Water participates in multiple modes of degradation in photovoltaic (PV) modules including encapsulant yellowing, delamination, and contact corrosion. To mitigate moisture-induced degradation, we must understand the kinetics of moisture in state-of-the-art encapsulants and module architectures. In this article, we present a robust optical method to quantify water content in the front and rear side encapsulants of bifacial silicon PV modules, then use such measurements to validate a model for simulating water concentration in these modules. First, we quantify the solubility of water in four modern encapsulants: ethylene vinyl acetate and polyolefin, each with and without UV-blocking additives. Second, we use water reflectometry detection to measure the diffusion of moisture within glass-backsheet modules as a function of time and environmental condition. Third, we present a model of moisture transport in bifacial silicon PV modules and show it to be consistent with our measurements. Crucially, our methodology enables the separate evaluation of water content in the front encapsulant and the rear polymers within glass-backsheet modules. Overall, our work presents a quantitative picture of moisture in emerging module architectures and a framework to extend this approach other encapsulants and module designs.

We demonstrate the versatile use of UV-ozone oxide (UVo) in surface cleaning, surface passivation, diffused junction passivation, and current tunneling applications of crystalline silicon (c-Si) solar cells. A UV-ozone generated oxide is used as a surface clean for random textured c-Si samples and the effectiveness of surface clean is determined by capping with a thin layer of aluminum oxide (AlO x ). Our developed UVo clean has resulted in a cleaning efficiency almost comparable to that of the benchmarked RCA clean, yielding a saturation current density of 12 fA/cm 2 . When planar and textured c-Si samples are capped by a stack of UVo and AlO x , a UV-ozone growth time of no more than 3 min is found to provide an optimum surface passivation. When tested on phosphorus and boron diffused junctions (with sheet resistance, R sh of 110–120 $\Omega\!/\!{\scriptstyle\square} $ ), the UVo and AlO x stack resulted in a J 0 of 11 fA/cm 2 or lower. The high-resolution transmission electron microscope imaging revealed that UVo structure is stable upon annealing for passivation activation. Last, when applied as a tunneling contact, the UVo realizes a contact resistivity ( ρc ) of ∼1 mΩ-cm 2 and ∼20 mΩ-cm 2 for boron and phosphorus doped metal-insulator-semiconductor contact structures, respectively, with moderately doped diffusions.

The application of machine learning techniques in silicon photovoltaics research and production has been gaining traction. Learning from the existing data has given the potential to research labs and industries of discovering optimized processing parameters, device architectures, and fabrication recipes. It has also been utilized for defect detection and quality inspection. The increasing computational capacities of modern computers and abstraction of machine learning algorithms, along with the increasing community support for open-source software libraries has increased the accessibility of learning-based algorithms that were perceived as complex to be implemented for interdisciplinary research and development just a few years back. In this article, we present a review of the efforts in the literature that have utilized machine learning techniques for commercial silicon solar cell devices in recent times. The emphasis is to categorize and investigate specific learning techniques that are best suited for one particular device or fabrication process parameter optimization. We also provide insight into possible expansions of current research methodologies that can further improve the prediction accuracy while minimizing the computational costs and extract other useful information from a machine learning model, such as prediction uncertainty, scalability, and generalization of a particular model.

Degradation modes affecting series resistance ( R deg,modes ) are one of the major causes of performance degradation in outdoor operating photovoltaic (PV) modules. They have distinct loss incurring mechanisms under different climatic conditions. In this article, major R deg,modes have been investigated for their impact on the electrical parameters and differentiated based on their distinct electrical signatures at both cell and module levels. For this purpose, the major R deg,modes have been identified through cross-characterization of nine unaged PV modules subjected to temperature cycle, humidity freeze, and damp heat test conditions, using electroluminescence imaging and visual inspection. These R deg,modes have been broadly categorized as metallization interruption, metallization corrosion, and cracks with loss in the active cell area. The spatial characteristics of various R deg,modes have been modeled using distributed diode model of a solar cell, which has been experimentally validated. The change in fill factor has been chosen as a base metric to compare various R deg,modes . The results show that combined case of metallization interruptions can be more severe than metallization corrosion. The maximum loss in power of up to 40% has been calculated for interruption at the finger-busbar interface. Furthermore, based on the loss incurring mechanism, various R deg,modes exhibited distinct electrical signatures that were analyzed using normalized percentage change in voltage and current at the maximum power point, which has also been used to distinguish them. The distinct electrical signatures can be used for identification and nondestructive investigation of R deg,modes in the PV modules under field-operating conditions.

