Search Results (42)

Large-scale integration of electric vehicle charging stations into the energy distribution network introduces highly variable power demands leading to additional voltage drops, increase in power losses, and quality degradation. Conventional mitigation strategies, including reactive power control only and multi-loop dq-axis-based controllers, often suffer from high computational complexity and limited flexibility for simultaneous active and reactive power control. This study presents a delay-modulated pulse width modulation control scheme for coordinated active and reactive power control in an energy distribution network with high penetration of electric vehicle charging load that are both time-varying and site-shifting in nature. The scheme uses a unified system comprising a solar photovoltaic array, battery storage system and a distribution STATCOM system. In this scheme, the control of active and reactive power is directly incorporated in the PWM pulse generation process by adding an adjustable delay parameter that controls the phase shift between the inverter current and the grid voltage. The proposed scheme is validated using a representative distribution feeder supplying the electric vehicle charging loads. The result illustrates that the feeder receiving end bus voltage drop is about 35% lower, the active power losses are about 40% lower, and the total harmonic distortion is at about 3%, which is within the IEEE 519 limit recommendations. Thus, the proposed control scheme is seen to be effective and computationally efficient, providing a scalable solution for real-time voltage regulation and power loss reduction. Full article

Traditional methods for preparing rare-earth-doped TiO₂ nanotubes are multi-step and often result in uneven dopant distribution, while pure TiO₂ is limited by its wide bandgap and rapid charge recombination. In this study, a one-step in situ synchronous anodization strategy is developed to fabricate gadolinium (Gd)-doped TiO₂ nanotube arrays directly on a titanium substrate. By adding gadolinium nitrate to an ethylene glycol–NH₄F electrolyte, Gd incorporation and nanotube growth are achieved simultaneously, reducing the processing steps by over 60%. The obtained Gd–TiO₂ nanotubes exhibit extended visible-light absorption with an edge beyond 500 nm and show a methylene blue degradation efficiency of 90% within 60 min, which is 50% higher than that of undoped TiO₂. Scavenger experiments reveal that ·OH radicals play the predominant role in the photocatalytic process. First-principles calculations further confirm significant bandgap narrowing from 2.89 eV to 2.46 eV after Gd doping. This work provides a simple, efficient, and scalable synthesis route for high-performance TiO₂-based photocatalysts with enhanced solar-driven activity. Full article

The inverter is the key element that converts the intermittent DC power of the PV array into a quality AC flow to the grid and simultaneously performs functions such as power factor control, reactive services, and grid code compliance. Therefore, the detailed operating profile of the inverter, how the power, dynamics, power quality, and efficiency evolve over time, is critical for both the scientific understanding of the system and the daily operation (O&M). Monitoring only aggregated energy indicators or single KPIs (e.g., PR) is often insufficient: it does not distinguish weather-related variations from technical limitations (clipping, curtailment), does not show dynamic loads (ramp rate), and does not provide confidence in the quality of the injected energy (PF, P–Q behavior). These deficiencies motivate research that simultaneously covers the physical side of the conversion, the operational dynamics, and the climatic reference of the resource. The analysis covers the window of 25 January–15 April 2025 (winter→spring). Due to the pronounced seasonality of the solar resource and temperature regime, all quantitative results and conclusions regarding efficiency, dynamics, clipping, and degradation are valid only for this window; generalizations to other seasons require additional data. In the next stage, we will add ≥12 months of data and perform a comparable seasonal analysis. Full specifications of the measuring equipment (DC/AC current/voltage, clock synchronization, separate high-frequency $PQ$-logger) and quantitative uncertainty estimates, including distribution to key indicators ($\eta$, $PR$, $THD$, $I_{DC}$), are presented. The PVGIS per-kWp climate reference is anchored to the nameplate DC peak and cross-checked against percentile scaling; $a\pm \epsilon$ scale error shifts $PR$ by $\epsilon$ and changes $\Delta E$ proportionally only on hours with $\widehat{P}>P$. The capacity for the climate reference (PVGIS per-kWp) is calibrated to the tabulated DC peak power $C_{cert}$ and is cross-validated using a percentile scale ($Q_{0.99}$). Full article

