bluuebunny/crossref_metadata_2025 huggingface dataset:
Calorimetric Evaluation of Thermal Stability of Organic Liquid Hydrogen Storage Materials and Metal Oxide Additives
Authors: Xie Lin-Jie , Jiang Jun-Cheng , Huang An-Chi , Tang Yan , Liu Ye-Cheng , Zhou Hai-Lin , Xing Zhi-Xiang
Published: March 2022

The effects of two different metal oxide catalysts, SnO and Li2O, on the dehydrogenation temperature of Carbazole and N-Ethylcarbazole (NE), respectively, were investigated by the Thermogravimetric analyzer and Differential Scanning Calorimetry. Thermogravimetric experiments were performed with 10wt% SnO and Li2O added to Carbazole and N-Ethylcarbazole, respectively, and compared to pure Carbazole and N-Ethylcarbazole. The results showed that the dehydrogenation temperature of N-Ethylcarbazole was lower than that of Carbazole, and the dehydrogenation temperature of N-Ethylcarbazole +SnO was the lowest, and SnO is an ideal dehydrogenation catalyst for N-Ethylcarbazole. Experiments using Differential Scanning Calorimetry and a Thermogravimetric analyzer showed that with the addition of catalyst, the activation energy of the mixture was more significant and stable, and the thermal hazard was reduced, whereas the relative dehydrogenation temperature was increased. This study provides important information for improving the design of dehydrogenation catalysts for organic liquid hydrogen storage processes.
Waste Heat Recovery in Automotive Paint Shop via Organic Rankine Cycle and Thermal Energy Storage System—Selected Thermodynamic Issues
Authors: Daniarta Sindu , Kolasiński Piotr , Rogosz Barbara
Published: March 2022

Baking ovens are necessary to be installed in a paint shop of assembly automotive manufacturers for drying the paint of automotive bodies (i.e., in the coating process). In this process, a large amount of heat is provided by burning the natural gas in the gas burner. Practically, the design of the heat confinement in the oven is often poor, which results in considerable heat losses (i.e., waste heat) which are released during the drying process and significantly raise the temperature of a working environment thereby lowering the thermal comfort of the factory staff. To address this issue and limit the waste heat transfer to the surroundings, the application of a waste heat recovery system of a specific design employing the organic Rankine cycle (ORC) may be a viable alternative solution. A combined design of such a system utilizing an evaporator and thermal energy storage (TES) device in a simple ORC layout will be discussed in this article. The obtained simulation result was computed using MATLAB coupled with thermophysical properties libraries, i.e., CoolProp. The obtained results indicate that the sustainability of the studied system scheme appears to be favorably implemented in the selected paint shop and may benefit to lower the temperature of the working area, improve the thermal comfort of factory staff and at the same time produce electricity since some car/automotive manufacturers likely run the production for over 20 hours per day.
Integration of European Electricity Balancing Markets
Authors: Roumkos Christos , Biskas Pandelis N. , Marneris Ilias G.
Published: March 2022

Achieving a fully integrated energy market under the EU electricity target model constitutes an ongoing process. Given that the integration of spot markets is already at a mature stage, the next step forward is the successful integration of the balancing markets across European control areas. An analytical review of all the aspects governing the European balancing market integration is presented in this paper, providing a detailed description on the European regulatory framework on this topic. In addition, the design variables that need to be harmonized among national balancing markets as well as the available balancing market arrangements for the exchange of cross-border balancing services are presented. Numerical examples of the essence of the balancing market integration are provided, and the implementation projects initiated by European transmission system operators (TSOs) towards this direction are described. The review concludes that balancing market integration may indeed lead to a significant reduction in the balancing costs for the participating control areas, but further effort is still required to move from a regional level to a European-wide real-time balancing market, so that the whole potential of such a new landscape is revealed to the benefit of end-consumers.
Water, Energy and Food Algorithm with Optimal Allocation and Sizing of Renewable Distributed Generation for Power Loss Minimization in Distribution Systems (WEF)
Authors: Hassan Abdurrahman Shuaibu , Sun Yanxia , Wang Zenghui
Published: March 2022

Distributed generation (DG) plays a vital role in electrical power networks. However, power loss reduction, voltage profile improvement, friendly environment, and reliability are all benefits of DG units. In this research work, a worthwhile methodology is recommended for optimal allocation of traditional (gas turbine) and renewable energy sources that are based on distributed generators which include solar and wind in the distribution system. The major objective of the research paper is the minimization of real, reactive power losses and emissions produced during the application of these conventional sources. Originally, the best locations to place this DG are identified using the concept of water, energy, and food algorithm (WEFA). The number and sizes of these renewable energy sources selected (wind and solar) are determined by applying the concepts of the Dragonfly Algorithm. The Weibull and beta distribution functions are modeled to extract the exact position to fix our DGs to minimize losses within the distribution network. To assess the performance of WEF five different cases scenario considered are DG capacity, Location of Bus, voltage profile, maximum power loss as well as utilization rate. The proposed WEF Algorithm is tested on the IEEE standard 33-bus system. The simulated results were compared with others found in literature and found to be better in terms of power loss reductions.
An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants
Authors: Akhter Muhammad Naveed , Mekhilef Saad , Mokhlis Hazlie , Almohaimeed Ziyad M. , Muhammad Munir Azam , Khairuddin Anis Salwa Mohd , Akram Rizwan , Hussain Muhammad Majid
Published: March 2022

