bluuebunny/crossref_metadata_2025 huggingface dataset:
Manifold Optimization-Based Data Detection Algorithm for Multiple-Input–Multiple-Output Orthogonal Frequency-Division Multiplexing Systems under Time-Varying Channels
Authors: Li Yumeng , Hu Die
Published: July 2024

Recently, multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems have gained significant attention in the field of wireless communications. The utilization of the Riemannian manifold has become prevalent in MIMO-OFDM systems. However, the existing data detection algorithms for MIMO-OFDM systems are mostly designed for block fading channels. Additionally, these algorithms often suffer from high computational complexity. In this paper, we propose a data detection algorithm on the basis of Riemannian manifold optimization for MIMO-OFDM systems under time-varying channels. The core concept of this algorithm is to optimize the transmitted signals by solving the manifold optimization problem in the case of time-varying channels. In order to reduce the computational complexity of the algorithm, we improve the proposed algorithm by dividing the transmitted signals into multiple subframes for solving the optimization problem separately and using the pilots to maintain the performance of the algorithm. In the simulation, the performance of multiple proposed algorithms and the forced-zero detection algorithm under different parameter settings are compared. The simulation results show that the proposed algorithm demonstrates good bit error rate and computational complexity performances.
N-DEPTH: Neural Depth Encoding for Compression-Resilient 3D Video Streaming
Authors: Siemonsma Stephen , Bell Tyler
Published: July 2024

Recent advancements in 3D data capture have enabled the real-time acquisition of high-resolution 3D range data, even in mobile devices. However, this type of high bit-depth data remains difficult to efficiently transmit over a standard broadband connection. The most successful techniques for tackling this data problem thus far have been image-based depth encoding schemes that leverage modern image and video codecs. To our knowledge, no published work has directly optimized the end-to-end losses of a depth encoding scheme sandwiched around a lossy image compression codec. We present N-DEPTH, a compression-resilient neural depth encoding method that leverages deep learning to efficiently encode depth maps into 24-bit RGB representations that minimize end-to-end depth reconstruction errors when compressed with JPEG. N-DEPTH’s learned robustness to lossy compression expands to video codecs as well. Compared to an existing state-of-the-art encoding method, N-DEPTH achieves smaller file sizes and lower errors across a large range of compression qualities, in both image (JPEG) and video (H.264) formats. For example, reconstructions from N-DEPTH encodings stored with JPEG had dramatically lower error while still offering 29.8%-smaller file sizes. When H.264 video was used to target a 10 Mbps bit rate, N-DEPTH reconstructions had 85.1%-lower root mean square error (RMSE) and 15.3%-lower mean absolute error (MAE). Overall, our method offers an efficient and robust solution for emerging 3D streaming and 3D telepresence applications, enabling high-quality 3D depth data storage and transmission.
Sliding Mode Speed Control for PMSM Based on Model Predictive Current
Authors: Zhou Weihong , Song Zhe , Xiao Xi , Guo Yougui , Mo Yu
Published: July 2024

To enhance the dynamic performance and disturbance rejection capability of the permanent magnet synchronous motor speed control system, a novel speed control method based on a novel sliding mode control (NSMC) and load torque observer is proposed on the basis of model predictive current control (MPCC) with a sliding mode disturbance observer. First, on the basis of MPCC, the influence of parameters such as resistance, inductance, and flux linkage on MPCC is analyzed. To address the aggregated disturbance caused by parameter mismatches, a piecewise square-root switching function sliding mode disturbance observer (SMDO) is designed to enhance the robustness of the parameters. To address the poor dynamic performance and inadequate robustness resulting from the proportional-integral-controller (PI) velocity loop control in the MPCC, a novel NSMC velocity control method is proposed. This method utilizes the hyperbolic sine function and fractional-order integral sliding mode surface, resolving the dilemma faced by traditional slide mode controllers (SMC) in balancing fast response and reduced vibration. Additionally, to enhance the system’s disturbance rejection capability, a sliding mode torque observer (SMTO) is designed to continuously update the observed load torque value into the NSMC controller, achieving speed compensation control. Finally, through comparative experiments among the proportional integral controller (PI), SMC, NSMC, and NSMC + SMTO, the results indicate that the proposed NSMC + SMTO exhibits the best speed response, steady-state characteristics, and disturbance rejection capability.
A Nonlinear Hybrid Modeling Method for Pump Turbines by Integrating Delaunay Triangulation Interpolation and an Improved BP Neural Network
Authors: Yang Qiuling , Zhang Yangning , Zhang Yingchen , Deng Changhong
Published: July 2024

