Power Source Research Articles

Balancing Security and Efficiency: A Power Consumption Analysis of a Lightweight Block Cipher

This research paper presents a detailed analysis of a lightweight block cipher’s (LWBC) power consumption and security features, specifically designed for IoT applications. To accurately measure energy consumption during the execution of the LWBC algorithm, we utilised the Qoitech Otii Arc, a specialised tool for optimising energy usage. Our experimental setup involved using the Otii Arc as a power source for an Arduino NodeMCU V3, running the LWBC security algorithm. Our methodology focused on energy consumption analysis using the shunt resistor technique. Our findings reveal that the LWBC is highly efficient and provides an effective solution for energy-limited IoT devices. We also conducted a comparative analysis of the proposed cipher against established LWBCs, which demonstrated its superior performance in terms of energy consumption per bit. The proposed LWBC was evaluated based on various key dimensions such as power efficiency, key and block size, rounds, cipher architecture, gate area, ROM, latency, and throughput. The results of our analysis indicate that the proposed LWBC is a promising cryptographic solution for energy-conscious and resource-limited IoT applications.

Advanced Sliding Mode Design for Optimal Automatic Generation Control in Multi-Area Multi-Source Power System Considering HVDC Link

The multi-area multi-source power system (MAMSPS), which uses a variety of power sources including gas, hydro, thermal, and renewable energy, has recently been implemented to balance the growing demand for electricity and the overall capacity for power generation. In this paper, an integral sliding mode control with a single-phase technique (ISMCSP) is applied to two areas, with each area including gas–wind–thermal power systems with HVDC system. Firstly, a two-area gas–wind–thermal power system with HVDC (TAGWTPSH) is the first model in this scheme to consider the parameter uncertainties of a MAMSPS. Secondly, sliding mode design law with a single-phase technique is introduced to alleviate chattering and oscillation problems. Then, power system stability is ensured by the Lyapunov control theory based on the new LMIs technique. Thirdly, the ISMCSP’s effectiveness in a MAMSPS is also assessed under random load patterns and parameter variations regarding settling time and over-/undershoot. The ISMCSP was created to alter the fundamental sliding mode control, and therefore the suggested approach performs better than recently published approaches. This is demonstrated by the frequency overshoot deviation value in frequency deviations: 0.7 × 10−3 to 2.8 × 10−3 for the TAGWTPSH with the suggested ISMCSP. In the last case, for random changes in load from −0.4 to +0.5 p. u, the proposed ISMCSP method still stabilizes the frequency of the areas meeting the standard requirements for AGC.

IMPROVING THE COMPETENCE OF HUMAN RESOURCES OF MSMES IN CIGUNDUL VILLAGE, SUKABUMI REGENCY

PKM objectives: to improve quality source Power man from the UMKM Faculty of Economics and Business. The PKM activity method consists of from: Business Model Canvas Analysis, innovation management business, and evaluation business. PKM Results: (1) Innovation system management business foot based doormat entrepreneurship with method develop customer relationship (join) with community lovers goods recycle repeat and marketing through track community social; (2) Marketing media innovation use social media Facebook and WhatsApp (already) utilise technology for sell product crafts produced ; (3) Creating field work in Cikundul Village Where there are 20 mothers House the stairs that join become craftsman doormat; (4) Income craftsman doormat increased​ as much as 15% because existence improvement amount consumers; (5) Increase understanding craftsman doormat on concept entrepreneurship (craftsmen) doormat capable​ learn values, capacity (ability) and attitudes​ somebody when face challenge life For get opportunity with various possible risks​ faced with during operate business; (6) Increased sense of trust self craftsman in operate operational business and execution sale product with system on line.

Quantitative assessment of the effectiveness of adaptive spatial processing algorithms in searching for low-noise underwater vehicles in surface shipping conditions of different density

The article describes the methodology and provides the results of a model quantitative assessment of the effectiveness of solving the problem of detecting and tracking a low-noise underwater object using three algorithms for spatial signal processing at the output of a multi-element antenna — the non-adaptive Bartlett algorithm, the Capon algorithm, and the Capon algorithm combined with a projection procedure limiting the signal power of strong local sources.

Design and Optimization of MoS2@rGO@NiFeS Nanocomposites for Hybrid Supercapattery Performance and Sensitive Electrochemical Detection.

