibrate imaging systems, to evaluate image quality, to normalize images and to extract features are presented. For image quality measurement the signal-to-noise ratio (SNR) is obtained from a set of EL images. Its spatial average depends on the size of the background area within the EL image. In this work the SNR will be calculated spatially resolved and as (background independent) averaged parameter using only one EL image and no additional information of the imaging system. This thesis presents additional methods to measure image sharpness spatially resolved and introduces a new parameter to describe resolvable object size. This allows equalising images of different resolutions and of different sharpness allowing artefact-free comparison. The flat field image scales the emitted EL signal to the detected image intensity. It is often measured through imaging a homogeneous light source such as a red LCD screen in close distance to the camera lens. This measurement however only partially removes vignetting the main contributor to the flat field. This work quantifies the vignetting correction quality and introduces more sophisticated vignetting measurement methods. Especially outdoor EL imaging often includes perspective distortion of the measured PV device. This thesis presents methods to automatically detect and correct for this distortion. This also includes intensity correction due to different irradiance angles. Single-time-effects and hot pixels are image artefacts that can impair the EL image quality. They can conceivably be confused with cell defects. Their detection and removal is described in this thesis. The methods presented enable direct pixel-by-pixel comparison for EL images of the same device taken at different measurement and exposure times, even if imaged by different contractors. EL statistics correlating cell intensity to crack length and PV performance parameters are extracted from EL and dark I-V curves. This allows for spatially resolved performance measurement without the need for laborious flash tests to measure the light I-V- curve. This work aims to convince the EL community of certain calibration- and imaging routines, which will allow setup independent, automatable, standardised and therefore comparable results. Recognizing the benefits of EL imaging for quality control and failure detection, this work paves the way towards cheaper and more reliable PV generation. The code used in this work is made available to public as library and interactive graphical application for scientific image processing

Machine learning for advanced characterisation of silicon solar cells

Improving the efficiency, reliability, and durability of photovoltaic cells and modules is key to accelerating the transition towards a carbon-free society. With tens of millions of solar cells manufactured every day, this thesis aims to leverage the available characterisation data to identify defects in solar cells using powerful machine learning techniques. Firstly, it explores temperature and injection dependent lifetime data to characterise bulk defects in silicon solar cells. Machine learning algorithms were trained to model the recombination statistics’ inverse function and predict the defect parameters. The proposed image representation of lifetime data and access to powerful deep learning techniques surpasses traditional defect parameter extraction techniques and enables the extraction of temperature dependent defect parameters. Secondly, it makes use of end-of-line current-voltage measurements and luminescence images to demonstrate how luminescence imaging can satisfy the needs of end-of-line binning. By introducing a deep learning framework, the cell efficiency is correlated to the luminescence image and shows that a luminescence-based binning does not impact the mismatch losses of the fabricated modules while having a greater capability of detecting defects in solar cells. The framework is shown in multiple transfer learning and fine-tuning applications such as half-cut and shingled cells. The method is then extended for automated efficiency-loss analysis, where a new deep learning framework identifies the defective regions in the luminescence image and their impact on the overall cell efficiency. Finally, it presents a machine learning algorithm to model the relationship between input process parameters and output efficiency to identify the recipe for achieving the highest solar cell efficiency with the help of a genetic algorithm optimiser. The development of machine learning-powered characterisation truly unlocks new insight and brings the photovoltaic industry to the next level, making the most of the available data to accelerate the rate of improvement of solar cell and module efficiency while identifying the potential defects impacting their reliability and durability

Design and Characterization of Hybrid Perovskite for New Generation Solar Cells

Le perovskiti sono semiconduttori con caratteristiche optoelettroniche ideali per l’applicazione fotovoltaica (elevato assorbimento, alta mobilità di carica) e con un basso costo. Tuttavia, la loro applicazione commerciale è limitata dall’instabilità di questo materiale. Il degrado della perovskite è causato da alcuni fattori esterni come ossigeno e umidità. In questa tesi sono state studiate entrambe le strutture (NIP e PIN) delle celle solari a perovskite. Per renderle più stabili ed efficienti sono state trattate con nuovi metodi di passivazione e con materiali di recente sintesi che hanno portato al raggiungimento dell’obiettivo. Sono state anche usate diverse tecniche di caratterizzazione per comprendere le ragioni dei miglioramenti nelle prestazioni di questi dispostivi. Questo ha permesso di ottenere una maggiore conoscenza dei meccanismi dei miglioramenti e di conseguenza a una più vicina commercializzazione di questo materiale.Perovskites are semiconductors with ideal optoelectronic properties (like high absorption and high charge mobility) for photovoltaic applications and at a low cost. However, their commercial application is limited by the instability of this material. The degradation of perovskite is caused by some external factors such as oxygen and humidity. In this thesis both structures (NIP and PIN) of perovskite solar cells have been studied. To make them more stable and efficient, they have been treated with new passivation methods and with recently synthesized materials that have led to the achievement of the goal. Various characterization techniques were also used to understand the reasons for the performance improvements of these devices. This allowed obtaining a greater knowledge of the mechanisms of the improvements and consequently to closer commercialization of these perovskites

