Simulation of particle deposition on solar photovoltaic panels based on a new critical capture velocity criterion.

Solar energy, as a clean energy source, is becoming increasingly important in the global energy mix. However, particle deposition on the surface of photovoltaic (PV) panels can significantly reduce their power generation efficiency. In this study, the collision-deposition behaviour between silica particles and the surface of PV modules is investigated. The impact process of 13 µm silica particles on the glass surface was recorded by using a high-speed digital camera at various incident velocities and angles. A particle dynamics model was developed to predict the critical capture velocity of particles at different incident angles. It was observed that the critical capture velocity of the particles decreases as the angle of incidence increases. Subsequently, a correlation equation was established between the incident angle and the critical capture velocity, serving as the deposition criterion. Computational Fluid Dynamics (CFD) numerical simulation was employed to simulate particle deposition on PV surfaces under different wind speeds and installation tilting angles. The simulation results demonstrate that the mass of 13 µm silica particles deposited on the surface of PV panels decreases with increasing wind speed. Moreover, under identical inlet wind speeds, the particle deposition mass exhibits an initial decrease followed by a subsequent increase as the installation tilt angle of the PV panel increases. The distribution pattern of particle deposition on PV panel surfaces is diverse; however, predominantly concentrated at the mid-bottom region.

Methodology for Calculating the Damaged Surface and Its Relationship with Power Loss in Photovoltaic Modules by Electroluminescence Inspection for Corrective Maintenance.

Photovoltaic panels are exposed to various external factors that can cause damage, with the formation of cracks in the photovoltaic cells being one of the most recurrent issues affecting their production capacity. Electroluminescence (EL) tests are employed to detect these cracks. In this study, a methodology developed according to the IEC TS 60904-13 standard is presented, allowing for the calculation of the percentage of type C cracks in a PV panel and subsequently estimating the associated power loss. To validate the methodology, it was applied to a polycrystalline silicon module subjected to incremental damage through multiple impacts on its rear surface. After each impact, electroluminescence images and I-V curves were obtained and used to verify power loss estimates. More accurate estimates were achieved by assessing cracks at the PV cell level rather than by substring or considering the entire module. In this context, cell-level analysis becomes indispensable, as the most damaged cell significantly influences the performance of the photovoltaic model. Subsequently, the developed methodology was applied to evaluate the conditions of four photovoltaic panels that had been in operation, exemplifying its application in maintenance tasks. The results assisted in decision making regarding whether to replace or continue using the panels.

Detection of visual faults in photovoltaic modules using a stacking ensemble approach.

Faults in photovoltaic (PV) modules may occur due to various environmental and physical factors. To prevent faults and minimize investment losses, fault diagnosis is crucial to ensure uninterrupted power production, extended operational lifespan, and a high level of safety in PV modules. Recent advancements in inspection techniques and instrumentation have significantly reduced the cost and time required for inspections. A novel stacking-based ensemble approach was performed in the present study for the accurate classification of PV module visible faults. The present study utilizes AlexNet (a pre-trained network) to extract image features from the aerial images of PV modules with the aid of MATLAB software. Furthermore, J48 algorithm was applied to perform the feature selection task to determine the most relevant features. The features derived as output from the J48 algorithm were passed onto train eight base classifiers namely, Naïve Bayes, logistic regression (LR), J48, random forest (RF), multilayer perceptron (MLP), logistic model tree (LMT), support vector machines (SVM) and k-nearest neighbors (kNN). The best performing five classifiers on the front run with higher classification accuracies were selected to formulate three categories of stacking ensemble groups as follows: (i) three-class ensemble (SVM, kNN, and LMT), (ii) four-class ensemble (SVM, kNN, LMT, and RF), and (iii) five-class ensemble (SVM, kNN, LMT, RF, and MLP). A comparison in the performance of the aforementioned stacked ensembles was evaluated with different meta classifiers. The obtained results infer that the four-class stacking ensemble model (SVM, kNN, LMT, and RF) with RF as the predictor achieved the highest possible classification accuracy of 99.04%.

Green separation and decomposition of crystalline silicon photovoltaic module's backsheet by using ethanol.

