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The allocation of communication resources is extremely important for the device-to-device (D2D) communication network with unmanned aerial vehicle (UAV) relays, but this field currently mainly focuses on the communication between UAVs and ground devices. While neglecting the comprehensive optimization of air-to-ground communication and internal ground communication. This paper studies the optimization problem of communication for ground D2D devices based on UAV relays in urban environment. We study the comprehensive optimization considering the air-to-ground communication and the mobile ground communication devices. We construct the D2D communication neighbor table, and the terrestrial D2D devices will sort and select the best D2D link for pairing based on the Euclidean distance and gain set around them. We construct a communication model for this scenario and propose an improved Dual-Actor Network Softmax Twin Delayed Deep Deterministic Policy Gradient algorithm based on deep reinforcement learning. We perform transmit power optimization to address the interference caused by D2D communication using frequency division multiplexing technology. By designing the reward function and training mechanism, we can achieve good power control and reduce communication interference. The results of the simulation analysis performed in this scenario show an effective reduction in communication latency and an increase in D2I (device-to-infrastructure) channel capacity, D2D connectivity success, and energy efficiency. The feasibility and effectiveness of the algorithm is verified by comparing the baseline algorithm. [ABSTRACT FROM AUTHOR]

Significant attempts have been made to make the electrical grid more intelligent and responsive to better meet customers' requirements while boosting the stability and efficiency of current power systems. Smart grid technologies, which have just recently emerged, facilitated the incorporation of demand response (DR) by introducing an information and communication backbone to the current system. The Internet of Things (IoT) has emerged as a key technology for smart energy grids. Security concerns have emerged as a major obstacle to the widespread adoption of IoT‐enabled devices because of the inherent Internet connectivity of these smart gadgets. Therefore, security is a crucial factor to address before the widespread implementation of IoT‐based devices in power grids. In this study, the framework and architecture of smart grids that are enabled by the IoT are first examined. Then, the role of IoT for DR in smart grids and different approaches adopted worldwide to make DR schemes more effective, have been discussed in detail. Finally, the authors discuss how IoT‐enabled smart grids can benefit from cutting‐edge solutions and technologies that make them more secure and resistant to cyber and physical attacks. [ABSTRACT FROM AUTHOR]

This paper presents the experimental results of a representative aircraft turbo-electric powertrain. The 180-kW hybrid gas-electric ground test rig was designed, fabricated, and experimentally evaluated. Hybrid turbo-electric power systems are an enabler for future medium- to long-range electrified aircraft, offering higher energy density over current battery technologies. Previous studies have focused on analytical models of turbo-electric power systems. However, as industry stakeholders continue to advance toward implementing turbo-electric parallel hybrid aircraft, there is a lack of practical knowledge regarding their implementation. The objectives of this study are twofold. First, the study aims to evaluate the real-time transient performance of turbo-electric aircraft. The second objective aims to characterize the real-world challenges of safely constructing and operating a hybrid turbo-electric aircraft. To accomplish both objectives, a turboprop-powered ground test vehicle was designed and fabricated. The ground test vehicle was constructed from a modified Cessna-172 aircraft, a modified 180-kW PBS-TP100 turboprop, two wing-mounted electric motors, and a purpose-built hybrid-turbo-electric powertrain. A portion of the turboprop shaft power is extracted to generate electricity for the battery system and wing-mounted electric motors. The test vehicle was successfully brought to full engine power and the wing-mounted electric motors were run through a series of operating points to experimentally evaluate transient electrical and mechanical performance. Observations are made regarding the interdependent time responses of the electromechanical systems. Results from the test run include engine performance metrics, current, voltage, and acoustic data. The generator peaked at 4-kW and was augmented by 13-kW of battery power to drive distributed propulsors, while the turboprop generated 142-kW of power for the variable pitch propeller. Practical recommendations for safe integration are identified, such as the need for a precharge circuit, crowbar circuit, and future short protection circuit. This study provides critical insight into the design and practical implementation of turbo-electric power systems for future electrified aircraft. [ABSTRACT FROM AUTHOR]

The introduction of cloud‐native technology has significantly changed the architecture of applications and the mechanism for the collaborative operation of components in power distribution edge computing terminals (PDECT). To develop an effective quantitative analysis tool for PDECT performance, the composition and characteristics of cloud‐native PDECT are studied, and the modelling and simulation of cloud‐native PDECT are proposed. Subsequently, modelling is implemented through the simulation software CloudSim, achieving the simulation of microservices, containers, declarative configuration, and container orchestration with the consideration of power distribution scenarios. Then, by the proposed simulation scenario module, various elements of the power distribution scenarios can be self‐defined. Finally, by demonstrating the principles and implementation mechanisms of the proposed modelling method and simulation tool, and comparing simulation results for different service time ranges, access devices, resource configurations of PDECT, request occurrence rates, and resource scheduling strategies, the validity and effectiveness of the proposed modelling method and simulation tool are verified. [ABSTRACT FROM AUTHOR]