The use of image processing to determine cell defects in polycrystalline solar modules

This research aims to use image processingtodetermine cell defects in polycrystalline solar modules. Image processing is a process of enhancing images for differentapplications. One domain that seems to not yet utilise the use of image processing, is photovoltaics. An increased use of fossil fuels is damaging the earth and a call to protect the earth has resulted in the emergence of pollutant-free technologies such as polycrystalline photovoltaic (PV) cells, which are connected to make up solar modules. However, defects often affect the performance of PV cells and consequently solar modules. Electroluminescence (EL) images are used to examine polycrystalline solar (PV) modules to determine if the modules are defective. The main research question that this research addressed is“How can an image processing technique be used to effectively identify defective polycrystalline PV cells from EL images of such cells?“. The experimental research methodology was used to address the main research question. The initial investigation into the problem revealed that certain sectors within industry, as well as the Physics Department at Nelson Mandela University(NMU), do not currently utiliseimage processing when examining EL images of solar modules. The current process is a tedious, manual process whereby solar modules are manually inspected. An analysis of the current processes enabled the identification of ways in which to automatically examine EL images of solar modules. An analysis of literatureprovided a better understanding of the different techniques that are used to examine solar modules, and it was identified how image processing can be applied to EL images. Further analysis of literatureprovided a better understanding of image processing and how image classification experiments using Deep Learning (DL) as an image processing technique can be used to address the main research question. The outcome of the experiments conducted in this research weredifferentadaptive models(LeNet, MobileNet, Xception)that can classify EL images of PV cellsaccording to known standardsused by the Physics Department at NMU. The known standards yielded four classes; normal, uncritical, critical and very critical, which were used for the classification of EL images of PV cells. The adaptive models were evaluated to obtain the precision, recall and F1–scoreof the models.The precession, recall, and F1–score were required to determine how effective the models were in identifying defective PV cells from EL images.The results indicated that an image processing technique canbe used to identify defective polycrystalline PV cells from EL images of such cells. However, further research needs to be conducted to improve the effectiveness of the adaptive models

Systems integration of concentrator photovoltaics and thermoelectrics for enhanced energy harvesting

Alongside other photovoltaic technologies, Concentrator photovoltaics (CPV) capitalise on the recent progress for high-efficiency III:V based multi-junction photovoltaic cells, combining them with low cost optics for increased power production. Thermoelectrics are semiconductor devices that can act as solid-state heat pumps (Peltier mode) or to generate electrical power from temperature differentials (Seebeck effect). In this work, new designs for the integration of a thermoelectric module within a CPV cell receiver were proposed and substantiated as a reliable and accurate temperature control platform. The thermoelectric was used for accurate and repeatable cooling, exhibiting high temporal-thermal sensitivity. Testing was done under varying irradiance and temperature conditions. A novel Closed Loop Integrated Cooler (CLIC) technique was tested, demonstrated and validated as a useful experimental metrology tool for measuring sub-degree cell temperature within hybrid devices using the material properties of the thermoelectric module. Proof-of-concept circuitry and a LabVIEW based deployment of the technique were designed built and characterised. The technique was able to detect thermal anomalies and fluctuations present when undertaking an I-V curve, something otherwise infeasible with a standard k or t-type thermocouple. A full CPV-TE hybrid module with primary and secondary optical elements (POE-SOE-CPV-TE) was built using a further optimised receiver design and tested on-sun for evaluation under outdoor operation conditions in southern Spain. A unique TE-based “self-soldering” process was investigated to improve manufacture repeatability, reproducibility and minimise thermal resistance. A manually-tracked gyroscopic test rig was designed, built and used to gain valuable outdoor baseline comparison data for a commercially available CPV module and a Heterojunction Intrinsic Thinlayer (HIT) flat plate panel with the POE-SOE-CPV-TE hybrid device. An energetic break-even between the power consumed by the TE and the power gain of the CPV cell from induced temperature change was experimentally measured. This work demonstrated the unique functionalities a thermoelectric device can improve CPV power generation. The potential of a TEM to improve CPV power generation through active cooling was highlighted and quantified

