Low Irradiance Levels Research Articles

Performance analysis of a photovoltaic component integrated into a hybrid power plant in Southeast Mauritania

This study investigated the performance of photovoltaic components of the 1.3MW KIFFA hybrid power plant in Mauritania. Data from the plant's monitoring system (January-December 2021) was used to assess various performance metrics. The analysis revealed a high daily reference yield (5.60 h/d), indicating good solar resource availability. However, final and array yields (4.78 h/d and 4.86 h/d, respectively) suggested potential for improvement. System component efficiencies were within acceptable ranges, with particularly high inverter efficiency (98.31%). Array capture losses were moderate (0.74 h/d), and system losses were minimal (0.08 h/d). The annual performance ratio (86.33%) and capacity factor (19.91%) indicated good overall plant performance. These findings were then compared with data from similar installations in various climate zones to understand the impact of climatic variations on photovoltaic performance. Compared to installations in temperate zones with lower irradiation levels, the KIFFA plant's reference yield was significantly higher. However, the final and array yields were closer due to potentially higher operating temperatures in Mauritania affecting module efficiency. Interestingly, comparisons with installations in other desert regions with similarly high irradiation levels revealed lower performance, particularly in terms of final yield, (4.71 h/d) in Algéria (Adrar) and (4.10 h/d) in Oman (Muscat). This suggests that climatic factors beyond just sunlight availability, such as dust accumulation, may have played a significant role in their performance compared to the KIFFA plant.

Novel isolates expand the physiological diversity of Prochlorococcus and illuminate its macroevolution.

Prochlorococcus is a diverse picocyanobacterial genus and the most abundant phototroph on Earth. Its photosynthetic diversity divides it into high-light (HL)- or low-light (LL)-adapted groups representing broad phylogenetic grades-each composed of several monophyletic clades. Here, we physiologically characterize four new Prochlorococcus strains isolated from below the deep chlorophyll maximum in the North Pacific Ocean. We combine these physiological properties with genomic analyses to explore the evolution of photosynthetic antennae and discuss potential macroevolutionary implications. The isolates belong to deeply branching low-light-adapted clades that have no other cultivated representatives and display some unusual characteristics. For example, despite its otherwise low-light-adapted physiological characteristics, strain MIT1223 has low chl b2 content similar to high-light-adapted strains. Isolate genomes revealed that each strain contains a unique arsenal of pigment biosynthesis and binding alleles that have been horizontally acquired, contributing to the observed physiological diversity. Comparative genomic analysis of all picocyanobacteria reveals that Pcb, the major pigment carrying protein in Prochlorococcus, greatly increased in copy number and diversity per genome along a branch that coincides with the loss of facultative particle attachment. Collectively, these observations support a recently developed macroevolutionary model, in which niche-constructing radiations allowed ancestral lineages of picocyanobacteria to transition from a particle-attached to planktonic lifestyle and broadly colonize the euphotic zone.IMPORTANCEThe marine cyanobacterium, Prochlorococcus, is among the Earth's most abundant organisms, and much of its genetic and physiological diversity remains uncharacterized. Although field studies help reveal the scope of diversity, cultured isolates allow us to link genomic potential to physiological processes, illuminate eco-evolutionary feedbacks, and test theories arising from comparative genomics of wild cells. Here, we report the isolation and characterization of novel low-light (LL)-adapted Prochlorococcus strains that fill in multiple evolutionary gaps. These new strains are the first cultivated representatives of the LLVII and LLVIII paraphyletic grades of Prochlorococcus, which are broadly distributed in the lower regions of the ocean euphotic zone. Each of these grades is a unique, highly diverse section of the Prochlorococcus tree that separates distinct ecological groups: the LLVII grade branches between monophyletic clades that have facultatively particle-associated and constitutively planktonic lifestyles, whereas the LLVIII grade lies along the branch that leads to all high-light (HL)-adapted clades. Characterizing strains and genomes from these grades yields insights into the large-scale evolution of Prochlorococcus. The new LLVII and LLVIII strains are adapted to growth at very low irradiance levels and possess unique light-harvesting gene signatures and pigmentation. The LLVII strains represent the most basal Prochlorococcus group with a major expansion in photosynthetic antenna genes. Furthermore, a strain from the LLVIII grade challenges the paradigm that all LL-adapted Prochlorococcus exhibit high ratios of chl b:a2. These findings provide insights into the photophysiological evolution of Prochlorococcus and redefine what it means to be a low- vs high-light-adapted Prochlorococcus cell.

