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This paper presents the design and the non-linearity optimization of a new vertical non-contact angle sensor based on the electromagnetic induction principle. The proposed sensor consists of a stator part (with one solenoidal excitation coil and three sinusoidal receiver coils) and a rotor part (with six rectangular metal sheets). The receiver coil was designed based on the differential principle, which eliminates the effect of the excitation coil on the induced voltage of the receiver coil, and essentially decouples the excitation field from the eddy current field. Moreover, the induced voltages in the three receiver coils are three-phase sinusoidal signals with a phase difference of 10°, which are linearized by CLARK transformation. To minimize the sensor non-linearity, the Plackett–Burman technique was used, which identified the stator radius and the rotor blade thickness as the key factors affecting the sensor linearity. Then, the particle swarm algorithm with decreasing inertia weights was utilized to optimize the sensor linearity. A sensor prototype was made and tested in the laboratory, where the experimental results showed that the sensor non-linearity was only 0.648% and 0.645% in the clockwise and counterclockwise directions, respectively. Notably, the non-linearity of the sensor was less than −0.696% at different speeds. [ABSTRACT FROM AUTHOR]

Energy sources are crucial for the development and growth of economies and civilizations. Solar energy is an alternative energy to generate electrical power. The challenges of solar photovoltaic panels (PV) are the low output power and efficiency and the huge installation area beside PVs need a tracking system for better efficiency. The motivation of this paper is to design an innovative solar sphere system, which is a new concentrated photovoltaic technology that has better performance (efficiency and output power) than the normal conventional solar panel (PV) with a smaller installation area and without any tracking system. This design consists of an acrylic solar sphere entirely filled with cooking oil (sunflower or corn oil) that captures solar radiation and concentrates it on a focal point. The focal point is adjusted over a multi-junction cell that acts as a collector device (concentrator solar cell). This focused solar energy can generate a massive amount of power, which is used to produce more electricity than normal photovoltaic panels. The experiments were carried out in order to discover the best acrylic models or shape designs, which is the sphere, the best materials or media in the sphere, that is oil, the best sphere's size and volume, and that is larger, the best sphere thickness, which at first is lower, the best fluid oil type, which is cooking oil, and finally the best fluid amount or volume inside the sphere, and this is the entire volume. Then, these factors mentioned above are compared with normal photovoltaics (PV) that have the same section area as these shapes. The results revealed that these factors have significant effects on the output power value and efficiency. It has been demonstrated that our innovative concentrated solar sphere system can produce nearly four times the output power or electricity greater than that of a conventional solar panel PV with the same cross-sectional area. This specific sort of compression is crucial because it shows that less space is required to establish this system than it would to install conventional solar panels. The performance of the system per unit of the square area it occupies was compared to the latest generation of flat panel PV available at the market performance; hence, the installation space will be decreased by 40% to 60%. Our system has about twice as much efficiency as solar PV and does not require a tracking system and maintenance. Our technology also has the benefit of not being impacted by extreme temperatures, clouds, dust, and humidity. [ABSTRACT FROM AUTHOR]

Self‐powered, light wavelength distinguishable photodetectors (PDs) are appealing components to build a robust, secure, and low energy consumption underwater wireless sensor network (UWSN). However, achieving such devices is extremely difficult even today. In this context, the first self‐powered, light wavelength distinguishable PDs with photoelectrochemical (PEC) principles and using tunnel junction (TJ) to overcome the technical hurdles for self‐powered, light wavelength distinguishable PEC‐PDs with p–n junction working electrode is reported. For such devices, a single photoelectrode is used, that is, one photoelectrode is able to distinguish different light wavelengths without using any external electrical power, and they are able to distinguish light wavelengths in both the ultraviolet (UV) and blue wavelength ranges. High responsivities reaching mA/W range and ultrafast response time with less than 10 ms are achieved in self‐powered operation mode. Moreover, such devices are able to operate not only in acidic but also in NaCl electrolyte, making them potentially attractive for applications in ocean environment. In the end, it is demonstrated that leveraging such PEC‐PDs, excellent data security can be achieved in the data communication mimicking that in an UWSN in ocean environment. This study not only represents a breakthrough in PDs, but also significantly advances the development of UWSNs, especially for ocean environment. [ABSTRACT FROM AUTHOR]