Solid-state laser beam sources offer the possibility of generating high-brilliance laser beams with low expansion and high usable intensity at the focal point. New approaches include beam shaping with the use of core and ring fiber and, therefore, variable power distribution in the laser beam focal point and material interaction area. Particularly, high-power laser beam welding benefits from beam shaping because of the stabilizing effect on the weld pool. Furthermore, the technical progress achieved with regard to beam quality also allows one to achieve high Rayleigh lengths and, therefore, a more uniform beam diameter over the whole material thickness. In this study, investigations on high-power laser beam welding with a 24 kW disk laser beam source are conducted for three different materials (mild steel, aluminum alloy, and copper), which are of high interest for welding in different sectors. The influence of power distribution between the core and the ring as well as welding speed on weld geometry (depth and width), weld pool stability, and the resulting weld seam quality is investigated. It is shown that the welding process cannot just be scaled up in comparison with welding with lower laser beam power but has its own challenges. It is possible that high welding depths (12 mm for copper, more than 12 mm is possible for aluminum, and 25 mm for mild steel) could be achieved in one pass. To achieve this, aluminum needs the lowest energy per unit length per mm of sheet thickness and copper the highest. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1. [ABSTRACT FROM AUTHOR]

The issue of generation expansion planning in microgrids has become a challenging issue in electricity industry for two reasons: load growth and uncertainties in renewables' generation. Therefore, this issue is considered here. Here, the modelling of generation expansion planning problem has been developed in a network of microgrids in a decentralized manner, considering normal and contingency conditions. On the other hand, in order to further develop the study considered, decentralized generation expansion planning model of microgrids by considering contingency conditions has been addressed in a cooperative approach to minimize total costs. In developed model, investment decisions are made at the higher level and operational constraints has been considered at lower one. Also, case studies are defined in three different scenarios: islanding operation of microgrids as first scenario and peer‐to‐peer trading of microgrids in non‐cooperative and cooperative approaches as second and third scenarios, respectively. The results of simulations have shown that by facilitating the transactions between microgrids, their total costs are reduced. The costs of the whole set of microgrids in the non‐cooperative scenario are reduced by 9.4% compared to the islanding scenario; and the costs are reduced by 7.5% in the cooperative scenario compared to the non‐cooperative scenario. [ABSTRACT FROM AUTHOR]

A new method for the generation of axial power distributions using artificial neural network (ANN) technique and real-time measured planar radial peaking factor, Fxy (hereinafter 'Live Fxy') method for digital core protection system of pressurized water reactors is presented. ANN with Simulated Annealing (SA) technique to find global optimum solution was used to calculate core average axial power distribution. In Live Fxy method, axial node-wise Fxys were calculated by multiplying pin/box factors to the measured assembly powers inferred from in-core detector signals without calculating detailed 3D power distributions. The hot pin power distribution for DNBR and LPD was then obtained by multiplying the core average axial power distribution and Fxys at axial nodes. The validation of the method was performed for various core conditions (e.g. core power levels, control rod positions, etc.) of Korean OPR1000 power plant. The result showed a decrease in RMS errors by 2.2% ~ 7.0% (3.6% on average), and the minimum thermal margins for DNBR and LPD were increased by 6.4% and 15.6%, respectively. The application of the method would improve the accuracy of axial power distribution and thermal margin, contributing to the operability and safety of digital core protection system. [ABSTRACT FROM AUTHOR]

Abstract The allocation of communication resources is extremely important for the device-to-device (D2D) communication network with unmanned aerial vehicle (UAV) relays, but this field currently mainly focuses on the communication between UAVs and ground devices. While neglecting the comprehensive optimization of air-to-ground communication and internal ground communication. This paper studies the optimization problem of communication for ground D2D devices based on UAV relays in urban environment. We study the comprehensive optimization considering the air-to-ground communication and the mobile ground communication devices. We construct the D2D communication neighbor table, and the terrestrial D2D devices will sort and select the best D2D link for pairing based on the Euclidean distance and gain set around them. We construct a communication model for this scenario and propose an improved Dual-Actor Network Softmax Twin Delayed Deep Deterministic Policy Gradient algorithm based on deep reinforcement learning. We perform transmit power optimization to address the interference caused by D2D communication using frequency division multiplexing technology. By designing the reward function and training mechanism, we can achieve good power control and reduce communication interference. The results of the simulation analysis performed in this scenario show an effective reduction in communication latency and an increase in D2I (device-to-infrastructure) channel capacity, D2D connectivity success, and energy efficiency. The feasibility and effectiveness of the algorithm is verified by comparing the baseline algorithm.