Silicon heterojunction solar cells: Excellent candidate for low light illuminations

The current solar cells and modules are marketed according to the behaviour at standard test conditions (STC), however, these devices are more often operated at lower irradiance levels. The dependence on illumination stems mainly from the voltage at the open circuit and at the maximum power point. The latter might also be strongly influenced by serial resistance, but that is more an engineering problem not addressed here. More fundamentally, the voltage is determined by quasi-Fermi levels at the contacts. In the case of unconstrained conductivity between the absorber and electrode, the quasi-Fermi levels are flat, and determined by their splitting in the absorber. Our analysis shows that the modulation doping mechanism working in Si heterojunction solar cell between the doped amorphous Si layer with a higher bandgap and crystalline absorber with a lower bandgap can, for certain parameter settings, lead to strongly depleted contact layer that limits conduction. This is a prerequisite for decoupling the quasi-Fermi level between contact and absorber and offers the possibility for additional voltage increase. Based on this understanding, we simulate a heterojunction solar cell with a varying thickness and doping of amorphous silicon p-type contact. We demonstrate that for a certain combination of thinner or lower-doped contact, higher efficiency at low illumination can be achieved compared to the technological baseline. This is fully in line with the experimental findings. This analysis is crucial not only for using solar cells for indoor applications but also for designing photovoltaic modules optimized for low irradiance, potentially increasing the level of self-sufficiency of buildings.

The effectiveness of a hybrid MPPT controller based on an artificial neural network and fuzzy logic in low-light conditions

Technological advancement and economic progress have made power consumption a big issue. Concern is growing as traditional energy sources dwindle. In the future, numerous fossil fuels will be insufficient to satisfy human requirements. This motivates research into the feasibility of using renewable energy sources. Renewable energy sources offer a multitude of advantages, including their cost-effectiveness, lack of environmental impact, and sustainable nature. Sunlight is currently the most prevalent source of energy because it is both free and readily accessible. Consequently, photovoltaic (PV) energy is gaining importance in the field of electricity generation. Tracking the maximum power point (MPP) in a solar PV system is challenging due to varying meteorological conditions (irradiance and temperature). To maximise the efficiency of a solar power installation, it is essential to monitor the PV array's optimum power point. This analysis compares the perturb and observe (PO), fuzzy logic (FL), and suggested artificial neural network (ANN)-fuzzy strategy for determining the MPP of a PV system with minimal radiation exposure. Simulation results show that at low irradiation levels, the proposed ANN-fuzzy maximum power point tracking (MPPT) unit controller is superior to the FL and PO MPPT controllers in terms of tracking maximum power.

Performance prediction of a novel disinfection-enhanced type Trombe wall with transverse fins

Trombe wall is an efficient passive solar heating system, while the utilization form of heated air needs to be expanded. Indoor microorganisms that can be transmitted through aerosol endanger the safety of human health. The fact that microorganisms can be thermally inactivated makes it possible to disinfect the air through Trombe wall. To expand and enhance the disinfection effect of the wall, extending the residence time of bioaerosols in the flow channel and raising the air temperature can be applied. Therefore, a transverse-finned-Trombe wall is proposed. Multiple physical field coupling is studied numerically. Migration, inactivation, and deposition of bioaerosols are described by Eulerian method. The results indicated as follows: As fin height increases, the thermal efficiency first rises and then drops; the single-pass inactivation ratio rises remarkably with its value reaching 90.96 % and 66.98 % for SARS-CoV-1 and SARS-CoV-2 even at low irradiance level (400 W/m2) at a fin height of 35 mm. As fin angle increases from −45° to +45°, the thermal efficiency drops first and then rises. Optimal disinfection performance is achieved at α = 0°. The contribution of deposition to removal of infectious bioaerosols plays a significant role when the thermal inactivation is not strong.

