Salp swarm and gray wolf optimizer for improving the efficiency of power supply network in radial distribution systems

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Intelligent Systems Pub Date : 2023-01-01 DOI:10.1515/jisys-2022-0221

I. Salman, K. M. Saffer, Hayder H. Safi, S. Mostafa, Bashar Ahmad Khalaf

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引用次数: 0

Abstract

Abstract The efficiency of distribution networks is hugely affected by active and reactive power flows in distribution electric power systems. Currently, distributed generators (DGs) of energy are extensively applied to minimize power loss and improve voltage deviancies on power distribution systems. The best position and volume of DGs produce better power outcomes. This work prepares a new hybrid SSA–GWO metaheuristic optimization algorithm that combines the salp swarm algorithm (SSA) and the gray wolf optimizer (GWO) algorithm. The SSA–GWO algorithm ensures generating the best size and site of one and multi-DGs on the radial distribution network to decrease real power losses (RPL) (kW) on lines and resolve voltage deviancies. Our novel algorithm is executed on IEEE 123-bus radial distribution test systems. The results confirm the success of the suggested hybrid SSA–GWO algorithm compared with implementing the SSA and GWO individually. Through the proposed SSA–GWO algorithm, the study decreases the RPL and improves the voltage profile on distribution networks with multiple DGs units.

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基于Salp群和灰狼优化算法的径向配电网效率优化研究

配电网有功潮流和无功潮流对配电网的效率有很大影响。目前，分布式能源发电机(dg)被广泛应用于配电系统中，以减小电力损耗和改善电压偏差。dg的最佳位置和体积可以产生更好的功率输出。本文提出了一种新的混合SSA - GWO元启发式优化算法，该算法将salp swarm算法(SSA)和灰狼优化器(GWO)算法相结合。SSA-GWO算法确保在径向配电网上生成一个和多个dg的最佳尺寸和位置，以降低线路上的实际功率损耗(RPL) (kW)并解决电压偏差。该算法已在IEEE 123总线径向配电测试系统上运行。与单独实现SSA和GWO相比，结果证实了所提出的SSA - GWO混合算法的成功。通过提出的SSA-GWO算法，降低了RPL，改善了多dg配电网的电压分布。

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

Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

5.90

自引率

3.30%

发文量

审稿时长

51 weeks

期刊介绍： The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.