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The dc-link voltage control is vitally important to ensure the operation of photovoltaic (PV) system at the maximum power voltage, where its performance affects the power quality injected into the grid. In this work, we propose a method, based on the Lyapunov function, for investigating the control system stability, during the design of a nonlinear dc-link voltage controller for single-stage single-phase grid-connected PV inverters. Furthermore, we demonstrate analytically that the non-linearity of the PV string's power-voltage (P-V) curve leads to system instability. Consequently, this article proposes an adaptive dc-link voltage controller, built from the P-V curve, as an application in order to establish a stability criterion. Simulations and experimental validations have been carried out on a grid-connected single-stage single-phase PV inverter test platform. The results confirm the feasibility of the analytical study developed in this article, as well as the proposed dc-link voltage controller with and without the stability analysis.

The heterogenous nature and spectral distribution of rear plane-of-array irradiance RPOA presents challenges when measured by small-area sensors such as pyranometers. Bifacial reference modules serving as large-area sensors can simplify irradiance monitoring because their electrical response follows that of the power generating modules in an array. This article compares RPOA and effective irradiance GE measured by calibrated reference modules against three commonly used small-area sensors including pyranometers, reference cells, and photodiodes. A technology-matched monofacial module is mounted side-by-side with the bifacial reference to decouple effective irradiance measurements into front and backside contributions. The results show that RPOA and GE measurements made with reference panels have the best correlation to reference cells. The mean absolute errors between the two measurement approaches are 9% relative, 4 W/m2 absolute for RPOA and 4% relative, 7 W/m2 absolute for GE. When GE measurements from the four sensor types are used to predict string-level power, the reference panel measurements show a 3.4% prediction error, which is comparable to that achieved when using GE measurements from pyranometers (3.0%) and reference cells (2.9%) thereby suggesting that reference modules can be used to accurately measure RPOA and GE in bifacial systems.

Combined-accelerated stress testing (C-AST) is developed to establish the durability of photovoltaic (PV) products, including for degradation modes that are not a priori known or examined in standardized tests. C-AST aims to comprehensively represent the sample, stress factors, and their combinations using levels at the statistical tails of the natural environment. Acceleration factors for relevant climate sequences within the C-AST cycle with respect to the Florida USA climate are estimated for selected degradation mechanisms. It is found that for degradation of the outer backsheet polymer layer, the acceleration factor of the tropical climate sequence (the longest of the climate sequences) is f (T, G) = 17.3 with ultraviolet photodegradation; for polyethylene terephthalate hydrolysis (backsheets), f (T, RH) = 426; for electrochemical corrosion (PV cell), f (I) = 14.1; and for PbSn solder fatigue f (ΔT, r (T)) = 17.3. Here, T is the module temperature, G is the broadband spectrum irradiance on the plane of array of the module, RH is the relative humidity on the module surface, I is the leakage current through the module packaging, and r(T), the number of temperature reversals. The methods discussed herein are generally applicable for evaluating acceleration factors in other accelerated test methods.

This article presents image processing methods for reconstructing and calibrating contactless electroluminescence (EL) images acquired with laser line scanning for diagnostic of photovoltaic modules. The method specifically focuses on two outputs: a contactless conventional EL that is calibrated to correspond to electrically biased EL and a contactless highlighted EL that emphasizes visualization of defects. Results are evaluated first by visual inspection, showing similar resolution to electrically biased EL at 2% or 5% Isc. A quantitative analysis by peak signal to noise ratio (PSNR) measures also assesses the resemblance between the reconstructed contactless conventional EL and the reference electrically biased EL.

Modeling the current-voltage (I–V) characteristics of photovoltaic (PV) modules is of importance for condition monitoring and fault diagnosis of PV arrays. The practicality of the one-diode model (ODM) is relying on the behaviors of parameter extraction and translation techniques. In this paper, a simple and robust dual-iteration algorithm is presented to extract the ODM parameters using only three points under limited conditions. These points can be easily obtained from both the datasheet and measurements, making the method universal and practical. Parameters under other unknown conditions are translated from the nearest acquired condition to make the proposed method applicable for various conditions. The modeling I–V curve obtained by the proposed method is in a close agreement with the field measurement, demonstrating its effectiveness to characterize the PV modules with different technologies. Moreover, three existing models are used to benchmark the accuracy of the proposed method. The comparison further confirms that the accuracy of the ODM has been enhanced especially at low irradiance using the proposed parameter translation method. Furthermore, a distance-weighted method is presented to reliably translate the parameters, whose superiority has been validated against measurements and the other four methods, thus being an effective output assessment for monitoring and diagnosis.

