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The purpose of this study is to investigate the annual degradation rates of photovoltaic (PV) systems composed of PV modules based on recent crystalline silicon (c‐Si) PV technologies. We investigated the annual degradation rates of four PV systems composed of different c‐Si PV technologies, comprising p‐type multi‐crystalline silicon with a passivated emitter rear cell, n‐type silicon heterojunction, p‐type single‐crystalline silicon (sc‐Si) with an aluminum back surface field, and n‐type (sc‐Si) solar cell technologies. These systems were located in Gunma Prefecture in Japan and were measured over 6 years. Furthermore, the effects of soiling on the annual degradation rates of these PV systems were examined by partially surface cleaning the PV arrays two times. The results obtained indicate that the apparent annual degradation rates of the PV strings before surface cleaning were 0.8, 1.6, 1.4, and 1.2%/year, respectively, because of optical losses due to dust particles. However, the inherent annual degradation rates of the PV strings after surface cleaning were 0.1, 0.6, 0.0, and 0.3%/year, respectively. These low degradation rates indicate that the PV systems composed of the recent c‐Si PV technologies all offered reasonably stable performance that was reduced by 3.6%, 5.5%, 7.3%, and 4.8%, respectively because of the effects of surface soiling, although the surfaces of the PV arrays had been washed by plentiful rainfall under their humid subtropical climatic operating conditions. [ABSTRACT FROM AUTHOR]

The global drive for sustainable development and carbon neutrality has heightened the need for energy-efficient buildings. Photovoltaic buildings, which aim to reduce energy consumption and carbon emissions, play a crucial role in this effort. However, the potential of the building envelope for electricity generation is often underutilized. This study introduces an efficient hybrid method that integrates Particle Swarm Optimization (PSO), Support Vector Machine (SVM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. This integrated approach was used to optimize the external envelope structure and photovoltaic components, leading to significant reductions: overall energy consumption decreased by 41% (from 105 kWh/m2 to 63 kWh/m2), carbon emissions by 34% (from 13,307 tCO2eq to 8817 tCO2eq), and retrofit and operating costs by 20% (from CNY 13.12 million to CNY 10.53 million) over a 25-year period. Sensitivity analysis further revealed that the window-to-wall ratio and photovoltaic windows play crucial roles in these outcomes, highlighting their potential to enhance building energy performance. These results confirm the feasibility of achieving substantial energy savings and emission reductions through this optimized design approach. [ABSTRACT FROM AUTHOR]

Optical switching of the electrical operating point of individual crystalline silicon modules has previously been demonstrated as an elegant noncontact method for outdoor photoluminescence image acquisition in full daylight, with the important advantage that no modifications to the system wiring are required. Herein, a modified approach for photoluminescence imaging acquisition in large photovoltaic arrays, enabled by simultaneous optical switching of all modules within a series‐connected string, is demonstrated. This improved method is a simpler approach and allows for significantly increased measurement throughput. Quantitative assessment of image data acquired in full daylight is possible since all modules in a string are series connected and operate at the same current. Excellent agreement is reported for voltage variations between modules that are inferred from daylight photoluminescence image data and measurements conducted under controlled laboratory conditions. [ABSTRACT FROM AUTHOR]

This study analyzes the possibilities of phasing out fossil and nuclear energy sources for Slovenia by 2050. Alternative carbon-free sources include renewable energy sources (RES) i.e. electricity, synthetic fuels and hydrogen from water electrolysis. The model is based on the use of currently mature low-carbon technologies and is adapted to Slovenia's natural conditions. Photovoltaic panels (PV) and hydropower plants are used for the majority of renewable electricity generation. To bridge the winter period with minimal PV production, storage with a pumped storage power plant is planned. One of the assumptions of the national climate strategy has been incorporated into the model, which envisages zero growth in final energy consumption by 2050. The result of the paper is an assessment of what some of the basic characteristics of the Slovenian energy system would look like after the phase-out of fossil and nuclear energy sources. The estimated storage capacity required is 5.1 MWh/capita. Abandoning fossil fuels with the currently mature RES technologies is not realistically feasible for technical and economic reasons. [ABSTRACT FROM AUTHOR]

While present trends in commercial photovoltaic (PV) are set on a‐Si/c‐Si heterojunctions and promising perovskites, the chalcogenides still attract interest as an excellent candidate for coming multi‐ and heterojunction devices with perovskites. Continuously enhanced over the years, typical metastabilities in thin‐film devices still cause issues under outdoor operation. Unfortunately, qualitative assessment in commercial PV is solely focused on data at the maximum power point of its operation, whether in simple on‐site PV plant monitoring or following latest standards for certified laboratory testing, thus metastabilities merely register as undefined power fluctuations. Herein, it is shown that dark current characteristics are a simple and yet field‐fit method, to identify metastable changes and take suitable counter measures. With this study, using commercial CdTe and CIGS showcase modules of various manufacturers and production generations, it is demonstrated how dark current characteristics allow to pinpoint physical changes of the individual module, between its pristine state, outdoor operation, dark degradation, and stabilization, like a fingerprint. Using the two‐diode model, it is shown that alterations to respective regimes of the characteristic can be assigned to typical defects, defining the module's current state, and be used to it as a guide to evaluate its recuperation via preconditioning treatments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hubei Minzu University (Natural Science Edition) is the property of Journal of Hubei Minzu University (Natural Sciences Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and costly manual inspections and enhancing production capacity. This paper presents a novel PV defect detection algorithm that leverages the YOLO architecture, integrating an attention mechanism and the Transformer module. We introduce a polarized self-attention mechanism in the feature extraction stage, enabling separate extraction of spatial and semantic features of PV modules, combined with the original input features, to enhance the network’s feature representation capabilities. Subsequently, we integrate the proposed CNN Combined Transformer (CCT) module into the model. The CCT module employs the transformer to extract contextual semantic information more effectively, improving detection accuracy. The experimental results demonstrate that the proposed method achieves a 77.9% mAP50 on the PVEL-AD dataset while preserving real-time detection capabilities. This method enhances the mAP50 by 17.2% compared to the baseline, and the mAP50:95 metric exhibits an 8.4% increase over the baseline.