Applications of spectral management in optoelectronic devices

The application and efficiency of optoelectronic devices depends on the ability to control the absorption and emission processes of photons in semiconductors. This thesis looks at three different applications of spectral management across a broad range of optoelectronic devices: photovoltaics (PVs), luminescent solar concentrators (LSCs) and light-emitting diodes (LEDs). Multiple excitation generation (MEG) – a process in which multiple charge-carrier pairs are generated from a single optical excitation - is a promising way to improve the photocurrent in photovoltaic devices and offers the potential to break the Shockley-Queisser limit. Here we present solar cells fabricated from PbSe nanorods which show external quantum efficiencies exceeding 100 %. This demonstrates the potential for substantial improvements in PV device performance due to MEG. Through spatial and spectral concentration, LSCs have the potential to reduce the cost of photovoltaic energy production and are attractive prospects for photobioreactors and building-integrated applications. Here we introduce versatile star-shaped donor-acceptor molecules based on a central BODIPY acceptor with oligofluorene donor side units. We perform comprehensive device measurements and Monte Carlo ray tracing simulations of LSCs. We find that the measured structures permit waveguide propagation lengths on a par with state-of-the-art nanocrystalline emitters, while proposed hypothetical structures can be seen as viable candidates for photobioreactor and energy production roles and should be synthesized. The efficiency of nanocrystal-based LEDs is inherently limited by the types of crystals used. Cesium lead halide perovskite nanocrystals exhibit photoluminescence quantum efficiencies approaching 100%. However, due to the large surface areas and anion mobility halogen exchange between perovskite nanocrystals of different compositions occurs rapidly, limiting applications. Here, we report significantly reduced halide exchange between chloride and iodide CsPbX3 (X= Cl, I) perovskite nanocrystals. We investigate perovskite-based multi-crystal component samples and their resulting optical and electrical interactions in bulk heterojunction LEDs. Efficient photon reabsorption from CsPbCl3 to CsPbI3 nanocrystals was found to improve LED device performance.• Cambridge Commonwealth, European and International Trusts Scholar • Cambridge Astralian Scholarships • Cambridge Australian Bragg Scholarshi

Measurement system for fast power and energy rating of photovoltaic devices

This thesis presents a new type of solar simulator and new measurement methods that allow for fast power rating of photovoltaic devices and for fast performance measurements for energy rating and energy yield predictions indoors under controlled, and more realistically simulated outdoor conditions. A novel indoor measurement system for photovoltaic device characterisation based on light emitting diodes (LEDs) as the light sources is described. The solar simulator is capable of reproducing spectral changes seen in natural sunlight, with its intricacies of variable air mass and weather conditions, to a better match than previously possible. Furthermore, it allows measurements under varying light intensity and device temperature. The prototype LED-based solar simulator developed is characterised and its measurement quality is analysed. The system achieves a class BAA solar simulator classification with a class B spectral match, class A light intensity uniformity and a class A temporal stability. It is the first system of its kind that meets the standards of a solar simulator in spectral match to the standard sunlight spectrum and in terms of minimum light intensity. An uncertainty analysis shows that calibration uncertainty for crystalline silicon solar cells is 5% in maximum power with a 95.45% level of confidence. Recommendations for further versions of the solar simulator are given and show potential of reducing this uncertainty down to 2.9% across all measurement spectra (1.8% with a primary calibrated reference cell). A new method for automated power-rating of single- and multi-junction devices is developed. The method uses a unique spectral response measurement and fitting method. It eliminates the need of external measurement equipment for determining spectral response. A simulated characterisation of an amorphous silicon single- and double-junction solar cell show accuracy of better than 0.5% in maximum power. First measurements on the LED-based solar simulator show a measurement error of 4.5% in maximum power, which is due to a lack of measurement feedback of spectral output and measurement irradiance. The first three-dimensional performance matrix for use in photovoltaic energy rating is reported, utilising the LED-based solar simulator. Device characteristics are measured indoors under varying irradiance, temperature and spectrum. A measurement method is detailed and utilised on a crystalline and amorphous silicon solar cell. It allows for the first time a direct investigation of spectral effects on photovoltaic devices under controlled conditions. Results show that amorphous silicon devices are very sensitive to changes in spectrum. Thus, spectral effects should not be neglected in energy yield predictions for such devices.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Carrier Recombination in Multicrystalline Silicon: A Study using Photoluminescence Imaging

This thesis applies photoluminescence imaging technique to study various carrier recombination mechanisms in multicrystalline silicon materials. One emphasis of the work has been recombination at grain boundaries, which is one of the limiting factors for the performance of multicrystalline silicon solar cells. An approach for quantifying the recombination activities of a grain boundary in terms of its effective surface recombination velocity, based on the photoluminescence intensity profile across the grain boundary, is developed. The approach is applied to compare the recombination properties of a large number of grain boundaries in wafers from different parts of a p-type boron doped directionally solidified multicrystalline silicon ingot. The results show that varying impurity levels along the ingot significantly impact the electrical properties of as-grown grain boundaries, and also their response to phosphorus gettering and hydrogenation. The work is then extended to various types of multicrystalline silicon materials. The electrical properties of conventionally solidified p-type, n-type and also recently developed high performance p-type multicrystalline silicon wafers were directly compared in terms of their electronic behaviours in the intra-grain regions, the grain boundaries and the dislocation networks. All studied samples reveal reasonably high diffusion lengths among the intra-grain regions after gettering and hydrogenation, suggesting that the main performance limiting factors are likely to be recombination at crystal defects. Overall, grain boundaries in the conventional p-type samples are found to be more recombination active than those in the high performance p-type and conventional n-type samples. As-grown grain boundaries and dislocations in the high performance p-type samples are not recombination active and only become active after thermal processes. In contrast, grain boundaries in the n-type samples are already recombination active in the as-grown state, but show a dramatic reduction in their recombination strength after gettering and hydrogenation. Apart from recombination through crystal defects within the bulk, recombination at surfaces acts as another significant loss mechanism in solar cells. This thesis also demonstrates the use of the photoluminescence imaging technique to study surface recombination in silicon wafers, and provides some examples of such applications