Metal sulfide-based composites have become increasingly common as materials used for electrodes in supercapacitors because of their excellent conductivity, electrochemical activity, and redox capacity. This study synthesized a composite of NiFeS@MoS2@rGO nanostructure using a simple hydrothermal approach. The synthesized nanocomposite consisted of the composite of nickel sulfide and iron sulfide doped with MoS2@rGO. A three-electrode cell is employed to investigate the electrochemical properties of the NiFeS@MoS2@rGO electrode. The results demonstrated an optimal specific capacitance of 3188 F/g at 1.4 A/g in a 1 M KOH electrolyte. Furthermore, a supercapattery is designed utilizing NiFeS@MoS2@rGO//AC as the positive electrode and activated carbon (AC) as the negative electrode materials. The resulting supercapattery is designed at a cell voltage of 1.6 V, achieving a specific capacity value of 189 C/g at 1.4 A/g. It also demonstrated an excellent energy density of 55 Wh/kg with an enhanced power density of 3800 W/kg. Furthermore, the hybrid device demonstrated remarkable stability with a cycling stability of 95% over 30,000 charge-discharge cycles at a current density of 1.4 A/g. The supercapattery, which has excellent energy storage capabilities, is used as a power source for operating different portable electronic devices.

Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched energy density and flexibility. However, as the world shifts toward renewable energy, overcoming the limitations of intermittent power sources requires a bold reimagining of energy storage and integration. Power-to-X (PtX) technologies, which convert excess renewable electricity into storable energy carriers, offer a promising solution for long-term energy storage and sector coupling. Recent advancements in machine learning (ML) have revolutionized PtX systems by enhancing efficiency, scalability, and sustainability. This review provides a detailed analysis of how ML techniques, such as deep reinforcement learning, data-driven optimization, and predictive diagnostics, are driving innovation in Power-to-Gas (PtG), Power-to-Liquid (PtL), and Power-to-Heat (PtH) systems. For example, deep reinforcement learning has improved real-time decision-making in PtG systems, reducing operational costs and improving grid stability. Additionally, predictive diagnostics powered by ML have increased system reliability by identifying early failures in critical components such as proton exchange membrane fuel cells (PEMFCs). Despite these advancements, challenges such as data quality, real-time processing, and scalability remain, presenting future research opportunities. These advancements are critical to decarbonizing hard-to-electrify sectors, such as heavy industry, transportation, and aviation, aligning with global sustainability goals.

Compact Vital-Sensing Band with Uninterrupted Power Supply for Core Body Temperature and Pulse Rate Monitoring.

Although wearable devices for continuous monitoring of vital signs have undergone significant advancements, their need for frequent recharging precludes continuous operation, potentially leading to adverse outcomes being overlooked. Additionally, the scattered locations of the sensors hamper wearability. Herein, we present a compact vital-sensing band with uninterrupted power supply designed for continuous monitoring of core body temperature (CBT) and pulse rate. The band─which comprises two sensors, a power source (i.e., a flexible thermoelectric generator (TEG) and a battery), and a flexible circuit─is worn on the forearm. The CBT is calculated by measuring the skin temperature and heat flux, while a triboelectric nanogenerator-based self-powered pressure sensor is utilized for pulse rate monitoring. The TEG is a flexible unit that converts body heat into electricity, accumulating a total energy of 314 mJ (100%). Out of this total energy, only 43.2 mJ (7.2%) is utilized for CBT measurements, while the remaining 270.80 mJ (92.8%) is stored in the battery. This enables reliable and continuous operation of the vital-sensing band, highlighting its potential for use in healthcare applications.

Nonlinear phase velocities in tri-directional functionally graded nanoplates coupled with NEMS patch using multi-physics simulation

