bluuebunny/crossref_metadata_2025 huggingface dataset:
A ConvNext-Based and Feature Enhancement Anchor-Free Siamese Network for Visual Tracking
Authors: Xu Qiguo , Deng Honggui , Zhang Zeyu , Liu Yang , Ruan Xusheng , Liu Gang
Published: August 2022

Existing anchor-based Siamese trackers rely on the anchor’s design to predict the scale and aspect ratio of the target. However, these methods introduce many hyperparameters, leading to computational redundancy. In this paper, to achieve outstanding network efficiency, we propose a ConvNext-based anchor-free Siamese tracking network (CAFSN), which employs an anchor-free design to increase network flexibility and versatility. In CAFSN, to obtain an appropriate backbone network, the state-of-the-art ConvNext network is applied to the visual tracking field for the first time by improving the network stride and receptive field. Moreover, A central confidence branch based on Euclidean distance is offered to suppress low-quality prediction frames in the classification prediction network of CAFSN for robust visual tracking. In particular, we discuss that the Siamese network cannot establish a complete identification model for the tracking target and similar objects, which negatively impacts network performance. We build a Fusion network consisting of crop and 3Dmaxpooling to better distinguish the targets and similar objects’ abilities. This module uses 3DMaxpooling to select the highest activation value to improve the difference between it and other similar objects. Crop unifies the dimensions of different features and reduces the amount of computation. Ablation experiments demonstrate that this module increased success rates by 1.7% and precision by 0.5%. We evaluate CAFSN on challenging benchmarks such as OTB100, UAV123, and GOT-10K, validating advanced performance in noise immunity and similar target identification with 58.44 FPS in real time.
Leveraging Deep Features Enhance and Semantic-Preserving Hashing for Image Retrieval
Authors: Zhao Xusheng , Liu Jinglei
Published: August 2022

The hash method can convert high-dimensional data into simple binary code, which has the advantages of fast speed and small storage capacity in large-scale image retrieval and is gradually being favored by an increasing number of people. However, the traditional hash method has two common shortcomings, which affect the accuracy of image retrieval. First, most of the traditional hash methods extract many irrelevant image features, resulting in partial information bias in the binary code produced by the hash method. Furthermore, the binary code made by the traditional hash method cannot maintain the semantic similarity of the image. To find solutions to these two problems, we try a new network architecture that adds a feature enhancement layer to better extract image features, remove redundant features, and express the similarity between images through contrastive loss, thereby constructing compact exact binary code. In summary, we use the relationship between labels and image features to model them, better preserve the semantic relationship and reduce redundant features, and use a contrastive loss to compare the similarity between images, using a balance loss to produce the resulting binary code. The numbers of 0s and 1s are balanced, resulting in a more compact binary code. Extensive experiments on three commonly used datasets—CIFAR-10, NUS-WIDE, and SVHN—display that our approach (DFEH) can express good performance compared with the other most advanced approaches.
A Modified Active-Disturbance-Rejection Control with Sliding Modes for an Uncertain System by Using a Novel Reaching Law
Authors: Zhang Dong , Wu Tao , Shi Shangyao , Dong Zhen
Published: August 2022

This article presents a modified active-disturbance-rejection control (ADRC) combined with a sliding mode control (SMC) regarding the tracking control problems for plants with unmatched uncertainty. The proposed modified active-disturbance-rejection control with sliding mode (ADRC-SM) employs a reduced-order extended state observer (ESO) for estimating various uncertainties of system in time, including unmatched and matched uncertainties. Meanwhile, a novel reaching law of SMC was designed by using the cycloid function as the main controller of ADRC, which ensures the robustness of the uncertain system. Due to the reduced-order ESO tracking and compensating for various uncertainties in the system as a total disturbance, the upper bound of the disturbance in the SMC is relaxed. The gain coefficient of the reaching law only needs to be designed to be larger than the limit of the lumped disturbance; thus, the chattering problem is greatly reduced. The designed new reaching law of the cycloid function shortens the time for the system state’s convergence to the sliding mode’s surface. The cycloid function replaces the switching function in the traditional reaching law, making the actual control input continuous and shortening the approach time. Compared with traditional ADRC-SM, the use of multiple ESOs or intelligent algorithms to approximate plant parameters can be avoided, the design is simplified, its robustness is enhanced, computational costs are reduced, and the convergence time is reduced. The controlled object with unmatched uncertainty is transformed into a system with matched uncertainty using state-space transformation, which reduces the complexity of the controller’s design. In addition, the stability analysis of the closed-loop system is carried out based on the Lyapunov method. Simulations and experiments verify that the modified ADRC-SM has the merits of fast response, small overshoot, small steady-state error, strong anti-interference competence, and high control accuracy.
A Deep Learning Method Based on the Attention Mechanism for Hardware Trojan Detection
Authors: Tang Wenjing , Su Jing , He Jiaji , Gao Yuchan
Published: August 2022