Power electronics, a pivotal field orchestrating electrical energy flow in the modern world, deals with efficient conversion, control, and management of electrical power across diverse applications. While its scope encompasses circuits for energy conversion, its challenges include transporting electrical energy over extended distances. Focusing on electrical‐to‐electrical conversions, the goal is minimal loss for delivering maximum power. The article explores the intricacies of power electronics, presenting key equations, and concepts. The current global power electronics market witnesses growth, driven by demand for energy‐efficient technologies, renewables integration, and the rise of electric vehicles. Future trends indicate continued growth, driven by renewable energy systems, and electric vehicles. Wide bandgap semiconductors, play a crucial role, with ultra‐wide bandgap semiconductors like diamond emerging as potential disruptors. A comparative analysis of semiconductor properties reveals diamond's unique attributes. Despite its challenges, diamond shows promise for power electronic applications, with ongoing research on components like Schottky diodes. Thermal considerations, substrate limitations, and dislocation challenges are discussed, emphasizing the need for advancements to harness the full potential of diamond in power electronics. Finally, some inputs about the importance of overcoming these challenges for the successful integration of diamond in power electronic systems are given. [ABSTRACT FROM AUTHOR]

Photovoltaic cells are intricate systems that transform solar energy into electrical power. Certain internal parameters, such as diode saturation current, conversion resistance, and series resistance, significantly influence the performance of electrical components. Frequently, manufacturers do not provide these parameters. At the moment, researchers need new and clear ways to measure these factors so they can get a better idea of how solar cells work and improve efficiency through simulations. We present a novel approach to accurately determining the five parameters (series resistance, shunt resistance, photovoltaic cell current, and diode saturation current) for multi-crystalline silicon solar cell models. This approach employs the Lambert function and the curve of parasitic resistance. By utilizing the extracted internal electrical parameters, this method will enhance the efficiency of solar cells through the facilitation of more accurate simulations. [ABSTRACT FROM AUTHOR]

Along with power transmission lines' efficiency, another crucial factor in electrical power transmission networks is reliability, which guarantees power transmission stability. One of the crucial and essential tasks for maintaining the continuity and stability of power transmission in transmission networks Capacity without any significant failures is identifying errors and malfunctions in power transmission lines as soon as possible. The goal of this article is to develop and apply ANN technology to overcome the obstacles faced by the electrical power transmission network. In order for the ANN to learn useful patterns and features from raw current measurements, pre-processing and feature extraction techniques are used during the training process. Real-time applications can benefit from the ANN's architecture, which is optimized for high accuracy, quick response times, and scalability. To validate the performance of the ANN-based fault detection system, extensive simulations are conducted using data from different transmission line scenarios, including various fault types that short-circuit. The results demonstrate the capability of the ANN model to accurately detect and classify faults, as well as disconnect the power grid after detect any fault. The results showed the accuracy and high speed of the proposed method using a neural network compared to traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, structural, electronic properties, optical spectra, stability, as well as the quantum efficiency of bulk, monolayer and two different stacking heterostructure configurations (XY2/XY2) of dichalcogenide compounds XY2 (where X = Mo or W and Y = S or Se) were performed using density functional technique. Our findings reveal the strong chemical bonds and high interlayer banding energy for MoSe2/WSe2 heterostructure of configuration-2, which confirms its stability and its feasibility for synthesizing. Besides, the elastic stiffness coefficients demonstrate all studied heterostructures are mechanically stable at ambient pressure, this means that the studied heterostructures are harder to separate their components, making them more durable and less reactive under certain conditions, especially MoSe2/WSe2 heterostructure-configuration 2. Otherwise, the electronic band structure highlights an indirect semiconductor behavior for bulk XY2. In contrast, a strong ionic bonding between the chalcogen and metal atoms leads to the electrons confinement in the direction perpendicular to the plane revealing direct semiconductor behavior for monolayers XY2 at Γ point. Besides, after combining two monolayers with different work functions the holes and electrons are accumulated in different layers, therefore indirect excitons have been observed for XY2/XY2 heterostructures (Γ → K). Furthermore, due to the presence of the type-II band alignment, the optical absorbance spectra proves that the absorbance is enhanced along both x and z directions for all investigated heterostructures. In addition, the MoSe2/WSe2 heterostructure of configuration-2, has about 65% of photons converted into usable electrical power, while 35% of photons are lost as heat and/or reflected. Finely, its high stability, its suitable forbidden band, its high visible light absorption, as well as its high quantum efficiency make the 2D Van der Waals MoSe2/WSe2 heterostructure-configuration-2 as new potential candidate for upcoming nano-electronic, photovoltaic and optoelectronic devices. [ABSTRACT FROM AUTHOR]