Incorporating solar energy into a grid necessitates an accurate power production forecast for photovoltaic (PV) facilities. In this research, output PV power was predicted at an hour ahead on yearly basis for three different PV plants based on polycrystalline (p-si), monocrystalline (m-si), and thin-film (a-si) technologies over a four-year period. Wind speed, module temperature, ambiance, and solar irradiation were among the input characteristics taken into account. Each PV plant power output was the output parameter. A deep learning method (RNN-LSTM) was developed and evaluated against existing techniques to forecast the PV output power of the selected PV plant. The proposed technique was compared with regression (GPR, GPR (PCA)), hybrid ANFIS (grid partitioning, subtractive clustering and FCM) and machine learning (ANN, SVR, SVR (PCA)) methods. Furthermore, different LSTM structures were also investigated, with recurrent neural networks (RNN) based on 2019 data to determine the best structure. The following parameters of prediction accuracy measure were considered: RMSE, MSE, MAE, correlation (r) and determination (R2) coefficients. In comparison to all other approaches, RNN-LSTM had higher prediction accuracy on the basis of minimum (RMSE and MSE) and maximum (r and R2). The p-si, m-si and a-si PV plants showed the lowest RMSE values of 26.85 W/m2, 19.78 W/m2 and 39.2 W/m2 respectively. Moreover, the proposed method was found to be robust and flexible in forecasting the output power of the three considered different photovoltaic plants.
Development of a Trajectory Period Folding Method for Burnup Calculations
Authors: Stanisz Przemysław , Oettingen Mikołaj , Cetnar Jerzy
Published: March 2022

In this paper, we present a trajectory period folding method for numerical modelling of nuclear transformations. The method uses the linear chain method, commonly applied for modelling of isotopic changes in matter. The developed method folds two consecutive periods of time and forms linear chain representations. In the same way as in the linear chain method, the mass flow of straight nuclide-to-nuclide transitions following the formation of nuclide transmutation chains in every step is considered over the total period of interest. Therefore, all quantitative data about the isotopic transformations for the period beyond a particular calculation step are preserved. Moreover, it is possible to investigate the formation history of any isotope from the beginning of irradiation to the arbitrary time step, including cooling periods and multi-recycling for any designed nuclear fuel cycle. We present a case study for the transition from 238U to 239Pu and define the properties of the developed method and its possible applications in reactor physics calculations.
An Optimized Time Sequence for Sensorless Control of IPMSM Drives via High-Frequency Square-Wave Signal Injection Scheme
Authors: Yu Ke , Wang Zuo , Li Ling
Published: March 2022

This paper presents a filterless sensorless control scheme with an optimized time sequence based on high-frequency (HF) square-wave voltage injection for a five-phase interior permanent magnet machine (IPMSM) drive. To avoid the utilization of low-pass filters (LPFs) in signal processing, an effective method without filters is proposed in this paper. Moreover, the cross-coupling magnetic saturation is analyzed and the online position error compensation is applied based on the offline measurements and finite-element analysis (FEA). Besides, compared with the conventional time sequence of senseorless control, the proposed optimized time sequence can eliminate the additional position estimation error caused by the time delay in digital implementation. Numerical simulations and experiments with a 2-kW five-phase IPMSM are carried out. The results verify the feasibility and effectiveness of the proposed sensorless control scheme with an optimized time sequence adopted by the IPMSM drives.
Analysis and Design of a Multi-Resonant Circuit for Applications of Wireless Capacitive Power Transmission
Authors: Estevez-Encarnacion Eduardo , Hernandez-Gonzalez Leobardo , Sanchez-Garcia Juan , Ramirez-Hernandez Jazmin , Cortes Domingo , Ponce-Silva Mario , Juarez-Sandoval Oswaldo
Published: March 2022

Capacitive technology for wireless power transmission has been shown to be a good option for charging the batteries of electric vehicles. It offers better coupling between the transmitter and the receiver than inductive power transmission. On the other hand, it has some disadvantages; for example, it is very sensitive to the distance between plates. Several ideas have recently been proposed to overcome this disadvantage. However, a proper analysis methodology is still lacking, as is a proper design procedure for the circuits used in capacitive transmission. In this paper, an analysis and design methodology is presented for applying the Z-tank resonant circuit to capacitive power transmission, and the theoretical design methodology is based on normalized equations with respect to the operating resonance frequency. The analysis methodology and design procedure result in a circuit where the resonance frequency remains constant despite changes in the distance between the capacitive plates. The simulation results validate the proposal; to ensure robust, realistic simulation results, parasitic elements were considered in all reactive components, and robust models were built for the switching elements, such as MOSFETs and diodes. The results show that the multi-resonant characteristic of the circuit achieves maximum energy transfer and high efficiency.
Contribution of the 2010 Maule Megathrust Earthquake to the Heat Flow at the Peru-Chile Trench
Authors: Dragoni Michele , Santini Stefano
Published: March 2022