The chip manufacturing of integrated circuits requires the participation of multiple parties, which greatly increases the possibility of hardware Trojan insertion and poses a significant threat to the entire hardware device landing; however, traditional hardware Trojan detection methods require gold chips, so the detection cost is relatively high. The attention mechanism can extract data with more adequate features, which can enhance the expressiveness of the network. This paper combines an attention module with a multilayer perceptron and convolutional neural network for hardware Trojan detection based on side-channel information, and evaluates the detection results by implementing specific experiments. The results show that the proposed method significantly outperforms machine learning classification methods and network-related methods, such as SVM and KNN, in terms of accuracy, precision, recall, and F1 value. In addition, the proposed method is effective in detecting data containing one or multiple hardware Trojans, and shows high sensitivity to the size of datasets.
Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection
Authors: Hussain Muhammad , Al-Aqrabi Hussain , Hill Richard
Published: August 2022

This paper presents a framework for photovoltaic (PV) fault detection based on statistical, supervised, and unsupervised machine learning (ML) approaches. The research is motivated by a need to develop a cost-effective solution that detects the fault types within PV systems based on a real dataset with a minimum number of input features. We discover the appropriate conditions for method selection and establish how to minimize computational demand from different ML approaches. Subsequently, the PV dataset is labeled as a result of clustering and classification. The labelled dataset is then trained using various ML models before evaluating each based on accuracy, precision, and a confusion matrix. Notably, an accuracy ranging from 94% to 100% is achieved with datasets from two different PV systems. The model robustness is affirmed by performing the approach on an additional real-world dataset that exhibits noise and missing values.
Transformation of Energy Markets: Description, Modeling of Functioning Mechanisms and Determining Development Trends
Authors: Pietrzak Michał Bernard , Kuc-Czarnecka Marta
Published: August 2022

One of the key contemporary economic and social issues today is the global energy transition [...]
Low Carbon Economic Dispatch Optimization of Regional Integrated Energy Systems Considering Heating Network and P2G
Authors: Luo Zhao , Wang Jinghui , Xiao Ni , Yang Linyan , Zhao Weijie , Geng Jialu , Lu Tao , Luo Mengshun , Dong Chenming
Published: August 2022

Against a background of the energy internet and low-carbon electricity, regional integrated energy system (RIES) has become a key way to achieve sustainable energy development, leading to reduced operating costs and system carbon emissions, and improved system operating efficiency. This paper puts forward a low-carbon economic dispatching optimization method for RIES with a heating network and power-to-gas (P2G). First, the heating network model and the mathematical model of P2G were constructed. Second, the carbon trading mechanism was introduced, the objective function being: to minimize the sum of the system operating cost and carbon trading cost; and ensure that the balance of cooling, heating, electric power, and the operating constraints—of RIES and the heating network—were comprehensively considered. Finally, the CPLEX optimization software simulation was used. The results show that the proposed method can take into account both low-carbon and economic factors, and can provide a reference for RIES low-carbon economic dispatch.
A Flexible DC–DC Converter with Multi-Directional Power Flow Capabilities for Power Management and Delivery Module in a Hybrid Electric Aircraft
Authors: Bhattacharya Sumantra , Anagnostou Dimosthenis , Schwane Patrick , Bauer Christiane , Kallo Josef , Willich Caroline
Published: August 2022

The development of fully electric and hybrid electric aircraft using fuel cells and batteries has lately received much attention since the technology can contribute to achieving an emission-free world. For hybridisation of fuel cells and batteries, the challenge of integrating different voltage sources to supply power to different loads at different voltage levels has to be met. Presently, this is achieved by using a combination of complex passive circuitry and DC–DC converters. In this paper, a new galvanically isolated DC–DC converter topology is proposed; it integrates a battery and fuel cells as input sources and supplies power to a high voltage (HV) and a low voltage (LV) load simultaneously. The converter enables power flow from the sources to the load, as well as between the sources and power flow back towards the battery during recuperation for recharging the battery during flight. The functionality of the converter design is verified using a real-time (RT) hardware-in-the-loop (HiL) test. The proposed concept is easily scalable and can be adopted in any fuel cell and battery drive train of all-electric flights of different sizes.
Integrated Predictive Control of PMLSM Current and Velocity Based on ST-SMO
Authors: Du Shenhui , Zhang Zihao , Wang Jinsong , Wang Kangtao , Zhao Hui , Li Zheng
Published: August 2022

To enhance the control performance of a permanent magnet linear synchronous motor (PMLSM) and to improve its dynamic response performance and steady-state accuracy, a PMLSM model predictive integrated control (MPC) system based on a super-twisting sliding mode observer (ST-SMO) is proposed. According to the mathematical model of a PMLSM, this paper designs a three-step model to predict the comprehensive control correction factor, optimize the prediction speed and current, reduce the response time, and enhance the system’s stability. Meanwhile, in order to solve the problem of the PMLSM’s high dependence on mechanical sensors, the ST-SMO is introduced to observe the rotation speed of PMLSM, which has better tracking performance and observation accuracy than a traditional sliding mode observer (SMO). Finally, the experimental verification is carried out on the PMLSM experimental platform. The software simulation and hardware experiment results show that the control system designed in this paper not only simplifies the overall structure of the system, but it also has better control performance and tracking ability. Compared with traditional control methods and SMO, it has better control performance, stability, and speed-tracking performance.
Dynamic Modeling and Comparison Study of Control Strategies of a Small-Scale Organic Rankine Cycle
Authors: Zhou Yuhao , Ruan Jiongming , Hong Guotong , Miao Zheng
Published: August 2022

