SIMULTANEOUS ALLOCATION OF MULTIPLE DISTRIBUTED GENERATION AND CAPACITORS IN RADIAL NETWORK USING GENETIC-SALP SWARM ALGORITHM

IF 1.6 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
C. Djabali, T. Bouktir
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引用次数: 5

Abstract

Purpose. In recent years, the problem of allocation of distributed generation and capacitors banks has received special attention from many utilities and researchers. The present paper deals with single and simultaneous placement of dispersed generation and capacitors banks in radial distribution network with different load levels: light, medium and peak using genetic-salp swarm algorithm. The developed genetic-salp swarm algorithm (GA-SSA) hybrid optimization takes the system input variables of radial distribution network to find the optimal solutions to maximize the benefits of their installation with minimum cost to minimize the active and reactive power losses and improve the voltage profile. The validation of the proposed hybrid genetic-salp swarm algorithm was carried out on IEEE 34-bus test systems and real Algerian distributed network of Djanet (far south of Algeria) with 112-bus. The numerical results endorse the ability of the proposed algorithm to achieve a better results with higher accuracy compared to the result obtained by salp swarm algorithm, genetic algorithm, particle swarm optimization and the hybrid particle swarm optimization algorithms. References 27, tables 10, figures 12.
基于遗传salp群算法的径向网络中多个分布式电源和电容器的同时分配
意图近年来,分布式发电和电容器组的分配问题受到了许多公用事业和研究人员的特别关注。本文利用遗传salp群算法研究了不同负荷水平(轻、中、峰值)的径向配电网中分散发电和电容器组的单次和同时布置。所开发的遗传-萨尔普群算法(GA-SSA)混合优化利用径向配电网的系统输入变量来寻找最优解,以最小的成本最大限度地提高其安装效益,最大限度地减少有功和无功功率损失,改善电压分布。在IEEE 34总线测试系统和Djanet(阿尔及利亚南部)的真实阿尔及利亚分布式网络(112总线)上对所提出的混合遗传salp群算法进行了验证。数值结果表明,与salp群算法、遗传算法、粒子群优化和混合粒子群优化算法相比,该算法能够以更高的精度获得更好的结果。参考文献27,表10,图12。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Electrical Engineering & Electromechanics
Electrical Engineering & Electromechanics ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
2.40
自引率
50.00%
发文量
53
审稿时长
10 weeks
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