bluuebunny/crossref_metadata_2025 huggingface dataset:
Intelligent Intrusion Detection Using Arithmetic Optimization Enabled Density Based Clustering with Deep Learning
Authors: Alrowais Fadwa , Marzouk Radwa , Nour Mohamed K. , Mohsen Heba , Hilal Anwer Mustafa , Yaseen Ishfaq , Alsaid Mohamed Ibrahim , Mohammed Gouse Pasha
Published: October 2022

Rapid advancements in the internet and communication domains have led to a massive rise in the network size and the equivalent data. Consequently, several new attacks have been created and pose several challenging issues for network security. In addition, the intrusions can launch several attacks and can be handled by the use of intrusion detection system (IDS). Though several IDS models are available in the literature, there is still a need to improve the detection rate and decrease the false alarm rate. The recent developments of machine learning (ML) and deep learning (DL)-based IDS systems are being deployed as possible solutions for effective intrusion detection. In this work, we propose an arithmetic optimization-enabled density-based clustering with deep learning (AOEDBC-DL) model for intelligent intrusion detection. The presented AOEDBC-DL technique follows a data clustering process to handle the massive quantity of network data traffic. To accomplish this, the AOEDBC-DL technique applied a density-based clustering technique and the initial set of clusters are initialized using the arithmetic optimization algorithm (AOA). In order to recognize and classify intrusions, a bidirectional long short term memory (BiLSTM) mechanism was exploited in this study. Eventually, the AOA was applied as a hyperparameter tuning procedure of the BiLSTM model. The experimental result analysis of the AOEDBC-DL algorithm was tested using benchmark IDS datasets. Extensive comparison studies highlighted the enhancements of the AOEDBC-DL technique over other existing approaches.
AP Association Algorithm Based on VR User Behavior Awareness
Authors: Ruan Jinjia , Wang Yuchuan , Fan Zhenming , Sun Yongqiang , Yang Taoning
Published: October 2022

With the rapid development of virtual reality (VR) technology, this paper proposes an access point (AP) correlation method based on VR user behavior awareness to address the problem of how current AP correlation methods only focus on the performance improvements of ordinary users and ignore the impact of VR user behavior on service quality. This paper analyzes the AP association method under the coverage scenario of a multi-access point (multi-AP) scenario environment and controls the performance improvement of VR user APs or APs under the access controller (AC) by association. Firstly, the VR network application scenario and system model were constructed, and secondly, the user behavior was sensed by analyzing the viewing habits of users. Then, the VR user association problem based on VR user behavior perception was transformed into a “many-to-many” matching problem between VR user devices and APs, and the generalized multidimensional multiple choice knapsack (GMMKP) model was established to solve the problem using the backpack problem theory; the suboptimal solution algorithm was selected to obtain the best VR user AP association strategy. The experimental results show by simulation that the proposed algorithm in this paper performed better in terms of the AP load balancing and average network download latency compared to the comparison algorithms.
Adaptive Discontinuous Control for Fixed-Time Consensus of Nonlinear Multi-Agent Systems
Authors: Wang Lu , Zou Min , Guo Wanli , Alsubaie Hajid , Alotaibi Ahmed , Osman Ahmed Taie Rasha , Jahanshahi Hadi
Published: October 2022

This paper mainly focuses on the fixed-time consensus (FXC) control problem for nonlinear multi-agent systems (MASs). For the cases of leader-following and leaderless, two adaptive discontinuous protocols are designed, respectively, to realize our control goals. Common adaptive control protocols always significantly increase the dimension of the considered system model, while the protocols presented here only require two adaptive update laws and are therefore simpler to apply in the engineering control. Moreover, no additional conditions are required to ensure that the system can achieve FXC successfully, except for some necessary assumptions. Simulation examples also illustrate that these two protocols are effective.
Creating an Immersive XR Learning Experience: A Roadmap for Educators
Authors: Meccawy Maram
Published: October 2022

The use of extended reality (XR) technologies, namely Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR) in education, has attracted much attention in recent years. Many educators have described how XR benefits learners and how useful AR and VR technologies are in the classroom. However, creating AR and VR educational tools, apps or learning environments is a complex process, hence providing an immersive learning experience using these technologies is not a straightforward journey. As a result, the adoption of these emerging technologies in education might be delayed or halted despite their reported benefits to today’s learners. In this paper, websites, technical articles, academic journals, reports and mobile app stores, relating to the use of XR technologies in education, have been examined. A number of themes have emerged and been reported in this paper, which provides a roadmap for those who would like to create XR experiences for learning and training purposes. The paper also describes the factors that should be considered when selecting an option to follow to introduce such immersive learning experiences.
A Data Preprocessing Based on Cluster and Testing of Parameter Identification Method in Power Distribution Network
Authors: Li Bin , Chen Haoran , Hu Ke
Published: October 2022