The treatment and recycling of discarded crystalline silicon photovoltaic modules (c-Si PV modules) has become a research focus, but few research have paid attention to the standardized treatment of c-Si PV module's fluorinated backsheet. Improper management of fluorinated backsheet can pose ecological and human health risks. Therefore, this study presents a novel method for processing the backsheet. The proposed approach entailed the utilization of ethanol (CH3CH2OH) to separate the backsheet from the PV module. Subsequently, the separated backsheet underwent decomposition using an alkaline ethanol (NaOH-CH3CH2OH) solution. Finally, the backsheet was recovered in the form of terephthalic acid (TPA) with a purity of 97.47 %. This recovered TPA can then serve as a valuable raw material for producing new backsheets, fostering a closed-loop material circulation. Experimental results demonstrate that immersing the PV module in a 75 % CH3CH2OH-H2O solution at a temperature of 343 K for 30 min achieved 100 % separation of the backsheet. Furthermore, subjecting the separated backsheet to a 60 min reaction in an NaOH-CH3CH2OH solution with a temperature of 343 K and a NaOH concentration of 1.0 mol/L achieved complete decomposition. The reaction mechanism was analyzed through characterization methods such as SEM/EDS, NMR, FTIR and XRD. This method is efficient, non-toxic organic reagent-free and environmentally friendly, so it holds significant potential for further development in the field of c-Si PV module recycling.

Mechanical integrity of photovoltaic panels under hailstorms: Mono vs. poly-crystalline comparison.

The performance of Photovoltaic (PV) modules heavily relies on their structural strength, manufacturing methods, and materials. Damage induced during their lifecycle leads to degradation, reduced power generation and efficiency. Mechanical stresses, originating from manufacturing, transportation, and operational phases impose significant loads on PV modules. These in-service loads encompass various environmental forces such as wind, snow, dust, hail, rain, and heat. In-service loads encompass static and dynamic forces such as wind, snow, dust, hail, rain, and heat. Among these factors, the mechanical loads from hail impacts play a crucial role in PV module performance and require a comprehensive investigation. This research focuses on evaluating the impact of hail loads on different PV modules, following international standards like ASTM 1038-10 and IEC-61215-2. The developed simulator effectively assesses the reliability of PV modules. The number of busbars within a PV module was identified as a key factor influencing the module's resilience to hail impacts. Notably, mono-crystalline PV modules exhibited better resistance to hail loads compared to their poly-crystalline counterparts. The PV modules experience micro-cracking due to hail impacts, leading to an efficiency reduction of 4.15% in mono-crystalline modules and 12.59% in poly-crystalline modules. Similarly, the generated power output decreased by 3.3% and 12.5%, respectively, in these module types.

Low-Cost Fabrication of Efficient Perovskite Photovoltaics Using Low-Purity Lead Iodide via Powder Engineering.

Low cost is the eternal theme for any commercial production. Numerous efforts have been explored to realize low-cost, high-efficiency perovskite solar cells (PSCs), such as replacing the traditional spin-coating method with an economical printing strategy, simplifying the device structure, reducing the number of functional layers, etc. However, there are few reports on the use of low-cost precursors. Herein, we enable the low-cost fabrication of efficient PSCs based on a very cheaper low-purity PbI2 via powder engineering. The low-purity PbI2 is blended with formamidinium iodide followed by dissolving in a 2-methoxyethanol solvent, and then, the high-quality FAPbI3 powders are formed via an inverse temperature crystallization process and solvent washing after several simple processes to reduce the impurities. As a result, the devices fabricated using the as-synthesized black powders based on the low-purity PbI2 exhibit a champion power conversion efficiency (PCE) of 23.9% and retained ∼95% of the initial PCE after ∼400 h of storage in the conditions of 25 ± 5 °C and 25 ± 5 RH% without encapsulation. In addition, the upscaling fabrication of a 5 cm × 5 cm solar minimodule also demonstrates an impressive efficiency of 19.5%. Our findings demonstrate an economic strategy for the commercialization of PSCs from the perspective of low-cost production.

Optimization of pv cells/modules parameters using a modified quasi-oppositional logistic chaotic rao-1 (QOLCR) algorithm.

Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. Moreover, the most important information required to configure an optimal PV system is unavailable in all manufacturer's datasheets. In this context, a novel method is recommended to optimize PV cells/module parameters with the ability to correctly characterize the I-V and P-V curves of different PV models. In the present article, a chaotic map is incorporated in the so-called quasi-oppositional Rao-1 algorithm to improve its efficiency, and the resulting algorithm is named quasi-oppositional logistic chaotic Rao-1 (QOLCR) algorithm. Numerical results indicate that the QOLCR algorithm has presented very good performance in terms of accuracy and robustness. The idea is to minimize the root mean square error (RMSE) between the estimated and the actual data. Simulation results in the single diode model give an RMSE of value [Formula: see text], and in the double diode model, an RMSE of value [Formula: see text] has been reached as the minimum value among the other compared optimization methods. Hence, the QOLCR approach also converges faster than the basic Rao-1 algorithm and its other variants. Moreover, the modified QO Rao-1 algorithm shows its perfectness and could be involved as tools for optimal designing of PV systems.

Physical and chemical properties of dust in the Pre-Aral region of Uzbekistan.

The aim of this work is study of physical and chemical properties of dust of the Pre-Aral region of Uzbekistan such as Karakalpakstan and Khorezm that are located near the three deserts such as the Aralkum, Karakum, and Kyzylkum. The dust particles fell on glass have been collected in Karakalpakstan and Khorezm and studied systematically by employing wide range of methods. Particle volume vs size distribution has been measured with maximum around 600 nm and ~ 10 µm. The major and minor constituent materials present in the dust have been studied systematically by X-ray fluorescence spectroscopy, energy dispersive X-ray diffraction, and inductively coupled plasma optical emission spectroscopy. Main characteristic absorption bands corresponding to Si-O, Si-O-Si bonding in quartz and Fe-O bonds in hematite Fe2O3 have been identified by infrared and Raman spectroscopy. Quartz, hematite, lime, corundum, magnesia, and several other trace minerals have been identified in the dust particles. X-ray diffraction peaks corresponding to quartz, hematite, and corundum are sharp and are found to be more crystalline with some level of disorder. Analysis of the particle size and crystallinity on human being has been performed: disordered or crystalline quartz can create the lung disease; the particles in the size of 0.5-0.7 µm may produce diseases such as chronic silicosis, silicosis, and silica tuberculosis whereas hematite might create lung disease. Dust particles worsen optical transmittance of glass of the panels.

Recovery of porous silicon from waste crystalline silicon solar panels for high-performance lithium-ion battery anodes.

A low-cost and easy-available silicon (Si) feedstock is of great significance for developing high-performance lithium-ion battery (LIB) anode materials. Herein, we employ waste crystalline Si solar panels as silicon raw materials, and transform micro-sized Si (m-Si) into porous Si (p-Si) by an alloying/dealloying approach in molten salt where Li+ was first reduced and simultaneously alloyed with m-Si to generate Li-Si alloy at the cathode. Subsequently, the as-prepared Li-Si alloy served as the anode in the same molten salt to release Li+ into the molten salt, resulting in the production of p-Si by taking advantage of the volume expansion/contraction effect. In the whole process, Li+ was shuttled between the electrodes in molten LiCl-KCl, without consuming Li salt. The obtained p-Si was applied as an anode in a half-type LIBs that delivered a capacity of 2427.7 mAh g-1 at 1 A g-1 after 200 cycles with a capacity retention rate of 91.5% (1383.3 mAh g-1 after 500 cycles). Overall, this work offers a straightforward way to convent waste Si panels to high-performance Si anodes for LIBs, giving retired Si a second life and alleviating greenhouse gas emissions caused by Si production.

A combination of Newton-Raphson method and heuristics algorithms for parameter estimation in photovoltaic modules.

Parameters extraction is instrumental to standard PV cells design. Reports indicates that heuristic algorithms are the most effective methods for accurately determinining the values of parameters. However, local concentration is against recent heuristic methods, and they are inhibited producing optimal results. This paper seeks to show that combining the heuristics algorithms with the Newton Raphson method can considerably increased the accuracy of results. An inspired artifact technique from the drone squadron simulation from control center is proposed for the extraction of the best constitutive parameters. This study equally provides clarifications on the approaches recently reported and proposed to build objective function. Furthermore, comparative evaluation of the current ten best heuristics algorithms that are published in the PV estimation domain is also undertaken. Moreover, this study investigates the convergence of algorithms when points of the number of current-voltage characteristics are varied. The results from this study highlight the differences between the two formulation, and it shows the best formulation accuracy. The results obtained from seven study cases that are considered in this present study, with the combined Newton Raphson performance method and Drone Squadron optimisation, were employed to extract precise PV module parameters.The study of the numbers of points reveals that the algorithm converges and is more precise when the numbers of points of the I-V characteristic are reduced. However, if these points are minimal, the algorithm will be hindered from returning optimal results.

