Search Results (81)

Transient increments of X-ray radiation and extreme ultraviolet (EUV) during solar flares are strong drivers of thermospheric dynamics on Mars, yet their class-dependent impacts remain poorly measured. This work provides the first direct, side-by-side study of Martian thermospheric reactions to flares X8.2 on 10 September 2017 and M6.0 on 17 September 2017. This study shows nonlinear, class-dependent effects, compositional changes, and recovery processes not recorded in previous investigations. Species-specific responses deviated significantly from irradiance proportionality, even though the soft X-ray flux in the X8.2 flare was 13 times greater. Argon (Ar) concentrations rose 3.28× (compared to 1.13× for M6.0), and radiative cooling led CO₂ heating to approach a halt at ΔT = +40 K (X8.2) against +19 K (M6.0) at exobase altitudes (196–259 km). N₂ showed the largest class difference, where temperatures rose by +126 K (X8.2) instead of +19 K (M6.0), therefore displaying flare-magnitude dependent thermal sensitivity. The 1.95× increase in O concentrations during X8.2 and the subsequent decrease following M6.0 (−39 K cooling) illustrate the contradiction between photochemical production and radiative loss. The O/CO₂ ratio at 225 km dropped 46% during X8.2, revealing compositional gradients boosted by flares. Recovery timeframes varied by class; CO₂ quickly re-equilibrated because of effective cooling, whereas inert species (Ar, N₂) stabilized within 1–2 orbits after M6.0 but needed >10 orbits of the MAVEN satellite after the X8.2 flare. The observations of the X8.2 flare came from the western limb of the Sun, but the M6.0 flare happened on the far side. The CME shock was the primary driver of Mars’ EUV reaction. These findings provide additional information on atmospheric loss and planetary habitability by indicating that Mars’ thermosphere has a saturation threshold where strong flares induce nonlinear energy partitioning that encourages the departure of lighter species. Full article

The quality effects of spectral irradiance conditions during papaya (Carica papaya L.) drying were investigated using three different dryers: a solar dryer with dynamic irradiance control (SDIC), a cylindrical solar dryer (CSD), and a solar simulator dryer (SSD). This study builds upon previous PDLC film applications in solar drying by specifically examining its impact on phytochemical preservation and color degradation, addressing gaps in spectral-specific effects on food quality parameters. The drying conditions were as follows: a temperature of 50 °C for each method, 700 w/m² for both SDIC and solar simulator dryers (SSD), and full solar irradiance for the cylindrical solar dryer (CSD). The cylindrical solar dryer exhibited 210 min of drying time due to higher solar irradiance than SDIC (300 min), while SSD lasted 180 min. Drying rates were highest for CSD (0.056 g H₂O/g d.m. min⁻¹), followed by SDIC (0.027 g H₂O/g d.m. min⁻¹). Color analysis revealed that CSD resulted in the most significant color degradation, followed by SSD and SDIC. This was attributed to the varying spectral composition of radiation in each method. The CSD, with a full solar spectrum, including higher UV and visible radiation, induced more pronounced color changes than SDIC, which received lower intensity radiation in these ranges. Chemical analyses showed that SSD samples had the highest antioxidant activity (1432.91 µmol TE/g dw by ABTS) and phenolic content (58.92 mg GAE/100 g), suggesting simulated conditions may better preserve certain phytochemicals. SDIC maintained better carotenoid-related color parameters while showing intermediate antioxidant levels (1084.09 µmol TE/g dw). These results demonstrate that irradiance control significantly impacts drying efficiency and quality parameters. Full article

Photovoltaic (PV) power generation may vary with respect to several factors such as solar radiation, temperature, power conditioning units, environmental effects, and shading conditions. The partial shading of PV modules is one of the most crucial factors that causes the performance degradation of PV systems. The main reason for efficiency reduction under partial shading conditions is the creation of multiple local maximums and one global maximum operating point. The classical Maximum Power Point Tracking (MPPT) algorithm fails to determine the global maximum operating point to prevent power losses under partial shading conditions. In this study, a novel hybrid MPPT method based on Perturb & Observe and Particle Swarm Optimization that mainly aims to determine global operating point, is proposed. The proposed hybrid MPPT method is tested under different partial shading conditions and variable irradiance levels. In this manner, the dynamic response of the system is remarkably increased by the proposed MPPT method. To show the superiority of the developed method, a performance comparison is conducted with the P&O- and Kalman-Filter-based MPPT methods. The obtained results illustrate an improvement around 1.5 V in undershoot voltage and 0.2 ms in convergence speed. In addition, the overall system efficiency of the PV system is increased around 2% when compared to the P&O- and Kalman-Filter-based MPPT methods. Consequently, the proposed method seems to be an efficient method in terms of undershoot voltage, convergence time, tracking accuracy, and efficiency under partial shading conditions. Full article