The 2010 Maule earthquake was a megathrust event that occurred along the Peru–Chile Trench. The earthquake source can be modelled as a fault with two asperities with different areas and strengths. By employing a discrete fault model, where asperities are the basic elements, the event can be described as a sequence of three dynamic modes involving simultaneous asperity slip. Interaction between asperities by mutual stress transfer plays a crucial role during fault slip. With a careful choice of values for the model parameters, the mode durations, the slip distribution, the seismic moment rate and the final moment calculated from the model are found to be consistent with the observed values. An important amount of frictional heat is produced by an event of this size and is calculated by summing up the contributions of each asperity. The seismic event produces a heat pulse propagating through the Earth’s crust and contributing to the average heat flow in the region. The calculated heat production is equal to about 2×1017 J and the peak value of the heat pulse is equal to 6×10−3 mW m−2 or about 10−4 of the average surface heat flow density, with a characteristic diffusion time in the order of 106 a.
A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines
Authors: Paldino Gian Marco , De Caro Fabrizio , De Stefani Jacopo , Vaccaro Alfredo , Villacci Domenico , Bontempi Gianluca
Published: March 2022

The limitation of transmission lines thermal capacity plays a crucial role in the safety and reliability of power systems. Dynamic thermal line rating approaches aim to estimate the transmission line’s temperature and assess its compliance with the limitations above. Existing physics-based standards estimate the temperature based on environment and line conditions measured by several sensors. This manuscript shows that estimation accuracy can be improved by adopting a data-driven Digital Twin approach. The proposed method exploits machine learning by learning the input–output relation between the physical sensors data and the actual conductor temperature, serving as a digital equivalent to physics-based standards. An experimental assessment on real data, comparing the proposed approach with the IEEE 738 standard, shows a reduction of 60% of the Root Mean Squared Error and a decrease in the maximum estimation error from above 10 °C to below 7 °C. These preliminary results suggest that the Digital Twin provides more accurate and robust estimations, serving as a complement, or a potential alternative, to traditional methods.
Evaluation of the Communication Delay in a Hybrid Real-Time Simulator for Weak Grids
Authors: Pan Sisi , Jiang Wei , Li Ming , Geng Hua , Wang Jieyun
Published: March 2022

Real-time Simulation (RTS) is one of the effective means via which to study device level or system level dynamics, such as power converter online testing, evaluation, and control, and power system stability analysis. The RTS -enabled design-chain offers a time -effective, low-cost, and fail-safe development process. As the penetration of renewable energy is becoming higher, the demand in hybrid system real-time simulation becomes imperative, where fast-dynamic device level power converters and slow -dynamic large -scale power systems are simulated at the same time. This paper introduces a novel hybrid real-time simulation architecture based on the central processing unit (CPU) and the field-programmable gate array (FPGA). Compared with the off-the-shelf power system real-time simulation system, it offers both wide time scale simulation and high accuracy. The multi-time scale model can perform electromechanical electromagnetic transient hybrid simulation, which can be applied to the research of power systems penetrated with power converters. In the proposed simulation platform, the communication delay is introduced when different RTS platforms exchange real-time data. The communication delay should be considered in the stability analysis of the grid-connected inverters in a weak grid environment. Based on the virtual impedance characteristic formed by the control loop with and without communication delay, the impedance characteristics are analyzed and inter-simulator delay impacts are revealed in this paper. Theoretical analysis indicates that the communication delay, contrary to expectation, can improve the virtual impedance characteristics of the system. With the same hardware simulation parameters, the grid-converter system is verified on both the Typhoon system alone and the Typhoon-dSPACE-SpaceR hybrid simulation platform. The THD value of grid current in a weak grid environment that works in the Typhoon system is 4.98%, and 2.38% in the Typhoon-dSPACE-SpaceR hybrid simulation platform. This study eventually reveals the fact that the inter-simulation delay creates the illusion that the control system built in the novel hybrid real-time simulation is more stable under weak grid conditions.
Collaborative Charging Scheduling of Hybrid Vehicles in Wireless Rechargeable Sensor Networks
Authors: Chen Jing-Jing , Yu Chang-Wu
Published: March 2022