Previously undiscovered failure modes in photovoltaic (PV) modules continue to emerge in field installations despite passing protocols for design qualification and quality assurance. Failure to detect these modes prior to widespread use could be attributed to the limitations of present-day standard accelerated stress tests (ASTs), which are primarily designed to identify known degradation or failure modes at the time of development by applying simultaneous or sequential stress factors (usually two at most). Here, we introduce an accelerated testing method known as the combined-accelerated stress test (C-AST), which simultaneously combines multiple stress factors of the natural environment. Simultaneous combination of multiple stress factors allows for improved identification of failure modes with better ability to detect modes not known a priori. A demonstration experiment was conducted that reproduced the field-observed cracking of polyamide- (PA-) and polyvinylidene fluoride (PVDF)–based backsheet films, a failure mode that was not detected by current design qualification and quality assurance testing requirements. In this work, a two-phase testing protocol was implemented. The first cycle (“Tropical”) is a predominantly high-humidity and high-temperature test designed to replicate harsh tropical climates. The second cycle (“Multi-season”) was designed to replicate drier and more temperate conditions found in continental or desert climates. Testing was conducted on 2 × 2-cell crystalline-silicon cell miniature modules constructed with both ultraviolet (UV)–transmitting and UV-blocking encapsulants. Cracking failures were observed within a cumulative 120 days of the Tropical condition for one of the PA-based backsheets and after 84 days of Tropical cycle followed by 42 days of the Multi-season cycle for the PVDF-based backsheet, which are both consistent with failures seen in fielded modules. In addition to backsheet cracking, degradation modes were observed including solder/interconnect fatigue, various light-induced degradation modes, backsheet delamination, discoloration, corrosion, and cell cracking. The ability to simultaneously apply multiple stress factors may allow many of the test sequences within the standardized design qualification procedure to be performed using a single test setup.

This article proposes an improved maximum power point tracking (MPPT) method that features a simple design, and improved efficiency in fast-changing irradiance conditions. The method uses three consecutive measurements and compares the power difference between each two consecutive samples, furthermore the voltage variation between the last two successive samples is observed. According to the obtained result of these comparisons, the algorithm applies the suitable action either increasing or decreasing the voltage. This simple concept allows easy implementation and reduces the implementation cost and calculation burden. Second, the method has a prompt tracking response during fast changes in solar irradiance (e.g., due to passing clouds). The proposed method is validated through experimental tests using solar irradiance profiles according to the EN50530 standard and is compared to the classical Perturb and Observe method. The experimental results show that the proposed MPPT effectively identifies the change in solar irradiance, and maintains high tracking efficiency even in fast-changing conditions.

Electroluminescence (EL) imaging is a powerful tool used to identify defects in photovoltaic solar cells. Typically, this type of characterization is performed in the dark using a current injection that equals short-circuit current measured at standard test conditions (STC). Restricting imaging to such a temperature range limits the information obtained about the module and cells. However, it is not trivial to develop a tool that would allow for EL imaging to be performed under a wider range of temperatures. Here we demonstrate an in situ EL imaging capability developed within an environmental chamber that allows for control of sample temperatures between-40 and 90 °C. Additionally, we demonstrate EL imaging of 4-cell mini-modules (MiMo) under front-side mechanical loading. A Raspberry Pi-connected camera with short-pass filter removed is used for EL imaging. The camera is low-cost with a small form-factor, appropriate for use in a harsh, enclosed environment. The camera is installed within a thermally isolating housing mounted within the environmental chamber. Three example cases are given for MiMos that exhibit various forms of degradation including solder fatigue and cell cracking. It is shown that by measuring at conditions above and below STC, different behaviors may be identified. In some cases, restricting characterization to STC may lead to a failure to detect damage existing in the sample.