Solar-powered pumping systems using series pumps are commonly applied in the delivery of water to remote agricultural regions, particularly in hilly tropical terrain. The synchronization of these pumps typically depends on reliable communication; however, dense vegetation, elevation changes, and weather conditions often disrupt signals. To address these limitations, a fully decentralized, communication-free control system is proposed. Each pumping station operates independently while maintaining synchronized operation through emulated neighbor sensing. The system applies a discrete-time control algorithm with virtual sensing that estimates neighboring pump statuses. Each station consists of a solar photovoltaic (PV) array, variable-speed drive, variable inlet valve, reserve tank, and local control unit. The controller adjusts the valve positions and pump power based on real-time water level measurements and virtual neighbor sensing. The simulation results across four scenarios, including clear sky, cloudy conditions, temporary outage, and varied irradiance, demonstrated steady-state operation with no water overflow or shortage and a steady-state error less than 4% for 3 m³ transfer. The error decreased as the average power increased. The proposed method maintained system functionality under simulated power outage and variable irradiance, confirming its suitability for remote agricultural areas where communication infrastructure is limited. Full article

Levofloxacin is an antibiotic classified as an emerging contaminant. Its presence in aquatic environments represents potential risks to ecosystems and human health, making its removal during wastewater treatment of relevant importance. Here, we present a comprehensive kinetic analysis of levofloxacin photodegradation under UVB solar irradiation, with emphasis on the influence of pH and dissolved oxygen, two conditions that can vary widely in wastewater and impact treatment efficiency. We also investigated the formation and role of reactive oxygen species in the degradation mechanism, as well as the cytotoxicity and antibacterial activity of photoproducts. Our findings reveal that the efficiency of levofloxacin photodegradation is highly dependent on environmental conditions; it requires neutral or slightly alkaline pH and a high concentration of dissolved oxygen, a situation not always observed in contaminated waters. Several reactive oxygen species are generated, with singlet oxygen being the most reactive with the antibiotic. We report for the first time the singlet oxygen quantum yield from levofloxacin. Bioassays demonstrated that photoproducts neither exhibit antibacterial activity nor induce significant cytotoxicity. Our study suggests that UVB treatment of contaminated effluent containing levofloxacin could be an effective and environmentally safe strategy for the antibiotic degradation under certain conditions of pH and dissolved oxygen. Full article

The theory of orbital forcing as formulated by Milankovitch involves the mediation by the advance (retreat) of ice sheets and the resulting variations in terrestrial albedo. This approach poses a major problem: that of the period of glacial cycles, which varies over time, as happened during the Mid-Pleistocene Transition (MPT). Here, we show that various hypotheses are called into question because of the finding of a second transition, the Early Quaternary Transition (EQT), resulting from the million-year period eccentricity parameter. We propose to complement the orbital forcing theory to explain both the MPT and the EQT by invoking the mediation of western boundary currents (WBCs) and the resulting variations in heat transfer from the low to the high latitudes. From observational and theoretical considerations, it appears that very long-period Rossby waves winding around subtropical gyres, the so-called “gyral” Rossby waves (GRWs), are resonantly forced in subharmonic modes from variations in solar irradiance resulting from the solar and orbital cycles. Two mutually reinforcing positive feedbacks of the climate response to orbital forcing have been evidenced: namely the change in the albedo resulting from the cyclic growth and retreat of ice sheets in accordance with the standard Milankovitch theory, and the modulation of the velocity of the WBCs of subtropical gyres. Due to the inherited resonance properties of GRWs, the response of the climate system to orbital forcing is sensitive to small changes in the forcing periods. For both the MPT and the EQT, the transition occurred when the forcing period merged with one of the natural periods of the climate system. The MPT occurred 1.25 Ma ago, when the dominant period shifted from 41 ka to 98 ka, with both periods corresponding to changes in the Earth’s obliquity and eccentricity. The EQT occurred 2.38 Ma ago, when the dominant period shifted from 408 ka to 786 ka, with both periods corresponding to changes in the Earth’s eccentricity. Through this paradigm shift, the objective of this self-consistent approach is essentially to spark new debates around a problem that has been pending since the discovery of glacial–interglacial cycles, where many hypotheses have been put forward without, however, fully answering all our questions. Full article