Abstract Significant attempts have been made to make the electrical grid more intelligent and responsive to better meet customers' requirements while boosting the stability and efficiency of current power systems. Smart grid technologies, which have just recently emerged, facilitated the incorporation of demand response (DR) by introducing an information and communication backbone to the current system. The Internet of Things (IoT) has emerged as a key technology for smart energy grids. Security concerns have emerged as a major obstacle to the widespread adoption of IoT‐enabled devices because of the inherent Internet connectivity of these smart gadgets. Therefore, security is a crucial factor to address before the widespread implementation of IoT‐based devices in power grids. In this study, the framework and architecture of smart grids that are enabled by the IoT are first examined. Then, the role of IoT for DR in smart grids and different approaches adopted worldwide to make DR schemes more effective, have been discussed in detail. Finally, the authors discuss how IoT‐enabled smart grids can benefit from cutting‐edge solutions and technologies that make them more secure and resistant to cyber and physical attacks.

Abstract The introduction of cloud‐native technology has significantly changed the architecture of applications and the mechanism for the collaborative operation of components in power distribution edge computing terminals (PDECT). To develop an effective quantitative analysis tool for PDECT performance, the composition and characteristics of cloud‐native PDECT are studied, and the modelling and simulation of cloud‐native PDECT are proposed. Subsequently, modelling is implemented through the simulation software CloudSim, achieving the simulation of microservices, containers, declarative configuration, and container orchestration with the consideration of power distribution scenarios. Then, by the proposed simulation scenario module, various elements of the power distribution scenarios can be self‐defined. Finally, by demonstrating the principles and implementation mechanisms of the proposed modelling method and simulation tool, and comparing simulation results for different service time ranges, access devices, resource configurations of PDECT, request occurrence rates, and resource scheduling strategies, the validity and effectiveness of the proposed modelling method and simulation tool are verified.

Abstract The adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1.

Abstract The issue of generation expansion planning in microgrids has become a challenging issue in electricity industry for two reasons: load growth and uncertainties in renewables' generation. Therefore, this issue is considered here. Here, the modelling of generation expansion planning problem has been developed in a network of microgrids in a decentralized manner, considering normal and contingency conditions. On the other hand, in order to further develop the study considered, decentralized generation expansion planning model of microgrids by considering contingency conditions has been addressed in a cooperative approach to minimize total costs. In developed model, investment decisions are made at the higher level and operational constraints has been considered at lower one. Also, case studies are defined in three different scenarios: islanding operation of microgrids as first scenario and peer‐to‐peer trading of microgrids in non‐cooperative and cooperative approaches as second and third scenarios, respectively. The results of simulations have shown that by facilitating the transactions between microgrids, their total costs are reduced. The costs of the whole set of microgrids in the non‐cooperative scenario are reduced by 9.4% compared to the islanding scenario; and the costs are reduced by 7.5% in the cooperative scenario compared to the non‐cooperative scenario.

The needs of solid oxide fuel cells (SOFC) and batteries in large ships are analyzed by estimating the amount of fuel consumption and CO2 emissions. Three types of systems are proposed: 1) fuel cells-battery-generator engine system, 2) battery-generator engine system, and 3) generator engine system. This study is based on the time-varying electric power demand and adopted the sequential quadratic programming (SQP) method to find the optimal power distribution of mixed power sources. CO2 emissions in the SOFC hybrid system are 11.6% lower than in the conventional generator engine system, and those in the battery-generator engine system are 5.1% lower than in the conventional system. The reduction of CO2 emissions come from three factors: the operation of the generator engine at a relatively high load rate, the battery sharing the operation of the generator engine, and the high efficiency of the SOFC running. The SOFC hybrid system can be a sustainable technology in large vessels. [ABSTRACT FROM AUTHOR]

This paper discusses the findings of a study of the student-tutor relationship with reference to Chinese international students in a UK university self-access writing centre. The study collected semi-structured interview data from multiple cohorts, including Chinese students, writing tutors (professional English for Academic Purposes (EAP) teachers rather than student peer tutors) and the writing centre director. Three types of relationships (asymmetrical, symmetrical and quasi-symmetrical) are thereby identified and discussed. The most reported relationship found in this study was an equal/symmetrical relationship. This egalitarianism was interpreted from the perspectives of a) the learning atmosphere; b) responsibility distribution; and c) differences between the writing tutor and the degree programme lecturer (i.e., disciplinary faculty). The findings enhance the understanding of the student-tutor relationship between professional EAP writing tutors and international students in the UK higher education (HE) context, and pedagogical implications are duly put forward.