Wireless rechargeable sensor networks (WRSN) are utilized in environmental monitoring, traffic video surveillance, medical services, etc. In most existing schemes, WRSNs provide sustainable energy for sensor nodes by employing one or more wireless charging vehicles (WCVs). However, two essential drawbacks, regional limitations and traveling speed limitations, constrain these schemes when applied in hostile and large-scale environments. On the other hand, benefiting from the intrinsic flexibility, high flight speed, low cost, and small size of drones, some works have used drones to charge sensor nodes. However, suffering from limited battery capacities, it is also hard to only use drones in large-scale WRSNs. To overcome the drawbacks of WCVs and drones, we proposed a novelty wireless charging system that deploys WCV, WCV-carried drones, and wireless charging pads (pads) in a large-scale wireless sensor network. Based on this new wireless charging system, we first formulated a pad deployment problem for minimizing the total number of pads subject to each sensor in the pad region that only can be charged by drones. In this work, three near-optimal algorithms, i.e., greedy, K-mean, and static, for the pad deployment problem are proposed. Then, to form a sustainable WRSN, we elucidated the collaborative charging scheduling problem with the deadlines of sensors. To guarantee the maximum number of sensors to be charged before the deadlines, we also presented an approximation algorithm to find the collaborative charging scheduling of WCV and WCV-carried drones with the help of pads based on the three deployment pad schemes. Through extensive simulations, we demonstrate the effectiveness of the proposed deployment pad schemes. and that the number of pads obtained by the greedy and K-mean scheme was generally lower than that of the static scheme with respect to network density, WCV region, and flight range. Then, we also examined the proposed collaborative charging scheduling scheme by extensive simulations. The results were compared and showed the effectiveness of the proposed schemes in terms of lifetime, the percentage of nodes being charged in time, the average move time of drones, the percentage of nodes being charged late by the drones, and the charge efficiency of all vehicles under different traffic loads. Related statistical analyses showed that the percentage of nodes being charged in time and the percentage of nodes being charged late based on the greedy and K-mean schemes were slightly better than those of the static scheme, but the charge efficiency of drones of the static scheme was significantly superior to that of the K-mean scheme under a busy network.
Energy Sustainability on an Offshore Island: A Case Study in Taiwan
Authors: Huang Chia-Yun , Yu Ting-To , Lin Wei-Min , Chung Kung-Ming , Chang Keh-Chin
Published: March 2022

This study determines the energy usage pattern for Penghu, which is an island in the Taiwan Strait, for which electricity is the major energy source. There is an increase in electricity consumption as the number of inhabitants and tourists increases. Water supply from desalination plants represents another major electricity demand. To reduce energy consumption and carbon emissions, subsidy programs are used to encourage the use of LED lighting and energy-efficient appliances (air conditioners and refrigerators), but not for common appliances, such as chest freezers. Abundant solar radiation in summer and the northeast monsoon in autumn/winter make Penghu an ideal location to take advantage of solar energy and wind power. An undersea cable between Penghu and Taiwan is now available and can be used to stabilize the isolated grid system in Penghu. The target for a low-carbon island can be achieved by installing PV systems and onshore and offshore wind turbines to fulfill the energy demand for the tourism industry.
Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis
Authors: Meng Xiangrui , Chang Haoqian , Wang Xiangqian
Published: March 2022

Methane is one of the most dangerous gases encountered in the mining industry. During mining operations, methane can be broadly classified into three states: mining excavation, stoppage safety check, and abnormal methane concentration, which is usually a precursor to a gas accident, such as a coal and gas outburst. Consequently, it is vital to accurately predict methane concentrations. Herein, we apply three deep learning methods—a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU)—to the problem of methane concentration prediction and evaluate their efficacy. In addition, we propose a novel prediction method that combines classical time series analysis with these deep learning models. The results revealed that GRU has the least root mean square error (RMSE) loss of the three models. The RMSE loss can be further reduced by approximately 35% by using the proposed combined approach, and the models are also less likely to result in overfitting. Therefore, combining deep learning methods with classical time series analysis can provide accurate methane concentration prediction and improve mining safety.
A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting
Authors: Saeed Faisal , Paul Anand , Seo Hyuncheol
Published: March 2022

Smart grids provide a unique platform to the participants of energy markets to tweak their offerings based on demand-side management. Responding quickly to the needs of the market can help to improve the reliability of the system, as well as the cost of capital investments. Electric load forecasting is important because it is used to make and run decisions about the power grid. However, people use electricity in nonlinear ways, which makes the electric load profile a complicated signal. Even though there has been a lot of research done in this field, an accurate forecasting model is still needed. In this regard, this article proposed a hybrid cross-channel-communication (C3)-enabled CNN-LSTM model for accurate load forecasting which helps decision making in smart grids. The proposed model is the combination of three different models, i.e., a C3 block to enable channel communication of a CNN (convolutional neural networks) model, two convolutional layers to extract the features and an LSTM (long short-term memory network) model for forecasting. In the proposed hybrid model, Leaky ReLu (rectified linear unit) was used as activation function instead of sigmoid. The channel communication in CNN model makes the proposed model very light and efficient. Extensive experimentation was done on electricity load data. The results show the model’s high efficiency. The proposed model shows 98.3% accuracy and 0.4560 MAPE error.
Characteristics and Affecting Factors of K2qn1 Member Shale Oil Reservoir in Southern Songliao Basin, China
Authors: Li Zhongcheng , Bao Zhidong , Wei Zhaosheng , Wang Hongxue , Zhao Wanchun , Dong Wentao , Shen Zheng , Wu Fan , Tian Wanting , Li Lei
Published: March 2022