Electroluminescence (EL) imaging is a photovoltaic (PV) module characterization technique, which provides high accuracy in detecting defects and faults, such as cracks, broken cells interconnections, shunts, among many others; furthermore, the EL technique is used extensively due to a high level of detail and direct relationship to injected carrier density. However, this technique is commonly practiced only indoors—or outdoors from dusk to dawn—because the crystalline silicon luminescence signal is several orders of magnitude lower than sunlight. This limits the potential of such a powerful technique to be used in utility scale inspections, and therefore, the interest in the development of electrical biasing tools to make outdoor EL imaging truly fast and efficient. With the focus of quickly acquiring EL images in daylight, we present in this article a drone-based system capable of acquiring EL images at a frame rate of 120 frames per second. In a single second during high irradiance conditions, this system can capture enough EL and background image pairs to create an EL PV module image that has sufficient diagnostic information to identify faults associated with power loss. The final EL images shown in this work reached representative quality SNRAVG of 4.6, obtained with algorithms developed in previous works. These drone-based EL images were acquired with global horizontal solar irradiance close to one sun in the plane of the array.

In order to maximize the use of renewable-based distributed generators (DGs), in addition to dealing with the effects of the inherent power management uncertainties of microgrids (MGs), applying storage devices is essential in the electrical system. The main goal of this paper is to minimize the total operation cost as well as the emissions of MG energy resources, alongside the better utilization of renewable energy sources (RES) and energy storage systems. The uncertainties of wind speed, solar irradiation, market price and electrical load demand are modeled using reduced unscented transformation (RUT) method. Simulation results reveal that, as expected, by increasing the battery efficiency, the achievable minimum daily operational cost of the system is reduced. For example, with 93% battery efficiency, the operational cost equals EUR 9200, while for an efficiency of 97%, the achievable minimum daily operational cost is EUR 8900. Moreover, the proper economic/environmental performance of the suggested approach, which contributes to the possibility of selecting a compromise solution for the MG operator in accordance with technical and economic constraints, is justified.

The ongoing energy transition and incoming water scarcity crisis demand coordinated research to ensure a fossil-free future for humankind. Aiming to increase energy efficiency, reduce curtailment and decarbonize water production, this paper proposes a novel energy management system (EMS) for a hybrid plant compound by a small modular nuclear reactor acting as cogeneration unit, a wind and solar farms as generators. Additionally reverse osmosis and multi-stage flash desalination plants are included as demand responsive units along with a freshwater storage. Mixed integer linear programming (MILP) is employed to formulate this stochastic optimization problem, where piecewise linear functions define operational costs and efficiencies of SMR and desalination motivating energy efficiency and safety. Renewable availability point forecasts are obtained with physics informed machine learning models whose error is characterised by fitting the predictor's residuals to different statistical distributions following an unsupervised methodology. The suitability of the EMS is addressed in two study cases, one exploring the flexibility exploitation of the algorithm and another proving its suitability for real-time implementation. The dispatcher manages to keep unaltered the SMR's core reaction while satisfying both electrical and water demand in different renewable availability regimes by fully exploiting sector coupling flexibility. Simultaneously, renewable curtailment is kept to a minimum.

Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to three days ahead. The only requirement is a basic dataset including power, wind speed and air temperature measurements. Then, these are recombined into physics informed metrics able to capture the operational point of the PV. In this way, the models learn about the physical relationships of the different features, effectively easing training. In order to generalise the results, we also present a methodology evaluating this physics informed approach. We present a study-case of a PV system in Denmark to validate our claims by extensively evaluating five different ML methods: Random Forest, Support Vector Machine, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and a hybrid CNN–LSTM. The results show consistently how the best predictors use the proposed physics-informed features disregarding the particular ML-method, and forecasting horizon. However, also, how there is a threshold regarding the number of previous samples to be included that appears as a convex function.

Solar energy generated from photovoltaics (PV) suffers from intermittent and fluctuating power output, as it generates in limited hours during the day, affected by irradiation fluctuations, and power generation does not necessarily follow the consumption profile. Intermittent and fluctuating power is mitigated by energy storage, as the generated power can be stored and used at the needed time. Integrating a supercapacitor as energy storage with the PV panel provides advantages such as increased system flexibility but also adds challenges. Using supercapacitors as energy storage outdoors, exposes the whole system to variable temperature, and this will affect the storage capacity. This paper is determining the optimal sizing of supercapacitor energy storage through performing laboratory testing under different temperatures in climatic chambers and different C-rates of discharging. Moreover, a system model for an off-grid PV-supercapacitor system with variable load is also presented together with an optimization method to size the supercapacitor storage unit that is capable of powering the loads 24 hours a day under all weather conditions.