The control strategy is crucial for the effective and safe operation of the ORC system. A transient model of the ORC system was developed in the present work and validated by the experimental data of a 4 kW ORC prototype. Then, the effect of heat source temperature on the dynamic response and operation characteristics of the ORC system were analyzed. Five control strategies were compared: the constant working fluid mass flow rate mode, constant vapor superheat mode, constant vapor temperature mode, constant evaporation pressure mode and constant output power load mode. Under the constraint that the working fluid at the expander inlet should be superheated, we found that the constant vapor superheat mode enabled the safe operation with the largest range of heat source temperature, while the other four modes were only available for a certain temperature range. Apart from the constant output power mode, the constant evaporation pressure mode can also provide a relatively stable performance for the ORC unit. The variation of the thermal efficiency was limited when the heat source temperature was higher than 125 °C, except for the constant vapor temperature mode. Considering the high performance and stable operation of the ORC system, it is necessary to have different operation modes combined in the control strategy according to the specific working scenarios.
Hierarchical Stochastic Optimal Scheduling of Electric Thermal Hydrogen Integrated Energy System Considering Electric Vehicles
Authors: Jia Shiduo , Kang Xiaoning , Cui Jinxu , Tian Bowen , Xiao Shuwen
Published: August 2022

After a large number of electric vehicles (EVs) are connected to the integrated energy system, disorderly charging and discharging of EVs will have a negative impact on the safe and stable operation of the system. In addition, EVs’ uncertain travel plans and the stochastic fluctuation of renewable energy output and load power will bring risks and challenges. In view of the above problems, this paper establishes a hierarchical stochastic optimal scheduling model of an electric thermal hydrogen integrated energy system (ETH-IES) considering the EVs vehicle-to-grid (V2G) mechanism. The EVs charging and discharging management layer aims to minimize the variance of the load curve and minimize the dissatisfaction of EV owners participating in V2G. The multi-objective sand cat swarm optimization (MSCSO) algorithm is used to solve the proposed model. On this basis, the daily stochastic economic scheduling of ETH-IES is carried out with the goal of minimizing the operation cost. The simulation results show that the proposed strategy can better achieve a win-win situation between EV owners and microgrid operators, and the operation cost of the proposed strategy is reduced by 16.55% compared with that under the disorderly charging and discharging strategy, which verifies the effectiveness of the proposed model and algorithm.
DevOps Model Appproach for Monitoring Smart Energy Systems
Authors: Lévy Loup-Noé , Bosom Jérémie , Guerard Guillaume , Amor Soufian Ben , Bui Marc , Tran Hai
Published: August 2022

Energy systems are often socio-technical complex systems that are facing new challenges regarding technological and environmental changes. Because of their complex nature, they cannot be approached solely through analytical modeling, hence the inefficiency of most classical modeling approaches. In this article, a Hybrid Approach based on both systemic and analytical modeling is presented and applied to a case study. From this novel approach, a model—the Multi-Institution Building Energy System—is presented. It allowed us to highlight and detail the need for greater governance of energy systems. The socio-technical solutions identified to answer the issues of governance (Accuracy, Reliability and Fairness) were DevOps methodology and the use of Distributed Microservices Architecture. Based on this framework, the design of a Decision Support System assuring and exploiting state-of-the-art scalable tools for data management and machine learning factories is described in this article. Moreover, we wish to set up the conceptual basis necessary for the design of a generic theoretical framework of optimization applicable to complex socio-technical systems in the context of the management of a shared resource.
A Bibliometric Analysis of Solar Energy Forecasting Studies in Africa
Authors: Zwane Nosipho , Tazvinga Henerica , Botai Christina , Murambadoro Miriam , Botai Joel , de Wit Jaco , Mabasa Brighton , Daniel Siphamandla , Mabhaudhi Tafadzwanashe
Published: August 2022

