Application of coyote optimizer for Optimal DG Placement in Radial Distribution Systems

Kamel, Salah; Amin, Amal; Selim, Ali; Ahmed, Mohammed Hassan

doi:10.1109/iccceee46830.2019.9070817

Cited by 3 publications

(9 citation statements)

References 18 publications

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“…Heuristic pdf method Ant-lion optimization [42] coyote optimizer [43] Modified sine-cosine algorithm [44] Particle swarm optimization [45] From Tables 2-7, it is observed that the selection of DGs location is better found under GA for CPLF and CILF in comparison to other methods. By comparing the real power loss, sensitivity, and accuracy, it can be concluded that bus 17 is the best location for the placement of DG for both CPLF and CILF.…”

Section: Ga Methodsmentioning

confidence: 99%

“…Ant-lion optimization [42] Coyote optimizer [43] Modified sine-cosine algorithm [44] Particle swarm optimization [45]…”

Section: Heuristic Pdf Methodsmentioning

confidence: 99%

“…Figures 6 and 7 provide the performance parameter comparison of GA with the heuristic method at different buses for CPLF and CILF. Moreover, in order to have comprehensive analysis and comparison for the DG location with GA and heuristic pdf, the results are compared with those obtained with the ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45]. The performance parameter evaluation for the estimation of DG location by using the ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45] is shown in Tables 4-7 for CPLF and CILF type of load.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…In this article, the heuristic probability distribution method and GA are applied for finding the best location and sizing of DG in the IEEE 33 bus system for the constant power load model (CPLF) and the constant impedance load model (CILF). The performance parameters show the superiority of GA over the heuristic probability distribution method and other existing methods such as ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45] in the IEEE 33 standard bus system. By considering lower real power losses, improved accuracy, and sensitivity, selection criteria of three DGs location are decided, and after that, size of DG is decided in terms of real and reactive power.…”

Section: Introductionmentioning

confidence: 96%

“…It is also observed that the proposed technique gives better results in comparison to existing techniques presented in literature [4,9,12,15,17,20,28,30,32,[35][36][37][38][39][40][41][42][43][44][45]. However, to analyze the efficacy of the proposed work in this paper, the results achieved are compared with the existing techniques such as ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45]. The GA seems to have the main advantage of utilizing entity definition, i.e., strings, instead of handling such entities themselves.…”

Section: Introductionmentioning

confidence: 98%

See 4 more Smart Citations

Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

et al. 2022

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This article presents the selection of location and sizing of multiple distributed generators (DGs) for boosting performance of the radial distribution system in the case of constant power load flow and constant impedance load flow. The consideration of placing and sizing of DGs is to meet the load demand. This article tries to overcome the limitations of existing techniques for determining the appropriate location and size of DGs. The selection of DG location is decided in terms of real power losses, accuracy, and sensitivity. The size of DG is measured in terms of real and reactive power. Both positioning and sizing of DG are analyzed with the genetic algorithm and the heuristic probability distribution method. The results are compared with other existing methods such as ant-lion optimization algorithm, coyote optimizer, modified sine-cosine algorithm, and particle swarm optimization. Further, the power quality improvement of the network is assessed by positioning D-STATCOM, and its location is decided on the basis of the nearby bus having poor voltage profile and high total harmonic distortion (THD). The switching and controlling of D-STATCOM are assessed with fuzzy logic controller (FLC) for improving the performance parameters such as voltage profile and THD at that particular bus. The proposed analytical approach for the system is tested on the IEEE 33 bus system. It is observed that the performance of the system with the genetic algorithm gives a better solution in comparison to heuristic PDF and other existing methods for determining the optimal location and size of DG. The introduction of D-STATCOM into the system with FLC shows better performance in terms of improved voltage profile and THD in comparison to existing techniques.

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Section: Ga Methodsmentioning

confidence: 99%

“…Ant-lion optimization [42] Coyote optimizer [43] Modified sine-cosine algorithm [44] Particle swarm optimization [45]…”

Section: Heuristic Pdf Methodsmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 98%

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Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

et al. 2022

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Advances in Coyote Optimization Algorithm: Variants and Applications

Meraihi

Gabis

Ramdane-Chérif

et al. 2022

Advances in Computational Intelligence and Communication

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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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Application of coyote optimizer for Optimal DG Placement in Radial Distribution Systems

Cited by 3 publications

References 18 publications

Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

Advances in Coyote Optimization Algorithm: Variants and Applications

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

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