In order to solve the problem that the data processing accuracy of the characteristic curves is not high, which affects the accuracy of the simulation of variable-speed pumped storage units, this paper proposes a nonlinear hybrid modeling method for pump turbines by integrating Delaunay triangulation interpolation and an improved back-propagation neural network. Firstly, the improved Sutter transform is used to preprocess the original curve, and the convex hull of the transformed curve is calculated. With the unknown point inside the convex hull, Delaunay triangulation interpolation is used to model the transformed curve. With the unknown point outside the convex hull, the back-propagation neural network is first established based on the input and output, and then the initial weights and thresholds of the network are determined using the mind evolution algorithm to complete the modeling and simulation of the curve. Simulation results show that the proposed method fully integrates the high-precision features of interpolation and the powerful nonlinear fitting ability of the neural network, which greatly improves the efficiency and accuracy of pump turbine modeling.
Analytical Calculation of the No-Load Magnetic Field of a Hybrid Excitation Generator
Authors: Xiong Yiyong , Zhao Jinghong , Yan Sinian , Wei Kun
Published: July 2024

Hybrid excitation generators combine the advantages of electric excitation motors and permanent magnet generators. Focusing on a permanent magnet generator as the object of study, an auxiliary excitation winding is introduced for voltage regulation. The main magnetic field is established by the permanent magnet, and the auxiliary excitation winding provides the magnetic potential required to regulate the air-gap magnetic field. While improving the voltage regulation performance of permanent magnet generators, it also reduces the loss of excitation windings in electrically excited generators. Based on a hybrid excitation generator with dual excitation windings, in the following article, we present a hybrid excitation generator equivalent to a full permanent magnet motor with the minimum output voltage, and an accurate subdomain model of the full permanent magnet motor is established considering the influence of slot opening. By establishing a matrix, the distribution curve of air-gap magnetic density was solved and ultimately verified using finite element analysis. The results of the present study lay a solid foundation for solving the air-gap magnetic density distribution of various parts of the hybrid excitation generator and studying its performance in the future.
Deep Residual-in-Residual Model-Based PET Image Super-Resolution with Motion Blur
Authors: Tian Xin , Chen Shijie , Wang Yuling , Han Dongqi , Lin Yuan , Zhao Jie , Chen Jyh-Cheng
Published: July 2024

Positron emission tomography (PET) is a non-invasive molecular imaging technique. The limited spatial resolution of PET images, due to technological and physical imaging constraints, directly affects the precise localization and interpretation of small lesions and biological processes. The super-resolution (SR) technique aims to enhance image quality by improving spatial resolution, thereby aiding clinicians in achieving more accurate diagnoses. However, most conventional SR methods rely on idealized degradation models and fail to effectively capture both low- and high-frequency information present in medical images. For the challenging SR reconstruction of PET images exhibiting motion-induced artefacts, a degradation model that better aligns with practical scanning scenarios was designed by us. Furthermore, we proposed a PET image SR method based on the deep residual-in-residual network (DRRN), focusing on the recovery of both low- and high-frequency information. By incorporating multi-level residual connections, our approach facilitates direct feature propagation across different network levels. This design effectively mitigates the lack of feature correlation between adjacent convolutional layers in deep networks. Our proposed method surpasses benchmark methods in both full-reference and no-reference metrics and subjective visual effects across small animal PET (SAPET), phantoms, and Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets. The experimental findings confirm the remarkable efficacy of DRRN in enhancing spatial resolution and mitigating blurring in PET images. In comparison to conventional SR techniques, this method demonstrates superior proficiency in restoring low-frequency structural texture information while simultaneously maintaining high-frequency details, thus showcasing exceptional multi-frequency information fusion capabilities.
Evaluating Multimodal Techniques for Predicting Visibility in the Atmosphere Using Satellite Images and Environmental Data
Authors: Tsai Hui-Yu , Tseng Ming-Hseng
Published: July 2024

Visibility is a measure of the atmospheric transparency at an observation point, expressed as the maximum horizontal distance over which a person can see and identify objects. Low atmospheric visibility often occurs in conjunction with air pollution, posing hazards to both traffic safety and human health. In this study, we combined satellite remote sensing images with environmental data to explore the classification performance of two distinct multimodal data processing techniques. The first approach involves developing four multimodal data classification models using deep learning. The second approach integrates deep learning and machine learning to create twelve multimodal data classifiers. Based on the results of a five-fold cross-validation experiment, the inclusion of various environmental data significantly enhances the classification performance of satellite imagery. Specifically, the test accuracy increased from 0.880 to 0.903 when using the deep learning multimodal fusion technique. Furthermore, when combining deep learning and machine learning for multimodal data processing, the test accuracy improved even further, reaching 0.978. Notably, weather conditions, as part of the environmental data, play a crucial role in enhancing visibility prediction performance.
Fast Depth Map Coding Algorithm for 3D-HEVC Based on Gradient Boosting Machine
Authors: Su Xiaoke , Liu Yaqiong , Zhang Qiuwen
Published: July 2024

