A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges
AUTOR(ES)
Oliveira, Rodrigo M. S. de, Araújo, Ramon C. F., Barros, Fabrício J. B., Segundo, Adriano Paranhos, Zampolo, Ronaldo F., Fonseca, Wellington, Dmitriev, Victor, Brasil, Fernando S.
FONTE
J. Microw. Optoelectron. Electromagn. Appl.
DATA DE PUBLICAÇÃO
2017-09
RESUMO
Abstract Partial discharge (PD) monitoring is widely used in rotating machines to evaluate the condition of stator winding insulation, but its practice on a large scale requires the development of intelligent systems that automatically process these measurement data. In this paper, it is proposed a methodology of automatic PD classification in hydro-generator stator windings using neural networks. The database is formed from online PD measurements in hydro-generators in a real setting. Noise filtering techniques are applied to these data. Then, based on the concept of image projection, novel features are extracted from the filtered samples. These features are used as inputs for training several neural networks. The best performance network, obtained using statistical procedures, presents a recognition rate of 98%.
Documentos Relacionados
- Analysis and Comparison of Sensors for Measurements of Partial Discharges in Hydrogenerator Stator Windings
- Spectral Method for Localization of Multiple Partial Discharges in Dielectric Insulation of Hydro-Generator Coils: Simulation and Experimental Results
- Analysis of Thermo-Mechanical Stress in Fiber Bragg Grating Used for Hydro-Generator Rotor Temperature Monitoring
- Heartbeat classification system based on neural networks and dimensionality reduction
- Synergistic control of forearm based on accelerometer data and artificial neural networks