Unlike diesel generators or energy storage systems, photovoltaic (PV) arrays lack inherent rotational inertia and have output limitations due to their operational environmental dependencies. These characteristics restrict their suitability as primary power system backbone components. This study proposes a grid-forming (GF) control strategy for PV inverters in low voltage grid (LVG) using a model predictive control (MPC) approach. The proposed method introduces a novel predictive model accounting for capacitor dynamics to precisely regulate both AC-side output voltage and DC-side voltage. Furthermore, in this paper, P-V droop control replaces the traditional frequency regulation, achieving the real-time balance of DC/AC power and seamless sharing of multiple photovoltaic power sources. By integrating a modified cost function, the controller can flexibly switch between maximum power point tracking (MPPT) mode and power reserve mode according to varying output demands. The proposed strategy can provide advanced frequency stability, MPPT accuracy, and fast dynamic response under rapidly changing solar irradiance and load conditions. Simulation and experimental tests are carried out to validate the effectiveness of the proposed strategy. Full article

This study presents a novel approach to improving the efficiency and reliability of solar water pumping systems by integrating a proportional–integral–derivative (PID) controller with the Jellyfish Algorithm (PID-JC) and artificial neural networks (ANN). Solar water-pumping systems are gaining attention due to their sustainable and eco-friendly nature; however, their performance is often limited by fluctuating solar irradiance and varying water demand. To address these challenges, Monte Carlo simulations were employed to account for system uncertainties. Traditional PID controllers, although widely used, often struggle to adapt effectively to dynamic environmental conditions. The proposed system utilizes an ANN to predict solar irradiance and water demand patterns based on historical data, enabling real-time adjustments of pump operations through the PID-JC. This approach is inspired by the adaptive behavior of jellyfish in dynamic environments. The PID-JC adjusts PID parameters dynamically based on ANN predictions, optimizing pump performance. Simulation and experimental results conducted on a solar water-pumping system in Mrada City, Northeastern Libya, demonstrated significant improvements in water delivery, energy consumption, and system reliability compared to conventional PID controllers. The PID-JC’s ability to adapt to diverse environmental conditions ensures robust performance across various geographical locations and seasonal changes. Additionally, comparisons to other optimization algorithms, such as Firefly and Golden Eagle Optimization, show that the Jellyfish Algorithm outperforms them with a 6.30% improvement in the cost function and a 28.13% reduction in processing time compared to Firefly, and a 26.81% improvement in the cost function and a 20.69% reduction in processing time compared to Golden Eagle Optimization. Full article

Emerging trends in heat pump (HP) and electric vehicle (EV) adoption within communities aim to reduce carbon emissions in the heating and transportation sectors. However, these technologies rely on grid electricity, whose carbon intensity varies over time. This study explores how the carbon-saving potential of these technologies can be further enhanced through demand-shifting operations and renewable energy integration. The research compares photovoltaic–thermal (PV/T) and hybrid solar heat pump systems that integrate EV charging and PCM-enhanced heat storage to improve space heating efficiency under low solar irradiance in the UK while reducing CO₂ emissions. The study simulates solar collector configurations and sizes, combining PV modules and heat pumps to enhance system performance. Control systems synchronize operations with periods of low grid CO₂ intensity, minimizing the environmental impact. The analysis evaluates PV/T systems, separate PV and thermal collectors, highlighting their energy efficiency and CO₂ reduction potential. Control systems further optimize HP operation and EV charging during periods of high renewable energy availability, preventing uncontrolled use that could result in elevated emissions. Using real weather data and a detailed building model, the findings show that a solar-assisted HP with 100% thermal collectors achieves a daily COP of 3.49. Reducing thermal collectors to 60% lowers the COP to 2.57, but PV output compensates, maintaining similar emission levels. The system achieves the lowest emission with high-efficiency evacuated flat plate PV/T collectors. Full article