Three-Dimensional High-Efficiency Video Coding (3D-HEVC) has been extensively researched due to its efficient compression and deep image representation, but encoding complexity continues to pose a difficulty. This is mainly attributed to redundancy in the coding unit (CU) recursive partitioning process and rate–distortion (RD) cost calculation, resulting in a complex encoding process. Therefore, enhancing encoding efficiency and reducing redundant computations are key objectives for optimizing 3D-HEVC. This paper introduces a fast-encoding method for 3D-HEVC, comprising an adaptive CU partitioning algorithm and a rapid rate–distortion-optimization (RDO) algorithm. Based on the ALV features extracted from each coding unit, a Gradient Boosting Machine (GBM) model is constructed to obtain the corresponding CU thresholds. These thresholds are compared with the ALV to further decide whether to continue dividing the coding unit. The RDO algorithm is used to optimize the RD cost calculation process, selecting the optimal prediction mode as much as possible. The simulation results show that this method saves 52.49% of complexity while ensuring good video quality.
Automated Left Ventricle Segmentation in Echocardiography Using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment
Authors: Balasubramani Madankumar , Sung Chih-Wei , Hsieh Mu-Yang , Huang Edward Pei-Chuan , Shieh Jiann-Shing , Abbod Maysam F.
Published: July 2024

Accurate segmentation of the left ventricle (LV) using echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially superior accuracy. This study explores the efficacy of employing a YOLO (You Only Look Once) segmentation model for automated LV segmentation in Echo images. YOLO, a cutting-edge object detection model, achieves exceptional speed–accuracy balance through its well-designed architecture. It utilizes efficient dilated convolutional layers and bottleneck blocks for feature extraction while incorporating innovations like path aggregation and spatial attention mechanisms. These attributes make YOLO a compelling candidate for adaptation to LV segmentation in Echo images. We posit that by fine-tuning a pre-trained YOLO-based model on a well-annotated Echo image dataset, we can leverage the model’s strengths in real-time processing and precise object localization to achieve robust LV segmentation. The proposed approach entails fine-tuning a pre-trained YOLO model on a rigorously labeled Echo image dataset. Model performance has been evaluated using established metrics such as mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 50% (mAP50) with 98.31% and across a range of IoU thresholds from 50% to 95% (mAP50:95) with 75.27%. Successful implementation of YOLO for LV segmentation has the potential to significantly expedite and standardize Echo image analysis. This advancement could translate to improved clinical decision-making and enhanced patient care.
Electro-Hydraulic Variable-Speed Drive Network Technology—First Experimental Validation
Authors: Schmidt Lasse , van Binsbergen-Galán Mikkel
Published: July 2024

The improvement of the energy efficiency of hydraulic systems remains an essential challenge for industry, and the demand for more sustainable solutions is increasing. A main focus in this endeavor is the ability to eliminate or strongly reduce the use of throttle control valves which have been the preferred control element in industrial hydraulic systems for decades. Components have been subject to continuous evolution, and current industrial grade hydraulic pumps and motors are both efficient and reliable. Even though few percentages of energy efficiency can still be achieved, the main achievements in terms of efficiency are associated with novel system designs rather than further development of components. An area subject to increasing attention is the field of variable-speed displacement control, allowing to avoid the main control valve throttle losses. Systems using this technology are, however, mainly developed as standalone drive systems, necessitating maximum force, speed, and power installed in each axis, with limited hydraulic power distribution capability compared to valve-controlled systems. An emerging field addressing this challenge is that of so-called electro-hydraulic variable-speed drive networks, which allow to completely eliminate the use of control valves and enable power sharing both electrically and hydraulically, potentially reducing the necessary installed power in many cases. The idea of such a technology was first proposed in 2022, and so far developments reported in the literature have mainly been of a theoretical nature. This article presents the first ever experimental results for a dual-cylinder electro-hydraulic variable-speed drive network prototype. The prototype was developed for an industrial application, but has initially been implemented in a laboratory testbench. Extensive data acquisition has been conducted while subject to the associated industrial motion cycle, under different load conditions. The data obtained are further used in combination with models to predict the total efficiency of the drive network prototype under higher loads than what could be achieved in the laboratory, suggesting a total efficiency from the electric supply to the cylinder pistons of 68%. Re-configuring the prototype to a known standalone drive system structure implies comparable efficiencies. Finally, the drive network is theoretically compared to a valve drive solution, generally suggesting that the prototype drive network can provide efficiency improvements of at least 40% in comparison.
Research on the Throttling Performance and Anti-Erosion Structure of Trapezoidal Throttle Orifices
Authors: Zhao Jianguo , Zheng Haotian , Xie Chong , Peng Hanxiu
Published: July 2024