Member 1 of the Cretaceous Qingshankou Formation (K2qn1 Member) in the Southern Songliao Basin, composed of mainly semi-deep and deep lacustrine shale layers, is rich in shale oil. Previous studies on shale reservoir characteristics mainly focused on marine shale strata, but few studies have considered lacustrine shale strata, so the pore-throat features and differences between the lacustrine shale reservoir and marine shale reservoir need to be studied. Taking the Class-I and II sweet spot sections and Class-III non-sweet spot section of Da’an shale oil demonstration area as examples, SEM (scanning electron microscopy) was used to qualitatively and semi-quantitatively describe the morphology and occurrence characteristics of the shale. Full-scale pore size distributions of lacustrine shale samples were quantitatively measured by N2GA (nitrogen absorption) combined with dominant pore size segments tested by experiments. Finally, the lacustrine shale reservoir was compared with classical marine shale reservoirs, and factors influencing semi-deep lacustrine and deep lacustrine shale oil in a large depression basin were analyzed by XRD (X-ray diffraction). The results show that Class-I and II sweet spots are rich in organic matter, quartz, and carbonate minerals, have mainly type H2 nitrogen adsorption hysteresis loops, and contain mainly inorganic pores, such as intergranular and intragranular pores in nano-scale, forming nano-scale reservoirs. Lacustrine shale is obviously different from marine shale in terms of pore structure, and the development characteristics of the lacustrine shale pore structure are more influenced by mineral components. Factors affecting the development of shale oil reservoirs in K2qn1 member include mineral components, TOC (total organic carbon), and diagenetic processes. Quartz and carbonate minerals are good for enhancing reservoir quality, while clay minerals are destructive to the development of reservoirs. TOC is the material foundation and main factor for forming organic pores, but the higher the TOC, the smaller the diameter of the organic pores will be. Compaction, cementation, and dissolution are the main diagenetic processes controlling the development of reservoir space.
Scalable IoT Architecture for Monitoring IEQ Conditions in Public and Private Buildings
Authors: Calvo Isidro , Espin Aitana , Gil-García Jose Miguel , Fernández Bustamante Pablo , Barambones Oscar , Apiñaniz Estibaliz
Published: March 2022

This paper presents a scalable IoT architecture based on the edge–fog–cloud paradigm for monitoring the Indoor Environmental Quality (IEQ) parameters in public buildings. Nowadays, IEQ monitoring systems are becoming important for several reasons: (1) to ensure that temperature and humidity conditions are adequate, improving the comfort and productivity of the occupants; (2) to introduce actions to reduce energy consumption, contributing to achieving the Sustainable Development Goals (SDG); and (3) to guarantee the quality of the air—a key concern due to the COVID-19 worldwide pandemic. Two kinds of nodes compose the proposed architecture; these are the so-called: (1) smart IEQ sensor nodes, responsible for acquiring indoor environmental measures locally, and (2) the IEQ concentrators, responsible for collecting the data from smart sensor nodes distributed along the facilities. The IEQ concentrators are also responsible for configuring the acquisition system locally, logging the acquired local data, analyzing the information, and connecting to cloud applications. The presented architecture has been designed using low-cost open-source hardware and software—specifically, single board computers and microcontrollers such as Raspberry Pis and Arduino boards. WiFi and TCP/IP communication technologies were selected, since they are typically available in corporative buildings, benefiting from already available communication infrastructures. The application layer was implemented with MQTT. A prototype was built and deployed at the Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), using the existing network infrastructure. This prototype allowed for collecting data within different academic scenarios. Finally, a smart sensor node was designed including low-cost sensors to measure temperature, humidity, eCO2, and VOC.
Techno-Economic Analysis of Intermediate Pyrolysis with Solar Drying: A Chilean Case Study
Authors: Zimmer Tobias , Rudi Andreas , Glöser-Chahoud Simon , Schultmann Frank
Published: March 2022

Intermediate pyrolysis can be used to obtain high-quality biofuels from low-value residues such as sewage sludge or digestate. A major obstacle is the high water content of sludgy biomass, which requires an energy-intensive and expensive drying step before pyrolysis. Solar greenhouse drying is an efficient and sustainable alternative to a thermally heated belt dryer. In this study, a techno-economic assessment of intermediate pyrolysis with solar drying is carried out. Marketable products of the process are bio-oil, a substitute for diesel or heating oil, and bio-char with various possible applications. Chile is chosen as the setting of the study as its 4000 km long extension from north to south gives the opportunity to evaluate different locations and levels of solar irradiation. It is found that solar drying results in higher capital investment, but lower fuel costs. Depending on the location and solar irradiation, solar drying can reduce costs by 5–34% compared to belt drying. The break-even price of bio-char is estimated at 300–380 EUR/ton after accounting for the revenue from the liquid bio-oil.
Single-Phase Charging of EV Embedded Batteries in an MMC with Submodule Override Capability
Authors: Omar Alaa , Wood Alan , Laird Hamish , Gaynor Paul
Published: March 2022