During orbital operations, spaceborne microstrip antennas are continuously exposed to solar radiation and the cold thermal sink of space, enduring extreme temperature variations. These extreme temperature variations induce significant thermal stress, which leads to deformation in spaceborne antennas, inevitably degrading their operational performance. To address this issue, an optimized design method for antenna array structure based on strain compensation is proposed in this paper. The proposed method uses the COMSOL Multiphysics 6.2 to analyze thermal-structural-electromagnetic coupling behavior of spaceborne microstrip arrays under extreme temperature conditions. The simulation quantifies the thermal-strain distribution. Accordingly, different slits are introduced in regions of high-strain concentration, effectively redistributing the strain to minimize thermal deformation. This optimized configuration maintains superior electrical performance while significantly enhancing thermal stability. Both simulation and measurement results verify the effectiveness of the proposed optimization design method. Notably, the proposed method offers a novel solution for mitigating thermal-induced performance degradation in spaceborne antenna systems without requiring active thermal control. Full article

The ATLAS lidar on NASA’s Earth-orbiting ICESat-2 satellite has operated continuously since its launch in September 2018, with no sign of degradation. Compared to previous international single-beam spaceborne lidars, which operated at a few tens of Hz, the single-photon-sensitive, six-beam ATLAS pushbroom lidar provides 60,000 surface measurements per second and has accumulated almost 3 trillion surface measurements during its six years of operation. It also features a 0.5 m² telescope aperture and a single, 5 Watt, frequency-doubled Nd:YAG laser generating a 10 KHz train of 1.5-nanosecond pulses at a green wavelength of 532 nm. The current paper investigates how, with minor modifications to the ATLAS lidar, this capability might be extended to other planets within our solar system. Crucial to this capability is the need to minimize the solar background seen by the lidar while simultaneously providing, for long time intervals (multiple months), an uninterrupted, modestly powered, multimegabit per second interplanetary laser communications link to a terminal in Earth orbit. The proposed solution is a pair of Earth and planetary satellites in high, parallel, quasi-synchronized orbits perpendicular to their host planet’s orbital planes about the Sun. High orbits significantly reduce the time intervals over which the interplanetary communications link is blocked by their host planets. Initial establishment of the interplanetary communications link is simplified during two specific time intervals per orbit when the sunlit image of the two planets are not displaced from their actual positions (“zero point ahead angle”). In this instance, sunlit planetary images and the orbiting satellite laser beacon can be displayed on the same pixelated detector array, thereby accelerating the coalignment of the two communication terminals. Various tables in the text provide insight for each of the eight planets regarding the impact of solar distance on the worst-case Signal-to-Noise Ratio (SNR), the effect of satellite orbital height on the duration of the unblocked interplanetary communications link, and the resulting planetary surface continuity and resolution in both the along-track and cross-track directions. For planets beyond Saturn, the laser power and/or transmit/receive telescope apertures required to transmit multimegabit-per-second lidar data back to Earth are major challenges given current technology. Full article

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO₂ emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024. Full article

This paper focuses on the triple-junction gallium arsenide solar array of a MEO (Medium Earth Orbit) satellite that has been in orbit for 7 years. Through a combination of theoretical and data-driven methods, it conducts research on anti-radiation design verification and life prediction. This study integrates the Long Short-Term Memory (LSTM) algorithm into the full life cycle management of MEO satellite solar arrays, providing a solution that combines theory and engineering for the design of high-reliability energy systems. Based on semiconductor physics theory, this paper establishes an output current calculation model. By combining radiation attenuation factors obtained from ground experiments, it derives the theoretical current values for the initial orbit insertion and the end of life. Aiming at the limitations of traditional physical models in addressing solar performance degradation under complex radiation environments, this paper introduces an LSTM algorithm to deeply mine the high-density current telemetry data (approximately 30 min per point) accumulated over 7 years in orbit. By comparing the prediction accuracy of LSTM with traditional models such as Recurrent Neural Network (RNN) and Feedforward Neural Network (FNN), the significant advantage of LSTM in capturing the long-term attenuation trend of solar arrays is verified. This study integrates deep learning technology into the full life cycle management of solar arrays, constructs a closed-loop verification system of “theoretical modeling–data-driven intelligent prediction”, and provides a solution for the long-life and high-reliability operation of the energy system of MEO orbit satellites. 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