The throttling performance of conventional throttle orifice structures of fluid control valves is very low. Therefore, this paper proposes a novel trapezoidal throttle orifice with excellent throttling performance. The effect of the taper of the throttle orifice on the erosion was researched. Firstly, two schemes of trapezoidal throttle orifice were proposed according to the fluid control valve. Secondly, the excellent throttling performance of the trapezoidal throttle orifice was compared and optimized. Finally, a numerical simulation method of the erosion-resistant ability of the trapezoidal throttle orifice was established. It was found that for the same throttling area, the differential pressure of the trapezoidal orifice was higher than that of the conventional rectangular orifice by about 18.6%. The taper had little effect on the gas production, which increased by only 3.3% during the 10° to 30° change. The maximum erosion was firstly reduced and then increased with increases in the angle from 0 to 25°of the taper. Moreover, the minimum was achieved at about a 20° taper angle. The above research methods provide a theoretical basis for optimizing the size and structure of orifices and the sealing reliability of fluid control valves.
EOR Technology (Patents) and Science (Articles) Assessment of BRICS and nonBRICS with Growth Rates and Specializations within Responsible Global Energy Transition: A Critical Review
Authors: Quintella C. M. , Rodrigues P. D. , Nicoleti J. L. , Ramos-de-Souza E. , Carvalho E. B. , Hanna S. A.
Published: July 2024

To achieve a low-carbon energy transition, it is essential to ensure that, as long as fossil fuels are needed, their production is sustainable, minimizing the environmental impact and securing resources for advancing greener technologies, in alignment with SDGs 7, 13, and 14. Enhanced oil recovery (EOR) increases the recovery rates without new developments. The recent expansion of the BRICS consortium, involving major producers, underscores the need to evaluate their EOR technologies, particularly potential gaps that could hinder global energy transition strategies. We analyzed intermediate levels of technological readiness levels (TRLs) utilizing patents (TRL4-5) and articles (TRL3) for 18 EOR methods between 2002 and 2021. Composite indicators derived from patents including compound annual growth rate, specialization, concentration, diversification, and Gini inequality were employed. Both BRICS and nonBRICS exhibited analogous distributions in the articles (particularly Norway, United Kingdom, Canada) and patents (particularly Russia, China, and Ukraine). The decline in growth rates among BRICS and negative rates in nonBRICS suggest a technological plateau for traditional methods. However, environmentally low-impact EOR methods are experiencing exponential emergence (low salinity water, MEOR, polymers and macromolecular compounds, their associations with surfactants, and WAG). Both groups are self-sufficient in EOR, ensuring a responsible and low-impact energy transition. This ensures energy quality while facilitating the maturation of renewable technologies.
Coordinated Frequency Modulation Control Strategy of Wind Power and Energy Storage Considering Mechanical Load Optimization
Authors: Zhang Chaoyu , Li Jiabin , Liu Shiyi , Hu Peng , Feng Jiangzhe , Ren Haoyang , Zhang Ruizhe , Jia Jiaoxin
Published: July 2024

When a doubly fed induction generator (DFIG) participates in primary frequency modulation by rotor kinetic energy control, the torque of the generator is changed sharply and the mechanical load pressure of the shaft increases rapidly, which aggravates the fatigue damage of shafting. In order to alleviate the fatigue load of shafting, energy storage was added in the primary frequency modulation of a wind turbine, and a coordinated frequency modulation control strategy of wind power and energy storage based on fuzzy control was proposed. The wind-storage frequency modulation power command was allocated to reduce the response speed of the wind turbine to alleviate the load pressure on the shafting by the fuzzy controller considering the rotor speed range and the state of energy storage charge, and the remaining demand power was supplemented by energy storage. Finally, the joint simulation model based on GH Bladed–Matlab was used to verify the effectiveness of the proposed control strategy. Compared with the traditional integrated control of virtual inertia, the proposed method can reduce the load pressure and fatigue damage of the shafting while satisfying the requirement of frequency modulation.
Modeling the Drying Process of Onion Slices Using Artificial Neural Networks
Authors: Francik Sławomir , Łapczyńska-Kordon Bogusława , Hajos Michał , Basista Grzegorz , Zawiślak Agnieszka , Francik Renata
Published: July 2024

One of the food preservation technologies is the drying process, which requires heat and is significantly energy-intensive, resulting in high costs. This caused the search for new design solutions for dryers, which requires continuous experimental research and the creation of new decision-supporting models for the optimization of drying processes. In this work, four models of the kinetics of convective onion drying were developed using Artificial Neural Networks (ANNs), taking into account pre-treatment before drying and the different temperatures of the drying agent. The moisture content in the dried material at a specific moment in time was taken as the dependent variable (ANN output). The following were accepted as independent variables (ANN inputs): drying temperature, initial sample thickness, initial moisture content, initial mass of the sample, time of drying, and material pre-treatment (no pre-treatment—blanching–osmotic dehydration). Four semantic models were formulated, the general Ann1 model taking into account all input variables and three detailed Ann2 models for individual types of pre-treatment. For the best Ann1, the MAPE values were 5.88–7.02% (for different data: Training, Test, Validation). For the detailed Ann2 models, the error values were more than twice lower. The MAPE values ranged from 1.14% to 3.12%.
The Modeling of Concentrators for Solar Photovoltaic Systems
Authors: Machado da Costa Ana Francisca , Marques Lameirinhas Ricardo A. , Pinho Correia Valério Bernardo Catarina , Neto Torres João Paulo , Santos Marcelino
Published: July 2024