Surface albedo is a key variable influencing ground-reflected solar irradiance, which is a vital factor in boosting the energy gains of bifacial solar installations. Therefore, surface albedo is crucial towards estimating photovoltaic power generation of both bifacial and tilted solar installations. Varying across daylight hours, seasons, and locations, surface albedo is assumed to be constant across time by various models. The lack of granular temporal observations is a major challenge to the modeling of intra-day albedo variability. Though satellite observations of surface reflectance, useful for estimating surface albedo, provide wide spatial coverage, they too lack temporal granularity. Therefore, this paper considers a novel approach to temporal downscaling with imaging time series of satellite-sensed surface reflectance and limited high-temporal ground observations from surface radiation (SURFRAD) monitoring stations. Aimed at increasing information density for learning temporal patterns from an image series and using visual redundancy within such imagery for temporal downscaling, we introduce temporally shifted heatmaps as an advantageous approach over Gramian Angular Field (GAF)-based image time series. Further, we propose Multispectral-WaveMix, a derivative of the mixer-based computer vision architecture, as a high-performance model to harness image time series for surface albedo forecasting applications. Multispectral-WaveMix models intra-day variations in surface albedo on a 1 min scale. The framework combines satellite-sensed multispectral surface reflectance imagery at a 30 m scale from Landsat and Sentinel-2A and 2B satellites and granular ground observations from SURFRAD surface radiation monitoring sites as image time series for image-to-image translation between remote-sensed imagery and ground observations. The proposed model, with temporally shifted heatmaps and Multispectral-WaveMix, was benchmarked against predictions from models image-to-image MLP-Mix, MLP-Mix, and Standard MLP. Model predictions were also contrasted against ground observations from the monitoring sites and predictions from the National Solar Radiation Database (NSRDB). The Multispectral-WaveMix outperformed other models with a Cauchy loss of 0.00524, a signal-to-noise ratio (SNR) of 72.569, and a structural similarity index (SSIM) of 0.999, demonstrating the high potential of such modeling approaches for generating granular time series. Additional experiments were also conducted to explore the potential of the trained model as a domain-specific pre-trained alternative for the temporal modeling of unseen locations. As bifacial solar installations gain dominance to fulfill the increasing demand for renewables, our proposed framework provides a hybrid modeling approach to build models with ground observations and satellite imagery for intra-day surface albedo monitoring and hence for intra-day energy gain modeling and bifacial deployment planning. Full article

The inherent variability of solar energy presents a significant challenge for grid operators, particularly when it comes to maintaining stability. Studying ramping phenomena is therefore crucial to understanding and managing fluctuations in power supply. In line with this goal, this study proposes a new classification approach for solar irradiance ramps, categorizing them into four distinct classes. We have proposed a methodology including adaptation and extension of a wind ramp classification to solar ramp classification titled the Irradiance-Based Extreme Day Identification method. Our proposal includes an agglomerative algorithm to find new ramp class boundaries. The strength of the proposed method relies on that it allows its generalization to any dataset. We assessed it on three datasets from distinct geographic regions—Oregon (northwestern United States), Hawaii (central Pacific Ocean), and Portugal (southwestern Europe)—each with varying temporal resolutions of five seconds, ten seconds, and one minute. The class boundaries for each dataset results in different limits of Z score value, as a consequence of the different climatic characteristics of each location and the time resolution of the datasets. The “low” class includes values less than 0.62 for Portugal, less than 2.17 for Oregon, and less than 2.19 for Hawaii. The “moderate” class spans values from 0.62 to 3.51 for Portugal, from 2.17 to 5.01 for Oregon, and from 2.19 to 5.88 for Hawaii. The “high” class covers values greater than 3.51 and up to 6 for Portugal, greater than 5.01 and up to 10.72 for Oregon, and greater than 5.88 and up to 8.01 for Hawaii. Lastly, the “severe” class includes values greater than 6 for Portugal, greater than 10.72 for Oregon, and greater than 8.01 for Hawaii. Under cloudy sky conditions, it is observed that the proposed algorithm is able to classify the four classes. These thresholds show how the proposed methodology adapts to the unique characteristics of each regional dataset. Full article