Foreign objects identification in the distribution network is an important link in the security of electric power, and is of great significance to the normal transportation of electric power. At present, a lot of equipment in the distribution network is in the open air environment, facing a large number of foreign interference. These foreign objects not only bring potential safety hazards to the distribution network, but also easily lead to short circuit, causing power supply difficulties within the region. Therefore, the research first constructs an optimized triplet feature learning model. On this basis, the HOG-SVM depth feature recognition model is proposed. In HOG-SVM, AM is introduced to improve recognition accuracy. In addition, the research enhances the night vision ability of the model by standardizing the features in the image region block. The results show that the AP of the model is stable at more than 90.54%, the average FPR is 2.21%, and the average FNR is 3.17%. The performance of HOG-SVM is significantly better than that of traditional SVM. It verifies the contribution of this research in the field of foreign object recognition and application value in ensuring the security of distribution network. [ABSTRACT FROM AUTHOR]

The application of nonlinear vibration absorbers in a variety of dynamic systems is attracting significant attention. The so-called nonlinear energy sink (NES) has the ability to provide high vibration attenuation in a broad frequency with robustness. However, the effectiveness of this device can be limited with the increase of excitation, as it may induce high amplitude (i.e., detach zone) oscillation and leading to sudden failure. To address these limitations, this work proposes an investigation into an electromagnetic-enhanced nonlinear energy sink (NES-EH) aiming to combine hybrid vibration control and energy harvesting. The device comprises the conventional NES, with a cubic stiffness spring, coupled with a magnet-coil assembly. The electro-magneto-mechanical coupling provides nonlinear damping dynamics allowing mitigation of the detached zone and an efficient conversion of the absorbed energy into electrical power. Two different assemblies, varying the relative static position of the magnet coil, are considered to explore alternative nonlinear damping models. The harmonic balance method is used to build analytical solutions to characterize the dynamics of coupled systems. After a numerical validation of the analytical approach, a complete characterization of the system dynamics is performed regarding frequency-response and slow invariant manifold (SIM) curves. Parametric analysis and optimization procedure are employed to design the NES-EH parameters. The performance of the designed absorber is compared with the classical NES solution, and their robustness is also investigated. [ABSTRACT FROM AUTHOR]

A major incident occurred at the Fukushima Daiichi Nuclear Power Station following the tsunami triggered by the Tohoku-Pacific Ocean Earthquake in March 2011, whereby seawater entered the torus room in the basement of the reactor building. Here, we identify and analyze the bacterial communities in the torus room water and several environmental samples. Samples of the torus room water (1 × 109 Bq137Cs/L) were collected by the Tokyo Electric Power Company Holdings from two sampling points between 30 cm and 1 m from the bottom of the room (TW1) and the bottom layer (TW2). A structural analysis of the bacterial communities based on 16S rRNA amplicon sequencing revealed that the predominant bacterial genera in TW1 and TW2 were similar. TW1 primarily contained the genus Limnobacter, a thiosulfate-oxidizing bacterium. γ-Irradiation tests on Limnobacter thiooxidans, the most closely related phylogenetically found in TW1, indicated that its radiation resistance was similar to ordinary bacteria. TW2 predominantly contained the genus Brevirhabdus, a manganeseoxidizing bacterium. Although bacterial diversity in the torus room water was lower than seawater near Fukushima, ~70% of identified genera were associated with metal corrosion. Latent environment allocation--an analytical technique that estimates habitat distributions and co-detection analyses--revealed that the microbial communities in the torus room water originated from a distinct blend of natural marine microbial and artificial bacterial communities typical of biofilms, sludge, and wastewater. Understanding the specific bacteria linked to metal corrosion in damaged plants is important for advancing decommissioning efforts. IMPORTANCE In the context of nuclear power station decommissioning, the proliferation of microorganisms within the reactor and piping systems constitutes a formidable challenge. Therefore, the identification of microbial communities in such environments is of paramount importance. In the aftermath of the Fukushima Daiichi Nuclear Power Station accident, microbial community analysis was conducted on environmental samples collected mainly outside the site. However, analyses using samples from on-site areas, including adjacent soil and seawater, were not performed. This study represents the first comprehensive analysis of microbial communities, utilizing meta 16S amplicon sequencing, with a focus on environmental samples collected from the radioactive element-containing water in the torus room, including the surrounding environments. Some of the identified microbial genera are shared with those previously identified in spent nuclear fuel pools in countries such as France and Brazil. Moreover, our discussion in this paper elucidates the correlation of many of these bacteria with metal corrosion. [ABSTRACT FROM AUTHOR]