Concentrating photovoltaic (CPV) systems have emerged as a transformative technology that incorporates radiation concentrators into the photovoltaic system to enable radiation to be concentrated onto a receiver—the solar cells. Different concentrator configurations have different impacts on the performance of the solar photovoltaic system. This research work aims to analyze the impact of different concentrators, comparing and identifying the most efficient structures for capturing and concentrating solar energy. Aiming at a deep analysis and comparison among concentrators shapes, this research work presents a unique investigation and revision among different structures such as flat, triangular, LFR, and parabolic concentrators. Moreover, since, in the UV–visible–NIR region, metals’ reflectance varies with the incident wavelength, five metals were considered: aluminum, gold, platinum, copper, and silver. Additionally, the research focuses on studying the effects of parameters critical to the quality of the concentration on the power obtained and on the uniformity of the radiation distribution on the surface of the receiver, as well as on the number of solar rays that reach the receiver. The power on the receiver increases proportionally with the number of reflector concentrators in the system and their reflectance. For parabolic geometries, the optical efficiency is affected by the receiver’s shadow on the concentrator and, in the case of the LFR, by a non-ideal alignment of the reflectors in relation to the receiver. However, in parabolic concentrator geometries, uniformity is usually lower, since in these configurations, the radiation is focused on specific areas of the receiver, usually the central zone.
Innovation in Methods for Incorporating Magnetite into Biocellulose for Electromagnetic Interference Shielding Effectiveness Applications
Authors: de Souza Thaís Cavalcante , dos Santos Alexsandro Ramos , Chacon João Luiz da Silva Pereira , Durval Ítalo José Batista , Costa Andréa Fernanda de Santana , Padrón Hernández Eduardo , Converti Attilio , Vinhas Glória Maria , Sarubbo Leonie Asfora
Published: July 2024

Materials with magnetic properties are essential in various electric sector technologies. However, the generation of pollutants is of concern, increasing the interest in developing new sustainable, low-cost magnetic materials. These materials have notable applications in protecting against electromagnetic interference (EMI), which can lead to health problems as well as environmental pollution. Therefore, the aims of the present study were to produce a sustainable magnetic polymer using different methods of magnetite incorporation, investigate its magnetic properties, and determine its EMI shielding potential. The magnetic BC materials were obtained via in situ and ex situ magnetic incorporation in processed BC membranes and BC hydrogels. Analyses were carried out using XRD, FTIR, SEM, and VSM, and tests were performed to assess electromagnetic interference shielding effectiveness (EMI SE). The results revealed that the magnetite incorporation method influences the final size of nanoparticles, the arrangement among BC fibers, and the magnetic properties. Materials produced from processed BC had a higher percentage of incorporated magnetite and greater magnetic saturation, whereas those containing nanoparticles with a larger diameter had a stronger coercive field. Although samples did not have high EMI SE, magnetite increased the wave reflection and absorption of the material. This biomaterial can drive important innovations in the energy sector, particularly in efficient and ecological electrical infrastructure.
Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images
Authors: Lekavičius Justinas , Gružauskas Valentas
Published: July 2024

With the popularity of solar energy in the electricity market, demand rises for data such as precise locations of solar panels for efficient energy planning and management. However, these data are not easily accessible; information such as precise locations sometimes does not exist. Furthermore, existing datasets for training semantic segmentation models of photovoltaic (PV) installations are limited, and their annotation is time-consuming and labor-intensive. Therefore, for additional remote sensing (RS) data creation, the pix2pix generative adversarial network (GAN) is used, enriching the original resampled training data of varying ground sampling distances (GSDs) without compromising their integrity. Experiments with the DeepLabV3 model, ResNet-50 backbone, and pix2pix GAN architecture were conducted to discover the advantage of using GAN-based data augmentations for a more accurate RS imagery segmentation model. The result is a fine-tuned solar panel semantic segmentation model, trained using transfer learning and an optimal amount—60% of GAN-generated RS imagery for additional training data. The findings demonstrate the benefits of using GAN-generated images as additional training data, addressing the issue of limited datasets, and increasing IoU and F1 metrics by 2% and 1.46%, respectively, compared with classic augmentations.
Optimization Strategy for Low-Carbon Economy of Integrated Energy System Considering Carbon Capture-Two Stage Power-to-Gas Hydrogen Coupling
Authors: He Kangjie , Zeng Linjun , Yang Jun , Gong Yongguo , Zhang Zhenhua , Chen Kun
Published: July 2024