We present a data prepossessing method for parameter identification based on clustering and hypothesis testing in a power distribution network to successfully achieve a more accurate result. This method considers the similarities of data in both spatial relationship and statistical theory, then builds a sophisticated data processing method to improve the performance of dynamic model-based parameter identification models, i.e., Markov chain Monte Carlo and sequential model-based global optimization. We applied this data processing method to the actual feeder data with no adjustment of the other condition. The experiment shows that our method achieves a 4.8% improvement in accuracy at most.
Chromium-Coated Zirconium Cladding Neutronics Impact for APR-1400 Reactor Core
Authors: Alrwashdeh Mohammad , Alameri Saeed A.
Published: October 2022

The accident-tolerant fuel concept involves replacing the conventional cladding system (zirconium) with a new material or coating that has specific thermomechanical properties. The aim of this study is to evaluate the neutronics performance of a chromium coating concept and design solutions. A Zircaloy–uranium fuel system (Zr–U) is currently used as a standard fuel system in pressurized water reactors around the world. This investigation presents the benefits of utilizing an alternative cladding material such as chromium coating and the effects on the thermal neutron parameters of the way in which the chromium coating is introduced in the reactor fuel. Among these significant benefits is an increase in the reactor fuel’s thermal conductivity, which improves reactor safety. Two types of fuel-cladding systems were investigated: Zircaloy–uranium (Zr–U) and Zircaloy–chromium (Zr–Cr–U) coating fuel systems. Neutronics analysis evaluations were performed for the selected fuel assemblies and a two-dimensional full core based on an APR-1400 reactor design. Neutronics analyses were performed for the application of the new fuel-cladding material systems using the reactor-physics Monte Carlo code Serpent 2.31.
Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid
Authors: Albogamy Fahad R.
Published: October 2022

Energy consumption schedulers have been widely adopted for energy management in smart microgrids. Energy management aims to alleviate energy expenses and peak-to-average ratio (PAR) without compromising user comfort. This work proposes an energy consumption scheduler using heuristic optimization algorithms: Binary Particle Swarm Optimization (BPSO), Wind Driven Optimization (WDO), Genetic Algorithm (GA), Differential Evolution (DE), and Enhanced DE (EDE). The energy consumption scheduler based on these algorithms under a price-based demand response program creates a schedule of home appliances. Based on the energy consumption behavior, appliances within the home are classified as interruptible, noninterruptible, and hybrid loads, considered as scenario-I, scenario-II, and scenario-III, respectively. The developed model based on optimization algorithms is the more appropriate solution to achieve the desired objectives. Simulation results show that the expense and PAR of schedule power usage in each scenario are less compared to the without-scheduling case.
Energy Efficient Routing and Dynamic Cluster Head Selection Using Enhanced Optimization Algorithms for Wireless Sensor Networks
Authors: Adumbabu I. , Selvakumar K.
Published: October 2022

A large number of spatially dispersed nodes on the wireless network create Wireless Sensor Networks (WSNs) to collect and analyze the physical data from the environment. The issues that affected the network and had an impact on network energy consumption were cluster head random selection, working node redundancy, and cluster head transmission path construction. Consequently, this energy constraint also has an impact on the network lifetime and energy-efficient routing. Therefore, the primary goals of this research are to decrease energy consumption and lengthen the network’s lifespan. So, using improved optimization algorithms, this paper presents a dynamic cluster head-based energy-efficient routing system. The Improved Coyote Optimization Algorithm (ICOA), in this case, consists of three phases setup, transmission, and measurement phase. The Improved Jaya Optimization Algorithm with Levy Flight (IJO-LF) then determines the route between the BS and the CH. It selects the most effective course based on the distance, node degree, and remaining energy. The proposed approach is compared with traditional methods and the routing protocols Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and Threshold sensitive Energy Efficient Sensor Network protocol (TEEN) during implementation on the MATLAB platform. Performance indicators for the suggested methodology are evaluated based on data packets collected by the BS, energy usage, alive nodes, and dead nodes. The outputs of the suggested methodology performed better than the conventional plans.
Production of Biohydrogen from Organ-Containing Waste for Use in Fuel Cells
Authors: Fedorov Mikhail , Maslikov Vladimir , Korablev Vadim , Politaeva Natalia , Chusov Aleksandr , Molodtsov Dmitriy
Published: October 2022