Maximum power point (MPP) tracking algorithms have always been at the forefront in photovoltaic (PV) systems to counter the nonlinearity caused by PV arrays and, thereby, harvest maximum power. There are several MPP trackers, including fixed and variable step size. The application of a fixed step-size MPP tracker leads to steady-state power oscillations around MPP. On the other hand, a conventional variable step-size MPP tracker employs a constant multiplying factor to increase the convergence rate; however, its response is found to be sluggish under PV parameters’ uncertainties. Therefore, in this article, an optimized fractional nonlinear synergetic controller (FNSC) driven MPP tracker is proposed to meticulously detect the true MPP. The proposed optimized FNSC provides a large dynamic range and ensures minimal sustained power oscillations around MPP even under unequal irradiances. The algorithm based on fractional calculus utilizes macrovariables for improving the performance of the proposed FNSC under steady-state and dynamic operating conditions. The effectiveness of the proposed optimized FNSC is verified using OPAL-RT (OP5700). The outcomes of this study validate the practicability of an FNSC-driven MPP tracker to guarantee less MPP tracking time. The MPP tracking time recorded with the proposed FNSC during steady-state and dynamic conditions is less than 21.80 and 20.80 ms , respectively. Besides, it guarantees 99.7% tracking efficiency and, thereby, outperforms the existing MPP trackers.

There are several methods for the evaluation of the performance of solar cells through the voltage loss. However, the advantages and disadvantages of each method have not been thoroughly discussed nor elucidated. This study compares these various methods to clarify their advantages and disadvantages. The bandgap offset W OC has been used for the evaluation of the voltage loss. However, it was found that such approach gives inaccurate assessment of the voltage loss in thin-bulk and quantum-structure solar cells. Our study discusses the reasons for such inaccuracy and proves that we can accurately evaluate the nonradiative voltage loss in quantum-structure solar cells using the optoelectronic reciprocity theorem and detailed-balance theory. Our findings enable a fair, accurate, and accessible evaluation of the nonradiative voltage loss in solar cells which facilitates the development of high-efficiency quantum-structure solar cell.

The diurnal and seasonal changes in the solar spectrum affect the photovoltaic (PV) module performance. In this article, the transient solar spectrum was integrated with the thermal–electrical model of the PV module for precise estimation of diurnal and seasonal PV performance. The transient meteorological parameters were considered for determining the dynamic power output of the PV. The SMARTSv2.9.5, COMSOL Multiphysics, and MATLAB were used for modeling different sections and integrated throughout. The cell temperature and power output were obtained and experimentally validated for various seasons. The statistical errors, such as mean absolute error (MAE), mean relative error, and root-mean-square error (RMSE) values, and the coefficient of determination ( R 2 ) value were calculated for the developed model during various seasons. The RMSE of back surface temperature and power output from monocrystalline ranges from 1.67 to 2.59 °C and 1.83 to 2.92 W, respectively, and the same for polycrystalline PV module ranges from 1.93 to 2.29 °C and 1.70 to 3.63 W, respectively. The MAE of the I sc and V oc ranges between 0.10 and 0.29 A and 0.41 and 0.58 V, respectively, for monocrystalline, 0.13–0.43 A and 0.39–0.52 V, respectively, for polycrystalline PV modules. The present model is a simple integration and uses basic mathematical equations and parameters to predict the PV outputs.

Soiling is the accumulation of dirt in solar panels that leads to a decreasing trend in solar energy yield and may be the cause of vast revenue losses. The effect of soiling can be reduced by washing the panels, which is, however, a procedure of non-negligible cost. Moreover, soiling monitoring systems are often unreliable or very costly. We study the problem of estimating the soiling ratio in photovoltaic (PV) modules, i.e., the ratio of the real power output to the power output that would be produced if solar panels were clean. A key advantage of our algorithms is that they estimate soiling, without needing to train on labeled data, i.e., periods of explicitly monitoring the soiling in each park, and without relying on generic analytical formulas that do not take into account the peculiarities of each installation. We consider as input a time series comprising a minimum set of measurements that are available to most PV park operators. Our experimental evaluation shows that we significantly outperform current state-of-the-art methods for estimating soiling ratio.

The underlying mechanisms of the initial stages of hot-spot and therefore defect creation due to reverse breakdown in Cu(In,Ga)S $\text{e}_{2}$ solar cells are not well understood. We test the thesis, that permanent damage is created due to a positive feedback loop of local temperature enhancing the local current and vice versa, resulting in a thermal runaway. We present experiments on reverse stress with simultaneously introducing local heat. Depending on the temperature profile of the introduced heat, the local current density is enhanced and leads to a gain in the local temperature. This feedback loop is shown to lead to reverse breakdown, causing permanent damage.