This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions. [ABSTRACT FROM AUTHOR]

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption. [ABSTRACT FROM AUTHOR]

The Economic Load Dispatch (ELD) problem is an essential aspect of power system planning and operational scheduling. Different techniques and algorithms have been recommended to solve it, aiming to minimize the cost of power generation with satisfying the load requirements. In this paper, a new algorithm called Electric Fish Optimization (EFO) is used to solve the ELD problem by considering the line losses, ramp rate limits, maximum and minimum capacities of the generators and prohibited operating zones (POZ). The algorithm has been utilized in test systems consisting of 6 and 15 units and its outcomes have been compared to those from previous research studies. The proposed algorithm has been shown to achieve minimum cost, indicating its superiority and effectiveness in addressing power system planning challenges. It is evident that the presented algorithm offers a valuable solution for optimizing ELD problems. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is of utmost importance, especially in the context of addressing climate change. However, while numerous studies have explored the role of renewable energy in achieving carbon neutrality, there is a noticeable gap in the literature regarding how uncertainties affect the impact of renewable energy on electric power CO2. To bridge this gap, the present study employs recently developed nonparametric techniques, namely multivariate quantile-on-quantile regression (MQQR) and time-varying quantile causality (TVQC), to investigate the relationship between renewable energy and electric power CO2 in the presence of uncertainties. The study utilizes monthly data spanning from January 1988 to May 2023. The bivariate results reveal that renewable energy and low uncertainties contribute to improving ecological quality by reducing electric power CO2. Furthermore, the multivariate quantile-on-quantile regression results highlight the substantial influence of low uncertainties on the impact of renewable energy in lowering electric power CO2. Additionally, the TVQC analysis demonstrates that both renewable energy and uncertainties possess predictive power regarding electric power sector CO2. The study discoveries showcase that the influence of renewable energy on electric power CO2 is subject to external moderation. The suggested policy framework in this study is structured to assist the United States in accomplishing the goals outlined in SDG 7. [ABSTRACT FROM AUTHOR]

When a medium–low-speed (MLS) maglev train is braking, part of its regenerative braking (RB) power consumption may cause a significant rise in the positive rail (PR) voltage. For RB energy re-utilization, an RB energy feedback system (RBEFS) is a promising application, but there is still no specific research in the field of MLS maglev trains. From this perspective, this article focuses on identifying the PR voltage rise behavior and investigating the application of an RBEFS on the over-voltage inhibition. Some development trends of the MLS maglev train, including the DC 3 kV traction grid system and the speed being raised to 160~200 km/h, are also considered in the analyzed scenarios. At first, a modeling scheme of a detailed vehicle–grid electrical power model with the RBEFS is established. On this basis, the PR voltage rise characteristics are analyzed with consideration of three pivotal influencing factors: RB power, PR impedance and supply voltage level. Subsequently, to stabilize the PR voltage fluctuations, the influence rules of the RBEFS on the voltage rise and the mutual transient voltage influences under the operating status switching for multiple vehicles running on the same power supply section are analyzed. [ABSTRACT FROM AUTHOR]

This paper proposes a two-level control strategy based on a super-twisting sliding-mode algorithm (STA) to optimally allocate power imbalances in shipboard microgrids (SMGs) while achieving frequency regulation. The strategy employs an STA observer to estimate the unknown power load demand imbalances in finite time. This estimate is then passed to an online high-level optimal control framework to periodically determine the optimal sequence of power reference values for each energy storage device (ESS), minimising the operational cost of the SMG. The online optimised power reference values are interpolated and passed to the low-level STA control strategy to control the output power of each ESS. The efficacy of the proposed methods is demonstrated through numerical simulations conducted on a prototypical model of an SMG equipped with two ESSs, namely batteries and fuel cells with associated hydrogen storage. [ABSTRACT FROM AUTHOR]