The production of low-carbon hydrogen based on renewable energy sources is considered a promising direction in the development of the modern world economy. The purpose of the presented research is to develop technologies and study the processes of converting biogases into hydrogen, as well as its use in low-temperature fuel cells. The methodology for organizing a multi-stage laboratory experiment for obtaining biogas, its purification from impurities and, in the future, the production of biohydrogen was developed based on field studies in Peter the Great St. Petersburg Polytechnic University. The results of modeling studies have shown that during biogas reforming, it is possible to obtain a hydrogen mixture with a hydrogen content of 98% vol and methane 2% vol. Based on the results of the research, the possibility of using the significant potential of “weak” biogas containing methane 30–45% vol to produce biohydrogen (more than 93% vol) was proved. A technique for using biohydrogen in low-temperature fuel cells for energy generation has been substantiated and tested.
Modeling and Simulation of a Low-Cost Fast Charging Station Based on a Micro Gas Turbine and a Supercapacitor
Authors: Gilev Bogdan , Andreev Miroslav , Hinov Nikolay , Angelov George
Published: October 2022

In recent years, micro turbine technology has become a continuously reliable and viable distributed generation system. The application of distributed energy power generation sources, such as micro gas turbines (MGT), to charge electric vehicles offers numerous technical, economical benefits, and opportunities. MGT are considered as they are smaller than conventional heavy-duty gas turbines. They also are capable of accepting and operating with different fossil fuels in the range of low–high pressure levels as well as co-generation opportunities. The MGT could provide the fast and reliable output power guaranteed and needed for grid stability. This paper provides a mathematical representation, modelling, and simulation of a low-cost fast charging station based on a micro gas turbine and a super capacitor forming altogether a power generation system suitable for use especially as energy source in fast charging stations and dynamic power systems. All the micro gas turbine’s parameters are estimated according to available performance and operational data. The proposed system generates up to 30 kW output power assuming that it operates with natural gas. The developed model of the system is simulated in the environment of MATLAB/Simulink. Each part of the micro turbine generation system is represented by a mathematical model. On the basis of the developed model of the system, the minimum value of the supercapacitor was determined, which ensures the charging schedule of a selected electric vehicle.
Trough-Type Free-Form Secondary Solar Concentrator for CPV/T Application
Authors: Meng Xian-long , Ren Fu-Peng , Zhang Peng , Tang Zi-xuan
Published: October 2022

Imaging concentrators like the parabolic trough solar concentrators have been widely employed for energy production in solar power plants. The conventional imaging solar concentrators form a non-uniform Gaussian distribution on receiving absorbers yielding the highest temperatures. The traditional CSP system normally truncated a peripheral region of heat flux to better use the central part. CPV/T systems using the waste heat recovery method can largely improve the total efficiency. However, for the CPV module, the coolant temperature was usually below 80 °C, which limited the applications of the thermal cycle such as the ORC system. In this article, a novel trough-type free-form secondary solar concentrator (TFSC) for PV/Thermal hybrid application has been proposed. Different from other CPV/T concepts using a combined PV panel and cooling tunnel/tube, the current concept separates the receiver in two parts. The secondary free-form reflector is generated by the geometric construction method, resulting in uniform heat flux in the edge region and high concentration in the central region. Through the ray tracing method, the optical properties have been verified. Sensitivity analysis of the concentrating structure is also conducted. The results provide supports for the design and applications of novel CPV/T systems.
Geochemical Characteristics of Coal in the Taiyuan Formation in the Center and North of the Xishan Coalfield
Authors: Wang Gang , Qin Yong , Xie Yiwei
Published: October 2022

The Xishan coalfield is an important coking coal-producing area in China. The No. 8 and No. 9 coals of the Taiyuan formation were sampled and tested from nine mines in the center and north of the Xishan coalfield, and the coal’s quality characteristics, element occurrence characteristics, enrichment characteristics, and sedimentary environment characteristics were analyzed to provide a foundation for clean and efficient coal utilization and identification of associated coal resources. The results obtained from the experiment show that Li in No. 8 coal is the enrichment element. The enrichment coefficient of the U element of ML8 is 13.78, which is close to the industrial index of recycling. The enrichment hazardous elements are Cu, Mo, Cd of ML8, Mo of DQ8, and Th of ZCD8. Along with K value and Sr/Ba ratio, seawater has a greater influence on the south of No. 8 coal than on the north and on the east of No. 9 coal than on the west. No. 8 coal (except ML8) was formed under an oxidation environment based on the U/Th, Ni/Co, and V/Cr ratios. The oxidation or suboxic to the dysoxic environment resulted in the formation of No. 9 coal. Based on the Al2O3/TiO2 ratio, the sediments of No. 8 and No. 9 coals are mainly from felsic volcanic rocks.
Fault Ride-Through Operation Analysis of Doubly Fed Induction Generator-Based Wind Energy Conversion Systems: A Comparative Review
Authors: Ansari Aftab Ahmed , Dyanamina Giribabu
Published: October 2022

