Optimal Placement of Distribution Static Compensators in Radial Distribution Systems Using Hybrid Analytical-Coyote optimization Technique

Amin, Amal; Kamel, Salah; Selim, Ali; Nasrat, Loai

doi:10.1109/mepcon47431.2019.9007924

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…• COA is a population-based meta-heuristic that is easy to implement with only two control parameters. • COA has shown good results when solving several placement problems such as sizing and location of renewable distributed generations [41]- [43], optimal placement of photovoltaic distributed generations [44]- [47], and perfect position of static compensators [48]. • Presence of some similarities between the mesh routers placement problem and the COA theory as illustrated in Table 3.…”

Section: Generating New Solutionsmentioning

confidence: 99%

Solving the Mesh Router Nodes Placement in Wireless Mesh Networks Using Coyote Optimization Algorithm

Taleb

Meraihi

Gabis³

et al. 2022

IEEE Access

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Wireless Mesh Networks (WMNs) have rapid real developments during the last decade due to their simple implementation at low cost, easy network maintenance, and reliable service coverage. Despite these properties, the nodes placement of such networks imposes an important research issue for network operators and influences strongly the WMNs performance. This challenging issue is known to be an NP-hard problem, and solving it using approximate optimization algorithms (i.e. heuristic and metaheuristic) is essential. This motivates our attempts to present an application of the Coyote Optimization Algorithm (COA) to solve the mesh routers placement problem in WMNs in this work. Experiments are conducted on several scenarios under different settings, taking into account two important metrics such as network connectivity and user coverage. Simulation results demonstrate the effectiveness and merits of COA in finding optimal mesh routers locations when compared to other optimization algorithms such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Bat Algorithm (BA), African Vulture Optimization Algorithm (AVOA), Aquila Optimizer (AO), Bald Eagle Search optimization (BES), Coronavirus herd immunity optimizer (CHIO), and Salp Swarm Algorithm (SSA).

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Section: Generating New Solutionsmentioning

confidence: 99%

Solving the Mesh Router Nodes Placement in Wireless Mesh Networks Using Coyote Optimization Algorithm

Taleb

Meraihi

Gabis³

et al. 2022

IEEE Access

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“…Thus, the RPO can result in considerable economic benefits and ensure reliable power grid operation 2 . It includes generator terminal voltage and DG output coordination, transformer trap adjustment, and reactive power compensation system input capacity and tool location 23 . Via optimization of power system reactive power's scheduling and configuration optimization, the RPO can both reduce reactive and active power loss and enhance operation stability of power system 24,25 …”

Section: Introductionmentioning

confidence: 99%

Reactive power control of hybrid systems using improved coyote optimizer

Chen

Zhou

et al. 2023

Concurrency and Computation

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SummaryA new method of reactive power control by photovoltaic and hydrogen is presented in this study. Photovoltaic has been employed for harvesting the hydrogen which is based on considering the weather conditions. The proposed system includes a combination of photovoltaic, hydrogen, and fuel cell along with a DG to connect to the grid and to improve the supply power quality. The main contribution of this paper is to direct an improved metaheuristic algorithm, called improved coyote optimization (CO) algorithm for achieving a proper DG placement. The improved version of the CO algorithm has benefitted from a spiral policy that is derived from Whale optimization algorithm. This process makes better control for the social behavior of the coyotes. Reactive power optimization (RPO) has been established after the size selection objective function. Big data technology is also used for improving the historical solution matching‐based RPO appliance. Cosine distance is used for measurement purposes of the historical solution matching‐based similarity technique during the computation time of conventional RPO and PVH‐FC features. As a result of using the suggested CO, the costs for electricity and losses are reduced by around 86.6% and 26.9%, respectively. Additionally, realized profits showed that applying the suggested strategy reduced the overall cost from 9.315e6 to 4.435e6 units. After optimization, the network loss and power are finally reduced to 1325 Kvar and 1371 kW, respectively. The results show that the suggested RPO technique has higher speed of calculation in comparison with some latest algorithms. Achievements also show that the suggested method provides a proper and optimal solution for RPO.

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