There are two main beneficial characteristics that doubly salient permanent magnet (PM) electrical machines present for aircraft applications: armature windings and PMs excitation sources placed on the stator side (maintenance and thermal management), and having a clear-cut rotor without PMs or excitation windings (vulnerable at high speeds due to associated centripetal mechanical stresses). Within this framework, a doubly salient permanent magnet (DSPM) generator was conceived by optimizing the stator size and rotor structure to minimize the torque ripple and maximize the root-mean-square (RMS) voltage value per turn of each generator phase. Firstly, a comparison between the 2D and 3D finite element method (FEM) models is made considering the results of 3D finite element analysis (FEA) as our benchmark in order to understand the accuracy of the 2D results against our benchmark model, the 3D one. A multi-objective design strategy based on a 2D FEA is made, it is set to have characteristics closest to optimal for a Boeing 767 turbine, that is, the necessary electromotive force for a required power of 90 kW at 3000 rpm, feeding a simplified Boeing 767 electrical power distribution system. The results show that the machine could not deliver the required power at 3000 rpm since the 2D FEA demonstrates that the 2D model gives optimistic results when compared with the 3D FEM model. However, with a 3D FEA of the machine feeding the aircraft load, it was seen that the machine's efficiency is 92%, suggesting that this machine can be a plausible solution. [ABSTRACT FROM AUTHOR]

This document provides an overview of innovations in thermoelectric technology, with a focus on materials and applications. It discusses thermoelectric generators (TEGs), which convert thermal energy into electrical energy, and their versatility in various settings such as space, medical applications, and vehicles. The document also highlights advancements in thermoelectric materials, such as Cu2−xSe and PbTe0.7S0.3, which improve the efficiency of TEGs. The included research papers cover topics such as TEG design, durability under different conditions, and the development of nanostructured materials. The findings emphasize the need for further research and collaboration to enhance the efficiency and practicality of thermoelectric systems. [Extracted from the article]

As the operating speed of electric multiple units (EMUs) in high-speed railways increases, pantograph–catenary (PC) detachment arcing occurs frequently. The resulting vehicular-grounding electromagnetic transients are related to the dynamic characteristics of the arc length. During large detachment, the processes of arc extinction and arc reignition may occur, resulting in more severe train body (TB) over-voltages and adverse effects on some vehicular electronic devices. As an extension of the previous works, this paper aims to establish a suitable PC arc model to examine the TB transient voltages. To begin with, the arc length dynamic characteristics are reasonably analyzed to deduce the relationship between the detachment distance and the arc length via the chain arc model. Then, the dynamic characteristics of the arc length are introduced, and an arc modeling scheme is proposed to elaborate the vehicle-grid electric power model for EMUs encountering various arcing scenarios. Based on this, the transient over-voltages are analyzed, accounting for both the arc extinction and arc reignition, as well as the mutual influences of multiple detachments in a short time. The influential factors, including arc length characteristics, phase angle, excitation inductance, and grounding parameters, are also involved in the performed analyses. [ABSTRACT FROM AUTHOR]

Background: Healthcare activities significantly contribute to greenhouse gas (GHG) emissions. Blood transfusions require complex, interlinked processes to collect, manufacture, and supply. Their contribution to healthcare emissions and avenues for mitigation is unknown. Study Design and Methods: We performed a life cycle assessment (LCA) for red blood cell (RBC) transfusions across England where 1.36 million units are transfused annually. We defined the process flow with seven categories: donation, transportation, manufacturing, testing, stockholding, hospital transfusion, and disposal. We used direct measurements, manufacturer data, bioengineering databases, and surveys to assess electrical power usage, embodied carbon in disposable materials and reagents, and direct emissions through transportation, refrigerant leakage, and disposal. Results: The central estimate of carbon footprint per unit of RBC transfused was 7.56 kg CO2 equivalent (CO2eq). The largest contribution was from transportation (2.8 kg CO2eq, 36% of total). The second largest was from hospital transfusion processes (1.9 kg CO2eq, 26%), driven mostly by refrigeration. The third largest was donation (1.3 kg CO2eq, 17%) due to the plastic blood packs. Total emissions from RBC transfusion are ~10.3 million kg CO2eq/year. Discussion: This is the first study to estimate GHG emissions attributable to RBC transfusion, quantifying the contributions of each stage of the process. Primary areas for mitigation may include electric vehicles for the blood service fleet, improving the energy efficiency of refrigeration, using renewable sources of electricity, changing the plastic of blood packs, and using methods of disposal other than incineration. [ABSTRACT FROM AUTHOR]