Intraoperative Staff Radiation Exposure During Aortic Endovascular Procedures

The risk of radiation exposure in the surgical operating room (OR) and/or catheterization laboratory is now well established. Complex endovascular procedures often require multiple approaches and different positioning of the staff members around the patient, potentially increasing the levels of radiations exposure. Our goal was to evaluate the levels of radiation exposure of the members of the staff during endovascular aortic procedures in order to propose radioprotection optimization. We included 41 aortic endovascular procedures out of 114 procedures performed between January 12, 2014, and August 31, 2015, including 24 standard endovascular aortic aneurysm repair (EVAR), 7 EVAR with iliac branch (EVARib), 8 complex fenestrated/branched EVAR (F/B EVAR), and 2 thoracic EVAR (TEVAR). Procedures were performed in an OR equipped with a carbon fiber table and a mobile fluoroscopy C-arm. We collected the usual dosimetry data given by the C-arm as well as the patient's peak skin dose (PSD). In all staff members, radiation exposure was measured with thermoluminescent chip dosimeters placed on both temples, on posterior sides of both hands, and on both lower legs. PSD levels were low for EVAR because 24 patients had values below the reading threshold. PSD significantly increased with more complex procedures. Main operator (MO) received the higher level of irradiation on whole body, hands, and ankles. Eye lenses irradiation was higher on both assistant operators (AOs). Other members received low levels of irradiation. We found a high ranges of radiation exposure with a high risk of exposure for the AO, mainly for F/B EVAR and EVARib. Even if all personal protections are used, staff positioning is a major point that must be considered. If MO is supposed to be the most exposed to X-rays, specific conditions of positioning of the AO may be at risk of exposure.

Realization of a Novel Artificial Intelligence Fuzzy Controller with Solar Irradiance-altered Maximum Power

This paper reports the realization of a novel artificial intelligence fuzzy Logic controller (AI-FLC) that attained the optimum power of the photovoltaic cell (PV array) with solar irradiance altered (from 300 to 1000 w/m2) using the boost-converter circuit. Experiments were conducted using the Simulink /MATLAB coding. The nonlinear current-voltage character of the PV array was observed to be affected by the weather conditions, solar irradiance level, and ambient temperature. In addition, unique maximum power point for each irradiance level was achieved which was ascribed to the rapid response of the FLC to the atmospheric changes without requiring any system parameters. The obtained accuracy was as much as 98.875%. The post converter circuit was connected to the buck converter for taking the advantage of the generated power, thereby stabilizing the synchronous generator-emanated voltage. The response of the proposed system was demonstrated to be better than the conventional PD control, wherein the output voltage was stable at 220 V even at low levels of the solar irradiance.

Continuous Solar Thermal Energy Production Based on Critical Irradiance Levels for Industrial Applications

The design of a solar thermal installation is based on the lowest irradiance levels that occur during winter. However, there are consecutive days with irradiance levels well below those used for the design, which are called in this work “critical irradiance levels”. To solve this challenge, a statistical analysis is carried out to find a representative percentile of 22 years of consecutive days with “critical irradiance levels”. A case study of a cotton-dyeing industrial process requires 18.5 m3 of hot water and operates for 2.75 h at temperatures between 40 and 90 °C. Environmental variables for 22 years were analyzed and validated to design a solar thermal installation (solar collector network and storage system) and a coupled heat pump. The fifth percentile, with three consecutive days and low irradiance levels, was the most repetitive. For this case, a storage system of 46.5 m3 guaranteed heat load at target temperature. The simple payback was 14.1 years, and the energy cost was 0.094 USD/kWh, which was competitive against the energy cost from using fossil fuels, 0.064 USD/kWh. The design based on critical environmental conditions guarantees a continuous supply of energy to the industrial process and defines the minimum availability of solar energy to supply a process.