Aiming at the increasing demand for electric energy in aircraft in the future, a multi-objective optimization aircraft engine constrained model predictive control method based on generation power distribution is proposed. Firstly, based on the aircraft engine component level model and the equilibrium manifold theory, the aircraft engine equilibrium manifold expansion model is established. Secondly, the influence of the power generation is modeled, and the influence of the low- and high-pressure shaft generators on the normal operation of the aircraft engine is studied and compared. The control variables such as fuel flow and total generation power are taken as the constraint conditions to design the constraint model predictive controller. Furthermore, the multi-objective grey wolf optimization algorithm is introduced to intelligently optimize the parameters of the designed controller. At last, the simulation based on the component level model shows that the high-pressure shaft generator has less influence on the state quantity, including engine thrust, than the low-pressure shaft generator. The proposed control method using the multi-objective gray wolf optimization (MOGWO) algorithm has rapid response and no steady-state error. [ABSTRACT FROM AUTHOR]

The features of the integral tractor concept are considered. The values of controlled interaxle and interwheel power distribution mechanisms are analyzed. Transmission options and simplified kinematic diagrams of interaxle and interwheel power distribution mechanisms are considered. Gear ratios in characteristic operating modes, types of their control, and the required technological bases for their design and manufacture are determined. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: Joan McCord was an experimental criminologist who advocated for evaluating social programs for efficacy, benefits, and potential harms to guide crime prevention policy. This paper argues that criminal justice reform should be guided by evidence for effective social programs that guard against unintended harms. Programs that focus on social control are consistent with basic facts of crime and guard against the tail risk of surges in serious crime and violence. This paper discusses the evidence from evaluations of social programs which are based on principals of social control in families, schools, neighborhoods, and the criminal justice system. Methods: This paper draws upon the evaluations of social programs that have been published based on at least one high-quality experiment or two rigorous quasi-experiments, that can be scaled to entire populations, and are sustainable over time. Results: A review of experimental and quasi-experimental evidence found that social programs focused on increasing social control (formal or informal) in families, schools, communities, and by the criminal justice system are effective at preventing serious crime. Some of the social programs with rigorous evidence of preventing crime could be scaled to entire populations for prolonged durations with adequate planning and implementation models. Conclusion: Many contemporary criminal justice reforms have little evidence of efficacy, and run the risk of generating unintended adverse outcomes related to the spread of serious crime and violence. Existing evidence suggests that social programs that focus on social control can act as buffers against the tail risk of serious crime. The social programs with evidence of preventing crime should be expanded, monitored for fidelity in implementation, and continuously evaluated to improve their efficacy and sustainability as effective safeguards against the rise in serious crime and violence. [ABSTRACT FROM AUTHOR]

In this article, a distributed controller based on adaptive dynamic programming is proposed to solve the minimum loss problem of flywheel energy storage systems (FESS). We first formulate a performance function aiming to reduce total losses of FESS in power distribution applications. Then we use the Hamilton–Jacobi–Bellman (HJB) equation to solve this optimal control problem. The solution of the HJB equation is approximated by neural networks. To achieve distributed control, we estimate the global variables in the HJB equation by using the dynamic average consensus algorithm. A barrier Lyapunov function and a saturation function are introduced to handle the issue of state and input constraints, respectively. Then the stability of the system is proved through the Lyapunov stability analysis. Finally the effectiveness of the proposed strategy is verified by simulations. Simulation results show that FESS can track the power command while minimizing total power losses by interacting with neighbors. The proposed algorithm leads to a loss reduction of 1.13%$$ 1.13\% $$ compared to the equal power distribution strategy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractOpenNode, a new open-source Fortran code, enables the simulation of fixed-source reactors. Powered by the nodal expansion method (NEM) and seamless Python integration, OpenNode competes with existing nuclear reactor simulation tools. The fixed-source problem plays a crucial role in radioprotection, providing flux calculations and detailed insights into heating effects and dose rates. Users can define and configure reactor models via a JSON input file, specifying critical parameters like geometry, materials, cross-section data, boundary conditions, and fixed sources.OpenNode further empowers users with a Python interface for preprocessing and postprocessing, streamlining result analysis. Distinguishing itself from conventional NEM codes, OpenNode supports three-dimensional (3D) Cartesian geometries, facilitating intricate reactor design simulations. Customization options, including mesh sizes, polynomial orders, and calculation modes, enhance precision and efficiency.In our comprehensive study, we verified OpenNode’s fixed-source mode using the 3D-IAEA reactor, comparing it with the SANM code KOMODO. The results underscore OpenNode’s exceptional accuracy in computing critical parameters, like the effective multiplication factor, power distribution, and nodal flux, within fixed-source reactors. OpenNode stands as a reliable, user-friendly tool poised to advance nuclear reactor simulations. [ABSTRACT FROM AUTHOR]