In present electrical power systems, wind energy conversion systems based on doubly fed induction generators represent one of the most commonly accepted systems in the global market due to their excellent performance under different power system operations. The high wind energy penetration rate makes it challenging for these wind turbines to follow grid code requirements. All operations of a wind energy system during a dip in voltage require special attention; these operations are critically known as fault ride-through and low voltage ride-through. In this paper, various fault ride-through techniques of doubly fed induction generator-based wind energy conversion systems, such as protective circuitry, reactive power injection, and control methods for transient and steady state operations, have been presented to improve the performance. During system disturbances, protective circuitry or control mechanisms are typically used to limit the over-current of the rotor and the generated inappropriate DC link over-voltage. Simultaneously, the reactive power injection system overcomes the reactive power scarcity and enhances the transient response, further limiting the DC bus voltage and rotor current. This review paper compares and suggests appropriate FRT methods that are driven by external modifications and internal system improvements. Furthermore, typical case studies are discussed to illustrate and support the FRT system. The impact of each case study was evaluated and analyzed using the results obtained from the MATLAB/Simulink application and the OPAL-RT (OP4500) real time simulator (RTS).
Evaluation, Analysis and Diagnosis for HVDC Transmission System Faults via Knowledge Graph under New Energy Systems Construction: A Critical Review
Authors: Wu Jiyang , Li Qiang , Chen Qian , Peng Guangqiang , Wang Jinyu , Fu Qiang , Yang Bo
Published: October 2022

High voltage direct current (HVDC) transmission systems play a critical role to optimize resource allocation and stabilize power grid operation in the current power grid thanks to their asynchronous networking and large transmission capacity. To ensure the operation reliability of the power grid and reduce the outage time, it is imperative to realize fault diagnosis of HVDC transmission systems in a short time. Based on the prior research on fault diagnosis methods of HVDC systems, this work comprehensively summarizes and analyzes the existing fault diagnosis methods from three different angles: fault type, fault influence, and fault diagnosis. Meanwhile, with the construction of the digital power grid system, the type, quantity, and complexity of power equipment have considerably increased, thus, traditional fault diagnosis methods can basically no longer meet the development needs of the new power system. Artificial intelligence (AI) techniques can effectively simplify solutions’ complexity and enhance self-learning ability, which are ideal tools to solve this problem. Therefore, this work develops a knowledge graph technology-based fault diagnosis framework for HVDC transmission systems to remedy the aforementioned drawbacks, in which the detailed principle and mechanism are introduced, as well as its technical framework for intelligent fault diagnosis decision.
Design and Sizing of Electric Bus Flash Charger Based on a Flywheel Energy Storage System: A Case Study
Authors: Shadnam Zarbil Mohammad , Vahedi Abolfazl , Azizi Moghaddam Hossein , Khlyupin Pavel Aleksandrovich
Published: October 2022

At present, the trend of all sectors and industries, especially the oil and gas industry, is towards reducing carbon dioxide emissions. Along with the modernization of technological processes, special attention has been paid to the reduction in greenhouse gas emissions from vehicles that run on gasoline and diesel. An effective solution in this field is the transition from vehicles with combustion engines to electric vehicles, similar to the use of the electric bus in public transportation. How to charge these electric buses is a challenge for researchers. By proposing a flash charging method for electric buses, the feasibility of using these buses is obtained. Due to the pulsating nature of the power demand in this charging method, the prevention of negative effects on the network by this type of load should be considered. These negative effects can include power quality problems, voltage drop, frequency instability, and overload of transmission lines and transformers. This paper presents a flywheel energy storage system (FESS)-based flash charging station for electric buses. The specifications of the components of this charging station are designed and sized for a case study for line 1 of Tehran Bus Rapid Transit (BRT). A charging strategy based on the proposed charging cycle is presented to minimize the high-power short-duration demand from the grid. The energy consumption of the electric bus based on the electricity consumption model is calculated. Based on theoretical calculations, for the case study, there is a need for installing 12 flash charging stations based on FESS in line 1 Tehran BRT. In this line, an electric bus with a battery capacity of 80 kWh is proposed. The power and energy capacity of these charging stations are sized to 600 kW and 3.334 kWh, respectively. Additionally, an economic comparison regarding the proposed charging station is conducted. The theoretical results of the design and sizing of the proposed charging station are validated based on simulation and experimental results for a small-scale laboratory setup.
The Impact of Soiling on PV Module Performance in Saudi Arabia
Authors: Al Garni Hassan Z.
Published: October 2022