Background. The objective of the present study was to compare the outcome of direct composite restorations, with and without the use of additional magnification. Methods. Twenty extracted molars were selected for the study. Class 1 Black cavities were prepared. All teeth were etched, and bonding agent was applied. Teeth were assigned randomly into two groups of ten each. Group I: restorations were done without the use of magnification, first by placing a layer of SDR® flow +Bulk Fill Flowable on the base of the cavity, and then restoring the morphology with Ceram.X SphereTEC® One. Group II: the same protocol was applied, but using the dental microscope. Teeth were then evaluated before and after the finishing protocol by 5 dental professionals using a series of established criteria. The results were documented and statistically analyzed using the non-parametric Mann-Whitney test. Results. Statistically significant differences were found when using magnification, before the polishing protocol in “marginal adaptation integrity” and “excess material”, and after polishing in “marginal adaptation integrity”. Conclusions. Magnification may be used to increase the quality of the final direct posterior restoration by improving the marginal adaptation integrity, reducing excess material, preventing marginal microleakage, and avoiding subsequent failure.

A new model of photovoltaic (PV) panel is proposed. The model precisely replicates sub-cell level degradation, such as cracks and interconnect failures, and reproduces their effect at the panel level I–V characteristic. Moreover, a regression method is proposed, which infers the model’s parameters from combination of electroluminescent (EL) image and degraded I–V characteristic. The combination of quantitative (EL image) and qualitative (I–V characteristic) enables to characterize the degradation of the cells embedded in the PV panel, without physical access to the cells. The proposed model and the regression problem is experimentally verified on a set of 3 single cell measurements and a set of 4 crystalline PV panels with various levels of degradation.

Measuring horizon shading and modeling available irradiance at a prospective site is necessary for accurate estimation of the energy yield of photovoltaic (PV) systems, as well as the expected operation and availability of PV powered products. Fisheye sky imaging is a relatively simple approach to characterize the surrounding horizon, however it's accuracy in estimating the shading loss on short timescales is not well quantified in the literature. In this work we evaluate the shade irradiance loss estimation accuracy of a horizon shading model based on fisheye sky images compared to actual local irradiance measurements from the site of interest. The results show that small errors in he horizon line estimation can lead to high irradiance estimation errors, for short timescales. However, these are mostly averaged out if the sampling period is 15 minutes or higher. The horizon shading maps obtained both with a commercial shading analysis tool, as well as self-calculated from raw fisheye images, tend to overestimating the direct beam shading and small relative errors are observed. Furthermore, ground reflected irradiance has a great influence on vertical surfaces, which was not accounted for in this work.

In this work we investigate the characteristics of solar cells cracks in photovoltaic (PV) modules for understanding the extent to which the solar cell electrical parameters change due to cell crack degradation. The experimental investigation is performed on two custom nine-cell mini-modules of mono- and multi-crystalline silicon, respectively, where each solar cell in the module has a junction box, allowing individual and module level characterization. Results show that power loss caused by cell cracks is driven primarily by a reduction of the cell’s maximum power point current, in particular B type cracks. C cracks also affect the short circuit current of the cells, whereas cells with combined B and C cracks show the most reduction of the short circuit current. Equivalent circuit diode model curve fitting and analysis of the light or dark I-V curves proved of limited used in analyzing degradation of cracked cells, as the model assumptions break down. However, a comparative analysis of dark and light I-V curves with a Suns-V oc curve was better suited for understanding the evolution of diode parameters on cracked cells for increasing levels of degradation.

Electroluminescence (EL) and photoluminescence (PL) imaging are powerful solar cell and panel characterization techniques that are capable of detecting early stage solar cell failures and degradation with high sensitivity and level of detail required for proper preventive maintenance. The electrical contacts needed during measurement pose a challenge for many inspection scenarios, both for EL and PL. Light or Laser induced luminescence (LIL) is a contactless way to generate spatially resolved images of PV panels that highlight poorly connected cell areas, such as finger failures, areas with high series resistance or disconnected cell cracks. In this study, we analyze the characteristics of the LIL scan imaging method in detecting cracks in mono-Si cells, comparing single frames with reconstructed contactless EL images built with a full LIL scan image stack. A comparison between contactless EL generated by LIL and conventional contact EL is made, and finally an estimation of the required laser intensity for outdoor LIL is provided, in correlation to known contact EL performance during nighttime and daylight conditions.