Planning an optimal installation site to maximize power-generation efficiency is crucial for the effective operation of photovoltaic power plants. Achieving this requires accurate, reliable information on solar irradiation across different regions. However, ground-based measurements using pyranometers are resource-intensive, requiring substantial time and human effort, and their measurement range is limited, complicating data collection. To address this, we propose a method to accurately estimate surface solar irradiation (SSI) using satellite data and feature engineering. By leveraging satellite data as the primary input, we overcome the spatial and temporal limitations of ground-based measurements. Additionally, we improve SSI-estimation performance through designed features based on the geometric information of the Sun and satellite. A hybrid deep neural network model is used for SSI estimation, effectively handling data of varying dimensions. Hourly SSI data from 12 synoptic observation stations collected over one year, excluded from the model’s training and validation sets, are utilized to evaluate the proposed method. Experimental results demonstrate strong SSI-estimation performance, with an average root mean square error (RMSE) of 0.1813 MJ/m², a relative RMSE of 0.1601, mean absolute error of 0.1159 MJ/m², and coefficient of determination of 0.9680. Full article

Contemporary environmental challenges are increasingly significant. The primary cause is the drastic changes in climates. The prediction of solar radiation is a crucial aspect of solar energy applications and meteorological forecasting. The amount of solar radiation reaching Earth’s surface (Global Horizontal Irradiance, GHI) varies with atmospheric conditions, geographical location, and temporal factors. This paper presents a novel methodology for estimating surface sun exposure using advanced deep learning techniques. The proposed method is tested and validated using the data obtained from NASA’s Goddard Earth Sciences Data and Information Services Centre (GES DISC) named the SORCE (Solar Radiation and Climate Experiment) dataset. For analyzing and predicting accurate data, features are extracted using a deep learning method, Deep-FS. The method extracted and provided the selected features that are most appropriate for predicting the surface exposure. Time series analysis was conducted using Convolutional Neural Networks (CNNs), with results demonstrating superior performance compared to traditional methodologies across standard performance metrics. The proposed Deep-FS model is validated and compared with the traditional approaches and models through the standard performance metrics. The experimental results concluded that the proposed model outperforms the traditional models. Full article

The integration of photovoltaic (PV) systems into DC motor drives has prompted the enhancement of motor performance. This study explores the application of photovoltaic generators (PVs) to independently power and excite a Separately Excited Direct-Current (SEDC) system by utilizing a proportional open-circuit voltage method as a strategy for tracking the maximum power point. This approach offers an effective means of optimizing energy output from PV systems. The primary aim was to optimize power output from photovoltaic generators across varying solar intensity levels. This paper describes the nonlinear current/voltage behaviour of PV generators under different levels of irradiation, along with the magnetic characteristics of the core material in an SEDC motor, utilizing advanced polynomial equations for accurate mathematical representation. Furthermore, we conducted a dynamic analysis of the SEDC motor, powered by the PV generators, under varying solar intensities. This study investigates the operational performance of the SEDC motor under varying solar irradiance levels by developing a realistic model using MATLAB software, R2022a, for numerical simulations, followed by implementation on high-performance computing platforms, including a real-time simulator and testbed, using a real-time digital simulator (RTDS). Full article

Flexible thermoelectric generators (TEGs) with pn-junction single-walled carbon nanotube (SWCNT) films on a polyimide substrate have attracted considerable attention for energy harvesting. This is because they generate electricity through the photo-thermoelectric effect by self-generated temperature gradient under uniform sunlight irradiation. To increase the performance and durability of the pn-junction TEGs, n-type films need to be improved as a priority. In this study, bismuth telluride (Bi₂Te₃) nanoplates synthesized by the solvothermal method were added to the n-type SWCNT films, including a cationic surfactant to form the nanocomposite films because Bi₂Te₃ has high n-type thermoelectric properties and high durability. The performances of the pn-junction TEGs were investigated by varying the heat treatment times. When the artificial sunlight was uniformly irradiated to the pn-junction TEGs, a stable output voltage of 0.47 mV was observed in the TEG with nanocomposite films heat-treated at 1 h. The output voltage decreased with increasing heat treatment time due to the decrease in the p-type region. The output voltage of TEG at 1 h is higher than that of the TEGs without Bi₂Te₃ nanoplates under the same conditions. Therefore, the addition of Bi₂Te₃ nanoplates was found to improve the performance of the pn-junction TEGs. These findings may aid in the development of facile and flexible optical devices, including photodetectors and hybrid devices integrating solar cells. Full article