Search Results (57)

Bird-dropping fouling and hotspot anomalies remain the most prevalent and detrimental defects in utility-scale photovoltaic (PV) plants; their co-occurrence on a single module markedly curbs energy yield and accelerates irreversible cell degradation. However, markedly disparate visual–thermal signatures of the two phenomena impede high-fidelity concurrent detection in existing robotic inspection systems, while stringent onboard compute budgets also preclude the adoption of bulky detectors. To resolve this accuracy–efficiency trade-off for dual-defect detection, we present YOLOv8-SG, a lightweight yet powerful framework engineered for mobile PV inspectors. First, a rigorously curated multi-modal dataset—RGB for stains and long-wave infrared for hotspots—is assembled to enforce robust cross-domain representation learning. Second, the HSV color space is leveraged to disentangle chromatic and luminance cues, thereby stabilizing appearance variations across sensors. Third, a single-head self-attention (SHSA) block is embedded in the backbone to harvest long-range dependencies at negligible parameter cost, while a global context (GC) module is grafted onto the detection head to amplify fine-grained semantic cues. Finally, an auxiliary bounding box refinement term is appended to the loss to hasten convergence and tighten localization. Extensive field experiments demonstrate that YOLOv8-SG attains 86.8% mAP@0.5, surpassing the vanilla YOLOv8 by 2.7 pp while trimming 12.6% of parameters (18.8 MB). Grad-CAM saliency maps corroborate that the model’s attention consistently coincides with defect regions, underscoring its interpretability. The proposed method, therefore, furnishes PV operators with a practical low-latency solution for concurrent bird-dropping and hotspot surveillance. Full article

Conjugated polymers (CPs) offer many attractive features for photodiodes and photovoltaics, including solution processability, ease of scale-up, light weight, low cost, and mechanical flexibility. CPs have a wide range of energy gaps; thus, the choice of the specific polymer determines the optimum operational wavelength range. However, there are relatively few CPs with a strong absorption in the blue region of the spectrum where the human eye is most sensitive (440 to 470 nm) and none with an energy gap at 2.75 eV (450 nm), which corresponds to the peak of the CIE-1931 z(λ) color-matching function and the dominant blue light emission wavelength in computer and smartphone displays. Blue-light detectors in this wavelength range are important for light hazard control, sky polarization studies, and for blue-light information devices, where 450 nm corresponds to the principal emission of GaN-based light sources. We report on a new CP called Atums Green (AG), which shows promising characteristics as a blue-light photodetection polymer optimized for exactly this range of wavelengths centered around 450 nm. We built and measured a simple photodetector made from spin-coated films of AG and showed that its photosensitivity can be improved by the addition of asphaltene, a low-cost carbonaceous waste product. Full article

The performance degradation of photovoltaic (PV) systems, comprising solar panels and DC-DC converters, is often caused by various anomalies related to manufacturing defects, operational conditions, or environmental factors. These faults significantly reduce energy output, preventing the system from reaching its nominal power and expected production levels. Given the demonstrated impact of such faults on PV system efficiency, an effective diagnostic method is essential for proactive maintenance and optimal performance. This paper presents a fault detection algorithm based on a Mamdani-type fuzzy logic approach. The proposed method utilizes three key inputs—panel current, panel voltage, and converter voltage—to assess system health. By computing the distortion ratios of these electrical parameters and processing them through a fuzzy logic controller, the algorithm accurately identifies fault conditions. Simulation results validate the effectiveness of this approach, demonstrating its capability to detect and classify 12 distinct faults in both the PV array and the DC-DC converter. The study highlights the potential of fuzzy logic-based diagnostics in enhancing the reliability and maintenance of photovoltaic systems. Full article