One limitation of photovoltaic energy is the intermittent and fluctuating power output, which does not necessarily follow the consumption profile. Energy storage can mitigate this issue as the generated power can be stored and used at the needed time. Integrating energy storage directly in the PV panel provides advantages in terms of simplified system design, reduced overall cost and increased system flexibility. Incorporating supercapacitors directly in the PV panel on module or cell level raises some challenges regarding the electrical integration, such as charge controlling for the capacitors, capacitor matching, as well as internal power electronics layout. Physical integration of graphene supercapacitors with solar cells, at module-or cell-level presents challenges related to physical dimensioning, thermal management and life expectation of the entire system. The main goal of this article is to review the supercapacitor technologies and perform a comparison between the available supercapacitors in the market and selecting the most suitable type for developing supercapacitor-based integrated PV-energy storage systems, to achieve optimal electrical and physical integration.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. An accurate model to represent the photovoltaic modules is essential to facilitate the efficient deployment of these systems in terms of design, analysis, and monitoring considerations. In this respect, this study proposes a new approach to improve the accuracy of the widely used five-parameter single-diode model. Two new physical equations are introduced to represent the series and shunt resistances, while the other parameters are represented by well established physical expressions. In the proposed model, most of the parameters are in terms of the cell temperature, irradiance, and datasheet values, while a few parameters need to be tuned. The model is compared with four well-known methodologies to extract the parameters of the single-diode and double-diode models. The simulation studies make use of the different I-V characteristics provided in the photovoltaics (PVs) datasheets, characteristics extracted from an outdoor module, as well as the ones simulated with the software PC1D. The results show an improved precision of the proposed model to estimate the power characteristics for a wide range of temperatures and irradiances, not only in the maximum power point but also in the whole range of voltages. Furthermore, the proposed physical model can be easily applied to other kind of studies where a physical meaning of the PV parameters is of great importance.

Introduction: Students must possess good theoretical knowledge in order to perform correct clinical treatments with good prognosis and in safe conditions but merging theoretical knowledge and clinical practice in dental education is a challenge for both the training staff and the students. Up until now, no studies have been published in our country regarding the challenges faced by dental students in their clinical works performance for restorative dentistry and endodontic treatments. Aim: The aim of this study was to assess the factors that may influence the performance of restorative and endodontic treatment procedures performed by 5th year dental students and their own perception on clinical work. Materials and Methods: This was a cross-sectional study carried out in February 2020. A self-made questionnaire with 13 questions was distributed via e-mail link to 5th year dental students at the University of Medicine and Pharmacy Iuliu Hat‚ieganu Cluj Napoca, Romania, Department of Conservative Dentistry, all 5th year dental student who had previously undergone two years of clinical training. One hundred eleven students were selected after applying the inclusion criteria that were, having two years of clinical training experience and having passed their theoretical ergonomics, restorative dentistry and endodontics examinations. Descriptive Statistics were used for data analysis. Results: The majority of the students 83% (n=92) encountered difficulties applying the rules of ergonomics related to posture. The most common causes that prevented students from applying the rules of ergonomics during the clinical work were challenges related to working in indirect vision (n=90), lack of help by an assistant during treatment (n=52), working time allocated being too short (n=50), lack of sufficient space around the unit (n=43). Regarding the theoretical level required for restorative cases diagnosis, treatment plan and treatment itself, 64% (n=71) of students claimed to have had the necessary theoretical knowledge in most cases they met and only 25% (n=28) could confidently treat all cases. Establishing the diagnosis and treatment plan for endodontic cases was difficult stage for 3.6% (n=4) of students, of average difficulty in the case of 81.1% (n=90) of students, while 15.3% (n=17) did not consider them difficult at all. Conclusion: The results indicated that students had high levels of confidence in their theoretical knowledge when establishing the diagnosis and the treatment plan, but they encountered multiple challenges both with working ergonomically and with mastering the practical skills needed in different stages of the treatment.

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.

The number of photovoltaic panels installed globally is continuously growing, requiring an automatic inspection procedure for operation and maintenance. Drones can be a useful tool to this aim as they enable fast acquisition of various imaging modalities: visual, infrared, or electroluminescence (EL). Image distortions due to perspective must be corrected to allow further automatic processing. It can be done by estimating the corresponding rotation angles to control the camera gimbal or as postprocessing to rectify the images. This article presents two methods to achieve both goals by identifying known points in the acquired image. The first method detects the four panel corners, whereas the second method finds the corners of each cell. The performance evaluation is performed first quantitatively on a validation dataset composed of 113 EL images and their corresponding ground-truth orientations. A qualitative evaluation shows satisfying performance of the rectification similarly for both methods. The quantitative performance is varying for each rotation axis. The average absolute error is 2.78 $^{\circ }$ along the $x$ -axis, 2.64 $^{\circ }$ along the $y$ -axis, and 1.28 $^{\circ }$ along the $z$ -axis for the panel method and 3.26 $^{\circ }$ , 2.05 $^{\circ }$ , and 1.24 $^{\circ }$ for the cell method. As a proof of concept, a final test on drone-acquired EL images shows good performance for the image rectification in real-life conditions.