Low‐head turbines are becoming an agricultural imperative due to their high efficiency, low cost, ability to operate at low flow rates and minimal environmental impact. Therefore, the Archimedes screw turbine (AST) can play a leading role for producing electric power, especially in Pakistan's rural areas where most of the places have less than 1 m head. In this research work, performance evaluation of AST was carried out at different flow velocities in terms of power coefficient and torque generated. Design parameters such as blade width, blade pitches, and blade rotational angles are also used for performance evaluation. For this purpose, computational fluid dynamic (CFD) analyses of AST blades were conducted at different water flow velocities (e.g., 1, 1.5, 2, 2.5, 3, and 3.5 m/s). ANSYS FLUENT was used for AST blade simulations using three different design parameters such as blade width, blade pitch, and blade rotational angles. Additionally, CFD simulations have inherent errors and uncertainties that may lead to findings and deviations from their exact or real values. To prevent these uncertainties and errors, an experimental study was also conducted to provide validation for the CFD simulation results. The results revealed from CFD simulations for optimized design parameters were then compared with experimental data. From the results, it was examined that the numerical findings were in good agreement with the experiment data. The highest power coefficient and power output values were obtained under optimized design parameters such as inner and outer diameter, blade pitch, blade width, blade rotation angles and shaft length (e.g., 40 mm, 120 mm, 130 mm, 2 mm, 60°, and 850 mm respectively). The findings can be useful to implement the AST unit for those places where the available water head is ranging from 1 to 6.5 m and a flow rate of 0.2–6.5 m3/s, especially for rural areas of Pakistan. [ABSTRACT FROM AUTHOR]

In the ship's electrical power system, the largest percentage of a load powered by an induction machine (IM) can be represented through the fan load characteristics. Likewise, it is well known that, from the point of view of the current load, the "most problematic" way of starting the induction machine is the direct start. This paper deals with a novel analytical modeling of the IM direct start-up speed–time curve under fan load. The derived analytical expressions have been used for modeling the start of induction machines of different powers and voltage levels. Also, the obtained results have been compared with the corresponding results provided by using the realized MATLAB/Simulink model of the induction machine coupled with fan load. Moreover, the results obtained have been compared with the methods presented in the literature as regards the modeling of start-up characteristics. The experimental validation of the proposed analytical expressions for modeling the direct start of IM driving fan load has also been carried out on a 300 W machine in a laboratory environment. Both the simulations and experimental verification have demonstrated an exceptional accuracy of the derived analytical expressions. The paper also provides the implemented MATLAB code for determining the speed–time curve of IM during direct start under fan-type load. [ABSTRACT FROM AUTHOR]

This article aims to present the efficacy analysis of electric vehicle (EV) charging/discharging process as a sink or a distributed energy source (DES) in the framework of LFC problem of multi-area multi-source deregulated power system (MAMS-DPS). The energy storage/extraction of EVs acts as a noteworthy role in mitigation of deviation frequency of DPS. A problem of AGC system for two-area DPS having two thermal power generators of non-reheat type and aggregated fleet of EVs in each area is considered to study here. The electric power contract between the distribution (DISCOs) and generation (GENCOs) companies in the restructured environment of DPS is specified through a contract participation matrix named as the DPM. In DPS area deviation frequency, generated and tie-line powers fluctuate in case of variation of load. To resolve these problems the efficacy of EV is accessed applying the optimal control scheme using TLBO algorithm and compared performances with the control schemes based on the optimization algorithms of GA as well as PSO, and PI, PID control schemes. The comparative performance of responses of system variables of the deviation frequency, generated power, and tie-line power are analysed applying the control schemes as PI control for without/with EV cases, and PID control, and the GA/PSO/TLBO-tuned PI/PID control schemes for with EV case. The TLBO-PID control scheme outperforms to mitigate the deviations observing the steady-state error, undershoot, overshoot, and settling time performance indices. [ABSTRACT FROM AUTHOR]

The rapid development of electric vehicles (EVs) has created more possibilities for their flexible participation in electric power dispatching. Considering the clustering and fast mobility of EVs coinciding with real-time market requirements for responsive demands, a bidding strategy is proposed in this paper to assist EV aggregators with submitting reasonable quotations for the real-time market. Based on Bayesian updating, the strategy in this paper considers relevant factors such as aggregator cost and expected return. First, a quotation strategy in electricity spot market is proposed for EV aggregators. Second, the applicable scope of the strategy is given under different scenarios. Then, the objective function of learning is proposed, and the specific process of Bayesian updating is demonstrated. Finally, a case study of the aggregator quotation strategy based on Bayesian updating is simulated to verify the effectiveness and feasibility of the proposed strategy, which helps improve the bidding success rate of EVs and peak-to-valley balance of the grid. [ABSTRACT FROM AUTHOR]