Solar photovoltaic (PV) deployment is rapidly expanding around the world. However, the soiling factor has an impact on its performance. Saudi Arabia has high solar irradiation and plans to diversify its energy mix for electricity generation by deploying more solar PV across the country. However, it is located in an arid and desert environment, making it a challenging project due to dust accumulation on solar modules. The soiling and PV performance in Saudi Arabia are examined in this paper. Furthermore, it highlights several mitigation techniques that can be used to maintain PV performance through preventive and restorative measures. Furthermore, this study looks into the size and characterization of dust in Saudi Arabia, as well as the entire life cycle of dust accumulation on PV modules. In this review study, the performance of solar PV systems is evaluated under soiling in different regions of Saudi Arabia. Depending on the local environment and other factors, the PV performance has been reduced by somewhere between 2% and 50%. A single sandstorm reduced the module power output by 20%. As revealed in Dhahran, the PV module was exposed to an outdoor environment and not cleaned for 6 months resulting in a power drop of more than 50%. It is strongly advised to clean PV panels once a month or fewer to maintain a high-performance system. However, in the event of a dust storm, it is advised to clean the system immediately to avoid a major decline in PV performance. The bi-facial PV solar panels technology associated with solar trackers and utilizing robotic cleaning systems have maximized the received solar irradiation and minimized the soiling loss efficiently. The most common elements found in dust particles are primarily derived from the natural desert. It has been noted that the composition and sizes of dust particles depend heavily on the location of the PV module. It is concluded that dust accumulation and cleaning costs are not a significant barrier to large-scale, cost-effective solar PV deployments in Saudi Arabia, particularly in the central region, which is considered a high-suitable region for utility-size PV plants due to many factors. The results of this study are essential for enlightening the PV engineering community, investors, and the research community about how soiling may affect regions with significant solar potential, such as Saudi Arabia, and what potential soiling mitigation strategies may be considered to maintain high-performance solar PV projects.
Net Zero: The Remaining Global Market Volume for Internal Combustion Engines in Light-Duty Vehicles under a 1.5 °C Carbon Budget Trajectory
Authors: Teske Sven , Bratzel Stefan , Tellermann Ralf , Stephan Benjamin , Vargas Mauricio
Published: October 2022

To achieve the goals of the Paris Climate Agreement, decarbonization targets for the global automotive industry are required. We assess the quantity of light-duty vehicles (LDVs) with internal combustion engines (ICEs) that can be manufactured within the identified carbon budget and compare it with the current sales plans of the four largest automobile manufacturers—Volkswagen, General Motors, Toyota, and Hyundai/Kia—as representative of traditional car manufacturers. We first describe the quantification of a carbon budget for LDVs under the 1.5 °C target and a methodology for calculating the market shares that will allow different drive-train technologies to stay within it. The global LDV market for new sales and historic and future vehicle retirement rates are presented, together with assumptions for car usage (in passenger kilometres per year) and fuel efficiencies. We calculate the quantity of ICE LDVs that can be sold before the manufacture of ICEs must cease globally. We then compare this upper global limit with the current sales plans of car companies. The plans of the four manufacturers differ, but all considerably exceed the number of ICE vehicle sales required to meet the 1.5 °C target. This analysis does not forecast the development of the global LDV market, but assesses the gap between manufacturers’ intention and the requirement under a 1.5 °C pathway.
Impact of NaOH on the Hydrothermal Oxidation of Guaiacol for the Production of Value-Added Products
Authors: Zeng Xu , Yin Guodong , Zhou Yangyuan , Zhao Jianfu
Published: October 2022

In this study, the impact of NaOH on the hydrothermal oxidation of guaiacol was investigated. It was found that NaOH significantly accelerated the production of formic acid and acetic acid with H2O2 or CuO as the oxidant. With the strong oxidant, H2O2, the highest acetic acid yield (15.73%) and formic acid (5.64%) were obtained at 300 °C for 90 s with NaOH 1.0 mol·L−1 and a 100% H2O2 oxygen supply. In comparison, with CuO as the oxidant, the highest values of acetic acid (13.42%) and formic acid (4.21%) were acquired at 250 °C for 6 h with NaOH 1.0 mol·L−1. Formic acid and acetic acid were generated through the oxidation of intermediates, such as levulinic acid, fumaric acid, maleic acid, etc. These results demonstrated that NaOH catalytic hydrothermal oxidation has potential for the production of value-added chemicals from biomass materials. When CuO is used as the oxidant, this process could also be used as a green method for copper smelting along with the utilization of lignin biomass.
Experimental Validation of an Enhanced MPPT Algorithm and an Optimal DC–DC Converter Design Powered by Metaheuristic Optimization for PV Systems
Authors: Mendez-Flores Efrain , Ortiz Alexandro , Macias Israel , Molina Arturo
Published: October 2022