During field operation, photovoltaic cells (PVs) are often subject to rapid variations in incident light, due to fast moving clouds. This causes variations in the operating temperature of the PVs as well. In this work we study the effect of rapid concentrated light variation over the PVs as an ageing method. The irradiance and thermal cycles were performed using a self-developed system with a controlled shutter that modulates the highly concentrated light incident on the PVs. Through the load we developed based on the impedance variation of a capacitor and a coil during the charge/discharge process, the main DC and AC parameters of the PVs are monitored in-situ through the I-V characteristics and RLC method measurements. Preliminary results show a degradation of 0.6% of the PVs caused by the light and temperature stress.

The photovoltaic (PV) cell temperature strongly affects the performance and efficiency of the entire PV module. Thus, the accurate estimation of the cell temperature plays an important role in the health monitoring and energy assessment of PV systems. This article proposes a multi-state dynamic thermal model for PV modules, considering the heat-transfer mechanisms between the module and its environments, as well as between layers. The proposed model is benchmarked against field measurements at Aalborg University, Denmark. The results demonstrate the effectiveness of the model to characterize the internal behavior of the PV module under varying weather conditions. The performance of the proposed thermal model is also compared with prior-art models, i.e., two benchmark models, a one-state thermal model and two typical empirical equation-based models. The comparison further confirms that the estimation of cell temperature using the developed model is more accurate, presenting a reliable prediction of power production for further monitoring and diagnosis.

With the rapid increase in photovoltaic energy production, there is a need for smart condition monitoring systems ensuring maximum throughput. Complex methods such as drone inspections are costly and labor intensive; hence, condition monitoring by utilizing sensor data is attractive. In order to recognize meaningful patterns from the sensor data, there is a need for expressive machine learning models. However, supervised machine learning, e.g., regression models, suffer from the cumbersome process of annotating data. By utilizing a recent state-of-the-art semi-supervised machine learning based on probabilistic modeling, we were able to perform condition monitoring in a photovoltaic system with high accuracy and only a small fraction of annotated data. The modeling approach utilizes all the unsupervised data by jointly learning a low-dimensional feature representation and a classification model in an end-to-end fashion. By analysis of the feature representation, new internal condition monitoring states can be detected, proving a practical way of updating the model for better monitoring. We present (i) an analysis that compares the proposed model to corresponding purely supervised approaches, (ii) a study on the semi-supervised capabilities of the model, and (iii) an experiment in which we simulated a real-life condition monitoring system. With the rapid increase in photovoltaic energy production, there is a need for smart condition monitoring systems ensuring maximum throughput. Complex methods such as drone inspections are costly and labor intensive; hence, condition monitoring by utilizing sensor data is attractive. In order to recognize meaningful patterns from the sensor data, there is a need for expressive machine learning models. However, supervised machine learning, e.g., regression models, suffer from the cumbersome process of annotating data. By utilizing a recent state-of-the-art semi-supervised machine learning based on probabilistic modeling, we were able to perform condition monitoring in a photovoltaic system with high accuracy and only a small fraction of annotated data. The modeling approach utilizes all the unsupervised data by jointly learning a low-dimensional feature representation and a classification model in an end-to-end fashion. By analysis of the feature representation, new internal condition monitoring states can be detected, proving a practical way of updating the model for better monitoring. We present (i) an analysis that compares the proposed model to corresponding purely supervised approaches, (ii) a study on the semi-supervised capabilities of the model, and (iii) an experiment in which we simulated a real-life condition monitoring system.

In order to provide Internet access to rural areas and places without a reliable economic electricity grid, self-sustainable drone-based cellular networks have recently been presented. However, the difficulties of power consumption and mission planning lead to the challenge of optimal sizing of the power supply for future cellular telecommunication networks. In order to deal with this challenge, this paper presents an optimal approach for sizing the photovoltaic (PV)-battery power supply for drone-based cellular networks in remote areas. The main objective of the suggested approach is to minimize the total cost, including the capital and operational expenditures. The suggested framework is applied to an off-grid cellular telecommunication network with drone-based base stations that are powered by PV-battery systems-based recharging sites in a rural location. The PV-battery system is optimally designed for three recharging sites with three different power consumption profiles with different peak and cumulative loads. Results show that the optimal design of the PV-battery system is dependent on geographical data, solar irradiation, and ambient temperature, which affect the output power of the PV system, as well as the power consumption profile, which affects the required number of PV panels and battery capacity.