This study proposes an optimal approach for enhancing the quality of a weak electrical power distribution system by utilizing thyristor‐controlled series capacitors (TCSC). The proposed approach considers various system components, including shunt capacitors and distributed generators that rely on renewable energy sources, which are characterized by uncertainty due to changing weather conditions. The proposed system maximizes the benefits of these components by updating its parameters to improve the distribution power system's quality under all operating conditions, which change due to the varying energy generated by the distributed generators and load changes. The optimal TCSC values are calculated for all operating conditions using the Hyper‐Spherical Search with graded objective functions management strategy (HSSA‐GOFMS) algorithm. The GOFMS converts all restrictions into objective functions with limits and prioritizes the search for objectives according to a sequential system of priorities. This strategy prevents the possibility of non‐convergence. The results demonstrate the effectiveness of the proposed approach in improving the performance quality of a weak electrical distribution system. The proposed strategy achieves the target voltage level limits within nominal values, even with the interference of the system with generators distributed at variable power levels. Furthermore, the proposed approach enhances the performance quality of the system under a wide range of load changes. [ABSTRACT FROM AUTHOR]

Over time, there has been a continuous surge in the demand for electrical energy, necessitating the development of larger and more intricate electrical power networks. These extensive networks pose a significant challenge, primarily in the form of considerable loss of electrical energy, which, if not effectively addressed, may lead to persistent and imperceptible losses. In response to this challenge, this research proposes the application of quantum binary particle swarm optimization (QBPSO) for the coordinated management of on-load tap changers (OLTC) in loaded transformers within a distribution network, with a specific emphasis on reducing power losses. The experimental results demonstrate that the implementation of QBPSO results in a reduction of power loss from 21.756107 kW to 19.157321 kW and an increase in the average voltage from 19.00467941 kV to 19.93068 kV in a 20 kV 34-bus distribution network. This has the potential to significantly enhance overall system efficiency. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to study the thermal identification issue by harvesting both solar energy and atmospheric thermal updraft for a solar-powered unmanned aerial vehicle (SUAV) and its electric energy performance under continuous soaring conditions. Design/methodology/approach: The authors develop a specific dynamic model for SUAVs in both soaring and cruise modes. The support vector machine regression (SVMR) is adopted to estimate the thermal position, and it is combined with feedback control to implement the SUAV soaring in the updraft. Then, the optimal path model is built based on the graph theory considering the existence of several thermals distributed in the environment. The procedure is proposed to estimate the electricity cost of SUAV during flight as well as soaring, and making use of dynamic programming to maximize electric energy. Findings: The simulation results present the integrated control method could allow SUAV to soar with the updraft. In addition, the proposed approach allows the SUAV to fly to the destination using distributed thermals while reducing the electric energy use. Originality/value: Two simplified dynamic models are constructed for simulation considering there are different flight mode. Besides, the data-driven-based SVMR method is proposed to support SUAV soaring. Furthermore, instead of using length, the energy cost coefficient in optimization problem is set as electric power, which is more suitable for SUAV because its advantage is to transfer the three-dimensional path planning problem into the two-dimensional. [ABSTRACT FROM AUTHOR]