The modular multilevel converter with embedded batteries is a viable alternative in electric vehicle drive systems. This research investigates integrated charging of the embedded batteries in a three-phase converter from a single-phase source without any additional hardware requirements. Two phases of the converter are connected to the supply while the third converter leg is not connected. In a typical charging of the converter batteries, the batteries with a lower state of charge are prioritised. Then all batteries are balanced at an average global state of charge. This research proposes a new sorting algorithm of the converter batteries with an override capability to bypass any individual submodule if required. This bypassing capability is modulation method independent, thus can be integrated with any modulation method. Simulation results and a real-time emulation of the charging system validate the proposed control method and the override algorithm. In addition, an open-loop switching technique is integrated with a new nearest level control-based measurement method of the battery terminal voltage to estimate its initial state of charge. This measurement facilitates a reduced number of voltage sensors for hardware implementation. This method was simulated and validated by comparing the calculated and the measured values of the battery initial state of charge. Real-time emulation of the system utilising hardware-in-the-loop apparatus was carried out, which confirmed the developed control system functions as expected.
Online Estimation of the Mechanical Parameters of a Wind Turbine with Doubly Fed Induction Generator by Utilizing Turbulence Excitations
Authors: Lai Yening , Zhu Ling , Pan Xueping , Guo Jinpeng , He Dazhuang , Liang Wei
Published: March 2022

In this paper, a new method using wind turbulence excitation is proposed to estimate the parameters of the mechanical system (drivetrain and pitch angle controller) in a Doubly Fed Induction Generator (DFIG) Wind Turbine (WT). Firstly, simulations were carried out for a DFIG WT under turbulence excitations. The spectral contents of the responses imply that the transients of the electrical system (generator and converter), which are much faster than those of the mechanical system, can be neglected when estimating the mechanical parameters. Based on this, a simplified model related to the mechanical system of the DFIG WT was derived by applying the model reduction technique. Secondly, the parameter sensitivity of Power Spectral Density (PSD) was used to quantify the impacts of individual parameters on the dynamics of the mechanical system, and the influential parameters were selected on the basis of the sensitivity results. Finally, a weighted least-squares optimization problem, which is suitable for a system with close oscillation modes, was formulated for parameter estimation. The estimation results based on two different types of optimization methods were compared, and their estimation accuracies validate the effectiveness of the proposed method.
A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend
Authors: Rana Md Masud , Atef Mohamed , Sarkar Md Rasel , Uddin Moslem , Shafiullah GM
Published: March 2022

This study aims to review the potential benefits of peak load shaving in a microgrid system. The relevance of peak shaving for a microgrid system is presented in this research review at the outset to justify the peak load shaving efficacy. The prospective benefits of peak shaving in microgrid systems, including technological, economic, and environmental advantages, are thoroughly examined. This review study also presents a cost–benefit numerical analysis to illustrate the economic viability of peak load shaving for a microgrid system. Different peak shaving approaches are briefly discussed, as well as the obstacles of putting them into practice. Finally, this review study reveals some potential future trends and possible directions for peak shaving research in microgrid systems. This review paper lays a strong foundation for identifying the potential benefits of peak shaving in microgrid systems and establishing suitable projects for practical effectuation.
Qualitative Analysis of Household Energy Awareness in Poland
Authors: Rosak-Szyrocka Joanna , Żywiołek Justyna
Published: March 2022

Because of disruptive changes in energy businesses and services, as well as changes in people’s lifestyles around the world, energy demand and consumption have risen quickly in recent decades. A household energy awareness literature review was conducted. The survey was conducted in Poland with over 1097 respondents. Additionally, it was given a qualitative analysis. At the same time, in order to consider why energy consumption in households is increasing, a traditional quality management tool was used—the Ishikawa diagram. The Pareto–Lorenz diagram was used to analyze the causes of the increase in energy consumption in households. A model of the causes of increasing energy consumption in households has been built. The researchers calculated the chi-square test, which allows them to determine Pearson’s C coefficient. The C-Pearson coefficient can be calculated using the Chi square value. The results of the study clearly show that the respondents have knowledge of energy and the factors that influence the increase in its consumption but are not aware of why energy should be saved and what it means for the environment. The authors propose ways to build the awareness of household users through a modern quality management tool called the interrelationship diagram.
Study on Geometric Characteristics and Quantitative Description Method of Casing Deformation during Shale Reservoir Hydraulic Fracturing
Authors: Wang Kongyang , Deng Jingen , Yan Wei , Nguu Dickson Muchiri , Yin Qiwu , Huang Shengsong
Published: March 2022