In this work we present a test platform for the rapid prototyping of power electronic converters. The platform consists of a Texas Instruments (TI) digital-signal-controller (DSC) TMS320F28379D connected to an Opal-RT real-time digital simulator capable of hardware-in-the-Ioop (HIL) simulations. Both the HIL simulator and the DSC are programmed based on Matlab/Simulink, with the latter making use of the embedded coder for the C2000 processors family. The proposed test platform allows high flexibility and fast development for control of power electronic converters, all based on a single software development environment. A complete specification of the platform components and a corresponding step-by-step implementation is presented. An example of a grid connected single-phase T-type converter illustrates the system operation and the platform capabilities.

Partial shading affects the performance and reliability of thin-film and crystalline-silicon (c-Si) photovoltaic (PV) modules. In this paper, the thin-film and c-Si modules are experimentally benchmarked by introducing various partial shading patterns over the modules. More specifically, experiments are performed using SPISUN 5600 SLP-based test-rig. The benchmarking reveals that thin-film and c-Si technologies behave differently under the same shading patterns. Furthermore, thermographic images are also presented to explore the effect of partial shadingon both modules.

Residential photovoltaic (PV) systems with integrated battery energy storage (BES) are used to increase energy self-consumption, especially in countries with low feed-in tariffs and high electricity prices. In recent years, lithium-ion batteries have been established as common technology for this application. However, a major challenge is their performance degradation over time, which is influenced by how the BES is operated. In this work, the impact of the PV + BES system's Energy Management System (EMS) on the lifetime of the battery is investigated, by developing a performance model of the PV+BES system and analyzing the battery degradation under different operation scenarios, using a battery lifetime model. Simulation results show that the battery lifetime can be extended and a high self-consumption ratio achieved, by operating the EMS at different charging rates, depending on predicted future PV production based on weather forecast.

Current-voltage (IV) curve tracing and electroluminescence (EL) imaging have been developed for in-situ performance characterization of photovoltaic (PV) devices in a Xe lamp-based weathering chamber for combined-accelerated stress testing. The capability allows for progressive failure monitoring during accelerated ageing with dynamic control of the characterization environment (e.g. imaging at specific temperatures and mechanical stress levels). Both light and dark IV curve tracing are implemented with techniques to overcome light and temperature instability inherent to the chamber. A Raspberry Pi-connected camera with infra-red filter removed is used for EL imaging, providing a low-cost, small form-factor solution which is desirable for use in a harsh environment. The camera is installed within a thermally-isolated housing mounted within the climate chamber. Measurement and control are achieved via LabVIEW, where characterization is integrated as part of the test protocol and performed automatically.

The “Framing” (local discoloration along cell edges) was induced by a simple sequential accelerated stress test (consisting of hygrothermal- and UV-stressors) applied to the PV modules with high OTR (oxygen transmission rate) backsheet, irrespective of the inclusion of UV-absorber in poly(ethylene-co-vinyl acetate) (EVA) encapsulant. UV-fluorescence imaging of the PV modules suggests that the spatially-inhomogeneous degradation of EVA material under UV-irradiating conditions is correlated to this “Framing” indicating an underlying common mechanism. These findings would contribute to the development of test procedures to broadly mimic the actual failures observed in fielded PV.

Electroluminescence (EL) imaging and infrared (IRT) thermography techniques have become indispensable tools in recent years for health diagnostic of photovoltaic modules in solar industry application. We propose a diagnostic setup, which performs lock-in EL for accurate analysis of different types of faults occurring in a solar module. The setup is built around a high-speed SWIR camera, which can acquire images at very short integration time (1µs) and high frame rate (301 fps). In addition, a state-of-the-art imaging chamber allows for introducing controlled levels of ambient light noise for developing new light noise removal methods, rotation of panel frame in 3 axes plane for developing perspective distortion correction techniques. The paper also gives an insight of different system and communication delays that affects the performance of overall EL lock-in imaging system integration. The purpose of the diagnostic setup is to support research in PV failure quantification through EL imaging, which can also be useful for aerial drone imaging of PV plants.