New energy industries, such as wind and photovoltaic (PV) power generation, are key to supporting the carbon peaking and carbon neutrality goals of China. The installed capacity of wind and PV power in China is the largest worldwide; therefore, it is necessary to secure the supply of key metal materials and conduct a more accurate management of emerging solid wastes. This study sets different development scenarios based on the historical data and planning objectives of wind and PV power industries of China. It assesses the demand, waste, and supply of key metals in China’s new energy industry using a life distribution model of wind and PV power generation equipment, and focuses on identifying the supply pressure of silver, copper, gallium, silver, steel, and neodymium,thereby providing a basic support for realizing the green and low-carbon development of the energy industry by 2060. In the baseline scenario, the decommissioned volume of the wind and PV power generation industries will reach 4.6 GW and 28.3 GW in 2035,respectively; and the total decommissioned volume (by mass) of both wind and PV power generation equipment will reach 2.54 ×106 t and 1.048 ×107 t in 2035 and 2060, respectively. In terms of the supply of key metals for the new energy industry during 2030‒2060,iron and steel have a low supply risk (≤5%), neodymium has a medium-high supply risk (25%‒50%), copper and silver have a high supply risk (50%‒100%), and the supply of gallium and indium are at an extremely high risk owing to their excessive peak demand.To improve the security and diversity of the supply chain of the new energy industry, it is necessary to ensure the sustainable supply of metal mineral resources and promote material recycling and efficient use. Furthermore, we propose the following recommendations:(1) treating decommissioned equipment for wind and PV power generation as waste electrical and electronic products, (2) incorporating wind and PV power generation enterprises into the Catalogue of Classified Management of Pollutant Discharge Permits for Stationary Pollution Sources, (3) improving the distributed new-energy solid-waste recycling system, and (4) developing recycling technologies for emerging solid wastes.

As the most common energy source of spacecraft, photovoltaic (PV) power generation has become one of the hottest research fields. During the on-orbit operation of spacecraft, the influence of various uncertain factors and the unbalanced inertial force will make the solar PV wing vibrate and degrade its performance. In this study, we investigated the influence of mechanical vibration on the output characteristics of PV array systems. Specifically, we focused on a three-segment solar panel commonly found on satellites, analyzing both its dynamic response and electrical output characteristics under mechanical vibration using numerical simulation software. The correctness of the simulation model was partly confirmed by experiments. The results showed that the maximum output power of the selected solar panel was reduced by 5.53% and its fill factor exhibited a decline from the original value of 0.8031 to 0.7587, provided that the external load applied on the panel increased to 10 N/m2, i.e., the vibration frequency and the maximal deflection angle were 0.3754 Hz and 74.9871°, respectively. These findings highlight a significant decrease in the overall energy conversion efficiency of the solar panel when operating under vibration conditions. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Photovoltaic (PV) modules are often situated in hot and windy environments, such as deserts, where dust accumulation poses a significant problem. The build-up of dust can result in an increase in PV module leakage current, making the modules more vulnerable to potential-induced degradation (PID), ultimately leading to a reduction in the efficiency of PV power generation. In this study, we investigate the impact of dust accumulation on the surface of PV modules on leakage current. A dust model is developed based on the Arrhenius relation, taking into account the impact of temperature and density on dust conductivity. The equation for leakage current due to dust accumulation is derived based on the clean module leakage current equation. We undertake a simulation of natural conditions in a laboratory setting to analyze the impact of dust on the leakage current of photovoltaic modules. The results show the following: At high temperatures, the leakage current will significantly increase due to the elevated conductivity of the dust. The conductivity increased by 27.1%, 48.9%, 64.3%, and 118% for the four groups of dusty PV modules, respectively. Leakage current prediction has a better accuracy when dust is equated to series conductance. Dust can reduce the activation energy of PV modules by up to 3.48%. [ABSTRACT FROM AUTHOR]

In order to harness the abundant solar energy in the desert environment, more and more large-scale photovoltaic systems have been installed in deserts terrains. However, the typical sandstorms and accumulation of dust on the solar panels are the challenges to reckon with in order to effectively harvest the high intensity solar radiation. The conventional dust mitigation techniques demand large instrumentation, electric power, and huge quantity of water, enormous manpower and logistics at the remote and hostile locations. A very low power consuming instant dust repelling system, based on electrostatic and electro-dynamic forces has been developed and tested for its dust mitigating performance. The design of the system is basically fabricating an interdigitated electrode on the surface of the solar panel, energized by the low power, low frequency single-phased alternating voltage source. The standing electric wave established in between the electrode levitates the dust particles and is simultaneously repelled by electrostatic forces. The system design was electrically and geometrically optimized and tested in the lab and also in real-life condition, and the efficiency of dust removal as high as 90 ± 1 ℅ was achieved, and this dust elimination helped to restore the initial open circuit voltage and the short current of the tested solar cells. The attractive features of the developed electro-dynamic dust repelling system are that it is automatic, unmanned, low cost, low power, and quite efficient. [ABSTRACT FROM AUTHOR]