High precision orientation mapping from 4D-STEM precession electron diffraction data through quantitative analysis of diffracted intensities

The association of scanning transmission electron microscopy (STEM) and detection of a diffraction pattern at each probe position (so-called 4D-STEM) represents one of the most promising approaches to analyze structural properties of materials with nanometric resolution and low irradiation levels. This is widely used for texture analysis of materials using automated crystal orientation mapping (ACOM). Herein, we perform orientation mapping in InP nanowires exploiting precession electron diffraction (PED) patterns acquired by an axial CMOS camera. Crystal orientation is determined at each probe position by the quantitative analysis of diffracted intensities minimizing a residue comparing experiments and simulations in analogy to x-ray structural refinement. Our simulations are based on the two-beam dynamical diffraction approximation and yield a high angular precision (∼0.03°), much lower than the traditional ACOM based on pattern matching algorithms (∼1°). We anticipate that simultaneous exploration of both spot positions and high precision crystal misorientation will allow the exploration of the whole potentiality provided by PED-based 4D-STEM for the characterization of deformation fields in nanomaterials.

Machine learning-enhanced detection of minor radiation-induced defects in semiconductor materials using Raman spectroscopy

Radiation damage in semiconductor materials is a crucial concern for electronic applications, especially in the fields of space, military, nuclear, and medical electronics. With the advancements in semiconductor fabrication techniques and the trend of miniaturization, the quality of semiconductor materials and their susceptibility to radiation-induced defects have become more important than ever. In this context, machine learning (ML) algorithms have emerged as a promising tool to study minor radiation-induced defects in semiconductor materials. In this study, we propose a sensitive non-destructive technique for investigating radiation-induced defects using multivariate statistical analyses combined with Raman spectroscopy. Raman spectroscopy is a contactless and non-destructive method widely used to characterize semiconductor materials and their defects. The multivariate statistical methods applied in analyzing the Raman spectra provide high sensitivity in detecting minor radiation-induced defects. The proposed technique was demonstrated by categorizing 100–500 kGy irradiated GaAs wafers into samples with low and high irradiation levels using linear discrimination analysis ML algorithms. Despite the high similarity in the obtained Raman spectra, the ML algorithms correctly predicted the blind testing samples, highlighting the effectiveness of ML in defect study. This study provides a promising approach for detecting minor radiation-induced defects in semiconductor materials and can be extended to other semiconductor materials and devices.

Performance investigation of tempered glass based photovoltaic panel integrated with back cooling hollow chamber

ABSTRACT At present, the whole world is facing a huge fuel crisis and those who adopt the extraction of energy from renewable sources are getting much benefit both environmentally and economically. Photovoltaic systems are the most mature and scaled renewable energy generation systems worldwide. However, the optimal condition for maximizing the output from Photovoltaic (PV) panels is characterized by low temperatures and high irradiation levels. With the increasing temperature and irradiation, the solar panel becomes warmer and thus the efficiency drops. Hence, by cooling the PV panels, the efficiency can be increase. Various thermal collector designs and different water flowing methods (Top cooling & Back cooling) has been employed to cool the panels. In this research study, an experiment of building a new PVT panel attached to a hollow chamber (Thermal Collector) at the back of the panel is carried out. In hollow chamber the warm water was kept inside and release that when the water was hot enough. This experiment was conducted in various outdoor conditions. On average, the electrical efficiency was measured 8.7 to 9.9% higher than the simple PV system. Maximum overall efficiency was recorded as 76%. The experiment also figured out due to low irradiation in cloudy weather the output power drops significantly.