Multi-messenger astronomy and time-domain astronomy are strongly linked even if they do not have the same objectives. Multi-messenger astronomy is an astrophysical observation approach born by the simultaneous, even if casual, detection of a few events discovered up to now. In contrast, time-domain astronomy is a recent technological trend that aims to make observations to explore the sky not with imaging, astrometry, photometry or spectroscopy but through the fast dynamic behavior of celestial objects. Time-domain astronomy aims to detect events on a temporal scale between seconds and nanoseconds. In this paper, a time-domain infrared fast detector for ground-based telescopes is proposed. This instrument can be useful for multi-messenger observations, and it is able to detect fast astronomical signals in the order of 1 ns. It is based on HgCdTe photoconductors, but the InAsSb photovoltaic detector has also been tested. The detection system designed to detect fast mid-infrared bursts includes trigger modules, an off-line noise-canceling strategy, and a classifier of the transients. Classification is derived from the analysis of fast instabilities in particle circular accelerators. This paper aims to be a preliminary feasibility study. Full article

Low-dimensional organic–inorganic hybrid metal halides (OIMHs) have garnered significant research attention due to their remarkable optical, electrical, and mechanical properties. These materials feature tunable optoelectronic characteristics, high photovoltaic efficiency, exceptional scalability and processability and ease of fabrication. By selecting appropriate organic and inorganic components, it is possible to achieve molecular-level dimensional control of the metal halides. Here, this review provides an in-depth analysis of the structure and synthesis methods of OIMHs materials, explores their optical properties, and summarizes their current applications in areas such as white-light LEDs, X-ray detectors, sensors, and solar cells. Finally, we also discuss the challenges faced by these materials and offer a perspective on their future development, aiming to serve as a reference for advancing research in OIMHs. Full article

The Cryo-PoF project is an R&D project funded by the Italian Insitute for Nuclear Research (INFN) in Milano-Bicocca (Italy). The technology at the basis of the project is Power over Fiber (PoF). By sending laser light through an optical fiber, this technology delivers electrical power to a photovoltaic power converter, in order to power sensors or electrical devices. Among the several advantages this solution can provide, we can underline the spark-free operation when electric fields are present, the removal of noise induced by power lines, the absence of interference with electromagnetic fields, and robustness in hostile environments. R&D for the application of PoF in cryogenic environments started at Fermilab in 2020; for the DUNE Vertical Drift detector, it was needed to operate the Photon Detector System on a high-voltage cathode surface. Cryo-PoF, starting from this project, developed a single-laser input line system to power, at cryogenic temperatures, both an electronic amplifier and Photon Detection devices, tuning their bias by means of the input laser power, without adding ancillary fibers. The results obtained in Milano-Bicocca will be discussed, presenting the tests performed using power photosensors at liquid nitrogen temperature. Full article

Integrating intelligent grids with the internet increases the amount of unauthorized input data which directly or indirectly influences electrical system control and decision-making. Photovoltaic (PV) farms that are linked to the power grid are susceptible to cyber attacks which may disrupt energy infrastructure and compromise the security, stability, and resilience of the electrical system. This research proposes a new model for cyber threat detection in PV farm, named as Cyber Detection in PV farm (CDPV), which makes use of deep learning methods based solely on point-of-common coupling (PCC) detectors. In this paper, a thorough cyber attack model for a photovoltaic (PV) farm is developed, where the simulation of four kinds of cyber attacks is provided. Furthermore, this paper evaluates the role of three deep learning techniques including convolutional neural network (CNN), artificial neural network (ANN), and long short-term memory (LSTM), in PV cyber threat detection. The findings demonstrate that, at the DC/DC converter and DC/AC inverter sides, the proposed CDPV model based on deep learning techniques (CNN, ANN, and LSTM) can improve the cyber detection accuracy and resilience under various attack scenarios. Full article