Nowadays, photovoltaic (PV) systems are responsible for over 994 TWH of the worldwide energy supply, which highlights their relevance and also explains why so much research has arisen to enhance their implementation; among this research, different optimization techniques have been widely studied to maximize the energy harvested under different environmental conditions (maximum power point tracking) and to optimize the efficiency of the required power electronics for the implementation of MPPT algorithms. On the one hand, an earthquake optimization algorithm (EA) was introduced as a multi-objective optimization tool for DC–DC converter design, mostly to overcome component shortages by optimal replacement, but it had never been tested (until now) for PV applications. On the other hand, the original EA was also taken as inspiration for a promising EA-based MPPT, which presumably enabled a solution with simple parametric calibration and improved dynamic behavior; yet prior to this research, the EA-MPPT had never been experimentally validated. Hence, this work fills the gap and provides the first implementation of the EA-based MPPT, validating its performance and suitability under real physical conditions, where the experimental testbed was optimized through the EA design methodology for DC–DC converters and implemented for the first time for PV applications. The results present energy waste reduction between 12 and 36% compared to MPPTs based on perturb and observe and particle swarm optimization; meanwhile, the designed converter achieved 7.3% current ripple, which is between 2.7 and 12.7% less than some industrial converters, and it had almost 90% efficiency at nominal operation. Finally, the EA-MPPT proved simple enough to be implemented even through an 8-bit MCU (ATmega328P from Arduino UNO).
Reactive Compensation Planning in Unbalanced Electrical Power Systems
Authors: Salazar Jair , Carrión Diego , Jaramillo Manuel
Published: October 2022

This research focuses on finding the optimal location of reactive compensation in an unbalanced electrical power system (EPS). An EPS is named unbalanced when there is uncertainty in the behaviour of the power demand, which causes changes in the voltage profile and angle of each of the electrical power phases. For this reason, using reactive compensation will improve the voltage profiles and the magnitudes of reactive power in the transmission lines. The proposed methodology uses the optimal AC power flows as a basis, which is applied to each power phase and, through this methodology, the operating conditions of the EPS are determined. Then, based on the voltage profiles of each power phase, it was possible to determine the critical nodes of the system, so that afterwards, through heuristics, it was possible to find the optimal location of the reactive compensation that independently meets the needs of each phase. To evaluate the proposed methodology, the IEEE test systems of 9, 14, 30 and 118 bus bars were used, as well as the 230 kV ring of the Ecuadorian transmission system. Finally, using the proposed methodology, it was possible to independently compensate for each of the power phases, rectifying the unbalanced voltage profiles that appeared in the EPS.
Collaborative Control Strategy of Power Quality Based on Residual Capacity of Photovoltaic Inverter
Authors: Sun Zekun , Liu Hao , Ding Yueming , Luo Haopeng , Liu Ting , Tan Qinyue
Published: October 2022

With the large-scale distributed PV connected to the grid, the random and intermittent nature of PV output, the non-linearity of the inverter, as well as the low daytime base-load and large-scale back feeding cause outstanding power quality problems such as overvoltage, three-phase unbalance, and high harmonic content at the end of the power supply system, which seriously affects the safe and stable operation of the grid and power efficiency. Based on the residual capacity of the inverter, this paper investigates a cooperative power quality control strategy that integrates active power filter, reactive power compensation, and inverter functions to achieve comprehensive power quality control and management without adding additional hardware equipment. The validation results show that the control strategy strongly improves the power quality, grid security, stability, and efficiency. This strategy indicates significant economic benefits.
Cenozoic Depositional Evolution and Stratal Patterns in the Western Pearl River Mouth Basin, South China Sea: Implications for Hydrocarbon Exploration
Authors: Liu Entao , Deng Yong , Lin Xudong , Yan Detian , Chen Si , Shi Xianbin
Published: October 2022

Investigating the deposition evolution and stratal stacking patterns in continental rift basins is critical not only to better understand the mechanism of basin fills but also to reveal the enrichment regularity of hydrocarbon reservoirs. The Pearl River Mouth Basin (PRMB) is a petroliferous continental rift basin located in the northern continental shelf of the South China Sea. In this study, the depositional evolution process and stacking pattern of the Zhu III Depression, western PRMB were studied through the integration of 3D seismic data, core data, and well logs. Five types of depositional systems formed from the Eocene to the Miocene, including the fan delta, meandering river delta, tidal flat, lacustrine system, and neritic shelf system. The representative depositional systems changed from the proximal fan delta and lacustrine system in the Eocene–early Oligocene, to the tidal flat and fan delta in the late Oligocene, and then the neritic shelf system in the Miocene. The statal stacking pattern varied in time and space with a total of six types of slope break belts developed. The diversity of sequence architecture results from the comprehensive effect of tectonic activities, sediment supply, sea/lake level changes, and geomorphic conditions. In addition, our results suggest that the types of traps are closely associated with stratal stacking patterns. Structural traps were developed in the regions of tectonic slope breaks, whereas lithological traps occurred within sedimentary slope breaks. This study highlights the diversity and complexity of sequence architecture in the continental rift basin, and the proposed hydrocarbon distribution patterns are applicable to reservoir prediction in the PRMB and the other continental rift basins.
The Transient Thermal Disturbance in Surrounding Formation during Drilling Circulation
Authors: Jang Minsoo , Chun Troy , An Jaewoo
Published: October 2022