Solar energy forecasting is considered an essential scientific aspect in supporting efforts to integrate solar energy into power grids. Moreover, solar energy forecasting plays an essential role in mitigating greenhouse gas emissions and conserving energy for future use. This study conducted a bibliometric analysis to assess solar energy forecasting research studies evolution at the continental (Africa) and southern Africa levels. Key aspects of analysis included (i) scientific research trends, (ii) nature of collaboration networks, (iii) co-occurrence of keywords and (iv) emerging themes in solar energy forecasting over the last two decades, between the years 2000–2021. The results indicate that solar energy forecasting research has, on average, expanded by 6.4% and 3.3% in Africa and southern Africa, respectively. Based on the study context, solar energy forecasting research only gained momentum in 2015, peaking in 2019, but it is generally still subtle. The scientific mapping illustrated that only South Africa ranks among the leading countries that have produced high numbers of published documents and also leads in contributions to the research area in both Africa and southern Africa. Three emerging topics were identified from the thematic map analysis—namely, “solar irradiance”, “artificial intelligence” and “clear sky”, which implies that researchers are paying attention to solar irradiance, using modelling techniques that incorporate machine learning techniques. Overall, this study contributes to scientific information on the potential bankability of renewable energy projects that could assist power utilities, governments and policymakers in Africa to enforce the green economy through accelerated decarbonisation of the energy systems and building relationships with developed countries for support and better transitioning to solar energy. From a Water–Energy–Food nexus perspective, the results of this work could assist the scientific community in Africa to take advantage of the inherent interconnectedness of water, energy and food resources, whilst also advancing the use of integrated solutions to shape the focus of solar energy research into a more systems thinking and transdisciplinary approach involving the interconnected primary resources and stakeholders pursuit of the Sustainable Development Goals.
Robust Multi-Objective H2/H∞ Load Frequency Control of Multi-Area Interconnected Power Systems Using TS Fuzzy Modeling by Considering Delay and Uncertainty
Authors: Mohseni Naser Azim , Bayati Navid
Published: August 2022

The main objective of this paper is to design a robust multi-objective H2/H∞ delayed feedback controller for load frequency control of a multi-area interconnected power system by taking into account all theoretical and practical constraints. To achieve more precise modelling and analysis, the limitation of valve position, governor, and transmission delay are considered to guarantee of LFC system’s stability in practical applications. The nonlinear delayed system is approximated by the Takagi–Sugeno fuzzy model. Then, a parallel distributed compensation scheme is utilized for designing the control system of the overall system. The proposed multi-objective and robust H2/H∞ controller simultaneously minimizes the H2 and H∞ control performance indexes. Finally, simulation results verify the robustness and effectiveness of the proposed scheme in dealing with the impact of load disturbances, model uncertainties, transmission time delays, and nonlinearities in the model.
Do Smart Cities Restrict the Carbon Emission Intensity of Enterprises? Evidence from a Quasi-Natural Experiment in China
Authors: Liu Yituan , Li Qihang , Zhang Zheng
Published: August 2022

The concept of “smart cities” plays a positive role in the overall green and sustainable development of a nation. However, it is still debated whether smart cities can restrain the carbon emission intensity at the micro-level and promote the green transformation of enterprises. To this end, based on China’s smart city policy (SCP) and regional enterprise data from 2008 to 2015, we study the impact of SCP on the carbon emission intensity of local enterprises, using the difference-in-differences method. The results show that SCP significantly reduces the carbon emission intensity of enterprises, and the estimated results remain significant after the propensity score matching. The mechanism analysis finds that digital transformation, innovation by enterprises, and urban green innovation all strengthen the impact of SCP on the carbon emission intensity of enterprises. The conclusions extend the scope of the existing research and provide suggestions for micro-enterprises to take advantage of SCP for better development.
2D Combustion Modeling of Cell Venting Gas in a Lithium-Ion Battery Pack
Authors: Zhang Yuanying , Wang Enhua , Li Cheng , Wang Hewu
Published: August 2022

With the rapid development of lithium-ion battery technology, powertrain electrification has been widely applied in vehicles. However, if thermal runaway occurs in a lithium-ion battery pack, the venting gas in the cells will spread and burn rapidly, which poses a great threat to safety. In this study, a 2D CFD simulation of the combustion characteristics of cell venting gas in a lithium-ion battery pack is performed, and the possibility of detonation of the battery pack is explored. First, a numerical model for the premixed combustion of venting gas is established using a two-step combustion mechanism. The combustion characteristics are then simulated in a 2D channel for the stoichiometric combustible mixture, and the variations in the flame velocity and pressure increment in the flow channel are analyzed. Next, the effects of the initial conditions inside the battery pack, including the pressure, temperature, and excess air coefficient, on the flame propagation process and pressure variation are evaluated. The results indicate that the flame velocity increases with the increase in the initial pressure or temperature and that the influence of the initial temperature is more acute. The maximum flame speed is achieved with a slightly rich mixture, about 450 mm·s−1. When the excess air coefficient is around 0.9, the flame propagation changes from a slow deflagration to a fast deflagration, which causes a high risk of explosion for the battery pack.
A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion
Authors: Huang Qiuhong , Wang Xiao
Published: August 2022

