ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM

Purpose. In recent years, the problem of voltage instability has received special attention from many utilities and researchers. The present paper deals with the on-line evaluation of voltage stability in power system using Adaptive Neuro-Fuzzy Inference System (ANFIS). The developed ANFIS model tak...

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Autores principales:	Arif Bourzami, Mohammed Amroune, Tarek Bouktir
Formato:	article
Lenguaje:	EN RU UK
Publicado:	National Technical University "Kharkiv Polytechnic Institute" 2019
Materias:	voltage stability line voltage stability index Moth-Flame optimization adaptive neuro-fuzzy inference system Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e63585d85c51475c80336c1e8a0871af
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spelling	oai:doaj.org-article:e63585d85c51475c80336c1e8a0871af2021-12-02T14:31:35ZON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM10.20998/2074-272X.2019.2.072074-272X2309-3404https://doaj.org/article/e63585d85c51475c80336c1e8a0871af2019-04-01T00:00:00Zhttp://eie.khpi.edu.ua/article/view/2074-272X.2019.2.07/163478https://doaj.org/toc/2074-272Xhttps://doaj.org/toc/2309-3404Purpose. In recent years, the problem of voltage instability has received special attention from many utilities and researchers. The present paper deals with the on-line evaluation of voltage stability in power system using Adaptive Neuro-Fuzzy Inference System (ANFIS). The developed ANFIS model takes the voltage magnitudes and their phases obtained from the weak buses in the system as input variables. The weak buses identification is formulated as an optimization problem considering the operating cost, the real power losses and the voltage stability index. The recently developed Moth-Flame Optimization (MFO) algorithm was adapted to solve this optimization problem. The validation of the proposed on-line voltage stability assessment approach was carried out on IEEE 30-bus and IEEE 118-bus test systems. The obtained results show that the proposed approach can achieve a higher accuracy compared to the Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks.Arif BourzamiMohammed AmrouneTarek BouktirNational Technical University "Kharkiv Polytechnic Institute"articlevoltage stabilityline voltage stability indexMoth-Flame optimizationadaptive neuro-fuzzy inference systemElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENRUUKElectrical engineering & Electromechanics, Iss 2, Pp 47-54 (2019)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	voltage stability line voltage stability index Moth-Flame optimization adaptive neuro-fuzzy inference system Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	voltage stability line voltage stability index Moth-Flame optimization adaptive neuro-fuzzy inference system Electrical engineering. Electronics. Nuclear engineering TK1-9971 Arif Bourzami Mohammed Amroune Tarek Bouktir ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM
description	Purpose. In recent years, the problem of voltage instability has received special attention from many utilities and researchers. The present paper deals with the on-line evaluation of voltage stability in power system using Adaptive Neuro-Fuzzy Inference System (ANFIS). The developed ANFIS model takes the voltage magnitudes and their phases obtained from the weak buses in the system as input variables. The weak buses identification is formulated as an optimization problem considering the operating cost, the real power losses and the voltage stability index. The recently developed Moth-Flame Optimization (MFO) algorithm was adapted to solve this optimization problem. The validation of the proposed on-line voltage stability assessment approach was carried out on IEEE 30-bus and IEEE 118-bus test systems. The obtained results show that the proposed approach can achieve a higher accuracy compared to the Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks.
format	article
author	Arif Bourzami Mohammed Amroune Tarek Bouktir
author_facet	Arif Bourzami Mohammed Amroune Tarek Bouktir
author_sort	Arif Bourzami
title	ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM
title_short	ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM
title_full	ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM
title_fullStr	ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM
title_full_unstemmed	ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM
title_sort	on-line voltage stability evaluation using neuro-fuzzy inference system and moth-flame optimization algorithm
publisher	National Technical University "Kharkiv Polytechnic Institute"
publishDate	2019
url	https://doaj.org/article/e63585d85c51475c80336c1e8a0871af
work_keys_str_mv	AT arifbourzami onlinevoltagestabilityevaluationusingneurofuzzyinferencesystemandmothflameoptimizationalgorithm AT mohammedamroune onlinevoltagestabilityevaluationusingneurofuzzyinferencesystemandmothflameoptimizationalgorithm AT tarekbouktir onlinevoltagestabilityevaluationusingneurofuzzyinferencesystemandmothflameoptimizationalgorithm
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ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM

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