Various faults of photovoltaic (PV) modules inevitably occur in the work process, since PV modules are installed in hostile situation. To obtain the types of failure, a novel fault diagnosis method based on back propagation (BP) neural network with Levenberg-Marquardt (L-M) algorithm for PV modules is proposed. Through the in-depth analysis the output of PV modules under normal and fault conditions, the input variables of the diagnosis model are acquired. The high-speed and real-time fault diagnosis model for PV modules is first designed based on TMS320VC5402 DSP and long-distance wireless fault diagnosis is realized by Zigbee technology. The simulation and experimental results show that the fault diagnosis method for PV modules based on BP network with L-M algorithm can effectively detect four types of fault for PV modules such as open circuit, short circuit, partial shading and abnormal degradation. The numerical results verify the effectiveness and correctness of the proposed method, which can provide a great educational benefit of PV operation technology. [ABSTRACT FROM AUTHOR]

Global advancement of solar energy and its emerging technologies are good indicators to assess the general response of energy users to clean energy as enshrined in the sustainable development goal (SDG) 7. However, the reality that solar panels and other components would become electronic waste someday and ultimately contribute to chemical hazards in the environment is a source of concern. This study aimed to evaluate the amounts of heavy metals in solar photovoltaic (PV) modules using atomic absorption spectroscopy and estimate the health risks associated with these heavy metals. Six samples of solar PV were collected and evaluated for Chromium (Cr), Cadmium (Cd), Lead (Pb), and Arsenic (As). Using the health risk index (HRI) and the target hazard quotient (THQ), the human dangers presented by the presence of heavy metals were evaluated. The heavy metal content ranged as follows: Pb (6.00–6.25 mg/kg); Cd (0.10–1.25 mg/kg); Cr (8.00–12.00 mg/kg); and As (0.60–1.25 mg/kg). An estimate of the average weight for children and adults was used to calculate the health risk index. The result showed that health risks for children and adults are high with heavy metal concentrations escalating in the order Pb > Cd > Cr > Ar. The THQ in all metals was < 1 in all the solar PV samples, which indicates they do not pose serious health risk concerns in a single panel. However, considering the large number of waste panels, the risk would be significant. Also, it was found that there were naturally occurring radioactive materials (NORM) in the PV modules and their effects are more in the short term. Hence, heavy metals are envisaged as long-term hazards, and NORM in PV modules are envisaged as short-term hazards. Using the EPA dose compliance concentrations (DCC) (EPA-DCC), the radioactive exposures from the selected photovoltaics were proven to be directly dependent on the number of PVs in use and their distance from the PV installations. Several recommendations were made to curb death in the process of pursuing cleaner energy technologies. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, driven by advancements in the photovoltaic industry, solar power generation has emerged as a crucial energy source in China and the globe. A progressive annotation approach is employed to pinpoint and label defect samples to enhance the precision of automated detection technology for minor defects within photovoltaic modules. Subsequently, computer vision techniques are harnessed to segment photovoltaic modules and defect samples amidst intricate backgrounds accurately. Finally, a transfer learning training model is deployed to classify and identify defects effectively. The results indicate that the mask-region convolutional neural network model achieves remarkable accuracy and recall rates of 98.7% and 0.913, respectively. Furthermore, the detection speed and inference time are 280.69 frames per second and 3.53 ms, respectively. In essence, the defect detection and classification algorithm utilizing computer vision techniques significantly enhances the precision of automated detection technology in identifying minor defects within complex environments. This advancement holds profound practical significance in ensuring photovoltaic modules’ quality and operational reliability.

Performance degradation is a prevalent phenomenon in solar photovoltaic systems globally, with varying aging profiles and effects depending on environmental and climatic conditions. This paper presents an extensive investigation of the electrical performance, aging mechanisms, and degradation analysis of field-aged PV modules under the tropical climate of Malaysia. Utilizing a combination of visual inspection, I-V curve measurement, and thermal imaging techniques, this research provides a comprehensive assessment of the electrical and thermal properties of field-aged PV modules from two locations in Malaysia over extended periods (8 years at UMPSA and 10 years at Pasir Mas). The multi-faceted approach of the study allows for a deeper understanding of the degradation process, offering insights into the causes and effects of higher current and lower voltage in aged modules. The study found the average annual degradation rates at UMPSA were 0.3% for open circuit voltage (Voc), 0.23% for short circuit current (Isc), 0.81% for maximum power (Pmax), and 0.35% for fill factor (FF) and at Pasir Mas, the average annual degradation rates were 1.124% for Voc, −0.166% for Isc, 1.276% for Pmax, and 0.43% for FF. The study also found that monocrystalline silicon (m-Si) panels experienced an average power degradation of 6.48%, while polycrystalline silicon (p-Si) panels showed a higher degradation of 12.76%. However, Thermal imaging revealed significant temperature variations across the modules, with hotspots reaching up to 11.2 °C above cooler areas in UMPSA panels and an even more pronounced 26.1 °C difference in Pasir Mas modules. These temperature disparities highlight the uneven heat distribution and potential performance issues in the panels. This research contributes to the understanding of PV module degradation in tropical climates and aligns with sustainable development, climate change mitigation efforts, and SDG 7 by promoting sustainable energy solutions.

