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

• Дата: 2019
• Автори: Bourzami, Arif, Amroune, Mohammed, Bouktir, Tarek
• 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
• Теми: 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
• Онлайн доступ: http://eie.khpi.edu.ua/article/view/2074-272X.2019.2.07
• Назва журналу: Electrical Engineering & Electromechanics

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.