Visual and electrical degradation data of five years aged rooftop photovoltaic modules.

Sun light as a renewable energy resource is getting popular day by day. Power production from a solar power plant is extensively dependent on the weather condition, daylight duration, available sunlight, air quality etc. Thus installation of large scale solar power plant requires long term feasibility study, previous five years weather data, lifetime of the solar panel, projected maintenance requirement and man power. In this survey we have visited several sites of roof top solar power plants in the Dhaka city which are aged at least five years. Firstly, we have conducted visual study of the solar panels on the roof top for visible degradation due to environment and ageing. Then we have measured Current-Voltage characteristics under sun light using portable PV-200 Seaward I-V Tracer. The Current-Voltage data were analyzed using 'Seaward Solar Chart' data analysis tool. The tool was used to plot Current-Voltage and Power-Voltage curves. From the data we have estimated the Standard Test Condition (STC) power, Fill Factor (F.F) and Efficiency of selected photovoltaic modules. To get a clear view over the experience of installed rooftop solar photovoltaic modules in Dhaka city, the data will be useful. The data will help us to project the challenges and provide a guide line to maintain an economically viable solar photovoltaic installation.

Data from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithms.

This paper presents the data of multimodal functions that emulate the performance of an array of five photovoltaic modules under partial shading conditions. These functions were obtained through mathematical modeling and represent the P-V curves of a photovoltaic module with several local maximums and a global maximum. In addition, data from a feedforward neural network are shown, which represent an approximation of the multimodal functions that were obtained with mathematical modeling. The modeling of multimodal functions, the architecture of the neural network and the use of the data were discussed in our previous work entitled "Search for Global Maxima in Multimodal Functions by Applying Numerical Optimization Algorithms: A Comparison Between Golden Section and Simulated Annealing" [1]. Data were obtained through simulations in a C code, which were exported to DAT files and subsequently organized into four Excel tables. Each table shows the voltage and power data for the five modules of the photovoltaic array, for multimodal functions and for the approximation of the multimodal functions implemented by the artificial neural network. In this way, a dataset that can be used to evaluate the performance of optimization algorithms and system identification techniques applied in multimodal functions was obtained.

Optimal parameters estimation and modelling of photovoltaic modules using analytical method.

This paper introduces a proposed approach to estimate the optimal parameters of the photovoltaic (PV) modules using in-field outdoor measurements and manufacturers' datasheet as well as employing the nonlinear least-squares fitting algorithm. The main goal is to determine the optimal parameter values of the implemented model which are: series resistance, reverse saturation current, photocurrent, ideality factor and shunt resistance in case of the five parameters model. A Microsoft Excel spreadsheet is developed in order to perform modeling and analysis of the parameters analytical initial values using manufacturer datasheet specifications regarding to the changing in solar irradiance and ambient temperature. Then, the sum of the squared residuals between in-field measured and simulated data are calculated and minimized using Excel solver in order to obtain the optimal values of the parameters simultaneously, to describe the best fit for the outdoor measured data. The proposed approach is used to find the optimal parameters of the PV module TRINA TSM-295 using an array tester. The convergence confidences of the estimated parameters are presented and assessed in an easy way. This approach allows all parameters to be optimized, simultaneously. The results are verified and compared with other research studies for different PV cell technologies. The obtained results are useful for the tested PV module manufacturer and assess the performance of the products in different weather conditions.

An analysis of the dust deposition on solar photovoltaic modules.