Due to the growing demand for clean and sustainable energy sources, there has been an increasing interest in solar cells and photovoltaic panels. Nevertheless, determining the right design parameters to achieve the most efficient energy output that aligns with the energy system's needs can be quite challenging. This complexity arises from the intricate models and the inherent inaccuracies in the available information. To tackle this challenge, this paper introduces the adaptive sine–cosine particle swarm optimization algorithm (ASCA-PSO) as a method for estimating the parameters of solar cells and photovoltaic modules. The ASCA-PSO approach combines the strengths of the SCA and PSO algorithms in a two-tier process. In this process, SCA search agents explore the search space, while the PSO search agents leverage the outcomes derived from SCA exploration. This study evaluates the effectiveness of ASCA-PSO in accurately estimating the parameters of single- and double-diode models using data from two commercial solar cells. The findings are compared with those of cutting-edge methods. It is demonstrated that ASCA-PSO can identify global solutions for multifaceted and intricate objective functions. Furthermore, it proves to be a viable option for designing solar cells even in the presence of noise. [ABSTRACT FROM AUTHOR]

This research aims to improve thermal performance and compare the performance of two common crystalline PV panel types (Mono and poly). Modules with a back-cooling system were designed and numerically analyzed with SolidWorks and ANSYS-Fluent-2021-R2 for the simulation under Baghdad weather at noon. The cooling system used consists of a phase-change material, paraffin wax (RT55), with a thickness of 5 cm and a heatsink with 33 fins with heights of 10, 20, and 30 mm and thicknesses of 2, 4, and 6 mm. to select the best height of the wax 1, 3, 5, 10, 20 cm examined. The result showed that for polycrystals, the panel temperature was reduced by 8.4°C using PCM and 11.9°C using PCM-fins. Also, output power was enhanced to 200.6 W by 10.2 W, and efficiency improved by 5%. Similarly, using PCM and PCM-fins lowered the temperature of the monocrystalline by 8.3 and 12.5°C, respectively. Therefore, the output power is enhanced to 202.4 W by 10.7 W and improves the electrical efficiency by 5.2%. The results of the study showed that mono had better performance than poly. This result is acceptable and is in good agreement with previous studies. [ABSTRACT FROM AUTHOR]

Shading on photovoltaic (PV) modules due to shadows, covering, dust, etc., usually characterized as semi-transparent, will significantly affect the power generation capacity. No systematic study has considered the impact of semi-transparent coverings on the power generation capacity of PV modules. This paper covers a single cell in the PV module using a covering with a transmittance of 18.55% and systematically investigates its impact on the power generation capacity. The open-circuit voltage (Voc) of the PV module is nearly unaffected by semi-transparent coverings because the covered cell can be considered as working at a lower irradiance and thus can output a voltage close to that of the uncovered cell. The short-circuit current (Isc) is significantly affected by coverings because it is co-contributed by the photocurrent (evaluated based on the covering ratio R and transmittance) and the reverse bias current ΔIsc (the covered cell is in a reverse bias state). The ΔIsc increases with R because more charge accumulates at the bi-ends of the covered cell; but, it decreases at full covering, which implies that in a partially covered case the uncovered part contributes more to ΔIsc than the covered part. The fill factor (FF) of the PV module first increases and then decreases with R, as the equivalent resistance of the covered cell increases rapidly with R, which replaces the wire resistance in dominating the series resistance of the PV module when R > 0.6. This work is of great theoretical significance in analyzing the output characteristics of PV modules under real conditions. [ABSTRACT FROM AUTHOR]

Solar energy has emerged as a prominent contender in this arena, attracting significant attention across the globe. Governments worldwide have undertaken extensive efforts to encourage the adoption of renewable energy, increasing the usage of solar panels. Despite its benefits, the deployment of photovoltaic (PV) modules generates significant waste, thereby posing a major environmental challenge. This study explores several recycling techniques, including physical, thermal, and chemical methods, that could be employed to manage solar panel waste. An in-depth analysis of separation techniques presently employed and underdevelopment was studied and compared to determine the physical treatment necessary for the separation of glass and aluminium. Extraction of rare earth metals cadmium, copper and tellurium requires chemical treatments using organic and inorganic solvents along with thermal treatment at 500–600°C to remove the EVA polymer. Recovery of silicon wafers and rare metals through various metal extraction processes is further examined. Europe was concluded as a frontrunner in solar waste management policies after analysis of the governmental policies of developed and developing nations of the world. The circular economy model developed portrayed a systematic approach for the removal of different components of a solar panel and reintegration into the manufacturing process. The implementation of a robust circular economy for renewable energy systems is conditional upon the optimization of resource recovery while minimizing energy consumption and this serves as the governing framework of this review. [ABSTRACT FROM AUTHOR]

The utilization of photovoltaic (PV) generation to charge storage batteries in recreational vehicles (RVs) is becoming increasingly prevalent. However, the performance of PV generation systems is hindered by the mismatch caused by different module types and varying environmental conditions. This discrepancy negatively impacts the output performance of PV modules, resulting in reduced system efficiency. To address this issue, this paper explored the series–parallel output characteristics of different types of PV modules and summarized the methods for configuring PV modules in a mixed-structure PV generation system for RV energy supplementation. Building upon this foundation, a novel equalization scheme based on extremum-seeking control (ESC) is introduced. The scheme initially employs a forward–flyback converter (FFC) to equalize the current among series-connected PV modules, followed by matching the voltage between parallel-connected PV module strings. Finally, the ESC is utilized to optimize the real-time output power of the PV generation system, thereby enhancing overall system efficiency. Through simulation experiments conducted on a PV generation system with four types of mixed-connection PV modules employing the PLECS simulation platform, simulated results demonstrate the effectiveness of the proposed scheme in improving PV module output performance and maximum power tracking efficiency. The simulation data reveal that the proposed scheme achieves an impressive average tracking efficiency of 99.15%, surpassing the efficiency of the global maximum power point tracking scheme based on an enhanced perturb and observe algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Dust removal coatings for polyimide (PI)-based photovoltaic modules used in lunar rovers were fabricated successfully through the blade-coating method using silicon dioxide (SiO2) nanoparticles and γ-aminopropyltriethoxysilane (KH550). The dust removal performance, morphology, transparency, and adhesive force of the coating can be optimized by adjusting the pH and the mass ratios of SiO2 and KH550. The designed coating demonstrates excellent dust removal performance, achieving an percentage of over 85 %. Moreover, the coating has minimal impact on the transparency of the PI substrate and exhibits strong adhesion to it. Additionally, the coating shows remarkable resistance to both high and low temperatures. Even after undergoing five cycles of thermal treatment ranging from −196 to 160 °C, there were no significant changes in the morphology or dust removal performance of the coating. Therefore, this coating exhibits tremendous potential for application in the dust removal of photovoltaic modules in lunar rovers.