Modelling and performance analysis of improved incremental conductance MPPT technique for water pumping system

Utilization of Solar Photo Voltaic Cell (SPVC) has significantly increased in residential, commercial, and industrial due to freely available. From the power-voltage (P–V) curve, fluctuation has been revealed over an hour because of varying irradiance, and temperature of sun rays. Maximum power point tracking (MPPT) has been essential to track power and maximize the output from SPVC constantly. In this study, a novel Improved Incremental Conductance (IIC) MPPT technique is proposed, and integrated with a boost converter to take out the maximum power from SPVC. The output power has been provided to a brushless DC motor (BLDC). Pulse Width Modulation (PWM) commutation system has been employed to control the BLDC motor through the 3-level bridge converter. The proposed study has been implemented in MATLAB/Simulink. The complete analysis has been examined at different irradiation conditions. Power consumption, loss, and efficiency have been estimated. Simulation results have been compared with conventional incremental conductance (IC). The proposed IIC MPPT has an improved 5% of tracking efficiency than conventional MPPT techniques. The results show that the effectiveness of the proposed IIC technique has attained a steady state in varying irradiation levels. In spite of the low irradiation level of 250 W/m2, the system remains effective.

An adjustable machine learning gradient boosting-based controller for PV applications

Traditional Proportional, Integral, and Derivative (PID) controllers are employed in most industrial control applications because they are simple and easy to implement. However, they are fallible and exhibit poor performance in complicated, non-linear, time-delayed systems, and noisy feedback loops. In photovoltaic systems applications such as maximum power point (MPP) tracking, PI controller performance in low irradiance levels, varying load conditions, and partial shading scenarios is considerably inefficient in tracking the corresponding reference current, which generates MPP and causes considerable steady-state error. Machine learning can be used with selective input/output data from the PI controller to build a model that has the ability to adjust in case of steady-state error. Thus, an adjustable Machine Learning Gradient Boosting-based (MLGB) controller with a PV module and a DC-DC boost converter is proposed in this work. To create the raw dataset, a PI controller was simulated, and the input/output signals were recorded. Data preprocessing, utilizing feature engineering and Shapley Additive Explanations (SHAP) values, was used to explain the dynamic behavior of each feature, the inter-feature dependability, and their significance with reference to the model output. Hyperparameters were tuned with cross-validation while the model was being created using the CatBoost method. To assess the adjustment of each feature to minimize the error, particularly for lower irradiance values, varying load conditions, and partial shading, a regression model was built. Using the MATLAB Simulink Environment, a complete system controlled by both the PI and the MLGB controller was developed and tested. A random irradiance level fluctuation profile, along with varying load and partial shading scenarios, was used to compare three controllers, namely the PI, conventional MLGB, and adjusted MLGB controllers, to see how they respond to rapidly changing environmental conditions. It has been proven through a thorough simulation analysis that the novel adjustable MLGB controller exhibits good tracking performance to follow the DC-DC boost converter's inductor current. When compared to the traditional PI controller, the new controller exhibits better results in terms of steady-state behavior and transient responsiveness in general, with a much lower mean (3.059E-03), median (1.908E-03), and RMS value (2.168E-02) of the signal error under various conditions.

Heat and electric power production using heat pumps assisted with solar thermal energy for industrial applications

Solar thermal devices with large installation areas represent technical, economic, and environmental challenges to design, construct, and operate them. Heat pump assisted with a solar thermal installation (STI) guarantee heat load supply at target temperature of a process, minimizing STI occupied area, environment impacts and associated costs. This paper deals with the design of two heat pumps, each one assisted with a STI. For designing solar network (which together the storage tank makes up the STI), lower irradiance levels than winter period are considered, guaranteeing continuous process operation. For designing each heat pump, two refrigerants with Ozone Depletion Potential of zero and Global Warming Potential of 7 (in one of them) are evaluated. This designing methodology is applied to a second-generation production of bioethanol case study. When heat is generated through a heat pump assisted with STI, the solar network absorber area is reduced by 85%, comparing with required area if only a STI produces heating. The same thing happens to generate electric power. Solar fraction value is one, 81,360 tons/year of not burned bagasse avoids 63,142 tons/year of greenhouse gases emissions. Still always guaranteeing the supply of energy to process, the impacts on environment and soil are minimized.