Solid particles impair the performance of the photovoltaic (PV) modules. This results in power losses which lower the efficiency of the system as well as the increases of temperature which additionally decreases the performance and lifetime. The deposited dust chemical composition, concentration and formation of a dust layer on the PV surface differ significantly in reference to time and location. In this study, an evaluation of dust deposition on the PV front cover glass during the non-heating season in one of the most polluted European cities, Kraków, was performed. The time-dependent particle deposition and its correlation to the air pollution with particulate matter were analysed. Dust deposited on several identical PV modules during variable exposure periods (from 1 day up to 1 week) and the samples of total suspended particles (TSP) on quartz fibre filters using a low volume sampler were collected during the non-heating season in the period of 5 weeks. The concentration of TSP in the study period ranged between 12.5 and 60.05 µg m-3 while the concentration of PM10 observed in the Voivodeship Inspectorate of Environmental Protection traffic station, located 1.2 km from the TSP sampler, ranged from 14 to 47 µg m-3. It was revealed that dust deposition density on a PV surface ranged from 7.5 to 42.1 mg m-2 for exposure periods of 1 day while the measured weekly dust deposition densities ranged from 25.8 to 277.0 mg m-2. The precipitation volume and its intensity as well as humidity significantly influence the deposited dust. The rate of dust accumulation reaches approximately 40 mg m-2day-1 in the no-precipitation period and it was at least two times higher than fluxes calculated on the basis of PM10 and TSP concentrations which suggest that additional forces such as electrostatic forces significantly influence dust deposition.

The field experiments and model of the natural dust deposition effects on photovoltaic module efficiency.

The maximisation of the efficiency of the photovoltaic system is crucial in order to increase the competitiveness of this technology. Unfortunately, several environmental factors in addition to many alterable and unalterable factors can significantly influence the performance of the PV system. Some of the environmental factors that depend on the site have to do with dust, soiling and pollutants. In this study conducted in the city centre of Kraków, Poland, characterised by high pollution and low wind speed, the focus is on the evaluation of the degradation of efficiency of polycrystalline photovoltaic modules due to natural dust deposition. The experimental results that were obtained demonstrated that deposited dust-related efficiency loss gradually increased with the mass and that it follows the exponential. The maximum dust deposition density observed for rainless exposure periods of 1 week exceeds 300 mg/m2 and the results in efficiency loss were about 2.1%. It was observed that efficiency loss is not only mass-dependent but that it also depends on the dust properties. The small positive effect of the tiny dust layer which slightly increases in surface roughness on the module performance was also observed. The results that were obtained enable the development of a reliable model for the degradation of the efficiency of the PV module caused by dust deposition. The novelty consists in the model, which is easy to apply and which is dependent on the dust mass, for low and moderate naturally deposited dust concentration (up to 1 and 5 g/m2 and representative for many geographical regions) and which is applicable to the majority of cases met in an urban and non-urban polluted area can be used to evaluate the dust deposition-related derating factor (efficiency loss), which is very much sought after by the system designers, and tools used for computer modelling and system malfunction detection.

Protection of Grid Connected Photovoltaic Systems (GCPVS)

ABSTRACT Installations of photovoltaic systems connected or not to the electrical network have become increasingly popular, but it is often carried out by unqualified people using low quality components. The present study aims to describe the methodology adopted for the protection of grid connected photovoltaic systems (GCPVS) and the implications caused by their lack. Photovoltaic modules are typically installed in galvanized, painted, stainless aluminum or steel structures, which must be connected to a common ground, as these structures and any other components of the system could be energized by the photovoltaic array and may cause serious injuries or even death during routine maintenance, these fatalities can still result in ramifications for the entire industry, with millions of dollars in liability (for death or injury), negative publicity for GCPVS, and increased complications in obtaining licenses. Also, its components must be of good quality and the systems of protection must be well dimensioned and correctly installed, in order to avoid fire and electric damages, with reduction of possible damages in cases of short circuits.

Small-Molecule Organic Photovoltaic Modules Fabricated via Halogen-Free Solvent System with Roll-to-Roll Compatible Scalable Printing Method.