Casing deformation is a common but serious problem experienced during hydraulic fracturing operations in shale reservoirs. The Multi-Finger Imaging Tool is used to measure the casing deformation where the casing inner diameter is the only parameter used to characterize the deformation. Many deformed casing geometric details are often ignored, and these geometric characteristics are helpful for revealing the casing deformation mechanism. In this study, we established a quantitative method to describe the casing deformation using methods of judging the similarity of curves. By comparing the field casing deformation sections and the initial casing section, we categorized the casing deformation sections into concave and elliptical types. Furthermore, using the centroid calculation, elliptical type was sub-divided into symmetric ellipse type and eccentric ellipse type. On the basis of the Weiyuan and Guandong oil field’s fault distribution maps, we demonstrated that the fault slip could be the main cause of concave type and eccentric ellipse type. A numerical study was then carried out to ascertain whether fault slip can cause concave type and eccentric ellipse type casing deformations and to establish the relationship between fault slip magnitude and casing deformation. The results support the idea that concave type and eccentric ellipse type casing deformation are caused by the fault slip. Sensitivity analysis showed that the shape of the casing section was largely influenced by the dip angle, while the change of the casing inner diameter was largely influenced by the strike angle. The method proposed herein presents a useful step towards the prediction of the causes of casing deformation and provides a relationship between casing inner diameter change and fault slip.
Understanding Off-Gassing of Biofuel Wood Pellets Using Pellets Produced from Pure Microcrystalline Cellulose with Different Additive Oils
Authors: Siwale Workson , Frodeson Stefan , Finell Michael , Arshadi Mehrdad , Jonsson Carina , Henriksson Gunnar , Berghel Jonas
Published: March 2022

Fuel wood pellets have the tendency of undergoing self-heating and off-gassing during storage and transportation. Self-heating can lead to spontaneous combustion and cause fires while toxic gasses such as carbon monoxide and some volatile organic compounds released due to off-gassing are a human health and environmental hazard. Previous research suggests that the self-heating and off-gassing of wood pellets are as a result of the oxidation of wood extractives. The aim of this study was to identify the extractives, i.e., fatty and resin acids that are responsible for the emissions of carbon monoxide, carbon dioxide and methane from wood pellets by testing the off-gassing tendencies of pellets produced from synthetic microcrystalline cellulose and different additive oils. The additive oils were intentionally selected to represent different types of wood extractives (mainly fatty and resin acids) and they included: tall oil, pine rosin, linseed oil and coconut oil. The highest mean concentrations of carbon monoxide, carbon dioxide and methane were recorded from cellulose pellets with added linseed oil. The concentrations of carbon monoxide and methane for the other four pellet types were negligible and there was no carbon dioxide emission. Pellets with added linseed oil had high off-gas emissions due to the high content of unsaturated fatty acids compared to other pellet types.
An Electrothermal Model to Predict Thermal Characteristics of Lithium-Ion Battery under Overcharge Condition
Authors: Hamisi Charles Mohamed , Chombo Pius Victor , Laoonual Yossapong , Wongwises Somchai
Published: March 2022

Understanding the thermal characteristics of lithium-ion batteries (LIBs) under various operating situations is critical for improving battery safety. Although the application of LIBs in the real world is mostly transient, many previous models consider the phenomenon of the constant state. This study examines thermal behavior by developing a 2D electrothermal model to predict the thermal behavior of LIBs with overcharge abuse in high thermal conditions. The 18,650 cylindrical LiCoO2 graphite is investigated in a thermally controlled chamber at 35, 50, and 60 °C with a K-type thermocouple mounted on the LIB surface under charging rates of 1C, 2C, and 3C to acquire quantitative data regarding the thermal response of LIBs. Maximum critical temperatures are found at 62.6 to 78.9 °C, 66.4 to 83.5 °C, and 72.1 to 86.6 °C at 1C, 2C, and 3C, respectively. Comparing simulation analysis and experimental conditions, the highest relative error of 1.71% was obtained. It was found that relative errors increase as the charging rate increases. Moreover, increasing the charging current and surrounding temperature significantly increases the battery’s surface temperature. Furthermore, battery heat distribution appears almost uniform and tends to increase towards the positive terminal because cathode material is highly resistant. In addition, increasing the LIB heat transfer coefficient could positively improve the battery performance by eventually curbing the rise in battery temperature and reducing non-uniformity.
Investigation of Heat Transfer and Pressure Drop for R744 in a Horizontal Smooth Tube of R744/R404A Hybrid Cascade Refrigeration System—Part 1: Intermediate Temperature Region
Authors: Jeon Min-Ju
Published: March 2022