Objective: This study's objective was to estimate hearing loss prevalence for noise-exposed US Utilities workers. Methods: In this cross-sectional study of a retrospective cohort, audiograms were examined for 1.3 million workers (13,595 within utilities) from 2010-2019. Hearing loss prevalence and adjusted risk as compared with a reference industry were estimated. Results: The hearing loss prevalence for noise-exposed Utilities workers (25%) was higher than for noise-exposed workers in all industries combined (20%). Some subsectors surpassed the prevalence for all industries combined and/or had adjusted risks significantly higher than the reference industry. The highest prevalence subsectors were Hydroelectric Power Generation (37%) and steam and Air-Conditioning Supply (29%). Conclusions: While often overlooked, noise-exposed workers in this sector have among the highest prevalences of hearing loss and significantly higher risks. Increased attention and better hearing conservation strategies are needed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused fluctuations in electricity demand, altering people's lifestyles and electricity usage patterns, thereby affecting the accuracy of demand predictions. However, existing studies on electricity forecasting have not adequately considered the incorporation of COVID-19-related features and the analysis of electricity usage characteristics across different regions of the UK. Therefore, this paper, based on data of the UK's national electricity demand, conducts an analysis around the scenario of a large-scale health emergency in society. We explore the changing patterns and regional characteristics of electricity consumption during the COVID-19 pandemic, comparing the forecast results before and during the pandemic to illustrate its impact on the UK's electricity demand. By introducing COVID-19-related features into the models, we compare the forecast results before and after their inclusion. The results indicate that the COVID-19 pandemic has had a certain impact on the electricity prediction in the UK, leading to a 22.8% decrease in prediction accuracy. However, the models' correlation improved with the inclusion of COVID-19-related features, resulting in a 13.2% enhancement in prediction accuracy compared to the previous models. Additionally, the study summarizes other factors influencing electricity demand, such as power imports/exports and clean energy usage, as considerations for electricity distribution planning. This contributes to improving the accuracy of predicting the UK's electricity demand during COVID-19 pandemic, enabling the government to adjust power dispatching plans reasonably based on relevant factors, achieving rational distribution and efficient scheduling of power resources. [ABSTRACT FROM AUTHOR]

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems. [ABSTRACT FROM AUTHOR]

We introduce a novel technique for producing several families of distributions: the alpha-log-power transformed method. The novelty of our new approach lies in the fact that it adds one new shape parameter and was not derived from any established parent model. Some examples of the new family are presented. Also, some important statistical properties of the new family are studied. The maximum likelihood estimation approach is utilized to estimate the model parameters of the new family. To evaluate the performance of the estimators, Monte Carlo simulation is conducted using some arbitrary baseline distributions namely the Weibull, Burr-XII and Pareto distribution. Two real datasets are used to empirically show the potential significance and applicability of the alpha log power transformed Weibull. The alpha log power transformed Weibull is a very competitive model for characterizing observations in survival analysis. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces a novel metaheuristic technique, a COOT‐based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop‐configured network. This method significantly narrows the optimization gap by leveraging a COOT‐based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus‐33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus‐33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno‐economic basis.

Abstract Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness.

Abstract Mixture distributions are naturally extra attractive to model the heterogeneous environment of processes in reliability analysis than simple probability models. This focus of the study is to develop and Bayesian inference on the 3-component mixture of power distributions. Under symmetric and asymmetric loss functions, the Bayes estimators and posterior risk using priors are derived. The presentation of Bayes estimators for various sample sizes and test termination time (a fact of time after that test is terminated) is examined in this article. To assess the performance of Bayes estimators in terms of posterior risks, a Monte Carlo simulation along with real data study is presented.

