ELECTRICAL ENGINEERING电气工程

ELECTRICAL ENGINEERING(英文缩写 ELECTR ENG),ISSN 0948-7921,eISSN 1432-0487,中文译名:电气工程 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。

2026 年数据 · 影响因子
2.700
JCR 分区
Q3
CAS 分区
B4
近一年发文量
0
本站 PubMed 收录统计

发文量统计区间:2025-10-01 至 2026-09-30,按本站收录文献的发表日期统计。

ISSN: 0948-7921 · eISSN: 1432-0487 · 缩写: ELECTR ENG ·中文: 电气工程

期刊介绍

选择期刊介绍栏目

期刊简介

《Electrical Engineering》是电气工程领域的国际性学术期刊,涵盖电力系统、电机驱动、电力电子、高电压技术及智能电网等方向。该刊注重工程应用与理论创新,读者群包括电气工程研究人员、工程师及高校师生,为传统电力与新兴能源技术提供交流平台。

研究方向

主要发表电力系统分析与控制、电机设计与驱动、电力电子变换器、可再生能源集成、高电压与绝缘技术、智能电网及储能等方面的研究。论文类型包括原创研究、综述和短篇通讯,侧重理论建模、实验验证与工程应用。

期刊特色

研究取向兼顾理论深度与工程实用性,鼓励解决实际电力工程问题。论文通常包含仿真或实验验证,强调方法创新与结果可重复性。适合电气工程领域的研究生、高校教师及工业界研发人员阅读和投稿。

投稿难度

投稿难度中等,对理论严谨性和实验完整性有一定要求。建议在投稿前明确工程贡献,完善对比实验与数据分析,并注意英文表达清晰。因审稿标准综合,不能仅凭分区判断录用难易,需认真准备。

历年影响因子趋势

JCR 数据年份影响因子JCR 分区
20211.630Q3
20221.800Q3
20231.600Q3
20241.900Q3
20252.700Q3

ELECTRICAL ENGINEERING 最新收录文献

  1. JCR分区: Q3 CAS分区: B4 影响因子: 2.7

    1. Optimal placement of distributed generation based on DISCO's financial benefit with loss and emission reduction using hybrid Jaya-Red Deer optimizer.

    作者:
    G V Naga Lakshmi, A Jayalaxmi, Venkataramana Veeramsetty
    日期:
    2022-12-24

    The optimal location of distributed generation (DG) is a critical challenge for distribution firms in order to keep the distribution network running smoothly. The optimal placement of DG units is an optimization challenge in which the objective function is to maximize distribution firms' financial benefit owing to reduced active power losses and emissions in the network. Bus voltage limits and feeder thermal limits are considered as constraints. To overcome the problem of trapping the solution toward the local optimal point and to achieve strong local and global searching capabilities, a new hybrid Jaya-Red Deer optimizer is proposed as an optimization approach in this study to determine the best placement and size of distributed generating units. In the MATLAB environment, the suggested method is implemented on IEEE 15 and PG & E 69 bus distribution systems and validated with Red Deer Optimizer, Dragonfly Algorithm, Genetic Algorithm, Particle Swarm Optimization, Jaya Algorithm and Black Widow Optimizer. Based on the simulation results, distribution firms may operate their networks with the greatest financial advantage by properly positioning and sizing their DG units.

  2. JCR分区: Q3 CAS分区: B4 影响因子: 2.7

    2. Calculated versus measured iron losses and instantaneous magnetization power functions of electrical steel.

    作者:
    Helmut Pfützner, Georgi Shilyashki, Emanuel Huber
    日期:
    2022-01-25

    Magnetic energy loss of soft magnetic laminations like SiFe sheets tends to be expressed through an integral over the power product · d/d. Already in earlier papers, we stressed that distinctions are needed for the quantities and . However, they are not considered in practically consistent ways, in the so far literature. Here, we discuss these distinctions in closer ways, comparing loss determination by calculation and measurement, respectively. A physically consistent procedure is described for the determination of loss and magnetization power functions through measurement of bi-located quantities and (S surface, C cross section). On the other hand, it is concluded that corresponding quantitative calculations-based on co-located quantities and -are impeded by the high amount of technological parameters of modern steel products. For example, they result from chemical additions, and-in particular-also from specific technologies of rolling, annealing, coating or scribing.

  3. JCR分区: Q3 CAS分区: B4 影响因子: 2.7

    3. ANN-based dynamic control and energy management of inverter and battery in a grid-tied hybrid renewable power system fed through switched Z-source converter.

    作者:
    B Kavya Santhoshi, K Mohanasundaram, L Ashok Kumar
    日期:
    2021-02-17

    The multidimensional purposes of grid-tied hybrid renewable system such as tracking of maximum power, increasing the power conversion efficiency, reducing the harmonic distortions in the injected current and control over power injected into the grid are presented in this paper by developing a laboratory-scale setup. To ensure continuous current operation at the shoot through mode of grid connected inverter, a switched Z-source converter is utilized at the PV side. The PWM rectifier connected with the wind turbine transforms AC power into dc. Individual power converters with conventional PI controllers have been dedicated for each power source, and control strategy uses only one reference voltage so as to increase the maximum power tracking speed from both PV and wind sources. The battery energy management is performed by artificial neural network (ANN) to enhance the stable power flow and increase the lifespan of the storage system. Finally, the voltage at the point of common coupling is fed to ANN-based space vector-modulated three-phase inverter and the converted AC power is injected to the grid. The overall system performance is measured by estimating the quality of injected power. A stable operation of the proposed microgrid system is verified by varying input and load at the grid. A continuous-time simulation model is realized in MATLAB and is validated using experimental prototype. This benchmark system provides various research scopes for the future smart grids.

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