In this study, the evaporation heat transfer characteristics of an intermediate temperature of R744 in a smooth horizontal tube, when operating as an indirect refrigeration system (IRS) among hybrid cascade refrigeration systems (HCRSs), are evaluated. Studies on the characteristics of intermediate-temperature evaporation heat transfer under the operating conditions of evaporators used in actual refrigeration systems, such as IRS, cascade refrigeration systems, and HCRS, used in supermarkets are lacking. Thus, this study provides basic data on the characteristics of evaporation heat transfer of R744 in the evaporators of refrigerators used in supermarkets. The tube employed to the evaporation experiment in this study was a horizontal smooth copper tube with a length of 8000 mm and an inner diameter of 11.46 mm. The experimental variables were measured over a wide range of mass flux of 200–500 kg/(m2·s), heat flux of 10–40 kW/m2, and saturation temperature of −40–0 °C. The main results are summarized as follows: (1) The application of Kandlikar’s correlation formula at an evaporation temperature of −20 °C in an IRS helps in a good prediction of the R744 evaporation heat transfer coefficient. (2) The pressure drop according to the heat and mass flux showed the same heat transfer coefficient trend, but the pressure drop at saturation temperature was different from the trend of heat transfer coefficient.
LPSRS: Low-Power Multi-Hop Synchronization Based on Reference Node Scheduling for Internet of Things
Authors: Elsharief Mahmoud , El-Gawad Mohamed A. Abd , Ko Haneul , Pack Sangheon
Published: March 2022

Time synchronization is one of the most fundamental problems on the internet of things (IoT). The IoT requires low power and an efficient synchronization protocol to minimize power consumption and conserve battery power. This paper introduces an efficient method for time synchronization in the IoT called low-power multi-hop synchronization (LPSRS). It employs a reference node scheduling mechanism to avoid packet collisions and minimize the communication overhead, which has a big impact on power consumption. The performance of LPSRS has been evaluated and compared to previous synchronization methods, HRTS and R-Sync, via real hardware networks and simulations. The results show that LPSRS achieves a better performance in terms of power consumption (transmitted messages). In particular, for a large network of 450 nodes, LPSRS reduced the total number of transmitted messages by 53% and 49% compared to HRTS and R-Sync, respectively.
Evaluation Methodology of Rotary Flow Dividers Used as Pressure Intensifiers with Creation of a New Pressure Multiplying Efficiency
Authors: Hradecký Jakub Milan , Bubák Antonín , Dub Martin
Published: March 2022

For most of the technical community, rotary flow dividers are known for synchronization of two or more hydraulic actuators. However, there is also a possibility to use them for pressure multiplication, flow regeneration, or speed control. For those applications, there is a need to describe the behavior of its quantities. This article reveals a new evaluation methodology for rotary flow dividers when they are unconventionally used as pressure multipliers and also reveals a new quantity-pressure multiplying efficiency. Then, there is an experiment provided between two rotary flow dividers with different designs, where there is a new evaluation methodology used. On the base of that, it is possible to compare and decide which divider is more likely to be used in multiplying circuits and more suitable for further investigation from the perspective of new designs. With this evaluation methodology, it is possible to compare much more than two different dividers. It is possible to run more tests and experiments with arbitrary dividers, and their new design changes to reach as efficient a pressure multiplication or flow regeneration as possible.
Research on Fuel Cell Fault Diagnosis Based on Genetic Algorithm Optimization of Support Vector Machine
Authors: Huo Weiwei , Li Weier , Sun Chao , Ren Qiang , Gong Guoqing
Published: March 2022

The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learning machine and a support vector machine are applied to classify the usual faults of fuel cells, including air compressor faults, air supply pipe and return pipe leaks, stack flooding faults and temperature controller faults. The accuracy of fault classification was 78.67% and 83.33% respectively. In order to improve the efficiency of fault classification, a genetic algorithm is used to optimize the parameters of the support vector machine. The simulation results show that the accuracy of fault classification was improved to 94% after optimization.
Dual-Active-Bridge Model and Control for Supporting Fast Synthetic Inertial Action
Authors: Cuoghi Stefania , Mandrioli Riccardo , Pittala Lohith Kumar , Cirimele Vincenzo , Ricco Mattia
Published: March 2022

This article proposes a dual-active-bridge control to support the fast synthetic inertial action in DC microgrids. First of all, the selection of the isolated DC/DC converter to link an energy storage system with the DC bus in a microgrid is analyzed and the advantages of the dual-active-bridge converter controlled by a single-phase shift modulation justify its selection. An active front-end can be then adapted to connect the DC bus with an AC grid. Secondly, this paper presents the design of a discrete PI controller for supporting fast synthetic inertial action. In particular, a discrete dual-active-bridge model based on the transferred power between both converter bridges, which overcomes the approximations of the output current linearization model, is proposed. Moreover, the article introduces a novel equation set to directly and dynamically tune discrete PI parameters to fulfill the design frequency specifications based on the inversion formulae method. In this way, during the voltage/power transients on the DC bus, the controller actively responds and recovers those transients within a grid fundamental cycle. Since the developed set of control equations is very simple, it can be easily implemented by a discrete control algorithm, avoiding the use of offline trial and error procedures which may lead to system instability under large load variations. Finally, the proposed control system is evaluated and validated in PLECS simulations and hardware-in-the-loop tests.