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

Hybrid energy storage system (HESS) can stabilize renewable energy power generation, but unreasonable energy storage power distribution and photovoltaic-load forecast errors will affect the economic benefits of the whole system. Aiming at the microgrid (MG) composed of photovoltaic (PV) and HESS, an energy management strategy (EMS) of MG considering forecast errors is proposed. Firstly, an optimization model considering the depreciation cost of battery is established. Secondly, day-ahead EMS is implemented under multiple operating modes considering minimum fluctuation and optimal economy. Then, according to the real-time forecast results and the feedback of system operation status, the intraday rolling energy management strategy (REMS) is developed to alleviate the impact of forecast errors. Finally, the real-time state of charge (SOC) of the supercapacitor (SC) is introduced to adjust the filter coefficient, which avoids the SC working in the charge/discharge restricted area for a long time and improves the adjustment effect. The results of the case analysis show that the proposed intraday REMS can effectively reduce the influence of forecast errors on energy management.

Urban air mobility (UAM) represents a promising solution for alleviating contemporary ground traffic congestion challenges. Consequently, a comprehensive analysis of power system operational characteristics within typical flight mission profiles for flying cars is of utmost importance. Due to constraints associated with battery energy density and the limited range of all-electric flying cars, the adoption of an oil-electric hybrid power system for flying cars is a more pragmatic choice. An overview of pertinent research concerning the operational performance of hybrid systems in vertical takeoff and landing ( VTOL) vehicles was provided. Utilizing MATLAB Simulink, a hybrid power coordination method was proposed that accounted for both operational modes. Distinct operational modes were predefined in accordance with actual operational conditions, encompassing vertical take-off and landing as well as level flight scenarios. Building upon this foundation, the coordination of the battery was explored, serving as the primary power source, to regulate power flow and enhance the efficiency of the range extender. The DC bus voltage was subject to regulation through a DC/ DC converter. To promote the overall longevity of the battery pack, power sharing mechanisms were introduced during both charging and discharging phases. Simulation results underscore the proficient control of the DC bus voltage, and power distribution between the range extender and the battery aligns with the predetermined design parameters. [ABSTRACT FROM AUTHOR]

This study investigates the reliability of commonly utilized digital spiking neurons and the potential side-channel vulnerabilities in neuromorphic systems that employ them. Through our experiments, we have successfully decoded the parametric information of Izhikevich and leaky integrate-and-fire (LIF) neuron-based spiking neural networks (SNNs) using differential power analysis. Furthermore, we have demonstrated the practical application of extracted information from the 92% accurate pretrained standard spiking convolution neural network classifier on the FashionMNIST dataset. These findings highlight the potential dangers of utilizing internal information for side-channel and denial-of-service attacks, even when using the usual input as the attack vector. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a novel metaheuristic technique, a COOT‐based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop‐configured network. This method significantly narrows the optimization gap by leveraging a COOT‐based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus‐33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus‐33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno‐economic basis. [ABSTRACT FROM AUTHOR]

Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness. [ABSTRACT FROM AUTHOR]

Wireless and power line communication hybrid relay technology can realize complementary advantages and comprehensively improve the communication coverage and performance of power Internet of Things. In order to study the mechanism of the physical layer and Media Access Control (MAC) layer algorithm that affects the performance of hybrid relay systems, the cross-layer performance modeling, optimization, and simulation analysis are carried out for the non-orthogonal multiple access (NOMA) technology. Firstly, a two-hop NOMA media access control protocol is designed based on the CSMA algorithm. Considering the effects of non-ideal channel transmission at the physical layer and competitive access at the MAC layer on the system performance, a cross-layer performance analysis model of hybrid wireless and power line communication relay system under NOMA is established. Finally, a cross-layer optimization model based on multi-objective programming is established for the hybrid relay system. By analyzing the relationship between transmitting power and performance index, the joint optimization of transmitting power and power distribution factor between users is realized. Simulation results verify the validity and reliability of the proposed cross-layer model. The results show that the hybrid relay algorithm combined with NOMA and CSMA can effectively improve the performance of the system throughput, packet loss probability, and delay. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article presents the issues related to load-power sharing in direct-current grid and a novel control method has advantages over known solutions. Unlike many similar-sounding papers, this article shows an attempt at creating fully controllable non-isolated system that allows for load-power sharing between a permanent magnet alternator equipped with machine-side converter (MSC) and a dual active bridge (DAB) tied to batteries or supercapacitor. The current-based load-power sharing is an essential feature of parallel-connected direct-current generators, and all types of voltage sources, in this way are contributing power to the system. To keep the optimal efficiency of the alternator, the rotational speed changes rely on proper mapping of the driving combustion engine. System components include a self-excited synchronous generator (SESG), operating at variable shaft speed, as well as batteries and supercapacitors that provide electricity for sudden electrical-load changes on the distribution grid. The core of the presented system is in a power-distribution method that consists of a programmed-controller structure allowing precise current distribution. A novelty of the proposed method is the use of a cascaded system of current and DC voltage regulators that allows for precise power-distribution control. In contrast with previously presented solutions, the proposed system allows for fast and accurate control of currents, loading parallel-connected DC voltage sources for wide-range generator speed changes. In the presented solution, both converters have been equipped with Schottky diodes, preventing the flow of equalizing currents between closed transistors in the parallel mode of operation. An experimental test-stand of the described system is presented with its theoretical basis and experimental results. [ABSTRACT FROM AUTHOR]