Bifacial photovoltaic modules are able to convert the solar radiation reaching their front and rear sides, which means that more electricity can be produced using the same array area as monofacial modules with similar ratings. In some locations, the cost per power unit for such a technology has already become cost-competitive with conventional monofacial modules. The so-called effective irradiance and the bifacial gain are useful metrics, respectively, to assess the solar resource and the performance of bifacial arrays. To calculate the effective irradiance, studies previously published employ rear-side irradiance measurements, whereas to compute the bifacial gain, other works make use of monofacial modules with rating similar to those of the bifacial modules under analysis. In this article, a straightforward method is presented, allowing to calculate the effective irradiance from the maximum power current, and to calculate the bifacial gain using a power scaling relation. The proposed method was experimentally tested using an outdoor platform with a dual-axis tracking system with bifacial modules. The effective irradiance was calculated using the novel method presented nRMSE of 2.88%, relative to the results obtained using the consolidated method. The bifacial gains obtained were 6.24% and 6.69%, respectively, using the proposed and traditional calculation methods. The procedure presented in this study might be useful for the quantification of the effective irradiance and the bifacial gain for PV installations, which do not have extensive monitoring hardware.

A system of 180 monocrystalline aluminum back-surface field modules were installed in Cocoa, Florida, for 10 years. In total, 156 modules are characterized and compared to 3 controls. Power degradation rates vary between $-$ 0.14% to $-$ 3.22% per year, with median and average rates of −0.92% and −1.05% per year, respectively. The losses are primarily resistive with minor optical and recombination loss contributions. Electroluminescence imaging shows a characteristic pattern, which is shown to be resistive in nature when compared to photoluminescence. Resistive losses are due to corrosion of the rear contact Ag/solder interface and, to a much lesser degree, gridline Ag oxidation. Moisture ingress through the backsheet is likely responsible for mediating corrosion. Optical losses are due mostly to a combination of antireflection coating degradation, minor encapsulant browning, and delamination. Minor front contact corrosion may contribute to recombination. This study expands upon previous work on this vintage of the module by examining a large sample set, comprehensive characterization including techniques not previously used on these modules, and a comparison between two other systems of different climates.

Spectral responsivity (SR) measurements are key in characterizing photovoltaic (PV) devices and calculating spectral mismatch correction factors (MMF). SR is measured using different wavelengths of monochromatic light. In practical measurement systems, this light has a finite bandwidth, meaning that it is only quasi-monochromatic. The data analysis, however, typically assumes perfectly monochromatic light. The accuracy of the calculated SR then depends not only on the actual bandwidth of the monochromatic light used during the measurement but also on the intensity distribution inside the bandwidth. In our SR measurement system, we use quasi-monochromatic light obtained from a solar simulator and a range of bandpass filters. First, we measured and analyzed the spectral content of light transmitted for each filter. Then, we developed an iterative correction scheme for the measured SR. This numerical approach assumes no previous knowledge about the SR of the device under test (DUT). Applying this scheme for bandwidth correction to a range of PV cells with varying SR curves, we found that the corrections for most wavelengths are of minor importance. However, for those wavelengths where the shape of the SR curve of the reference device and DUT differ notably, the corrections are significant up to 20% and higher. We investigated the change in the spectral MMF, which was found to be negligible in most cases but not always. Therefore, the bandwidth correction is mainly important not only for improving SR measurements of PV devices but also relevant for the determination of spectral mismatch factors.

For flexible photovoltaic (PV) applications, the dominant material for the frontsheet is poly(ethylene-co-tetrafluoroethylene). As a fluoropolymer, it resists soiling by letting the water run off easily, is resistant to degradation by exposure to ultraviolet light, and is more mechanically durable than most fluoropolymers. To keep costs down, less expensive alternative polymers are desirable. In this study, highly accelerated ultraviolet light and heat stresses are applied to candidate materials, and the degradation kinetics are determined to provide information to evaluate their suitability for use in a PV application. Because of the uncertainty in service life prediction, the acceleration parameters are instead used primarily to evaluate the relevance of the applied stresses. Here, we find that the best materials are fluoropolymer based and that even when exposed to high irradiance at high temperatures, relatively little degradation is seen. For the 15 materials tested here, we found the Arrhenius activation energy for various degradation processes to be 39 ± 22 kJ/mol with a power law dependence on irradiance of 0.49 ± 0.22 with a negative correlation coefficient of −0.606 (i.e., more highly thermally activated processes are less dependent on the irradiance level). For frontside exposure, the most severe conditions used here (4 W/m 2 /nm @340 nm, 70 °C, for 4000 h) were on average equal to about 11.4 y in Riyadh, Saudi Arabia when mounted with insulation on the backside. Thus, to get relevant amounts of ultraviolet exposure with unmodified commercial equipment (∼0.8 W/m 2 /nm @340 nm) requires extraordinarily long exposure times, especially if conducted at lower irradiance levels.