To improve the predicting accuracy of wind power, this paper proposes a forecasting model of wind power based on the IPSO–LSTM model and classified fusion, which not only overcomes the shortcoming of the artificially determined parameters of LSTM, but also solves the problem that the fused accuracy may be reduced by the environment when adopting a single fusion model. Firstly, some wind speed sub-series were obtained by decomposing the original wind speed according to the wavelet packet decomposition (WPD), and the data sets formed by combining these sub-series with meteorological elements. Subsequently, the wind power components formed by wind speed decomposition are predicted through the long short-term memory neural network (LSTM), which is optimized by the improved particle swarm optimization (IPSO). Consequently, the predicting value of the final wind power was acquired by adopting the method of classified fusion to calculate the wind power components. Several case studies were carried out on the proposed model with the help of Python. It is found from those relevant results that the RMSE and MAE of the proposed model is 1.2382 and 0.8210, respectively. Moreover, the R2 is 0.9952. Those simulating results show that the proposed model may be better for fitting the actual curve of wind power and has excellent predicting accuracy.
Interaction Mechanism of the Upper and Lower Main Roofs with Different Properties in Close Coal Seams: A Case Study
Authors: Xie Shengrong , Wu Yiyi , Guo Fangfang , Chen Dongdong , Wang En , Zhang Xiao , Zou Hang , Liu Ruipeng , Ma Xiang , Li Shijun
Published: August 2022

Close-distance coal seams are widely distributed in China, and the mining of overlying coal seams leads to floor damage. To grasp the properties and the fracture spans of the damaged main roof in the underlying coal seam, combining the calculation of the floor damage depth with rock damage theory and the formulas for calculating the first and periodic weighting intervals of the damaged main roof and the instability conditions of the damaged key blocks are obtained. Three interaction stability mechanics models are proposed for key blocks with different properties of the upper and lower main roof, and the instability conditions of the lower damaged key blocks are obtained when the fracture lines overlap. When combined with a specific example, the field monitoring verified the calculation results. The research results are as follows: (1) The first and periodic weighting intervals, horizontal thrust between blocks, and critical load of instability of the damaged main roof are significantly reduced. Still, there are differences in its reduction under different loads, rotation angles, and lumpiness. (2) When the fracture lines of the upper and lower main roofs overlap, the stability of the damaged key blocks is the lowest. There are three linkage stability regions in the critical load curves of the two key blocks. (3) In this case, the damage equivalent of the main roof is 0.397, which belongs to the local damage type. Its first and periodic weighting intervals are 40 m and 16 m, which is 22% and 24% less than when there is no damage. (4) A supporting load of 0.489 MPa is required to maintain the stability of the upper key block, and the lower damaged key block is prone to rotary and sliding instability during the first and periodic weighting, respectively. Thus, the supports need to bear a total of 0.988 MPa and 0.761 MPa to maintain the stability of the two key blocks simultaneously. The ground pressure data monitored on-site is in accord with the calculation results.
Functional Logistic Regression for Motor Fault Classification Using Acoustic Data in Frequency Domain
Authors: Poręba Jakub , Baranowski Jerzy
Published: August 2022

Motor diagnostics is an important subject for consideration. Electric motors of different types are present in a multitude of object, from consumer goods through everyday use devices to specialized equipment. Diagnostic assessment of motors using acoustic signals is an interesting field, as microphones are present everywhere and are relatively easy sensors to process. In this paper, we analyze acoustic signals for the purpose of motor diagnostics using functional data analysis. We represent the spectrum (FFT) of the acoustic signals on a B-Spline basis and construct a classifier based on that representation. The results are promising, especially for binary classifiers, while multiclass (softmax regression) shows more sensitivity to dataset size. In particular, we show that while we are able to obtain almost perfect classification for binary cases, multiclass classifiers can struggle depending on the training/testing split. This is especially visible for determining the number of broken teeth, which is a non-issue for binary classifiers.
A Game-Theoretic Approach of Optimized Operation of AC/DC Hybrid Microgrid Clusters
Authors: Pan Xuewei , Yang Fan , Ma Peiwen , Xing Yijin , Zhang Jinye , Cao Lingling
Published: August 2022

To maximize the benefits of microgrid clusters, a general model and analysis method for studying the optimized operation of AC/DC microgrid clusters using non-cooperative games is proposed. This paper first establishes the optimized objective function of an AC/DC microgrid for economic operations. Based on the supply and demand theory, the dynamic adjustment mechanism of electricity price is introduced into microgrid clusters, and a game model for the optimal operation of multiple microgrids is established. The Nash equilibrium solution of the established model is obtained by iterative search algorithm, and the convergence of the Nash equilibrium solution is also proven. Finally, the validity and economy of the proposed model are verified by the actual case.
Identification of Breakpoints in Carbon Market Based on Probability Density Recurrence Network
Authors: Zhu Mengrui , Xu Hua , Gao Xingyu , Wang Minggang , Vilela André L. M. , Tian Lixin
Published: August 2022

The scientific judgement of the structural abrupt transition characteristics of the carbon market price is an important means to comprehensively analyze its fluctuation law and effectively prevent carbon market risks. However, the existing methods for identifying structural changes of the carbon market based on carbon price data mostly regard the carbon price series as a deterministic time series and pay less attention to the uncertainty implied by the carbon price series. We propose a framework for identifying abrupt transitions in the carbon market from the perspective of a complex network by considering the influence of random factors on the carbon price series, expressing the carbon price series as a sequence of probability density functions, using the distribution of probability density to reveal the uncertainty information implied by carbon price series and constructing a recurrence network of carbon price probability density. Based on the community structure, the break index and statistical test method are defined. The simulation verifies the effectiveness and superiority of the method compared with traditional methods. An empirical analysis uses the carbon price data of the European Union carbon market and seven pilot carbon markets in China. The results show many abrupt transitions in the carbon price series of the two markets, whose occurrence period is closely related to major events.
Towards Balanced Aerodynamic Axle Loading of a Car with Covered Wheels—Inflatable Splitter
Authors: Szudarek Maciej , Kamieniecki Konrad , Tudruj Sylwester , Piechna Janusz
Published: August 2022