When a diesel engine operates at non constant power output, it not only has low fuel efficiency but may also generate significant mechanical stress, leading to wear and damage of mechanical components. In order to ensure the efficient operation of diesels within diesel multiple units (DMU), this paper proposes a proportional-integral (PI) control approach based on the operational mode of the diesel generator sets featuring constant power output. This method was devised to steadily synchronize power of the traction systems on the load side with the target output power of the diesel generator sets. In turn, constant power operation on the load side guarantees a constant power output from the generator sets, thereby improving the fuel economy of the diesels. Through simulation and experimental tests, steady state fluctuations in load power were maintained within ±2%, demonstrating the effective constant power operation and the real-time alignment of load power with the target value.

Electric power distribution is a crucial element in meeting the needs of the community, especially in the workshop and electronic equipment repair sector. The transmission process aims to optimize efficiency and reduce power losses. PT PLN (Persero) UP2D SUMBAR has a high commitment in responding to the needs of the community, related to the distribution of electricity, especially in the 20 KV distribution network. Although it already uses two Early Warning Systems connected to SMART VU SCADA, the DCC room alarm sound is disabled to minimize noise. Therefore, this research focuses on implementing an additional Early Warning System with LED warning lights. The research methodology includes literature study, initial testing (Experimental Method), system design, hardware design, second testing (successful/unsuccessful), direct testing in the DCC room, and analysis of results. It is expected that this research can improve alarm notifications, speed up dispatcher response time, and increase vigilance in handling disturbances in the 20 KV distribution network.

Show the prospects for the development of local electric networks with distributed generation. Recent changes in the electric power industry are forcing the revision of the requirements for both infrastructure and power facilities. Show that the connection of distributed generators and consumers of electrical energy to the network is fraught with certain difficulties. Solving the problem of high-quality and uninterrupted power supply to consumers requires a new approach associated with the use of modern Smart Grid technologies, equally related to energy and information technology. It is noted that for the implementation of such a large-scale task, the "road map" "Energynet" of the National Technology Initiative was developed, among the main directions of which are the digitalization of the infrastructure of distribution networks and the deep decentralization of electricity production. Consider, in accordance with the identified areas, the segments of the smart energy market, which include: reliable and flexible distribution grids, smart distributed energy and consumer services. Propose a solution to the problem using the example of a local low voltage microgrid with distributed generation. METHODS. The solution to the problem is proposed on the basis of creating a simulation model, with the help of which it is possible to perform a number of tests and obtain the optimal network mode within a given error area. implement various options for the structure of the microgrid, the number and type of generators, the load level. The structural diagram of the local network is proposed. RESULTS. The article describes the process of managing power flows between producers (local distributed sources) and consumers of electricity. This process is carried out through a single control center, which receives information about the state of the micro-grid objects - the level of electricity generation by generators and the level of the current load of consumers. Based on the results of information processing in the control center, a decision is made on the redistribution of power between objects. A scheme describing the dynamic process of power transfer for efficient resource management in the power distribution network is proposed. CONCLUSION. The considered power management algorithm is a simple implementation of classical load balancing, and at the same time allows for optimal energy distribution within a given error. Optimal, in this case, will be the state of the system, in which all distributed renewable energy sources are used in full to meet the needs of the local micro-network, while reducing to a minimum the consumption from the network. At the same time, it is also necessary to introduce corrections for the unevenness and instability of electricity generation by renewable energy sources.

