Allocation-Based Optimal Reactive Power Dispatch Considering Polynomial Load Model Using Improved Grey Wolf Optimizer

Hosseini-Hemati, Saman; Sheisi, Gholam Hossein; Karimi, Shahram

doi:10.1007/s40998-021-00419-8

Cited by 14 publications

(6 citation statements)

References 47 publications

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“…Several works of literature suggested a new position update mechanism in an attempt to improve the original algorithm. In [4], Hosseini et al use the positions of the former alpha and beta as co-decision makers along with the current alpha, beta, and delta to update the position of the current individual. The effect of the former alpha and beta is linearly faded out using the control parameter according to Eq.…”

Section: Modifying Conventional Algorithm's Equationmentioning

confidence: 99%

“…The traditional position updates equation emphasizes repositioning the search agents based on the fittest three agents, resulting in poor exploration abilities. As a result, many improvements, such as [4], [7], and [10], attempt to improve the exploration by modifying the conventional position update equation.…”

Section: Summary Of Gwo Improvementsmentioning

confidence: 99%

“…The impact of the improvements on the original GWO for each proposed variant in the literature was presented in Sect. 4. Finally, the main findings of the presented study and the possible future work are presented in Sect.…”

Section: Introductionmentioning

confidence: 96%

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Grey Wolf Optimization Algorithm: A Survey

Hashem,

Abdullah,

Ghathwan

2023

Iraqi Journal of Science

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The Gray Wolf Optimizer (GWO) is a population-based meta-heuristic algorithm that belongs to the family of swarm intelligence algorithms inspired by the social behavior of gray wolves, in particular the social hierarchy and hunting mechanism. Because of its simplicity, flexibility, and few parameters to be tuned, it has been applied to a wide range of optimization problems. And yet it has some disadvantages, such as poor exploration skills, stagnation at local optima, and slow convergence speed. Therefore, different variants of GWO have been proposed and developed to address these disadvantages. In this article, some literature, especially from the last five years, has been reviewed and summarized by well-known publishers. First, the inspiration and the mathematical model of GWO were explained. Subsequently, the improved GWO variants were divided into four categories and discussed. After that, each variant's methodology and experiments were explained and clarified. The study ends with a summary conclusion of the main foundation of GWO and suggests some possible future directions that can be explored further.

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Section: Modifying Conventional Algorithm's Equationmentioning

confidence: 99%

Section: Summary Of Gwo Improvementsmentioning

confidence: 99%

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Grey Wolf Optimization Algorithm: A Survey

Hashem,

Abdullah,

Ghathwan

2023

Iraqi Journal of Science

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“…Alpha is primarily in charge of deciding about hunting, sleeping locations, and waking times, among other things. The group is dictated to by Alpha's choices [67]. On the other hand, Alpha has been shown to follow the other wolves in the group in a democratic manner.…”

Section: Grey Wolf Algorithmmentioning

confidence: 99%

A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm

et al. 2021

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Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP-hard, meta-heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy-aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works.

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“…В сетях 35-220 кВ правильное моделирование нагрузки и точное определение коэффициентов моделей позволяет решать не только технические задачи, такие как поддержание устойчивости сетей [1,2], но и техникоэкономические задачи по организации оптимальных перетоков мощности [3,4]. В распределительных сетях СХН важны для определения оптимальных условий компенсации реактивной мощности [5] и управления сетями [6].…”

Section: Introductionunclassified

Calculation of Zip Load Model Coefficients in the Nearby Parts of the Electrical Network Using Measurement Data

Dziuba¹,

Safonov²

2022

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The article discusses the methodology for recalculating the coefficients of ZIP load model in nodes of electric networks 35–220 kV, according to the experimental data obtained for some another network nodes. The need to recalculate the static load characteristics for nearby parts of the network arises due to problems between networks owners and the insufficient number of certified measuring equipment during the active experiment, as well as the impossibility of connecting the measuring equipment in some places of the electrical network. For recalculation, a model of an electrical network section is formed and standard models of network elements such as transformers, currentlimiting reactors and power lines are used. These models are divided into elementary multipoles and the calculation is performed sequentially for each of them. The given technique is general and allows recalculating the ZIP model coefficients of the load at an arbitrary point of the electrical network according to experimental data at some points of the network.

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Allocation-Based Optimal Reactive Power Dispatch Considering Polynomial Load Model Using Improved Grey Wolf Optimizer

Cited by 14 publications

References 47 publications

Grey Wolf Optimization Algorithm: A Survey

Grey Wolf Optimization Algorithm: A Survey

A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm

Calculation of Zip Load Model Coefficients in the Nearby Parts of the Electrical Network Using Measurement Data

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