The response of photosynthesis to temperature and irradiance in a green alga, Ryuguphycus kuaweuweu (Ulvales) reveals adaptation to a subtidal environment in the northern Ryukyu Islands

We investigated the photosynthetic response of Ryuguphycus kuaweuweu (=Umbraulva kuaweuweu, Ulvales), a subtidal green alga from the northern Ryukyu Islands (35–40 m deep), Kagoshima, Japan, to temperature, irradiance, and light spectral limitation using a pulse amplitude modulation (PAM)-chlorophyll fluorometer and dissolved oxygen sensors. The photochemical efficiency in photosystem II (maximum quantum yield, Fv/Fm and effective quantum yield, ΔF/Fm′) response during 3-day temperature exposure (8–40 °C) revealed that Fv/Fm, measured in darkness, was not significantly affected by temperatures below 32 °C. In contrast, ΔF/Fm′ measured at 50 μmol photons m−2 s−1 was sensitive to temperature and showed an optimum temperature of 26.1 °C. The gross photosynthesis (GP rate, 8–40 °C) at 200 μmol photons m−2 s−1 showed that the maximum GP rate occurred at 29.5 °C. The photosynthesis–irradiance (P–E) responses (0–1000 μmol photons m−2 s−1) measured at 12, 20, and 28 °C showed that the net photosynthesis quickly saturated at low irradiance levels. However, as temperature increased, the irradiances required for photosynthetic saturation also increased. Additionally, the P–E curves obtained under blue (450 nm), green (525 nm), red (660 nm), and white (metal halide lamp) lights indicated that this alga exhibited higher levels of maximum photosynthetic output under blue and green lights. We further observed that during continuous 6-h exposure to irradiance levels of 100, 200, 500, and 1000 μmol photons m−2 s−1 at 20 °C, ΔF/Fm′ decreased progressively as the irradiance levels increased. At 100 μmol photons m−2 s−1 and 12, 20, and 28 °C, the ΔF/Fm′ was stable at 20 and 28 °C but declined at 12 °C, suggesting the occurrence of low temperature-light stress. In conclusion, the results suggest that R. kuaweuweu is adapted to the study site's subtidal temperature and light environments where green and blue wavelengths can effectively penetrate.

Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets

For flexible photovoltaic (PV) applications, the dominant material for the frontsheet is poly(ethylene-co-tetrafluoroethylene). As a fluoropolymer, it resists soiling by letting the water run off easily, is resistant to degradation by exposure to ultraviolet light, and is more mechanically durable than most fluoropolymers. To keep costs down, less expensive alternative polymers are desirable. In this study, highly accelerated ultraviolet light and heat stresses are applied to candidate materials, and the degradation kinetics are determined to provide information to evaluate their suitability for use in a PV application. Because of the uncertainty in service life prediction, the acceleration parameters are instead used primarily to evaluate the relevance of the applied stresses. Here, we find that the best materials are fluoropolymer based and that even when exposed to high irradiance at high temperatures, relatively little degradation is seen. For the 15 materials tested here, we found the Arrhenius activation energy for various degradation processes to be 39 ± 22 kJ/mol with a power law dependence on irradiance of 0.49 ± 0.22 with a negative correlation coefficient of −0.606 (i.e., more highly thermally activated processes are less dependent on the irradiance level). For frontside exposure, the most severe conditions used here (4 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /nm @340 nm, 70 °C, for 4000 h) were on average equal to about 11.4 y in Riyadh, Saudi Arabia when mounted with insulation on the backside. Thus, to get relevant amounts of ultraviolet exposure with unmodified commercial equipment (∼0.8 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /nm @340 nm) requires extraordinarily long exposure times, especially if conducted at lower irradiance levels.