Preventive tree pruning is an efficient way to reduce the risks of outages in the electrical network, such as cable rupture, short circuits, and fires, which may occur due to interferences between vegetation and urban electrical distribution lines. These interferences are listed as one of the main causes of power supply interruptions by distribution companies and, consequently, generate significant financial losses due to the decline in quality indicators and the need for maintenance. Vegetation management inspection is essential to maintain the reliability and continuity of the power distribution, but manual monitoring is expensive, time-consuming, and subjective. This paper proposes a new method based on Deep Learning (DL) and high-resolution sensors for automated vegetation inspection in urban power distribution lines. The method combines Light Detection and Ranging (LiDAR) and spherical images and overcomes the limitations of aerial images using vehicle-borne sensors. The dataset was obtained using the proposed capture system in four Brazilian cities. The results demonstrated the suitability of the proposed method in an innovative way through a proof of concept. Among the main contributions, the functional evaluation of the system for Artificial Intelligence-based real-time vegetation inspection in power line distribution is highlighted. The proposed method achieved high rates of interference identification using a web platform and mobile application (achieving a precision of over 94%). The results demonstrate the potential of the proposed approach for automated vegetation inspection in real-world scenarios and open up opportunities for the deployment and evaluation of large-scale applications.

In this paper, a method for power distribution control in communication-free multiple-input and multiple-output (MIMO) wireless power transfer (WPT) through load impedance and mutual inductance estimation is proposed. The proposed method intentionally adjusts the resonant frequency of the secondary side to introduce a reactance, utilizing this information to estimate the load impedance and mutual inductance for power distribution control without the need for communication. The principle of the power transmission algorithm proposed in this paper is designed to be effective regardless of whether the power or frequency is high or low. Therefore, the experimental setup is not designed by specifying the coil size, system frequency, or separation distance between the transmitting (Tx) and receiving (Rx) coils to match specific real applications. To verify the proposed method, four Tx coils and two Rx coils of arbitrary sizes are fabricated, and a MIMO-WPT environment is implemented using LC matching. In the experiment, since the circuit designed for the Tx is not capable of withstanding high currents, power is intentionally transmitted at a low range of less than 1 W to validate the feasibility of the method. Because the transmission power range is set low, the distance between the Tx and Rx coils is set close to approximately 2 cm. Consequently, it is verified that the algorithm for parameter estimation and power transmission to multiple Rx coils is effective as long as the environment allows for the current flowing through the Tx coil to change due to the presence of the Rx coils. All experiments for uniform and non-uniform power distribution to two Rx coils showed good agreement between the desired power and the actual received power.

Power distribution systems consist of critical infrastructure with various categories of assets that require rigorous maintenance programs to ensure their reliability and performance for all stakeholders. Maintenance program prioritization is essential, considering asset significance, failure likelihood, and consequences. This research presents a pioneering methodology for functional failure criticality assessment and control in power distribution systems. It uniquely combines NORSOK Z008 guidelines for functional failure identification of all types of assets and a multiple criteria decision-making tool named analytic hierarchy process (AHP) for prioritization of resources, and yearly budgets for the identified critical assets using asset failure analysis. This innovative approach addresses the shortcomings of existing methods by comprehensively enhancing cost-effectiveness, efficiency, and stakeholder decision-making, while adhering to industrial standards. The rationale stems from the necessity to bridge the gap between the theoretical frameworks and practical decision-making in infrastructure maintenance. By conducting a case study in a densely populated area of a developing nation, the study validates the effectiveness of the proposed approach in mitigating risks and improving system performance. The overarching goal of the research is to equip power distribution utilities with a robust toolset to optimize resource allocation, enhance system reliability, and ensure regulatory compliance. This strategic initiative aims to empower stakeholders with informed decision-making capabilities, ultimately fostering a safer, more reliable power distribution infrastructure that benefits both utilities and communities they serve.

In recent years, the unmanned aerial vehicle-assisted internet of vehicles has been extensively studied to enhance communication and computation services in vehicular environments where ground infrastructures are limited or absent. However, due to the limited-service range and battery life of unmanned aerial vehicles, along with the high mobility of vehicles, an unmanned aerial vehicle cannot continuously cover and serve the same vehicle, leading to interruptions in vehicular application services. Therefore, this paper proposes a joint optimization strategy for task migration and power allocation based on soft actor-critic (JOTMAP-SAC). First, communication models, computational resource allocation models, and computation models are established sequentially based on the computational resource and dynamic coordinate of each node. The joint optimization problem of task migration and power allocation is then formulated. Considering the dynamic nature of the unmanned aerial vehicle-assisted internet of vehicles environment and the continuity of the action space, a soft actor-critic based algorithm for task migration and power allocation is designed. This algorithm iteratively finds the optimal solution to the joint optimization problem, thereby reducing the processing delay in unmanned aerial vehicle-assisted internet of vehicles and ensuring the continuity of internet of vehicles task processing.