Abstract
PD(Partial discharges) are small electrical sparks that occur within the electric insulation of cables, transformers and windings on motors. PD analysis is a proactive diagnostic approach that uses PD measurements to evaluate the integrity of this equipment. Recently, several diagnostic algorithms for classifying the type of PD and locating the defect position have been developed. In this work, a new PD recognition system is proposed, which utilizes approximate coefficients of wavelet transform as a feature vector, furthermore, introduces bank of Elman networks to recognize the various PD phenomena. In order to verify the performance of the proposed scheme, it is applied to the simulated PD data.
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