The nonlinear phase velocities analysis of tri-directional functionally graded (FG) nanoplates is crucial for optimizing their performance as nano-electro-mechanical systems (NEMS). By understanding the nonlinear phase velocities of various wave modes within the nanoplate, engineers can accurately predict and enhance the efficiency of NEMS. This analysis allows for the precise tuning of the piezoelectric patches to capture vibrational energy more effectively, ensuring maximum energy conversion efficiency. Moreover, it helps in identifying the optimal placement and orientation of the piezoelectric patches, minimizing energy loss and enhancing the reliability and durability of the NEMS. Ultimately, this leads to more efficient utilization of ambient vibrations in airplanes, providing a sustainable power source for various onboard sensors and monitoring systems, contributing to reduced reliance on external power sources, and improved overall energy management. For this issue, for the first time, nonlinear phase velocity in the tri-directional functionally graded nanoplate coupled with a piezoelectric patch via COMSOL multi-physics simulation, physics-informed deep neural networks (PIDNNs), and mathematics simulation are presented. In the mathematics simulation domain, nonlocal strain gradient theory for modeling both the hardening and softening behavior of the current nanoplate is presented. The electromechanical coupling effect and the abrupt change in material properties at the interfaces will have a major influence on the mechanical performance of tri-directional functionally graded (TD-FG) nanoplate coupled with a piezoelectric patch if transverse shear deformations cannot be well modeled. Thereby, in the current simulation, a quasi-3D refined theory with 10 variables is presented. Also, for coupling the composite structure with the piezoelectric patch, compatibility conditions are considered. An analytical solution procedure is presented for solving the nonlinear partial differential equations of the current electrical system.

Dynamics of soluble salts in cooling systems of circulating water supply

A model of the dynamics of soluble salts in the water of cooling systems of circulating water supply was developed based on mass balance principles. The proposed model is expressed in a general form under the assumption of a constant volume of circulating water and non-stationary input and output flows. The model accounts for scenarios where the concentration of soluble salts exceeds their concentration in the circulating water, thereby considering the technological necessity to change the system's power source in the event of an emergency at the feedwater treatment facilities. The developed model enables accurate prediction of the water-chemical regime in both existing and new recirculating cooling systems, sufficient for engineering calculations. It also facilitates the selection of optimal management strategies for such systems, including stabilization methods for recirculating water, feedwater preparation, blowdown volumes, etc. The model was validated under laboratory conditions for cases of concentration and dilution of highly soluble NaCl salt, with an observed error margin not exceeding 2%, attributed to experimental and instrumental factors. The variation in the concentration factor was analyzed for different blowdown rates, assuming initial equality of soluble salt concentrations in feedwater and circulation water. It was shown that establishing dynamic equilibrium between the circulation and feed waters can require dozens or even hundreds of circulation cycles for blowdown rates of 1–4%, and this system inertia must be considered during operation and when forecasting the performance of cooling systems with circulating water supply.

Labour process theory: in and beyond the core: continuities, challenges, and choices

Since the publication of Braverman’s seminal Labor and Monopoly Capital (1974), labour process theory (LPT) has become a successful theory-building project driven by empirical expansion and conceptual innovation, operating as the equivalent of a normal science, with a distinctive scope and specific empirical focus on workplace regimes in the context of capitalist political economy. It’s toolkit or conceptual architecture combines underpinning and ordering principles with contingent and flexible operational categories concerning labour power and control. Though LPT has built stronger conceptual connections between workplace and accumulation regimes, changes in the global order have brought new theoretical and empirical challenges that are requiring it to adjust some of its core concepts and boundaries to consider development such as new sources of labour power, migration, and mobility.

Combined ventilation mode in extra-long road tunnels: Energy efficient channel utilization with the inclined shafts

The ventilation mode plays a dominate role in addressing the feasibility of an extra-long tunnel. In this paper, a combined ventilation mode, i.e. energy efficient channel utilization with the inclined shafts, is proposed to ensure the air quality and fire safety in the extra-long road tunnel. Combined with the equations of ideal air, mass and energy conservation, and boundary conditions, the velocity of internal airflow and the energy consumption of tunnel can be analytically solved. As regards the case study in the Tongzi Tunnel in China, the scheme of combined ventilation mode of single channel and inclined shafts was proposed. The effects of natural wind pressure, traffic wind pressure and mechanical ventilation pressure on tunnel ventilation energy consumption were considered. In addition, the energy efficient channel is a strong substitution for shafts to enhance the efficiency of the ventilation. Based on the results, the position of the vertical shaft and single channel in combined ventilation was optimized. Compared with original ventilation scheme, the new method can save up to 40% ventilation energy consumption in daily operation, and saving 1.02 million CNY in electricity costs per year and improving long-term economic efficiency. In the fire emergency, the combined ventilation mode can more effectively control smoke for larger scale tunnel fires. In particular, when the power of the fire source exceeds 30 MW, the required critical wind speed increase rate is reduced to 0.01 m/s per MW. The analysis of ventilation mode undertaken here, has extended our knowledge of reducing energy consumption and maintenance costs in the extra-long tunnel operation.