Generating aerodynamic downforce for the wheels on the front axle of a car is a much more difficult task than for the rear axle. This paper, submitted to the special issue of Energies “Future of Road Vehicle Aerodynamics”, presents an unusual solution to increase the aerodynamic downforce of the front axle for cars with covered wheels, with the use of an elastic splitter. The effect of the inflatable splitter on the aerodynamic forces and moments was studied in a DrivAer passenger car and a fast sports car, Arrinera Hussarya. Providing that the ground clearance was low enough, the proposed solution was successful in increasing the front axle downforce without a significant increase in drag force. The possibility of emergency application of such a splitter in the configuration of the body rotated by up to 2 degrees with the front end raised was also analyzed. An elastic, deformed splitter remained effective for the nonzero pitch case. The results of the calculations are presented in the form of numerical data of aerodynamic forces, pressure and velocity distributions, and their comparisons. The benefits of the elastic splitter are documented, and the noted disadvantages are discussed.
Energy Decision Making: Problems, Methods, and Tools—An Overview
Authors: Łatuszyńska Małgorzata , Nermend Kesra
Published: August 2022

Making decisions concerning the use of energy sources is a very difficult and complicated task [...]
Experimental Characterization of Transitory Functioning Regimes of a Biomass Stirling Micro-CHP
Authors: Voronca Ștefan-Dominic , Siroux Monica , Darie George
Published: August 2022

Micro-cogeneration (micro-combined heat and power) is a technology that simultaneously produces decentralized thermal and electrical energy with a power of less than 50 kWel. This technology consists of using the waste heat generated by a thermodynamic process to meet the heating and hot water demands of buildings. The use of biomass as a fuel offers important advantages: use of a renewable energy, carbon neutrality, availability, and low cost. Furthermore, the analysis and optimization of hybrid energy systems, which include existing micro-cogeneration systems powered by renewable energy, is a scientific challenge needing experimental characterization of such micro-cogeneration systems. In this context, a biomass Stirling micro-CHP unit (μCHP), was tested to characterize its energy performance. A dynamic model based on these experimental investigations was developed to evaluate its thermal power output and energy efficiencies. The dependence of the nominal load on the water flow rate of the consumer and the inlet temperature of the fluid heated by the cogeneration system was studied. Results showed that the flow rate of the heat transfer fluid rejecting heat from the μCHP unit influences the temperature of the heat transfer fluid exiting the μCHP to supply domestic hot water to the user, which, if too high, will prompt the self-guarding mechanism of the machine.
A Novel Hybrid MPPT Technique Based on Harris Hawk Optimization (HHO) and Perturb and Observer (P&O) under Partial and Complex Partial Shading Conditions
Authors: Hafeez Muhammad Annas , Naeem Ahmer , Akram Muhammad , Javed Muhammad Yaqoob , Asghar Aamer Bilal , Wang Yong
Published: August 2022

Photovoltaic (PV) systems have been used extensively worldwide over the past few years due to the mitigation of fossils fuels; it is the best source because of its eco-friendly nature. In PV systems, the main research area concerns its performance under partial shading (PS) and complex partial shading (CPS) conditions. PV sources perform perfectly under ideal conditions, but under practical conditions, their performance depends upon many factors, including shading conditions, temperature, irradiance, and the angle of inclination, which can bring a photovoltaic or solar system into a PS or CPS condition. In these conditions, many power peaks appear, and it is hard to find the global peak among many local peaks. The ability to track the maximum power peak and maintain it to avoid fluctuations depends on the maximum power point tracking (MPPT) technique used in a photovoltaic system. This article is based on the implementation of a hybrid algorithm, combining Harris hawk’s optimization (HHO), a new technique which is based on natural inspiration, and a conventional perturb and observe (P&O) technique. The hybrid technique was tested under different weather conditions in MATLAB Simulink and showed less computational time, a fast convergence speed, and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the hybrid technique was made with bio-inspired particle swarm optimization (PSO), adaptive cuckoo search optimization (ACS), the dragonfly algorithm (DFO), and the water cycle algorithm (WCA). The hybrid technique achieves 99.8% efficiency on average and performs very well among the rest of the competing techniques.
A Novel Voltage Sag Detection Method Based on a Selective Harmonic Extraction Algorithm for Nonideal Grid Conditions
Authors: Li Zhenyu , Yang Ranchen , Guo Xiao , Wang Ziming , Chen Guozhu
Published: August 2022

