Merging slime mould with whale optimization algorithm for optimal allocation of hybrid power flow controller in power system

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-03-25 DOI:10.1080/0952813X.2022.2040598
A. A. Bhandakkar, Lini Mathew
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引用次数: 4

Abstract

ABSTRACT This manuscript proposes the optimal allocation of hybrid power flow controller (HPFC) using hybrid technique. The proposed technique is the implementation of Integrated Slime Mould Algorithm (ISMA). The searching behaviour of Slime Mould Algorithm (SMA) is enhanced by the position updating behaviour of the whale optimisation algorithm (WOA). HPFC, a hybrid topology, has VAR compensator or an impedance-type FACTS device, most probably obtainable at power system, and two voltage source converters depend on controllers share a general DC link. The novel contributions of allocating HPFC at optimal location for multi-objective fitness functions denote minimal real power loss of system as well as minimal generation cost using ISMA method. Here, ISMA method optimises maximum line of power loss as appropriate location of unified power flow controller (UPFC). The optimal location parameters and dynamic stability restrictions are restored with normal constraints, employing UPFC optimal capacity has been optimised to decreased cost with the help of ISMA technique.
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将黏菌优化算法与鲸鱼优化算法相结合用于电力系统中混合潮流控制器的优化配置
摘要:本文提出了一种基于混合动力技术的混合潮流控制器(HPFC)的优化配置方法。所提出的技术是集成黏菌算法(ISMA)的实现。鲸鱼优化算法(WOA)的位置更新行为增强了黏菌算法(SMA)的搜索行为。HPFC是一种混合拓扑,具有无功补偿器或阻抗型FACTS器件,最可能在电力系统中获得,两个电压源转换器依赖于控制器共享一个通用直流链路。在多目标适应度函数的最优位置分配HPFC的新贡献是利用ISMA方法实现系统实际功率损耗最小和发电成本最小。在这里,ISMA方法优化最大线路功率损耗作为统一潮流控制器(UPFC)的适当位置。在常规约束条件下恢复最优位置参数和动态稳定性约束,并利用ISMA技术对UPFC最优容量进行优化,以降低成本。
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来源期刊
CiteScore
6.10
自引率
4.50%
发文量
89
审稿时长
>12 weeks
期刊介绍: Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving
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