On-line voltage stability evaluation using neuro-fuzzy inference system and Moth-Flame optimization algorithm

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 vo...

Повний опис

Збережено в:
Бібліографічні деталі
Дата:2019
Автори: Bourzami, A., Amroune, M., Bouktir, T.
Формат: Стаття
Мова:English
Опубліковано: Інститут технічних проблем магнетизму НАН України 2019
Назва видання:Електротехніка і електромеханіка
Теми:
Онлайн доступ:http://dspace.nbuv.gov.ua/handle/123456789/159061
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Назва журналу:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Цитувати:On-line voltage stability evaluation using neuro-fuzzy inference system and Moth-Flame optimization algorithm / A. Bourzami, M. Amroune, T. Bouktir // Електротехніка і електромеханіка. — 2019. — № 2. — С. 47-54. — Бібліогр.: 37 назв. — англ.

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Digital Library of Periodicals of National Academy of Sciences of Ukraine
Опис
Резюме: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.