The injecting drilling mud is typically at the ambient temperature, relatively much colder than the deep formation, inducing a cooling effect in the formation. Although the cooled formation temperature gradually returns to its original temperature after drilling circulation, the recovery speed is slow due to low thermal diffusivity. Considering that any well tests begin in a short period after drilling ends, temperature recovery is not fully achieved before the tests. It means that the measured temperature of producing fluid is not that of the actual formation, significantly impairing the robustness of the subsequent thermal applications. Furthermore, there has been no quantified concept of thermal disturbance in the formation and its analysis. In this work, a proposed numerical transient heat transfer model computes the radial temperature in the drill pipe, annulus, and formation. The concept of quantifying thermal disturbance, named thermally disturbed radius (TDR), indicates how long the thermal disturbance occurs radially in the formation. The TDR increases with the more significant temperature difference between circulating fluid and formation. Thus, the TDR appears to be the largest at the bottom-hole depth. In the sensitivity of TDR of various operational parameters, circulation time (i.e., drilling time) is the most influential factor. Meanwhile, the other parameters do not significantly affect TDR: circulation rate, injecting mud temperature, and mud density. The sensitivity analysis concludes that as long as the operators control the drilling time, the uncertainty of the measured temperature after drilling can be manageable without limiting any other operational parameters.
Forecasting Day-Ahead Carbon Price by Modelling Its Determinants Using the PCA-Based Approach
Authors: Rudnik Katarzyna , Hnydiuk-Stefan Anna , Kucińska-Landwójtowicz Aneta , Mach Łukasz
Published: October 2022

Accurate price forecasts on the EU ETS market are of interest to many production and investment entities. This paper describes the day-ahead carbon price prediction based on a wide range of fuel and energy indicators traded on the Intercontinental Exchange market. The indicators are analyzed in seven groups for individual products (power, natural gas, coal, crude, heating oil, unleaded gasoline, gasoil). In the proposed approach, by combining the Principal Component Analysis (PCA) method and various methods of supervised machine learning, the possibilities of prediction in the period of rapid price increases are shown. The PCA method made it possible to reduce the number of variables from 37 to 4, which were inputs for predictive models. In the paper, these models are compared: regression trees, ensembles of regression trees, Gaussian Process Regression (GPR) models, Support Vector Machines (SVM) models and Neural Network Regression (NNR) models. The research showed that the Gaussian Process Regression model turned out to be the most advantageous and its price prediction can be considered very accurate.
Fault Detection of UHV Converter Valve Based on Optimized Cost-Sensitive Extreme Random Forest
Authors: Xiong Fuqiang , Cao Chenhuan , Tang Mingzhu , Wang Zhihong , Tang Jun , Yi Jiabiao
Published: October 2022

Aiming at the problem of unbalanced data categories of UHV converter valve fault data, a method for UHV converter valve fault detection based on optimization cost-sensitive extreme random forest is proposed. The misclassification cost gain is integrated into the extreme random forest decision tree as a splitting index, and the inertia weight and learning factor are improved to construct an improved particle swarm optimization algorithm. First, feature extraction and data cleaning are carried out to solve the problems of local data loss, large computational load, and low real-time performance of the model. Then, the classifier training based on the optimization cost-sensitive extreme random forest is used to construct a fault detection model, and the improved particle swarm optimization algorithm is used to output the optimal model parameters, achieving fast response of the model and high classification accuracy, good robustness, and generalization under unbalanced data. Finally, in order to verify its effectiveness, this model is compared with the existing optimization algorithms. The running speed is faster and the fault detection performance is higher, which can meet the actual needs.
Torque Control for PMSG-Based Wind-Power System Using Stationary abc-Reference Frame
Authors: da Costa Israel Divan Lopes , Brandao Danilo Iglesias , Seleme Seleme Isaac , Morais Lenin Martins Ferreira
Published: October 2022