Unmanned aerial vehicles (UAVs) with high-resolution optical and infrared ( IR ) imaging have been introduced in recent years to perform inexpensive and fast inspections in operation and maintenance activities of solar power plants, reducing the labor needed, while lowering the on-site inspection time. Even though UAVs can acquire images extremely quickly, the analysis of those images is still a time-consuming procedure that should be performed by a trained professional. Therefore, a computer vision approach may be used to accelerate image analysis. In this work, a dataset of IR images was created from a 10-MW solar power plant and a comparative analysis between mask R- convolutional neural network (CNN) and U-Net was performed for two experiments. Concerning the defective module segmentation, the mask R-CNN algorithm achieved a mean average precision at intersection over union (IoU) = 0.50 of 0.96, using augmentation data. Regarding the segmentation and classification of failure type, the algorithm reached a value of 0.88 considering the same evaluation metric and data augmentation. When compared to the U-Net in terms of IoU, the mask R-CNN outperformed it with 0.87 and 0.83 for the first and second experiments, respectively.

In this article, the impact of different hydrogen configurations and their evolution on the extent and kinetics of light- and elevated-temperature-induced degradation (LeTID) is investigated in float-zone silicon via charge carrier lifetime measurements, low-temperature Fourier-transform infrared spectroscopy, and four-point-probe resistance measurements. Degradation conditions were light soaking at 77 °C and 1 sun-equivalent illumination intensity and dark anneal at 175 °C. The initial configuration of hydrogen is manipulated by varying the wafer thickness, the cooling ramp of the fast-firing process, and the dopant type (B- or P-doped). We find lower hydrogen concentrations in thinner samples and samples with a slower cooling ramp. This suggests that hydrogen diffuses out of the sample during the cool-down, which strongly affects the final concentration of hydrogen molecules H 2 , and to a smaller degree the concentration of boron-hydrogen (BH) pairs. A regeneration of potential LeTID defects and a presumed LeTID degradation during dark annealing is found in n-type Si. In p-type Si, the LeTID extent was found to scale with H 2 , suggesting a direct link between both. The temporal evolution of BH pairs, LeTID degradation/regeneration, and surface degradation depends on wafer thickness and the cooling ramp of the fast-firing process. Based upon these findings, we formulate a theory of the hydrogen-related mechanism behind LeTID: Hydrogen originating from H 2 moves between different temporary traps. First, hydrogen binds to LeTID precursors and acceptor atoms in the silicon bulk, later moving toward the surface. This leads first to the LeTID degradation and regeneration and then to the degradation of surface passivation.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

An all-inclusive investigation of the effect of postprocessing thermal treatment on all vacuum-deposited small-molecule organic solar cells (SM-OSC) is presented. Herein, DTDCTB is chosen as the donor (D) molecule, and three fullerene-derivative acceptor (A) molecules, namely, ICBA, C 70 , and C 60 , are chosen for the study, and the devices were optimized for PV. As the first step, OSC cells were fabricated and characterized for photophysical, morphology, as well as various photovoltaic parameters, such as ${V}_{\text{OC}}$ , ${J}_{\text{SC}}$ , $FF,{\rm{and\ }}\eta $ , and external quantum efficiency for different processing parameters, such as active layer concentration ratios and annealing temperatures. The devices based on ICBA were found to have outperformed the devices using C 70 and C 60 . OSC devices using ICBA as an acceptor are chosen for further characterization to establish the role of thermal treatment on their device performance. To this, 1-diode Shockley equation modeling is employed, and a qualitative relationship between diode saturation current and thermal annealing is obtained. Additionally, the charge recombination dynamics of the binary bulk heterojunction systems were investigated using the light intensity-dependent J–V characteristics, and the role of annealing in the reduction of trap-assistance recombination was established that corroborates well with the obtained annealing-dependent morphology information from AFM measurement.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Development of photovoltaic (PV)-powered vehicles is very important to play a critical role in a future carbon neutrality society because it has been reported that the vehicle integrated PVs (VIPVs) have great ability to reduce CO 2 emission from the transport sector. Usage of high-efficiency solar cell modules is essential due to the limited installable area of PV on vehicle exterior. This article presents test driving data of the Toyota Prius demonstration car installed with high-efficiency III-V compound triple-junction solar cell module with an efficiency of more than 30%. Average daily driving distance (DD) of 17 km/day under usage of air conditioning and 62% CO 2 emission reduction are demonstrated by actual driving in Nagoya, Japan. In addition, analytical results for impact of high-efficiency VIPV modules of more than 35% on increases in DD of more than 30 km/day average and reducing CO 2 emission of PV-powered vehicles of more than 70% reduction are also shown.

Presents the 2022 author/subject index for this issue of the publication.