Voltage sag detection is utilized to capture the sag occurrence moment and calculate the sag depth of power grid voltage in real time, so as to generate reference voltage for controlling voltage interactive equipment such as dynamic voltage restorers (DVRs). However, the traditional voltage sag detection methods based on synchronously rotating frames (SRFs) are unable to acquire high-precision sag information under nonideal grid conditions such as unbalance or harmonic interference. In order to enhance the immunity of the sag detection, a method based on a selective harmonic extraction algorithm (SHEA) is proposed in this paper. Firstly, the state-space model of SHEA is established using discrete orthogonal basis to decouple and separate the signal of target frequency and the signal of interference frequency. The controllability, stability and convergence of SHEA are analyzed theoretically and serve as the criteria for parameter tuning. Moreover, a gain compensator (GC) is used to improve the low and middle frequency gains of the voltage sag detection method based on SHEA so that the dynamic response speed for sag judgment can be optimized quantitatively. The simulation results indicate that the proposed voltage sag detection method has good dynamic and steady-state performance under nonideal power grid conditions such as unbalanced sag, frequency drift, phase variation and harmonic interference.
Developing a Grey Forecasting Model for the Air Flowing across the Parallel Plate Duct
Authors: Dhaundiyal Alok
Published: August 2022

The focus of this work is on developing a nonlinear grey model for the laminar flow regime of carrier fluid across the flat plate collector. The trust region reflective algorithm was used to solve the nonlinear laminar flow problem and handle the sparse matrix. The stream function, dimensionless velocity, gradients of velocity components, carrier fluid temperature, and the absorber plate temperature were estimated for a flat plate collector operated with and without a circular chimney. Similarly, the same technique was adopted to determine a model for heat transfer across the absorber plate. The numerically obtained solution was also compared with the parameters obtained through the instrumental measurement. The relative tolerance was kept at 10−5 for the white and grey box solutions. The developed model was noticed to have a smaller deviation than the conventional analytical model to predict the experimental values. The slip condition was observed for the proposed scheme. The uncertainty in the temperature measurement through the proposed model varies from ±0.53 to ±1.47 K. The minimum absolute percentage error (MAPE) lies in the domain of 0.52–1.67% for the nonlinear grey model, whereas the linear grey model for measuring absorber plate temperature has a percentage error of 0.0011–0.02%.
Assessment of Renewable Acceptance by Electric Network Development Exploiting Operation Islands
Authors: Carlini Enrico , De Cesare Alfonso , Gadaleta Corrado , Giordano Chiara , Migliori Michela , Forte Giuseppe
Published: August 2022

The framework of energy transition poses significant challenges in subtransmission network development, where the increased renewable energy generation is collected, in order to efficiently convey power production, avoiding limitations in a range of operating conditions. In this paper, a method to evaluate possible margins for further renewable penetration due to electric network development is assessed, by means of scenario evaluation for the concretisation of renewable initiatives, combined producibility analysis, and load flow studies, accounting for operation islands in subtransmission network organisation, carried out in N and N-1 conditions. The method is applied to a provisional model of the southern part of the Italian power system.
An Anomaly Detection Method of Time Series Data for Cyber-Physical Integrated Energy System Based on Time-Frequency Feature Prediction
Authors: Chen Jinyi , Zhou Suyang , Qiu Yue , Xu Boya
Published: August 2022

An integrated energy system (IES) is vulnerable to network attacks due to the coupling features of multi-energy systems, as well as the deep integration between a physical system and an information system. The anomaly detection of the time-series data in an IES is a key problem to defend against network attacks and ensure the cyber-physical security of IES. Aiming at false data injection attacks (FDIAs) on IES, this paper proposes an anomaly detection method for time-series data in a cyber-physical integrated energy system based on time-frequency feature prediction. The time-frequency features of the time-series data are extracted based on three time-frequency transform methods (DWT, EMD, and EWT). Then the extracted time-frequency features are input to the autoencoder (AE) to capture the hidden features and nonlinear structure of the original data in the frequency domain. The time-domain data within the detected time period are predicted by applying regression prediction on the top-layer features of AE. Considering the uncertainty of regression prediction, kernel density estimation (KDE) is used to estimate the probability density function of prediction error and the interval of the predicted data is estimated accordingly. The estimated lower boundary value of the predicted data is selected as the attack judgment threshold for anomaly detection. The results of the case study verify the advantages of the proposed method in reducing the false positive rate and improving the anomaly detection accuracy.
Multi-Pulse Rectifier Based on an Optimal Pulse Doubling Technique
Authors: Abdollahi Rohollah , Gharehpetian Gevork B. , Mohammadi Fazel , Prakash P Saravana
Published: August 2022