To further optimize the low-carbon economy of the integrated energy system (IES), this paper establishes a two-stage P2G hydrogen-coupled electricity–heat–hydrogen–gas IES with carbon capture (CCS). First, this paper refines the two stages of P2G and introduces a hydrogen fuel cell (HFC) with a hydrogen storage device to fully utilize the hydrogen energy in the first stage of power-to-gas (P2G). Then, the ladder carbon trading mechanism is considered and CCS is introduced to further reduce the system’s carbon emissions while coupling with P2G. Finally, the adjustable thermoelectric ratio characteristics of the combined heat and power unit (CHP) and HFC are considered to improve the energy utilization efficiency of the system and to reduce the system operating costs. This paper set up arithmetic examples to analyze from several perspectives, and the results show that the introduction of CCS can reduce carbon emissions by 41.83%. In the CCS-containing case, refining the P2G two-stage and coupling it with HFC and hydrogen storage can lead to a 30% reduction in carbon emissions and a 61% reduction in wind abandonment costs; consideration of CHP and HFC adjustable thermoelectric ratios can result in a 16% reduction in purchased energy costs.
Frequency-Dependent Grounding Impedance of a Pair of Hemispherical Electrodes: Inductive or Capacitive Behavior?
Authors: Brandão Faria José , Fernandes João Pereira , Maló Machado Vitor , Pedro Maria Eduarda
Published: July 2024

This article is the authors’ last contribution to a trilogy of research papers submitted to Energies’ Special Issue on Electromagnetic Field Computation, aimed at the theoretical analysis and numerical computation of the frequency-dependent complex impedance of hemispherical electrodes. In this work, we consider a pair of distant identical hemispherical electrodes buried in the ground, whose constitutive parameters (conductivity and permittivity) are assigned diverse values. Simulation experiments carried out using a full-wave finite element method, considering different combinations of the earth’s constitutive parameters, reveal that the grounding impedance of the electrode system can exhibit surprisingly varied frequency behavior. For frequencies close to zero, the impedance can start out inductive or capacitive, then go through a number of resonant transitions between inductive and capacitive states, finally tending towards purely resistive behavior. The results are interpreted using theoretical approximations valid for low- and high-frequency regimes.
Variation in Flow Characteristics and Power Performance Due to Axial Distance Optimization in the Design of Counter-Rotating Tidal Turbines
Authors: Jeong Haechang , Yang Changjo
Published: July 2024

Counter-rotating turbines, designed to enhance the performance efficiency of tidal turbines, exhibit variable operational characteristics depending on the axial distance between the front and rear blades. This study encompassed both numerical analyses and performance experiments to establish the optimal design by examining the relationship between flow field alterations and the performance of a counter-rotating tidal turbine with varied axial distances. The blades of the counter-rotating tidal turbine, based on a 10-kW single turbine, were designed utilizing the Blade Element Momentum technique. The turbine blades were assessed for changes in output performance attributed to flow separation by analyzing the velocity distribution and separation points within the flow, demonstrating a maximum power coefficient of 40.3% at a design Tip Speed Ratio of 3. At y/D = 0.3. The counter-rotating tidal turbine achieved a maximum power coefficient of 47%, with performance enhancements of the rear blades driven by the accelerated wake of the front blades. Furthermore, the pressure coefficients of the blades, influenced by their shape, inflow velocity, and angle, were detailed separately for the suction and pressure sides. The study also explored the correlation between the flow characteristics and the output performance of each blade by analyzing the distribution of pressure coefficients.
Design of a Wave Generation System Using an Oscillating Paddle-Type Device Anchored to Fixed Structures on the Coast
Authors: Castaño Serna Juan Pablo , Rubio-Clemente Ainhoa , Chica Edwin
Published: July 2024

Wave energy, a form of renewable energy, is derived from the movement of sea waves. Wave energy generation devices are technologies designed to harness this resource and convert it into electricity. These devices are classified based on their location, size, wave direction, and operating principle. This work presents the design of an oscillating device for harnessing wave energy. For this purpose, computational fluid dynamics and response surface methodology were employed to evaluate the influence of the percentage of the blade height submerged below the water surface (X1) and the distance from the device to the breakwater in terms of the percentage of the wave length (X2). The response variable studied was the hydrodynamic efficiency (η) of the device. Transient fluid dynamic simulations were carried out using Ansys Fluent software 2023 R1, with input conditions based on a wave spectrum characteristic of the Colombian Pacific Ocean. Analysis of variance determined that both factors and their interaction have significant effects on the response variable. Using the obtained regression model, the optimal point of the system was determined. Numerical results showed that the maximum η of the system was achieved when the device was submerged at 75% of its height and was positioned 10% of the wave length away from the vertical breakwater. Under this configuration, η was 64.8%. Experimental validations of the optimal configuration were conducted in a wave channel, resulting in a η of 45%. The difference in efficiencies can be attributed to mechanical losses in the power take-off system, which were not considered during the numerical simulations.
Real-Time Simulation System for Small Scale Regional Integrated Energy Systems
Authors: Jiang Wei , Qi Renjie , Xu Song , Hashimoto Seiji
Published: July 2024