[Objective] Under the background of energy crisis, countries (regions) around the world have accelerated the distribution of photovoltaic (PV) renewable energy, so it is important to explore the dynamic evolution characteristics and driving mechanism of the global PV module trade network structure. [Methods] The complex network analysis method and temporal exponential random graph model (TERGM) were used to explore the structural evolution characteristics and driving mechanism of the global PV module trade network. [Results] (1) The global photovoltaic module trade network is highly clustered and has a short path length, and the trade gap between countries (regions) is widening, forming a close trade network dominated by intraregional trade. (2) The global PV module trade network has a typical core-periphery structure, and China is at the center of the core circle. (3) The worldwide PV module trade network exhibits reciprocal effects, structure-dependent effects, and time-dependent effects. (4) The impacts of national economic development, trade freedom, environmental performance index, and carbon emissions on the establishment of global photovoltaic module trade network are significantly positive, while the national urbanization and industrialization rates are negative. The exogenous networks that have a significant impact on them are the national border network, the income grade network, and the WTO member network. [Conclusion] China should take its central position at the global PV trade network as an opportunity to strengthen technological innovation, strengthen the resilience of the PV supply chain, speed up the convergence of the PV industry, and achieve differentiated development of products. The country should give full play to its geographical proximity to global and regional markets, promote trade diversification, actively promote international cooperation and integrated development of the PV industrial chain, and push forward the energy revolution to achieve the dual carbon goals.

The global drive for sustainable development and carbon neutrality has heightened the need for energy-efficient buildings. Photovoltaic buildings, which aim to reduce energy consumption and carbon emissions, play a crucial role in this effort. However, the potential of the building envelope for electricity generation is often underutilized. This study introduces an efficient hybrid method that integrates Particle Swarm Optimization (PSO), Support Vector Machine (SVM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. This integrated approach was used to optimize the external envelope structure and photovoltaic components, leading to significant reductions: overall energy consumption decreased by 41% (from 105 kWh/m2 to 63 kWh/m2), carbon emissions by 34% (from 13,307 tCO2eq to 8817 tCO2eq), and retrofit and operating costs by 20% (from CNY 13.12 million to CNY 10.53 million) over a 25-year period. Sensitivity analysis further revealed that the window-to-wall ratio and photovoltaic windows play crucial roles in these outcomes, highlighting their potential to enhance building energy performance. These results confirm the feasibility of achieving substantial energy savings and emission reductions through this optimized design approach.

Photovoltaic power generation is one of the most effective measures to reduce greenhouse gas emissions, and the surface of photovoltaic modules in desert areas is mainly affected by sand erosion and cover, which affect power output. Therefore, a wind–sand erosion system was established to simulate the desert wind–sand environment, analyze the influence of dust erosion on the output power of the component, and observe the surface erosion morphology of the component. Then, dust particles of different sizes were selected to cover the surface of the photovoltaic module, and the temperature change and output characteristics of the backplane of the module were studied. The results show that the erosion rate increases with the increase in the erosion angle. When the erosion rate is 25 m/s and 30 m/s, the output power decreases by 9.82%~16.00% and 15.42%~24.46% at different erosion angles, respectively. As the particle size (0.05 mm~0.30 mm) deposited on the surface of the photovoltaic module gradually increases, the open-circuit voltage of the module changes little, and its maximum difference is 0.25 V. Short-circuit current and output power vary greatly; the maximum difference in short-circuit current is about 13.00%, and the maximum difference in output power is about 17.00%. Through our research, this study provides a certain reference for maximizing power generation efficiency and the clean planning of desert photovoltaic power stations. [ABSTRACT FROM AUTHOR]

Accurately evaluating dust deposition characteristics on photovoltaic modules is vital for improving power generation prediction and formulating cleaning cycles. This study analyzed the composition and size characteristics of dust collected from a solar photovoltaic power station in the Kubuqi Desert. Relationships were established among dust density, solar cell output parameters, actual radiation intensity, and spectral transmittance through experiments. Based on this, a monitoring model for dust deposition on PV modules was proposed and validated in the laboratory. Results showed that the composition and particle size of dust deposited on PV modules did not significantly differ across various areas of the station.Under the same dust deposited density, spectral transmittance exponentially decayed with increased dust density, with shorter wavelengths being more affected. Operating current loss accurately reflected dust deposition compared to other output parameters. The proposed monitoring model achieved 94% accuracy in reflecting the degree of dust deposition. This study provides theoretical support for intelligent operation schemes of solar PV power generation stations. [ABSTRACT FROM AUTHOR]

