Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)

Swarm-based nature-inspired optimization techniques are developing rapidly due to their universal acceptance and capability in solving various real-life challenges efficiently. Hybridization of swarm intelligence based optimization algorithms with multi-objective based solution techniques is creatin...

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Autores principales:	Quazi Nafees Ul Islam, Ashik Ahmed, Saad Mohammad Abdullah
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Multi-objective Dynamic load Static load Non-dominated sorting SPSS Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/ad46a838ded149208f0f235a263aed10
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spelling	oai:doaj.org-article:ad46a838ded149208f0f235a263aed102021-11-22T04:20:55ZOptimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)2090-447910.1016/j.asej.2021.01.035https://doaj.org/article/ad46a838ded149208f0f235a263aed102021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2090447921001374https://doaj.org/toc/2090-4479Swarm-based nature-inspired optimization techniques are developing rapidly due to their universal acceptance and capability in solving various real-life challenges efficiently. Hybridization of swarm intelligence based optimization algorithms with multi-objective based solution techniques is creating a wide door in the field of optimization. The main focus of this work is, developing a hybrid Non-dominated Sorting Whale Optimization Algorithm (NSWOA), where swarm-based Whale Optimization Algorithm (WOA) is hybridized with multi-objective based, non-dominated sorting technique. This is done to develop an algorithm with efficient optima searching ability and faster computational speed. The application of NSWOA in optimizing the controller parameters of an islanded microgrid consisting of both static and dynamic load has been also described. SPSS software has been used to compare the performance of proposed NSWOA with non-dominated sorting genetic algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA) technique in optimizing the controller parameters of islanded microgrid model with multi-objective problem. It is obtained that, NSWOA requires an average of 4 iterations to reach the best optimum solution, which is less than other existing algorithms. Moreover, the computational time required by NSWOA is 2.9201 s, which proves that, it converges at a much faster rate compared to existing NSGA-II and SPEA algorithms.Quazi Nafees Ul IslamAshik AhmedSaad Mohammad AbdullahElsevierarticleMulti-objectiveDynamic loadStatic loadNon-dominated sortingSPSSEngineering (General). Civil engineering (General)TA1-2040ENAin Shams Engineering Journal, Vol 12, Iss 4, Pp 3677-3689 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multi-objective Dynamic load Static load Non-dominated sorting SPSS Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Multi-objective Dynamic load Static load Non-dominated sorting SPSS Engineering (General). Civil engineering (General) TA1-2040 Quazi Nafees Ul Islam Ashik Ahmed Saad Mohammad Abdullah Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)
description	Swarm-based nature-inspired optimization techniques are developing rapidly due to their universal acceptance and capability in solving various real-life challenges efficiently. Hybridization of swarm intelligence based optimization algorithms with multi-objective based solution techniques is creating a wide door in the field of optimization. The main focus of this work is, developing a hybrid Non-dominated Sorting Whale Optimization Algorithm (NSWOA), where swarm-based Whale Optimization Algorithm (WOA) is hybridized with multi-objective based, non-dominated sorting technique. This is done to develop an algorithm with efficient optima searching ability and faster computational speed. The application of NSWOA in optimizing the controller parameters of an islanded microgrid consisting of both static and dynamic load has been also described. SPSS software has been used to compare the performance of proposed NSWOA with non-dominated sorting genetic algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA) technique in optimizing the controller parameters of islanded microgrid model with multi-objective problem. It is obtained that, NSWOA requires an average of 4 iterations to reach the best optimum solution, which is less than other existing algorithms. Moreover, the computational time required by NSWOA is 2.9201 s, which proves that, it converges at a much faster rate compared to existing NSGA-II and SPEA algorithms.
format	article
author	Quazi Nafees Ul Islam Ashik Ahmed Saad Mohammad Abdullah
author_facet	Quazi Nafees Ul Islam Ashik Ahmed Saad Mohammad Abdullah
author_sort	Quazi Nafees Ul Islam
title	Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)
title_short	Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)
title_full	Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)
title_fullStr	Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)
title_full_unstemmed	Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)
title_sort	optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (nswoa)
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/ad46a838ded149208f0f235a263aed10
work_keys_str_mv	AT quazinafeesulislam optimizedcontrollerdesignforislandedmicrogridusingnondominatedsortingwhaleoptimizationalgorithmnswoa AT ashikahmed optimizedcontrollerdesignforislandedmicrogridusingnondominatedsortingwhaleoptimizationalgorithmnswoa AT saadmohammadabdullah optimizedcontrollerdesignforislandedmicrogridusingnondominatedsortingwhaleoptimizationalgorithmnswoa
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Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)

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