Hasil untuk "Applications of electric power"

Z. Cano, Dustin Banham, Siyu Ye et al.

M. Hannan, M. M. Hoque, A. Mohamed et al.

H. Akagi, E. Watanabe, M. Aredes

R. Kötz, M. Carlen

Kailong Liu, Kang Li, Q. Peng et al.

Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

C. Fiori, K. Ahn, Hesham A Rakha

Yuting Jia, G. Alva, G. Fang

The commercial solar cells are currently less efficient in converting solar radiation into electricity. During electric power convention, most of the absorbed energy is dissipated to the surroundings. In order to improve energy efficiency, many efforts have been made to investigate and develop hybrid photovoltaic and thermal collector systems. A photovoltaic–thermal (PV/T) system does both the generation of electric power and collection of thermal energy at the same time. Thus, the overall efficiency of the photovoltaic–thermal (PV/T) system can increase accordingly. In this work, we attempt to summarize various research works on technologies like flat–plate PV/T systems and concentrator type PV/T systems, using different kinds of working fluids under a variety of environmental conditions. The purpose of this review is to define the appropriate environmental conditions and applications for different kinds of PV/T systems. Besides, it is also presented that the applications and developments of the PV/T systems. In order to develop novel PV/T systems, more effort is needed in accurate modeling, exploration of novel materials, enhancement of PV/T system stability and the design of a supporting energy storage system.

A. K. Ozcanli, Fatma Yaprakdal, M. Baysal

Over the past decades, electric power systems (EPSs) have undergone an evolution from an ordinary bulk structure to intelligent flexible systems by way of advanced electronics and control technologies. Moreover, EPS has become a more complex, unstable and nonlinear structure with the integration of distributed energy resources in comparison with traditional power grids. Unlike classical approaches, physical methods, statistical approaches and computer calculation techniques are commonly used to solve EPS problems. Artificial intelligent (AI) techniques have especially been used recently in many fields. Deep neural networks have become increasingly attractive as an AI approach due to their robustness and flexibility in handling nonlinear complex relationships on large scale data sets. Major deep learning concepts addressing some problems in EPS have been reviewed in the present study by a comprehensive literature survey. The practices of deep learning and its combinations are well organized with up‐to‐date references in various fields such as load forecasting, wind and solar power forecasting, power quality disturbances detection and classifications, fault detection power system equipment, energy security, energy management and energy optimization. Furthermore, the difficulties encountered in implementation and the future trends of this method in EPS are discussed subject to the findings of current studies. It concludes that deep learning has a huge application potential on EPS, due to smart technologies integration that will increase considerably in the future.

Mouna Zerzeri, Adel Khedher

This paper presents a robust sensorless control strategy for a dual-inverter doubly fed induction motor (DFIM) designed for high-performance electric vehicle (EV) traction systems. The proposed scheme eliminates the mechanical speed sensor by employing a sliding-mode observer (SMO) for real-time estimation of rotor speed and flux, ensuring accurate feedback under load disturbances and thereby enhancing reliability while reducing implementation cost. The DFIM is powered by two voltage-source inverters that independently control the stator and rotor windings through space vector pulse-width modulation (SVPWM). A power-sharing strategy optimally distributes the electromagnetic power between the two converters, ensuring smooth transitions between sub-synchronous and super-synchronous operating modes. Furthermore, a stator-flux-oriented vector control (SFOC) scheme incorporating a graphical torque optimization algorithm is developed to maximize torque while satisfying inverter and machine constraints across both base-speed and flux-weakening regions. The stability of the SMO-based estimation and closed-loop control is rigorously validated using Lyapunov theory. Comprehensive MATLAB <i>R2024b</i>/Simulink simulations conducted under the WLTC-Class 3 driving cycle confirm high accuracy and robustness, showing fast dynamic response, precise speed estimation, and smooth torque behavior across the full speed range. The results demonstrate that the SMO-based DFIM drive offers a cost-effective and reliable solution for next-generation EV traction applications.

M. H. Nehrir, Caisheng Wang, K. Strunz et al.

Jayachandra Malavatu, Peter Kepplinger, Bernhard Faessler

The objective of this study is to address the power imbalance between supply and demand caused by the adoption of electric vehicles and renewable energy sources. Due to power imbalance at the point of common coupling, additional peaks and valleys will be created. The novelty of this work lies in proposing a hybrid energy storage system that combines power-dense and energy-dense batteries, optimized using a Norm-2 approach, to mitigate these imbalances effectively. The methodology involves a simulation study using realistic distribution grid load curves, focusing on two case studies. The results of this study reveal that, with an optimally sized energy storage system, power-dense batteries reduce the peak power demand by 15 % and valley filling by 9.8 %, while energy-dense batteries fill the valleys by 15 % and improve the peak power demand by 9.3 %. Furthermore, a hybrid energy storage system outperforms and is useful for multiple grid applications when compared with a single type of energy storage system. The study identifies an optimal capacity share of 40 % power-dense and 60 % energy-dense batteries as providing an effective balance between power and energy requirements. The findings highlight the proposed system successfully manages not only the highest peaks and valleys, but also intermediate fluctuations caused by renewable energy and electric vehicle integration. During a one-year simulation using a hybrid energy storage system, peak power demand decreased by 11 %, peak-to-average ratio improved by 12 %, and power variance was reduced by 29 %, indicating more stable and efficient grid performance compared to without any storage system.

