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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| Дата: | 2019 |
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| Автори: | , , |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine”
2019
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| Онлайн доступ: | http://eie.khpi.edu.ua/article/view/2074-272X.2019.2.07 |
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| Назва журналу: | Electrical Engineering & Electromechanics |
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Electrical Engineering & Electromechanics| id |
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eiekhpieduua-article-1644222019-04-16T12:30:41Z ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM Bourzami, Arif Amroune, Mohammed Bouktir, Tarek voltage stability line voltage stability index Moth-Flame optimization adaptive neuro-fuzzy inference system 621.3 стабильность напряжения показатель стабильности напряжения сети оптимизация методом мотылька и пламени адаптивная нейро-нечеткая система вывода 621.3 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. В последние годы проблема нестабильности напряжения привлекла особое внимание многих служб эксплуатации и исследователей. Настоящая статья посвящена оценке в режиме онлайн стабильности напряжения в энергосистеме с использованием адаптивной нейро-нечеткой системы вывода (ANFIS). Разработанная модель ANFIS принимает в качестве входных переменных величины напряжения и их фазы, полученные от шин в системе. Идентификация шин сформулирована как задача оптимизации, учитывающая эксплуатационные расходы, реальные потери мощности и показатель стабильности напряжения. Недавно разработанный алгоритм оптимизации методом мотылька и пламени (MFO) адаптирован для решения данной задачи оптимизации. Проверка предложенного подхода к онлайн оценке стабильности напряжения в сети проводилась на тестовых системах IEEE с 30 шинами и IEEE со 118 шинами. Полученные результаты показывают, что предлагаемый подход может обеспечить более высокую точность по сравнению с многоуровневыми нейронными сетями (MLP) и нейронными сетями с радиальными базисными функциями (RBF). National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine” 2019-04-16 Article Article application/pdf http://eie.khpi.edu.ua/article/view/2074-272X.2019.2.07 10.20998/2074-272X.2019.2.07 Electrical Engineering & Electromechanics; No. 2 (2019); 47-54 Электротехника и Электромеханика; № 2 (2019); 47-54 Електротехніка і Електромеханіка; № 2 (2019); 47-54 2309-3404 2074-272X en http://eie.khpi.edu.ua/article/view/2074-272X.2019.2.07/163478 Copyright (c) 2019 Arif Bourzami, Mohammed Amroune, Tarek Bouktir https://creativecommons.org/licenses/by-nc/4.0 |
| institution |
Electrical Engineering & Electromechanics |
| collection |
OJS |
| language |
English |
| topic |
voltage stability line voltage stability index Moth-Flame optimization adaptive neuro-fuzzy inference system 621.3 стабильность напряжения показатель стабильности напряжения сети оптимизация методом мотылька и пламени адаптивная нейро-нечеткая система вывода 621.3 |
| spellingShingle |
voltage stability line voltage stability index Moth-Flame optimization adaptive neuro-fuzzy inference system 621.3 стабильность напряжения показатель стабильности напряжения сети оптимизация методом мотылька и пламени адаптивная нейро-нечеткая система вывода 621.3 Bourzami, Arif Amroune, Mohammed Bouktir, Tarek ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM |
| topic_facet |
voltage stability line voltage stability index Moth-Flame optimization adaptive neuro-fuzzy inference system 621.3 стабильность напряжения показатель стабильности напряжения сети оптимизация методом мотылька и пламени адаптивная нейро-нечеткая система вывода 621.3 |
| format |
Article |
| author |
Bourzami, Arif Amroune, Mohammed Bouktir, Tarek |
| author_facet |
Bourzami, Arif Amroune, Mohammed Bouktir, Tarek |
| author_sort |
Bourzami, Arif |
| 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 |
| title_alt |
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. |
| publisher |
National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine” |
| publishDate |
2019 |
| url |
http://eie.khpi.edu.ua/article/view/2074-272X.2019.2.07 |
| work_keys_str_mv |
AT bourzamiarif onlinevoltagestabilityevaluationusingneurofuzzyinferencesystemandmothflameoptimizationalgorithm AT amrounemohammed onlinevoltagestabilityevaluationusingneurofuzzyinferencesystemandmothflameoptimizationalgorithm AT bouktirtarek onlinevoltagestabilityevaluationusingneurofuzzyinferencesystemandmothflameoptimizationalgorithm |
| first_indexed |
2024-06-01T14:39:14Z |
| last_indexed |
2024-06-01T14:39:14Z |
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1800670024913911808 |