Industrial growth led to the expansion of existing environmental problems, where different kinds of pollutants can enter the environment by many known routes, particularly through wastewater. Among other contaminants, pharmaceuticals, such as diazepam, once released, pose a significant challenge related to their removal from complex environmental matrices due to their persistence and potential toxicity. For this reason, it is a great challenge to find suitable methods for the treatment of wastewater. The aim of this paper was to investigate the stability of diazepam, subjecting it to various degradation processes (hydrolysis and photolysis), focusing on photocatalysis, an advanced oxidation process commonly used for the purification of industrial wastewater. The photocatalytic system consisted of UV-A and simulated solar irradiation with titanium dioxide (TiO₂) immobilized on a glass mesh as a photocatalyst, with an additional reaction performed in the presence of an oxidizing agent, i.e., hydrogen peroxide, to improve diazepam removal from water matrices. The kinetic rate of diazepam degradation was monitored with a high-performance liquid chromatograph coupled with a photodiode array detector (HPLC-PDA). The target compound was characterized as a hydrolytically and photolytically stable compound with t_1/2 = 25 h. The presence of an immobilized TiO₂ catalyst contributed significantly to the degradation of diazepam under the influence of UV-A and simulated solar radiation, with t_1/2 in the range of 1.61–2.56 h. Five degradation products of diazepam were identified at the laboratory scale by MS analysis (m/z = 267, m/z = 273, m/z = 301, m/z = 271, and m/z = 303), while the toxicity assessment revealed that diazepam exhibited developmental toxicity and a low bioaccumulation factor. The pilot-scale process resulted in significant improvements in diazepam degradation with the fastest degradation kinetics (0.6888 h⁻¹). These results obtained at the pilot scale highlight the potential for industrial-scale implementation, offering a promising and innovative solution for pharmaceutical removal from wastewater. Full article

In response to the stalling fault of the solar array drive assembly (SADA) in an in-orbit MEO satellite, an analysis and research on the energy balance algorithm are conducted. This is performed under the continuous changes in the light period, shadow period, and the incident angle of the solar panels. An output energy model of the solar panels is presented. It is proven that this model is a continuous function, and the optimal stalling angle for energy output is deduced. By simulating and calculating the energy output under different stalling angles and taking into account the on-orbit performance degradation of the solar cell array, the energy output curve within one orbital period is obtained, which provides support for the on-orbit operation and maintenance of the satellite. Moreover, on-orbit verification was carried out in the case of a stalling fault of the -Y-wing SADA of a certain MEO-orbiting satellite. Full article

The performance analysis of a 10 kW_p rooftop grid connected solar photovoltaic (PV) system located in Sagolband, Imphal, India has been studied for 5 years. The key technical parameters such as array yield ($Y_A$), reference yield ($Y_R$), final yield ($Y_F$), capacity utilization factor (CUF), PV system efficiency (${\eta }_{Sys}$), and performance ratio (PR) were used to investigate its performance. In this study, the experimentally measured results of the system’s performance for the five years (i.e., July 2018 to June 2023) were compared with the predicted results, which were obtained using PVsyst V7.3.0 software. The measured energy generation in 5 years (including 40 days OFF due to inverter failure on 17 June 2019 because of a surge, which was resolved on 27 July 2019) was 58,911.3 kWh as compared to the predicted 77,769 kWh. The measured daily average energy yield was 3.2 kWh/kW_p as compared to the predicted 4.2 kWh/kW_p. It can be seen that there was a large difference between the real and predicted values, which may be due to inverter downtime, local environmental variables (e.g., lower-than-expected solar irradiation and temperature impacts), and the possible degradation of photovoltaic modules over time. The measured daily average PR of the system was 70.71%, and the maximum occurred in the months of October, November, December, and January, which was almost similar to the predicted result. The measured daily average CUF of the system was 13.36%, and the maximum occurred in the months of March, April, and May. The measured daily average system efficiency was 11.31%. Moreover, the actual payback was 4 years and 10 months, indicating strong financial viability despite the system’s estimated lifespan of 25 years. This study highlights the importance of regular maintenance, fault detection, and better predictive modelling for more accurate energy projections, and also offers an understanding of real-world performance fluctuations. Full article

