A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges

Rodrigo Melo e Silva de Oliveira, Ramon Araújo, Fabrício José Brito Barros, Adriano Paranhos Segundo, Ronaldo Zampolo, Wellington Fonseca, Victor Dmitriev, Fernando Brazil

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%.

Keywords


Artificial Intelligence, Condition Monitoring, Hydro-generator, Neural Networks, Partial Discharge

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References


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DOI: http://dx.doi.org/10.1590/2179-10742017v16i3854

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