Regional Integrated Energy Systems (RIESs) integrate wide spectrum of energy sources and storage with optimized energy management and further pollution reduction. This paper presents a real-time simulation system for RIESs powered by multiple digital signal processors (DSPs) with different means of data exchange. The RIES encompasses the DC microgrid (DMG), the district heat network (DHN), and the natural gas network (NGN). To realize multi-energy flow simulation, averaged switch models are investigated for different types of device-level units in the DMG, and the unified energy path method is used to build circuit-dual models of the DHN and NGN. A hierarchical island strategy (HIS) and a multi-energy dispatch strategy (MEDS) are proposed to enhance the energy flow control and operating efficiency. The two-layer HIS can adjust the operating status of device-level units in real time to achieve bus voltage stability in the DMG; MEDS uses energy conversion devices to decouple multi-energy flows and adopts the decomposed flow method to calculate the flow results for each network. The real-time simulation hardware platform is built, and both electricity-led and thermal-led experiments are carried out to verify the accuracy of models and the effectiveness of the proposed strategy. The proposed system with an energy management strategy aims to provide substantial theoretical and practical contributions to the control and simulation of RIESs, thus supporting the advancement of integrated energy systems.
Enhanced Four-Level Active Nested Neutral Point-Clamped Inverter
Authors: Odeh Charles , Lewicki Arkadiusz , Morawiec Marcin , Jąderko Andrzej
Published: July 2024

The classical four-level nested neutral point-clamped (4L NNPC) inverter-leg is a hybrid of the flying-capacitor and diode-clamped 4L-inverter-leg configurations. Though uniform reduced voltage stress (1/3 of input voltage) on constituting switches is evident in the 4L NNPC inverter-leg, trails of the drawbacks of the diode-clamping concept still exist. With the significantly rated off-the-shelf IGBT switch modules (6.5 kV, 1200 A), chances of deployment of the newly evolved Four-Level Nested T-Type inverter (4L NTTI) in certain applications is high. Compared with the classical 4L NNPC inverter, 4L NTTI involves a smaller number of power switches and low conduction losses. However, in 4L NTTI, two of the six active switches have a blocking voltage rating of 2/3 of the input voltage. Considering this limiting topological feature in 4L NTTI, a six-switch inverter-leg for the four-level active neutral point-clamped (4L ANNPC) inverter is presented in this paper. In the proposed 4L ANNPC inverter-leg, only one switch has a voltage stress of 2/3 of the input voltage. This ameliorated voltage stress translates to low-cost and -loss inverter implementation. The operational characteristics and competitiveness of the 4L ANNPC inverter are analyzed in detail and demonstrated with a prototype.
Parameter Estimation and Preliminary Fault Diagnosis for Photovoltaic Modules Using a Three-Diode Model
Authors: Huang Chao-Ming , Chen Shin-Ju , Yang Sung-Pei , Huang Yann-Chang , Huang Pao-Yuan
Published: July 2024

Accurate estimation of photovoltaic (PV) power generation can ensure the stability of regional voltage control, provide a smooth PV output voltage and reduce the impact on power systems with many PV units. The internal parameters of solar cells that affect their PV power output may change over a period of operation and must be re-estimated to produce a power output close to the actual value. To accurately estimate the power output for PV modules, a three-diode model is used to simulate the PV power generation. The three-diode model is more accurate but more complex than single-diode and two-diode models. Different from the traditional methods, the 9 parameters of the three-diode model are transformed into 16 parameters to further provide more refined estimates. To accurately estimate the 16 parameters in the model, an optimization tool that combines enhanced swarm intelligence (ESI) algorithms and the dynamic crowing distance (DCD) index is used based on actual historical PV power data and the associated weather information. When the 16 parameters for a three-diode model are accurately estimated, the I–V (current-voltage) curves for different solar irradiances are plotted, and the possible failures of PV modules can be predicted at an early stage. The proposed method is verified using a 200 kWp PV power generation system. Three different diode models that are optimized using different ESI algorithms are compared for different weather conditions. The results affirm the reliability of the proposed ESI algorithms and the value of creating more refined estimation models with more parameters. Preliminary fault diagnosis results based on the differences between the actual and estimated I–V curves are provided to operators for early maintenance reference.
Integrated Transmission and Distribution Co-Simulation Platform for Demonstration of Bulk Grid Services Using Distributed Energy Resources
Authors: Motakatla Venkateswara Reddy , Liu Weijia , Hao Jun , Padullaparti Harsha Vardhana , Kumar Utkarsh , Choi Seong Lok , Mendoza Ismael
Published: July 2024

In September 2020, the Federal Energy Regulatory Commission (FERC) released Order 2222, which opens wholesale markets to small-capacity distributed energy resources (DERs), recognizing their potential in improving operational efficiency by providing bulk grid services. Therefore, a co-simulation capability that can connect transmission and distribution (T&D) simulations and evaluate the impacts of DER provision of bulk grid services is needed. In this paper, we present a new integrated T&D co-simulation platform that incorporates T&D system simulators, DER aggregator/group strategies, and a co-simulation coordinator. Industry-standard communication protocols are employed to mimic real-world conditions. Secondary frequency regulation is selected as the representative bulk grid service, and we simulate the responses of DERs to the frequency regulation signals. The simulation results for a solar-rich distribution feeder in Colorado, USA, demonstrate how the T&D co-simulation setup is used to evaluate the contributions of DERs to minimize the bulk grid frequency deviation.
Plasma-Assisted One-Step Direct Methanol Conversion to Ethylene Glycol and Hydrogen: Process Intensification
Authors: Ayodele Olumide Bolarinwa
Published: July 2024