Robert A. Mostoghiu Paun, Darren Croton, Chris Power et al.

Traditional N-body methods introduce localised perturbations in the gravitational forces governing their evolution. These perturbations lead to an artificial fragmentation in the filamentary network of the Large Scale Structure, often referred to as "beads-on-a-string." This issue is particularly apparent in cosmologies with a suppression of the matter power spectrum at small spatial scales, such as warm dark matter models, where the perturbations induced by the N-body discretisation dominate the cosmological power at the suppressed scales. Initial conditions based on third-order Lagrangian perturbation theory, which allow for a late-starting redshift, have been shown to minimise numerical errors contributing to such artefacts. In this work, we investigate whether the additional use of a spatially adaptive softening for dark matter particles, based on the gravitational tidal field, can reduce the severity of artificial fragmentation. Tidal adaptive softening significantly improves force accuracy in idealised filamentary collapse simulations over a fixed softening approach. However, it does not substantially reduce spurious haloes in cosmological simulations when paired with such optimised initial conditions. Nevertheless, tidal adaptive softening induces a shift in halo formation times in warm dark matter simulations compared to a fixed softening counterpart, an effect not seen in cold dark matter simulations. Furthermore, initialising the initial conditions at an earlier redshift generally results in z=0 haloes forming from Lagrangian volumes with lower average sphericity. This sphericity difference could impact post-processing algorithms identifying spurious objects based on Lagrangian volume morphology. We propose potential strategies for reducing spurious haloes without abandoning current N-body methods.

Larissa Jerrim, Stas Shabala, Ross Turner et al.

We investigate the effect of turbulent magnetic fields on the observed spectral properties of synchrotron radio emission in large-scale radio galaxy lobes. We use three-dimensional relativistic magnetohydrodynamic simulations of fast, high-powered jets to study the structure of the lobe magnetic fields and how this structure affects the radio spectrum of the lobes. It has previously been argued that lobe ages inferred from radio spectra underestimate the true ages of radio galaxies due to re-acceleration of electrons in the lobe, mixing of electron populations, or the presence of turbulent magnetic fields in the lobes. We find that the spectral ages with and without accounting for the lobe magnetic field structure are consistent with each other, suggesting that mixing of radiating populations of different ages is the primary cause of the underestimation of radio lobe ages. By accounting for the structure of lobe magnetic fields, we find greater spectral steepening in the equatorial regions of the lobe. We demonstrate that the assumptions of the continuous injection, Jaffe-Perola, and Tribble models for radio lobe spectra do not hold in our simulations, and we show that young particles with high magnetic field strengths are the dominant contributors to the overall radio lobe spectrum.

Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick et al.

Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these planets throughout their orbits would depend on the wavelength-dependent albedo of surface CO2 ice that may accumulate at or near apoastron and vary according to the spectral energy distribution of the host star. To explore these possible effects, we incorporated a CO2 ice-albedo parameterization into a one-dimensional energy balance climate model. With the inclusion of this parameterization, our simulations demonstrated that F-dwarf planets require 29% more orbit-averaged flux to thaw out of global water ice cover compared with simulations that solely use a traditional pure water ice-albedo parameterization. When no eccentricity is assumed, and host stars are varied, F-dwarf planets with higher bond albedos relative to their M-dwarf planet counterparts require 30% more orbit-averaged flux to exit a water snowball state. Additionally, the intense heat experienced at periastron aids eccentric planets in exiting a snowball state with a smaller increase in instellation compared with planets on circular orbits; this enables eccentric planets to exhibit warmer conditions along a broad range of instellation. This study emphasizes the significance of incorporating an albedo parameterization for the formation of CO2 ice into climate models to accurately assess the habitability of eccentric planets, as we show that, even at moderate eccentricities, planets with Earth-like atmospheres can reach surface temperatures cold enough for the condensation of CO2 onto their surfaces, as can planets receiving low amounts of instellation on circular orbits.

Marco Antonio Islas-Herrera, David Sánchez-Luna, Jorge Miguel Jaimes-Ponce et al.

Energy harvesting is a clean technique for obtaining electrical energy from environmental energy. Mechanical vibrations are an energy source that can be used to produce electricity using piezoelectric energy harvesters. Vibrations and wind in bridges have the potential to produce clean energy that can be employed to supply energy to electronic devices with low consumption. The purpose of this paper was to validate the functioning of an energy harvester and test the electrical power generation potential of a system based on the oscillation of a rod with a tip mass to stimulate piezoelectric transducers by impact. The obtained results showed the electric energy productions for different test conditions. Experimentally, the proposed structure produced 0.337 µJ of energy after 14 s of testing. In addition, after one hour of operation, an estimated production of 10.4 mJ was obtained, considering four stacks of 25 piezoelectric disks each when periodic impacts of 50 N at 5.7 Hz stimulated the transducers. In future work, we will focus on taking advantage of the vibrations produced in the proposed structure induced by the mechanical vibration of bridges and vortex-induced vibration (VIV) through interaction with wind to produce clean energy that is useful for low-power applications.

