Detection and Classification of Voltage Disturbances in Electrical Power Systems Using a Modified Euclidean ARTMAP Neural Network with Continuous Training

• Autor(a) principal: Barros, Ana Claudia [UNESP]
• Data de Publicação: 2015
• Outros Autores: Tonelli-Neto, Mauro S., Magalini Santos Decanini, Jose Guilherme, Minussi, Carlos Roberto [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1080/15325008.2015.1073814; http://hdl.handle.net/11449/160906
• topic: wavelet transform; Euclidean ARTMAP neural network; continuous training; power quality disturbance; power distribution system

This article presents a method to detect and classify voltage disturbances in electric power distribution systems using a modified Euclidean ARTMAP neural network with continuous training. This decision-making tool accelerates the procedures to restore the normal operation conditions providing security, reliability, and profits to utilities. Furthermore, it allows the diagnosis system to adapt to changes from the constant evolution of the electric system. The voltage signals features or signatures are extracted using discrete wavelet transform, multiresolution analysis, and the energy concept. Results show that the proposed methodology is robust and efficient, providing a fast diagnosis process. The data set used to validate the proposal is obtained by simulations in a real distribution system using ATP software.