This perspective reports a process intensification strategy that converts methanol into ethylene glycol (MeOH-2-EG) in a single step to circumvent multi-step naphtha cracking into ethylene followed by ethylene epoxidation to ethylene oxide (EO) and the subsequent hydrolysis of EO to ethylene glycol (EG). Due to the thermodynamic restriction for the direct MeOH-2-EG, plasma-assisted catalysis was introduced, and platinum group metals were identified as prospective transition metal catalysts that can achieve the formation of strong metal hydride bonds and guarantee the controlled C–C coupling of two plasma-activated hydroxymethyl radicals (*CH2OH) from methanol, both of which are essential for the single-step MeOH-2-EG.
Inertial Energy Storage Integration with Wind Power Generation Using Transgenerator–Flywheel Technology
Authors: Deng Yi , Ehsani Mehrdad
Published: July 2024

A new type of generator, a transgenerator, is introduced, which integrates the wind turbine and flywheel into one system, aiming to make flywheel-distributed energy storage (FDES) more modular and scalable than the conventional FDES. The transgenerator is a three-member dual-mechanical-port (DMP) machine with two rotating members (inner and outer rotors) and one stationary member (stator). The transgenerator–flywheel system is introduced with its configuration, transgenerator overview, flywheel operation principle and power management strategies, and control system. Simulations are performed in MATLAB 2023b/Simulink to verify the system viability, including control system verification and flywheel storage performance evaluation. The results show that the inner and outer rotors can be controlled independently with an accurate and fast control response, and the grid-side control works properly. The flywheel performs well, with considerable charging power and storage capacity.
A Multiphase and Multicomponent Model and Numerical Simulation Technology for CO2 Flooding and Storage
Authors: Li Qiaoyun , Ning Zhengfu , Wu Shuhong , Wang Baohua , Li Qiang , Li Hua
Published: July 2024

In recent years, CO2 flooding has become an important technical measure for oil and gas field enterprises to further improve oil and gas recovery and achieve the goal of “dual carbon”. It is also one of the concrete application forms of CCUS. Numerical simulation based on CO2-EOR plays an indispensable role in the study of the mechanism of CO2 flooding and buried percolation, allowing for technical indicators to be selected and EOR/EGR prediction to be improved for reservoir engineers. This paper discusses the numerical simulation techniques related to CO2 flooding and storage, including mathematical models and solving algorithms. A multiphase and multicomponent mathematical model is developed to describe the flow mechanism of hydrocarbon components–CO2–water underground and to simulate the phase diagram of the components. The two-phase P-T flash calculation with SSI (+DEM) and the Newton method is adopted to obtain the gas–liquid phase equilibrium parameters. The extreme value judgment of the TPD function is used to form the phase stability test and miscibility identification model. A tailor-made multistage preconditioner is built to solve the linear equation of the strong-coupled, multiphase, multicomponent reservoir simulation, which includes the variables of pressure, saturation, and composition. The multistage preconditioner improves the computational efficiency significantly. A numerical simulation of CO2 injection in a carbonate reservoir in the Middle East shows that it is effective for researching the recovery factor and storage quantity of CO2 flooding based on the above numerical simulation techniques.
Study on the Applicability of Autothermic Pyrolysis In Situ Conversion Process for Low-Grade Oil Shale: A Case Study of Tongchuan, Ordos Basin, China
Authors: Ren Dazhong , Wang Zhendong , Yang Fu , Zeng Hao , Lü Chenyuan , Wang Han , Wang Senhao , Xu Shaotao
Published: July 2024

The feasibility of the autothermic pyrolysis in situ conversion (ATS) process for low-grade oil shale (OS) has not been determined. In this research, the pyrolysis and combustion properties of Tongchuan OS, with a 4.04% oil yield, were systematically analyzed. The findings revealed that temperatures between 350 and 425 °C favored oil production, while temperatures from 450 to 520 °C resulted in a higher rate of gaseous generation. At 300 °C, the volume expansion and ignition coking caused by the large amount of bitumen generated resulted in severe pore plugging, which significantly increased the combustion activation energy of the residue, while the presence of substantial flammable bitumen also significantly decreased the ignition and combustion temperatures. From 300 to 520 °C, the combustion performance of residue decreases continuously. In addition, pyrolysis residues of Tongchuan exhibited a slightly higher calorific value, between 425 and 520 °C, owing to its higher fixed carbon content (10.79%). Based on the ideal temperature screening method outlined for Tongchuan OS, the recommended preheating temperature for Tongchuan OS was 425 °C, while the optimum temperature for the retorting zone should be 510 °C, considering a heat utilization rate of 40%. These findings contribute valuable insights for the application of the ATS process to low-grade OS.