This paper presents pulse multiplication technology based on an optimal Pulse Doubling Technique (PDT) to upgrade a 28-Pulse Rectifier (28-PR) to a 56-PR. The optimal PDT comprises a Tapped Interphase Reactor (TIPR) with a low kVA-rating and two diodes. The number of pulses can be increased from 28 to 56 using the PDT so that the input current harmonics are reasonably mitigated. Additionally, the 14-phase Polygon-Connected Autotransformer (PCA) is designed in such a way that it can be used for retrofit applications. A detailed simulation analysis in the MATLAB/Simulink environment is carried out, and the results show that the improved quality indices of the final AC input and DC output power are equivalent to the IEEE 519-2014 standard and meet sensitive industrial application requirements with an input current Total Harmonic Distortion (THD) lower than 3%. Moreover, the power factor also maintained unity for a wide operating range. The optimal PDT scheme is affordable and easy to implement as only a small-capacity PDT (only 1% of the output power) is needed to double the pulse number. An experimental prototype is developed to verify the simulation results.
Lignin Biodegradation and Its Valorization
Authors: Cui Lingwei , Wang Zheyi , Zeng Yan , Yang Niping , Liu Mengshuang , Zhao Youxi , Zheng Yanning
Published: August 2022

Lignin, a rigid polymer composed of phenolic subunits with high molecular weight and complex structure, ranks behind only cellulose in the contribution to the biomass of plants. Therefore, lignin can be used as a new environmentally friendly resource for the industrial production of a variety of polymers, dyes and adhesives. Since laccase was found to be able to degrade lignin, increasing attention had been paid to the valorization of lignin. Research has mainly focused on the identification of lignin-degrading enzymes, which play a key role in lignin biodegradation, and the potential application of lignin degradation products. In this review, we describe the source, catalytic specificity and enzyme reaction mechanism of the four classes of the lignin-degrading enzymes so far discovered. In addition, the major pathways of lignin biodegradation and the applications of the degradative products are also discussed. Lignin-degrading bacteria or enzymes can be used in combination with chemical pretreatment for the production of value-added chemicals from lignin, providing a promising strategy for lignin valorization.
Inclusive Hyper- to Dilute-Concentrated Suspended Sediment Transport Study Using Modified Rouse Model: Parametrized Power-Linear Coupled Approach Using Machine Learning
Authors: Kumar Sanny , Singh Harendra , Balaji Srinivas , Hanmaiahgari Prashanth , Pu Jaan
Published: August 2022

The transfer of suspended sediment can range widely from being diluted to being hyper-concentrated, depending on the local flow and ground conditions. Using the Rouse model and the Kundu and Ghoshal (2017) model, it is possible to look at the sediment distribution for a range of hyper-concentrated and diluted flows. According to the Kundu and Ghoshal model, the sediment flow follows a linear profile for the hyper-concentrated flow regime and a power law applies for the dilute concentrated flow regime. This paper describes these models and how the Kundu and Ghoshal parameters (linear-law coefficients and power-law coefficients) are dependent on sediment flow parameters using machine-learning techniques. The machine-learning models used are XGboost Classifier, Linear Regressor (Ridge), Linear Regressor (Bayesian), K Nearest Neighbours, Decision Tree Regressor, and Support Vector Machines (Regressor). The models were implemented on Google Colab and the models have been applied to determine the relationship between every Kundu and Ghoshal parameter with each sediment flow parameter (mean concentration, Rouse number, and size parameter) for both a linear profile and a power-law profile. The models correctly calculated the suspended sediment profile for a range of flow conditions (0.268 mm≤d50≤2.29 mm, 0.00105gmm3≤particle density≤2.65gmm3, 0.197mms≤vs≤96mms, 7.16mms≤u*≤63.3mms, 0.00042≤cˉ≤0.54), including a range of Rouse numbers (0.0076≤P≤23.5). The models showed particularly good accuracy for testing at low and extremely high concentrations for type I to III profiles.
AI-2/Lux-S Quorum Sensing of Lactobacillus plantarum SS-128 Prolongs the Shelf Life of Shrimp (Litopenaeus vannamei): From Myofibril Simulation to Practical Application
Authors: Li Yuan , Liu Taige , Meng Xianghong , Qian Yilin , Yan Shijie , Liu Zunying
Published: August 2022

Retarding the protein deterioration of shrimp during storage is important for maintaining its quality. Lactobacillus plantarum SS-128 (L. plantarum SS-128) is a biocontrol bacterium that can effectively maintain the fresh quality of food. This research establishes a myofibril simulation system and refrigerated control system to explore the impact of L. plantarum SS-128 on the quality and shelf life of refrigerated shrimp (Litopenaeus vannamei). Through the bacterial growth assay and AI-2 signal molecule measurement, the effect of the AI-2/LuxS quorum sensing (QS) system of L. plantarum SS-128 and shrimp spoilage bacteria was established. In the myofibril simulation system, a study on protein degradation (dimer tyrosine content, protein solubility, sulfhydryl content, and carbonyl content) showed that adding L. plantarum SS-128 effectively slowed protein degradation by inhibiting the growth of food pathogens. The application to refrigerated shrimp indicated that the total volatile basic nitrogen (TVB-N) value increased more slowly in the group with added L. plantarum SS-128, representing better quality. The total viable count (TVC) and pH results exhibited similar trends. This study provides theoretical support for the application of L. plantarum SS-128 in storing aquatic products.