Economic Emission Dispatch With Stochastic Renewable Power and Voltage Source Converters via Reinforcement Learning Multi-Objective Differential Evolution

Economic Emission Dispatch With Stochastic Renewable Power and Voltage Source Converters via Reinforcement Learning Multi-Objective Differential Evolution

New physics as a possible explanation for the Amaterasu particle

The Telescope Array experiment has recently reported the most energetic event detected in the hybrid technique era, with a reconstructed energy of 240 EeV, which has been named “Amaterasu” after the Shinto deity. Its origin is intriguing since no powerful enough candidate sources are located within the region consistent with its propagation horizon and arrival direction. In this work, we investigate the possibility of describing its origin in a scenario of new physics, specifically under a Lorentz Invariance Violation (LIV) assumption. The kinematics of UHECR propagation under a phenomenological LIV approach is investigated. The total mean free path for a particle with Amaterasu's energy increases from a few Mpc to hundreds of Mpc for -δ had,0 > 10-22, expanding significantly the region from which it could have originated. A combined fit of the spectrum and composition data of Telescope Array under different LIV assumptions was also performed. The data is best fitted with some level of LIV both with and without Amaterasu. Robustness with data from the Pierre Auger Observatory is investigated by exploring an intermediate composition scenario. Similar improvements in the description of the data with LIV are found for that. New physics in the form of LIV could, thus, provide a plausible and robust explanation for the Amaterasu particle.

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

Evaluation of Distributed Power and Electric Vehicle Grid-connected Carrying Capacity Based on Genetic Algorithm Optimisation Neural Network

Abstract The integration of new types of loads, such as large electric vehicles (EVs) and distributed energy sources, and the conventional structure and operation of the power distribution network have been significantly transformed. This presents a significant challenge in maintaining the network’s capacity to handle the load. To address this challenge, a carrying capacity assessment method based on a machine learning model optimized by a bionic optimization algorithm is proposed. Firstly, the assessment model’s index system is established, and the distribution network data are collected and preprocessed. Secondly, considering the complexity of the distribution network carrying capacity assessment, a backpropagation (BP) neural network is constructed within the machine learning model. The genetic algorithm, inspired by biological optimization, is used to fine-tune the machine learning model’s parameters. Subsequently, the model’s accuracy and generalization capability are evaluated, and the optimized machine learning model is used to assess the carrying capacity of EVs and distributed power sources. The results of the carrying capacity assessment can provide an effective basis for subsequent power distribution network planning. This method combines the benefits of machine learning with bionic optimization algorithms to tackle the challenges associated with incorporating new load types into the power distribution network. The model’s ability to accurately assess the carrying capacity can support effective planning and management of the evolving distribution network.

An evaluate of ORR catalysts in fuel cell: Current Advances and Applications

Abstract. This thesis presents a comprehensive review of fuel cell technology, with a specific emphasis at the latest trends and programs of oxygen reduction response (ORR) catalysts. The examine starts offevolved with the aid of using evaluating the benefits and drawbacks of hydrogen fuel cells with different alternative power sources, which include sun power and secondary batteries, highlighting the ability of fuel cells to update traditional inner combustion engines because of their high power density and environmental benefits. The thesis then explores the essential running ideas of fuel cells and examines their operational mechanisms in numerous chemical environments. In the context of ORR catalysts, the assessment evaluates quite a number catalysts, which include precious metal catalysts, non-precious metal catalysts, and single-atom catalysts, that specialize in techniques to decorate their activity and stability. Finally, the thesis discusses the demanding situations and destiny possibilities for the industrial application of ORR catalysts in hydrogen fuel cells.