Solar photovoltaic (SPV) arrays play a pivotal role in advancing clean and sustainable energy systems, with a worldwide total installed capacity of 1.6 terawatts and annual investments reaching USD 480 billion in 2023. However, climate disaster effects, particularly extremely hot weather events, can compromise the performance and resilience of SPV panels through thermal deterioration and degradation, which may lead to lessened operational life and potential failure. These heatwave-related consequences highlight the need for timely inspection and precise anomaly diagnosis of SPV panels to ensure optimal energy production. This case study focuses on intelligent remote inspection by employing aerial radiometric infrared thermography within a predictive maintenance framework to enhance diagnostic monitoring and early scrutiny capabilities for SPV power plant sites. The proposed methodology leverages pre-trained deep learning (DL) algorithms, enabling a deep transfer learning approach, to test the effectiveness of multiclass classification (or diagnosis) of various thermal anomalies of the SPV panel. This case study adopted a highly imbalanced 6-class thermographic radiometric dataset (floating-point temperature numerical values in degrees Celsius) for training and validating the pre-trained DL predictive classification models and comparing them with a customized convolutional neural network (CNN) ensembled model. The performance metrics demonstrate that among selected pre-trained DL models, the MobileNetV2 exhibits the highest F1 score (0.998) and accuracy (0.998), followed by InceptionV3 and VGG16, which recorded an F1 score of 0.997 and an accuracy of 0.998 in performing the smart inspection of 6-class thermal anomalies, whereas the customized CNN ensembled model achieved both a perfect F1 score (1.000) and accuracy (1.000). Furthermore, to create trust in the intelligent inspection system, we investigated the pre-trained DL predictive classification models using perceptive explainability to display the most discriminative data features, and mathematical-structure-based interpretability to portray multiclass feature clustering. Full article

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. Full article

Photodetectors have a wide range of applications across various fields. Self-powered photodetectors that do not require external energy have garnered significant attention. The photoelectrochemical type of photodetector is a self-powered device that is both simple to fabricate and offers high performance. However, developing photoelectrochemical photodetectors with superior quality and performance remains a significant challenge. The electrolyte, which is a key component in these detectors, must maintain extensive contact with the semiconductor without degrading its material quality and efficiently catalyze the redox reactions of photogenerated electrons and holes, while also facilitating rapid charge carrier transport. In this study, $\alpha$-Ga₂O₃ nanorod arrays were synthesized via a cost-effective hydrothermal method to achieve a self-powered solar-blind photodetector. The impacts of different electrolytes—Na₂SO₄, NaOH, and Na₂CO₃—on the photodetector was investigated. Ultimately, a self-powered photodetector with Na₂SO₄ as the electrolyte demonstrated a stable photoresponse, with the maximum responsivity of 0.2 mA/W at 262 nm with the light intensity of 3.0 mW/cm², and it exhibited rise and decay times of 0.16 s and 0.10 s, respectively. The $\alpha$-Ga₂O₃ nanorod arrays and Na₂SO₄ electrolyte provided a rapid pathway for the transport of photogenerated carriers and the built-in electric field at the semiconductor–liquid heterojunction interface, which was largely responsible for the effective separation of photogenerated electron–hole pairs that provided the outstanding performance of our photodetector. Full article

Currently, fault identification in most photovoltaic systems primarily relies on experienced engineers conducting on-site tests or interpreting data. However, due to limited human resources, it is challenging to meet the vast demands of the solar photovoltaic market. Therefore, we propose to identify fault types through the current–voltage curves of solar arrays, obtaining curves for various conditions (normal, aging faults, shading faults, degradation faults due to potential differences, short-circuit faults, hot-spot faults, and crack faults) as training data for the model. We employ a multi-input model architecture that combines convolutional neural networks with deep neural networks, allowing both the imagery and feature values of the current–voltage curves to be used as input data for fault identification. This study demonstrates that by inputting the current–voltage curves, irradiance, and module specifications of solar string arrays into the trained model, faults can be identified quickly using actual field data. Full article