Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system

Sultana, Umbrin; Khairuddin, Azhar; Mokhtar, Ahmad Safawi; Zareen, Naila; Sultana, Beenish

doi:10.1016/j.energy.2016.05.128

Cited by 165 publications

(61 citation statements)

References 35 publications

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“…Esfahani et al [313] Optimisation (GWO) [345], Krill Herd Algorithm (KHA) [338], Invasive Weed Optimisation (IWO) [346], and Cat Swarm Optimisation (CSO) [344] are introduced with enhanced optimisation performance. Hybridisation of modern mathematical modelling optimisation methods is another strategy for performance improvement.…”

Section: Maleki and Askarzadehmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

222

101

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Section: Maleki and Askarzadehmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

222

101

View full text Add to dashboard Cite

“…Omega wolf (ω) represents the rest of the solution. In this algorithm, hunting is processed by α, β, and δ, and ω follows these three preceding wolves …”

Section: Gwo Algorithmmentioning

confidence: 99%

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Selvaraj

Kanimozhi

2019

Int Trans Electr Energ Syst

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Summary The distribution flexible alternating current system (D‐FACT) devices are being increasingly placed in the distribution system due to their several practical and environmental benefits. In this work, a novel grey wolf optimizer (GWO) algorithm as a meta‐heuristic technique is applied to solve the reconfiguration problem, the installation of distribution static compensator (D‐STATCOM) problem, and the combination of these two problem. The aim of this research is to diminish the total power loss and the load‐balancing (LB) index of the radial distribution system. First, the index vector (IV) method is employed to suggest the candidate busses for D‐STATCOM allocation. Then, the GWO algorithm is applied to establish the locations and sizes of the D‐STATCOM from the candidate busses. An overall accurateness and consistency of the proposed GWO algorithm has been validated and experienced on a 33‐bus, a 69‐bus, and a real‐time 31‐bus radial distribution system, India with three kinds of load levels. In addition, the proposed method results are ideal as compared with other intellectual methods like genetic algorithm (GA) and fuzzy GA.

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“…[9][10][11][12][13][14][15][16] The significant aim of the present work is to provide an efficient PEMFC model according to Amphlett equation model that simulates the practical performance of the cell at various operational circumstances. [31][32][33][34][35][36] So in this article, SBO is used to establish variables of PEMFC model. In the earlier decade, metaheuristic approaches developing as optimization issue have applied by many researchers.…”

Section: Introductionmentioning

confidence: 99%

“…The mentioned advantage of SBO method is the main reason of researcher to use in many problems of electrical engineering. [31][32][33][34][35][36] So in this article, SBO is used to establish variables of PEMFC model.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

2019

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Summary This study proposes precise modeling for the proton exchange membrane fuel cells which present desirable advantages compared with other energy management systems. The presented model can be applied for the simulation of the actual behavior of the proton exchange membrane fuel cells such as the electrical, electrochemical, and mechanical. In the present literature, a newly presented optimizer namely Satin Bowerbird is implemented for the evaluation of the proton exchange membrane fuel cell performance criteria. The Satin Bowerbird optimizer is an evolutionary algorithm that imitates the mating process of the Bowerbirds in the mating season. The Satin Bowerbird optimizer is applied to the different commercial benchmark of proton exchange membrane fuel cell stack to assess the performance of the proposed algorithm. The statistical study is also carried out to show the superiority of the proposed method compared with other schemes. The standard deviation for the Satin Bowerbird optimizer is obtained 0.0941 which is the lowest value amongst the other well‐known approaches. Also, the lowest sum of squared error is calculated for the proposed algorithm. Moreover, the validation of the presented method is done with the experimental data which shows good agreement between the experimental and modeling data.

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Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system

Cited by 165 publications

References 35 publications

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

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