Publisher Correction: Rapid biomechanical imaging at low irradiation level via dual line-scanning Brillouin microscopy

Publisher Correction: Rapid biomechanical imaging at low irradiation level via dual line-scanning Brillouin microscopy

Growth response to nitrate enrichment helps facilitate success of an alien Potamogeton in New Zealand streams

Motivated by stream ecosystem degradation by eutrophication, we mimicked slow flowing lowland stream conditions with a novel experimental setup to further our understanding of aquatic plant responses to increases in nitrate and light. We conducted a mesocosm growth experiment of two species from the genus Potamogeton: P. crispus (alien) and P. ochreatus (native), grown at four nitrate and four light levels. We hypothesised that (i) internal nutrient status of the plants would scale with water column nutrient concentration, and that (ii) plant performance would reflect the nutrient status of the plant. Furthermore, we hypothesised that (iii) a low irradiance level would negate the effects of an increased nitrate level. In relation to (ii) we hypothesised that (iv) the traits of the alien species would enable it to outperform the native species where both the availability of light and nutrient resources was high. Internal tissue N content was broadly similar in the two higher (>250 μg NO3− L−1) and the two lower nutrient treatments (<20 μg NO3− L−1) in both species and plants were therefore collapsed into high and low N-groups. High-N individuals had higher growth rates than low-N ones regardless of species or light treatment and plants had reduced growth rates at the lowest light treatment, however this response was less evident for P. crispus. The highest growth rate was found at the high-N individuals of P. crispus at the highest light treatment, and correspondingly, in this treatment this species exhibited an increase in branching degree and lateral spread from the low-N plants. As P. crispus spreads by fragmentation, our results show it to be a highly effective competitor in anthropogenically impacted areas compared to its native counterpart. Our study exemplifies how light can influence eutrophication responses of plants and how both need to be accounted for in management decisions.

Fault detection and diagnosis in grid-connected PV systems under irradiance variations

Nowadays, photovoltaic (PV) energy is considered as one of the most encouraging renewable energy sources. Nevertheless, the power delivered by a PV field is strongly attached to irradiance which undergoes rapid variations depending on the climatic conditions. Accordingly, it becomes extremely difficult to distinguish if it refers to faulty status in the system or healthy status under the irradiance variation (IV). Therefore, PV monitoring considering IV condition is fundamental in ensuring high reliability as well as improving power production of PV systems.In fault detection and diagnosis (FDD) field, researchers have considered the variation of irradiance (especially under low irradiance level) as faulty operating mode while others have considered it as fixed parameter during detecting faults.In this paper, therefore, firstly, the IV is introduced in the dynamic model of the grid connected PV (GCPV) system in different operating conditions. Then, an efficient and robust FDD approach based on machine learning and deep learning techniques is proposed in order to identify the healthy and faulty operating conditions.The obtained results through simulated data of a 12 kW PV module are extremely encouraging with a high accuracy under different studied cases.

Rapid biomechanical imaging at low irradiation level via dual line-scanning Brillouin microscopy.

Brillouin microscopy is a technique for mechanical characterization of biological material without contact at high three-dimensional resolution. Here, we introduce dual line-scanning Brillouin microscopy (dLSBM), which improves acquisition speed and reduces irradiation dose by more than one order of magnitude with selective illumination and single-shot analysis of hundreds of points along the incident beam axis. Using tumor spheroids, we demonstrate the ability to capture the sample response to rapid mechanical perturbations as well as the spatially resolved evolution of the mechanical properties in growing spheroids.