The combination of ZnO with narrow bandgap materials such as CuO is now a common method to synthesize high-performance optoelectronic devices. This study focuses on optimizing the performance of p-CuO/n-ZnO heterojunction pyroelectric photodetectors, fabricated through magnetron sputtering, by leveraging the pyro-phototronic effect. The devices’ photoresponse to UV (365 nm) and visible (405 nm) lasers is thoroughly examined. The results show that when the device performance is regulated by adjusting the three parameters—sputtering power, sputtering time, and sputtering oxygen–argon ratio—the optimal sputtering parameters should be as follows: sputtering power of 120 W, sputtering time of 15 min, and sputtering oxygen–argon ratio of 1:3. With the optimal sputtering parameters, the maximum responsivity of the pyroelectric effect and the traditional photovoltaic effect ${\mathrm{R}}_{\mathrm{pyro}+\mathrm{photo}}$ of the detector is 4.7 times that under the basic parameters, and the maximum responsivity of the traditional photovoltaic effect ${\mathrm{R}}_{\mathrm{photo}}$ is also 5.9 times that under the basic parameters. This study not only showcases the extensive potential of the pyro-phototronic effect in enhancing heterojunction photodetectors for high-performance photodetection but also provides some ideas for fabricating high-performance photodetectors. Full article

Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture. To address the low detection accuracy for Crack and Star crack defects and the imbalanced dataset, a novel data augmentation method, the Linear Feature Augmentation (LFA) module, specifically designed for linear features, is introduced. LFA effectively improves model training performance and robustness. Furthermore, the Efficient Feature Enhancement Module (EFEM) is presented to enhance the receptive field, suppress redundant information, and emphasize meaningful features. To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. A Multi-Scale Multi-Feature Pyramid Network (MMFPN) is employed to selectively aggregate boundary and category information, thereby improving the accuracy of multi-scale target recognition. Experimental results on a large-scale photovoltaic panel dataset demonstrate that the LEM-Detector achieves a detection accuracy of 94.7% for multi-scale defects, outperforming several state-of-the-art methods. This approach effectively addresses the challenges of photovoltaic panel defect detection, paving the way for more reliable and accurate defect identification systems. This research will contribute to the automatic detection of surface defects in industrial production, ultimately enhancing production efficiency. Full article

With the progression of mankind and the development of technology, great strides have been made regarding the application of inorganic crystalline materials in a number of fields such as high-energy and nuclear physics, environmental and safety inspection, the optoelectronics and communication fields, energy, and aerospace engineering, particularly the industrialization of photovoltaic and detector materials, which has brought mankind’s knowledge of natural disciplines to an all-time high [...] Full article

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions. Full article

Although perovskite has great potential in optoelectronic devices, the simultaneous satisfaction of material stability and high performance is still an issue that needs to be solved. Most perovskite optoelectronic devices use quantum dot spin coating or the gas-phase growth of perovskite thin films as the photoelectric conversion layer. Due to stability limitations, these materials often experience a significant decrease in photoelectric conversion efficiency when encountering liquid reagents. The self-assembled growth of hybrid perovskite crystals determines superior lattice ordering and stability. There are three types of ionic liquids—[Emim]BF4, EMIMNTF2, and HMITFSI—that can effectively enhance the X-ray photoelectric conversion performance of hybrid perovskite crystal CH₃NH₃PbI₃ (MAPbI₃), and the enhancement in the photocurrent leads to an improvement in the sensitivity of X-ray detectors. We soak the perovskite crystals in an ionic liquid and perform two treatment methods: electrification and dilution with ETOH solution. It is interesting to find that MAPbI₃ perovskite single crystal materials choose the same optimized ionic liquid species in X-ray detection and photovoltaic power generation applications, and the effect is quite the opposite. Compared with untreated MAPbI₃ crystals, the average photocurrent density of Electrify-HMITFSI MAPbI₃ increased by 826.85% under X-ray excitation and the sensitivity of X-ray detectors made from these treated MAPbI₃ crystals significantly increased by 72.6%, but the intensity of the PL spectrum decreased to 90% of the untreated intensity. Full article