CHEN Leilei, NIAN Heng, ZHAO Jianyong et al.

The utilization of a new energy hydrogen production system is an effective approach to enhance the absorption capacity of renewable energies such as wind and solar power. The current research on energy management of electrolyzer, both domestically and internationally, primarily focuses on single-electrolyzer. The energy management of single-electrolyzer fails to account for the nonlinearity in its operational characteristics, thereby posing challenges in considering the hydrogen production efficiency of multi-electrolyzers and its impact on system economics. The present study focuses on the energy management of a novel hydrogen production system incorporating multi-electrolyzers. The energy management optimization model incorporates wind power, photovoltaic systems, batteries, and multiple electrolyzers to achieve targets for new energy consumption rate, economic income, and hydrogen production rate. Taking into account the operational characteristics of a single electrolyzer and production constraints, the multi-objective optimization problem is solved by strength Pareto evolutionary algorithm 2 (SPEA2). The simulation research demonstrates that the proposed energy management strategy can achieve a 100% absorption rate of newly generated power from renewable sources, while simultaneously increasing the hydrogen production efficiency per unit by 5.15%. The effective management of energy in a multi-electrolyzers hydrogen production system is crucial for enhancing the efficiency of hydrogen production and effectively addressing the limitations associated with single-electrolyzer operation and energy management.

WANG Xiangyu, CHEN Wuhui, GUO Xiaolong et al.

With the development and evolution of the information revolution, promoting the integration of a new generation of digital technology with traditional power generation system, and promoting the digital construction of power generation systems is an important way to support energy transformation and digital grid construction. Based on the digital business needs of power generation system, this paper summarized the business needs of data in various scenarios such as the full life cycle management, intelligent operation and maintenance, and intelligent operation. The architecture of power generation system was expounded from the aspects of network structure and digital technology architecture. The key technologies and applications in the process of digitalization of power generation system were sorted out. Finally, the problems that need to be solved in the process of digitalization of power generation system were discussed.

Matthew Deakin, Xu Deng

A low-cost reconfiguration stage connected at the output of balanced three-phase, multi-terminal ac/dc/ac converters can increase the feasible set of power injections substantially, increasing converter utilization and therefore achieving a lower system cost. However, the approach has yet to be explored for phase unbalance mitigation in power distribution networks, an important application for future energy systems. This study addresses this by considering power converter reconfiguration's potential for increasing the feasible set of power transfers of four-wire power converters. Reconfigurable topologies are compared against both conventional four-wire designs and an idealised, fully reconfigurable converter. Results show that conventional converters need up to 75.3% greater capacity to yield a capability chart of equivalent size to an idealised reconfigurable converter. The number and capacity of legs impact the capability chart's size, as do constraints on dc-side power injections. The proposed approach shows significant promise for maximizing the utilization of power electronics used to mitigate impacts of phase unbalance.

G. Pierrou, C. Valero-De La Flor, G. Hug

In this paper, a novel Energy Management System (EMS) algorithm to achieve optimal Electric Vehicle (EV) charging scheduling at the parking lots of electric railway stations is proposed. The proposed approach uncovers the potential of leveraging EV charging flexibility to prevent overloading in the combined EV charging and railway operation along with renewable generation, railway regenerative capabilities, and energy storage. Specifically, to realize end-user flexibility, each EV state of charge at departure time is introduced as an optimization variable. Peak load constraints are included in the railway EMS to properly adjust EV charging requirements during periods of high railway demand. A comprehensive numerical study using a scenario-tree approach on an actual railway line in Switzerland demonstrates the effectiveness and the feasibility of the proposed method in a practical setting under multiple scenarios.

S. Venkatesan, K. Manickavasagam, Nikita Tengenkai et al.

Electric mobility has become an essential part of the future of transportation. Detection, diagnosis and prognosis of fault in electric drives are improving the reliability, of electric vehicles (EV). Permanent magnet synchronous motor (PMSM) drives are used in a large variety of applications due to their dynamic performances, higher power density and higher efficiency. In this study, health monitoring and prognosis of PMSM is developed by creating intelligent digital twin (i-DT) in MATLAB/Simulink. An artificial neural network (ANN) and fuzzy logic are used for mapping inputs distance, time of travel of EV and outputs casing temperature, winding temperature, time to refill the bearing lubricant, percentage deterioration of magnetic flux to compute remaining useful life (RUL) of permanent magnet (PM). Health monitoring and prognosis of EV motor using i-DT is developed with two approaches. Firstly, in-house health monitoring and prognosis is developed to monitor the performance of the motor in-house. Secondly, Remote Health Monitoring and Prognosis Centre (RHMPC) is developed to monitor the performance of the motor remotely using cloud communication by the service provider of the EV. The simulation results prove that the RUL of PM and time to refill the bearing lubricant obtained by i-DT twins theoretical results.