Application of stress factors in the combinations and sequences as they occur in the natural environment better reproduces failures seen in the field. Further, choice of sample type for testing that best represents the final product is most likely to yield relevant results. Tests meeting these criteria are more likely to discover potential weaknesses that are not a priori known in new module designs, reduce risk, accelerate time to market, achieve bankability and reduce costly overdesign.

An in-situ method is proposed for monitoring and estimating the power degradation of mc-Si photovoltaic (PV) modules undergoing thermo-mechanical degradation tests that primarily manifest through cell cracking, such as mechanical load tests, thermal cycling and humidity freeze tests. The method is based on in-situ measurement of the module’s dark current-voltage (I-V) characteristic curve during the stress test, as well as initial and final module flash testing on a Sun simulator. The method uses superposition of the dark I-V curve with final flash test module short-circuit current to account for shunt and junction recombination losses, as well as series resistance estimation from the in-situ measured dark I-Vs and final flash test measurements. The method is developed based on mc-Si standard modules undergoing several stages of thermo-mechanical stress testing and degradation, for which we investigate the impact of the degradation on the modules light I-V curve parameters, and equivalent solar cell model parameters. Experimental validation of the method on the modules tested shows good agreement between the in-situ estimated power degradation and the flash test measured power loss of the modules, of up to 4.31 % error (RMSE), as the modules experience primarily junction defect recombination and increased series resistance losses. However, the application of the method will be limited for modules experiencing extensive photo-current degradation or delamination, which are not well reflected in the dark I-V characteristic of the PV module.

A progression of potential-induced degradation (PID) mechanisms is observed in CdTe modules, which are dependent on the stress level and moisture ingress. This includes shunting, junction degradation, and two different manifestations of series resistance. The dark I–V method for in situ characterization of P max based on superposition was adapted for the thin-film modules undergoing PID in view of the degradation mechanisms observed. An exponential model based on module temperature and relative humidity (RH) was fit to the PID rate for multiple stress levels in chamber tests and validated by predicting the observed degradation of the module type in the field.

The experimentation with a movable outdoor electroluminescence (EL) testbed is performed in this work. For EL inspections of PV power plants, the fastest scenario will include the use of unmanned aerial vehicle (UAV) performing image acquisition in continuous motion. With this motivation, we investigate the EL image quality of an acquisition in motion and the extent of image processing required to correct scene displacement. The results show processed EL images with a high level of information even when acquired at 1 m/s camera speed and at frame rate of 120 fps.

Good quality images are necessary for electroluminescence (EL) image analysis and failurequantification in solar panels. In this work, a method for determining image quality in terms of more accurate failure detection in PV panels through EL imaging is proposed. The goal of the paper is to highlight the different methods for image quality improvement and to determine if the enhanced image provides more useful diagnostic information for accurate micro cracks and fracture detection. From the work carried out in this paper, it is to be noted that averaging technique helps in improving the SNR value. Additionally, subtracting the background from the obtained averaged EL image proves to be an enhancing method for cell fracture identification and more number of edges are also detected which can be useful for micro crack quantification.

With the significant growth in the number of photovoltaic (PV) installations and their size, regular PV system inspection has become a challenge. Aerial drone imaging, based on visual, thermographic, and more recently luminescence, can be viable solutions for PV inspection. However, to achieve effective detection and quantification of failure based on images acquired form Unmanned Aerial Vehicle, there is need for image quality enhancement and correction of distortions, inherent to the drone measurement process. In this work we propose methods to automatically correct the perspective distortion in electroluminescent (EL) images of PV panels. We identified two main cases of perspective distortion: when the imaging plane is parallel to the panel plane or not, and propose methods to correct both. For both cases, the proposed method yields good results, as assessed by visual evaluation.

We validated the use of coulombs transferred between the active cell circuit and ground as an index for quantitatively predicting degradation rate in the field for two thin-film module types undergoing potential-induced degradation (PID). The dependence was determined by comparing the degradation rate and leakage current in the field (Cocoa, Florida) to accelerated tests $( 85 ^{circ}\mathrm {C}$, 85% relative humidity), both with the application of -1,000 V bias to the cell circuit. The two module types, which degraded about 4% and 11% in power after application of 96 h of bias in chamber, degraded 5% in the field by PID within 200 days and 6 days, respectively. The signatures of PID in the thin-film modules by electroluminescence, photoluminescence, and thermography are shown so that PID in fielded thin-film modules can be identified.