In contrast to homogeneous mechanical load according to IEC 61215, photovoltaic modules in the field are mainly exposed to inhomogeneous loads like snow or wind. This paper deals with such inhomogeneous loads using computational fluid dynamics and finite element method simulations. Temperatures different to room temperature and the choice of encapsulates have significant influences on the thermomechanics of a photovoltaic module in case of snow load. Polyolefin is the encapsulant with the lowest storage modulus and has the lowest overall stress in solar cells and glass down to −30°C. Furthermore, with colder temperatures, the first principal stress decreases in solar cells but increases in the glass. For wind loads, the impact of module orientation, wind direction, module inclination angle, and wind speed is analyzed. A crosswind scenario is found to be most critical. Additionally, as a rule of thumb, higher module inclination angles result in higher stresses. Finally, general thermomechanical rules are extracted allowing for a deeper understanding of the underlying effects and therefore help to build more robust modules in the future. [ABSTRACT FROM AUTHOR]

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This paper particularly explains potential-induced degradation (PID) of wafer-based standard p-type crystalline silicon technology. We present a single-encapsulation method that is useful for simulating PID in the laboratory to facilitate microanalyses of module-level solar cells. The PID testing is performed for the module with single-encapsulation and conventional modules. Their current–voltage characteristics and electroluminescence images are investigated before and after the PID tests. As described herein, we infer that the single-encapsulation modules generate PID almost in the same way as conventional modules do, and that the degradation trend is almost identical to that of conventional modules. Results of PID recovery tests indicate that the time necessary for PID recovery is always less than that for PID generation, irrespective of the encapsulation method. [ABSTRACT FROM AUTHOR]

Since photovoltaic ( PV) arrays installationin in harsh outdoor environments, PV faults frequently occur during their operation. To accurately identify the fault types of PV arrays, a sand cat swarm optimization support vector machine (SCSO-SVM) was proposed for PV module fault identification. In addition, the SCSO-SVM, support vector machine ( SVM), particle swarm optimized support vector machine (PSO-SVM), genetic optimized support vector machine (GA-SVM), sparrow optimized support vector machine (SSA-SVM), gray wolf optimized support vector machine (GWO-SVM) and whale optimized support vector machine (WOA-SVM) algorithms were compared. First of all, all six SVM hybrid algorithms overcome the disadvantage that SVM diagnosis results were easily affected by the initial values of parameters, and the recognition accuracy was improved compared with traditional SVM algorithms, but the recognition time was increased for all of them. Secondly, SCSO-SVM recognition was the best among the seven algorithms, which overcomed the vulnerability of SVM to the initial values of parameters and improved the recognition accuracy by about 9. 459 4% compared to SVM. Because it is more effective in finding the SVM penalty factors and kernel function parameters. Then, for the same algorithm, the recognition accuracy of the algorithm decreased with decreasing input features because the fewer the input features, the less effective it was to characterize the output properties of the PV modules under different fault types. However, the recognition time of the algorithm was not brief with the decrease of the input features. Therefore, the appropriate input features were selected to balance the fault recognition accuracy and efficiency of the algorithm. Finally, it was found that the recognition effect of the seven algorithms depends on the effect of the dataset. The reason may be that there are differences in generalizability due to excessive selection of parameters for each algorithm and dependence on the initial value selection of parameters. [ABSTRACT FROM AUTHOR]

The accurate detection and classification of faults in photovoltaic (PV) systems contribute to enhancing their performance. Bypass diode (BD) heating is a common issue afflicting field‐installed PV modules. Although different fault modes exist, they exhibit similar characteristics, which hinder their early detection and classification. Thus, a real‐time fault detection and classification method for heated BDs in PV modules using thermoelectric devices (TEs) is proposed. When PV modules experience partial shading (PS) or exhibit bypass diode faults (BDFs), the temperature of the BD rises. By attaching a TE to the BD, the heat generated due to the PS or BDF can be converted into electrical energy. Additionally, the cause of bypass heating can be classified in real time by analyzing the TE's power‐generation capacity, considering the inverter status and ambient conditions. For example, the power generated by the TE is in the ranges of 8.5–14.7 (PS) and 28–49.4 mW cm−2 (BDF) under operating conditions. Moreover, the power generated by the TE under the PS and BDF conditions is sufficiently high to drive a communication system, guaranteeing the reliability of the detection system. Therefore, the proposed bypass heating detection system can classify the fault modes of PV systems in real time without external power. [ABSTRACT FROM AUTHOR]

Large deployment of photovoltaic (PV) installation worldwide demands reliability assurance of the systems to maintain the confidence in the markets. With the diversity of PV applications, it is essential the detection of circumstances that can be the root of failure mechanisms, such as transportation and installation of the modules. They are one of the main sources of induced vibrations, which, in its turn, can provoke defects and damages in the PV modules. In this work, we have measured and analyzed tri-axial accelerations and mechanical vibration that photovoltaic crystalline modules withstand during transportation by road, including loading and unloading operations. We have also analyzed the natural oscillation frequencies, the resonance phenomena and the highest impacts. It is concluded that a proper fastening of the load inside the truck is necessary and that high impacts during loading and unloading should be avoided. Packing the modules to reduce vibrations is also recommendable. Several solutions to perform this are proposed. [ABSTRACT FROM AUTHOR]