For the first time, the photovoltaic modules composed of small molecule were successfully fabricated by using roll-to-roll compatible printing techniques. In this study, blend films of small molecules, BTR and PC71BM were slot-die coated using a halogen-free solvent system. As a result, high efficiencies of 7.46% and 6.56% were achieved from time-consuming solvent vapor annealing (SVA) treatment and roll-to-roll compatible solvent additive approaches, respectively. After successful verification of our roll-to-roll compatible method on small-area devices, we further fabricated large-area photovoltaic modules with a total active area of 10 cm2, achieving a power conversion efficiency (PCE) of 4.83%. This demonstration of large-area photovoltaic modules through roll-to-roll compatible printing methods, even based on a halogen-free solvent, suggests the great potential for the industrial-scale production of organic solar cells (OSCs).

Cu(In,Ga)Se2 Solar Cells with Amorphous In2O3-Based Front Contact Layers.

Amorphous (a-) In2O3-based front contact layers composed of transparent conducting oxide (TCO) and transparent oxide semiconductor (TOS) layers were proved to be effective in enhancing the short-circuit current density (Jsc) of Cu(In,Ga)Se2 (CIGS) solar cells with a glass/Mo/CIGS/CdS/TOS/TCO structure, while maintaining high fill factor (FF) and open-circuit voltage (Voc). An n-type a-In-Ga-Zn-O layer was introduced between the CdS and TCO layers. Unlike unintentionally doped ZnO broadly used as TOS layers in CIGS solar cells, the grain-boundary(GB)-free amorphous structure of the a-In-Ga-Zn-O layers allowed high electron mobility with superior control over the carrier density (N). High FF and Voc values were achieved in solar cells containing a-In-Ga-Zn-O layers with N values broadly ranging from 2 × 1015 to 3 × 1018 cm-3. The decrease in FF and Voc produced by the electronic inhomogeneity of solar cells was mitigated by controlling the series resistance within the TOS layer of CIGS solar cells. In addition, a-In2O3:H and a-In-Zn-O layers exhibited higher electron mobilities than the ZnO:Al layers conventionally used as TCO layers in CIGS solar cells. The In2O3-based layers exhibited lower free carrier absorption while maintaining similar sheet resistance than ZnO:Al. The TCO and TOS materials and their combinations did not significantly change the Voc of the CIGS solar cells and the mini-modules.

Solar Trees: First Large-Scale Demonstration of Fully Solution Coated, Semitransparent, Flexible Organic Photovoltaic Modules.

The technology behind a large area array of flexible solar cells with a unique design and semitransparent blue appearance is presented. These modules are implemented in a solar tree installation at the German pavilion in the EXPO2015 in Milan/IT. The modules show power conversion efficiencies of 4.5% and are produced exclusively using standard printing techniques for large-scale production.

Multifunctional microstructured polymer films for boosting solar power generation of silicon-based photovoltaic modules.

We propose two-dimensional periodic conical micrograting structured (MGS) polymer films as a multifunctional layer (i.e., light harvesting and self-cleaning) at the surface of outer polyethylene terephthalate (PET) cover-substrates for boosting the solar power generation in silicon (Si)-based photovoltaic (PV) modules. The surface of ultraviolet-curable NOA63 MGS polymer films fabricated by the soft imprint lithography exhibits a hydrophobic property with water contact angle of ∼121° at no inclination and dynamic advancing/receding water contact angles of ∼132°/111° at the inclination angle of 40°, respectively, which can remove dust particles or contaminants on the surface of PV modules in real outdoor environments (i.e., self-cleaning). The NOA63 MGS film coated on the bare PET leads to the reduction of reflection as well as the enhancement of both the total and diffuse transmissions at wavelengths of 300-1100 nm, indicating lower solar weighted reflectance (RSW) of ∼8.2%, higher solar weighted transmittance (TSW) of ∼93.1%, and considerably improved average haze ratio (HAvg) of ∼88.3% as compared to the bare PET (i.e., RSW ≈ 13.5%, TSW ≈ 86.9%, and HAvg ≈ 9.1%), respectively. Additionally, it shows a relatively good durability at temperatures of ≤160 °C. The resulting Si PV module with the NOA63 MGS/PET has an enhanced power conversion efficiency (PCE) of 13.26% (cf., PCE = 12.55% for the reference PV module with the bare PET) due to the mainly improved short circuit current from 49.35 to 52.01 mA, exhibiting the PCE increment percentage of ∼5.7%. For light incident angle-dependent PV module current-voltage characteristics, superior solar energy conversion properties are also obtained in a broad angle range of 10-80°.