Conventional sandwich structure photoelectrochemical UV detectors cannot detect UV light below 300 nm due to UV filtering problems. In this work, we propose to place the electron collector inside the active material, thus avoiding the effect of electrodes on light absorption. We obtained a TiO₂-nanotubes@Ti@quartz photoanode structure by precise treatment of a commercial Ti mesh by anodic oxidation. The structure can absorb any light in the near-UV band and has superior stability to other metal electrodes. The final encapsulated photoelectrochemical UV detectors exhibit good switching characteristics with a response time below 100 ms. The mechanism of the oxidation conditions on the photovoltaic performance of the device was investigated by the electrochemical impedance method, and we obtained the optimal synthesis conditions. Response tests under continuous spectroscopy confirm that the response range of the device is extended from 300–400 nm to 240–400 nm. This idea of a built-in collector is an effective way to extend the response range of a photoelectrochemical detector. Full article

Epitaxy is the process of crystallization of monocrystalline layers and nanostructures on a crystalline substrate. It allows for the crystallization of various semiconductor layers on a finite quantity of semiconductor substrates, like GaAs, InP, GaP, InGaP, GaP, and many others. The growth of epitaxial heterostructures is very complicated and requires special conditions and the precise control of the growth temperature, the pressure in the reactor, and the flow of the precursors. It is used to grow epitaxial structures in lasers, diodes, detectors, photovoltaic structures, and so on. Semiconductors themselves are not suitable materials for application in surface-enhanced Raman spectroscopy (SERS) due to poor plasmonic properties in the UV/VIS range caused by missing free electrons in the conduction band due to the existing band gap. A plasmonic material is added on top of the nanostructured pattern, allowing for the formation of mixed photon–plasmon modes called localized surface plasmon-polaritons which stand behind the SERS effect. Typically, gold and silver are used as functional plasmonic layers. Such materials could be deposited via chemical or physical process. Attention has also been devoted to other plasmonic materials, like ones based on the nitrides of metals. The SERS performance of a functional surface depends both on the response of the plasmonic material and the morphology of the underlying semiconductor epitaxial layer. In the context of SERS, epitaxial growth allows for the fabrication of substrates with well-defined 3D nanostructures and enhanced electromagnetic properties. In this work, we described the possible potential plasmonic modification, composed of various coatings such as noble metals, TiN, and others, of well-developed epitaxial nanostructures for the construction of a new type of highly active SERS platforms. This abstract also highlights the role of epitaxial growth in advancing SERS, focusing on its principles, methods, and impact. Furthermore, this work outlines the potential of epitaxial growth to push the boundaries of SERS. The ability to design substrates with tailored plasmonic properties opens avenues for ultralow concentration detection. Full article

Infrared detectors have broad application prospects in the fields of detection and communication. Using ideal materials and good device structure is crucial for achieving high-performance infrared detectors. Here, we utilized black phosphorus (BP) and single-walled carbon nanotube (SWCNT) films to construct a vertical van der Waals heterostructure, resulting in high-performance photovoltaic infrared detectors. In the device, a strong built-in electric field was formed in the heterojunction with a favored energy-band matching between the BP and the SWCNT, which caused a good photovoltaic effect. The fabricated devices exhibited a diode-like rectification behavior in the dark, which had a high rectification ratio up to a magnitude of 10⁴ and a low ideal factor of 1.4. Under 1550 nm wavelength illumination, the 2D BP/SWCNT film photodetector demonstrated an open-circuit voltage of 0.34 V, a large external power conversion efficiency (η) of 7.5% and a high specific detectivity (D*) of 3.1 × 10⁹ Jones. This external η was the highest among those for the photovoltaic devices fabricated with the SWCNTs or the heterostructures based on 2D materials and the obtained D* was also higher than those for most of the infrared detectors based on 2D materials or carbon materials. This work showcases the application potential of BP and SWCNTs in the detection field. Full article