Outdoor tests of photovoltaics module are crucial both for marketing and for research and technological developments. The electric generation performance and their degradation rates and lifetime are also related to different climatic conditions of the regions. In this work, the outdoor tests are carried out for six different photovoltaic (PV) modules under Arid-steppe Climate condition of Ankara, Türkiye. Their degradation rates are calculated by using linear regression (LR) and year on year (YOY) methods. The comparison between LR and YOY are carried out and with the other performed studies of different regions of world. In addition, it is investigated that how effective the climatic conditions on daily degradation rates. The results obtained are as follows: Mono-Si and Hetero-junction Silicon (HIT) cell modules degradation rates of 0.71/1.56 %/year and 0.84 %/year are respectively obtained by LR method and 0.57/0.90 %/year and 0.85%/year are respectively by YOY method. The degradation rates for Cupper Indium Selenide (CIS), Cupper Indium Gallium Selenide (CIGS) and microcrystalline Silicon/Amorphous Silicon (µc-Si/a-Si) modules have 1.73/1.49 %/year, 11.55/9.52 %/year and 1.48 %/year for LR method and 1.28/1.12 %/year, 9.94/9.53 %/year and 0.99 %/year for YOY method are obtained respectively. It is also obtained for the Polycrystalline Silicon Modules as 1.20/1.86 %/year degradation rates by LR method and 0.79/1.88 %/year degradation rates by YOY method. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) cells are integral in harnessing solar energy, yet their performance is hindered by excessive heat generation, impacting efficiency and sustainability. Addressing the challenge of efficiency loss in photovoltaic (PV) cells due to overheating, this study focuses on optimizing active water cooling control for PV modules. The aim is to develop a dynamic, sustainable model and integrate a PID controller tuned by Sine Cosine Algorithm (SCA), targeting optimal operating temperatures. This study introduces a dynamic model and a closed-loop control system to manage PV cell temperature, investigating the correlation between water flow and temperature regulation. Experimental data is gathered using a pseudo-random binary sequence (PRBS) as an excitation signal, forming the foundation of an Auto Regressive eXogenous (ARX) model. The closed-loop system incorporates a PID controller and tuned using the Sine Cosine Algorithm (SCA) to optimize performance. The resulting model is rigorously validated through experimental investigation, demonstrating its precision in capturing the system’s dynamics. Moreover, the implementation of a controller-based cooling system substantiates the model’s practical efficacy. The research demonstrates significant improvements when implementing a controller-based water-cooling system for photovoltaic (PV) modules. Compared to the baseline scenario without cooling, the system achieves a 34.5% reduction in average PV temperature (from 59.2°C to 38.9°C) and a 9.46% increase in average power output (from 196.7W to 215.3W). Moreover, this system utilizes only 248.8 liters of water, marking a substantial 64% decrease in water consumption compared to traditional free-flow cooling methods, which use 790.9 liters. The research demonstrates that the controller-based cooling approach is a sustainable option, delivering power output comparable to the free-flow method, yet significantly lowering water consumption. This research signifies a turning point for sustainability, offering an efficient and waterconscious approach for enhancing PV system performance, a crucial step toward a greener and more environmentally responsible energy future. [ABSTRACT FROM AUTHOR]

In order to identify and locate flaws in solar thermal images, this research suggests using an optimization-tuned CNN classifier. The input thermal images are initially pre-processed to remove the noise present in them. After pre-processing, features like LBP, LDP, and LOOP are extracted. The collected features are then combined to produce a feature vector, which is the input to the proposed CNN classifier. Single hotspots, multiple hotspots, and string hotspots are the three types of faults that are supposed to be classified. After the classification process, the defects are located using the VGG-16 model. The weights of the CNN and VGG-16 models are modified using the proposed AqWH algorithm, which includes the distinctive characteristics of the wild horse and the Aquila search agents, to enhance classification and localization accuracy. The suggested possesses accuracy levels of 90% for classification and 96.11% for localization tasks, showing its superiority over conventional methods. [ABSTRACT FROM AUTHOR]

In view of the increasing interest and market share of bifacial cells and modules, suitable substrates such as glass and transparent backsheets along with ethylene vinyl acetate (EVA) and polyolefin elastomer (POE) encapsulants were examined in combined‐accelerated stress testing (C‐AST) to evaluate and compare degradation modes. Testing with both monofacial and bifacial cells, we found that glass–glass modules with monofacial cells led to greater grid finger breakage than those with polymeric backsheets. This is attributed to previous X‐ray topography and modeling work showing higher stress in cells and interconnections in glass–glass modules than glass backsheet modules. Consistent with the objectives of C‐AST, which stresses modules at levels corresponding to the limits seen in the natural environment, we observed the UV‐fluorescence signatures of modules tested in C‐AST (considering the degradation associated with developing chromophores, moisture penetration, and photobleaching effects) to be like those in fielded modules, more so than other chamber stress testing implemented for comparison. We found light‐induced degradation (LID) in module types with regenerated (inactive) cells with C‐AST, suggesting the possibility of LID destabilization in some field conditions. We could also distinguish potential‐induced degradation (PID) on the back of the bifacial passivated emitter and rear cells (PERC) in C‐AST. Confirming with ex situ tests, we found polarization‐type PID most prevalent in glass‐glass modules with EVA as would be anticipated considering the greater leakage current through such module encapsulation. Unlike PID tests performed in the dark, which can lead to false positive PID test results, field‐representative illumination is experienced by the modules on the front and back sides while −1200 V system voltage is applied in C‐AST, supporting the conclusion that this module type with glass‐glass construction would be susceptible to PID in the field. [ABSTRACT FROM AUTHOR]

