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La capacité de détecter et de classer le type de défaut joue un grand rôle dans la protection du système d'alimentation. Cette procédure doit être précise sans prendre de temps. Dans ce papier, la détection du type de défaut a été mise en œuvre en utilisant l'analyse en ondelettes avec le principe d'entropie en ondelettes. La simulation du système d'alimentation est réalisée à l'aide de PSCAD/EMTDC. Différents types de défauts ont été étudiés pour obtenir différentes formes d'onde de courant. Ces formes d'onde actuelles ont été décomposées à l'aide d'une analyse en ondelettes en différentes approximations et détails. Les entropies en ondelettes de ces décompositions sont analysées pour atteindre une méthodologie réussie pour la classification des failles. L'approche suggérée est testée en utilisant différents types de défauts et une identification réussie prouvée pour le type de défaut.
La capacidad de detectar y clasificar el tipo de falla juega un papel importante en la protección del sistema de energía. Se requiere que este procedimiento sea preciso sin consumo de tiempo. En este documento, la detección del tipo de falla se ha implementado utilizando el análisis de wavelets junto con el principio de entropía de wavelets. La simulación del sistema de potencia se realiza mediante PSCAD/EMTDC. Se estudiaron diferentes tipos de fallas obteniendo diversas formas de onda de corriente. Estas formas de onda de corriente se descompusieron utilizando el análisis de ondículas en diferentes aproximaciones y detalles. Las entropías wavelet de tales descomposiciones se analizan alcanzando una metodología exitosa para la clasificación de fallas. El enfoque sugerido se prueba utilizando diferentes tipos de fallas y una identificación exitosa comprobada para el tipo de falla.
The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropies of such decompositions are analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault.
تلعب القدرة على اكتشاف نوع الخطأ وتصنيفه دورًا كبيرًا في حماية نظام الطاقة. يجب أن يكون هذا الإجراء دقيقًا دون استهلاك للوقت. في هذه الورقة، تم تنفيذ الكشف عن نوع الخطأ باستخدام تحليل المويجة جنبًا إلى جنب مع مبدأ إنتروبيا المويجة. تتم محاكاة نظام الطاقة باستخدام PSCAD/EMTDC. تمت دراسة أنواع مختلفة من الأعطال للحصول على أشكال موجية مختلفة للتيار. تم تحليل هذه الأشكال الموجية الحالية باستخدام تحليل الموجات الصغيرة إلى تقريب وتفاصيل مختلفة. يتم تحليل إنتروبيا الموجات الصغيرة لمثل هذه التحاليل للوصول إلى منهجية ناجحة لتصنيف الأعطال. يتم اختبار النهج المقترح باستخدام أنواع مختلفة من الأخطاء وتحديد ناجح مثبت لنوع الخطأ.
Artificial intelligence, Machine Fault Diagnosis and Prognostics, Wavelet Transform, Pattern recognition (psychology), Quantum mechanics, Electric power system, Fault (geology), Engineering, Condition Assessment of Power Transformers, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Entropy (arrow of time), Electrical and Electronic Engineering, Fault classification, wavelet transform, Seismology, Physics, Waveform, Voltage, Geology, FOS: Earth and related environmental sciences, Power (physics), Fault Detection, Fault Diagnosis, waveletentropy., Computer science, Adaptive Protection Schemes for Microgrids, Algorithm, Control and Systems Engineering, Electrical engineering, Physical Sciences, Thermodynamics, Vibration Analysis, Wavelet transform, Statistical physics, Transformer Fault Diagnosis, Wavelet, Mathematics
Artificial intelligence, Machine Fault Diagnosis and Prognostics, Wavelet Transform, Pattern recognition (psychology), Quantum mechanics, Electric power system, Fault (geology), Engineering, Condition Assessment of Power Transformers, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Entropy (arrow of time), Electrical and Electronic Engineering, Fault classification, wavelet transform, Seismology, Physics, Waveform, Voltage, Geology, FOS: Earth and related environmental sciences, Power (physics), Fault Detection, Fault Diagnosis, waveletentropy., Computer science, Adaptive Protection Schemes for Microgrids, Algorithm, Control and Systems Engineering, Electrical engineering, Physical Sciences, Thermodynamics, Vibration Analysis, Wavelet transform, Statistical physics, Transformer Fault Diagnosis, Wavelet, Mathematics
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| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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