Prediction of Li-ion conductivity in Ca and Si co-doped LiZr2(PO4)3 using a denoising autoencoder for experimental data

All-solid-state batteries composed of inorganic materials are in high demand as power sources for electric vehicles owing to their improved safety, energy density, and overall lifespan. However, the low ionic conductivity of inorganic solid electrolytes has limited the performance and adoption of inorganic all-solid-state batteries. The solid electrolyte LiZr2(PO4)3 has attracted attention owing to its high Li-ion conductivity. The ionic conductivity of LiZr2(PO4)3 changes with the crystalline phase obtained, which varies based on composition control through elemental substitution and process conditions such as sintering temperature. Traditionally, optimizing such parameters and understanding their relationship to physical properties have relied on researcher experience and intuition. However, a recent use of a materials informatics approach utilizing machine learning shows promise for more efficient property optimization. This study proposes a deep learning model to correlate powder X-ray diffraction (XRD) profiles with the activation energy (Ea) for Li-ion conduction, thereby enhancing the interpretability of the measurement data. XRD profiles, which contain information on crystal structure, lattice strain, and particle size, were used as-is (i.e., without preprocessing) in the deep learning model. An attention mechanism was introduced to the deep learning model that focuses on XRD crystal-structure information and visualization of important factors embedded in the XRD profiles. The highlighted areas in the output of this model successfully predict LiZr2(PO4)3 phases with low Ea (high Li conductivity) and high Ea (low Li conductivity). Moving forward, this deep learning model can offer new insights to materials researchers, potentially contributing to the discovery of new solid electrolyte materials.

Heat transfer study on a stator-permanent magnet electric motor: A hybrid estimation model for real-time temperature monitoring and predictive maintenance

When electrical machines operate without a specific cooling system, the surrounding environment plays a crucial role in the rise of temperature and the duty cycle of operation. More clearly, a natural convection cooling system with a low value of heat transfer coefficient carries the risk of thermal breakdown, insufficient safety, and reliability. This paper studies the heat transfer aspects of a low-power flux switching permanent magnet (FSPM) motor under natural convection cooling to implement a novel real-time sensor-less temperature monitoring system. Thermal and electromagnetic experiments are carried out to create foundations for transient and steady-state numerical models. A data-driven, deep learning algorithm estimates the core and permanent magnet (PM) eddy current losses in real-time, besides the already available copper and friction losses. Subsequently, a two-node thermal equivalent circuit in a hybrid model with a feed-forward neural network estimates the dynamic temperature profile of windings and PMs. It is indicated that the worst-case estimation error is below 7.5%, and the configuration is applicable under a wide range of operation states and environmental conditions. Lastly, the system, including the power source, FSPM motor, and hybrid temperature estimation unit, will be implemented in MATLAB/Simulink to investigate the fault prediction and operation management capabilities.

Human-safe based electric vehicle evacuation in transportation-power networks during emergencies: A two-stage response framework

The proliferation of electric vehicles (EVs) and the deepening interconnection between transportation and the power grid have heightened the complexity and vulnerability of the coupling transportation-power networks (TPNs). EV evacuation can exert significant dynamic pressure on the TPNs during emergencies. This article presents a baseline reproducible two-stage emergency response framework. In the first stage, we develop a platform for the comprehensive simulation called EV evacuation integrated simulation model (EVEISM), utilizing agent-based modeling. The interaction between the agent models and the GIS-based environment models captures the behavior of EVs in the complete evacuation period, including pre-evacuation and post-evacuation charging, receiving notifications, making destination decisions, planning routes to avoid risks, and traffic dynamics. In the second stage, leveraging EVEISM results to enhance situational awareness, strategic pre-positioning of mobile energy storage systems (MESS) and optimal routing are conducted. A mixed-integer second-order cone programming model is established for the coordinated response of multiple power sources to dispatch MESS dynamically. Case studies are conducted on coupling transportation-power systems, exemplified through the toxic gas leak scenario. The numerical results demonstrate the method's effectiveness in ensuring the safe evacuation of residents and optimizing coordinated response to enhance the system's safety and economic efficiency.

Airflow sensing and energy harvesting applications of PVDF-TPU piezoelectric nanofibers

Wind energy is one of the abundant potential power sources that can be found both indoors and outdoors. Recently, the focus has been placed on the potential of ambient energy gathering using natural airflow as a small-scale wind energy source. Here, highly piezoelastic nanofiber mats were fabricated from a pure polyvinylidene fluoride (PVDF) and PVDF-TPU (thermoplastic polyurethane) composites via the electrospinning approach. They have exceptional electrical energy harvesting and airflow sensing capabilities. The potential of these composite nanomats for piezoelectric energy harvesting was studied, depending on airflow perturbations in the surrounding environment. PVDF blended with 15 weight percent (wt.%) TPU exhibited the optimum sensitivity, clearly demonstrating the scope of these developed prototypes in the field of airflow sensing and energy harvesting technology.