The aim of this study is to propose a photovoltaic (PV) module simulation model with high accuracy under practical working conditions and strong applicability in the engineering field to meet various PV system simulation needs. Unlike previous model-building methods, this study combines the advantages of analytical and metaheuristic algorithms. First, the applicability of various metaheuristic algorithms is comprehensively compared and the seven parameters of the PV cell under standard test conditions are extracted using the double diode model, which verifies that the artificial hummingbird algorithm has higher accuracy than other algorithms. Then, the seven parameters under different conditions are corrected using the analytical method. In terms of the correction method, the ideal factor correction is added on the basis of previous methods to solve the deviation between simulated data and measured data in the non-linear section. Finally, the root mean squared error between the simulated current data and the measured current data of the proposed model under three different temperatures and irradiance is 0.0697, 0.0570 and 0.0289 A, respectively. [ABSTRACT FROM AUTHOR]

Front glass structured with random subwavelength structures provides photovoltaic (PV) modules with a spectrally broadband omnidirectional antireflective property, enhancing also the incident angle modifier factor and boosting energy production in the field. However, long‐term outdoor monitoring allowing a monthly quantification of the electrical improvement of PV devices laminated with nanostructured glasses has been missing so far. Herein, two mini‐modules, one laminated with flat glass and the other laminated with nanostructured glass, are installed in a single‐axis solar tracker, and the solar irradiance and the short‐circuit current (Isc) are monitored for over 4 months, from September to December. This monitoring provides the quantification of the impact of a broad range of sunlight's angle of incidence (AOI) on the electrical enhancement of the device. The relative Isc improvement between two samples averaged for each month is calculated, ranging from 3.0% in September up to 4.0% in December. Besides, the influence of the solar AOI on the Isc improvement is investigated for sunny days, analyzing separately the Isc performance in the morning/afternoon hours, which corresponds to a normal incidence, and at noon, when the AOI is higher, obtaining more than 5.0% with AOI higher than 60°. [ABSTRACT FROM AUTHOR]

Photovoltaic modules are one of the renewable energy sources with great potential for application in various regions of the world as well as for different terrain. They are, however, sensitive to external factors, affecting the overall amount of energy generated, such as solar irradiance, shading effects and any form of soil build-up on the front glass cover of solar device. The latter issue happens over a course of weeks, months and years and the exact pace is determined for a specific location. Nevertheless, dust remaining on the module surface limits the amount of solar irradiation that can reach solar cells. It then leads to a lowered maximum power and correspond to a decrease in energy yield. A way to mitigate soiling effect, outside of natural washing dependent on precipitation, is a regular debris removal. The proposed methods utilise different approaches, namely active cleaning such as manual, mechanical or electrodynamic, or passive cleaning by applying additional hydrophobic or hydrophilic coating to slow down the accumulation tempo. [ABSTRACT FROM AUTHOR]

This research aims to improve thermal performance and compare the performance of two common crystalline PV panel types (Mono and poly). Modules with a back-cooling system were designed and numerically analyzed with SolidWorks and ANSYS-Fluent-2021-R2 for the simulation under Baghdad weather at noon. The cooling system used consists of a phase-change material, paraffin wax (RT55), with a thickness of 5 cm and a heatsink with 33 fins with heights of 10, 20, and 30 mm and thicknesses of 2, 4, and 6 mm. to select the best height of the wax 1, 3, 5, 10, 20 cm examined. The result showed that for polycrystals, the panel temperature was reduced by 8.4°C using PCM and 11.9°C using PCM-fins. Also, output power was enhanced to 200.6 W by 10.2 W, and efficiency improved by 5%. Similarly, using PCM and PCM-fins lowered the temperature of the monocrystalline by 8.3 and 12.5°C, respectively. Therefore, the output power is enhanced to 202.4 W by 10.7 W and improves the electrical efficiency by 5.2%. The results of the study showed that mono had better performance than poly. This result is acceptable and is in good agreement with previous studies.

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%.

As the most common energy source of spacecraft, photovoltaic (PV) power generation has become one of the hottest research fields. During the on-orbit operation of spacecraft, the influence of various uncertain factors and the unbalanced inertial force will make the solar PV wing vibrate and degrade its performance. In this study, we investigated the influence of mechanical vibration on the output characteristics of PV array systems. Specifically, we focused on a three-segment solar panel commonly found on satellites, analyzing both its dynamic response and electrical output characteristics under mechanical vibration using numerical simulation software. The correctness of the simulation model was partly confirmed by experiments. The results showed that the maximum output power of the selected solar panel was reduced by 5.53% and its fill factor exhibited a decline from the original value of 0.8031 to 0.7587, provided that the external load applied on the panel increased to 10 N/m2, i.e., the vibration frequency and the maximal deflection angle were 0.3754 Hz and 74.9871°, respectively. These findings highlight a significant decrease in the overall energy conversion efficiency of the solar panel when operating under vibration conditions.

Photovoltaic (PV) modules are often situated in hot and windy environments, such as deserts, where dust accumulation poses a significant problem. The build-up of dust can result in an increase in PV module leakage current, making the modules more vulnerable to potential-induced degradation (PID), ultimately leading to a reduction in the efficiency of PV power generation. In this study, we investigate the impact of dust accumulation on the surface of PV modules on leakage current. A dust model is developed based on the Arrhenius relation, taking into account the impact of temperature and density on dust conductivity. The equation for leakage current due to dust accumulation is derived based on the clean module leakage current equation. We undertake a simulation of natural conditions in a laboratory setting to analyze the impact of dust on the leakage current of photovoltaic modules. The results show the following: At high temperatures, the leakage current will significantly increase due to the elevated conductivity of the dust. The conductivity increased by 27.1%, 48.9%, 64.3%, and 118% for the four groups of dusty PV modules, respectively. Leakage current prediction has a better accuracy when dust is equated to series conductance. Dust can reduce the activation energy of PV modules by up to 3.48%.