The power system of wind farms is generally characterized by a weak grid, in which voltages may be heavily distorted and imbalanced, challenging the control scheme of wind-power converters that must be impervious to such disturbances. The control scheme in the stationary natural abc-frame has shown good performance under non-ideal voltage conditions, and then this paper proposes to analyze the operational performance of a wind-power system based on a permanent magnet synchronous generator subject to non-ideal conditions of the grid voltage, with its control scheme devised in the abc-reference frame. The proposed control scheme considers the torque control decoupling the flux and torque for the generator-side, showing the possibility to implement the machine torque control, without any coordinates transformation using a closed loop dot-product approach, between the field flux and stator currents. For the grid-side converter, the load current compensation is proposed, using the load current decomposition and conservative power theory (CPT), improving the grid power quality. The simulation results estimate the performance of the grid-side control under distorted and asymmetrical voltages, and the generator-side control against torque disturbances due to wind speed variation. Finally, experimental results in a small-scale test bench validate the proposed control scheme in injecting active and reactive power into the grid, and the torque control under wind speed variation.
Improvement of Operating Efficiency of Energy Cooperatives with the Use of “Crypto-Coin Mining”
Authors: Sołtysik Maciej , Kozakiewicz Mariusz , Jasiński Jakub
Published: October 2022

Poland remains the most coal-dependent economy in the EU. To minimize that problem, which is particularly clear and dangerous in the period of a shortage of fossil fuels, the Polish authorities have decided to establish various institutions, organizational and regulatory solutions. Their role is to support the development of renewable energy sources and local energy communities. The latter are to focus on optimizing the production and consumption of energy in pursuit of energy self-sufficiency on a local scale. One such institution, set up in Poland over the last several years, is the energy cooperative, which is intended to increase the efficient use of the potential of renewable energy sources in rural and urban–rural areas. The authors of this article verify the assumptions, for instance, the number, composition or production and members’ consumption profiles, under which such a relatively new institution has the chance to develop. A novelty in this research paper is that the interests of the entities composing a given energy cooperative may additionally be secured by the use of surplus generation for crypto-coin mining, and thus the storage of energy in virtual currency. A dedicated mathematical model in mixed-integer programming technology was used, enriched with respect to previous research, making it possible for members of the cooperative to achieve energy independence while maximizing self-consumption and using their excess energy for processing cryptocurrency. This is in line with the global trend of “greening”; the processes of acquiring electronic money.
Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts
Authors: Schreiber Jens , Sick Bernhard
Published: October 2022

Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts.
A Dual-Stage Controller for Frequency Regulation in a Two-Area Realistic Diverse Hybrid Power System Using Bull–Lion Optimization
Authors: Singh Balvinder , Slowik Adam , Bishnoi Shree Krishna
Published: October 2022

In this article, a dual-stage proportional integral–proportional derivative with filter (PI–PDF) controller has been proposed for a hybrid two-area power system model having thermal-, hydro-, gas-, wind-, and solar-based power generating sources. Superconductor magnetic energy storage (SMES) units to cope with the transient power deviations have been incorporated in both areas. Governor dead-band (GDB) is considered in the governor model of thermal, and a generation rate constraint (GRC) is considered in the thermal and hydro turbine models to analyze the impact of system nonlinearity. The parameters of the proposed control strategy are optimally tuned by deploying a newly developed bull–lion optimization (BLO) to maintain optimal frequency and power response during system load deviations. Variations in wind speed and PV solar irradiance data have been included to examine the effectiveness of the BLO-based PI–PDF controller with system uncertainties and variability of renewable energy sources. The obtained results are validated by comparison with recently developed existing optimization techniques. The results revealed that the proposed control strategy is efficient for regulating the frequency and tie-line power of renewable integrated power systems. Further, the BLO-based PI–PDF control strategy improved the performance in terms of performance indices like settling time and peak overshoot/undershoot in wide scale.
Experimental Proof-of-Concept of a Hybrid Wave Energy Converter Based on Oscillating Water Column and Overtopping Mechanisms
Authors: Simonetti Irene , Esposito Andrea , Cappietti Lorenzo
Published: October 2022

This paper presents the results of laboratory tests on a hybrid wave energy converter concept, the O2WC (Oscillating-Overtopping Water Column) device. The proposed device aims at providing an alternative to the classical OWC concept, storing part of the wave energy of the highly energetic sea states in a second chamber at atmospheric pressure, through overtopping phenomena. In this way, the maximum airflow rate and air pressure in the OWC chamber are reduced, possibly aiding the safe functioning of the air turbine, and allowing to exploit the excess of energy instead of dissipating it through by-pass valves. The performance of the device is investigated under different incident wave conditions, for different design parameters. The height of the overtopping threshold from the second chamber of the device which allows to maximize the performance has been selected. Results show that the decrease of the primary conversion efficiency of the OWC component of the device caused by the decreased air pressure in the OWC chamber can be partially compensated by the additional energy stored in the overtopping chamber of the O2WC device. Overall, the studied O2WC device has capture width ratio values ranging between 0.3 and 0.7.