Surface flashover is an inevitable insulation issue for basin-type insulators in gas-insulated switchgears/lines, which significantly challenges the reliability of the electrical power systems. Previous studies have indicated that polymer/semiconductor-filler composite coatings effectively improve the insulation properties; however, the influence mechanism of the coating materials on flashover has not been demonstrated from a molecular perspective. In this work, epoxy/silicon-carbide (EP/SiC) composites were coated onto an EP substrate. The energy-level structure, surface trap, surface charging, and DC flashover voltage in SF6/N2 were calculated and characterized, and the process by which the tailored molecular energy level influences surface charge transport and flashover characteristics was elucidated. The incorporating of SiC particles reduced the width of the bandgap and introduced shallow traps, which improved carrier mobility and surface conductivity. Quantitative analysis of charge transport indicated that the improved carrier mobility and reduced surface trap level accelerated the surface charge dissipation. This reduced the tangential electrical field distortion and surface charge density and further impeded gas ionization. When the SiC concentration was 15 wt%, the flashover performance improved by 20.88%. This study describes the mechanism by which the EP/SiC coating regulates the surface charge distribution to improve the surface flashover performance by establishing a relationship among the microscopic molecular energy-level structures, mesoscopic charge transport, and macroscopic discharge phenomena. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite Systems (GNSS) provide valuable data for computing accurate and reliable Zenith Tropospheric Delay (ZTD) products used in GNSS precise positioning, climate, and meteorological studies. However, inconsistent GNSS data streaming in the East African region, caused by poor internet connectivity, equipment failure, and electrical power outages, results in data gaps that pose an impact on the applications of the ZTD products. To ensure continuous ZTD data availability, alternative approaches are necessary to fill the gaps. This paper evaluates the accuracy and feasibility of computing ZTD over East Africa using the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) model. The ERA5 ZTD is compared with ZTD data from 13 GNSS stations for the years 2013 to 2020 from the Nevada Geodetic Laboratory (NGL) products. Seven statistical evaluation metrics, including mean bias (MnB), root mean squared error (RMSE), Pearson correlation coefficient (R), coefficient of determination (R2), refined index of agreement (IA), Nash–Sutcliffe coefficient of efficiency (NSE), and Taylor skill score (TSS) were used to determine the agreement between ERA5 and GNSS tropospheric products. The results indicate that ERA5ZTD products match well with GNSS ZTD products, with an average MnB of −0.80 mm, RMSE of 8.94 mm, R of 0.976, R2 of 0.952, IA of 0.986, TSS of 0.975, and NSE of 0.945. The high accuracy and stability of ERA5 data in computing ZTD make it an excellent alternative source of ZTD data to augment GNSS ZTD products for positioning and meteorological applications in the East African region. [ABSTRACT FROM AUTHOR]

In thermophotovoltaic (TPV) systems, it is crucial that the selective emitters can tailor emission spectrum to match the bandgap of photovoltaic (PV) cells and largely enhance the system efficiency. In this work, a metamaterial emitter based on the metal-insulator-metal (MIM) structure is proposed to obtain the high energy conversion efficiencies. The geometric parameters of MIM emitter have been investigated to obtain an excellent radiation spectrum of emitter composed of W and HfO2. The excellent emission performance of MIM emitter is attributed to the excitation of surface plasmon polariton (SPP) and cavity resonance, and the structure of MIM emitter is insensitive for different polarization modes. The 21 material combinations of MIM emitters have been screened to obtain the optimal emitter matching GaSb and InGaAsSb cells. This work identifies the crucial role of structure and materials into the emitter of a TPV system. In the evaluation of MIM emitter and TPV System, when the operating temperature of emitter increases from 1400 K to 2000 K, the system efficiency of optimal W/HfO2/W MIM emitter increases from 20.26% to 30.41%, while the output electric power increases from 3.59 kW/m2 to 42.48 kW/m2. The phenomenon indicates that the MIM emitter with the optimal material combinations and geometric parameters can significantly improve the matching degree with GaSb and InGaAsSb cells. Our results will be helpful to expand the optimization scope of metamaterial emitters in TPV systems. [ABSTRACT FROM AUTHOR]

The aims of this study encompass the characterization of process parameters and the antimicrobial potential during operation of a novel non-thermal plasma (NTP) source in a duct system containing a particulate matter (PM) filter thus mimicking the interior of an air purifier. Simulating conditions of a long-term operation scenario, in which bacterial aerosols in indoor environments accumulate on PM filters, the filter surfaces were artificially inoculated with Escherichia coli (E. coli) and exposed to an air stream enriched with reactive species. Electrical power consumption, key plasma parameters, volume flow and air flow velocity, reactive gas species concentrations as well as inactivation rates of E. coli were assessed. The NTP operated at a gas temperature close to ambient air temperature and featured a mean electron energy of 9.4 eV and an electron density of 1∙1019 m−3. Ozone was found to be the dominating reactive gas species with concentrations of approx. 10 ppm in close vicinity to the PM filters. An inactivation rate of 99.96 % could be observed after exposure of the PM filters to the gas stream for 15 min. This inactivation efficiency appears very competitive in combating realistic bacterial aerosol concentrations in indoor environments. [ABSTRACT FROM AUTHOR]